diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Mudah Download Clash Royale Mod Apk dengan Fitur Unlimited Money.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Mudah Download Clash Royale Mod Apk dengan Fitur Unlimited Money.md
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-<h1>Cara Download Clash Royale Mod APK</h1>
-<p>Clash Royale is one of the most popular and addictive strategy games on mobile devices. It combines card collecting, tower defense, and real-time multiplayer battles in a fast-paced and fun gameplay. However, some players may find it hard to progress in the game without spending real money on gems and gold, which are the main currencies in the game. That's why some players resort to using Clash Royale Mod APK, which is a modified version of the game that gives them unlimited resources and access to all the cards in the game.</p>
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-<p>If you are one of those players who want to enjoy Clash Royale without any limitations, then this article is for you. In this article, we will show you how to download and install Clash Royale Mod APK on your Android device, what are the features of this mod, and whether it is safe and legal to use. Let's get started!</p>
- <h2>What is Clash Royale Mod APK?</h2>
-<p>Clash Royale Mod APK is a hacked version of the original Clash Royale game that was developed by third-party developers. This mod allows you to have unlimited gems and gold, which you can use to buy chests, cards, upgrades, and more. You can also unlock all the cards in the game, including the legendary ones, and use them in your deck. Moreover, you can play on custom mods and servers that offer different game modes, such as 2v2 battles, draft mode, unlimited elixir mode, and more.</p>
-<p>With Clash Royale Mod APK, you can enjoy the game without any restrictions or worries. You can experiment with different strategies, decks, and cards without losing any trophies or resources. You can also challenge your friends or other players online and show off your skills. However, you should be aware that using this mod may also have some risks and consequences, which we will discuss later in this article.</p>
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- <h3>Features of Clash Royale Mod APK</h3>
-<p>Clash Royale Mod APK has many features that make it different from the original game. Here are some of the main features of this mod:</p>
- <h4>Unlimited Gems and Gold</h4>
-<p>Gems and gold are the most important resources in Clash Royale. You need gems to buy chests, cards, emotes, skins, and more. You need gold to upgrade your cards and increase your level. However, gems and gold are very scarce and expensive in the game. You can only get them by completing quests, winning battles, opening chests, or spending real money.</p>
-<p>With Clash Royale Mod APK, you don't have to worry about gems and gold anymore. You will have unlimited amounts of them in your account. You can use them to buy anything you want in the game without any limitations. You can also use them to speed up the chest opening process and get more cards faster.</p>
- <h4>All Cards Unlocked</h4>
-<p>Cards are the core element of Clash Royale. They are used to create your deck and fight against your opponents. There are four types of cards in the game: common, rare, epic, and legendary. Each card has its own strengths, weaknesses, abilities, and costs. You can collect cards by opening chests or buying them from the shop.</p>
-<p>However, not all cards are easy to get in the game. Some cards are very rare and hard to find, especially the legendary ones. You may have to open hundreds of chests or spend thousands of gems to get them. And even if you get them, you still need to upgrade them to make them more powerful.</p>
-<p>With Clash Royale Mod APK, you don't have to worry about cards anymore. You will have all the cards unlocked in the game from the start. You can use any card you want in your deck without any restrictions or costs. You can also upgrade them to the maximum level with just one click.</p>
- <h4>Custom Mods and Servers</h4>
-<p>Another feature of Clash Royale Mod APK is that you can play on custom mods and servers that offer different game modes and experiences. For example, you can play on a mod that gives you unlimited elixir, which means you can spam any card you want without worrying about the elixir cost. You can also play on a mod that lets you draft your deck from a random pool of cards, which adds more variety and challenge to the game. You can also play on a mod that lets you use cards from other Supercell games, such as Brawl Stars, Clash of Clans, or Boom Beach.</p>
-<p>Moreover, you can play on custom servers that have different rules and settings than the official ones. For example, you can play on a server that has higher or lower trophy requirements, faster or slower chest opening times, more or less rewards, and more. You can also play on a server that has more players online, which means you can find matches faster and easier.</p>
- <h2>How to Download and Install Clash Royale Mod APK?</h2>
-<p>If you are interested in trying out Clash Royale Mod APK, then you need to follow these steps to download and install it on your Android device:</p>
- <h3>Step 1: Enable Unknown Sources</h3>
-<p>The first step is to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on. You may see a warning message, but don't worry, just tap OK.</p>
- <h3>Step 2: Download the APK File</h3>
-<p>The next step is to download the APK file of Clash Royale Mod APK. You can find many websites that offer this file, but be careful, some of them may contain viruses or malware. We recommend you to use this link, which is safe and reliable. Just click on the download button and wait for the file to be downloaded.</p>
- <h3>Step 3: Install the APK File</h3>
-<p>The third step is to install the APK file on your device. To do this, go to your file manager, then locate the downloaded file, then tap on it. You may see a pop-up message asking for your permission, just tap on install and wait for the process to finish.</p>
- <h3>Step 4: Launch the Game and Enjoy</h3>
-<p>The final step is to launch the game and enjoy it. To do this, go to your app drawer, then find the Clash Royale icon, then tap on it. You may see a loading screen, then a welcome message, then the game will start. You will notice that you have unlimited gems and gold, all cards unlocked, and access to custom mods and servers. You can now play the game as you wish and have fun!</p>
- <h2>Is Clash Royale Mod APK Safe and Legal?</h2>
-<p>Before you download and install Clash Royale Mod APK, you may have some questions about its safety and legality. Here are some answers to these questions:</p>
- <h3>Safety Issues</h3>
-<p>As we mentioned earlier, not all websites that offer Clash Royale Mod APK are safe and trustworthy. Some of them may contain viruses or malware that can harm your device or steal your personal information. That's why you should always use a reputable source like the one we provided above. You should also scan the file with an antivirus app before installing it.</p>
-<p>Another safety issue is that using Clash Royale Mod APK may cause your account to be banned by Supercell, the developer of the original game. This is because using this mod violates their terms of service and fair play policy. Supercell has the right to detect and ban any account that uses any third-party software or modification that gives an unfair advantage over other players. Therefore, if you use Clash Royale Mod APK, you should do it at your own risk and responsibility.</p>
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-<p>Besides safety issues, using Clash Royale Mod APK may also raise some legal issues. This is because using this mod infringes the intellectual property rights of Supercell, the developer of the original game. Supercell owns all the rights to the game content, such as graphics, sounds, characters, cards, etc. By modifying and distributing their game without their permission, you are violating their rights and breaking the law.</p>
-<p>Therefore, if you use Clash Royale Mod APK, you should be aware of the potential legal consequences that may arise from it. You may face lawsuits or fines from Supercell or other parties involved in the game development or distribution. You may also face criminal charges depending on the laws of your country or region. Therefore, you should be careful and cautious when using Clash Royale Mod APK.</p>
- <h2>Conclusion</h2>
-<p>Clash Royale Mod APK is a modified version of the original Clash Royale game that gives you unlimited gems and gold, all cards unlocked, and access to custom mods and servers. It can be a fun and exciting way to enjoy the game without any limitations or restrictions. However, it also has some risks and drawbacks, such as safety and legal issues. You should always use a reliable source to download the APK file, scan it with an antivirus app, and use it at your own risk and responsibility. We hope this article has helped you understand how to download and install Clash Royale Mod APK on your Android device. Have fun and clash on!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Clash Royale Mod APK:</p>
-<ul>
-<li><b>Q: Can I use Clash Royale Mod APK on iOS devices?</b></li>
-<li>A: No, Clash Royale Mod APK is only compatible with Android devices. You cannot use it on iOS devices, such as iPhones or iPads.</li>
-<li><b>Q: Can I play Clash Royale Mod APK with other players who use the original game?</b></li>
-<li>A: No, Clash Royale Mod APK uses different servers than the original game. You can only play with other players who use the same mod or server as you.</li>
-<li><b>Q: Can I update Clash Royale Mod APK to the latest version of the game?</b></li>
-<li>A: No, Clash Royale Mod APK is not updated automatically. You have to download and install the new version of the mod manually whenever there is an update from the original game.</li>
-<li><b>Q: Can I switch back to the original game after using Clash Royale Mod APK?</b></li>
-<li>A: Yes, you can switch back to the original game anytime you want. However, you may lose your progress and data in the modded game. You may also have to uninstall the modded game and reinstall the original game from the Google Play Store.</li>
-<li><b>Q: Can I use my existing account in Clash Royale Mod APK?</b></li>
-<li>A: No, you cannot use your existing account in Clash Royale Mod APK. You have to create a new account in the modded game. You may also have to use a different email address or phone number to register.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DLS 17 Mod Apk Unlock All Player and Play with Unlimited Money.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DLS 17 Mod Apk Unlock All Player and Play with Unlimited Money.md
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-<p>If you are a fan of soccer games, you have probably heard of Dream League Soccer (DLS), one of the most popular and realistic soccer games for mobile devices. DLS 17 is the latest edition of this game series, featuring amazing graphics, smooth animations, and addictive gameplay. However, if you want to take your soccer experience to the next level, you should consider downloading the mod apk version of DLS 17, which gives you access to unlimited money, all players unlocked, no ads, and many other features that will make you feel like a soccer superstar. In this article, we will show you what DLS 17 mod apk is, what are its benefits and risks, and how to download and install it on your android device.</p>
-<h2>What is DLS 17?</h2>
-<p>Dream League Soccer 17 (DLS 17) is an association football video game developed by British studio First Touch Games for iOS and Android devices. The game allows you to create and manage your own soccer team, advancing from the lower divisions of a fictitious league to the top division. You can also compete in league or cup matches against other teams from around the world. The game has the FIFPRO license, which means you can sign real-life players to your team via an in-game transfer market. The game also has a training mode where you can improve the skills of your players. The game has 3D graphics, realistic physics, stunning atmosphere, and tons of replay value.</p>
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-<p>A mod apk is a modified version of an original app that has been altered by a third-party developer to add or remove some features. In this case, DLS 17 mod apk is a version of DLS 17 that has been hacked to give you unlimited money, all players unlocked, no ads, license fixed, and other enhancements that make the game more fun and easy to play. With DLS 17 mod apk, you don't have to worry about running out of coins, signing expensive players, watching annoying ads, or facing any errors. You can simply enjoy the game as you wish and customize your team to your liking.</p>
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-<h2>How to Download and Install DLS 17 Mod APK Unlock All Player on Your Android Device</h2>
-<p>If you are ready to download and install DLS 17 mod apk unlock all player on your android device, you need to follow these steps carefully:</p>
-<h3>Step 1: Allow Unknown Sources on Your Device</h3>
-<p>The first step is to enable the option to install apps from unknown sources on your device. This will allow you to install the mod apk file that is not from the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message, but just tap OK to proceed.</p>
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-<p>The next step is to install a file manager app on your device that can extract and install apk and obb files. There are many file manager apps available on the Google Play Store, but we recommend XAPKS Installer, which is a free and easy-to-use app that can handle both apk and obb files. To install XAPKS Installer, go to  and tap Install. Then, open the app and grant it the necessary permissions.</p>
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-<h3>Step 3: Download the DLS 17 Mod APK and OBB Files to Your Device</h3>
-<p>The third step is to download the DLS 17 mod apk and obb files to your device. You can find these files from a reputable website, such as , which provides safe and fast downloads. To download these files, go to  and tap Download. Then, wait for the files to be downloaded to your device.</p>
-<h3>Step 4: Install the DLS 17 Mod APK File on Your Device</h3>
-<p>The fourth step is to install the DLS 17 mod apk file on your device. To do this, locate and tap the mod apk file that you downloaded in Step 3. You may see a pop-up window asking you to confirm the installation. Tap Install and wait for the installation process to finish.</p>
-<h3>Step 5: Extract and Copy the OBB File to Your Device Storage</h3>
-<p>The fifth step is to extract and copy the obb file that you downloaded in Step 3 to your device storage. To do this, open XAPKS Installer and locate the obb file that has a zip icon. Tap the obb file and select Extract. Then, select the folder that says com.firsttouchgames.dls3 and tap Copy. Next, go to Internal Storage > Android > OBB and paste the folder there. Make sure that the folder name matches the obb file name.</p>
-<h3>Step 6: Launch the DLS 17 Mod APK App and Enjoy</h3>
-<p>The final step is to launch the DLS 17 mod apk app and enjoy the game. To do this, go to your app drawer and tap the DLS 17 icon. You may see a loading screen and some messages, but just wait for them to finish. Then, you can start playing the game with all players unlocked and unlimited money. You can also customize your team, change the settings, and access other features of the game.</p>
-<h2>Conclusion</h2>
-<p>DLS 17 is one of the best soccer games for android devices, but it can be even better with the mod apk version that unlocks all players and gives you unlimited money. In this article, we showed you what DLS 17 mod apk is, what are its benefits and risks, and how to download and install it on your android device. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Now, go ahead and download DLS 17 mod apk unlock all player and enjoy the ultimate soccer experience on your android device.</p>
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-<p>Here are some frequently asked questions and answers about DLS 17 mod apk unlock all player:</p>
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-<p>A: DLS 17 mod apk is safe to download if you get it from a trusted source, such as . However, you should always be careful when downloading any mod apk file from unknown sources, as they may contain malware or viruses that can harm your device or data.</p>
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-<p>A: No, you cannot play online with DLS 17 mod apk, as it is not compatible with the official servers of the game. You can only play offline with the mod apk version. If you want to play online, you need to use the original version of the app.</p>
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-<p>A: No, you cannot update DLS 17 mod apk, as it is not supported by the Google Play Store. You need to uninstall the mod apk version and install the latest original version of the app if you want to update it. However, you may lose your modded features and progress in the game if you do so.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Bus Simulator  Ultimate Mod Apk 1.0 with Unlimited Money and Features.md b/spaces/1phancelerku/anime-remove-background/Download Bus Simulator  Ultimate Mod Apk 1.0 with Unlimited Money and Features.md
deleted file mode 100644
index ff5db297158ded509add4fb0a0d58c8ccb1355f6..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Bus Simulator  Ultimate Mod Apk 1.0 with Unlimited Money and Features.md	
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- <p>If you are a fan of simulation games, you might have heard of Bus Simulator Ultimate, a realistic and immersive game that lets you drive different buses across various countries and cities. In this game, you can create your own bus company, customize your buses, transport passengers, earn money, and compete with other players online. But what if you want to enjoy the game without any limitations or restrictions? That's where Bus Simulator Ultimate Hack APK 1.0 7 comes in handy. In this article, we will tell you what this hack is, how to download and install it, what are its benefits and drawbacks, and some FAQs about it.</p>
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- <p>Bus Simulator Ultimate is a popular simulation game developed by Zuuks Games, a Turkish game studio that also created other games like Truck Simulator 2018: Europe and Euro Truck Driver 2018. The game was released in June 2019 and has since gained over 100 million downloads on Google Play Store and over 4 million ratings with an average of 4.3 stars.</p>
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- <h2>How to download and install Bus Simulator Ultimate Hack APK 1.0 7?</h2>
- <p>If you are interested in trying Bus Simulator Ultimate Hack APK 1.0 7, you need to follow these simple steps to download and install it on your device:</p>
- <h3>Step 1: Enable unknown sources on your device</h3>
- <p>Before you can install any APK file on your device, you need to enable the option of unknown sources in your settings. This will allow you to install apps from sources other than the official Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on.</p>
-<p>[Bus Simulator : Ultimate Mod Apk 1.0 [Unlimited money] APK](^1^)</p>
- <h3>Step 2: Download the APK file from a trusted source</h3>
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- <p><a href="">Bus Simulator Ultimate Hack APK 1.0 7 Download Link</a></p>
- <h3>Step 3: Install the APK file on your device</h3>
- <p>After you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your downloads folder or wherever you saved it, and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on install and wait for the process to finish.</p>
- <h3>Step 4: Launch the game and enjoy the hack features</h3>
- <p>Finally, you can launch the game and enjoy the hack features. You will see that you have unlimited money and gold, all buses unlocked and upgraded, no ads and no root required, and other hack features that will make your gameplay easier and more enjoyable. You can now drive any bus you want, customize it as you like, transport passengers without any hassle, create your own bus company, compete with other players online, and have fun!</p>
- <h2>What are the benefits of using Bus Simulator Ultimate Hack APK 1.0 7?</h2>
- <p>Using Bus Simulator Ultimate Hack APK 1.0 7 has many benefits that will enhance your gaming experience and satisfaction. Some of these benefits are:</p>
- <h3>Unlimited money and gold</h3>
- <p>With unlimited money and gold, you can buy anything you want in the game without worrying about running out of resources or spending real money. You can buy new buses or upgrade them with different features and accessories. You can also buy VIP membership or gems that will give you more advantages and perks in the game.</p>
- <h3>All buses unlocked and upgraded</h3>
- <p>With all buses unlocked and upgraded, you can choose from over 30 different buses with realistic interiors and sounds, such as Mercedes-Benz, Setra, Volvo, MAN, Scania, IVECO, and more. You can also customize your buses with different skins, stickers, accessories, horns, lights, etc. You can drive any bus you want across various countries and cities without any limitations or restrictions.</p>
- <h3>No ads and no root required</h3>
- <p>With no ads and no root required, you can enjoy the game without any interruptions or annoyances. You don't have to watch ads or videos to get some rewards or bonuses in the game. You also don't have to root your device to use the hack features. This will save you time and effort and protect your device from potential risks.</p> <h2>What are the drawbacks of using Bus Simulator Ultimate Hack APK 1.0 7?</h2>
- <p>Using Bus Simulator Ultimate Hack APK 1.0 7 also has some drawbacks that you should be aware of before you decide to use it. Some of these drawbacks are:</p>
- <h3>Risk of malware and viruses</h3>
- <p>Since you are downloading and installing an APK file from an unknown source, you are exposing your device to the risk of malware and viruses that can harm your device or steal your data. Some of these malware and viruses can also affect your gameplay or performance, such as slowing down your device, crashing your game, deleting your files, etc. To avoid this, you should always scan the APK file with a reliable antivirus software before installing it.</p>
- <h3>Risk of banning and suspension</h3>
- <p>Since you are using a modified version of the original game that gives you unfair advantages over other players, you are violating the terms and conditions of the game developers and publishers. This means that they can detect your hack and ban or suspend your account from the game. This will result in losing your game progress and data, as well as being unable to play the game online or offline. To avoid this, you should always use the hack with caution and discretion, and avoid using it in multiplayer mode or online competitions.</p>
- <h3>Risk of losing game progress and data</h3>
- <p>Since you are using a modified version of the original game that is not compatible with the official updates or patches, you are risking losing your game progress and data if you update or uninstall the game. This means that you will have to start from scratch or lose some of your achievements or rewards in the game. To avoid this, you should always backup your game data before updating or uninstalling the game, and use a cloud service or an external storage device to save your data.</p>
- <h2>Conclusion</h2>
- <p>Bus Simulator Ultimate is a realistic and immersive simulation game that lets you drive different buses across various countries and cities. However, if you want to enjoy the game without any limitations or restrictions, you might want to try Bus Simulator Ultimate Hack APK 1.0 7. This is a modified version of the original game that gives you access to unlimited money and gold, all buses unlocked and upgraded, no ads and no root required, and other hack features that will make your gameplay easier and more enjoyable. However, using this hack also has some drawbacks, such as risk of malware and viruses, risk of banning and suspension, and risk of losing game progress and data. Therefore, you should weigh the pros and cons before using this hack, and always use it with caution and discretion.</p>
- <p>We hope this article has helped you understand what Bus Simulator Ultimate Hack APK 1.0 7 is, how to download and install it, what are its benefits and drawbacks, and some FAQs about it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bus Simulator Ultimate Hack APK 1.0 7:</p>
- <ol>
-<li><b>Is Bus Simulator Ultimate Hack APK 1.0 7 safe to use?</b></li>
-<p>Bus Simulator Ultimate Hack APK 1.0 7 is not completely safe to use, as it can expose your device to malware and viruses, as well as get your account banned or suspended from the game. Therefore, you should always scan the APK file with a reliable antivirus software before installing it, use the hack with caution and discretion, and avoid using it in multiplayer mode or online competitions.</p>
-<li><b>Is Bus Simulator Ultimate Hack APK 1.0 7 free to use?</b></li>
-<p>Yes, Bus Simulator Ultimate Hack APK 1.0 7 is free to use, as you don't have to pay any money to download or install it on your device. However, you might have to watch some ads or videos to access some of the hack features in the game.</p>
-<li><b>Does Bus Simulator Ultimate Hack APK 1.0 7 work on all devices?</b></li>
-<p>No, Bus Simulator Ultimate Hack APK 1.0 7 does not work on all devices, as it requires Android version 5.0 or higher to run on your device. It also requires at least 2 GB of RAM and 500 MB of free storage space on your device.</p>
-<li><b>Can I update Bus Simulator Ultimate Hack APK 1.0 7?</b></li>
-<p>No, you cannot update Bus Simulator Ultimate Hack APK 1.0 7, as it is not compatible with the official updates or patches from the game developers and publishers. If you update the game, you will lose the hack features and your game progress and data . To avoid this, you should always backup your game data before updating or uninstalling the game, and use a cloud service or an external storage device to save your data.</p>
- <li><b>Where can I get more information about Bus Simulator Ultimate Hack APK 1.0 7?</b></li>
-<p>If you want to get more information about Bus Simulator Ultimate Hack APK 1.0 7, you can visit the official website of the game developers and publishers, or the official social media pages of the game, such as Facebook, Twitter, Instagram, YouTube, etc. You can also join the online community of the game, such as forums, blogs, groups, etc., where you can interact with other players and share your tips, tricks, feedback, suggestions, etc.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/52Hz/SRMNet_thesis/main_test_SRMNet.py b/spaces/52Hz/SRMNet_thesis/main_test_SRMNet.py
deleted file mode 100644
index 8c2fcca2cf7fa89cf64bf9ae7a6003c5d69df51e..0000000000000000000000000000000000000000
--- a/spaces/52Hz/SRMNet_thesis/main_test_SRMNet.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import torch
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from PIL import Image
-import cv2
-import os
-from skimage import img_as_ubyte
-from tqdm import tqdm
-from natsort import natsorted
-import glob
-import argparse
-from model_arch.SRMNet_SWFF import SRMNet_SWFF
-from model_arch.SRMNet import SRMNet
-
-tasks = ['Deblurring_motionblur',
-         'Dehaze_realworld',
-         'Denoise_gaussian',
-         'Denoise_realworld',
-         'Deraining_raindrop',
-         'Deraining_rainstreak',
-         'LLEnhancement',
-         'Retouching']
-
-def main():
-    parser = argparse.ArgumentParser(description='Quick demo Image Restoration')
-    parser.add_argument('--input_dir', default='./test/', type=str, help='Input images root')
-    parser.add_argument('--result_dir', default='./result/', type=str, help='Results images root')
-    parser.add_argument('--weights_root', default='experiments/pretrained_models', type=str, help='Weights root')
-    parser.add_argument('--task', default='Retouching', type=str, help='Restoration task (Above task list)')
-
-    args = parser.parse_args()
-
-    # Prepare testing data
-    files = natsorted(glob.glob(os.path.join(args.input_dir, '*')))
-    if len(files) == 0:
-        raise Exception(f"No files found at {args.input_dir}")
-    os.makedirs(args.result_dir, exist_ok=True)
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    # Build model
-    model = define_model(args)
-    model.eval()
-    model = model.to(device)
-
-    print('restoring images......')
-
-    mul = 16
-
-    for i, file_ in enumerate(tqdm(files)):
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-
-        # Pad the input if not_multiple_of 8
-        h, w = input_.shape[2], input_.shape[3]
-        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-        padh = H - h if h % mul != 0 else 0
-        padw = W - w if w % mul != 0 else 0
-        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-        with torch.no_grad():
-            restored = model(input_)
-
-        restored = torch.clamp(restored, 0, 1)
-        restored = restored[:, :, :h, :w]
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(args.result_dir, f + '.png')), restored)
-        print('{}'.format(os.path.join(args.result_dir, f + '.png')))
-    clean_folder(args.input_dir)
-    print('finish !')
-
-
-def define_model(args):
-    # Enhance models
-    if args.task in ['LLEnhancement', 'Retouching']:
-        model = SRMNet(in_chn=3, wf=96, depth=4)
-        weight_path = os.path.join(args.weights_root, args.task + '.pth')
-        load_checkpoint(model, weight_path)
-
-    # Restored models
-    else:
-        model = SRMNet_SWFF(in_chn=3, wf=96, depth=4)
-        weight_path = os.path.join(args.weights_root, args.task + '.pth')
-        load_checkpoint(model, weight_path)
-
-    return model
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-        
-def clean_folder(folder):
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print('Failed to delete %s. Reason: %s' % (file_path, e))
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/AISuperheroes/08GR-KitchenSink-AIUIUX/README.md b/spaces/AISuperheroes/08GR-KitchenSink-AIUIUX/README.md
deleted file mode 100644
index 2275ae9cd95158f64d4424074c4b33fe1722b0ba..0000000000000000000000000000000000000000
--- a/spaces/AISuperheroes/08GR-KitchenSink-AIUIUX/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 08GR KitchenSink AIUIUX
-emoji: 📈
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIZ2H/Gradio331-3D-Models-AI-1/files/Readme.md b/spaces/AIZ2H/Gradio331-3D-Models-AI-1/files/Readme.md
deleted file mode 100644
index ba3cbde04295450ab0d39ad8c05f7d568795f361..0000000000000000000000000000000000000000
--- a/spaces/AIZ2H/Gradio331-3D-Models-AI-1/files/Readme.md
+++ /dev/null
@@ -1,2 +0,0 @@
-Duck & Fox:
-https://github.com/KhronosGroup/glTF-Sample-Models
\ No newline at end of file
diff --git a/spaces/AIatUIUC/CodeLATS/generators/py_generate.py b/spaces/AIatUIUC/CodeLATS/generators/py_generate.py
deleted file mode 100644
index 0e9576e9f25f68989e309f07429bdfea8c6a482a..0000000000000000000000000000000000000000
--- a/spaces/AIatUIUC/CodeLATS/generators/py_generate.py
+++ /dev/null
@@ -1,404 +0,0 @@
-from generators.model import ModelBase, message_to_str
-from .generator_types import Generator
-from .generator_utils import generic_generate_func_impl, generic_generate_internal_tests, generic_generate_self_reflection, generate_with_accumulated_context
-
-from typing import Optional, List, Union
-import ast
-import re
-from .parse import parse_code_block, add_code_block
-
-PY_SIMPLE_COMPLETION_INSTRUCTION = "# Write the body of this function only."
-PY_REFLEXION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature).\n\n-----"
-PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation.\n\n-----"
-USE_PYTHON_CODEBLOCK_INSTRUCTION = "Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```"
-
-PY_SIMPLE_CHAT_INSTRUCTION = "You are an AI that only responds with python code, NOT ENGLISH. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
-PY_SIMPLE_CHAT_INSTRUCTION_V2 = "You are an AI that only responds with only python code. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
-PY_REFLEXION_CHAT_INSTRUCTION = "You are an AI Python assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature)."
-PY_REFLEXION_CHAT_INSTRUCTION_V2 = "You are an AI Python assistant. You will be given your previous implementation of a function, a series of unit tests results, and your self-reflection on your previous implementation. Write your full implementation (restate the function signature)."
-PY_REFLEXION_FEW_SHOT_ADD = '''Example 1:
-[previous impl]:
-```python
-def add(a: int, b: int) -> int:
-    """
-    Given integers a and b, return the total value of a and b.
-    """
-    return a - b
-```
-
-[unit test results from previous impl]:
-Tested passed:
-
-Tests failed:
-assert add(1, 2) == 3 # output: -1
-assert add(1, 2) == 4 # output: -1
-
-[reflection on previous impl]:
-The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.
-
-[improved impl]:
-```python
-def add(a: int, b: int) -> int:
-    """
-    Given integers a and b, return the total value of a and b.
-    """
-    return a + b
-```
-'''
-
-PY_REFLEXION_FEW_SHOT = '''Example 1:
-[previous impl]:
-```python
-from typing import *
-def fullJustify(words: List[str], maxWidth: int) -> List[str]:
-    """
-    Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
-    You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
-    Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
-    For the last line of text, it should be left justified and no extra space is inserted between words.
-    Note:
-    A word is defined as a character sequence consisting of non-space characters only.
-    Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
-    The input array `words` contains at least one word.
-    """
-    res = []
-    cur_line = []
-    cur_len = 0
-
-    for word in words:
-        if cur_len + len(word) + len(cur_line) > maxWidth:
-            if len(cur_line) == 1:
-                res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
-            else:
-                spaces = maxWidth - cur_len
-                space_between = spaces // (len(cur_line) - 1)
-                extra_spaces = spaces % (len(cur_line) - 1)
-                line = ''
-                for i, w in enumerate(cur_line[:-1]):
-                    line += w + ' ' * (space_between + (i < extra_spaces))
-                line += cur_line[-1]
-                res.append(line)
-            cur_line = []
-            cur_len = 0
-        cur_line.append(word)
-        cur_len += len(word)
-
-    last_line = ' '.join(cur_line)
-    last_line += ' ' * (maxWidth - len(last_line))
-    res.append(last_line)
-
-    return res
-```
-
-[unit test results from previous impl]:
-Tested passed:
-
-Tests failed:
-assert fullJustify([], 10) == [] # output: ['          ']
-assert fullJustify([], 0) == [] # output: ['']
-
-[reflection on previous impl]:
-The implementation failed the test cases where the input list of words is empty. The issue arises because the code does not handle the case where there are no words to process. As a result, it still appends a line with spaces to the result list, even when there are no words. To fix this issue, we should add a condition at the beginning of the function to check if the input list is empty, and return an empty list if it is. This will ensure that the function returns the correct output for empty input lists.
-
-[improved impl]:
-```python
-from typing import *
-def fullJustify(words: List[str], maxWidth: int) -> List[str]:
-    """
-    Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
-    You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
-    Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
-    For the last line of text, it should be left justified and no extra space is inserted between words.
-    Note:
-    A word is defined as a character sequence consisting of non-space characters only.
-    Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
-    The input array `words` contains at least one word.
-    """
-    if not words:
-        return []
-
-    res = []
-    cur_line = []
-    cur_len = 0
-
-    for word in words:
-        if cur_len + len(word) + len(cur_line) > maxWidth:
-            if len(cur_line) == 1:
-                res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
-            else:
-                spaces = maxWidth - cur_len
-                space_between = spaces // (len(cur_line) - 1)
-                extra_spaces = spaces % (len(cur_line) - 1)
-                line = ''
-                for i, w in enumerate(cur_line[:-1]):
-                    line += w + ' ' * (space_between + (i < extra_spaces))
-                line += cur_line[-1]
-                res.append(line)
-            cur_line = []
-            cur_len = 0
-        cur_line.append(word)
-        cur_len += len(word)
-
-    last_line = ' '.join(cur_line)
-    last_line += ' ' * (maxWidth - len(last_line))
-    res.append(last_line)
-
-    return res
-```
-END EXAMPLES
-
-'''
-PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are a Python programming assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation."
-PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = "You are a Python programming assistant. You will be given a function implementation and a series of unit test results. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as guidance when you try again later. Only provide the few sentence description in your answer, not the implementation. You will be given a few examples by the user."
-PY_SELF_REFLECTION_FEW_SHOT = """Example 1:
-[function impl]:
-```python
-def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
-    n = len(nums)
-    left, right = 0, 0
-    max_length = 0
-    current_sum = 0
-    result = []
-    while right < n:
-        current_sum += nums[right]
-        while current_sum > target:
-            current_sum -= nums[left]
-            left += 1
-        if right - left + 1 >= max_length:
-            max_length = right - left + 1
-            result = nums[left:right+1]
-        right += 1
-    return result
-```
-[unit test results]:
-Tests passing:
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3]
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5]
-assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3]
-assert longest_subarray_with_sum_limit([], 10) == []
-assert longest_subarray_with_sum_limit([], 0) == []
-assert longest_subarray_with_sum_limit([], -5) == []  
-Tests failing:
-assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: [5]
-[self-reflection]:
-The implementation failed the where no subarray fulfills the condition. The issue in the implementation is due to the use of >= instead of > in the condition to update the result. Because of this, it returns a subarray even when the sum is greater than the target, as it still updates the result when the current subarray length is equal to the previous longest subarray length. To overcome this error, we should change the condition to only update the result when the current subarray length is strictly greater than the previous longest subarray length. This can be done by replacing >= with > in the condition.
-
-Example 2:
-[function impl]:
-```python
-def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
-    n = len(nums)
-    left, right = 0, 0
-    max_length = 0
-    current_sum = 0
-    result = []
-    while current_sum + nums[right] <= target:
-        current_sum += nums[right]
-        right += 1
-    while right < n:
-        current_sum += nums[right]
-        while current_sum > target:
-            current_sum -= nums[left]
-            left += 1
-        if right - left + 1 > max_length:
-            max_length = right - left + 1
-            result = nums[left:right+1]
-        right += 1
-    return result
-```
-[unit test results]:
-Tests passing:
-assert longest_subarray_with_sum_limit([], 10) == []
-assert longest_subarray_with_sum_limit([], 0) == []
-assert longest_subarray_with_sum_limit([], -5) == []
-Tests failing:
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] # output: list index out of range
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] # output: list index out of range
-assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: list index out of range
-assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] # output: list index out of range
-[self-reflection]:
-The implementation failed 4 out of the 7 test cases due to an IndexError. The issue stems from the while loop while current_sum + nums[right] <= target:, which directly accesses nums[right] without checking if right is within the bounds of the list. This results in a runtime error when right goes beyond the list length. To overcome this error, we need to add a bounds check for the right variable in the mentioned while loop. We can modify the loop condition to while right < len(nums) and current_sum + nums[right] <= target:. This change will ensure that we only access elements within the bounds of the list, thus avoiding the IndexError.
-END OF EXAMPLES
-"""
-
-PY_TEST_GENERATION_FEW_SHOT = """Examples:
-func signature:
-def add3Numbers(x, y, z):
-    \"\"\" Add three numbers together.
-    This function takes three numbers as input and returns the sum of the three numbers.
-    \"\"\"
-unit tests:
-assert add3Numbers(1, 2, 3) == 6
-assert add3Numbers(-1, 2, 3) == 4
-assert add3Numbers(1, -2, 3) == 2
-assert add3Numbers(1, 2, -3) == 0
-assert add3Numbers(-3, -2, -1) == -6
-assert add3Numbers(0, 0, 0) == 0
-"""
-
-PY_TEST_GENERATION_COMPLETION_INSTRUCTION = f"""You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. Call your function answer().
-
-{PY_TEST_GENERATION_FEW_SHOT}"""
-
-PY_TEST_GENERATION_CHAT_INSTRUCTION = """You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. Call your function answer()."""
-
-
-class PyGenerator(Generator):
-    def self_reflection(self, func: str, feedback: str, model: ModelBase) -> str:
-        return generic_generate_self_reflection(
-            func=func,
-            feedback=feedback,
-            model=model,
-            self_reflection_chat_instruction=PY_SELF_REFLECTION_CHAT_INSTRUCTION,
-            self_reflection_completion_instruction=PY_SELF_REFLECTION_COMPLETION_INSTRUCTION,
-            add_code_block=lambda x: add_code_block(x, "python"),
-            self_reflection_few_shot=PY_SELF_REFLECTION_FEW_SHOT
-        )
-
-    def func_impl(
-        self,
-        func_sig: str,
-        model: ModelBase,
-        strategy: str,
-        prev_func_impl: Optional[str] = None,
-        feedback: Optional[str] = None,
-        self_reflection: Optional[str] = None,
-        num_comps: int = 1,
-        temperature: float = 0.8,
-        acc_feedback: Optional[str] = None,
-        acc_reflection: Optional[str] = None,
-    ) -> Union[str, List[str]]:
-        if strategy == "mcts":
-            return generate_with_accumulated_context(
-                func_sig=func_sig,
-                model=model,
-                strategy="reflexion",
-                prev_func_impl=prev_func_impl,
-                accumulated_feedback=acc_feedback,
-                accumulated_reflection=acc_reflection,
-                num_comps=num_comps,
-                temperature=temperature,
-                reflexion_chat_instruction=PY_REFLEXION_CHAT_INSTRUCTION,
-                reflexion_few_shot=PY_REFLEXION_FEW_SHOT_ADD,
-                simple_chat_instruction=PY_SIMPLE_CHAT_INSTRUCTION,
-                reflexion_completion_instruction=PY_REFLEXION_COMPLETION_INSTRUCTION,
-                simple_completion_instruction=PY_SIMPLE_COMPLETION_INSTRUCTION,
-                code_block_instruction=USE_PYTHON_CODEBLOCK_INSTRUCTION,
-                parse_code_block=lambda x: parse_code_block(x, "python"),
-                add_code_block=lambda x: add_code_block(x, "python"),
-            )
-        else:
-            return generic_generate_func_impl(
-                func_sig=func_sig,
-                model=model,
-                strategy=strategy,
-                prev_func_impl=prev_func_impl,
-                feedback=feedback,
-                self_reflection=self_reflection,
-                num_comps=num_comps,
-                temperature=temperature,
-                reflexion_chat_instruction=PY_REFLEXION_CHAT_INSTRUCTION,
-                reflexion_few_shot=PY_REFLEXION_FEW_SHOT_ADD,
-                simple_chat_instruction=PY_SIMPLE_CHAT_INSTRUCTION,
-                reflexion_completion_instruction=PY_REFLEXION_COMPLETION_INSTRUCTION,
-                simple_completion_instruction=PY_SIMPLE_COMPLETION_INSTRUCTION,
-                code_block_instruction=USE_PYTHON_CODEBLOCK_INSTRUCTION,
-                parse_code_block=lambda x: parse_code_block(x, "python"),
-                add_code_block=lambda x: add_code_block(x, "python"),
-            )
-
-    def internal_tests(self, func_sig: str, model: ModelBase, max_num_tests: int = 4) -> List[str]:
-        def parse_tests(tests: str) -> List[str]:
-            return [test.strip() for test in tests.splitlines() if "assert" in test]
-        """
-        Generates tests for a function.
-        """
-        return generic_generate_internal_tests(
-            func_sig=func_sig,
-            model=model,
-            max_num_tests=max_num_tests,
-            test_generation_few_shot=PY_TEST_GENERATION_FEW_SHOT,
-            test_generation_chat_instruction=PY_TEST_GENERATION_CHAT_INSTRUCTION,
-            test_generation_completion_instruction=PY_TEST_GENERATION_COMPLETION_INSTRUCTION,
-            parse_tests=parse_tests,
-            is_syntax_valid=py_is_syntax_valid,
-        )
-
-
-DUMMY_FUNC_SIG = "def func():"
-DUMMY_FUNC_CALL = "func()"
-
-
-def handle_first_line_indent(func_body: str) -> str:
-    if func_body.startswith("    "):
-        return func_body
-    split = func_body.splitlines()
-    return f"    {split[0]}\n" + "\n".join(split[1:])
-
-
-def handle_entire_body_indent(func_body: str) -> str:
-    split = func_body.splitlines()
-    res = "\n".join(["    " + line for line in split])
-    return res
-
-
-def fix_turbo_response(func_body: str) -> str:
-    return fix_markdown(remove_unindented_signatures(func_body))
-
-
-def fix_markdown(func_body: str) -> str:
-    return re.sub("`{3}", "", func_body)
-
-
-def remove_unindented_signatures(code: str) -> str:
-    regex = r"^def\s+\w+\s*\("
-
-    before_signature = []
-    after_signature = []
-    signature_found = False
-
-    for line in code.split("\n"):
-        if re.match(regex, line):
-            signature_found = True
-            continue
-
-        if signature_found:
-            after_signature.append(line)
-        else:
-            if not line.startswith("    ") and line.strip():
-                line = "    " + line
-            before_signature.append(line)
-
-    return "\n".join(before_signature + after_signature)
-
-
-def py_fix_indentation(func_body: str) -> str:
-    func_body = fix_turbo_response(func_body)
-    """
-    3 cases:
-        1. good syntax
-        2. first line not good
-        3. entire body not good
-    """
-    def parse_indent_rec(f_body: str, cur_state: int) -> str:
-        f_body = fix_markdown(f_body)
-        if cur_state > 1:
-            return f_body
-        code = f'{DUMMY_FUNC_SIG}\n{f_body}\n{DUMMY_FUNC_CALL}'
-        try:
-            exec(code)
-            return f_body
-        except (IndentationError, SyntaxError):
-            p_func = handle_first_line_indent if cur_state == 0 else handle_entire_body_indent
-            return parse_indent_rec(p_func(func_body), cur_state + 1)
-        except Exception:
-            return f_body
-    return parse_indent_rec(func_body, 0)
-
-
-def py_is_syntax_valid(code: str) -> bool:
-    try:
-        ast.parse(code)
-        return True
-    except Exception:
-        return False
diff --git a/spaces/AUBMC-AIM/MammoGANesis/README.md b/spaces/AUBMC-AIM/MammoGANesis/README.md
deleted file mode 100644
index 0013fdb3edd85717040725803aee03aacc2c1809..0000000000000000000000000000000000000000
--- a/spaces/AUBMC-AIM/MammoGANesis/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: MammoGANesis
-emoji: 🔥
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/__init__.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/__init__.py
deleted file mode 100644
index 750e4bee526b17e354cbb6dcdda8e5ea759e9634..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from . import baseline
-from . import cbhg
-from . import gpt
-from . import seq2seq
-from . import tacotron_based
\ No newline at end of file
diff --git a/spaces/AdamOswald1/finetuned_diffusion/style.css b/spaces/AdamOswald1/finetuned_diffusion/style.css
deleted file mode 100644
index 9bfa78cc983f84693cf7cbab1e3bfd0e0d36c944..0000000000000000000000000000000000000000
--- a/spaces/AdamOswald1/finetuned_diffusion/style.css
+++ /dev/null
@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
-    display:inline-flex;
-    align-items:center;
-    gap:.8rem;
-    font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
-    font-weight:900;
-    margin-bottom:7px
-}
-.finetuned-diffusion-div p{
-    margin-bottom:10px;
-    font-size:94%
-}
-a{
-    text-decoration:underline
-}
-.tabs{
-    margin-top:0;
-    margin-bottom:0
-}
-#gallery{
-    min-height:20rem
-}
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/containerlite-plugin.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/containerlite-plugin.js
deleted file mode 100644
index bac1f94a2a83731c5b2d8084a73d410dd8a7c4c7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/containerlite-plugin.js
+++ /dev/null
@@ -1,27 +0,0 @@
-import Factory from './gameobjects/container/containerlite/Factory.js';
-import Creator from './gameobjects/container/containerlite/Creator.js';
-import ContainerLite from './gameobjects/container/containerlite/ContainerLite.js';
-import SetValue from './utils/object/SetValue.js';
-
-class ContainerLitePlugin extends Phaser.Plugins.BasePlugin {
-
-    constructor(pluginManager) {
-        super(pluginManager);
-
-        //  Register our new Game Object type
-        pluginManager.registerGameObject('rexContainerLite', Factory, Creator);
-    }
-
-    start() {
-        var eventEmitter = this.game.events;
-        eventEmitter.on('destroy', this.destroy, this);
-    }
-
-    getParent(child) {
-        return ContainerLite.GetParent(child);
-    }
-}
-
-SetValue(window, 'RexPlugins.GameObjects.ContainerLite', ContainerLite);
-
-export default ContainerLitePlugin;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ProportionMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ProportionMethods.js
deleted file mode 100644
index 23a2d83314e3a4fb03068dddd8d48bd19a1f71f3..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ProportionMethods.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export default {
-    getChildProportion(gameObject) {
-        return this.getSizerConfig(gameObject).proportion;
-    },
-
-    setChildProportion(gameObject, proportion) {
-        this.getSizerConfig(gameObject).proportion = proportion;
-        return this;
-    },
-
-}
\ No newline at end of file
diff --git a/spaces/AlekseyCalvin/Make-Putin-Queer/README.md b/spaces/AlekseyCalvin/Make-Putin-Queer/README.md
deleted file mode 100644
index e9ad90420a613f52f1d708d310a85e2163033fe2..0000000000000000000000000000000000000000
--- a/spaces/AlekseyCalvin/Make-Putin-Queer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Make Putin Queer! Use "trp" token in prompts
-emoji: 🏳️‍🌈
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlowaSawsan/Third-Molar-Segmentation/app.py b/spaces/AlowaSawsan/Third-Molar-Segmentation/app.py
deleted file mode 100644
index 87f324a376c4522ae69ecb4c105b1f1ad0195137..0000000000000000000000000000000000000000
--- a/spaces/AlowaSawsan/Third-Molar-Segmentation/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-#import required libraries-----------------------------------------------
-import streamlit as st
-from tensorflow import keras
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import cv2
-
-#loading model-----------------------------------------------------------
-model=keras.models.load_model("3rdm_att_UNet_50epochs_acc.h5")
-
-#User Interface----------------------------------------------------------
-st.header("Segmentation of the Lower Left Third Molar in Panoramic X-ray Images Using Attention U-Net")
-
-examples=["2.png","20.png","31.png"]
-
-def load_image(image_file):
-      img = Image.open(image_file)
-      return img
-
-st.subheader("Instruction:")
-st.subheader("Please select from the provided samples or upload dental panoramic X-ray image")
-image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
-
-col1, col2, col3 = st.columns(3)
-with col1:
-    ex=load_image(examples[0])
-    st.image(ex,width=200)
-    if st.button('Sample 1'):
-        image_file=examples[0]
-
-with col2:
-    ex1=load_image(examples[1])
-    st.image(ex1,width=200)
-    if st.button('Sample 2'):
-        image_file=examples[1]
-
-with col3:
-    ex2=load_image(examples[2])
-    st.image(ex2,width=200)
-    if st.button('Sample 3'):
-        image_file=examples[2]
-    
-#main--------------------------------------------------------------------
-   
-if image_file is not None:
-
-      img=load_image(image_file)
-      
-      st.text("Selected Image ....")
-      st.image(img,width=850)
-      
-      img = np.asarray(img)     
-      img = cv2.resize(img, (512, 256))
-      img = cv2.cvtColor(img, cv2.COLOR_BAYER_GR2GRAY)
-      img = np.expand_dims(img, axis=0)
-
-      prediction = model.predict(img)
-
-      output = prediction.reshape(256,512)
-
-
-      if output is not None :      
-          st.text("Result")  
-          #st.write(output.shape)
-          st.image(output,width=850)
-
-      st.text("DONE")
\ No newline at end of file
diff --git a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/symbols.py b/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/symbols.py
deleted file mode 100644
index 053a7105f7ce95aa51614f6995399fa2172b3eb2..0000000000000000000000000000000000000000
--- a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/symbols.py
+++ /dev/null
@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-'''# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-'''
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-'''# cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-'''
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/README.md
deleted file mode 100644
index 204d9c951c996fedabc169d9a32781be9f4c4cc1..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-## Diffusers examples with ONNXRuntime optimizations
-
-**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.**
-
-This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime.
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py
deleted file mode 100644
index 6eae78f2801ebaca42d3c22262d2f839e16403d0..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-
-from ...schedulers import DDIMScheduler
-from ...utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class DDIMPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for image generation.
-
-    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.).
-
-    Parameters:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-
-        # make sure scheduler can always be converted to DDIM
-        scheduler = DDIMScheduler.from_config(scheduler.config)
-
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        eta: float = 0.0,
-        num_inference_steps: int = 50,
-        use_clipped_model_output: Optional[bool] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            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. A value of `0` corresponds to
-                DDIM and `1` corresponds to DDPM.
-            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.
-            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
-                If `True` or `False`, see documentation for [`DDIMScheduler.step`]. If `None`, nothing is passed
-                downstream to the scheduler (use `None` for schedulers which don't support this 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.ImagePipelineOutput`] instead of a plain tuple.
-
-        Example:
-
-        ```py
-        >>> from diffusers import DDIMPipeline
-        >>> import PIL.Image
-        >>> import numpy as np
-
-        >>> # load model and scheduler
-        >>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom")
-
-        >>> # run pipeline in inference (sample random noise and denoise)
-        >>> image = pipe(eta=0.0, num_inference_steps=50)
-
-        >>> # process image to PIL
-        >>> image_processed = image.cpu().permute(0, 2, 3, 1)
-        >>> image_processed = (image_processed + 1.0) * 127.5
-        >>> image_processed = image_processed.numpy().astype(np.uint8)
-        >>> image_pil = PIL.Image.fromarray(image_processed[0])
-
-        >>> # save image
-        >>> image_pil.save("test.png")
-        ```
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.config.sample_size, int):
-            image_shape = (
-                batch_size,
-                self.unet.config.in_channels,
-                self.unet.config.sample_size,
-                self.unet.config.sample_size,
-            )
-        else:
-            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
-
-        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."
-            )
-
-        image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
-
-            # 2. predict previous mean of image x_t-1 and add variance depending on eta
-            # eta corresponds to η in paper and should be between [0, 1]
-            # do x_t -> x_t-1
-            image = self.scheduler.step(
-                model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
-            ).prev_sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
deleted file mode 100644
index 6f886e1c407ff9376929a7092f82e5508d2b1ac9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
deleted file mode 100644
index 008a70ae67454c3fd470c29ffd000b18db391c8e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 92accfc703fc398d2845d7dc2f1d5336f24738e8..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
deleted file mode 100644
index a3c5435142db6b1f81421f5fd96d07ece32b5f38..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/lraspp_m-v3-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-
-# Re-config the data sampler.
-data = dict(samples_per_gpu=4, workers_per_gpu=4)
-
-runner = dict(type='IterBasedRunner', max_iters=320000)
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/sd_api_pictures/style.css b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/sd_api_pictures/style.css
deleted file mode 100644
index 6f4994616a1d4ca52f3a8245f963ce0b7ebbb0d7..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/sd_api_pictures/style.css
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Align the elements for SD_api_picture extension */
-.SDAP #sampler_box {
-  padding-top: var(--spacing-sm);
-  padding-bottom: var(--spacing-sm);
-  border: 0;
-}
-
-.SDAP #steps_box {
-  border-radius: 0 0 var(--block-radius) var(--block-radius);
-}
-
-.SDAP #sampler_col {
-  gap: 0;
-  padding: 0;
-  background-color: transparent;
-}
-
-.SDAP #sampler_row {
-    border-bottom: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius) var(--block-radius) 0 0;
-    background: var(--block-background-fill);
-    gap: 0;
-}
-
-.SDAP #sampler_row .refresh-button {
-    margin-bottom: var(--spacing-sm);
-    margin-right: var(--spacing-lg);
-}
-
-.SDAP #seed_box,
-.SDAP #cfg_box {
-  padding-top: var(--spacing-md);
-}
-
-.SDAP #sampler_box span,
-.SDAP #seed_box span,
-.SDAP #cfg_box span,
-.SDAP #steps_box span {
-  margin-bottom: var(--spacing-sm);
-}
-
-.SDAP svg.dropdown-arrow {
-  flex-shrink: 0 !important;
-  margin: 0px !important;
-}
-
-.SDAP .hires_opts input[type="number"] {
-  width: 6em !important;
-}
diff --git a/spaces/Anonymous-sub/Rerender/gmflow_module/README.md b/spaces/Anonymous-sub/Rerender/gmflow_module/README.md
deleted file mode 100644
index 17449970f8861dec7fef8d8835fc7e92abeb2332..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/gmflow_module/README.md
+++ /dev/null
@@ -1,239 +0,0 @@
-# GMFlow
-
-
-Official PyTorch implementation of paper:
-
-[**GMFlow: Learning Optical Flow via Global Matching**](https://arxiv.org/abs/2111.13680), **CVPR 2022, Oral**
-
-Authors: [Haofei Xu](https://haofeixu.github.io/), [Jing Zhang](https://scholar.google.com.hk/citations?user=9jH5v74AAAAJ), [Jianfei Cai](https://jianfei-cai.github.io/), [Hamid Rezatofighi](https://scholar.google.com/citations?user=VxAuxMwAAAAJ), [Dacheng Tao](https://scholar.google.com/citations?user=RwlJNLcAAAAJ)
-
-
-**11/15/2022 Update: Check out our new work: [Unifying Flow, Stereo and Depth Estimation](https://haofeixu.github.io/unimatch/) and code: [unimatch](https://github.com/autonomousvision/unimatch) for extending GMFlow to stereo and depth tasks. [More pretrained GMFlow models](https://github.com/autonomousvision/unimatch/blob/master/MODEL_ZOO.md) with different speed-accuracy trade-offs are also released. Check out our [Colab](https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing) and [HuggingFace](https://huggingface.co/spaces/haofeixu/unimatch) demo to play with GMFlow in your browser!**
-
-
-
-**A [video introduction](https://www.bilibili.com/video/BV18A4y1R7PL) (in Chinese) of GMFlow is available at bilibili!**
-
-
-
-https://user-images.githubusercontent.com/19343475/174446408-520b8a6c-9714-4ff3-978c-98e23ab29c1f.mp4
-
-
-
-
-
-We streamline the optical flow estimation pipeline by reformulating optical flow as a **global matching** problem.
-
-
-
-
-<p align="center"><img width=90% src="assets/gmflow.png"></p>
-
-
-
-
-
-## Highlights
-
-- **Flexible & Modular design**
-
-  We decompose the end-to-end optical flow framework into five components:
-
-  feature extraction, feature enhancement, feature matching, flow propagation and flow refinement.
-
-  One can easily construct a customized optical flow model by combining different components.
-
-- **High accuracy**
-
-  With only one refinement, GMFlow outperforms 31-refinements RAFT on the challenging Sintel benchmark.
-
-- **High efficiency**
-
-  A basic GMFlow model (without refinement) runs at 57ms (V100) or 26ms (A100) for Sintel data (436x1024). 
-
-  GMFlow gains more speedup than RAFT on high-end GPUs (e.g., A100) since GMFlow doesn't require a large number of sequential computation.
-
-  GMFlow also simplifies backward flow computation without requiring to forward the network twice. The bidirectional flow can be used for occlusion detection with forward-backward consistency check.
-
-  <p align="center"><img width=90% src="assets/bidir_flow_occ.png"></p>
-
-  
-
-
-## Installation
-
-Our code is based on pytorch 1.9.0, CUDA 10.2 and python 3.8. Higher version pytorch should also work well.
-
-We recommend using [conda](https://www.anaconda.com/distribution/) for installation:
-
-```
-conda env create -f environment.yml
-conda activate gmflow
-```
-
-## Demos
-
-All pretrained models can be downloaded from [google drive](https://drive.google.com/file/d/1d5C5cgHIxWGsFR1vYs5XrQbbUiZl9TX2/view?usp=sharing).
-
-
-
-You can run a trained model on a sequence of images and visualize the results:
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_market_1 \
---output_path output/gmflow-norefine-sintel_market_1 \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-```
-
-You can also predict bidirectional flow with `--pred_bidir_flow` enabled and use `--fwd_bwd_consistency_check` for forward-backward consistency check. More examples can be found in [scripts/demo.sh](scripts/demo.sh).
-
-
-
-## Datasets
-
-The datasets used to train and evaluate GMFlow are as follows:
-
-* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
-* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
-* [Sintel](http://sintel.is.tue.mpg.de/)
-* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
-* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) 
-
-By default the dataloader [datasets.py](data/datasets.py) assumes the datasets are located in folder `datasets` and are organized as follows:
-
-```
-datasets
-├── FlyingChairs_release
-│   └── data
-├── FlyingThings3D
-│   ├── frames_cleanpass
-│   ├── frames_finalpass
-│   └── optical_flow
-├── HD1K
-│   ├── hd1k_challenge
-│   ├── hd1k_flow_gt
-│   ├── hd1k_flow_uncertainty
-│   └── hd1k_input
-├── KITTI
-│   ├── testing
-│   └── training
-├── Sintel
-│   ├── test
-│   └── training
-```
-
-It is recommended to symlink your dataset root to `datasets`:
-
-```shell
-ln -s $YOUR_DATASET_ROOT datasets
-```
-
-Otherwise, you may need to change the corresponding paths in [datasets.py](data/datasets.py).
-
-
-
-## Evaluation
-
-You can evaluate a trained GMFlow model by running:
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py --eval --val_dataset things sintel --resume pretrained/gmflow_things-e9887eda.pth 
-```
-
-More evaluation scripts can be found in [scripts/evaluate.sh](scripts/evaluate.sh).
-
-
-
-For submission to Sintel and KITTI online test sets, you can run [scripts/submission.sh](scripts/submission.sh).
-
-
-
-## Training
-
-All training scripts on FlyingChairs, FlyingThings3D, Sintel and KITTI datasets can be found in [scripts/train_gmflow.sh](scripts/train_gmflow.sh) and [scripts/train_gmflow_with_refine.sh](scripts/train_gmflow_with_refine.sh).
-
-Note that the basic GMFlow model (without refinement) can be trained on 4x 16GB V100 GPUs. For training GMFlow with refinement, 8x 16GB V100 or 4x 32GB V100 or 4x 40GB A100 GPUs are required by default. You may need to tune the batch size and training iterations according to your hardware. 
-
-
-
-We support using tensorboard to monitor and visualize the training process. You can first start a tensorboard session with
-
-```shell
-tensorboard --logdir checkpoints
-```
-
-and then access [http://localhost:6006](http://localhost:6006) in your browser.
-
-
-
-## Citation
-
-If you find our work useful in your research, please consider citing our paper:
-
-```
-@inproceedings{xu2022gmflow,
-  title={GMFlow: Learning Optical Flow via Global Matching},
-  author={Xu, Haofei and Zhang, Jing and Cai, Jianfei and Rezatofighi, Hamid and Tao, Dacheng},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={8121-8130},
-  year={2022}
-}
-```
-
-
-
-## Acknowledgements
-
-This project would not have been possible without relying on some awesome repos : [RAFT](https://github.com/princeton-vl/RAFT), [LoFTR](https://github.com/zju3dv/LoFTR), [DETR](https://github.com/facebookresearch/detr), [Swin](https://github.com/microsoft/Swin-Transformer), [mmdetection](https://github.com/open-mmlab/mmdetection) and [Detectron2](https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py). We thank the original authors for their excellent work.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Artrajz/vits-simple-api/utils/download.py b/spaces/Artrajz/vits-simple-api/utils/download.py
deleted file mode 100644
index 5be259bc8baeb28b6bee6e929eb3a9591b317dab..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/download.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import logging
-import os
-import hashlib
-import tarfile
-import urllib.request
-import zipfile
-
-from tqdm import tqdm
-from pathlib import Path
-from logger import logger
-from py7zr import SevenZipFile
-
-
-class TqdmUpTo(tqdm):
-    def update_to(self, b=1, bsize=1, tsize=None):
-        if tsize is not None:
-            self.total = tsize
-        self.update(b * bsize - self.n)
-
-
-def download_file(url, dest_path):
-    logging.info(f"Downloading: {url}")
-    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=url.split('/')[-1]) as t:
-        urllib.request.urlretrieve(url, dest_path, reporthook=t.update_to)
-
-
-def verify_md5(file_path, expected_md5):
-    md5 = hashlib.md5(file_path.read_bytes()).hexdigest()
-    if md5 != expected_md5:
-        return False, f"MD5 mismatch: {md5} != {expected_md5}"
-    return True, ""
-
-
-def extract_file(file_path, destination=None):
-    """
-    Extract a compressed file based on its extension.
-    If destination is not specified, it will be extracted to its parent directory.
-    """
-    if destination is None:
-        destination = Path(file_path).parent
-
-    logging.info(f"Extracting to {destination}")
-
-    if file_path.endswith('.zip'):
-        with zipfile.ZipFile(file_path, 'r') as zip_ref:
-            zip_ref.extractall(destination)
-    elif file_path.endswith('.tar.gz'):
-        with tarfile.open(file_path, 'r:gz') as tar_ref:
-            tar_ref.extractall(destination)
-    elif file_path.endswith('.tar.bz2'):
-        with tarfile.open(file_path, 'r:bz2') as tar_ref:
-            tar_ref.extractall(destination)
-    elif file_path.endswith('.7z'):
-        with SevenZipFile(file_path, mode='r') as z:
-            z.extractall(destination)
-    else:
-        logging.error(f"Unsupported compression format for file {file_path}")
-
-
-def download_and_verify(urls, target_path, expected_md5=None, extract_destination=None):
-    for url in urls:
-        try:
-            download_file(url, target_path)
-            break
-        except Exception as error:
-            logger.error(f"downloading from URL {url}: {error}")
-
-    else:  # This else is tied to the for loop, and executes if no download is successful
-        return False, "Error downloading from all provided URLs."
-
-    if expected_md5 is not None:
-        success, message = verify_md5(Path(target_path), expected_md5)
-        if not success:
-            os.remove(target_path)
-            return False, message
-
-    # If it's a compressed file, extract it
-    if target_path.endswith(('.zip', '.tar.gz', '.tar.bz2', '.7z')):
-        extract_file(target_path, extract_destination)
-        os.remove(target_path)
-
-    return True, "File downloaded, verified, and extracted successfully!"
-
-
-if __name__ == "__main__":
-    URLS = [
-        "YOUR_PRIMARY_URL_HERE",
-        "YOUR_FIRST_BACKUP_URL_HERE",
-        # ... you can add more backup URLs as needed
-    ]
-    TARGET_PATH = ""
-    EXPECTED_MD5 = ""
-    EXTRACT_DESTINATION = ""
-
-    success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5, EXTRACT_DESTINATION)
-    print(message)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/cachecontrol/caches/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/cachecontrol/caches/__init__.py
deleted file mode 100644
index 37827291fb5a76d7ef9a7a3a695710b2074ca09a..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/cachecontrol/caches/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from .file_cache import FileCache, SeparateBodyFileCache
-from .redis_cache import RedisCache
-
-
-__all__ = ["FileCache", "SeparateBodyFileCache", "RedisCache"]
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/style.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/style.py
deleted file mode 100644
index 313c889496d90cef94d5537c122e5c5e898e3bb4..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/style.py
+++ /dev/null
@@ -1,796 +0,0 @@
-import sys
-from functools import lru_cache
-from marshal import dumps, loads
-from random import randint
-from typing import Any, Dict, Iterable, List, Optional, Type, Union, cast
-
-from . import errors
-from .color import Color, ColorParseError, ColorSystem, blend_rgb
-from .repr import Result, rich_repr
-from .terminal_theme import DEFAULT_TERMINAL_THEME, TerminalTheme
-
-# Style instances and style definitions are often interchangeable
-StyleType = Union[str, "Style"]
-
-
-class _Bit:
-    """A descriptor to get/set a style attribute bit."""
-
-    __slots__ = ["bit"]
-
-    def __init__(self, bit_no: int) -> None:
-        self.bit = 1 << bit_no
-
-    def __get__(self, obj: "Style", objtype: Type["Style"]) -> Optional[bool]:
-        if obj._set_attributes & self.bit:
-            return obj._attributes & self.bit != 0
-        return None
-
-
-@rich_repr
-class Style:
-    """A terminal style.
-
-    A terminal style consists of a color (`color`), a background color (`bgcolor`), and a number of attributes, such
-    as bold, italic etc. The attributes have 3 states: they can either be on
-    (``True``), off (``False``), or not set (``None``).
-
-    Args:
-        color (Union[Color, str], optional): Color of terminal text. Defaults to None.
-        bgcolor (Union[Color, str], optional): Color of terminal background. Defaults to None.
-        bold (bool, optional): Enable bold text. Defaults to None.
-        dim (bool, optional): Enable dim text. Defaults to None.
-        italic (bool, optional): Enable italic text. Defaults to None.
-        underline (bool, optional): Enable underlined text. Defaults to None.
-        blink (bool, optional): Enabled blinking text. Defaults to None.
-        blink2 (bool, optional): Enable fast blinking text. Defaults to None.
-        reverse (bool, optional): Enabled reverse text. Defaults to None.
-        conceal (bool, optional): Enable concealed text. Defaults to None.
-        strike (bool, optional): Enable strikethrough text. Defaults to None.
-        underline2 (bool, optional): Enable doubly underlined text. Defaults to None.
-        frame (bool, optional): Enable framed text. Defaults to None.
-        encircle (bool, optional): Enable encircled text. Defaults to None.
-        overline (bool, optional): Enable overlined text. Defaults to None.
-        link (str, link): Link URL. Defaults to None.
-
-    """
-
-    _color: Optional[Color]
-    _bgcolor: Optional[Color]
-    _attributes: int
-    _set_attributes: int
-    _hash: Optional[int]
-    _null: bool
-    _meta: Optional[bytes]
-
-    __slots__ = [
-        "_color",
-        "_bgcolor",
-        "_attributes",
-        "_set_attributes",
-        "_link",
-        "_link_id",
-        "_ansi",
-        "_style_definition",
-        "_hash",
-        "_null",
-        "_meta",
-    ]
-
-    # maps bits on to SGR parameter
-    _style_map = {
-        0: "1",
-        1: "2",
-        2: "3",
-        3: "4",
-        4: "5",
-        5: "6",
-        6: "7",
-        7: "8",
-        8: "9",
-        9: "21",
-        10: "51",
-        11: "52",
-        12: "53",
-    }
-
-    STYLE_ATTRIBUTES = {
-        "dim": "dim",
-        "d": "dim",
-        "bold": "bold",
-        "b": "bold",
-        "italic": "italic",
-        "i": "italic",
-        "underline": "underline",
-        "u": "underline",
-        "blink": "blink",
-        "blink2": "blink2",
-        "reverse": "reverse",
-        "r": "reverse",
-        "conceal": "conceal",
-        "c": "conceal",
-        "strike": "strike",
-        "s": "strike",
-        "underline2": "underline2",
-        "uu": "underline2",
-        "frame": "frame",
-        "encircle": "encircle",
-        "overline": "overline",
-        "o": "overline",
-    }
-
-    def __init__(
-        self,
-        *,
-        color: Optional[Union[Color, str]] = None,
-        bgcolor: Optional[Union[Color, str]] = None,
-        bold: Optional[bool] = None,
-        dim: Optional[bool] = None,
-        italic: Optional[bool] = None,
-        underline: Optional[bool] = None,
-        blink: Optional[bool] = None,
-        blink2: Optional[bool] = None,
-        reverse: Optional[bool] = None,
-        conceal: Optional[bool] = None,
-        strike: Optional[bool] = None,
-        underline2: Optional[bool] = None,
-        frame: Optional[bool] = None,
-        encircle: Optional[bool] = None,
-        overline: Optional[bool] = None,
-        link: Optional[str] = None,
-        meta: Optional[Dict[str, Any]] = None,
-    ):
-        self._ansi: Optional[str] = None
-        self._style_definition: Optional[str] = None
-
-        def _make_color(color: Union[Color, str]) -> Color:
-            return color if isinstance(color, Color) else Color.parse(color)
-
-        self._color = None if color is None else _make_color(color)
-        self._bgcolor = None if bgcolor is None else _make_color(bgcolor)
-        self._set_attributes = sum(
-            (
-                bold is not None,
-                dim is not None and 2,
-                italic is not None and 4,
-                underline is not None and 8,
-                blink is not None and 16,
-                blink2 is not None and 32,
-                reverse is not None and 64,
-                conceal is not None and 128,
-                strike is not None and 256,
-                underline2 is not None and 512,
-                frame is not None and 1024,
-                encircle is not None and 2048,
-                overline is not None and 4096,
-            )
-        )
-        self._attributes = (
-            sum(
-                (
-                    bold and 1 or 0,
-                    dim and 2 or 0,
-                    italic and 4 or 0,
-                    underline and 8 or 0,
-                    blink and 16 or 0,
-                    blink2 and 32 or 0,
-                    reverse and 64 or 0,
-                    conceal and 128 or 0,
-                    strike and 256 or 0,
-                    underline2 and 512 or 0,
-                    frame and 1024 or 0,
-                    encircle and 2048 or 0,
-                    overline and 4096 or 0,
-                )
-            )
-            if self._set_attributes
-            else 0
-        )
-
-        self._link = link
-        self._meta = None if meta is None else dumps(meta)
-        self._link_id = (
-            f"{randint(0, 999999)}{hash(self._meta)}" if (link or meta) else ""
-        )
-        self._hash: Optional[int] = None
-        self._null = not (self._set_attributes or color or bgcolor or link or meta)
-
-    @classmethod
-    def null(cls) -> "Style":
-        """Create an 'null' style, equivalent to Style(), but more performant."""
-        return NULL_STYLE
-
-    @classmethod
-    def from_color(
-        cls, color: Optional[Color] = None, bgcolor: Optional[Color] = None
-    ) -> "Style":
-        """Create a new style with colors and no attributes.
-
-        Returns:
-            color (Optional[Color]): A (foreground) color, or None for no color. Defaults to None.
-            bgcolor (Optional[Color]): A (background) color, or None for no color. Defaults to None.
-        """
-        style: Style = cls.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = color
-        style._bgcolor = bgcolor
-        style._set_attributes = 0
-        style._attributes = 0
-        style._link = None
-        style._link_id = ""
-        style._meta = None
-        style._null = not (color or bgcolor)
-        style._hash = None
-        return style
-
-    @classmethod
-    def from_meta(cls, meta: Optional[Dict[str, Any]]) -> "Style":
-        """Create a new style with meta data.
-
-        Returns:
-            meta (Optional[Dict[str, Any]]): A dictionary of meta data. Defaults to None.
-        """
-        style: Style = cls.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = None
-        style._bgcolor = None
-        style._set_attributes = 0
-        style._attributes = 0
-        style._link = None
-        style._meta = dumps(meta)
-        style._link_id = f"{randint(0, 999999)}{hash(style._meta)}"
-        style._hash = None
-        style._null = not (meta)
-        return style
-
-    @classmethod
-    def on(cls, meta: Optional[Dict[str, Any]] = None, **handlers: Any) -> "Style":
-        """Create a blank style with meta information.
-
-        Example:
-            style = Style.on(click=self.on_click)
-
-        Args:
-            meta (Optional[Dict[str, Any]], optional): An optional dict of meta information.
-            **handlers (Any): Keyword arguments are translated in to handlers.
-
-        Returns:
-            Style: A Style with meta information attached.
-        """
-        meta = {} if meta is None else meta
-        meta.update({f"@{key}": value for key, value in handlers.items()})
-        return cls.from_meta(meta)
-
-    bold = _Bit(0)
-    dim = _Bit(1)
-    italic = _Bit(2)
-    underline = _Bit(3)
-    blink = _Bit(4)
-    blink2 = _Bit(5)
-    reverse = _Bit(6)
-    conceal = _Bit(7)
-    strike = _Bit(8)
-    underline2 = _Bit(9)
-    frame = _Bit(10)
-    encircle = _Bit(11)
-    overline = _Bit(12)
-
-    @property
-    def link_id(self) -> str:
-        """Get a link id, used in ansi code for links."""
-        return self._link_id
-
-    def __str__(self) -> str:
-        """Re-generate style definition from attributes."""
-        if self._style_definition is None:
-            attributes: List[str] = []
-            append = attributes.append
-            bits = self._set_attributes
-            if bits & 0b0000000001111:
-                if bits & 1:
-                    append("bold" if self.bold else "not bold")
-                if bits & (1 << 1):
-                    append("dim" if self.dim else "not dim")
-                if bits & (1 << 2):
-                    append("italic" if self.italic else "not italic")
-                if bits & (1 << 3):
-                    append("underline" if self.underline else "not underline")
-            if bits & 0b0000111110000:
-                if bits & (1 << 4):
-                    append("blink" if self.blink else "not blink")
-                if bits & (1 << 5):
-                    append("blink2" if self.blink2 else "not blink2")
-                if bits & (1 << 6):
-                    append("reverse" if self.reverse else "not reverse")
-                if bits & (1 << 7):
-                    append("conceal" if self.conceal else "not conceal")
-                if bits & (1 << 8):
-                    append("strike" if self.strike else "not strike")
-            if bits & 0b1111000000000:
-                if bits & (1 << 9):
-                    append("underline2" if self.underline2 else "not underline2")
-                if bits & (1 << 10):
-                    append("frame" if self.frame else "not frame")
-                if bits & (1 << 11):
-                    append("encircle" if self.encircle else "not encircle")
-                if bits & (1 << 12):
-                    append("overline" if self.overline else "not overline")
-            if self._color is not None:
-                append(self._color.name)
-            if self._bgcolor is not None:
-                append("on")
-                append(self._bgcolor.name)
-            if self._link:
-                append("link")
-                append(self._link)
-            self._style_definition = " ".join(attributes) or "none"
-        return self._style_definition
-
-    def __bool__(self) -> bool:
-        """A Style is false if it has no attributes, colors, or links."""
-        return not self._null
-
-    def _make_ansi_codes(self, color_system: ColorSystem) -> str:
-        """Generate ANSI codes for this style.
-
-        Args:
-            color_system (ColorSystem): Color system.
-
-        Returns:
-            str: String containing codes.
-        """
-
-        if self._ansi is None:
-            sgr: List[str] = []
-            append = sgr.append
-            _style_map = self._style_map
-            attributes = self._attributes & self._set_attributes
-            if attributes:
-                if attributes & 1:
-                    append(_style_map[0])
-                if attributes & 2:
-                    append(_style_map[1])
-                if attributes & 4:
-                    append(_style_map[2])
-                if attributes & 8:
-                    append(_style_map[3])
-                if attributes & 0b0000111110000:
-                    for bit in range(4, 9):
-                        if attributes & (1 << bit):
-                            append(_style_map[bit])
-                if attributes & 0b1111000000000:
-                    for bit in range(9, 13):
-                        if attributes & (1 << bit):
-                            append(_style_map[bit])
-            if self._color is not None:
-                sgr.extend(self._color.downgrade(color_system).get_ansi_codes())
-            if self._bgcolor is not None:
-                sgr.extend(
-                    self._bgcolor.downgrade(color_system).get_ansi_codes(
-                        foreground=False
-                    )
-                )
-            self._ansi = ";".join(sgr)
-        return self._ansi
-
-    @classmethod
-    @lru_cache(maxsize=1024)
-    def normalize(cls, style: str) -> str:
-        """Normalize a style definition so that styles with the same effect have the same string
-        representation.
-
-        Args:
-            style (str): A style definition.
-
-        Returns:
-            str: Normal form of style definition.
-        """
-        try:
-            return str(cls.parse(style))
-        except errors.StyleSyntaxError:
-            return style.strip().lower()
-
-    @classmethod
-    def pick_first(cls, *values: Optional[StyleType]) -> StyleType:
-        """Pick first non-None style."""
-        for value in values:
-            if value is not None:
-                return value
-        raise ValueError("expected at least one non-None style")
-
-    def __rich_repr__(self) -> Result:
-        yield "color", self.color, None
-        yield "bgcolor", self.bgcolor, None
-        yield "bold", self.bold, None,
-        yield "dim", self.dim, None,
-        yield "italic", self.italic, None
-        yield "underline", self.underline, None,
-        yield "blink", self.blink, None
-        yield "blink2", self.blink2, None
-        yield "reverse", self.reverse, None
-        yield "conceal", self.conceal, None
-        yield "strike", self.strike, None
-        yield "underline2", self.underline2, None
-        yield "frame", self.frame, None
-        yield "encircle", self.encircle, None
-        yield "link", self.link, None
-        if self._meta:
-            yield "meta", self.meta
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, Style):
-            return NotImplemented
-        return self.__hash__() == other.__hash__()
-
-    def __ne__(self, other: Any) -> bool:
-        if not isinstance(other, Style):
-            return NotImplemented
-        return self.__hash__() != other.__hash__()
-
-    def __hash__(self) -> int:
-        if self._hash is not None:
-            return self._hash
-        self._hash = hash(
-            (
-                self._color,
-                self._bgcolor,
-                self._attributes,
-                self._set_attributes,
-                self._link,
-                self._meta,
-            )
-        )
-        return self._hash
-
-    @property
-    def color(self) -> Optional[Color]:
-        """The foreground color or None if it is not set."""
-        return self._color
-
-    @property
-    def bgcolor(self) -> Optional[Color]:
-        """The background color or None if it is not set."""
-        return self._bgcolor
-
-    @property
-    def link(self) -> Optional[str]:
-        """Link text, if set."""
-        return self._link
-
-    @property
-    def transparent_background(self) -> bool:
-        """Check if the style specified a transparent background."""
-        return self.bgcolor is None or self.bgcolor.is_default
-
-    @property
-    def background_style(self) -> "Style":
-        """A Style with background only."""
-        return Style(bgcolor=self.bgcolor)
-
-    @property
-    def meta(self) -> Dict[str, Any]:
-        """Get meta information (can not be changed after construction)."""
-        return {} if self._meta is None else cast(Dict[str, Any], loads(self._meta))
-
-    @property
-    def without_color(self) -> "Style":
-        """Get a copy of the style with color removed."""
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = None
-        style._bgcolor = None
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = self._link
-        style._link_id = f"{randint(0, 999999)}" if self._link else ""
-        style._null = False
-        style._meta = None
-        style._hash = None
-        return style
-
-    @classmethod
-    @lru_cache(maxsize=4096)
-    def parse(cls, style_definition: str) -> "Style":
-        """Parse a style definition.
-
-        Args:
-            style_definition (str): A string containing a style.
-
-        Raises:
-            errors.StyleSyntaxError: If the style definition syntax is invalid.
-
-        Returns:
-            `Style`: A Style instance.
-        """
-        if style_definition.strip() == "none" or not style_definition:
-            return cls.null()
-
-        STYLE_ATTRIBUTES = cls.STYLE_ATTRIBUTES
-        color: Optional[str] = None
-        bgcolor: Optional[str] = None
-        attributes: Dict[str, Optional[Any]] = {}
-        link: Optional[str] = None
-
-        words = iter(style_definition.split())
-        for original_word in words:
-            word = original_word.lower()
-            if word == "on":
-                word = next(words, "")
-                if not word:
-                    raise errors.StyleSyntaxError("color expected after 'on'")
-                try:
-                    Color.parse(word) is None
-                except ColorParseError as error:
-                    raise errors.StyleSyntaxError(
-                        f"unable to parse {word!r} as background color; {error}"
-                    ) from None
-                bgcolor = word
-
-            elif word == "not":
-                word = next(words, "")
-                attribute = STYLE_ATTRIBUTES.get(word)
-                if attribute is None:
-                    raise errors.StyleSyntaxError(
-                        f"expected style attribute after 'not', found {word!r}"
-                    )
-                attributes[attribute] = False
-
-            elif word == "link":
-                word = next(words, "")
-                if not word:
-                    raise errors.StyleSyntaxError("URL expected after 'link'")
-                link = word
-
-            elif word in STYLE_ATTRIBUTES:
-                attributes[STYLE_ATTRIBUTES[word]] = True
-
-            else:
-                try:
-                    Color.parse(word)
-                except ColorParseError as error:
-                    raise errors.StyleSyntaxError(
-                        f"unable to parse {word!r} as color; {error}"
-                    ) from None
-                color = word
-        style = Style(color=color, bgcolor=bgcolor, link=link, **attributes)
-        return style
-
-    @lru_cache(maxsize=1024)
-    def get_html_style(self, theme: Optional[TerminalTheme] = None) -> str:
-        """Get a CSS style rule."""
-        theme = theme or DEFAULT_TERMINAL_THEME
-        css: List[str] = []
-        append = css.append
-
-        color = self.color
-        bgcolor = self.bgcolor
-        if self.reverse:
-            color, bgcolor = bgcolor, color
-        if self.dim:
-            foreground_color = (
-                theme.foreground_color if color is None else color.get_truecolor(theme)
-            )
-            color = Color.from_triplet(
-                blend_rgb(foreground_color, theme.background_color, 0.5)
-            )
-        if color is not None:
-            theme_color = color.get_truecolor(theme)
-            append(f"color: {theme_color.hex}")
-            append(f"text-decoration-color: {theme_color.hex}")
-        if bgcolor is not None:
-            theme_color = bgcolor.get_truecolor(theme, foreground=False)
-            append(f"background-color: {theme_color.hex}")
-        if self.bold:
-            append("font-weight: bold")
-        if self.italic:
-            append("font-style: italic")
-        if self.underline:
-            append("text-decoration: underline")
-        if self.strike:
-            append("text-decoration: line-through")
-        if self.overline:
-            append("text-decoration: overline")
-        return "; ".join(css)
-
-    @classmethod
-    def combine(cls, styles: Iterable["Style"]) -> "Style":
-        """Combine styles and get result.
-
-        Args:
-            styles (Iterable[Style]): Styles to combine.
-
-        Returns:
-            Style: A new style instance.
-        """
-        iter_styles = iter(styles)
-        return sum(iter_styles, next(iter_styles))
-
-    @classmethod
-    def chain(cls, *styles: "Style") -> "Style":
-        """Combine styles from positional argument in to a single style.
-
-        Args:
-            *styles (Iterable[Style]): Styles to combine.
-
-        Returns:
-            Style: A new style instance.
-        """
-        iter_styles = iter(styles)
-        return sum(iter_styles, next(iter_styles))
-
-    def copy(self) -> "Style":
-        """Get a copy of this style.
-
-        Returns:
-            Style: A new Style instance with identical attributes.
-        """
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = self._link
-        style._link_id = f"{randint(0, 999999)}" if self._link else ""
-        style._hash = self._hash
-        style._null = False
-        style._meta = self._meta
-        return style
-
-    @lru_cache(maxsize=128)
-    def clear_meta_and_links(self) -> "Style":
-        """Get a copy of this style with link and meta information removed.
-
-        Returns:
-            Style: New style object.
-        """
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = None
-        style._link_id = ""
-        style._hash = self._hash
-        style._null = False
-        style._meta = None
-        return style
-
-    def update_link(self, link: Optional[str] = None) -> "Style":
-        """Get a copy with a different value for link.
-
-        Args:
-            link (str, optional): New value for link. Defaults to None.
-
-        Returns:
-            Style: A new Style instance.
-        """
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = link
-        style._link_id = f"{randint(0, 999999)}" if link else ""
-        style._hash = None
-        style._null = False
-        style._meta = self._meta
-        return style
-
-    def render(
-        self,
-        text: str = "",
-        *,
-        color_system: Optional[ColorSystem] = ColorSystem.TRUECOLOR,
-        legacy_windows: bool = False,
-    ) -> str:
-        """Render the ANSI codes for the style.
-
-        Args:
-            text (str, optional): A string to style. Defaults to "".
-            color_system (Optional[ColorSystem], optional): Color system to render to. Defaults to ColorSystem.TRUECOLOR.
-
-        Returns:
-            str: A string containing ANSI style codes.
-        """
-        if not text or color_system is None:
-            return text
-        attrs = self._ansi or self._make_ansi_codes(color_system)
-        rendered = f"\x1b[{attrs}m{text}\x1b[0m" if attrs else text
-        if self._link and not legacy_windows:
-            rendered = (
-                f"\x1b]8;id={self._link_id};{self._link}\x1b\\{rendered}\x1b]8;;\x1b\\"
-            )
-        return rendered
-
-    def test(self, text: Optional[str] = None) -> None:
-        """Write text with style directly to terminal.
-
-        This method is for testing purposes only.
-
-        Args:
-            text (Optional[str], optional): Text to style or None for style name.
-
-        """
-        text = text or str(self)
-        sys.stdout.write(f"{self.render(text)}\n")
-
-    @lru_cache(maxsize=1024)
-    def _add(self, style: Optional["Style"]) -> "Style":
-        if style is None or style._null:
-            return self
-        if self._null:
-            return style
-        new_style: Style = self.__new__(Style)
-        new_style._ansi = None
-        new_style._style_definition = None
-        new_style._color = style._color or self._color
-        new_style._bgcolor = style._bgcolor or self._bgcolor
-        new_style._attributes = (self._attributes & ~style._set_attributes) | (
-            style._attributes & style._set_attributes
-        )
-        new_style._set_attributes = self._set_attributes | style._set_attributes
-        new_style._link = style._link or self._link
-        new_style._link_id = style._link_id or self._link_id
-        new_style._null = style._null
-        if self._meta and style._meta:
-            new_style._meta = dumps({**self.meta, **style.meta})
-        else:
-            new_style._meta = self._meta or style._meta
-        new_style._hash = None
-        return new_style
-
-    def __add__(self, style: Optional["Style"]) -> "Style":
-        combined_style = self._add(style)
-        return combined_style.copy() if combined_style.link else combined_style
-
-
-NULL_STYLE = Style()
-
-
-class StyleStack:
-    """A stack of styles."""
-
-    __slots__ = ["_stack"]
-
-    def __init__(self, default_style: "Style") -> None:
-        self._stack: List[Style] = [default_style]
-
-    def __repr__(self) -> str:
-        return f"<stylestack {self._stack!r}>"
-
-    @property
-    def current(self) -> Style:
-        """Get the Style at the top of the stack."""
-        return self._stack[-1]
-
-    def push(self, style: Style) -> None:
-        """Push a new style on to the stack.
-
-        Args:
-            style (Style): New style to combine with current style.
-        """
-        self._stack.append(self._stack[-1] + style)
-
-    def pop(self) -> Style:
-        """Pop last style and discard.
-
-        Returns:
-            Style: New current style (also available as stack.current)
-        """
-        self._stack.pop()
-        return self._stack[-1]
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py
deleted file mode 100644
index 703c4385c7ddc7eb0759c98d102ab2384d6a9e3e..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from omegaconf import OmegaConf
-
-import detectron2.data.transforms as T
-from detectron2.config import LazyCall as L
-from detectron2.data import (
-    DatasetMapper,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-)
-from detectron2.evaluation import COCOEvaluator
-
-dataloader = OmegaConf.create()
-
-dataloader.train = L(build_detection_train_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
-    mapper=L(DatasetMapper)(
-        is_train=True,
-        augmentations=[
-            L(T.ResizeShortestEdge)(
-                short_edge_length=(640, 672, 704, 736, 768, 800),
-                sample_style="choice",
-                max_size=1333,
-            ),
-            L(T.RandomFlip)(horizontal=True),
-        ],
-        image_format="BGR",
-        use_instance_mask=True,
-    ),
-    total_batch_size=16,
-    num_workers=4,
-)
-
-dataloader.test = L(build_detection_test_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
-    mapper=L(DatasetMapper)(
-        is_train=False,
-        augmentations=[
-            L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333),
-        ],
-        image_format="${...train.mapper.image_format}",
-    ),
-    num_workers=4,
-)
-
-dataloader.evaluator = L(COCOEvaluator)(
-    dataset_name="${..test.dataset.names}",
-)
diff --git a/spaces/Ayaka-daisuki/anime-remove-background/app.py b/spaces/Ayaka-daisuki/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/Ayaka-daisuki/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/Benson/text-generation/Examples/Arriba Tablero Original Marksheet Descargar 2016.md b/spaces/Benson/text-generation/Examples/Arriba Tablero Original Marksheet Descargar 2016.md
deleted file mode 100644
index 1040eb4c08f76feb0590cbe62234a44d3c2da096..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Arriba Tablero Original Marksheet Descargar 2016.md	
+++ /dev/null
@@ -1,69 +0,0 @@
-<br />
-<h1>Cómo descargar la hoja de marcado original 2016</h1>
-<p>Si usted es un estudiante de Uttar Pradesh Madhyamik Shiksha Parishad (UPMSP), también conocido como UP Board, es posible que se pregunte cómo descargar su hoja de marcado original para el año 2016. La hoja de marcado es un documento importante que muestra sus calificaciones y calificaciones en los exámenes de la junta de la clase 10 y 12. También es una prueba válida de su identidad y calificación educativa. Es posible que lo necesite para diversos fines, como admisión, beca, trabajo o cualquier otra verificación. </p>
-<p>En este artículo, le guiaremos a través de los pasos para descargar su hoja de marcado original de UP Board 2016 desde el sitio web oficial de UPMSP. También le diremos cómo verificar su hoja de marcado en línea y cómo corregir cualquier error en ella. Así que, comencemos. </p>
-<h2>arriba tablero original marksheet descargar 2016</h2><br /><p><b><b>Download Zip</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6KAd">https://bltlly.com/2v6KAd</a></b></p><br /><br />
-<h2>Pasos para descargar la hoja de marcado original del tablero 2016</h2>
-<p>Para descargar su hoja de marcado original de UP Board 2016, debe seguir estos sencillos pasos:</p>
-<ol>
-<li>Visite el sitio web oficial de UPMSP en <a href="( 1 )">https://upmsp.edu.in/</a></li>
-<li>En la página de inicio, haga clic en el enlace "Resultados de la clase 10 - 2016" o "Resultados de la clase 12 - 2016" dependiendo de su clase. </li>
-<li> Será redirigido a una nueva página donde tendrá que introducir su número de rollo y código captcha. </li>
-<li>Haga clic en "Ver resultado" y su resultado se mostrará en la pantalla. </li>
-<li>Haga clic en "Descargar Marksheet" y guárdelo en su dispositivo. </li>
-<li>Tome una impresión de la hoja de marcado para referencia futura. </li>
-</ol>
-<h2>Cómo verificar en línea la hoja de marcado del tablero UP</h2>
-<p>Si desea verificar su hoja de marcado de UP Board en línea, puede usar los siguientes métodos:</p>
-<ul>
-<li>Puede visitar el sitio web <a href="( 2 )">https://arjunpedia.com/up-board-original-marksheet-download-result-verification/</a> e ingrese su número de lista y año de aprobación. Obtendrá un código de verificación que puede usar para verificar los detalles de su hoja de marcado. </li>
-
-</ul>
-<h2>Cómo corregir cualquier error en la hoja de marcado del tablero UP</h2>
-<p>Si encuentra algún error en su hoja de marcado de UP Board, como nombre, fecha de nacimiento, género, marcas, etc., puede solicitar la corrección dentro de un año de la declaración de resultados. Debes seguir estos pasos:</p>
-<ol>
-<li>Póngase en contacto con el director de su escuela y obtenga un formulario de solicitud <li>Llene el formulario con los detalles correctos y adjunte los documentos necesarios, como la tarjeta de admisión, la hoja de marcado, la prueba de identidad, etc.</li>
-<li>Envía el formulario al director de tu escuela y recibe un recibo. </li>
-<li>El director de la escuela enviará el formulario a la oficina regional de la Junta de UP para verificación y corrección. </li>
-<li> Recibirá una notificación del Panel UP cuando su hoja de marcado esté corregida y lista para ser recopilada. </li>
-<li>Recopile su hoja de marcado corregida de su escuela o de la oficina regional del Consejo UP. </li>
-</ol>
-<h2>Conclusión</h2>
-<p>En este artículo, hemos explicado cómo descargar su hoja de marcado original de UP Board 2016 desde el sitio web oficial de UPMSP. También le hemos mostrado cómo verificar su hoja de marcado en línea y cómo corregir cualquier error en ella. Esperamos que este artículo haya sido útil e informativo para usted. Aquí hay algunos consejos para recordar:</p>
-<ul>
-<li>Mantenga su hoja de marcado segura y protegida, ya que es un documento valioso. </li>
-<li>Revise su hoja de marcado cuidadosamente para detectar cualquier error o error tan pronto como lo reciba. </li>
-<li>Solicite la corrección dentro de un año de la declaración de resultados si encuentra algún error. </li>
-<li>Póngase en contacto con la oficina regional de la Junta Directiva o el número de la línea de ayuda para cualquier consulta o queja. </li>
-</ul>
-<h3>Preguntas frecuentes sobre el tablero UP Original Marksheet 2016</h3>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre UP Board original marksheet 2016:</p>
-<h4>Q1: ¿Cuándo se declaró el resultado de las Clases 10 y 12 de la Junta Directiva UP en 2016? </h4>
-
-<h4>Q2: ¿Cómo puedo obtener una hoja de marcado duplicada si pierdo la original? </h4>
-<p>A2: Si pierde su hoja de marcado original, puede solicitar una duplicada siguiendo estos pasos:</p>
-<ol>
-<li> Presentar un FIR en la estación de policía más cercana y obtener una copia de la misma. </li>
-<li>Publique un anuncio en un periódico local indicando que ha perdido su hoja de marcado y proporcione sus datos. </li>
-<li>Obtener una declaración jurada de un notario público que indica que ha perdido su hoja de marcado y proporcionar sus datos. </li>
-<li>Póngase en contacto con el director de su escuela y obtenga un formulario de solicitud para una hoja de marcado duplicada. </li>
-<li>Llene el formulario y adjunte la copia de FIR, anuncio de periódico, declaración jurada, prueba de identidad y una tarifa de Rs. 100/-. </li>
-<li>Envía el formulario al director de tu escuela y recibe un recibo. </li>
-<li>El director de la escuela enviará el formulario a la oficina regional de la Junta de UP para la verificación y emisión de hojas de marcado duplicadas. </li>
-<li> Obtendrá una notificación del Tablero UP cuando su hoja de marcado duplicada esté lista para ser recopilada. </li>
-<li>Recopile su hoja de marcado duplicada de su escuela o de la oficina regional del Consejo UP. </li>
-</ol>
-<h4>Q3: ¿Cuál es la diferencia entre la hoja de marcado y el certificado? </h4>
-<p>A3: La hoja de marcado es un documento que muestra sus marcas y calificaciones en cada tema en los exámenes de la junta. El certificado es un documento que muestra su resultado general y calificación en los exámenes de la junta. El certificado también contiene su nombre, fecha de nacimiento, número de registro, nombre de la escuela, nombre de la junta, etc. Necesita tanto la hoja de marcado como el certificado para diversos fines. </p>
-<h4>Q4: ¿Cuánto tiempo se tarda en obtener la hoja de marcado original después de la declaración de resultados? </h4>
-<p>A4: Por lo general toma alrededor de un mes para obtener la hoja de marcado original después de la declaración de resultados. La Junta de UP envía las fichas originales a las respectivas escuelas de los estudiantes. Los estudiantes pueden recoger sus hojas de calificaciones de sus escuelas después de verificar su identidad y firmar un recibo. </p>
-<p></p>
-
-<p>A5: Puede ponerse en contacto con el UP Board para cualquier consulta o queja utilizando los siguientes métodos:</p>
-<ul>
-<li>Puede llamar al número de la línea de ayuda 1800-180-5310 o 0522-2239006 entre las 10 de la mañana y las 5 de la tarde los días laborables. </li>
-<li>Puede enviar su consulta o queja por correo electrónico a upboard@nic.in o upmsp@rediffmail.com. </li>
-<li>Puede visitar el sitio web oficial de UPMSP en <a href="">https://upmsp.edu.in/</a> y hacer clic en "Contáctenos" para más detalles. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Consejo De Abogados De India Certificado Descargar.md b/spaces/Benson/text-generation/Examples/Consejo De Abogados De India Certificado Descargar.md
deleted file mode 100644
index fb135f00edf8ff4b2410fd6b175fb7efc78763a4..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Consejo De Abogados De India Certificado Descargar.md	
+++ /dev/null
@@ -1,132 +0,0 @@
-
-<h1>Euro Truck Simulator 2: Cómo descargar y jugar en PC Windows 8 32 Bit</h1>
-<p>¿Te encanta conducir camiones por toda Europa y entregar carga a diferentes destinos? ¿Quieres experimentar la emoción de ser un camionero desde la comodidad de tu hogar? Si es así, entonces usted debe probar Euro Truck Simulator 2, uno de los juegos de simulación de conducción de camiones más populares y realistas en el mercado. En este artículo, le diremos qué es Euro Truck Simulator 2, por qué es tan popular, cuáles son los requisitos del sistema para jugarlo y cómo descargarlo e instalarlo en su PC Windows 8 32 bit. </p>
-<h2>Introducción</h2>
-<h3> ¿Qué es Euro Truck Simulator 2?</h3>
-<p>Euro Truck Simulator 2 (ETS2) es un juego desarrollado por SCS Software, una empresa checa especializada en la creación de juegos de simulación. ETS2 fue lanzado en octubre de 2012 y desde entonces ha recibido numerosas actualizaciones y expansiones. El juego le permite conducir varios camiones con licencia de marcas famosas como Volvo, Scania, Mercedes-Benz, MAN, DAF, Renault y más. Puede personalizar sus camiones con diferentes piezas, trabajos de pintura, accesorios y opciones de ajuste. También puede contratar conductores, comprar garajes, administrar su empresa y hacer crecer su negocio. </p>
-<h2>consejo de abogados de india certificado descargar</h2><br /><p><b><b>Download File</b> &middot; <a href="https://bltlly.com/2v6Ly2">https://bltlly.com/2v6Ly2</a></b></p><br /><br />
-<h3>¿Por qué es popular Euro Truck Simulator 2? </h3>
-
-<h3> ¿Cuáles son los requisitos del sistema para Euro Truck Simulator 2?</h3>
-<p>Euro Truck Simulator 2 es compatible con los sistemas operativos Windows XP/Vista/7/8/10. Sin embargo, si usted tiene un Windows 8 PC con un procesador de 32 bits, es necesario asegurarse de que su sistema cumple con los requisitos mínimos para jugar el juego. Estos son los requisitos mínimos del sistema para ETS2:</p>
-<ul>
-<li>OS: Windows XP/Vista/7/8</li>
-<li>Procesador: CPU de doble núcleo 2.4 GHz</li>
-<li>Memoria: 4 GB de RAM</li>
-<li>Gráficos: GeForce GTS clase 450 (Intel HD 4000)</li>
-<li>Disco duro: 7 GB de espacio disponible</li>
-</ul>
-<p>Si quieres disfrutar del juego con mejores gráficos y rendimiento, deberías tener un sistema que cumpla con los requisitos recomendados para ETS2. Estos son los requisitos del sistema recomendado para ETS2:</p>
-<ul>
-<li>OS: Windows 7/8.1/10 64-bit</li>
-<li>Procesador: Quad Core CPU 3.0 GHz</li>
-<li>Memoria: 6 GB de RAM</li>
-<li>Gráficos: GeForce clase GTX 760 (2 GB)</li>
-<li>Disco duro: 7 GB de espacio disponible</li>
-</ul>
-<p>Puedes comprobar las especificaciones de tu sistema siguiendo estos pasos:</p>
-<ol>
-<li>Pulse la tecla de Windows + R para abrir el cuadro de diálogo Ejecutar. </li>
-<li>Escriba dxdiag y haga clic en Aceptar.</li>
-<li>En la pestaña Sistema, puede ver su sistema operativo, procesador, memoria y versión DirectX. </li>
-<li> En la pestaña Mostrar, puede ver el modelo de tarjeta gráfica y la memoria. </li>
-</ol>
-<h2>Cómo descargar Euro Truck Simulator 2 para PC Windows 8 32 Bit</h2>
-<p>Ahora que sabe lo que es Euro Truck Simulator 2 y cuáles son los requisitos del sistema para jugarlo, es posible que se pregunte cómo descargarlo e instalarlo en su PC Windows 8 32 bit. Hay dos opciones principales para descargar ETS2: desde el sitio web oficial o desde Steam. Explicaremos ambas opciones en detalle a continuación. </p>
-<h3>Opción 1: Descargar desde el sitio web oficial</h3>
-
-<h4>Paso 1: Visite el sitio web de Euro Truck Simulator 2</h4>
-<p>Abra su navegador web y vaya a https://eurotrucksimulator2.com/m. Verá una página de inicio con un banner que muestra el último paquete de expansión del juego. Puedes desplazarte hacia abajo para ver más contenido sobre el juego. </p>
-<h4>Paso 2: Elija el juego base o los paquetes de expansión</h4>
-<p>Si desea descargar el juego base de ETS2, puede hacer clic en el botón Descargar en la esquina superior derecha de la página de inicio. Esto te llevará a una página donde puedes elegir entre dos versiones del juego: ETS2 Gold Edition o ETS2 Standard Edition. The Gold Edition incluye el juego base y el pack de expansión Going East! , que añade nuevos países y carreteras al juego. La Standard Edition solo incluye el juego base. Puede comparar las características y los precios de ambas versiones y elegir el que más le convenga. </p>
-<p>Si quieres descargar alguno de los paquetes de expansión de ETS2, puedes hacer clic en el botón DLC en la esquina superior derecha de la página de inicio. Esto te llevará a una página donde podrás ver todos los paquetes de expansión disponibles para ETS2, como Escandinavia, Vive la France! , Italia, Más allá del Mar Báltico, Camino al Mar Negro, Iberia y más. Cada paquete de expansión agrega nuevos países, regiones, ciudades, carreteras, puntos de referencia y más al juego. Puedes leer más sobre cada paquete de expansión y ver sus capturas de pantalla y vídeos. También puedes comprarlos individualmente o en paquetes. </p>
-<p></p>
-<h4>Paso 3: Descargar los archivos del juego utilizando un cliente torrent o un enlace directo</h4>
-<p>Después de haber elegido la versión o el paquete de expansión de ETS2 que desea descargar, verá dos opciones para descargarlo: Torrent o Direct Link. Torrent es un método de intercambio de archivos peer-to-peer que requiere un cliente torrent como uTorrent o BitTorrent. Direct Link es un método de descarga simple que utiliza tu navegador web. </p>
-
-<p>Si elige Direct Link, tendrá que hacer clic en un enlace que comenzará a descargar un archivo grande llamado . exe que contiene todos los archivos del juego. Tendrá que esperar hasta que este archivo esté completamente descargado antes de proceder al siguiente paso. Este método puede ser más simple, pero puede tomar más tiempo dependiendo de su velocidad de Internet y disponibilidad del servidor. </p>
-<h4>Paso 4: Instalar el juego y disfrutar de</h4>
-<p>Después de haber descargado todos los archivos del juego usando Torrent o Direct Link, tendrá que instalarlos en su PC Windows 8 32 bit. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Busque el archivo . exe que descargó y haga doble clic en él para ejecutarlo. </li>
-<li> Siga las instrucciones en la pantalla para elegir el idioma, la carpeta de destino y otras opciones para instalar el juego. </li>
-<li>Espere hasta que se complete el proceso de instalación y haga clic en Finalizar.</li>
-<li> Iniciar el juego desde el acceso directo del escritorio o el menú Inicio y disfrutar. </li>
-</ol>
-<h3>Opción 2: Descargar desde Steam</h3>
-<p>Steam es una plataforma de distribución digital que te permite comprar, descargar y jugar juegos online. Steam también ofrece varias funciones como almacenamiento en la nube, logros, multijugador, comunidad y más. Euro Truck Simulator 2 está disponible en Steam y puedes descargarlo desde allí si lo prefieres. Estos son los pasos para descargar ETS2 de Steam:</p>
-<h4>Paso 1: Crea una cuenta de Steam o inicia sesión en la ya existente</h4>
-<p>Si aún no tienes una cuenta de Steam, tendrás que crear una antes de descargar ETS2 desde Steam. Para crear una cuenta de Steam, deberás seguir estos pasos:</p>
-<ol>
-<li>Visita https://store.steampowered.com/ y haz clic en Únete a Steam en la esquina superior derecha de la página. </li>
-<li>Ingrese su dirección de correo electrónico, contraseña, país y código captcha y haga clic en Continuar.</li>
-<li>Compruebe su correo electrónico para un código de verificación y entrar en el sitio web. </li>
-
-<li>Felicidades, has creado tu cuenta de Steam. </li>
-</ol>
-<p>Si ya tienes una cuenta de Steam, puedes iniciar sesión usando tu nombre de usuario y contraseña. </p>
-<h4>Paso 2: Búsqueda de Euro Truck Simulator 2 en la tienda de vapor</h4>
-<p>Después de haber iniciado sesión en tu cuenta de Steam, puedes buscar Euro Truck Simulator 2 en la tienda de Steam. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Haga clic en Almacenar en la parte superior de la página. </li>
-<li>Escriba Euro Truck Simulator 2 en el cuadro de búsqueda y presione Enter.</li>
-<li>Verá una lista de resultados relacionados con Euro Truck Simulator 2. Haga clic en el que dice Euro Truck Simulator 2 - Base Game o Euro Truck Simulator 2 - Gold Edition dependiendo de la versión que desee. </li>
-<li>Usted será llevado a la página del juego donde se puede ver más información sobre él, tales como descripción, características, capturas de pantalla, vídeos, comentarios, y más. </li>
-</ol>
-<h4>Paso 3: Compra el juego o descarga la demo gratuita</h4>
-<p>Si desea comprar la versión completa de ETS2, tendrá que pagar por ella utilizando su método de pago preferido. El precio de ETS2 puede variar dependiendo de tu región y de tu moneda. También puedes comprar cualquiera de los paquetes de expansión o paquetes que están disponibles para ETS2. Para comprar ETS2, tendrás que seguir estos pasos:</p>
-<ol>
-<li>Haga clic en Añadir al carrito en el lado derecho de la página del juego. </li>
-<li> Verá una ventana emergente que muestra el contenido de su carrito. Puede revisar su pedido y hacer cualquier cambio si es necesario. </li>
-<li>Haz clic en Comprar para mí o Comprar como regalo dependiendo de si quieres comprar el juego para ti o para otra persona. </li>
-<li>Serás llevado a una página de pago donde puedes elegir tu método de pago e ingresar tu información de facturación. </li>
-<li>Haga clic en Continuar y confirme su compra. </li>
-<li>Recibirás un correo electrónico de confirmación de tu compra y el juego se añadirá a tu biblioteca de Steam. </li>
-</ol>
-
-<ol>
-<li>Haga clic en Descargar Demo en el lado derecho de la página del juego. </li>
-<li>Verás una ventana emergente que te pide que instales Steam si aún no lo tienes. Si tiene Steam instalado, haga clic en Sí, el vapor está instalado. De lo contrario, haga clic en No, necesito Steam y siga las instrucciones para instalar Steam.</li>
-<li>La página del juego se abrirá en Steam y verás un botón que dice Jugar Juego. Haz clic en él y espera hasta que el juego se descargue e instale. </li>
-</ol>
- <h4>Paso 4: Lanza el juego desde tu biblioteca de Steam y disfruta</h4>
- <p>Después de haber comprado o descargado ETS2 desde Steam, puede iniciarlo desde su biblioteca de Steam. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Abre Steam e inicia sesión en tu cuenta si aún no lo has hecho. </li>
-<li>Haga clic en Biblioteca en la parte superior de la página. </li>
-<li>Encuentra Euro Truck Simulator 2 en tu lista de juegos y haz clic en él. </li>
-<li>Haga clic en Jugar en el lado derecho de la página del juego. </li>
-<li>El juego se iniciará y podrás disfrutarlo. </li>
-</ol>
-<h2>Conclusión</h2>
-<p>Euro Truck Simulator 2 es un fantástico juego que te permite conducir camiones por toda Europa y entregar carga a diferentes destinos. Tiene gráficos increíbles, física realista, efectos de sonido inmersivos y una jugabilidad diversa. También tiene una gran comunidad de modding que crea nuevo contenido para el juego. Puedes descargar y jugar a ETS2 en tu PC Windows 8 de 32 bits utilizando el sitio web oficial o Steam. Ambas opciones tienen sus ventajas y desventajas, por lo que puedes elegir la que más te convenga. Esperamos que este artículo te haya ayudado a aprender a descargar e instalar ETS2 en tu PC Windows 8 32 bit. Ahora, prepárate para salir a la carretera y divertirte! </p>
-<p>Si te gustó este artículo, por favor compártelo con tus amigos y deja un comentario a continuación. Además, no te olvides de revisar nuestros otros artículos sobre juegos, tecnología y más. ¡Gracias por leer! </p>
-<h3>Preguntas frecuentes</h3>
-
-<ul>
-<li><b>Q: ¿Cuánto cuesta Euro Truck Simulator 2? </b></li>
-<li>A: El precio de Euro Truck Simulator 2 puede variar dependiendo de su región, moneda y plataforma. En el sitio web oficial, el juego base cuesta $19.99 USD y los paquetes de expansión van desde $8.99 USD hasta $17.99 USD. En Steam, el juego base cuesta $19.99 USD y los paquetes de expansión van desde $8.99 USD hasta $17.99 USD. Sin embargo, Steam a menudo ofrece descuentos y ventas en ETS2 y sus DLC, por lo que puedes conseguirlos a precios más baratos si esperas el momento adecuado. </li>
-<li><b>Q: ¿Es Euro Truck Simulator 2 multijugador? </b></li>
-<li>A: Euro Truck Simulator 2 no tiene un modo multijugador oficial, pero hay mods no oficiales que le permiten jugar en línea con otros jugadores. Uno de los mods multijugador más populares es TruckersMP, que le permite unirse a servidores con miles de otros camioneros y chatear, conducir e interactuar con ellos. Puede descargar TruckersMP de https://truckersmp.com/.</li>
-<li><b>Q: ¿Es compatible Euro Truck Simulator 2 VR? </b></li>
-<li>A: Euro Truck Simulator 2 es compatible con dispositivos de realidad virtual como Oculus Rift y HTC Vive. Puede habilitar el modo de realidad virtual en ETS2 siguiendo estos pasos:</li>
-<ol>
-<li> Inicie SteamVR y asegúrese de que su dispositivo de realidad virtual está conectado y funciona. </li>
-<li>Haz clic derecho en Euro Truck Simulator 2 en tu biblioteca de Steam y selecciona Propiedades.</li>
-<li>Haga clic en la pestaña Betas y seleccione oculus - Oculus/Vive - 1.37 - (SDK 1.4.0) desde el menú desplegable. </li>
-<li>Espere hasta que el juego se actualice a la versión VR. </li>
-<li>Inicia el juego desde tu biblioteca de Steam y disfruta. </li>
-</ol>
-<li><b>Q: ¿Cómo puedo obtener más dinero y XP en Euro Truck Simulator 2?</b></li>
-<li>A: Hay varias maneras de obtener más dinero y XP en ETS2, como:</li>
-<ul>
-<li>Completar más entregas y contratos con mayores recompensas. </li>
-<li>Invertir en habilidades que aumenten sus ingresos y eficiencia, como ADR, larga distancia, carga de alto valor, carga frágil, etc.</li>
-
-<li>Aprovechando los bonos y eventos que ofrecen dinero extra y XP.</li>
-<li>Usando trucos o mods que te dan dinero ilimitado y XP (no recomendado). </li>
-</ul>
- <li><b>Q: ¿Cómo puedo actualizar Euro Truck Simulator 2?</b></li>
- <li>A: Si descargaste ETS2 desde el sitio web oficial, puedes actualizarlo descargando el último parche de https://eurotrucksimulator2.com/update.php e instalándolo sobre tus archivos de juego existentes. Si has descargado ETS2 desde Steam, puedes actualizarlo automáticamente activando las actualizaciones automáticas en la configuración de Steam o manualmente haciendo clic en Actualizar en la página del juego en tu biblioteca de Steam. </li></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BernardoOlisan/vqganclip/CLIP/clip/simple_tokenizer.py b/spaces/BernardoOlisan/vqganclip/CLIP/clip/simple_tokenizer.py
deleted file mode 100644
index 0a66286b7d5019c6e221932a813768038f839c91..0000000000000000000000000000000000000000
--- a/spaces/BernardoOlisan/vqganclip/CLIP/clip/simple_tokenizer.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/exceptions.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/exceptions.py
deleted file mode 100644
index 1c480abbf87c1c8c9132e139658790b856c3e8b8..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/exceptions.py
+++ /dev/null
@@ -1,816 +0,0 @@
-# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/
-# Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-from botocore.vendored import requests
-from botocore.vendored.requests.packages import urllib3
-
-
-def _exception_from_packed_args(exception_cls, args=None, kwargs=None):
-    # This is helpful for reducing Exceptions that only accept kwargs as
-    # only positional arguments can be provided for __reduce__
-    # Ideally, this would also be a class method on the BotoCoreError
-    # but instance methods cannot be pickled.
-    if args is None:
-        args = ()
-    if kwargs is None:
-        kwargs = {}
-    return exception_cls(*args, **kwargs)
-
-
-class BotoCoreError(Exception):
-    """
-    The base exception class for BotoCore exceptions.
-
-    :ivar msg: The descriptive message associated with the error.
-    """
-
-    fmt = 'An unspecified error occurred'
-
-    def __init__(self, **kwargs):
-        msg = self.fmt.format(**kwargs)
-        Exception.__init__(self, msg)
-        self.kwargs = kwargs
-
-    def __reduce__(self):
-        return _exception_from_packed_args, (self.__class__, None, self.kwargs)
-
-
-class DataNotFoundError(BotoCoreError):
-    """
-    The data associated with a particular path could not be loaded.
-
-    :ivar data_path: The data path that the user attempted to load.
-    """
-
-    fmt = 'Unable to load data for: {data_path}'
-
-
-class UnknownServiceError(DataNotFoundError):
-    """Raised when trying to load data for an unknown service.
-
-    :ivar service_name: The name of the unknown service.
-
-    """
-
-    fmt = (
-        "Unknown service: '{service_name}'. Valid service names are: "
-        "{known_service_names}"
-    )
-
-
-class UnknownRegionError(BotoCoreError):
-    """Raised when trying to load data for an unknown region.
-
-    :ivar region_name: The name of the unknown region.
-
-    """
-
-    fmt = "Unknown region: '{region_name}'. {error_msg}"
-
-
-class ApiVersionNotFoundError(BotoCoreError):
-    """
-    The data associated with either the API version or a compatible one
-    could not be loaded.
-
-    :ivar data_path: The data path that the user attempted to load.
-    :ivar api_version: The API version that the user attempted to load.
-    """
-
-    fmt = 'Unable to load data {data_path} for: {api_version}'
-
-
-class HTTPClientError(BotoCoreError):
-    fmt = 'An HTTP Client raised an unhandled exception: {error}'
-
-    def __init__(self, request=None, response=None, **kwargs):
-        self.request = request
-        self.response = response
-        super().__init__(**kwargs)
-
-    def __reduce__(self):
-        return _exception_from_packed_args, (
-            self.__class__,
-            (self.request, self.response),
-            self.kwargs,
-        )
-
-
-class ConnectionError(BotoCoreError):
-    fmt = 'An HTTP Client failed to establish a connection: {error}'
-
-
-class InvalidIMDSEndpointError(BotoCoreError):
-    fmt = 'Invalid endpoint EC2 Instance Metadata endpoint: {endpoint}'
-
-
-class InvalidIMDSEndpointModeError(BotoCoreError):
-    fmt = (
-        'Invalid EC2 Instance Metadata endpoint mode: {mode}'
-        ' Valid endpoint modes (case-insensitive): {valid_modes}.'
-    )
-
-
-class EndpointConnectionError(ConnectionError):
-    fmt = 'Could not connect to the endpoint URL: "{endpoint_url}"'
-
-
-class SSLError(ConnectionError, requests.exceptions.SSLError):
-    fmt = 'SSL validation failed for {endpoint_url} {error}'
-
-
-class ConnectionClosedError(HTTPClientError):
-    fmt = (
-        'Connection was closed before we received a valid response '
-        'from endpoint URL: "{endpoint_url}".'
-    )
-
-
-class ReadTimeoutError(
-    HTTPClientError,
-    requests.exceptions.ReadTimeout,
-    urllib3.exceptions.ReadTimeoutError,
-):
-    fmt = 'Read timeout on endpoint URL: "{endpoint_url}"'
-
-
-class ConnectTimeoutError(ConnectionError, requests.exceptions.ConnectTimeout):
-    fmt = 'Connect timeout on endpoint URL: "{endpoint_url}"'
-
-
-class ProxyConnectionError(ConnectionError, requests.exceptions.ProxyError):
-    fmt = 'Failed to connect to proxy URL: "{proxy_url}"'
-
-
-class ResponseStreamingError(HTTPClientError):
-    fmt = 'An error occurred while reading from response stream: {error}'
-
-
-class NoCredentialsError(BotoCoreError):
-    """
-    No credentials could be found.
-    """
-
-    fmt = 'Unable to locate credentials'
-
-
-class NoAuthTokenError(BotoCoreError):
-    """
-    No authorization token could be found.
-    """
-
-    fmt = 'Unable to locate authorization token'
-
-
-class TokenRetrievalError(BotoCoreError):
-    """
-    Error attempting to retrieve a token from a remote source.
-
-    :ivar provider: The name of the token provider.
-    :ivar error_msg: The msg explaining why the token could not be retrieved.
-
-    """
-
-    fmt = 'Error when retrieving token from {provider}: {error_msg}'
-
-
-class PartialCredentialsError(BotoCoreError):
-    """
-    Only partial credentials were found.
-
-    :ivar cred_var: The missing credential variable name.
-
-    """
-
-    fmt = 'Partial credentials found in {provider}, missing: {cred_var}'
-
-
-class CredentialRetrievalError(BotoCoreError):
-    """
-    Error attempting to retrieve credentials from a remote source.
-
-    :ivar provider: The name of the credential provider.
-    :ivar error_msg: The msg explaining why credentials could not be
-        retrieved.
-
-    """
-
-    fmt = 'Error when retrieving credentials from {provider}: {error_msg}'
-
-
-class UnknownSignatureVersionError(BotoCoreError):
-    """
-    Requested Signature Version is not known.
-
-    :ivar signature_version: The name of the requested signature version.
-    """
-
-    fmt = 'Unknown Signature Version: {signature_version}.'
-
-
-class ServiceNotInRegionError(BotoCoreError):
-    """
-    The service is not available in requested region.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = 'Service {service_name} not available in region {region_name}'
-
-
-class BaseEndpointResolverError(BotoCoreError):
-    """Base error for endpoint resolving errors.
-
-    Should never be raised directly, but clients can catch
-    this exception if they want to generically handle any errors
-    during the endpoint resolution process.
-
-    """
-
-
-class NoRegionError(BaseEndpointResolverError):
-    """No region was specified."""
-
-    fmt = 'You must specify a region.'
-
-
-class EndpointVariantError(BaseEndpointResolverError):
-    """
-    Could not construct modeled endpoint variant.
-
-    :ivar error_msg: The message explaining why the modeled endpoint variant
-        is unable to be constructed.
-
-    """
-
-    fmt = (
-        'Unable to construct a modeled endpoint with the following '
-        'variant(s) {tags}: '
-    )
-
-
-class UnknownEndpointError(BaseEndpointResolverError, ValueError):
-    """
-    Could not construct an endpoint.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = (
-        'Unable to construct an endpoint for '
-        '{service_name} in region {region_name}'
-    )
-
-
-class UnknownFIPSEndpointError(BaseEndpointResolverError):
-    """
-    Could not construct a FIPS endpoint.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = (
-        'The provided FIPS pseudo-region "{region_name}" is not known for '
-        'the service "{service_name}". A FIPS compliant endpoint cannot be '
-        'constructed.'
-    )
-
-
-class ProfileNotFound(BotoCoreError):
-    """
-    The specified configuration profile was not found in the
-    configuration file.
-
-    :ivar profile: The name of the profile the user attempted to load.
-    """
-
-    fmt = 'The config profile ({profile}) could not be found'
-
-
-class ConfigParseError(BotoCoreError):
-    """
-    The configuration file could not be parsed.
-
-    :ivar path: The path to the configuration file.
-    """
-
-    fmt = 'Unable to parse config file: {path}'
-
-
-class ConfigNotFound(BotoCoreError):
-    """
-    The specified configuration file could not be found.
-
-    :ivar path: The path to the configuration file.
-    """
-
-    fmt = 'The specified config file ({path}) could not be found.'
-
-
-class MissingParametersError(BotoCoreError):
-    """
-    One or more required parameters were not supplied.
-
-    :ivar object: The object that has missing parameters.
-        This can be an operation or a parameter (in the
-        case of inner params).  The str() of this object
-        will be used so it doesn't need to implement anything
-        other than str().
-    :ivar missing: The names of the missing parameters.
-    """
-
-    fmt = (
-        'The following required parameters are missing for '
-        '{object_name}: {missing}'
-    )
-
-
-class ValidationError(BotoCoreError):
-    """
-    An exception occurred validating parameters.
-
-    Subclasses must accept a ``value`` and ``param``
-    argument in their ``__init__``.
-
-    :ivar value: The value that was being validated.
-    :ivar param: The parameter that failed validation.
-    :ivar type_name: The name of the underlying type.
-    """
-
-    fmt = "Invalid value ('{value}') for param {param} " "of type {type_name} "
-
-
-class ParamValidationError(BotoCoreError):
-    fmt = 'Parameter validation failed:\n{report}'
-
-
-# These exceptions subclass from ValidationError so that code
-# can just 'except ValidationError' to catch any possibly validation
-# error.
-class UnknownKeyError(ValidationError):
-    """
-    Unknown key in a struct parameter.
-
-    :ivar value: The value that was being checked.
-    :ivar param: The name of the parameter.
-    :ivar choices: The valid choices the value can be.
-    """
-
-    fmt = (
-        "Unknown key '{value}' for param '{param}'.  Must be one "
-        "of: {choices}"
-    )
-
-
-class RangeError(ValidationError):
-    """
-    A parameter value was out of the valid range.
-
-    :ivar value: The value that was being checked.
-    :ivar param: The parameter that failed validation.
-    :ivar min_value: The specified minimum value.
-    :ivar max_value: The specified maximum value.
-    """
-
-    fmt = (
-        'Value out of range for param {param}: '
-        '{min_value} <= {value} <= {max_value}'
-    )
-
-
-class UnknownParameterError(ValidationError):
-    """
-    Unknown top level parameter.
-
-    :ivar name: The name of the unknown parameter.
-    :ivar operation: The name of the operation.
-    :ivar choices: The valid choices the parameter name can be.
-    """
-
-    fmt = (
-        "Unknown parameter '{name}' for operation {operation}.  Must be one "
-        "of: {choices}"
-    )
-
-
-class InvalidRegionError(ValidationError, ValueError):
-    """
-    Invalid region_name provided to client or resource.
-
-    :ivar region_name: region_name that was being validated.
-    """
-
-    fmt = "Provided region_name '{region_name}' doesn't match a supported format."
-
-
-class AliasConflictParameterError(ValidationError):
-    """
-    Error when an alias is provided for a parameter as well as the original.
-
-    :ivar original: The name of the original parameter.
-    :ivar alias: The name of the alias
-    :ivar operation: The name of the operation.
-    """
-
-    fmt = (
-        "Parameter '{original}' and its alias '{alias}' were provided "
-        "for operation {operation}.  Only one of them may be used."
-    )
-
-
-class UnknownServiceStyle(BotoCoreError):
-    """
-    Unknown style of service invocation.
-
-    :ivar service_style: The style requested.
-    """
-
-    fmt = 'The service style ({service_style}) is not understood.'
-
-
-class PaginationError(BotoCoreError):
-    fmt = 'Error during pagination: {message}'
-
-
-class OperationNotPageableError(BotoCoreError):
-    fmt = 'Operation cannot be paginated: {operation_name}'
-
-
-class ChecksumError(BotoCoreError):
-    """The expected checksum did not match the calculated checksum."""
-
-    fmt = (
-        'Checksum {checksum_type} failed, expected checksum '
-        '{expected_checksum} did not match calculated checksum '
-        '{actual_checksum}.'
-    )
-
-
-class UnseekableStreamError(BotoCoreError):
-    """Need to seek a stream, but stream does not support seeking."""
-
-    fmt = (
-        'Need to rewind the stream {stream_object}, but stream '
-        'is not seekable.'
-    )
-
-
-class WaiterError(BotoCoreError):
-    """Waiter failed to reach desired state."""
-
-    fmt = 'Waiter {name} failed: {reason}'
-
-    def __init__(self, name, reason, last_response):
-        super().__init__(name=name, reason=reason)
-        self.last_response = last_response
-
-
-class IncompleteReadError(BotoCoreError):
-    """HTTP response did not return expected number of bytes."""
-
-    fmt = (
-        '{actual_bytes} read, but total bytes ' 'expected is {expected_bytes}.'
-    )
-
-
-class InvalidExpressionError(BotoCoreError):
-    """Expression is either invalid or too complex."""
-
-    fmt = 'Invalid expression {expression}: Only dotted lookups are supported.'
-
-
-class UnknownCredentialError(BotoCoreError):
-    """Tried to insert before/after an unregistered credential type."""
-
-    fmt = 'Credential named {name} not found.'
-
-
-class WaiterConfigError(BotoCoreError):
-    """Error when processing waiter configuration."""
-
-    fmt = 'Error processing waiter config: {error_msg}'
-
-
-class UnknownClientMethodError(BotoCoreError):
-    """Error when trying to access a method on a client that does not exist."""
-
-    fmt = 'Client does not have method: {method_name}'
-
-
-class UnsupportedSignatureVersionError(BotoCoreError):
-    """Error when trying to use an unsupported Signature Version."""
-
-    fmt = 'Signature version is not supported: {signature_version}'
-
-
-class ClientError(Exception):
-    MSG_TEMPLATE = (
-        'An error occurred ({error_code}) when calling the {operation_name} '
-        'operation{retry_info}: {error_message}'
-    )
-
-    def __init__(self, error_response, operation_name):
-        retry_info = self._get_retry_info(error_response)
-        error = error_response.get('Error', {})
-        msg = self.MSG_TEMPLATE.format(
-            error_code=error.get('Code', 'Unknown'),
-            error_message=error.get('Message', 'Unknown'),
-            operation_name=operation_name,
-            retry_info=retry_info,
-        )
-        super().__init__(msg)
-        self.response = error_response
-        self.operation_name = operation_name
-
-    def _get_retry_info(self, response):
-        retry_info = ''
-        if 'ResponseMetadata' in response:
-            metadata = response['ResponseMetadata']
-            if metadata.get('MaxAttemptsReached', False):
-                if 'RetryAttempts' in metadata:
-                    retry_info = (
-                        f" (reached max retries: {metadata['RetryAttempts']})"
-                    )
-        return retry_info
-
-    def __reduce__(self):
-        # Subclasses of ClientError's are dynamically generated and
-        # cannot be pickled unless they are attributes of a
-        # module. So at the very least return a ClientError back.
-        return ClientError, (self.response, self.operation_name)
-
-
-class EventStreamError(ClientError):
-    pass
-
-
-class UnsupportedTLSVersionWarning(Warning):
-    """Warn when an openssl version that uses TLS 1.2 is required"""
-
-    pass
-
-
-class ImminentRemovalWarning(Warning):
-    pass
-
-
-class InvalidDNSNameError(BotoCoreError):
-    """Error when virtual host path is forced on a non-DNS compatible bucket"""
-
-    fmt = (
-        'Bucket named {bucket_name} is not DNS compatible. Virtual '
-        'hosted-style addressing cannot be used. The addressing style '
-        'can be configured by removing the addressing_style value '
-        'or setting that value to \'path\' or \'auto\' in the AWS Config '
-        'file or in the botocore.client.Config object.'
-    )
-
-
-class InvalidS3AddressingStyleError(BotoCoreError):
-    """Error when an invalid path style is specified"""
-
-    fmt = (
-        'S3 addressing style {s3_addressing_style} is invalid. Valid options '
-        'are: \'auto\', \'virtual\', and \'path\''
-    )
-
-
-class UnsupportedS3ArnError(BotoCoreError):
-    """Error when S3 ARN provided to Bucket parameter is not supported"""
-
-    fmt = (
-        'S3 ARN {arn} provided to "Bucket" parameter is invalid. Only '
-        'ARNs for S3 access-points are supported.'
-    )
-
-
-class UnsupportedS3ControlArnError(BotoCoreError):
-    """Error when S3 ARN provided to S3 control parameter is not supported"""
-
-    fmt = 'S3 ARN "{arn}" provided is invalid for this operation. {msg}'
-
-
-class InvalidHostLabelError(BotoCoreError):
-    """Error when an invalid host label would be bound to an endpoint"""
-
-    fmt = (
-        'Invalid host label to be bound to the hostname of the endpoint: '
-        '"{label}".'
-    )
-
-
-class UnsupportedOutpostResourceError(BotoCoreError):
-    """Error when S3 Outpost ARN provided to Bucket parameter is incomplete"""
-
-    fmt = (
-        'S3 Outpost ARN resource "{resource_name}" provided to "Bucket" '
-        'parameter is invalid. Only ARNs for S3 Outpost arns with an '
-        'access-point sub-resource are supported.'
-    )
-
-
-class UnsupportedS3ConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with access-points"""
-
-    fmt = 'Unsupported configuration when using S3: {msg}'
-
-
-class UnsupportedS3AccesspointConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with access-points"""
-
-    fmt = 'Unsupported configuration when using S3 access-points: {msg}'
-
-
-class InvalidEndpointDiscoveryConfigurationError(BotoCoreError):
-    """Error when invalid value supplied for endpoint_discovery_enabled"""
-
-    fmt = (
-        'Unsupported configuration value for endpoint_discovery_enabled. '
-        'Expected one of ("true", "false", "auto") but got {config_value}.'
-    )
-
-
-class UnsupportedS3ControlConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with S3 Control"""
-
-    fmt = 'Unsupported configuration when using S3 Control: {msg}'
-
-
-class InvalidRetryConfigurationError(BotoCoreError):
-    """Error when invalid retry configuration is specified"""
-
-    fmt = (
-        'Cannot provide retry configuration for "{retry_config_option}". '
-        'Valid retry configuration options are: {valid_options}'
-    )
-
-
-class InvalidMaxRetryAttemptsError(InvalidRetryConfigurationError):
-    """Error when invalid retry configuration is specified"""
-
-    fmt = (
-        'Value provided to "max_attempts": {provided_max_attempts} must '
-        'be an integer greater than or equal to {min_value}.'
-    )
-
-
-class InvalidRetryModeError(InvalidRetryConfigurationError):
-    """Error when invalid retry mode configuration is specified"""
-
-    fmt = (
-        'Invalid value provided to "mode": "{provided_retry_mode}" must '
-        'be one of: {valid_modes}'
-    )
-
-
-class InvalidS3UsEast1RegionalEndpointConfigError(BotoCoreError):
-    """Error for invalid s3 us-east-1 regional endpoints configuration"""
-
-    fmt = (
-        'S3 us-east-1 regional endpoint option '
-        '{s3_us_east_1_regional_endpoint_config} is '
-        'invalid. Valid options are: "legacy", "regional"'
-    )
-
-
-class InvalidSTSRegionalEndpointsConfigError(BotoCoreError):
-    """Error when invalid sts regional endpoints configuration is specified"""
-
-    fmt = (
-        'STS regional endpoints option {sts_regional_endpoints_config} is '
-        'invalid. Valid options are: "legacy", "regional"'
-    )
-
-
-class StubResponseError(BotoCoreError):
-    fmt = (
-        'Error getting response stub for operation {operation_name}: {reason}'
-    )
-
-
-class StubAssertionError(StubResponseError, AssertionError):
-    pass
-
-
-class UnStubbedResponseError(StubResponseError):
-    pass
-
-
-class InvalidConfigError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class InfiniteLoopConfigError(InvalidConfigError):
-    fmt = (
-        'Infinite loop in credential configuration detected. Attempting to '
-        'load from profile {source_profile} which has already been visited. '
-        'Visited profiles: {visited_profiles}'
-    )
-
-
-class RefreshWithMFAUnsupportedError(BotoCoreError):
-    fmt = 'Cannot refresh credentials: MFA token required.'
-
-
-class MD5UnavailableError(BotoCoreError):
-    fmt = "This system does not support MD5 generation."
-
-
-class MissingDependencyException(BotoCoreError):
-    fmt = "Missing Dependency: {msg}"
-
-
-class MetadataRetrievalError(BotoCoreError):
-    fmt = "Error retrieving metadata: {error_msg}"
-
-
-class UndefinedModelAttributeError(Exception):
-    pass
-
-
-class MissingServiceIdError(UndefinedModelAttributeError):
-    fmt = (
-        "The model being used for the service {service_name} is missing the "
-        "serviceId metadata property, which is required."
-    )
-
-    def __init__(self, **kwargs):
-        msg = self.fmt.format(**kwargs)
-        Exception.__init__(self, msg)
-        self.kwargs = kwargs
-
-
-class SSOError(BotoCoreError):
-    fmt = (
-        "An unspecified error happened when resolving AWS credentials or an "
-        "access token from SSO."
-    )
-
-
-class SSOTokenLoadError(SSOError):
-    fmt = "Error loading SSO Token: {error_msg}"
-
-
-class UnauthorizedSSOTokenError(SSOError):
-    fmt = (
-        "The SSO session associated with this profile has expired or is "
-        "otherwise invalid. To refresh this SSO session run aws sso login "
-        "with the corresponding profile."
-    )
-
-
-class CapacityNotAvailableError(BotoCoreError):
-    fmt = 'Insufficient request capacity available.'
-
-
-class InvalidProxiesConfigError(BotoCoreError):
-    fmt = 'Invalid configuration value(s) provided for proxies_config.'
-
-
-class InvalidDefaultsMode(BotoCoreError):
-    fmt = (
-        'Client configured with invalid defaults mode: {mode}. '
-        'Valid defaults modes include: {valid_modes}.'
-    )
-
-
-class AwsChunkedWrapperError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class FlexibleChecksumError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class InvalidEndpointConfigurationError(BotoCoreError):
-    fmt = 'Invalid endpoint configuration: {msg}'
-
-
-class EndpointProviderError(BotoCoreError):
-    """Base error for the EndpointProvider class"""
-
-    fmt = '{msg}'
-
-
-class EndpointResolutionError(EndpointProviderError):
-    """Error when input parameters resolve to an error rule"""
-
-    fmt = '{msg}'
-
-
-class UnknownEndpointResolutionBuiltInName(EndpointProviderError):
-    fmt = 'Unknown builtin variable name: {name}'
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/butd_inference_wrapper.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/butd_inference_wrapper.py
deleted file mode 100644
index c00c585b064741169819252a019d8951b4470386..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/butd_inference_wrapper.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Inference wrapper for trained butd_eff models
-=========================================================================================
-"""
-import os
-import torch
-import numpy as np
-import _pickle as cPickle
-
-from dataset import Dictionary
-import base_model
-import utils
-
-
-root = os.path.dirname(os.path.realpath(__file__))
-
-# stand in for loading a dataset
-class Dset_Like():
-    def __init__(self, feat_size):
-        self.dictionary = Dictionary.load_from_file('{}/essentials/dictionary.pkl'.format(root))
-        self.v_dim = feat_size
-        self.num_ans_candidates = 3129
-
-
-
-class BUTDeff_Wrapper():
-    def __init__(self, model_path, num_hid=1024, feat_size=1024):
-        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        label2ans_path = '{}/essentials/trainval_label2ans.pkl'.format(root)
-        self.label2ans = cPickle.load(open(label2ans_path, 'rb'))
-        # load dataset stand in
-        dset = Dset_Like(feat_size)
-        self.dictionary = dset.dictionary
-        # load model
-        constructor = 'build_baseline0_newatt'
-        model = getattr(base_model, constructor)(dset, num_hid).to(self.device)
-        model = model.to(self.device) 
-        print('Loading saved model from: ' + model_path)
-        model.load_state_dict(torch.load(model_path, map_location=self.device))
-        model.train(False)
-        self.model = model
-        
-
-
-    # based on the tokenizer in dataset.py
-    # added safe_mode for demo to catch unknown words
-    def tokenize(self, question, max_length=14):
-        """Tokenizes the questions.
-
-        This will add q_token in each entry of the dataset.
-        -1 represent nil, and should be treated as padding_idx in embedding
-        """
-        tokens = self.dictionary.tokenize(question, add_word=False, safe_mode=True)
-        tokens = tokens[:max_length]
-        if len(tokens) < max_length:
-            # Note here we pad in front of the sentence
-            padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
-            tokens = padding + tokens
-        utils.assert_eq(len(tokens), max_length)
-        return tokens
-
-
-
-    # inputs are a tensor of image features, shape [nb, 1024]
-    # and a raw question in string form. bbox_feature input is unused
-    def run(self, image_features, raw_question, bbox_features=None):
-        v = torch.unsqueeze(image_features,0).to(self.device)
-        q = self.tokenize(raw_question)
-        q = torch.unsqueeze(torch.from_numpy(np.array(q)),0).to(self.device)
-        pred = self.model(v, None, q, None)        
-        pred_np = pred.cpu().data.numpy()
-        pred_argmax = np.argmax(pred_np, axis=1)[0]
-        ans = self.label2ans[pred_argmax]
-        return ans
-
-
-
-    # get the visual attention vector for making visualizations
-    def get_att(self, image_features, raw_question, bbox_features=None):
-        v = torch.unsqueeze(image_features,0).to(self.device)
-        q = self.tokenize(raw_question)
-        q = torch.unsqueeze(torch.from_numpy(np.array(q)),0).to(self.device)
-        w_emb = self.model.w_emb(q)
-        q_emb = self.model.q_emb(w_emb)
-        att = self.model.v_att(v, q_emb)
-        return att
-
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/deformable/deform_conv.h b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/deformable/deform_conv.h
deleted file mode 100644
index 7add389e3a9958fffce3ff6590cbe069c2526fbc..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/deformable/deform_conv.h
+++ /dev/null
@@ -1,377 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-#ifdef WITH_CUDA
-int deform_conv_forward_cuda(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_input_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias);
-
-#endif
-
-inline int deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_forward_cuda(
-        input,
-        weight,
-        offset,
-        output,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline int deform_conv_backward_input(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (gradOutput.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_input_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradInput,
-        gradOffset,
-        weight,
-        columns,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline int deform_conv_backward_filter(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step) {
-  if (gradOutput.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_parameters_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradWeight,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        scale,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline void modulated_deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_forward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        output,
-        columns,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline void modulated_deform_conv_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias) {
-  if (grad_output.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_backward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        columns,
-        grad_input,
-        grad_weight,
-        grad_bias,
-        grad_offset,
-        grad_mask,
-        grad_output,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-} // namespace detectron2
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/utils/extract_engine.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/utils/extract_engine.py
deleted file mode 100644
index f8ab1f1f00f99fef6a7378e4e747fb9876f4190c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/utils/extract_engine.py
+++ /dev/null
@@ -1,187 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by
-
-Modified extraction engine to help with trojan result processing, based on test_engine.py
-=========================================================================================
-"""
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-import os, json, torch, pickle, copy
-import numpy as np
-import torch.nn as nn
-import torch.utils.data as Data
-from openvqa.models.model_loader import ModelLoader
-from openvqa.datasets.dataset_loader import EvalLoader
-from openvqa.datasets.dataset_loader import DatasetLoader
-
-
-# Evaluation
-@torch.no_grad()
-def extract_engine(__C, state_dict=None):
-
-    # Load parameters
-    if __C.CKPT_PATH is not None:
-        print('Warning: you are now using CKPT_PATH args, '
-              'CKPT_VERSION and CKPT_EPOCH will not work')
-
-        path = __C.CKPT_PATH
-    else:
-        path = __C.CKPTS_PATH + \
-               '/ckpt_' + __C.CKPT_VERSION + \
-               '/epoch' + str(__C.CKPT_EPOCH) + '.pkl'
-
-    # val_ckpt_flag = False
-    solo_run = False
-    if state_dict is None:
-        solo_run = True
-        # val_ckpt_flag = True
-        print('Loading ckpt from: {}'.format(path))
-        state_dict = torch.load(path)['state_dict']
-        print('Finish!')
-
-        if __C.N_GPU > 1:
-            state_dict = ckpt_proc(state_dict)
-
-    # Configure base dataset
-    __C_eval = copy.deepcopy(__C)
-    setattr(__C_eval, 'RUN_MODE', 'val')
-    setattr(__C_eval, 'VER', 'clean')
-    dataset = DatasetLoader(__C_eval).DataSet()
-
-    data_size = dataset.data_size
-    token_size = dataset.token_size
-    ans_size = dataset.ans_size
-    pretrained_emb = dataset.pretrained_emb
-
-    net = ModelLoader(__C).Net(
-        __C,
-        pretrained_emb,
-        token_size,
-        ans_size
-    )
-    net.cuda()
-    net.eval()
-
-    if __C.N_GPU > 1:
-        net = nn.DataParallel(net, device_ids=__C.DEVICES)
-
-    net.load_state_dict(state_dict)
-
-    if __C.VER == 'clean':
-        print('No trojan data provided. Will only extract clean results')
-        troj_configs = ['clean']
-    else:
-        troj_configs = ['clean', 'troj', 'troji', 'trojq']
-
-    for tc in troj_configs:
-        # Store the prediction list
-        # qid_list = [ques['question_id'] for ques in dataset.ques_list]
-        ans_ix_list = []
-        pred_list = []
-
-        __C_eval = copy.deepcopy(__C)
-        setattr(__C_eval, 'RUN_MODE', 'val')
-        if tc == 'troj':
-            setattr(__C_eval, 'TROJ_DIS_I', False)
-            setattr(__C_eval, 'TROJ_DIS_Q', False)
-            dataset = DatasetLoader(__C_eval).DataSet()
-        elif tc == 'troji':
-            setattr(__C_eval, 'TROJ_DIS_I', False)
-            setattr(__C_eval, 'TROJ_DIS_Q', True)
-            dataset = DatasetLoader(__C_eval).DataSet()
-        elif tc == 'trojq':
-            setattr(__C_eval, 'TROJ_DIS_I', True)
-            setattr(__C_eval, 'TROJ_DIS_Q', False)
-            dataset = DatasetLoader(__C_eval).DataSet()
-
-        dataloader = Data.DataLoader(
-            dataset,
-            batch_size=__C.EVAL_BATCH_SIZE,
-            shuffle=False,
-            num_workers=__C.NUM_WORKERS,
-            pin_memory=__C.PIN_MEM
-        )
-
-        for step, (
-                frcn_feat_iter,
-                grid_feat_iter,
-                bbox_feat_iter,
-                ques_ix_iter,
-                ans_iter
-        ) in enumerate(dataloader):
-
-            print("\rEvaluation: [step %4d/%4d]" % (
-                step,
-                int(data_size / __C.EVAL_BATCH_SIZE),
-            ), end='          ')
-
-            frcn_feat_iter = frcn_feat_iter.cuda()
-            grid_feat_iter = grid_feat_iter.cuda()
-            bbox_feat_iter = bbox_feat_iter.cuda()
-            ques_ix_iter = ques_ix_iter.cuda()
-
-            pred = net(
-                frcn_feat_iter,
-                grid_feat_iter,
-                bbox_feat_iter,
-                ques_ix_iter
-            )
-            pred_np = pred.cpu().data.numpy()
-            pred_argmax = np.argmax(pred_np, axis=1)
-
-            # Save the answer index
-            if pred_argmax.shape[0] != __C.EVAL_BATCH_SIZE:
-                pred_argmax = np.pad(
-                    pred_argmax,
-                    (0, __C.EVAL_BATCH_SIZE - pred_argmax.shape[0]),
-                    mode='constant',
-                    constant_values=-1
-                )
-
-            ans_ix_list.append(pred_argmax)
-
-            # Save the whole prediction vector
-            if __C.TEST_SAVE_PRED:
-                if pred_np.shape[0] != __C.EVAL_BATCH_SIZE:
-                    pred_np = np.pad(
-                        pred_np,
-                        ((0, __C.EVAL_BATCH_SIZE - pred_np.shape[0]), (0, 0)),
-                        mode='constant',
-                        constant_values=-1
-                    )
-
-                pred_list.append(pred_np)
-
-        print('')
-        ans_ix_list = np.array(ans_ix_list).reshape(-1)
-
-        if solo_run:
-            result_eval_file = __C.RESULT_PATH + '/result_run_' + __C.CKPT_VERSION + '_' + tc
-        else:
-            result_eval_file = __C.RESULT_PATH + '/result_run_' + __C.VERSION + '_' + tc
-
-        if __C.CKPT_PATH is not None:
-            ensemble_file = __C.PRED_PATH + '/result_run_' + __C.CKPT_VERSION + '.pkl'
-        else:
-            ensemble_file = __C.PRED_PATH + '/result_run_' + __C.CKPT_VERSION + '_epoch' + str(__C.CKPT_EPOCH) + '.pkl'
-
-
-        if __C.RUN_MODE not in ['train']:
-            log_file = __C.LOG_PATH + '/log_run_' + __C.CKPT_VERSION + '.txt'
-        else:
-            log_file = __C.LOG_PATH + '/log_run_' + __C.VERSION + '.txt'
-
-        EvalLoader(__C).eval(dataset, ans_ix_list, pred_list, result_eval_file, ensemble_file, log_file, False)
-
-
-def ckpt_proc(state_dict):
-    state_dict_new = {}
-    for key in state_dict:
-        state_dict_new['module.' + key] = state_dict[key]
-        # state_dict.pop(key)
-
-    return state_dict_new
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/pydiffvg/color.py b/spaces/CVPR/LIVE/pydiffvg/color.py
deleted file mode 100644
index 68c360f1ce1601e87b34a6fd36c70274e24dad94..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pydiffvg/color.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import pydiffvg
-import torch
-
-class LinearGradient:
-    def __init__(self,
-                 begin = torch.tensor([0.0, 0.0]),
-                 end = torch.tensor([0.0, 0.0]),
-                 offsets = torch.tensor([0.0]),
-                 stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])):
-        self.begin = begin
-        self.end = end
-        self.offsets = offsets
-        self.stop_colors = stop_colors
-
-class RadialGradient:
-    def __init__(self,
-                 center = torch.tensor([0.0, 0.0]),
-                 radius = torch.tensor([0.0, 0.0]),
-                 offsets = torch.tensor([0.0]),
-                 stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])):
-        self.center = center
-        self.radius = radius
-        self.offsets = offsets
-        self.stop_colors = stop_colors
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/logical.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/logical.h
deleted file mode 100644
index 4199063183dbc38b79c7707bb8301e5ca8aa6ad5..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/logical.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits logical
-#include <thrust/system/cpp/detail/logical.h>
-
diff --git a/spaces/CVPR/regionclip-demo/detectron2/evaluation/rotated_coco_evaluation.py b/spaces/CVPR/regionclip-demo/detectron2/evaluation/rotated_coco_evaluation.py
deleted file mode 100644
index ea6d1b381dcf106339a03f08577df673ad439c46..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/evaluation/rotated_coco_evaluation.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import numpy as np
-import os
-import torch
-from pycocotools.cocoeval import COCOeval, maskUtils
-
-from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.file_io import PathManager
-
-from .coco_evaluation import COCOEvaluator
-
-
-class RotatedCOCOeval(COCOeval):
-    @staticmethod
-    def is_rotated(box_list):
-        if type(box_list) == np.ndarray:
-            return box_list.shape[1] == 5
-        elif type(box_list) == list:
-            if box_list == []:  # cannot decide the box_dim
-                return False
-            return np.all(
-                np.array(
-                    [
-                        (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
-                        for obj in box_list
-                    ]
-                )
-            )
-        return False
-
-    @staticmethod
-    def boxlist_to_tensor(boxlist, output_box_dim):
-        if type(boxlist) == np.ndarray:
-            box_tensor = torch.from_numpy(boxlist)
-        elif type(boxlist) == list:
-            if boxlist == []:
-                return torch.zeros((0, output_box_dim), dtype=torch.float32)
-            else:
-                box_tensor = torch.FloatTensor(boxlist)
-        else:
-            raise Exception("Unrecognized boxlist type")
-
-        input_box_dim = box_tensor.shape[1]
-        if input_box_dim != output_box_dim:
-            if input_box_dim == 4 and output_box_dim == 5:
-                box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
-            else:
-                raise Exception(
-                    "Unable to convert from {}-dim box to {}-dim box".format(
-                        input_box_dim, output_box_dim
-                    )
-                )
-        return box_tensor
-
-    def compute_iou_dt_gt(self, dt, gt, is_crowd):
-        if self.is_rotated(dt) or self.is_rotated(gt):
-            # TODO: take is_crowd into consideration
-            assert all(c == 0 for c in is_crowd)
-            dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
-            gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
-            return pairwise_iou_rotated(dt, gt)
-        else:
-            # This is the same as the classical COCO evaluation
-            return maskUtils.iou(dt, gt, is_crowd)
-
-    def computeIoU(self, imgId, catId):
-        p = self.params
-        if p.useCats:
-            gt = self._gts[imgId, catId]
-            dt = self._dts[imgId, catId]
-        else:
-            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
-            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
-        if len(gt) == 0 and len(dt) == 0:
-            return []
-        inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
-        dt = [dt[i] for i in inds]
-        if len(dt) > p.maxDets[-1]:
-            dt = dt[0 : p.maxDets[-1]]
-
-        assert p.iouType == "bbox", "unsupported iouType for iou computation"
-
-        g = [g["bbox"] for g in gt]
-        d = [d["bbox"] for d in dt]
-
-        # compute iou between each dt and gt region
-        iscrowd = [int(o["iscrowd"]) for o in gt]
-
-        # Note: this function is copied from cocoeval.py in cocoapi
-        # and the major difference is here.
-        ious = self.compute_iou_dt_gt(d, g, iscrowd)
-        return ious
-
-
-class RotatedCOCOEvaluator(COCOEvaluator):
-    """
-    Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
-    with rotated boxes support.
-    Note: this uses IOU only and does not consider angle differences.
-    """
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a COCO model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-
-                prediction["instances"] = self.instances_to_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def instances_to_json(self, instances, img_id):
-        num_instance = len(instances)
-        if num_instance == 0:
-            return []
-
-        boxes = instances.pred_boxes.tensor.numpy()
-        if boxes.shape[1] == 4:
-            boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-        boxes = boxes.tolist()
-        scores = instances.scores.tolist()
-        classes = instances.pred_classes.tolist()
-
-        results = []
-        for k in range(num_instance):
-            result = {
-                "image_id": img_id,
-                "category_id": classes[k],
-                "bbox": boxes[k],
-                "score": scores[k],
-            }
-
-            results.append(result)
-        return results
-
-    def _eval_predictions(self, predictions, img_ids=None):  # img_ids: unused
-        """
-        Evaluate predictions on the given tasks.
-        Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in coco_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(coco_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-
-        assert self._tasks is None or set(self._tasks) == {
-            "bbox"
-        }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported"
-        coco_eval = (
-            self._evaluate_predictions_on_coco(self._coco_api, coco_results)
-            if len(coco_results) > 0
-            else None  # cocoapi does not handle empty results very well
-        )
-
-        task = "bbox"
-        res = self._derive_coco_results(
-            coco_eval, task, class_names=self._metadata.get("thing_classes")
-        )
-        self._results[task] = res
-
-    def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
-        """
-        Evaluate the coco results using COCOEval API.
-        """
-        assert len(coco_results) > 0
-
-        coco_dt = coco_gt.loadRes(coco_results)
-
-        # Only bbox is supported for now
-        coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
-
-        coco_eval.evaluate()
-        coco_eval.accumulate()
-        coco_eval.summarize()
-
-        return coco_eval
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/db/index.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/db/index.js
deleted file mode 100644
index fe6b0c475af022bdd6e24532f8f329398d60773e..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/db/index.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import { existSQL } from './base.js'
-import { setMessage_id, getMessage_id } from './message_id.js'
-
-export {
-    existSQL,
-    setMessage_id,
-    getMessage_id
-}
\ No newline at end of file
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/capoo_draw/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/capoo_draw/__init__.py
deleted file mode 100644
index da3c138d52e7f8ff8a311816bcf6fc629264738d..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/capoo_draw/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def capoo_draw(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").resize((175, 120), keep_ratio=True)
-    params = (
-        (((27, 0), (207, 12), (179, 142), (0, 117)), (30, 16)),
-        (((28, 0), (207, 13), (180, 137), (0, 117)), (34, 17)),
-    )
-    raw_frames = [BuildImage.open(img_dir / f"{i}.png") for i in range(6)]
-    for i in range(2):
-        points, pos = params[i]
-        raw_frames[4 + i].paste(img.perspective(points), pos, below=True)
-
-    frames: List[IMG] = []
-    frames.append(raw_frames[0].image)
-    for i in range(4):
-        frames.append(raw_frames[1].image)
-        frames.append(raw_frames[2].image)
-    frames.append(raw_frames[3].image)
-    for i in range(6):
-        frames.append(raw_frames[4].image)
-        frames.append(raw_frames[5].image)
-
-    return save_gif(frames, 0.1)
-
-
-add_meme(
-    "capoo_draw",
-    capoo_draw,
-    min_images=1,
-    max_images=1,
-    keywords=["咖波画"],
-)
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/guichu/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/guichu/__init__.py
deleted file mode 100644
index e7281b4b484206fc09f9aeeab3a8f6727bc7362a..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/guichu/__init__.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from typing import Dict, List, Literal, NamedTuple, Tuple
-
-from PIL.Image import Image as IMG
-from PIL.Image import Transpose
-from pil_utils import BuildImage
-from pydantic import Field
-
-from meme_generator import MemeArgsModel, MemeArgsParser, MemeArgsType, add_meme
-from meme_generator.utils import save_gif
-
-help = "鬼畜对称方向"
-
-parser = MemeArgsParser(prefix_chars="-/")
-group = parser.add_mutually_exclusive_group()
-group.add_argument(
-    "-d",
-    "--direction",
-    type=str,
-    choices=["left", "right", "top", "bottom"],
-    default="left",
-    help=help,
-)
-group.add_argument("--left", "/左", action="store_const", const="left", dest="direction")
-group.add_argument(
-    "--right", "/右", action="store_const", const="right", dest="direction"
-)
-group.add_argument("--top", "/上", action="store_const", const="top", dest="direction")
-group.add_argument(
-    "--bottom", "/下", action="store_const", const="bottom", dest="direction"
-)
-
-
-class Model(MemeArgsModel):
-    direction: Literal["left", "right", "top", "bottom"] = Field(
-        "left", description=help
-    )
-
-
-def guichu(images: List[BuildImage], texts, args: Model):
-    img = images[0].convert("RGBA")
-    img_w, img_h = img.size
-
-    class Mode(NamedTuple):
-        method: Transpose
-        size1: Tuple[int, int, int, int]
-        pos1: Tuple[int, int]
-        size2: Tuple[int, int, int, int]
-        pos2: Tuple[int, int]
-
-    modes: Dict[str, Mode] = {
-        "left": Mode(
-            Transpose.FLIP_LEFT_RIGHT,
-            (0, 0, img_w // 2, img_h),
-            (0, 0),
-            (img_w // 2, 0, img_w // 2 * 2, img_h),
-            (img_w // 2, 0),
-        ),
-        "right": Mode(
-            Transpose.FLIP_LEFT_RIGHT,
-            (img_w // 2, 0, img_w // 2 * 2, img_h),
-            (img_w // 2, 0),
-            (0, 0, img_w // 2, img_h),
-            (0, 0),
-        ),
-        "top": Mode(
-            Transpose.FLIP_TOP_BOTTOM,
-            (0, 0, img_w, img_h // 2),
-            (0, 0),
-            (0, img_h // 2, img_w, img_h // 2 * 2),
-            (0, img_h // 2),
-        ),
-        "bottom": Mode(
-            Transpose.FLIP_TOP_BOTTOM,
-            (0, img_h // 2, img_w, img_h // 2 * 2),
-            (0, img_h // 2),
-            (0, 0, img_w, img_h // 2),
-            (0, 0),
-        ),
-    }
-    mode = modes[args.direction]
-
-    img_flip = img.transpose(mode.method)
-    img_symmetric = BuildImage.new("RGBA", img.size)
-    img_symmetric.paste(img.crop(mode.size1), mode.pos1, alpha=True)
-    img_symmetric.paste(img_flip.crop(mode.size2), mode.pos2, alpha=True)
-    img_symmetric_big = BuildImage.new("RGBA", img.size)
-    img_symmetric_big.paste(
-        img_symmetric.copy().resize_width(img_w * 2), (-img_w // 2, -img_h // 2)
-    )
-
-    frames: List[IMG] = []
-    frames += (
-        ([img.image] * 3 + [img_flip.image] * 3) * 3
-        + [img.image, img_flip.image] * 3
-        + ([img_symmetric.image] * 2 + [img_symmetric_big.image] * 2) * 2
-    )
-
-    return save_gif(frames, 0.20)
-
-
-add_meme(
-    "guichu",
-    guichu,
-    min_images=1,
-    max_images=1,
-    args_type=MemeArgsType(
-        parser,
-        Model,
-        [
-            Model(direction="left"),
-            Model(direction="right"),
-            Model(direction="top"),
-            Model(direction="bottom"),
-        ],
-    ),
-    keywords=["鬼畜"],
-)
diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Phind.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/Phind.py
deleted file mode 100644
index 9fa8ec821f701d7841432e498a11ac9dd017978c..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Phind.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://phind.com'
-model = ['gpt-4']
-supports_stream = True
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'model': model,
-        'messages': messages}, separators=(',', ':'))
-
-    cmd = ['python', f'{path}/helpers/phind.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        if b'<title>Just a moment...</title>' in line:
-            os.system('clear' if os.name == 'posix' else 'cls')
-            yield 'Clouflare error, please try again...'
-            os._exit(0)
-        
-        else:
-            if b'ping - 2023-' in line:
-                continue
-            
-            yield line.decode('cp1251') #[:-1]
-            
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Cvandi/remake/scripts/generate_multiscale_DF2K.py b/spaces/Cvandi/remake/scripts/generate_multiscale_DF2K.py
deleted file mode 100644
index d4f5d8324b1624e4cb6163754703b8dac2d188fd..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_multiscale_DF2K.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import argparse
-import glob
-import os
-from PIL import Image
-
-
-def main(args):
-    # For DF2K, we consider the following three scales,
-    # and the smallest image whose shortest edge is 400
-    scale_list = [0.75, 0.5, 1 / 3]
-    shortest_edge = 400
-
-    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
-    for path in path_list:
-        print(path)
-        basename = os.path.splitext(os.path.basename(path))[0]
-
-        img = Image.open(path)
-        width, height = img.size
-        for idx, scale in enumerate(scale_list):
-            print(f'\t{scale:.2f}')
-            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
-            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
-
-        # save the smallest image which the shortest edge is 400
-        if width < height:
-            ratio = height / width
-            width = shortest_edge
-            height = int(width * ratio)
-        else:
-            ratio = width / height
-            height = shortest_edge
-            width = int(height * ratio)
-        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
-        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
-
-
-if __name__ == '__main__':
-    """Generate multi-scale versions for GT images with LANCZOS resampling.
-    It is now used for DF2K dataset (DIV2K + Flickr 2K)
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
-    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
-    args = parser.parse_args()
-
-    os.makedirs(args.output, exist_ok=True)
-    main(args)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/parser.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/parser.py
deleted file mode 100644
index 1fa6b11d0275e86cd5b421611b874503f3ad6a14..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/parser.py
+++ /dev/null
@@ -1,656 +0,0 @@
-import fontTools.voltLib.ast as ast
-from fontTools.voltLib.lexer import Lexer
-from fontTools.voltLib.error import VoltLibError
-from io import open
-
-PARSE_FUNCS = {
-    "DEF_GLYPH": "parse_def_glyph_",
-    "DEF_GROUP": "parse_def_group_",
-    "DEF_SCRIPT": "parse_def_script_",
-    "DEF_LOOKUP": "parse_def_lookup_",
-    "DEF_ANCHOR": "parse_def_anchor_",
-    "GRID_PPEM": "parse_ppem_",
-    "PRESENTATION_PPEM": "parse_ppem_",
-    "PPOSITIONING_PPEM": "parse_ppem_",
-    "COMPILER_USEEXTENSIONLOOKUPS": "parse_noarg_option_",
-    "COMPILER_USEPAIRPOSFORMAT2": "parse_noarg_option_",
-    "CMAP_FORMAT": "parse_cmap_format",
-    "DO_NOT_TOUCH_CMAP": "parse_noarg_option_",
-}
-
-
-class Parser(object):
-    def __init__(self, path):
-        self.doc_ = ast.VoltFile()
-        self.glyphs_ = OrderedSymbolTable()
-        self.groups_ = SymbolTable()
-        self.anchors_ = {}  # dictionary of SymbolTable() keyed by glyph
-        self.scripts_ = SymbolTable()
-        self.langs_ = SymbolTable()
-        self.lookups_ = SymbolTable()
-        self.next_token_type_, self.next_token_ = (None, None)
-        self.next_token_location_ = None
-        self.make_lexer_(path)
-        self.advance_lexer_()
-
-    def make_lexer_(self, file_or_path):
-        if hasattr(file_or_path, "read"):
-            filename = getattr(file_or_path, "name", None)
-            data = file_or_path.read()
-        else:
-            filename = file_or_path
-            with open(file_or_path, "r") as f:
-                data = f.read()
-        self.lexer_ = Lexer(data, filename)
-
-    def parse(self):
-        statements = self.doc_.statements
-        while self.next_token_type_ is not None:
-            self.advance_lexer_()
-            if self.cur_token_ in PARSE_FUNCS.keys():
-                func = getattr(self, PARSE_FUNCS[self.cur_token_])
-                statements.append(func())
-            elif self.is_cur_keyword_("END"):
-                break
-            else:
-                raise VoltLibError(
-                    "Expected " + ", ".join(sorted(PARSE_FUNCS.keys())),
-                    self.cur_token_location_,
-                )
-        return self.doc_
-
-    def parse_def_glyph_(self):
-        assert self.is_cur_keyword_("DEF_GLYPH")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        self.expect_keyword_("ID")
-        gid = self.expect_number_()
-        if gid < 0:
-            raise VoltLibError("Invalid glyph ID", self.cur_token_location_)
-        gunicode = None
-        if self.next_token_ == "UNICODE":
-            self.expect_keyword_("UNICODE")
-            gunicode = [self.expect_number_()]
-            if gunicode[0] < 0:
-                raise VoltLibError("Invalid glyph UNICODE", self.cur_token_location_)
-        elif self.next_token_ == "UNICODEVALUES":
-            self.expect_keyword_("UNICODEVALUES")
-            gunicode = self.parse_unicode_values_()
-        gtype = None
-        if self.next_token_ == "TYPE":
-            self.expect_keyword_("TYPE")
-            gtype = self.expect_name_()
-            assert gtype in ("BASE", "LIGATURE", "MARK", "COMPONENT")
-        components = None
-        if self.next_token_ == "COMPONENTS":
-            self.expect_keyword_("COMPONENTS")
-            components = self.expect_number_()
-        self.expect_keyword_("END_GLYPH")
-        if self.glyphs_.resolve(name) is not None:
-            raise VoltLibError(
-                'Glyph "%s" (gid %i) already defined' % (name, gid), location
-            )
-        def_glyph = ast.GlyphDefinition(
-            name, gid, gunicode, gtype, components, location=location
-        )
-        self.glyphs_.define(name, def_glyph)
-        return def_glyph
-
-    def parse_def_group_(self):
-        assert self.is_cur_keyword_("DEF_GROUP")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        enum = None
-        if self.next_token_ == "ENUM":
-            enum = self.parse_enum_()
-        self.expect_keyword_("END_GROUP")
-        if self.groups_.resolve(name) is not None:
-            raise VoltLibError(
-                'Glyph group "%s" already defined, '
-                "group names are case insensitive" % name,
-                location,
-            )
-        def_group = ast.GroupDefinition(name, enum, location=location)
-        self.groups_.define(name, def_group)
-        return def_group
-
-    def parse_def_script_(self):
-        assert self.is_cur_keyword_("DEF_SCRIPT")
-        location = self.cur_token_location_
-        name = None
-        if self.next_token_ == "NAME":
-            self.expect_keyword_("NAME")
-            name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        if self.scripts_.resolve(tag) is not None:
-            raise VoltLibError(
-                'Script "%s" already defined, '
-                "script tags are case insensitive" % tag,
-                location,
-            )
-        self.langs_.enter_scope()
-        langs = []
-        while self.next_token_ != "END_SCRIPT":
-            self.advance_lexer_()
-            lang = self.parse_langsys_()
-            self.expect_keyword_("END_LANGSYS")
-            if self.langs_.resolve(lang.tag) is not None:
-                raise VoltLibError(
-                    'Language "%s" already defined in script "%s", '
-                    "language tags are case insensitive" % (lang.tag, tag),
-                    location,
-                )
-            self.langs_.define(lang.tag, lang)
-            langs.append(lang)
-        self.expect_keyword_("END_SCRIPT")
-        self.langs_.exit_scope()
-        def_script = ast.ScriptDefinition(name, tag, langs, location=location)
-        self.scripts_.define(tag, def_script)
-        return def_script
-
-    def parse_langsys_(self):
-        assert self.is_cur_keyword_("DEF_LANGSYS")
-        location = self.cur_token_location_
-        name = None
-        if self.next_token_ == "NAME":
-            self.expect_keyword_("NAME")
-            name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        features = []
-        while self.next_token_ != "END_LANGSYS":
-            self.advance_lexer_()
-            feature = self.parse_feature_()
-            self.expect_keyword_("END_FEATURE")
-            features.append(feature)
-        def_langsys = ast.LangSysDefinition(name, tag, features, location=location)
-        return def_langsys
-
-    def parse_feature_(self):
-        assert self.is_cur_keyword_("DEF_FEATURE")
-        location = self.cur_token_location_
-        self.expect_keyword_("NAME")
-        name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        lookups = []
-        while self.next_token_ != "END_FEATURE":
-            # self.advance_lexer_()
-            self.expect_keyword_("LOOKUP")
-            lookup = self.expect_string_()
-            lookups.append(lookup)
-        feature = ast.FeatureDefinition(name, tag, lookups, location=location)
-        return feature
-
-    def parse_def_lookup_(self):
-        assert self.is_cur_keyword_("DEF_LOOKUP")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        if not name[0].isalpha():
-            raise VoltLibError(
-                'Lookup name "%s" must start with a letter' % name, location
-            )
-        if self.lookups_.resolve(name) is not None:
-            raise VoltLibError(
-                'Lookup "%s" already defined, '
-                "lookup names are case insensitive" % name,
-                location,
-            )
-        process_base = True
-        if self.next_token_ == "PROCESS_BASE":
-            self.advance_lexer_()
-        elif self.next_token_ == "SKIP_BASE":
-            self.advance_lexer_()
-            process_base = False
-        process_marks = True
-        mark_glyph_set = None
-        if self.next_token_ == "PROCESS_MARKS":
-            self.advance_lexer_()
-            if self.next_token_ == "MARK_GLYPH_SET":
-                self.advance_lexer_()
-                mark_glyph_set = self.expect_string_()
-            elif self.next_token_ == "ALL":
-                self.advance_lexer_()
-            elif self.next_token_ == "NONE":
-                self.advance_lexer_()
-                process_marks = False
-            elif self.next_token_type_ == Lexer.STRING:
-                process_marks = self.expect_string_()
-            else:
-                raise VoltLibError(
-                    "Expected ALL, NONE, MARK_GLYPH_SET or an ID. "
-                    "Got %s" % (self.next_token_type_),
-                    location,
-                )
-        elif self.next_token_ == "SKIP_MARKS":
-            self.advance_lexer_()
-            process_marks = False
-        direction = None
-        if self.next_token_ == "DIRECTION":
-            self.expect_keyword_("DIRECTION")
-            direction = self.expect_name_()
-            assert direction in ("LTR", "RTL")
-        reversal = None
-        if self.next_token_ == "REVERSAL":
-            self.expect_keyword_("REVERSAL")
-            reversal = True
-        comments = None
-        if self.next_token_ == "COMMENTS":
-            self.expect_keyword_("COMMENTS")
-            comments = self.expect_string_().replace(r"\n", "\n")
-        context = []
-        while self.next_token_ in ("EXCEPT_CONTEXT", "IN_CONTEXT"):
-            context = self.parse_context_()
-        as_pos_or_sub = self.expect_name_()
-        sub = None
-        pos = None
-        if as_pos_or_sub == "AS_SUBSTITUTION":
-            sub = self.parse_substitution_(reversal)
-        elif as_pos_or_sub == "AS_POSITION":
-            pos = self.parse_position_()
-        else:
-            raise VoltLibError(
-                "Expected AS_SUBSTITUTION or AS_POSITION. " "Got %s" % (as_pos_or_sub),
-                location,
-            )
-        def_lookup = ast.LookupDefinition(
-            name,
-            process_base,
-            process_marks,
-            mark_glyph_set,
-            direction,
-            reversal,
-            comments,
-            context,
-            sub,
-            pos,
-            location=location,
-        )
-        self.lookups_.define(name, def_lookup)
-        return def_lookup
-
-    def parse_context_(self):
-        location = self.cur_token_location_
-        contexts = []
-        while self.next_token_ in ("EXCEPT_CONTEXT", "IN_CONTEXT"):
-            side = None
-            coverage = None
-            ex_or_in = self.expect_name_()
-            # side_contexts = [] # XXX
-            if self.next_token_ != "END_CONTEXT":
-                left = []
-                right = []
-                while self.next_token_ in ("LEFT", "RIGHT"):
-                    side = self.expect_name_()
-                    coverage = self.parse_coverage_()
-                    if side == "LEFT":
-                        left.append(coverage)
-                    else:
-                        right.append(coverage)
-                self.expect_keyword_("END_CONTEXT")
-                context = ast.ContextDefinition(
-                    ex_or_in, left, right, location=location
-                )
-                contexts.append(context)
-            else:
-                self.expect_keyword_("END_CONTEXT")
-        return contexts
-
-    def parse_substitution_(self, reversal):
-        assert self.is_cur_keyword_("AS_SUBSTITUTION")
-        location = self.cur_token_location_
-        src = []
-        dest = []
-        if self.next_token_ != "SUB":
-            raise VoltLibError("Expected SUB", location)
-        while self.next_token_ == "SUB":
-            self.expect_keyword_("SUB")
-            src.append(self.parse_coverage_())
-            self.expect_keyword_("WITH")
-            dest.append(self.parse_coverage_())
-            self.expect_keyword_("END_SUB")
-        self.expect_keyword_("END_SUBSTITUTION")
-        max_src = max([len(cov) for cov in src])
-        max_dest = max([len(cov) for cov in dest])
-        # many to many or mixed is invalid
-        if (max_src > 1 and max_dest > 1) or (
-            reversal and (max_src > 1 or max_dest > 1)
-        ):
-            raise VoltLibError("Invalid substitution type", location)
-        mapping = dict(zip(tuple(src), tuple(dest)))
-        if max_src == 1 and max_dest == 1:
-            if reversal:
-                sub = ast.SubstitutionReverseChainingSingleDefinition(
-                    mapping, location=location
-                )
-            else:
-                sub = ast.SubstitutionSingleDefinition(mapping, location=location)
-        elif max_src == 1 and max_dest > 1:
-            sub = ast.SubstitutionMultipleDefinition(mapping, location=location)
-        elif max_src > 1 and max_dest == 1:
-            sub = ast.SubstitutionLigatureDefinition(mapping, location=location)
-        return sub
-
-    def parse_position_(self):
-        assert self.is_cur_keyword_("AS_POSITION")
-        location = self.cur_token_location_
-        pos_type = self.expect_name_()
-        if pos_type not in ("ATTACH", "ATTACH_CURSIVE", "ADJUST_PAIR", "ADJUST_SINGLE"):
-            raise VoltLibError(
-                "Expected ATTACH, ATTACH_CURSIVE, ADJUST_PAIR, ADJUST_SINGLE", location
-            )
-        if pos_type == "ATTACH":
-            position = self.parse_attach_()
-        elif pos_type == "ATTACH_CURSIVE":
-            position = self.parse_attach_cursive_()
-        elif pos_type == "ADJUST_PAIR":
-            position = self.parse_adjust_pair_()
-        elif pos_type == "ADJUST_SINGLE":
-            position = self.parse_adjust_single_()
-        self.expect_keyword_("END_POSITION")
-        return position
-
-    def parse_attach_(self):
-        assert self.is_cur_keyword_("ATTACH")
-        location = self.cur_token_location_
-        coverage = self.parse_coverage_()
-        coverage_to = []
-        self.expect_keyword_("TO")
-        while self.next_token_ != "END_ATTACH":
-            cov = self.parse_coverage_()
-            self.expect_keyword_("AT")
-            self.expect_keyword_("ANCHOR")
-            anchor_name = self.expect_string_()
-            coverage_to.append((cov, anchor_name))
-        self.expect_keyword_("END_ATTACH")
-        position = ast.PositionAttachDefinition(
-            coverage, coverage_to, location=location
-        )
-        return position
-
-    def parse_attach_cursive_(self):
-        assert self.is_cur_keyword_("ATTACH_CURSIVE")
-        location = self.cur_token_location_
-        coverages_exit = []
-        coverages_enter = []
-        while self.next_token_ != "ENTER":
-            self.expect_keyword_("EXIT")
-            coverages_exit.append(self.parse_coverage_())
-        while self.next_token_ != "END_ATTACH":
-            self.expect_keyword_("ENTER")
-            coverages_enter.append(self.parse_coverage_())
-        self.expect_keyword_("END_ATTACH")
-        position = ast.PositionAttachCursiveDefinition(
-            coverages_exit, coverages_enter, location=location
-        )
-        return position
-
-    def parse_adjust_pair_(self):
-        assert self.is_cur_keyword_("ADJUST_PAIR")
-        location = self.cur_token_location_
-        coverages_1 = []
-        coverages_2 = []
-        adjust_pair = {}
-        while self.next_token_ == "FIRST":
-            self.advance_lexer_()
-            coverage_1 = self.parse_coverage_()
-            coverages_1.append(coverage_1)
-        while self.next_token_ == "SECOND":
-            self.advance_lexer_()
-            coverage_2 = self.parse_coverage_()
-            coverages_2.append(coverage_2)
-        while self.next_token_ != "END_ADJUST":
-            id_1 = self.expect_number_()
-            id_2 = self.expect_number_()
-            self.expect_keyword_("BY")
-            pos_1 = self.parse_pos_()
-            pos_2 = self.parse_pos_()
-            adjust_pair[(id_1, id_2)] = (pos_1, pos_2)
-        self.expect_keyword_("END_ADJUST")
-        position = ast.PositionAdjustPairDefinition(
-            coverages_1, coverages_2, adjust_pair, location=location
-        )
-        return position
-
-    def parse_adjust_single_(self):
-        assert self.is_cur_keyword_("ADJUST_SINGLE")
-        location = self.cur_token_location_
-        adjust_single = []
-        while self.next_token_ != "END_ADJUST":
-            coverages = self.parse_coverage_()
-            self.expect_keyword_("BY")
-            pos = self.parse_pos_()
-            adjust_single.append((coverages, pos))
-        self.expect_keyword_("END_ADJUST")
-        position = ast.PositionAdjustSingleDefinition(adjust_single, location=location)
-        return position
-
-    def parse_def_anchor_(self):
-        assert self.is_cur_keyword_("DEF_ANCHOR")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        self.expect_keyword_("ON")
-        gid = self.expect_number_()
-        self.expect_keyword_("GLYPH")
-        glyph_name = self.expect_name_()
-        self.expect_keyword_("COMPONENT")
-        component = self.expect_number_()
-        # check for duplicate anchor names on this glyph
-        if glyph_name in self.anchors_:
-            anchor = self.anchors_[glyph_name].resolve(name)
-            if anchor is not None and anchor.component == component:
-                raise VoltLibError(
-                    'Anchor "%s" already defined, '
-                    "anchor names are case insensitive" % name,
-                    location,
-                )
-        if self.next_token_ == "LOCKED":
-            locked = True
-            self.advance_lexer_()
-        else:
-            locked = False
-        self.expect_keyword_("AT")
-        pos = self.parse_pos_()
-        self.expect_keyword_("END_ANCHOR")
-        anchor = ast.AnchorDefinition(
-            name, gid, glyph_name, component, locked, pos, location=location
-        )
-        if glyph_name not in self.anchors_:
-            self.anchors_[glyph_name] = SymbolTable()
-        self.anchors_[glyph_name].define(name, anchor)
-        return anchor
-
-    def parse_adjust_by_(self):
-        self.advance_lexer_()
-        assert self.is_cur_keyword_("ADJUST_BY")
-        adjustment = self.expect_number_()
-        self.expect_keyword_("AT")
-        size = self.expect_number_()
-        return adjustment, size
-
-    def parse_pos_(self):
-        # VOLT syntax doesn't seem to take device Y advance
-        self.advance_lexer_()
-        location = self.cur_token_location_
-        assert self.is_cur_keyword_("POS"), location
-        adv = None
-        dx = None
-        dy = None
-        adv_adjust_by = {}
-        dx_adjust_by = {}
-        dy_adjust_by = {}
-        if self.next_token_ == "ADV":
-            self.advance_lexer_()
-            adv = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                adv_adjust_by[size] = adjustment
-        if self.next_token_ == "DX":
-            self.advance_lexer_()
-            dx = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                dx_adjust_by[size] = adjustment
-        if self.next_token_ == "DY":
-            self.advance_lexer_()
-            dy = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                dy_adjust_by[size] = adjustment
-        self.expect_keyword_("END_POS")
-        return ast.Pos(adv, dx, dy, adv_adjust_by, dx_adjust_by, dy_adjust_by)
-
-    def parse_unicode_values_(self):
-        location = self.cur_token_location_
-        try:
-            unicode_values = self.expect_string_().split(",")
-            unicode_values = [int(uni[2:], 16) for uni in unicode_values if uni != ""]
-        except ValueError as err:
-            raise VoltLibError(str(err), location)
-        return unicode_values if unicode_values != [] else None
-
-    def parse_enum_(self):
-        self.expect_keyword_("ENUM")
-        location = self.cur_token_location_
-        enum = ast.Enum(self.parse_coverage_(), location=location)
-        self.expect_keyword_("END_ENUM")
-        return enum
-
-    def parse_coverage_(self):
-        coverage = []
-        location = self.cur_token_location_
-        while self.next_token_ in ("GLYPH", "GROUP", "RANGE", "ENUM"):
-            if self.next_token_ == "ENUM":
-                enum = self.parse_enum_()
-                coverage.append(enum)
-            elif self.next_token_ == "GLYPH":
-                self.expect_keyword_("GLYPH")
-                name = self.expect_string_()
-                coverage.append(ast.GlyphName(name, location=location))
-            elif self.next_token_ == "GROUP":
-                self.expect_keyword_("GROUP")
-                name = self.expect_string_()
-                coverage.append(ast.GroupName(name, self, location=location))
-            elif self.next_token_ == "RANGE":
-                self.expect_keyword_("RANGE")
-                start = self.expect_string_()
-                self.expect_keyword_("TO")
-                end = self.expect_string_()
-                coverage.append(ast.Range(start, end, self, location=location))
-        return tuple(coverage)
-
-    def resolve_group(self, group_name):
-        return self.groups_.resolve(group_name)
-
-    def glyph_range(self, start, end):
-        return self.glyphs_.range(start, end)
-
-    def parse_ppem_(self):
-        location = self.cur_token_location_
-        ppem_name = self.cur_token_
-        value = self.expect_number_()
-        setting = ast.SettingDefinition(ppem_name, value, location=location)
-        return setting
-
-    def parse_noarg_option_(self):
-        location = self.cur_token_location_
-        name = self.cur_token_
-        value = True
-        setting = ast.SettingDefinition(name, value, location=location)
-        return setting
-
-    def parse_cmap_format(self):
-        location = self.cur_token_location_
-        name = self.cur_token_
-        value = (self.expect_number_(), self.expect_number_(), self.expect_number_())
-        setting = ast.SettingDefinition(name, value, location=location)
-        return setting
-
-    def is_cur_keyword_(self, k):
-        return (self.cur_token_type_ is Lexer.NAME) and (self.cur_token_ == k)
-
-    def expect_string_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.STRING:
-            raise VoltLibError("Expected a string", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_keyword_(self, keyword):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME and self.cur_token_ == keyword:
-            return self.cur_token_
-        raise VoltLibError('Expected "%s"' % keyword, self.cur_token_location_)
-
-    def expect_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            return self.cur_token_
-        raise VoltLibError("Expected a name", self.cur_token_location_)
-
-    def expect_number_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.NUMBER:
-            raise VoltLibError("Expected a number", self.cur_token_location_)
-        return self.cur_token_
-
-    def advance_lexer_(self):
-        self.cur_token_type_, self.cur_token_, self.cur_token_location_ = (
-            self.next_token_type_,
-            self.next_token_,
-            self.next_token_location_,
-        )
-        try:
-            if self.is_cur_keyword_("END"):
-                raise StopIteration
-            (
-                self.next_token_type_,
-                self.next_token_,
-                self.next_token_location_,
-            ) = self.lexer_.next()
-        except StopIteration:
-            self.next_token_type_, self.next_token_ = (None, None)
-
-
-class SymbolTable(object):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def exit_scope(self):
-        self.scopes_.pop()
-
-    def define(self, name, item):
-        self.scopes_[-1][name] = item
-
-    def resolve(self, name, case_insensitive=True):
-        for scope in reversed(self.scopes_):
-            item = scope.get(name)
-            if item:
-                return item
-        if case_insensitive:
-            for key in scope:
-                if key.lower() == name.lower():
-                    return scope[key]
-        return None
-
-
-class OrderedSymbolTable(SymbolTable):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def resolve(self, name, case_insensitive=False):
-        SymbolTable.resolve(self, name, case_insensitive=case_insensitive)
-
-    def range(self, start, end):
-        for scope in reversed(self.scopes_):
-            if start in scope and end in scope:
-                start_idx = list(scope.keys()).index(start)
-                end_idx = list(scope.keys()).index(end)
-                return list(scope.keys())[start_idx : end_idx + 1]
-        return None
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/config.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/config.py
deleted file mode 100644
index 76d9af14aaf7df47c4551c169f27b05abf9c269e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/config.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from __future__ import annotations
-
-import configparser
-import json
-import os
-import warnings
-from typing import Any
-
-conf: dict[str, dict[str, Any]] = {}
-default_conf_dir = os.path.join(os.path.expanduser("~"), ".config/fsspec")
-conf_dir = os.environ.get("FSSPEC_CONFIG_DIR", default_conf_dir)
-
-
-def set_conf_env(conf_dict, envdict=os.environ):
-    """Set config values from environment variables
-
-    Looks for variables of the form ``FSSPEC_<protocol>`` and
-    ``FSSPEC_<protocol>_<kwarg>``. For ``FSSPEC_<protocol>`` the value is parsed
-    as a json dictionary and used to ``update`` the config of the
-    corresponding protocol. For ``FSSPEC_<protocol>_<kwarg>`` there is no
-    attempt to convert the string value, but the kwarg keys will be lower-cased.
-
-    The ``FSSPEC_<protocol>_<kwarg>`` variables are applied after the
-    ``FSSPEC_<protocol>`` ones.
-
-    Parameters
-    ----------
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    envdict : dict-like(str, str)
-        Source for the values - usually the real environment
-    """
-    kwarg_keys = []
-    for key in envdict:
-        if key.startswith("FSSPEC_") and len(key) > 7 and key[7] != "_":
-            if key.count("_") > 1:
-                kwarg_keys.append(key)
-                continue
-            try:
-                value = json.loads(envdict[key])
-            except json.decoder.JSONDecodeError as ex:
-                warnings.warn(
-                    f"Ignoring environment variable {key} due to a parse failure: {ex}"
-                )
-            else:
-                if isinstance(value, dict):
-                    _, proto = key.split("_", 1)
-                    conf_dict.setdefault(proto.lower(), {}).update(value)
-                else:
-                    warnings.warn(
-                        f"Ignoring environment variable {key} due to not being a dict:"
-                        f" {type(value)}"
-                    )
-        elif key.startswith("FSSPEC"):
-            warnings.warn(
-                f"Ignoring environment variable {key} due to having an unexpected name"
-            )
-
-    for key in kwarg_keys:
-        _, proto, kwarg = key.split("_", 2)
-        conf_dict.setdefault(proto.lower(), {})[kwarg.lower()] = envdict[key]
-
-
-def set_conf_files(cdir, conf_dict):
-    """Set config values from files
-
-    Scans for INI and JSON files in the given dictionary, and uses their
-    contents to set the config. In case of repeated values, later values
-    win.
-
-    In the case of INI files, all values are strings, and these will not
-    be converted.
-
-    Parameters
-    ----------
-    cdir : str
-        Directory to search
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    """
-    if not os.path.isdir(cdir):
-        return
-    allfiles = sorted(os.listdir(cdir))
-    for fn in allfiles:
-        if fn.endswith(".ini"):
-            ini = configparser.ConfigParser()
-            ini.read(os.path.join(cdir, fn))
-            for key in ini:
-                if key == "DEFAULT":
-                    continue
-                conf_dict.setdefault(key, {}).update(dict(ini[key]))
-        if fn.endswith(".json"):
-            with open(os.path.join(cdir, fn)) as f:
-                js = json.load(f)
-            for key in js:
-                conf_dict.setdefault(key, {}).update(dict(js[key]))
-
-
-def apply_config(cls, kwargs, conf_dict=None):
-    """Supply default values for kwargs when instantiating class
-
-    Augments the passed kwargs, by finding entries in the config dict
-    which match the classes ``.protocol`` attribute (one or more str)
-
-    Parameters
-    ----------
-    cls : file system implementation
-    kwargs : dict
-    conf_dict : dict of dict
-        Typically this is the global configuration
-
-    Returns
-    -------
-    dict : the modified set of kwargs
-    """
-    if conf_dict is None:
-        conf_dict = conf
-    protos = cls.protocol if isinstance(cls.protocol, (tuple, list)) else [cls.protocol]
-    kw = {}
-    for proto in protos:
-        # default kwargs from the current state of the config
-        if proto in conf_dict:
-            kw.update(conf_dict[proto])
-    # explicit kwargs always win
-    kw.update(**kwargs)
-    kwargs = kw
-    return kwargs
-
-
-set_conf_files(conf_dir, conf)
-set_conf_env(conf)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/layouts.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/layouts.py
deleted file mode 100644
index 58a4f20ba2cc2b5a9527a212e067dd01c727f609..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/layouts.py
+++ /dev/null
@@ -1,393 +0,0 @@
-from __future__ import annotations
-
-import warnings
-from typing import TYPE_CHECKING, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.blocks import BlockContext
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import Changeable, Selectable
-
-if TYPE_CHECKING:
-    from gradio.blocks import Block
-
-set_documentation_group("layout")
-
-
-@document()
-class Row(BlockContext):
-    """
-    Row is a layout element within Blocks that renders all children horizontally.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Row():
-                gr.Image("lion.jpg", scale=2)
-                gr.Image("tiger.jpg", scale=1)
-        demo.launch()
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        *,
-        variant: Literal["default", "panel", "compact"] = "default",
-        visible: bool = True,
-        elem_id: str | None = None,
-        equal_height: bool = True,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            variant: row type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
-            visible: If False, row will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            equal_height: If True, makes every child element have equal height
-        """
-        self.variant = variant
-        self.equal_height = equal_height
-        if variant == "compact":
-            self.allow_expected_parents = False
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "row",
-            "variant": self.variant,
-            "equal_height": self.equal_height,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-    def style(
-        self,
-        *,
-        equal_height: bool | None = None,
-        **kwargs,
-    ):
-        """
-        Styles the Row.
-        Parameters:
-            equal_height: If True, makes every child element have equal height
-        """
-        warn_style_method_deprecation()
-        if equal_height is not None:
-            self.equal_height = equal_height
-        return self
-
-
-@document()
-class Column(BlockContext):
-    """
-    Column is a layout element within Blocks that renders all children vertically. The widths of columns can be set through the `scale` and `min_width` parameters.
-    If a certain scale results in a column narrower than min_width, the min_width parameter will win.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Row():
-                with gr.Column(scale=1):
-                    text1 = gr.Textbox()
-                    text2 = gr.Textbox()
-                with gr.Column(scale=4):
-                    btn1 = gr.Button("Button 1")
-                    btn2 = gr.Button("Button 2")
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        *,
-        scale: int = 1,
-        min_width: int = 320,
-        variant: Literal["default", "panel", "compact"] = "default",
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            scale: relative width compared to adjacent Columns. For example, if Column A has scale=2, and Column B has scale=1, A will be twice as wide as B.
-            min_width: minimum pixel width of Column, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in a column narrower than min_width, the min_width parameter will be respected first.
-            variant: column type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
-            visible: If False, column will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.scale = scale
-        self.min_width = min_width
-        self.variant = variant
-        if variant == "compact":
-            self.allow_expected_parents = False
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "column",
-            "variant": self.variant,
-            "scale": self.scale,
-            "min_width": self.min_width,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        variant: str | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "variant": variant,
-            "visible": visible,
-            "__type__": "update",
-        }
-
-
-class Tabs(BlockContext, Changeable, Selectable):
-    """
-    Tabs is a layout element within Blocks that can contain multiple "Tab" Components.
-    """
-
-    def __init__(
-        self,
-        *,
-        selected: int | str | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            selected: The currently selected tab. Must correspond to an id passed to the one of the child TabItems. Defaults to the first TabItem.
-            visible: If False, Tabs will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        BlockContext.__init__(self, visible=visible, elem_id=elem_id, **kwargs)
-        Changeable.__init__(self)
-        Selectable.__init__(self)
-        self.selected = selected
-
-    def get_config(self):
-        return {"selected": self.selected, **super(BlockContext, self).get_config()}
-
-    @staticmethod
-    def update(
-        selected: int | str | None = None,
-    ):
-        return {
-            "selected": selected,
-            "__type__": "update",
-        }
-
-
-@document()
-class Tab(BlockContext, Selectable):
-    """
-    Tab (or its alias TabItem) is a layout element. Components defined within the Tab will be visible when this tab is selected tab.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Tab("Lion"):
-                gr.Image("lion.jpg")
-                gr.Button("New Lion")
-            with gr.Tab("Tiger"):
-                gr.Image("tiger.jpg")
-                gr.Button("New Tiger")
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        label: str,
-        *,
-        id: int | str | None = None,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            label: The visual label for the tab
-            id: An optional identifier for the tab, required if you wish to control the selected tab from a predict function.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        BlockContext.__init__(self, elem_id=elem_id, **kwargs)
-        Selectable.__init__(self)
-        self.label = label
-        self.id = id
-
-    def get_config(self):
-        return {
-            "label": self.label,
-            "id": self.id,
-            **super(BlockContext, self).get_config(),
-        }
-
-    def get_expected_parent(self) -> type[Tabs]:
-        return Tabs
-
-    def get_block_name(self):
-        return "tabitem"
-
-
-TabItem = Tab
-
-
-@document()
-class Group(BlockContext):
-    """
-    Group is a layout element within Blocks which groups together children so that
-    they do not have any padding or margin between them.
-    Example:
-        with gr.Group():
-            gr.Textbox(label="First")
-            gr.Textbox(label="Last")
-    """
-
-    def __init__(
-        self,
-        *,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            visible: If False, group will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {"type": "group", **super().get_config()}
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-
-@document()
-class Box(BlockContext):
-    """
-    Box is a a layout element which places children in a box with rounded corners and
-    some padding around them.
-    Example:
-        with gr.Box():
-            gr.Textbox(label="First")
-            gr.Textbox(label="Last")
-    """
-
-    def __init__(
-        self,
-        *,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            visible: If False, box will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        warnings.warn("gr.Box is deprecated. Use gr.Group instead.", DeprecationWarning)
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {"type": "box", **super().get_config()}
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-    def style(self, **kwargs):
-        warn_style_method_deprecation()
-        return self
-
-
-class Form(BlockContext):
-    def __init__(self, *, scale: int = 0, min_width: int = 0, **kwargs):
-        """
-        Parameters:
-            scale: relative width compared to adjacent Columns. For example, if Column A has scale=2, and Column B has scale=1, A will be twice as wide as B.
-            min_width: minimum pixel width of Column, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in a column narrower than min_width, the min_width parameter will be respected first.
-        """
-        self.scale = scale
-        self.min_width = min_width
-        super().__init__(**kwargs)
-
-    def add_child(self, child: Block):
-        if isinstance(self.parent, Row):
-            scale = getattr(child, "scale", None)
-            self.scale += 1 if scale is None else scale
-            self.min_width += getattr(child, "min_width", 0) or 0
-        super().add_child(child)
-
-    def get_config(self):
-        return {
-            "type": "form",
-            "scale": self.scale,
-            "min_width": self.min_width,
-            **super().get_config(),
-        }
-
-
-@document()
-class Accordion(BlockContext):
-    """
-    Accordion is a layout element which can be toggled to show/hide the contained content.
-    Example:
-        with gr.Accordion("See Details"):
-            gr.Markdown("lorem ipsum")
-    """
-
-    def __init__(
-        self,
-        label,
-        *,
-        open: bool = True,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            label: name of accordion section.
-            open: if True, accordion is open by default.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.label = label
-        self.open = open
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "accordion",
-            "open": self.open,
-            "label": self.label,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        open: bool | None = None,
-        label: str | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "label": label,
-            "open": open,
-            "__type__": "update",
-        }
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_decoders.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_decoders.py
deleted file mode 100644
index 500ce7ffc3cc999085bf303833e2fa5ad98ea2ec..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_decoders.py
+++ /dev/null
@@ -1,324 +0,0 @@
-"""
-Handlers for Content-Encoding.
-
-See: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Encoding
-"""
-import codecs
-import io
-import typing
-import zlib
-
-from ._compat import brotli
-from ._exceptions import DecodingError
-
-
-class ContentDecoder:
-    def decode(self, data: bytes) -> bytes:
-        raise NotImplementedError()  # pragma: no cover
-
-    def flush(self) -> bytes:
-        raise NotImplementedError()  # pragma: no cover
-
-
-class IdentityDecoder(ContentDecoder):
-    """
-    Handle unencoded data.
-    """
-
-    def decode(self, data: bytes) -> bytes:
-        return data
-
-    def flush(self) -> bytes:
-        return b""
-
-
-class DeflateDecoder(ContentDecoder):
-    """
-    Handle 'deflate' decoding.
-
-    See: https://stackoverflow.com/questions/1838699
-    """
-
-    def __init__(self) -> None:
-        self.first_attempt = True
-        self.decompressor = zlib.decompressobj()
-
-    def decode(self, data: bytes) -> bytes:
-        was_first_attempt = self.first_attempt
-        self.first_attempt = False
-        try:
-            return self.decompressor.decompress(data)
-        except zlib.error as exc:
-            if was_first_attempt:
-                self.decompressor = zlib.decompressobj(-zlib.MAX_WBITS)
-                return self.decode(data)
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        try:
-            return self.decompressor.flush()
-        except zlib.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class GZipDecoder(ContentDecoder):
-    """
-    Handle 'gzip' decoding.
-
-    See: https://stackoverflow.com/questions/1838699
-    """
-
-    def __init__(self) -> None:
-        self.decompressor = zlib.decompressobj(zlib.MAX_WBITS | 16)
-
-    def decode(self, data: bytes) -> bytes:
-        try:
-            return self.decompressor.decompress(data)
-        except zlib.error as exc:
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        try:
-            return self.decompressor.flush()
-        except zlib.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class BrotliDecoder(ContentDecoder):
-    """
-    Handle 'brotli' decoding.
-
-    Requires `pip install brotlipy`. See: https://brotlipy.readthedocs.io/
-        or   `pip install brotli`. See https://github.com/google/brotli
-    Supports both 'brotlipy' and 'Brotli' packages since they share an import
-    name. The top branches are for 'brotlipy' and bottom branches for 'Brotli'
-    """
-
-    def __init__(self) -> None:
-        if brotli is None:  # pragma: no cover
-            raise ImportError(
-                "Using 'BrotliDecoder', but neither of the 'brotlicffi' or 'brotli' "
-                "packages have been installed. "
-                "Make sure to install httpx using `pip install httpx[brotli]`."
-            ) from None
-
-        self.decompressor = brotli.Decompressor()
-        self.seen_data = False
-        self._decompress: typing.Callable[[bytes], bytes]
-        if hasattr(self.decompressor, "decompress"):
-            # The 'brotlicffi' package.
-            self._decompress = self.decompressor.decompress  # pragma: no cover
-        else:
-            # The 'brotli' package.
-            self._decompress = self.decompressor.process  # pragma: no cover
-
-    def decode(self, data: bytes) -> bytes:
-        if not data:
-            return b""
-        self.seen_data = True
-        try:
-            return self._decompress(data)
-        except brotli.error as exc:
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        if not self.seen_data:
-            return b""
-        try:
-            if hasattr(self.decompressor, "finish"):
-                # Only available in the 'brotlicffi' package.
-
-                # As the decompressor decompresses eagerly, this
-                # will never actually emit any data. However, it will potentially throw
-                # errors if a truncated or damaged data stream has been used.
-                self.decompressor.finish()  # pragma: no cover
-            return b""
-        except brotli.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class MultiDecoder(ContentDecoder):
-    """
-    Handle the case where multiple encodings have been applied.
-    """
-
-    def __init__(self, children: typing.Sequence[ContentDecoder]) -> None:
-        """
-        'children' should be a sequence of decoders in the order in which
-        each was applied.
-        """
-        # Note that we reverse the order for decoding.
-        self.children = list(reversed(children))
-
-    def decode(self, data: bytes) -> bytes:
-        for child in self.children:
-            data = child.decode(data)
-        return data
-
-    def flush(self) -> bytes:
-        data = b""
-        for child in self.children:
-            data = child.decode(data) + child.flush()
-        return data
-
-
-class ByteChunker:
-    """
-    Handles returning byte content in fixed-size chunks.
-    """
-
-    def __init__(self, chunk_size: typing.Optional[int] = None) -> None:
-        self._buffer = io.BytesIO()
-        self._chunk_size = chunk_size
-
-    def decode(self, content: bytes) -> typing.List[bytes]:
-        if self._chunk_size is None:
-            return [content] if content else []
-
-        self._buffer.write(content)
-        if self._buffer.tell() >= self._chunk_size:
-            value = self._buffer.getvalue()
-            chunks = [
-                value[i : i + self._chunk_size]
-                for i in range(0, len(value), self._chunk_size)
-            ]
-            if len(chunks[-1]) == self._chunk_size:
-                self._buffer.seek(0)
-                self._buffer.truncate()
-                return chunks
-            else:
-                self._buffer.seek(0)
-                self._buffer.write(chunks[-1])
-                self._buffer.truncate()
-                return chunks[:-1]
-        else:
-            return []
-
-    def flush(self) -> typing.List[bytes]:
-        value = self._buffer.getvalue()
-        self._buffer.seek(0)
-        self._buffer.truncate()
-        return [value] if value else []
-
-
-class TextChunker:
-    """
-    Handles returning text content in fixed-size chunks.
-    """
-
-    def __init__(self, chunk_size: typing.Optional[int] = None) -> None:
-        self._buffer = io.StringIO()
-        self._chunk_size = chunk_size
-
-    def decode(self, content: str) -> typing.List[str]:
-        if self._chunk_size is None:
-            return [content]
-
-        self._buffer.write(content)
-        if self._buffer.tell() >= self._chunk_size:
-            value = self._buffer.getvalue()
-            chunks = [
-                value[i : i + self._chunk_size]
-                for i in range(0, len(value), self._chunk_size)
-            ]
-            if len(chunks[-1]) == self._chunk_size:
-                self._buffer.seek(0)
-                self._buffer.truncate()
-                return chunks
-            else:
-                self._buffer.seek(0)
-                self._buffer.write(chunks[-1])
-                self._buffer.truncate()
-                return chunks[:-1]
-        else:
-            return []
-
-    def flush(self) -> typing.List[str]:
-        value = self._buffer.getvalue()
-        self._buffer.seek(0)
-        self._buffer.truncate()
-        return [value] if value else []
-
-
-class TextDecoder:
-    """
-    Handles incrementally decoding bytes into text
-    """
-
-    def __init__(self, encoding: str = "utf-8"):
-        self.decoder = codecs.getincrementaldecoder(encoding)(errors="replace")
-
-    def decode(self, data: bytes) -> str:
-        return self.decoder.decode(data)
-
-    def flush(self) -> str:
-        return self.decoder.decode(b"", True)
-
-
-class LineDecoder:
-    """
-    Handles incrementally reading lines from text.
-
-    Has the same behaviour as the stdllib splitlines, but handling the input iteratively.
-    """
-
-    def __init__(self) -> None:
-        self.buffer: typing.List[str] = []
-        self.trailing_cr: bool = False
-
-    def decode(self, text: str) -> typing.List[str]:
-        # See https://docs.python.org/3/library/stdtypes.html#str.splitlines
-        NEWLINE_CHARS = "\n\r\x0b\x0c\x1c\x1d\x1e\x85\u2028\u2029"
-
-        # We always push a trailing `\r` into the next decode iteration.
-        if self.trailing_cr:
-            text = "\r" + text
-            self.trailing_cr = False
-        if text.endswith("\r"):
-            self.trailing_cr = True
-            text = text[:-1]
-
-        if not text:
-            return []
-
-        trailing_newline = text[-1] in NEWLINE_CHARS
-        lines = text.splitlines()
-
-        if len(lines) == 1 and not trailing_newline:
-            # No new lines, buffer the input and continue.
-            self.buffer.append(lines[0])
-            return []
-
-        if self.buffer:
-            # Include any existing buffer in the first portion of the
-            # splitlines result.
-            lines = ["".join(self.buffer) + lines[0]] + lines[1:]
-            self.buffer = []
-
-        if not trailing_newline:
-            # If the last segment of splitlines is not newline terminated,
-            # then drop it from our output and start a new buffer.
-            self.buffer = [lines.pop()]
-
-        return lines
-
-    def flush(self) -> typing.List[str]:
-        if not self.buffer and not self.trailing_cr:
-            return []
-
-        lines = ["".join(self.buffer)]
-        self.buffer = []
-        self.trailing_cr = False
-        return lines
-
-
-SUPPORTED_DECODERS = {
-    "identity": IdentityDecoder,
-    "gzip": GZipDecoder,
-    "deflate": DeflateDecoder,
-    "br": BrotliDecoder,
-}
-
-
-if brotli is None:
-    SUPPORTED_DECODERS.pop("br")  # pragma: no cover
diff --git a/spaces/DataDoggo/Visionary/README.md b/spaces/DataDoggo/Visionary/README.md
deleted file mode 100644
index 4a7e6c67255310e02ee1b8b1e5fefad91e4946a0..0000000000000000000000000000000000000000
--- a/spaces/DataDoggo/Visionary/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Visionary
-emoji: 🐠
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Detomo/ai-comic-generation/src/lib/loadImage.ts b/spaces/Detomo/ai-comic-generation/src/lib/loadImage.ts
deleted file mode 100644
index d2e7dcb6a548a9ce1937315486954e66e2c54746..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/lib/loadImage.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-export async function loadImage(image: string): Promise<HTMLImageElement> {
-  const img = new Image();
-  img.src = image;
-
-  const imgOnLoad = () => {
-    return new Promise<HTMLImageElement>((resolve, reject) => {
-      img.onload = () => { resolve(img) };
-      img.onerror = (err) => { reject(err) };
-    })
-  };
-
-  const loadImg = await imgOnLoad();
-  return loadImg
-}
\ No newline at end of file
diff --git a/spaces/DiegoLigtenberg/realtimespeech/instructions.md b/spaces/DiegoLigtenberg/realtimespeech/instructions.md
deleted file mode 100644
index 9d7f833f92ea0c434e6df9c611e806256ca359c2..0000000000000000000000000000000000000000
--- a/spaces/DiegoLigtenberg/realtimespeech/instructions.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Whisper UI - Transcriptions, Summaries & Analytics
-
----
-
-#### Run Whisper
-- Add a YouTube URL or select a local file on the left
-- Select the right Whisper model supported by your machine (extra configs have other whisper params if you want to play around with them)
-- Select whether you want to summarize the video. If so, enter a minimum and maximum length for the summary (usually between 50 and 100 words). Note that only the first 8 minutes of the video can be summarized in the current version.
-- Click Save settings.
-- Click "Transcribe"
-
-Once a transcription is created, it will be retained as a session variable so you can navigate around raw, summarized and time-annotated output.
-However, if you refresh or add a new video, the old transcription will be replaced.
-
----
diff --git a/spaces/Dipl0/Dipl0-pepe-diffuser-bot/app.py b/spaces/Dipl0/Dipl0-pepe-diffuser-bot/app.py
deleted file mode 100644
index 4c2da02a033d91ee480f2844f58ce46439f97c3b..0000000000000000000000000000000000000000
--- a/spaces/Dipl0/Dipl0-pepe-diffuser-bot/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Dipl0/pepe-diffuser").launch()
\ No newline at end of file
diff --git a/spaces/Dorado607/ChuanhuChatGPT/assets/custom.css b/spaces/Dorado607/ChuanhuChatGPT/assets/custom.css
deleted file mode 100644
index 904564120f962fb82242e6bba7cad57da6e16ccc..0000000000000000000000000000000000000000
--- a/spaces/Dorado607/ChuanhuChatGPT/assets/custom.css
+++ /dev/null
@@ -1,659 +0,0 @@
-:root {
-    --chatbot-color-light: #000000;
-    --chatbot-color-dark: #FFFFFF;
-    --chatbot-background-color-light: #F3F3F3;
-    --chatbot-background-color-dark: #121111;
-    --message-user-background-color-light: #95EC69;
-    --message-user-background-color-dark: #26B561;
-    --message-bot-background-color-light: #FFFFFF;
-    --message-bot-background-color-dark: #2C2C2C;
-    --switch-checkbox-color-light: #e5e7eb;
-    --switch-checkbox-color-dark: #515151;
-}
-
-
-#app_title {
-    font-weight: var(--prose-header-text-weight);
-    font-size: var(--text-xxl);
-    line-height: 1.3;
-    text-align: left;
-    margin-top: 6px;
-    white-space: nowrap;
-}
-#description {
-    text-align: center;
-    margin: 32px 0 4px 0;
-}
-
-/* 解决container=False时的错误填充 */
-div.form {
-    background: none !important;
-}
-
-/* 高级页面 */
-#advanced_warning {
-    display: flex;
-    flex-wrap: wrap;
-    flex-direction: column;
-    align-content: center;
-}
-
-/* gradio的页脚信息 */
-footer {
-    /* display: none !important; */
-    margin-top: .2em !important;
-    font-size: 85%;
-}
-#footer {
-    text-align: center;
-}
-#footer div {
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.60;
-}
-
-#float_display {
-    position: absolute;
-    max-height: 30px;
-}
-#toast-update {
-    position: absolute;
-    display: flex;
-    top: -500px;
-    width: 100%;
-    justify-content: center;
-    z-index: var(--layer-top);
-    transition: top 0.3s ease-out;
-}
-#check-chuanhu-update {
-    position: absolute;
-    align-items: center;
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    margin: var(--size-6) var(--size-4);
-    box-shadow: var(--shadow-drop-lg);
-    border: 1px solid var(--block-label-border-color);
-    border-radius: var(--container-radius);
-    background: var(--background-fill-primary);
-    padding: var(--size-4) var(--size-6);
-    min-width: 360px;
-    max-width: 480px;
-    overflow: hidden;
-    pointer-events: auto;
-}
-#version-info-title {
-    font-size: 1.2em;
-    font-weight: bold;
-    text-align: start;
-    width: 100%;
-}
-#release-note-wrap {
-    width: 100%;
-    max-width: 400px;
-    height: 120px;
-    border: solid 1px var(--border-color-primary);
-    overflow: auto;
-    padding: 0 8px;
-}
-#release-note-wrap.hideK {
-    display: none;
-}
-.btn-update-group {
-    display: flex;
-    justify-content: space-evenly;
-    align-items: center;
-    width: 100%;
-    padding-top: 10px;
-}
-.btn-update-group.hideK {
-    display: none;
-}
-/* user_info */
-#user_info.block {
-    white-space: nowrap;
-    position: absolute; left: 8em; top: .8em;
-    z-index: var(--layer-2);
-    box-shadow: var(--block-shadow);
-    border: none!important; border-radius: var(--block-label-radius);
-    background: var(--color-accent);
-    padding: var(--block-label-padding);
-    font-size: var(--block-label-text-size); line-height: var(--line-sm);
-    width: auto; max-height: 30px!important;
-    opacity: 1;
-    transition: opacity 0.3s ease-in-out;
-}
-#user_info.block .wrap {
-    opacity: 0;
-}
-#user_info p {
-    color: white;
-    font-weight: var(--block-label-text-weight);
-}
-#user_info.hideK {
-    opacity: 0;
-    transition: opacity 1s ease-in-out;
-}
-
-/* status_display */
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diff --git a/spaces/ECCV2022/PSG/style.css b/spaces/ECCV2022/PSG/style.css
deleted file mode 100644
index c3d85769278b98b0e5722a4c6a164048674da6ce..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/style.css
+++ /dev/null
@@ -1,26 +0,0 @@
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-}
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/seg_toy_data.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/seg_toy_data.py
deleted file mode 100644
index 7f0b7d8f4c520ec7847d69743d8e430b8795b656..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_datasets/seg_toy_data.py
+++ /dev/null
@@ -1,34 +0,0 @@
-prefix = 'tests/data/ocr_char_ann_toy_dataset/'
-
-train = dict(
-    type='OCRSegDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_train.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=100,
-        file_format='txt',
-        parser=dict(
-            type='LineJsonParser', keys=['file_name', 'annotations', 'text'])),
-    pipeline=None,
-    test_mode=True)
-
-test = dict(
-    type='OCRDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_test.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=True)
-
-train_list = [train]
-
-test_list = [test]
diff --git a/spaces/FauziNL/Voice_anime2/README.md b/spaces/FauziNL/Voice_anime2/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/FauziNL/Voice_anime2/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Fazzie/Pokemon-GAI/modules/details.py b/spaces/Fazzie/Pokemon-GAI/modules/details.py
deleted file mode 100644
index a73f2b63894ccbdd708361c1e680e337d81ffc79..0000000000000000000000000000000000000000
--- a/spaces/Fazzie/Pokemon-GAI/modules/details.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import random
-import json
-from typing import cast, Dict, List, Optional, TypedDict, Union
-
-
-class Attack(TypedDict):
-    name: str
-    cost: List[str]
-    convertedEnergyCost: int
-    damage: str
-    text: str
-
-
-ListCollection = Dict[str, Union[List[str], List[Attack]]]
-
-
-def load_lists(list_names: List[str], base_dir: str = "lists") -> ListCollection:
-    lists = {}
-
-    for name in list_names:
-        with open(f"{base_dir}/{name}.json", "r") as f:
-            lists[name] = json.load(f)
-
-    return lists
-
-
-def rand_hp() -> int:
-    # Weights from https://bulbapedia.bulbagarden.net/wiki/HP_(TCG)
-
-    hp_range: List[int] = list(range(30, 340 + 1, 10))
-
-    weights: List[int] = [156, 542, 1264, 1727, 1477, 1232, 1008, 640, 436, 515, 469, 279, 188,
-                          131, 132, 132, 56, 66, 97, 74, 23, 24, 25, 7, 15, 6, 0, 12, 18, 35, 18, 3]
-
-    return random.choices(hp_range, weights)[0]
-
-
-def rand_energy(can_be_none: bool = False) -> Union[str, None]:
-    types: List[str] = ['colorless', 'darkness', 'dragon', 'fairy', 'fighting',
-                        'fire', 'grass', 'lightning', 'metal', 'psychic', 'water']
-
-    if can_be_none:
-        return random.choices([random.choices(types)[0], None])[0]
-    else:
-        return random.choices(types)[0]
-
-
-def rand_name(energy_type: str = cast(str, rand_energy())) -> str:
-    lists: ListCollection = load_lists([energy_type], 'lists/names')
-
-    return cast(str, random.choices(lists[energy_type])[0])
-
-
-def rand_species(species: List[str]) -> str:
-    random_species: str = random.choices(species)[0]
-
-    return random_species[:1].upper() + random_species[1:]
-
-
-def rand_length() -> Dict[str, int]:
-    # Weights from https://bulbapedia.bulbagarden.net/wiki/List_of_Pok%C3%A9mon_by_height
-
-    feet_ranges: List[int] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16,
-                              17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, 35, 47, 65, 328]
-
-    weights: List[int] = [30, 220, 230, 176, 130, 109, 63, 27, 17, 17, 5, 5, 6,
-                          4, 3, 2, 2, 2, 1, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1]
-
-    return {
-        "feet": random.choices(feet_ranges, weights)[0],
-        "inches": random.randrange(0, 11 + 1, 1)
-    }
-
-
-def rand_weight() -> str:
-    # Weights from https://bulbapedia.bulbagarden.net/wiki/List_of_Pok%C3%A9mon_by_weight
-
-    weight_ranges: List[Dict[str, int]] = [
-        {"start": 1, "end": 22},
-        {"start": 22, "end": 44},
-        {"start": 44, "end": 55},
-        {"start": 55, "end": 110},
-        {"start": 110, "end": 132},
-        {"start": 132, "end": 218},
-        {"start": 218, "end": 220},
-        {"start": 221, "end": 226},
-        {"start": 226, "end": 331},
-        {"start": 331, "end": 441},
-        {"start": 441, "end": 451},
-        {"start": 452, "end": 661},
-        {"start": 661, "end": 677},
-        {"start": 677, "end": 793},
-        {"start": 794, "end": 903},
-        {"start": 903, "end": 2204}]
-
-    # 'weights' as in statistical weightings, not physical mass
-    weights: List[int] = [271, 145, 53, 204, 57, 122, 1, 11, 57, 28, 7, 34, 4, 17, 5, 31]
-
-    start: int
-    end: int
-    start, end = random.choices(weight_ranges, weights)[0].values()
-
-    random_weight: int = random.randrange(start, end + 1, 1)
-
-    return f'{random_weight} lbs.'
-
-
-def rand_attack(
-        attacks: List[Attack],
-        name: str, energy_type: Optional[str],
-        colorless_only_allowed: bool = False) -> Attack:
-    random_attack: Attack = random.choices(attacks)[0]
-
-    energy_type = energy_type.capitalize() if energy_type else None  # Energy is capitalised in the JSON lists
-
-    if energy_type and energy_type != 'Dragon':  # No attacks use Dragon energy so this would otherwise infinitely loop
-        if colorless_only_allowed:
-            while energy_type not in random_attack["cost"] and 'colorless' not in random_attack["cost"]:
-                random_attack = random.choices(attacks)[0]
-        else:
-            while energy_type not in random_attack["cost"]:
-                random_attack = random.choices(attacks)[0]
-
-    random_attack['text'] = random_attack['text'].replace('<name>', name)
-
-    return random_attack
-
-
-def rand_attacks(attacks: List[Attack], name: str, energy_type: Optional[str], n: int = 2) -> List[Attack]:
-    attack1: Attack = rand_attack(attacks, name, energy_type)
-
-    if n > 1:
-        attack2: Attack = rand_attack(attacks, name, energy_type, True)
-        while attack1['text'] == attack2['text']:
-            attack2 = rand_attack(attacks, name, energy_type, True)
-        return [attack1, attack2]
-    else:
-        return [attack1]
-
-
-def rand_retreat() -> int:
-    return random.randrange(0, 4, 1)
-
-
-def rand_description(descriptions) -> str:
-    return random.choices(descriptions)[0]
-
-
-def rand_rarity() -> str:
-    return random.choices(['●', '◆', '★'], [10, 5, 1])[0]
-
-
-lists: ListCollection = load_lists(['attacks', 'descriptions', 'species'])
-
-
-class Details(TypedDict):
-    name: str
-    hp: int
-    energy_type: str
-    species: str
-    length: Dict[str, int]
-    weight: str
-    attacks: List[Attack]
-    weakness: Union[str, None]
-    resistance: Union[str, None]
-    retreat: int
-    description: str
-    rarity: str
-
-
-def rand_details() -> Details:
-    energy_type: str = cast(str, rand_energy())
-    name: str = rand_name(energy_type)
-
-    return {
-        "name": name,
-        "hp": rand_hp(),
-        "energy_type": energy_type,
-        "species": rand_species(cast(List[str], lists["species"])),
-        "length": rand_length(),
-        "weight": rand_weight(),
-        "attacks": cast(List[Attack], rand_attacks(cast(List[Attack], lists["attacks"]), name, energy_type=energy_type)),
-        "weakness": rand_energy(can_be_none=True),
-        "resistance": rand_energy(can_be_none=True),
-        "retreat": rand_retreat(),
-        "description": rand_description(lists["descriptions"]),
-        "rarity": rand_rarity(),
-    }
diff --git a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Yqcloud.py b/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
deleted file mode 100644
index ad5c3a4326c68ceb7ee012fbf5bc072da72a7e40..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-import time
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://chat9.yqcloud.top/'
-model = [
-    'gpt-3.5-turbo',
-]
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, chatId: str, **kwargs):
-
-    headers = {
-        'authority': 'api.aichatos.cloud',
-        'origin': 'https://chat9.yqcloud.top',
-        'referer': 'https://chat9.yqcloud.top/',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
-    }
-
-    json_data = {
-        'prompt': str(messages),
-        'userId': f'#/chat/{chatId}',
-        'network': True,
-        'apikey': '',
-        'system': '',
-        'withoutContext': False,
-    }
-    response = requests.post('https://api.aichatos.cloud/api/generateStream',
-                             headers=headers, json=json_data, stream=True)
-    for token in response.iter_content(chunk_size=2046):
-        yield (token.decode('utf-8'))
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/modules/F0Predictor/HarvestF0Predictor.py b/spaces/FrankZxShen/so-vits-svc-models-ba/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index 122bdbb4c736feb4a8d974eca03df71aede76f69..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self,f0):
-        '''
-        对F0进行插值处理
-        '''
-    
-        data = np.reshape(f0, (f0.size, 1))
-    
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-    
-        ip_data = data
-    
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-    
-        return ip_data[:,0], vuv_vector[:,0]
-
-    def resize_f0(self,x, target_len):
-        source = np.array(x)
-        source[source<0.001] = np.nan
-        target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
-        res = np.nan_to_num(target)
-        return res
-        
-    def compute_f0(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.harvest(
-                wav.astype(np.double),
-                fs=self.hop_length,
-                f0_ceil=self.f0_max,
-                f0_floor=self.f0_min,
-                frame_period=1000 * self.hop_length / self.sampling_rate,
-            )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Frorozcol/financIA/pages/utils/plots.py b/spaces/Frorozcol/financIA/pages/utils/plots.py
deleted file mode 100644
index f91ca059e0c3fa17a251c8e0c97f05a4308a02ba..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/financIA/pages/utils/plots.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from matplotlib import pyplot as plt
-from spacy.lang.es.stop_words import STOP_WORDS as es_stopwords
-from wordcloud import WordCloud
-
-def plots_world_cloud(df, title, figsize=(10, 10)):
-    """This function is used to plot the world cloud"""
-    text = " ".join(df)
-    plt.figure(figsize=figsize)
-    wordcloud = WordCloud(background_color="white", stopwords=es_stopwords).generate(text)
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.axis("off")
-    plt.title(title)
-    plt.show()
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
deleted file mode 100644
index d05eb50c7cd501a5bab4ec403a98137b31b9b51b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
deleted file mode 100644
index 7cf5f307442e56b29460fb5477cef64bfd3476b9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
deleted file mode 100644
index f7aa7444d4c8022563db642478beec4dc5ab0dab..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 1a7cb708e551e90a12ad4267e2af6938c353f0ba..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(type='ResNet', depth=101))
diff --git a/spaces/Hahsgsgsy/teston/hash.py b/spaces/Hahsgsgsy/teston/hash.py
deleted file mode 100644
index 1e8a4712777c0d007979453bbaf3d4b3a3a781e6..0000000000000000000000000000000000000000
--- a/spaces/Hahsgsgsy/teston/hash.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import base64
-
-import urllib3
-from Crypto.Cipher import AES
-from Crypto.Util.Padding import pad, unpad
-
-urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
-
-class en:
-	def __init__(self, auth): self.key, self.iv = bytearray(self.secret(auth), "UTF-8"), bytearray.fromhex('00000000000000000000000000000000')
-
-	def secret(self, e):
-		t, n, s = e[0:8], e[16:24]+e[0:8]+e[24:32]+e[8:16], 0
-		while s < len(n):
-			t = chr((ord(n[s][0]) - ord('0') + 5) % 10 + ord('0')) if n[s] >= '0' and n[s] <= '9' else chr((ord(n[s][0]) - ord('a') + 9) % 26 + ord('a'))
-			n, s = self.replaceCharAt(n, s, t), s+1
-		return n
-
-	replaceCharAt = lambda self, e, t, i: e[0:t] + i + e[t + len(i):]
-	encrypt       = lambda self, text: base64.b64encode(AES.new(self.key, AES.MODE_CBC, self.iv).encrypt(pad(text.encode('UTF-8'), AES.block_size))).decode('UTF-8')
-	decrypt       = lambda self, text: unpad(AES.new(self.key, AES.MODE_CBC, self.iv).decrypt(base64.urlsafe_b64decode(text.encode('UTF-8'))), AES.block_size).decode('UTF-8')
diff --git a/spaces/Hisjhsshh/dreamlike-art-dreamlike-diffusion-1.0/README.md b/spaces/Hisjhsshh/dreamlike-art-dreamlike-diffusion-1.0/README.md
deleted file mode 100644
index e997247913d278f0c9b0488b22c4670b2feb1ad6..0000000000000000000000000000000000000000
--- a/spaces/Hisjhsshh/dreamlike-art-dreamlike-diffusion-1.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dreamlike Art Dreamlike Diffusion 1.0
-emoji: 📈
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/bsrgan.py b/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/bsrgan.py
deleted file mode 100644
index 32ef56169978e550090261cddbcf5eb611a6173b..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/bsrgan.py
+++ /dev/null
@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-
diff --git a/spaces/ICML2022/OFA/fairseq/examples/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/__init__.py
deleted file mode 100644
index 44bb24ae614941f23fea29c56d60167650c39bcb..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
-    from fairseq.version import __version__  # noqa
-except ImportError:
-    pass
diff --git a/spaces/ICML2022/OFA/fairseq/examples/discriminative_reranking_nmt/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/discriminative_reranking_nmt/__init__.py
deleted file mode 100644
index 0278f6a27340c7ff7e207d09348483d1b0d3a100..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/discriminative_reranking_nmt/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import criterions, models, tasks  # noqa
diff --git a/spaces/ICML2022/PointCloudC/GDANet_cls.py b/spaces/ICML2022/PointCloudC/GDANet_cls.py
deleted file mode 100644
index d1082e54d03677dc5d0a1eb3e4ca0e900f47fcd3..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/PointCloudC/GDANet_cls.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-from util.GDANet_util import local_operator, GDM, SGCAM
-
-
-class GDANET(nn.Module):
-    def __init__(self):
-        super(GDANET, self).__init__()
-
-        self.bn1 = nn.BatchNorm2d(64, momentum=0.1)
-        self.bn11 = nn.BatchNorm2d(64, momentum=0.1)
-        self.bn12 = nn.BatchNorm1d(64, momentum=0.1)
-
-        self.bn2 = nn.BatchNorm2d(64, momentum=0.1)
-        self.bn21 = nn.BatchNorm2d(64, momentum=0.1)
-        self.bn22 = nn.BatchNorm1d(64, momentum=0.1)
-
-        self.bn3 = nn.BatchNorm2d(128, momentum=0.1)
-        self.bn31 = nn.BatchNorm2d(128, momentum=0.1)
-        self.bn32 = nn.BatchNorm1d(128, momentum=0.1)
-
-        self.bn4 = nn.BatchNorm1d(512, momentum=0.1)
-
-        self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=True),
-                                   self.bn1)
-        self.conv11 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True),
-                                    self.bn11)
-        self.conv12 = nn.Sequential(nn.Conv1d(64 * 2, 64, kernel_size=1, bias=True),
-                                    self.bn12)
-
-        self.conv2 = nn.Sequential(nn.Conv2d(67 * 2, 64, kernel_size=1, bias=True),
-                                   self.bn2)
-        self.conv21 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True),
-                                    self.bn21)
-        self.conv22 = nn.Sequential(nn.Conv1d(64 * 2, 64, kernel_size=1, bias=True),
-                                    self.bn22)
-
-        self.conv3 = nn.Sequential(nn.Conv2d(131 * 2, 128, kernel_size=1, bias=True),
-                                   self.bn3)
-        self.conv31 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=1, bias=True),
-                                    self.bn31)
-        self.conv32 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=1, bias=True),
-                                    self.bn32)
-
-        self.conv4 = nn.Sequential(nn.Conv1d(256, 512, kernel_size=1, bias=True),
-                                   self.bn4)
-
-        self.SGCAM_1s = SGCAM(64)
-        self.SGCAM_1g = SGCAM(64)
-        self.SGCAM_2s = SGCAM(64)
-        self.SGCAM_2g = SGCAM(64)
-
-        self.linear1 = nn.Linear(1024, 512, bias=True)
-        self.bn6 = nn.BatchNorm1d(512)
-        self.dp1 = nn.Dropout(p=0.4)
-        self.linear2 = nn.Linear(512, 256, bias=True)
-        self.bn7 = nn.BatchNorm1d(256)
-        self.dp2 = nn.Dropout(p=0.4)
-        self.linear3 = nn.Linear(256, 40, bias=True)
-
-    def forward(self, x):
-        B, C, N = x.size()
-        ###############
-        """block 1"""
-        # Local operator:
-        x1 = local_operator(x, k=30)
-        x1 = F.relu(self.conv1(x1))
-        x1 = F.relu(self.conv11(x1))
-        x1 = x1.max(dim=-1, keepdim=False)[0]
-
-        # Geometry-Disentangle Module:
-        x1s, x1g = GDM(x1, M=256)
-
-        # Sharp-Gentle Complementary Attention Module:
-        y1s = self.SGCAM_1s(x1, x1s.transpose(2, 1))
-        y1g = self.SGCAM_1g(x1, x1g.transpose(2, 1))
-        z1 = torch.cat([y1s, y1g], 1)
-        z1 = F.relu(self.conv12(z1))
-        ###############
-        """block 2"""
-        x1t = torch.cat((x, z1), dim=1)
-        x2 = local_operator(x1t, k=30)
-        x2 = F.relu(self.conv2(x2))
-        x2 = F.relu(self.conv21(x2))
-        x2 = x2.max(dim=-1, keepdim=False)[0]
-
-        x2s, x2g = GDM(x2, M=256)
-
-        y2s = self.SGCAM_2s(x2, x2s.transpose(2, 1))
-        y2g = self.SGCAM_2g(x2, x2g.transpose(2, 1))
-        z2 = torch.cat([y2s, y2g], 1)
-        z2 = F.relu(self.conv22(z2))
-        ###############
-        x2t = torch.cat((x1t, z2), dim=1)
-        x3 = local_operator(x2t, k=30)
-        x3 = F.relu(self.conv3(x3))
-        x3 = F.relu(self.conv31(x3))
-        x3 = x3.max(dim=-1, keepdim=False)[0]
-        z3 = F.relu(self.conv32(x3))
-        ###############
-        x = torch.cat((z1, z2, z3), dim=1)
-        x = F.relu(self.conv4(x))
-        x11 = F.adaptive_max_pool1d(x, 1).view(B, -1)
-        x22 = F.adaptive_avg_pool1d(x, 1).view(B, -1)
-        x = torch.cat((x11, x22), 1)
-
-        x = F.relu(self.bn6(self.linear1(x)))
-        x = self.dp1(x)
-        x = F.relu(self.bn7(self.linear2(x)))
-        x = self.dp2(x)
-        x = self.linear3(x)
-        return x
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/data/realesrgan_paired_dataset.py b/spaces/Iceclear/StableSR/StableSR/basicsr/data/realesrgan_paired_dataset.py
deleted file mode 100644
index 3d0c6159d448f26fc8a256d6a9d0c51096b78fe0..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/data/realesrgan_paired_dataset.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import os
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
-from basicsr.data.transforms import augment, paired_random_crop
-from basicsr.utils import FileClient, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-
-
-@DATASET_REGISTRY.register(suffix='basicsr')
-class RealESRGANPairedDataset(data.Dataset):
-    """Paired image dataset for image restoration.
-
-    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
-
-    There are three modes:
-
-    1. **lmdb**: Use lmdb files. If opt['io_backend'] == lmdb.
-    2. **meta_info_file**: Use meta information file to generate paths. \
-        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
-    3. **folder**: Scan folders to generate paths. The rest.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-        dataroot_gt (str): Data root path for gt.
-        dataroot_lq (str): Data root path for lq.
-        meta_info (str): Path for meta information file.
-        io_backend (dict): IO backend type and other kwarg.
-        filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
-            Default: '{}'.
-        gt_size (int): Cropped patched size for gt patches.
-        use_hflip (bool): Use horizontal flips.
-        use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
-        scale (bool): Scale, which will be added automatically.
-        phase (str): 'train' or 'val'.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANPairedDataset, self).__init__()
-        self.opt = opt
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-        # mean and std for normalizing the input images
-        self.mean = opt['mean'] if 'mean' in opt else None
-        self.std = opt['std'] if 'std' in opt else None
-
-        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
-        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
-
-        # file client (lmdb io backend)
-        if self.io_backend_opt['type'] == 'lmdb':
-            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
-            self.io_backend_opt['client_keys'] = ['lq', 'gt']
-            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
-        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
-            # disk backend with meta_info
-            # Each line in the meta_info describes the relative path to an image
-            with open(self.opt['meta_info']) as fin:
-                paths = [line.strip() for line in fin]
-            self.paths = []
-            for path in paths:
-                gt_path, lq_path = path.split(', ')
-                gt_path = os.path.join(self.gt_folder, gt_path)
-                lq_path = os.path.join(self.lq_folder, lq_path)
-                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
-        else:
-            # disk backend
-            # it will scan the whole folder to get meta info
-            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
-            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
-
-        if 'num_pic' in self.opt:
-            self.paths = self.paths[:self.opt['num_pic']]
-        if 'phase' not in self.opt:
-            self.opt['phase'] = 'test'
-        if 'scale' not in self.opt:
-            self.opt['scale'] = 1
-
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
-        scale = self.opt['scale']
-
-        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
-        # image range: [0, 1], float32.
-        gt_path = self.paths[index]['gt_path']
-        img_bytes = self.file_client.get(gt_path, 'gt')
-        img_gt = imfrombytes(img_bytes, float32=True)
-        lq_path = self.paths[index]['lq_path']
-        img_bytes = self.file_client.get(lq_path, 'lq')
-        img_lq = imfrombytes(img_bytes, float32=True)
-
-        # augmentation for training
-        if self.opt['phase'] == 'train':
-            gt_size = self.opt['gt_size']
-            # random crop
-            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
-            # flip, rotation
-            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
-        # normalize
-        if self.mean is not None or self.std is not None:
-            normalize(img_lq, self.mean, self.std, inplace=True)
-            normalize(img_gt, self.mean, self.std, inplace=True)
-
-        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
-
-    def __len__(self):
-        return len(self.paths)
diff --git a/spaces/Illumotion/Koboldcpp/examples/server/completion.js.hpp b/spaces/Illumotion/Koboldcpp/examples/server/completion.js.hpp
deleted file mode 100644
index f0a071a69bca1cbf8cf997225c21233ed3c9a54c..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/server/completion.js.hpp
+++ /dev/null
@@ -1,428 +0,0 @@
-unsigned char completion_js[] = {
-  0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44,
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-};
-unsigned int completion_js_len = 5099;
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/tools/convert_hf_gptj.py b/spaces/Illumotion/Koboldcpp/otherarch/tools/convert_hf_gptj.py
deleted file mode 100644
index 8de785b27ab09092b07ac6b9ef92efd124ff7fc0..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/tools/convert_hf_gptj.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Convert GPT-J-6B h5 transformer model to ggml format
-#
-# Load the model using GPTJForCausalLM.
-# Iterate over all variables and write them to a binary file.
-#
-# For each variable, write the following:
-#   - Number of dimensions (int)
-#   - Name length (int)
-#   - Dimensions (int[n_dims])
-#   - Name (char[name_length])
-#   - Data (float[n_dims])
-#
-# By default, the bigger matrices are converted to 16-bit floats.
-# This can be disabled by adding the "use-f32" CLI argument.
-#
-# At the start of the ggml file we write the model parameters
-# and vocabulary.
-#
-
-import sys
-import struct
-import json
-import torch
-import numpy as np
-
-from transformers import GPTJForCausalLM
-
-# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
-    sys.exit(1)
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-fname_out = sys.argv[1] + "/ggml-model.bin"
-
-with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
-    encoder = json.load(f)
-
-with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
-    encoder_added = json.load(f)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
-    hparams = json.load(f)
-
-# possible data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-#
-# map from ftype to string
-ftype_str = ["f32", "f16"]
-
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
-        sys.exit(1)
-    fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
-
-
-model = GPTJForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
-#print (model)
-
-list_vars = model.state_dict()
-#print (list_vars)
-
-fout = open(fname_out, "wb")
-
-fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
-fout.write(struct.pack("i", hparams["vocab_size"]))
-fout.write(struct.pack("i", hparams["n_positions"]))
-fout.write(struct.pack("i", hparams["n_embd"]))
-fout.write(struct.pack("i", hparams["n_head"]))
-fout.write(struct.pack("i", hparams["n_layer"]))
-fout.write(struct.pack("i", hparams["rotary_dim"]))
-fout.write(struct.pack("i", ftype))
-
-byte_encoder = bytes_to_unicode()
-byte_decoder = {v:k for k, v in byte_encoder.items()}
-
-fout.write(struct.pack("i", len(encoder) + len(encoder_added)))
-
-for key in encoder:
-    text = bytearray([byte_decoder[c] for c in key])
-    fout.write(struct.pack("i", len(text)))
-    fout.write(text)
-
-for key in encoder_added:
-    text = bytearray([byte_decoder[c] for c in key])
-    fout.write(struct.pack("i", len(text)))
-    fout.write(text)
-
-for name in list_vars.keys():
-    data = list_vars[name].squeeze().numpy()
-    print("Processing variable: " + name + " with shape: ", data.shape)
-
-    # we don't need these
-    if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
-        print("  Skipping variable: " + name)
-        continue
-
-    n_dims = len(data.shape);
-
-    # ftype == 0 -> float32, ftype == 1 -> float16
-    ftype_cur = 0;
-    if ftype != 0:
-        if name[-7:] == ".weight" and n_dims == 2:
-            print("  Converting to float16")
-            data = data.astype(np.float16)
-            ftype_cur = 1
-        else:
-            print("  Converting to float32")
-            data = data.astype(np.float32)
-            ftype_cur = 0
-    else:
-        if data.dtype != np.float32:
-            print("  Converting to float32")
-            data = data.astype(np.float32)
-            ftype_cur = 0
-
-    # for efficiency - transpose these matrices:
-    # (note - with latest ggml this is no longer more efficient, so disabling it)
-    #  "transformer.h.*.mlp.fc_in.weight"
-    #  "transformer.h.*.attn.out_proj.weight"
-    #  "transformer.h.*.attn.q_proj.weight"
-    #  "transformer.h.*.attn.k_proj.weight"
-    #  "transformer.h.*.attn.v_proj.weight"
-    #if name.endswith(".mlp.fc_in.weight")     or \
-    #   name.endswith(".attn.out_proj.weight") or \
-    #   name.endswith(".attn.q_proj.weight")   or \
-    #   name.endswith(".attn.k_proj.weight")   or \
-    #   name.endswith(".attn.v_proj.weight"):
-    #    print("  Transposing")
-    #    data = data.transpose()
-
-    # header
-    str = name.encode('utf-8')
-    fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
-    for i in range(n_dims):
-        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
-    fout.write(str);
-
-    # data
-    data.tofile(fout)
-
-fout.close()
-
-print("Done. Output file: " + fname_out)
-print("")
\ No newline at end of file
diff --git a/spaces/Iqbalzz/hololive-rvc-models/README.md b/spaces/Iqbalzz/hololive-rvc-models/README.md
deleted file mode 100644
index 60a8823cdbcc5ddebc50a7e33fcf122a057ed498..0000000000000000000000000000000000000000
--- a/spaces/Iqbalzz/hololive-rvc-models/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Hololive Rvc Models
-emoji: 🎤🌸▶️
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: megaaziib/hololive-rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/JFoz/Dog-Pose-Editor-Controlnet/app.py b/spaces/JFoz/Dog-Pose-Editor-Controlnet/app.py
deleted file mode 100644
index b78bc3099de7e1fe0bda5128d18cb0cbc9e9ee3e..0000000000000000000000000000000000000000
--- a/spaces/JFoz/Dog-Pose-Editor-Controlnet/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import gradio as gr
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-from PIL import Image
-from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
-import cv2
-
-with open("test.html") as f:
-    lines = f.readlines()
-
-def create_key(seed=0):
-    return jax.random.PRNGKey(seed)
-
-#def addp5sketch(url):
-#    iframe = f'<iframe src ={url} style="border:none;height:525px;width:100%"/frame>'
-#    return gr.HTML(iframe)
-
-def wandb_report(url):
-    iframe = f'<iframe src ={url} style="border:none;height:1024px;width:100%"/frame>'
-    return gr.HTML(iframe)
-
-report_url = 'https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5'
-control_img = 'myimage.jpg'
-
-controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-    "JFoz/dog-cat-pose", dtype=jnp.bfloat16
-)
-pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
-)
-
-def infer(prompts, negative_prompts, image):
-
-    params["controlnet"] = controlnet_params
-    
-    num_samples = 1 #jax.device_count()
-    rng = create_key(0)
-    rng = jax.random.split(rng, jax.device_count())
-    image = Image.fromarray(image)
-    
-    prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
-    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
-    processed_image = pipe.prepare_image_inputs([image] * num_samples)
-    
-    p_params = replicate(params)
-    prompt_ids = shard(prompt_ids)
-    negative_prompt_ids = shard(negative_prompt_ids)
-    processed_image = shard(processed_image)
-    
-    output = pipe(
-        prompt_ids=prompt_ids,
-        image=processed_image,
-        params=p_params,
-        prng_seed=rng,
-        num_inference_steps=50,
-        neg_prompt_ids=negative_prompt_ids,
-        jit=True,
-    ).images
-    
-    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-    return output_images
-
-with gr.Blocks(theme='kfahn/AnimalPose') as demo:  
-  gr.Markdown(
-      """
-      # Animal Pose Control Net
-      ## This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
-      [Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset)  
-      [Diffusers model](https://huggingface.co/JFoz/dog-pose)  
-      [Github](https://github.com/fi4cr/animalpose)   
-      [Training Report](https://wandb.ai/john-fozard/AP10K-pose/runs/wn89ezaw)
-      """)
-  with gr.Row():
-    with gr.Column():
-      prompts  = gr.Textbox(label="Prompt")
-      negative_prompts  = gr.Textbox(label="Negative Prompt")
-      conditioning_image = gr.Image(label="Conditioning Image")
-      
-    with gr.Column():
-#      #keypoint_tool = addp5sketch(sketch_url)
-      keypoint_tool = gr.HTML(lines)
-      gallery = gr.Gallery(label="output")
-      submit_btn = gr.Button("Submit")
-      submit_btn.click(fn=infer, inputs = [prompts, negative_prompts, conditioning_image], outputs = gallery)
-    
-#gr.Interface(fn=infer, inputs = ["text", "text", "image"], outputs = "gallery",
-#            examples=[["a Labrador crossing the road", "low quality", "myimage.jpg"]])   
-    
-#with gr.Row():
- #   report = wandb_report(report_url)
-    
-
-demo.launch()
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/__init__.py b/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/commands/google_search.py b/spaces/Jamkonams/AutoGPT/autogpt/commands/google_search.py
deleted file mode 100644
index 7d38ce7568d2de207d521b077cfebd72527c9795..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/commands/google_search.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Google search command for Autogpt."""
-from __future__ import annotations
-
-import json
-
-from duckduckgo_search import ddg
-
-from autogpt.config import Config
-
-CFG = Config()
-
-
-def google_search(query: str, num_results: int = 8) -> str:
-    """Return the results of a Google search
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-    search_results = []
-    if not query:
-        return json.dumps(search_results)
-
-    results = ddg(query, max_results=num_results)
-    if not results:
-        return json.dumps(search_results)
-
-    for j in results:
-        search_results.append(j)
-
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-
-def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
-    """Return the results of a Google search using the official Google API
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = CFG.google_api_key
-        custom_search_engine_id = CFG.custom_search_engine_id
-
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-
-        # Send the search query and retrieve the results
-        result = (
-            service.cse()
-            .list(q=query, cx=custom_search_engine_id, num=num_results)
-            .execute()
-        )
-
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get(
-            "code"
-        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-            "message", ""
-        ):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-
-    # Return the list of search result URLs
-    return search_results_links
diff --git a/spaces/Jeff2323/ai-comic-factory/next.config.js b/spaces/Jeff2323/ai-comic-factory/next.config.js
deleted file mode 100644
index 4a29795b01a1f36b3e0f1d19f53852cdf63b9134..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/next.config.js
+++ /dev/null
@@ -1,11 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  output: 'standalone',
-  
-  experimental: {
-    serverActions: true,
-    serverActionsBodySizeLimit: '8mb',
-  },
-}
-
-module.exports = nextConfig
diff --git a/spaces/Josh98/nl2bash_m/README.md b/spaces/Josh98/nl2bash_m/README.md
deleted file mode 100644
index 4a393c7f47cea5228f191e851464a83282ddc7a1..0000000000000000000000000000000000000000
--- a/spaces/Josh98/nl2bash_m/README.md
+++ /dev/null
@@ -1,58 +0,0 @@
----
-title: nl2bash_m
-emoji: 🤗 
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
-  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
-  Accuracy = (TP + TN) / (TP + TN + FP + FN)
-  Where:
-  TP: True positive
-  TN: True negative
-  FP: False positive
-  FN: False negative
----
-
-# Metric Card for nl2bash_m
-
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
-
-## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
-
-## How to Use
-*Give general statement of how to use the metric*
-
-*Provide simplest possible example for using the metric*
-
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
-
-### Output Values
-
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
-
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
-
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
-
-### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
-
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
-
-## Citation
-*Cite the source where this metric was introduced.*
-
-## Further References
-*Add any useful further references.*
diff --git a/spaces/JustinLin610/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py b/spaces/JustinLin610/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py
deleted file mode 100644
index b19711da3af3bf6174958ec8fe897431ae3407a5..0000000000000000000000000000000000000000
--- a/spaces/JustinLin610/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py
+++ /dev/null
@@ -1,687 +0,0 @@
-#!/usr/bin/env python3
-# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import gzip
-import html
-import io
-import math
-from functools import lru_cache
-from typing import Callable, List, Optional
-
-import ftfy
-
-import numpy as np
-import regex as re
-import torch
-import torch.nn as nn
-from iopath.common.file_io import g_pathmgr
-from timm.models.layers import trunc_normal_
-
-from models.helpers import cast_if_src_dtype, VerboseNNModule
-
-
-def get_sinusoid_encoding_table(n_position, d_hid):
-    """Sinusoid position encoding table"""
-
-    # TODO: make it with torch instead of numpy
-    def get_position_angle_vec(position):
-        return [
-            position / np.power(10000, 2 * (hid_j // 2) / d_hid)
-            for hid_j in range(d_hid)
-        ]
-
-    sinusoid_table = np.array(
-        [get_position_angle_vec(pos_i) for pos_i in range(n_position)]
-    )
-    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
-    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
-
-    return torch.FloatTensor(sinusoid_table).unsqueeze(0)
-
-
-def interpolate_pos_encoding_2d(target_spatial_size, pos_embed):
-    N = pos_embed.shape[1]
-    if N == target_spatial_size:
-        return pos_embed
-    dim = pos_embed.shape[-1]
-    # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32
-    pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)
-    pos_embed = nn.functional.interpolate(
-        pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(
-            0, 3, 1, 2
-        ),
-        scale_factor=math.sqrt(target_spatial_size / N),
-        mode="bicubic",
-    )
-    if updated:
-        pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)
-    pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-    return pos_embed
-
-
-def interpolate_pos_encoding(
-    npatch_per_img,
-    pos_embed,
-    patches_layout,
-    input_shape=None,
-    first_patch_idx=1,
-):
-    assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none"
-    N = pos_embed.shape[1] - first_patch_idx  # since it's 1 if cls_token exists
-    if npatch_per_img == N:
-        return pos_embed
-
-    assert (
-        patches_layout[-1] == patches_layout[-2]
-    ), "Interpolation of pos embed not supported for non-square layouts"
-
-    class_emb = pos_embed[:, :first_patch_idx]
-    pos_embed = pos_embed[:, first_patch_idx:]
-
-    if input_shape is None or patches_layout[0] == 1:
-        # simple 2D pos embedding, no temporal component
-        pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)
-    elif patches_layout[0] > 1:
-        # pos embed has a temporal component
-        assert len(input_shape) == 4, "temporal interpolation not supported"
-        # we only support 2D interpolation in this case
-        num_frames = patches_layout[0]
-        num_spatial_tokens = patches_layout[1] * patches_layout[2]
-        pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)
-        # interpolate embedding for zeroth frame
-        pos_embed = interpolate_pos_encoding_2d(
-            npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)
-        )
-    else:
-        raise ValueError("This type of interpolation isn't implemented")
-
-    return torch.cat((class_emb, pos_embed), dim=1)
-
-
-def _get_pos_embedding(
-    npatch_per_img,
-    pos_embed,
-    patches_layout,
-    input_shape,
-    first_patch_idx=1,
-):
-    pos_embed = interpolate_pos_encoding(
-        npatch_per_img,
-        pos_embed,
-        patches_layout,
-        input_shape=input_shape,
-        first_patch_idx=first_patch_idx,
-    )
-    return pos_embed
-
-
-class PatchEmbedGeneric(nn.Module):
-    """
-    PatchEmbed from Hydra
-    """
-
-    def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):
-        super().__init__()
-
-        if len(proj_stem) > 1:
-            self.proj = nn.Sequential(*proj_stem)
-        else:
-            # Special case to be able to load pre-trained models that were
-            # trained with a standard stem
-            self.proj = proj_stem[0]
-        self.norm_layer = norm_layer
-
-    def get_patch_layout(self, img_size):
-        with torch.no_grad():
-            dummy_img = torch.zeros(
-                [
-                    1,
-                ]
-                + img_size
-            )
-            dummy_out = self.proj(dummy_img)
-        embed_dim = dummy_out.shape[1]
-        patches_layout = tuple(dummy_out.shape[2:])
-        num_patches = np.prod(patches_layout)
-        return patches_layout, num_patches, embed_dim
-
-    def forward(self, x):
-        x = self.proj(x)
-        # B C (T) H W -> B (T)HW C
-        x = x.flatten(2).transpose(1, 2)
-        if self.norm_layer is not None:
-            x = self.norm_layer(x)
-        return x
-
-
-class SpatioTemporalPosEmbeddingHelper(VerboseNNModule):
-    def __init__(
-        self,
-        patches_layout: List,
-        num_patches: int,
-        num_cls_tokens: int,
-        embed_dim: int,
-        learnable: bool,
-    ) -> None:
-        super().__init__()
-        self.num_cls_tokens = num_cls_tokens
-        self.patches_layout = patches_layout
-        self.num_patches = num_patches
-        self.num_tokens = num_cls_tokens + num_patches
-        self.learnable = learnable
-        if self.learnable:
-            self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
-            trunc_normal_(self.pos_embed, std=0.02)
-        else:
-            self.register_buffer(
-                "pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim)
-            )
-
-    def get_pos_embedding(self, vision_input, all_vision_tokens):
-        input_shape = vision_input.shape
-        pos_embed = _get_pos_embedding(
-            all_vision_tokens.size(1) - self.num_cls_tokens,
-            pos_embed=self.pos_embed,
-            patches_layout=self.patches_layout,
-            input_shape=input_shape,
-            first_patch_idx=self.num_cls_tokens,
-        )
-        return pos_embed
-
-
-class RGBDTPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        rgbt_stem: PatchEmbedGeneric,
-        depth_stem: PatchEmbedGeneric,
-        img_size: List = (3, 224, 224),
-        num_cls_tokens: int = 1,
-        pos_embed_fn: Callable = None,
-        use_type_embed: bool = False,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        stem = rgbt_stem if rgbt_stem is not None else depth_stem
-        (
-            self.patches_layout,
-            self.num_patches,
-            self.embed_dim,
-        ) = stem.get_patch_layout(img_size)
-        self.rgbt_stem = rgbt_stem
-        self.depth_stem = depth_stem
-        self.use_pos_embed = pos_embed_fn is not None
-        self.use_type_embed = use_type_embed
-        self.num_cls_tokens = num_cls_tokens
-
-        if self.use_pos_embed:
-            self.pos_embedding_helper = pos_embed_fn(
-                patches_layout=self.patches_layout,
-                num_cls_tokens=num_cls_tokens,
-                num_patches=self.num_patches,
-                embed_dim=self.embed_dim,
-            )
-        if self.num_cls_tokens > 0:
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
-            )
-        if self.use_type_embed:
-            self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style):
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-            if self.use_pos_embed:
-                nn.init.normal_(self.pos_embedding_helper.pos_embed)
-                self.pos_embedding_helper.pos_embed *= scale
-
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-        if self.use_type_embed:
-            nn.init.normal_(self.type_embed)
-
-    def tokenize_input_and_cls_pos(self, input, stem, mask):
-        # tokens is of shape B x L x D
-        tokens = stem(input)
-        assert tokens.ndim == 3
-        assert tokens.shape[2] == self.embed_dim
-        B = tokens.shape[0]
-        if self.num_cls_tokens > 0:
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            tokens = torch.cat((class_tokens, tokens), dim=1)
-        if self.use_pos_embed:
-            pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)
-            tokens = tokens + pos_embed
-        if self.use_type_embed:
-            tokens = tokens + self.type_embed.expand(B, -1, -1)
-        return tokens
-
-    def forward(self, vision=None, depth=None, patch_mask=None):
-        if patch_mask is not None:
-            raise NotImplementedError()
-
-        if vision is not None:
-            vision_tokens = self.tokenize_input_and_cls_pos(
-                vision, self.rgbt_stem, patch_mask
-            )
-
-        if depth is not None:
-            depth_tokens = self.tokenize_input_and_cls_pos(
-                depth, self.depth_stem, patch_mask
-            )
-
-        # aggregate tokens
-        if vision is not None and depth is not None:
-            final_tokens = vision_tokens + depth_tokens
-        else:
-            final_tokens = vision_tokens if vision is not None else depth_tokens
-        return_dict = {
-            "trunk": {
-                "tokens": final_tokens,
-            },
-            "head": {},
-        }
-        return return_dict
-
-
-class AudioPreprocessor(RGBDTPreprocessor):
-    def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:
-        super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)
-
-    def forward(self, audio=None):
-        return super().forward(vision=audio)
-
-
-class ThermalPreprocessor(RGBDTPreprocessor):
-    def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:
-        super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)
-
-    def forward(self, thermal=None):
-        return super().forward(vision=thermal)
-
-
-def build_causal_attention_mask(context_length):
-    # lazily create causal attention mask, with full attention between the vision tokens
-    # pytorch uses additive attention mask; fill with -inf
-    mask = torch.empty(context_length, context_length, requires_grad=False)
-    mask.fill_(float("-inf"))
-    mask.triu_(1)  # zero out the lower diagonal
-    return mask
-
-
-class TextPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        vocab_size: int,
-        context_length: int,
-        embed_dim: int,
-        causal_masking: bool,
-        supply_seq_len_to_head: bool = True,
-        num_cls_tokens: int = 0,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.context_length = context_length
-        self.token_embedding = nn.Embedding(vocab_size, embed_dim)
-        self.pos_embed = nn.Parameter(
-            torch.empty(1, self.context_length + num_cls_tokens, embed_dim)
-        )
-        self.causal_masking = causal_masking
-        if self.causal_masking:
-            mask = build_causal_attention_mask(self.context_length)
-            # register the mask as a buffer so it can be moved to the right device
-            self.register_buffer("mask", mask)
-
-        self.supply_seq_len_to_head = supply_seq_len_to_head
-        self.num_cls_tokens = num_cls_tokens
-        self.embed_dim = embed_dim
-        if num_cls_tokens > 0:
-            assert self.causal_masking is False, "Masking + CLS token isn't implemented"
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, embed_dim)
-            )
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style="openclip"):
-        # OpenCLIP style initialization
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.pos_embed, std=0.01)
-
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-    def forward(self, text):
-        # text tokens are of shape B x L x D
-        text_tokens = self.token_embedding(text)
-        # concat CLS tokens if any
-        if self.num_cls_tokens > 0:
-            B = text_tokens.shape[0]
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            text_tokens = torch.cat((class_tokens, text_tokens), dim=1)
-        text_tokens = text_tokens + self.pos_embed
-        return_dict = {
-            "trunk": {
-                "tokens": text_tokens,
-            },
-            "head": {},
-        }
-        # Compute sequence length after adding CLS tokens
-        if self.supply_seq_len_to_head:
-            text_lengths = text.argmax(dim=-1)
-            return_dict["head"] = {
-                "seq_len": text_lengths,
-            }
-        if self.causal_masking:
-            return_dict["trunk"].update({"attn_mask": self.mask})
-        return return_dict
-
-
-class Im2Video(nn.Module):
-    """Convert an image into a trivial video."""
-
-    def __init__(self, time_dim=2):
-        super().__init__()
-        self.time_dim = time_dim
-
-    def forward(self, x):
-        if x.ndim == 4:
-            # B, C, H, W -> B, C, T, H, W
-            return x.unsqueeze(self.time_dim)
-        elif x.ndim == 5:
-            return x
-        else:
-            raise ValueError(f"Dimension incorrect {x.shape}")
-
-
-class PadIm2Video(Im2Video):
-    def __init__(self, ntimes, pad_type, time_dim=2):
-        super().__init__(time_dim=time_dim)
-        assert ntimes > 0
-        assert pad_type in ["zero", "repeat"]
-        self.ntimes = ntimes
-        self.pad_type = pad_type
-
-    def forward(self, x):
-        x = super().forward(x)
-        if x.shape[self.time_dim] == 1:
-            if self.pad_type == "repeat":
-                new_shape = [1] * len(x.shape)
-                new_shape[self.time_dim] = self.ntimes
-                x = x.repeat(new_shape)
-            elif self.pad_type == "zero":
-                padarg = [0, 0] * len(x.shape)
-                padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]
-                x = nn.functional.pad(x, padarg)
-        return x
-
-
-# Modified from github.com/openai/CLIP
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str, context_length=77):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-
-        with g_pathmgr.open(bpe_path, "rb") as fh:
-            bpe_bytes = io.BytesIO(fh.read())
-            merges = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
-        merges = merges[1 : 49152 - 256 - 2 + 1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v + "</w>" for v in vocab]
-        for merge in merges:
-            vocab.append("".join(merge))
-        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {
-            "<|startoftext|>": "<|startoftext|>",
-            "<|endoftext|>": "<|endoftext|>",
-        }
-        self.pat = re.compile(
-            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
-            re.IGNORECASE,
-        )
-        self.context_length = context_length
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token + "</w>"
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder[token] for token in tokens])
-        text = (
-            bytearray([self.byte_decoder[c] for c in text])
-            .decode("utf-8", errors="replace")
-            .replace("</w>", " ")
-        )
-        return text
-
-    def __call__(self, texts, context_length=None):
-        if not context_length:
-            context_length = self.context_length
-
-        if isinstance(texts, str):
-            texts = [texts]
-
-        sot_token = self.encoder["<|startoftext|>"]
-        eot_token = self.encoder["<|endoftext|>"]
-        all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
-        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-        for i, tokens in enumerate(all_tokens):
-            tokens = tokens[:context_length]
-            result[i, : len(tokens)] = torch.tensor(tokens)
-
-        if len(result) == 1:
-            return result[0]
-        return result
-
-
-class IMUPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        kernel_size: int,
-        imu_stem: PatchEmbedGeneric,
-        embed_dim: int,
-        img_size: List = (6, 2000),
-        num_cls_tokens: int = 1,
-        pos_embed_fn: Callable = None,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        stem = imu_stem
-        self.imu_stem = imu_stem
-        self.embed_dim = embed_dim
-        self.use_pos_embed = pos_embed_fn is not None
-        self.num_cls_tokens = num_cls_tokens
-        self.kernel_size = kernel_size
-        self.pos_embed = nn.Parameter(
-            torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)
-        )
-
-        if self.num_cls_tokens > 0:
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
-            )
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style):
-        nn.init.normal_(self.pos_embed, std=0.01)
-
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-    def tokenize_input_and_cls_pos(self, input, stem):
-        # tokens is of shape B x L x D
-        tokens = stem.norm_layer(stem.proj(input))
-        assert tokens.ndim == 3
-        assert tokens.shape[2] == self.embed_dim
-        B = tokens.shape[0]
-        if self.num_cls_tokens > 0:
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            tokens = torch.cat((class_tokens, tokens), dim=1)
-        if self.use_pos_embed:
-            tokens = tokens + self.pos_embed
-        return tokens
-
-    def forward(self, imu):
-        # Patchify
-        imu = imu.unfold(
-            -1,
-            self.kernel_size,
-            self.kernel_size,
-        ).permute(0, 2, 1, 3)
-        imu = imu.reshape(imu.size(0), imu.size(1), -1)
-
-        imu_tokens = self.tokenize_input_and_cls_pos(
-            imu,
-            self.imu_stem,
-        )
-
-        return_dict = {
-            "trunk": {
-                "tokens": imu_tokens,
-            },
-            "head": {},
-        }
-        return return_dict
diff --git a/spaces/KOFTRFU204/AICoverGen/src/rmvpe.py b/spaces/KOFTRFU204/AICoverGen/src/rmvpe.py
deleted file mode 100644
index 8d0d57297d4301e43a4fdcda216ae39c5e3b83b4..0000000000000000000000000000000000000000
--- a/spaces/KOFTRFU204/AICoverGen/src/rmvpe.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import torch, numpy as np
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # frame length#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # frame length,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # frame length，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # frame length，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # frame length
-        devided = product_sum / weight_sum  # frame length
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # frame length
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("Quotations~1.wav") ### edit
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)
diff --git a/spaces/KyanChen/FunSR/models/baselines/arbrcan.py b/spaces/KyanChen/FunSR/models/baselines/arbrcan.py
deleted file mode 100644
index 5d9b5ec1bdc1c64c3f2d18f1eac141f95fcabf2c..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/baselines/arbrcan.py
+++ /dev/null
@@ -1,341 +0,0 @@
-import torch.nn as nn
-import torch
-import numpy as np
-import torch.nn.functional as F
-import math
-import models
-from models import register
-
-
-def default_conv(in_channels, out_channels, kernel_size, bias=True):
-    return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias)
-
-
-## Channel Attention (CA) Layer
-class CALayer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(CALayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv_du = nn.Sequential(
-            nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
-            nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        y = self.avg_pool(x)
-        y = self.conv_du(y)
-        return x * y
-
-
-## Residual Channel Attention Block (RCAB)
-class RCAB(nn.Module):
-    def __init__(
-            self, conv, n_feat, kernel_size, reduction,
-            bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
-
-        super(RCAB, self).__init__()
-        modules_body = []
-        for i in range(2):
-            modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
-            if bn: modules_body.append(nn.BatchNorm2d(n_feat))
-            if i == 0: modules_body.append(act)
-        modules_body.append(CALayer(n_feat, reduction))
-        self.body = nn.Sequential(*modules_body)
-        self.res_scale = res_scale
-
-    def forward(self, x):
-        res = self.body(x)
-        res += x
-        return res
-
-
-## Residual Group (RG)
-class ResidualGroup(nn.Module):
-    def __init__(self, conv, n_feat, kernel_size, reduction, act, res_scale, n_resblocks):
-        super(ResidualGroup, self).__init__()
-        modules_body = [
-            RCAB(
-                conv, n_feat, kernel_size, reduction, bias=True, bn=False, act=nn.ReLU(True), res_scale=1) \
-            for _ in range(n_resblocks)]
-        modules_body.append(conv(n_feat, n_feat, kernel_size))
-        self.body = nn.Sequential(*modules_body)
-
-    def forward(self, x):
-        res = self.body(x)
-        res += x
-        return res
-
-
-class SA_upsample(nn.Module):
-    def __init__(self, channels, num_experts=4, bias=False):
-        super(SA_upsample, self).__init__()
-        self.bias = bias
-        self.num_experts = num_experts
-        self.channels = channels
-
-        # experts
-        weight_compress = []
-        for i in range(num_experts):
-            weight_compress.append(nn.Parameter(torch.Tensor(channels//8, channels, 1, 1)))
-            nn.init.kaiming_uniform_(weight_compress[i], a=math.sqrt(5))
-        self.weight_compress = nn.Parameter(torch.stack(weight_compress, 0))
-
-        weight_expand = []
-        for i in range(num_experts):
-            weight_expand.append(nn.Parameter(torch.Tensor(channels, channels//8, 1, 1)))
-            nn.init.kaiming_uniform_(weight_expand[i], a=math.sqrt(5))
-        self.weight_expand = nn.Parameter(torch.stack(weight_expand, 0))
-
-        # two FC layers
-        self.body = nn.Sequential(
-            nn.Conv2d(4, 64, 1, 1, 0, bias=True),
-            nn.ReLU(True),
-            nn.Conv2d(64, 64, 1, 1, 0, bias=True),
-            nn.ReLU(True),
-        )
-        # routing head
-        self.routing = nn.Sequential(
-            nn.Conv2d(64, num_experts, 1, 1, 0, bias=True),
-            nn.Sigmoid()
-        )
-        # offset head
-        self.offset = nn.Conv2d(64, 2, 1, 1, 0, bias=True)
-
-    def forward(self, x, scale, scale2):
-        b, c, h, w = x.size()
-
-        # (1) coordinates in LR space
-        ## coordinates in HR space
-        coor_hr = [torch.arange(0, round(h * scale), 1).unsqueeze(0).float().to(x.device),
-                   torch.arange(0, round(w * scale2), 1).unsqueeze(0).float().to(x.device)]
-
-        ## coordinates in LR space
-        coor_h = ((coor_hr[0] + 0.5) / scale) - (torch.floor((coor_hr[0] + 0.5) / scale + 1e-3)) - 0.5
-        coor_h = coor_h.permute(1, 0)
-        coor_w = ((coor_hr[1] + 0.5) / scale2) - (torch.floor((coor_hr[1] + 0.5) / scale2 + 1e-3)) - 0.5
-
-        input = torch.cat((
-            torch.ones_like(coor_h).expand([-1, round(scale2 * w)]).unsqueeze(0) / scale2,
-            torch.ones_like(coor_h).expand([-1, round(scale2 * w)]).unsqueeze(0) / scale,
-            coor_h.expand([-1, round(scale2 * w)]).unsqueeze(0),
-            coor_w.expand([round(scale * h), -1]).unsqueeze(0)
-        ), 0).unsqueeze(0)
-
-
-        # (2) predict filters and offsets
-        embedding = self.body(input)
-        ## offsets
-        offset = self.offset(embedding)
-
-        ## filters
-        routing_weights = self.routing(embedding)
-        routing_weights = routing_weights.view(self.num_experts, round(scale*h) * round(scale2*w)).transpose(0, 1)      # (h*w) * n
-
-        weight_compress = self.weight_compress.view(self.num_experts, -1)
-        weight_compress = torch.matmul(routing_weights, weight_compress)
-        weight_compress = weight_compress.view(1, round(scale*h), round(scale2*w), self.channels//8, self.channels)
-
-        weight_expand = self.weight_expand.view(self.num_experts, -1)
-        weight_expand = torch.matmul(routing_weights, weight_expand)
-        weight_expand = weight_expand.view(1, round(scale*h), round(scale2*w), self.channels, self.channels//8)
-
-        # (3) grid sample & spatially varying filtering
-        ## grid sample
-        fea0 = grid_sample(x, offset, scale, scale2)               ## b * h * w * c * 1
-        fea = fea0.unsqueeze(-1).permute(0, 2, 3, 1, 4)            ## b * h * w * c * 1
-
-        ## spatially varying filtering
-        out = torch.matmul(weight_compress.expand([b, -1, -1, -1, -1]), fea)
-        out = torch.matmul(weight_expand.expand([b, -1, -1, -1, -1]), out).squeeze(-1)
-
-        return out.permute(0, 3, 1, 2) + fea0
-
-
-class SA_adapt(nn.Module):
-    def __init__(self, channels):
-        super(SA_adapt, self).__init__()
-        self.mask = nn.Sequential(
-            nn.Conv2d(channels, 16, 3, 1, 1),
-            nn.BatchNorm2d(16),
-            nn.ReLU(True),
-            nn.AvgPool2d(2),
-            nn.Conv2d(16, 16, 3, 1, 1),
-            nn.BatchNorm2d(16),
-            nn.ReLU(True),
-            nn.Conv2d(16, 16, 3, 1, 1),
-            nn.BatchNorm2d(16),
-            nn.ReLU(True),
-            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-            nn.Conv2d(16, 1, 3, 1, 1),
-            nn.BatchNorm2d(1),
-            nn.Sigmoid()
-        )
-        self.adapt = SA_conv(channels, channels, 3, 1, 1)
-
-    def forward(self, x, scale, scale2):
-        mask = self.mask(x)
-        adapted = self.adapt(x, scale, scale2)
-
-        return x + adapted * mask
-
-
-class SA_conv(nn.Module):
-    def __init__(self, channels_in, channels_out, kernel_size=3, stride=1, padding=1, bias=False, num_experts=4):
-        super(SA_conv, self).__init__()
-        self.channels_out = channels_out
-        self.channels_in = channels_in
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.num_experts = num_experts
-        self.bias = bias
-
-        # FC layers to generate routing weights
-        self.routing = nn.Sequential(
-            nn.Linear(2, 64),
-            nn.ReLU(True),
-            nn.Linear(64, num_experts),
-            nn.Softmax(1)
-        )
-
-        # initialize experts
-        weight_pool = []
-        for i in range(num_experts):
-            weight_pool.append(nn.Parameter(torch.Tensor(channels_out, channels_in, kernel_size, kernel_size)))
-            nn.init.kaiming_uniform_(weight_pool[i], a=math.sqrt(5))
-        self.weight_pool = nn.Parameter(torch.stack(weight_pool, 0))
-
-        if bias:
-            self.bias_pool = nn.Parameter(torch.Tensor(num_experts, channels_out))
-            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_pool)
-            bound = 1 / math.sqrt(fan_in)
-            nn.init.uniform_(self.bias_pool, -bound, bound)
-
-    def forward(self, x, scale, scale2):
-        # generate routing weights
-        scale = torch.ones(1, 1).to(x.device) / scale
-        scale2 = torch.ones(1, 1).to(x.device) / scale2
-        routing_weights = self.routing(torch.cat((scale, scale2), 1)).view(self.num_experts, 1, 1)
-
-        # fuse experts
-        fused_weight = (self.weight_pool.view(self.num_experts, -1, 1) * routing_weights).sum(0)
-        fused_weight = fused_weight.view(-1, self.channels_in, self.kernel_size, self.kernel_size)
-
-        if self.bias:
-            fused_bias = torch.mm(routing_weights, self.bias_pool).view(-1)
-        else:
-            fused_bias = None
-
-        # convolution
-        out = F.conv2d(x, fused_weight, fused_bias, stride=self.stride, padding=self.padding)
-
-        return out
-
-
-def grid_sample(x, offset, scale, scale2):
-    # generate grids
-    b, _, h, w = x.size()
-    grid = np.meshgrid(range(round(scale2*w)), range(round(scale*h)))
-    grid = np.stack(grid, axis=-1).astype(np.float64)
-    grid = torch.Tensor(grid).to(x.device)
-
-    # project into LR space
-    grid[:, :, 0] = (grid[:, :, 0] + 0.5) / scale2 - 0.5
-    grid[:, :, 1] = (grid[:, :, 1] + 0.5) / scale - 0.5
-
-    # normalize to [-1, 1]
-    grid[:, :, 0] = grid[:, :, 0] * 2 / (w - 1) -1
-    grid[:, :, 1] = grid[:, :, 1] * 2 / (h - 1) -1
-    grid = grid.permute(2, 0, 1).unsqueeze(0)
-    grid = grid.expand([b, -1, -1, -1])
-
-    # add offsets
-    offset_0 = torch.unsqueeze(offset[:, 0, :, :] * 2 / (w - 1), dim=1)
-    offset_1 = torch.unsqueeze(offset[:, 1, :, :] * 2 / (h - 1), dim=1)
-    grid = grid + torch.cat((offset_0, offset_1),1)
-    grid = grid.permute(0, 2, 3, 1)
-
-    # sampling
-    output = F.grid_sample(x, grid, padding_mode='zeros')
-
-    return output
-
-
-@register('arbrcan')
-class ArbRCAN(nn.Module):
-    def __init__(self, encoder_spec=None, conv=default_conv):
-        super(ArbRCAN, self).__init__()
-
-        n_resgroups = 10
-        n_resblocks = 20
-        n_feats = 64
-        kernel_size = 3
-        reduction = 16
-        act = nn.ReLU(True)
-        n_colors = 3
-        res_scale = 1
-
-        self.n_resgroups = n_resgroups
-
-        # head module
-        modules_head = [conv(n_colors, n_feats, kernel_size)]
-        self.head = nn.Sequential(*modules_head)
-
-        # body module
-        modules_body = [
-            ResidualGroup(conv, n_feats, kernel_size, reduction, act=act, res_scale=res_scale,
-                          n_resblocks=n_resblocks) \
-            for _ in range(n_resgroups)]
-        modules_body.append(conv(n_feats, n_feats, kernel_size))
-        self.body = nn.Sequential(*modules_body)
-
-        # tail module
-        modules_tail = [
-            None,                                              # placeholder to match pre-trained RCAN model
-            conv(n_feats, n_colors, kernel_size)]
-        self.tail = nn.Sequential(*modules_tail)
-
-        ##########   our plug-in module     ##########
-        # scale-aware feature adaption block
-        # For RCAN, feature adaption is performed after each backbone block, i.e., K=1
-        self.K = 1
-        sa_adapt = []
-        for i in range(self.n_resgroups // self.K):
-            sa_adapt.append(SA_adapt(64))
-        self.sa_adapt = nn.Sequential(*sa_adapt)
-
-        # scale-aware upsampling layer
-        self.sa_upsample = SA_upsample(64)
-
-    def set_scale(self, scale, scale2):
-        self.scale = scale
-        self.scale2 = scale2
-
-    def forward(self, x, size):
-        B, C, H, W = x.shape
-        H_up, W_up = size
-        scale = H_up / H
-        scale2 = W_up / W
-        # head
-        x = self.head(x)
-
-        # body
-        res = x
-        for i in range(self.n_resgroups):
-            res = self.body[i](res)
-            # scale-aware feature adaption
-            if (i+1) % self.K == 0:
-                res = self.sa_adapt[i](res, scale, scale2)
-
-        res = self.body[-1](res)
-        res += x
-
-        # scale-aware upsampling
-        res = self.sa_upsample(res, scale, scale2)
-
-        # tail
-        x = self.tail[1](res)
-
-        return x
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/layers/csp_layer.py b/spaces/KyanChen/RSPrompter/mmdet/models/layers/csp_layer.py
deleted file mode 100644
index c8b547b8994862bfe14739033bb6b254ef886f29..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/layers/csp_layer.py
+++ /dev/null
@@ -1,246 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-from mmengine.model import BaseModule
-from torch import Tensor
-
-from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
-from .se_layer import ChannelAttention
-
-
-class DarknetBottleneck(BaseModule):
-    """The basic bottleneck block used in Darknet.
-
-    Each ResBlock consists of two ConvModules and the input is added to the
-    final output. Each ConvModule is composed of Conv, BN, and LeakyReLU.
-    The first convLayer has filter size of 1x1 and the second one has the
-    filter size of 3x3.
-
-    Args:
-        in_channels (int): The input channels of this Module.
-        out_channels (int): The output channels of this Module.
-        expansion (float): The kernel size of the convolution.
-            Defaults to 0.5.
-        add_identity (bool): Whether to add identity to the out.
-            Defaults to True.
-        use_depthwise (bool): Whether to use depthwise separable convolution.
-            Defaults to False.
-        conv_cfg (dict): Config dict for convolution layer. Defaults to None,
-            which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Defaults to dict(type='BN').
-        act_cfg (dict): Config dict for activation layer.
-            Defaults to dict(type='Swish').
-    """
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 expansion: float = 0.5,
-                 add_identity: bool = True,
-                 use_depthwise: bool = False,
-                 conv_cfg: OptConfigType = None,
-                 norm_cfg: ConfigType = dict(
-                     type='BN', momentum=0.03, eps=0.001),
-                 act_cfg: ConfigType = dict(type='Swish'),
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(init_cfg=init_cfg)
-        hidden_channels = int(out_channels * expansion)
-        conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
-        self.conv1 = ConvModule(
-            in_channels,
-            hidden_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.conv2 = conv(
-            hidden_channels,
-            out_channels,
-            3,
-            stride=1,
-            padding=1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.add_identity = \
-            add_identity and in_channels == out_channels
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward function."""
-        identity = x
-        out = self.conv1(x)
-        out = self.conv2(out)
-
-        if self.add_identity:
-            return out + identity
-        else:
-            return out
-
-
-class CSPNeXtBlock(BaseModule):
-    """The basic bottleneck block used in CSPNeXt.
-
-    Args:
-        in_channels (int): The input channels of this Module.
-        out_channels (int): The output channels of this Module.
-        expansion (float): Expand ratio of the hidden channel. Defaults to 0.5.
-        add_identity (bool): Whether to add identity to the out. Only works
-            when in_channels == out_channels. Defaults to True.
-        use_depthwise (bool): Whether to use depthwise separable convolution.
-            Defaults to False.
-        kernel_size (int): The kernel size of the second convolution layer.
-            Defaults to 5.
-        conv_cfg (dict): Config dict for convolution layer. Defaults to None,
-            which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Defaults to dict(type='BN', momentum=0.03, eps=0.001).
-        act_cfg (dict): Config dict for activation layer.
-            Defaults to dict(type='SiLU').
-        init_cfg (:obj:`ConfigDict` or dict or list[dict] or
-            list[:obj:`ConfigDict`], optional): Initialization config dict.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 expansion: float = 0.5,
-                 add_identity: bool = True,
-                 use_depthwise: bool = False,
-                 kernel_size: int = 5,
-                 conv_cfg: OptConfigType = None,
-                 norm_cfg: ConfigType = dict(
-                     type='BN', momentum=0.03, eps=0.001),
-                 act_cfg: ConfigType = dict(type='SiLU'),
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(init_cfg=init_cfg)
-        hidden_channels = int(out_channels * expansion)
-        conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
-        self.conv1 = conv(
-            in_channels,
-            hidden_channels,
-            3,
-            stride=1,
-            padding=1,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.conv2 = DepthwiseSeparableConvModule(
-            hidden_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            padding=kernel_size // 2,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.add_identity = \
-            add_identity and in_channels == out_channels
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward function."""
-        identity = x
-        out = self.conv1(x)
-        out = self.conv2(out)
-
-        if self.add_identity:
-            return out + identity
-        else:
-            return out
-
-
-class CSPLayer(BaseModule):
-    """Cross Stage Partial Layer.
-
-    Args:
-        in_channels (int): The input channels of the CSP layer.
-        out_channels (int): The output channels of the CSP layer.
-        expand_ratio (float): Ratio to adjust the number of channels of the
-            hidden layer. Defaults to 0.5.
-        num_blocks (int): Number of blocks. Defaults to 1.
-        add_identity (bool): Whether to add identity in blocks.
-            Defaults to True.
-        use_cspnext_block (bool): Whether to use CSPNeXt block.
-            Defaults to False.
-        use_depthwise (bool): Whether to use depthwise separable convolution in
-            blocks. Defaults to False.
-        channel_attention (bool): Whether to add channel attention in each
-            stage. Defaults to True.
-        conv_cfg (dict, optional): Config dict for convolution layer.
-            Defaults to None, which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Defaults to dict(type='BN')
-        act_cfg (dict): Config dict for activation layer.
-            Defaults to dict(type='Swish')
-        init_cfg (:obj:`ConfigDict` or dict or list[dict] or
-            list[:obj:`ConfigDict`], optional): Initialization config dict.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 expand_ratio: float = 0.5,
-                 num_blocks: int = 1,
-                 add_identity: bool = True,
-                 use_depthwise: bool = False,
-                 use_cspnext_block: bool = False,
-                 channel_attention: bool = False,
-                 conv_cfg: OptConfigType = None,
-                 norm_cfg: ConfigType = dict(
-                     type='BN', momentum=0.03, eps=0.001),
-                 act_cfg: ConfigType = dict(type='Swish'),
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(init_cfg=init_cfg)
-        block = CSPNeXtBlock if use_cspnext_block else DarknetBottleneck
-        mid_channels = int(out_channels * expand_ratio)
-        self.channel_attention = channel_attention
-        self.main_conv = ConvModule(
-            in_channels,
-            mid_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.short_conv = ConvModule(
-            in_channels,
-            mid_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.final_conv = ConvModule(
-            2 * mid_channels,
-            out_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-
-        self.blocks = nn.Sequential(*[
-            block(
-                mid_channels,
-                mid_channels,
-                1.0,
-                add_identity,
-                use_depthwise,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg) for _ in range(num_blocks)
-        ])
-        if channel_attention:
-            self.attention = ChannelAttention(2 * mid_channels)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward function."""
-        x_short = self.short_conv(x)
-
-        x_main = self.main_conv(x)
-        x_main = self.blocks(x_main)
-
-        x_final = torch.cat((x_main, x_short), dim=1)
-
-        if self.channel_attention:
-            x_final = self.attention(x_final)
-        return self.final_conv(x_final)
diff --git a/spaces/LanguageBind/LanguageBind/open_clip/transformer.py b/spaces/LanguageBind/LanguageBind/open_clip/transformer.py
deleted file mode 100644
index 3c04436fb0cc26dbcbdf72e52397da93744b078c..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/open_clip/transformer.py
+++ /dev/null
@@ -1,737 +0,0 @@
-from collections import OrderedDict
-import math
-from typing import Callable, Optional, Sequence, Tuple
-
-import torch
-from einops import rearrange
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint
-
-from .utils import to_2tuple
-
-
-class LayerNormFp32(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm (with cast back to input dtype)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class LayerScale(nn.Module):
-    def __init__(self, dim, init_values=1e-5, inplace=False):
-        super().__init__()
-        self.inplace = inplace
-        self.gamma = nn.Parameter(init_values * torch.ones(dim))
-
-    def forward(self, x):
-        return x.mul_(self.gamma) if self.inplace else x * self.gamma
-
-
-class PatchDropout(nn.Module):
-    """
-    https://arxiv.org/abs/2212.00794
-    """
-
-    def __init__(self, prob, exclude_first_token=True):
-        super().__init__()
-        assert 0 <= prob < 1.
-        self.prob = prob
-        self.exclude_first_token = exclude_first_token  # exclude CLS token
-
-    def forward(self, x, B, T):
-        if not self.training or self.prob == 0.:
-            return x
-
-        if self.exclude_first_token:
-            cls_tokens, x = x[:, :1], x[:, 1:]
-        else:
-            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
-
-        batch = x.size()[0]
-        num_tokens = x.size()[1]
-
-        batch_indices = torch.arange(batch)
-        batch_indices = batch_indices[..., None]
-
-        keep_prob = 1 - self.prob
-        num_patches_keep = max(1, int(num_tokens * keep_prob))
-
-        if T == 1:
-            rand = torch.randn(batch, num_tokens)
-            patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
-        else:
-            rand = torch.randn(B, num_tokens)
-            patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
-            patch_indices_keep = patch_indices_keep.unsqueeze(1).repeat(1, T, 1)
-            patch_indices_keep = rearrange(patch_indices_keep, 'b t n -> (b t) n')
-
-
-
-
-
-        x = x[batch_indices, patch_indices_keep]
-
-        if self.exclude_first_token:
-            x = torch.cat((cls_tokens, x), dim=1)
-
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=True,
-            scaled_cosine=False,
-            scale_heads=False,
-            logit_scale_max=math.log(1. / 0.01),
-            attn_drop=0.,
-            proj_drop=0.
-    ):
-        super().__init__()
-        self.scaled_cosine = scaled_cosine
-        self.scale_heads = scale_heads
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-        self.logit_scale_max = logit_scale_max
-
-        # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
-        self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
-        if qkv_bias:
-            self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
-        else:
-            self.in_proj_bias = None
-
-        if self.scaled_cosine:
-            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
-        else:
-            self.logit_scale = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        if self.scale_heads:
-            self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
-        else:
-            self.head_scale = None
-        self.out_proj = nn.Linear(dim, dim)
-        self.out_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
-        L, N, C = x.shape
-        q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)
-        q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-
-        if self.logit_scale is not None:
-            attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
-            logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
-            attn = attn.view(N, self.num_heads, L, L) * logit_scale
-            attn = attn.view(-1, L, L)
-        else:
-            q = q * self.scale
-            attn = torch.bmm(q, k.transpose(-1, -2))
-
-        if attn_mask is not None:
-            if attn_mask.dtype == torch.bool:
-                new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
-                new_attn_mask.masked_fill_(attn_mask, float("-inf"))
-                attn_mask = new_attn_mask
-            attn += attn_mask
-
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = torch.bmm(attn, v)
-        if self.head_scale is not None:
-            x = x.view(N, self.num_heads, L, C) * self.head_scale
-            x = x.view(-1, L, C)
-        x = x.transpose(0, 1).reshape(L, N, C)
-        x = self.out_proj(x)
-        x = self.out_drop(x)
-        return x
-
-
-class AttentionalPooler(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            context_dim: int,
-            n_head: int = 8,
-            n_queries: int = 256,
-            norm_layer: Callable = LayerNorm
-    ):
-        super().__init__()
-        self.query = nn.Parameter(torch.randn(n_queries, d_model))
-        self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim)
-        self.ln_q = norm_layer(d_model)
-        self.ln_k = norm_layer(context_dim)
-
-    def forward(self, x: torch.Tensor):
-        x = self.ln_k(x).permute(1, 0, 2)  # NLD -> LND
-        N = x.shape[1]
-        q = self.ln_q(self.query)
-        out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0]
-        return out.permute(1, 0, 2)  # LND -> NLD
-
-    def _repeat(self, query, N: int):
-        return query.unsqueeze(1).repeat(1, N, 1)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            is_cross_attention: bool = False,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-        if is_cross_attention:
-            self.ln_1_kv = norm_layer(d_model)
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def attention(
-            self,
-            q_x: torch.Tensor,
-            k_x: Optional[torch.Tensor] = None,
-            v_x: Optional[torch.Tensor] = None,
-            attn_mask: Optional[torch.Tensor] = None,
-    ):
-        k_x = k_x if k_x is not None else q_x
-        v_x = v_x if v_x is not None else q_x
-
-        attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
-        return self.attn(
-            q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask
-        )[0]
-
-    def forward(
-            self,
-            q_x: torch.Tensor,
-            k_x: Optional[torch.Tensor] = None,
-            v_x: Optional[torch.Tensor] = None,
-            attn_mask: Optional[torch.Tensor] = None,
-    ):
-        k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
-        v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
-
-        x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-
-
-class CustomResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            scale_cosine_attn: bool = False,
-            scale_heads: bool = False,
-            scale_attn: bool = False,
-            scale_fc: bool = False,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = Attention(
-            d_model, n_head,
-            scaled_cosine=scale_cosine_attn,
-            scale_heads=scale_heads,
-        )
-        self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-            self,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.grad_checkpointing = False
-
-        self.resblocks = nn.ModuleList([
-            ResidualAttentionBlock(
-                width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)
-            for _ in range(layers)
-        ])
-
-    def get_cast_dtype(self) -> torch.dtype:
-        if hasattr(self.resblocks[0].mlp.c_fc, 'int8_original_dtype'):
-            return self.resblocks[0].mlp.c_fc.int8_original_dtype
-        return self.resblocks[0].mlp.c_fc.weight.dtype
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        for r in self.resblocks:
-            if self.grad_checkpointing and not torch.jit.is_scripting():
-                # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
-                x = checkpoint(r, x, None, None, attn_mask)
-            else:
-                x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisionTransformer(nn.Module):
-    output_tokens: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            image_size: int,
-            patch_size: int,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float,
-            ls_init_value: float = None,
-            global_average_pool: bool = False,
-            attentional_pool: bool = False,
-            n_queries: int = 256,
-            attn_pooler_heads: int = 8,
-            output_dim: int = 512,
-            patch_dropout: float = 0.,
-            input_patchnorm: bool = False,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            output_tokens: bool = False
-    ):
-        super().__init__()
-        self.output_tokens = output_tokens
-        image_height, image_width = self.image_size = to_2tuple(image_size)
-        patch_height, patch_width = self.patch_size = to_2tuple(patch_size)
-        self.grid_size = (image_height // patch_height, image_width // patch_width)
-        self.output_dim = output_dim
-
-        # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1
-        self.input_patchnorm = input_patchnorm
-
-        if input_patchnorm:
-            patch_input_dim = patch_height * patch_width * 3
-            self.patchnorm_pre_ln = LayerNorm(patch_input_dim)
-            self.conv1 = nn.Linear(patch_input_dim, width)
-        else:
-            self.patchnorm_pre_ln = nn.Identity()
-            self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        # class embeddings and positional embeddings
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))
-
-        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
-        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
-
-        self.ln_pre = norm_layer(width)
-        self.transformer = Transformer(
-            width,
-            layers,
-            heads,
-            mlp_ratio,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-
-        self.global_average_pool = global_average_pool
-        if attentional_pool:
-            self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)
-            self.ln_post = norm_layer(output_dim)
-            self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))
-        else:
-            self.attn_pool = None
-            self.ln_post = norm_layer(width)
-            self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-        self.init_parameters()
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        for param in self.parameters():
-            param.requires_grad = False
-
-        if unlocked_groups != 0:
-            groups = [
-                [
-                    self.conv1,
-                    self.class_embedding,
-                    self.positional_embedding,
-                    self.ln_pre,
-                ],
-                *self.transformer.resblocks[:-1],
-                [
-                    self.transformer.resblocks[-1],
-                    self.ln_post,
-                ],
-                self.proj,
-            ]
-
-            def _unlock(x):
-                if isinstance(x, Sequence):
-                    for g in x:
-                        _unlock(g)
-                else:
-                    if isinstance(x, torch.nn.Parameter):
-                        x.requires_grad = True
-                    else:
-                        for p in x.parameters():
-                            p.requires_grad = True
-
-            _unlock(groups[-unlocked_groups:])
-
-    def init_parameters(self):
-        # FIXME OpenAI CLIP did not define an init for the VisualTransformer
-        # TODO experiment if default PyTorch init, below, or alternate init is best.
-
-        # nn.init.normal_(self.class_embedding, std=self.scale)
-        # nn.init.normal_(self.positional_embedding, std=self.scale)
-        #
-        # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        # attn_std = self.transformer.width ** -0.5
-        # fc_std = (2 * self.transformer.width) ** -0.5
-        # for block in self.transformer.resblocks:
-        #     nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-        #     nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-        #     nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-        #     nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        #
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=self.scale)
-        pass
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        if self.global_average_pool:
-            return x.mean(dim=1), x
-        else:
-            return x[:, 0], x[:, 1:]
-
-    def forward(self, x: torch.Tensor):
-
-        # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
-        if self.input_patchnorm:
-            # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
-            x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])
-            x = x.permute(0, 2, 4, 1, 3, 5)
-            x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)
-            x = self.patchnorm_pre_ln(x)
-            x = self.conv1(x)
-        else:
-            x = self.conv1(x)  # shape = [*, width, grid, grid]
-            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-
-        # class embeddings and positional embeddings
-        x = torch.cat(
-            [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
-             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-
-        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
-        x = self.patch_dropout(x)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        if self.attn_pool is not None:
-            x = self.attn_pool(x)
-            x = self.ln_post(x)
-            pooled, tokens = self._global_pool(x)
-        else:
-            pooled, tokens = self._global_pool(x)
-            pooled = self.ln_post(pooled)
-
-        if self.proj is not None:
-            pooled = pooled @ self.proj
-
-        if self.output_tokens:
-            return pooled, tokens
-        
-        return pooled
-
-
-class TextTransformer(nn.Module):
-    output_tokens: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            context_length: int = 77,
-            vocab_size: int = 49408,
-            width: int = 512,
-            heads: int = 8,
-            layers: int = 12,
-            ls_init_value: float = None,
-            output_dim: int = 512,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            embed_cls: bool = False,
-            pad_id: int = 0,
-            output_tokens: bool = False,
-    ):
-        super().__init__()
-        self.output_tokens = output_tokens
-        self.num_pos = self.context_length = context_length
-        self.vocab_size = vocab_size
-        self.width = width
-        self.output_dim = output_dim
-        self.heads = heads
-        self.pad_id = pad_id
-
-        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
-
-        if embed_cls:
-            self.cls_emb = nn.Parameter(torch.empty(width))
-            self.num_pos += 1
-        else:
-            self.cls_emb = None
-
-        self.token_embedding = nn.Embedding(vocab_size, width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))
-        self.transformer = Transformer(
-            width=width,
-            layers=layers,
-            heads=heads,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-        self.ln_final = norm_layer(width)
-
-        self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
-
-        self.init_parameters()
-
-    def init_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-        if self.cls_emb is not None:
-            nn.init.normal_(self.cls_emb, std=0.01)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.num_pos, self.num_pos)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def build_cls_mask(self, text, cast_dtype: torch.dtype):
-        cls_mask = (text != self.pad_id).unsqueeze(1)
-        cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)
-        additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)
-        additive_mask.fill_(0)
-        additive_mask.masked_fill_(~cls_mask, float("-inf"))
-        additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)
-        return additive_mask
-
-    def _repeat(self, t, N: int):
-        return t.reshape(1, 1, -1).repeat(N, 1, 1)
-
-    def forward(self, text):
-        cast_dtype = self.transformer.get_cast_dtype()
-        seq_len = text.shape[1]
-
-        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
-        attn_mask = self.attn_mask
-        if self.cls_emb is not None:
-            seq_len += 1
-            x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)
-            cls_mask = self.build_cls_mask(text, cast_dtype)
-            attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]
-
-        x = x + self.positional_embedding[:seq_len].to(cast_dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x, attn_mask=attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        if self.cls_emb is not None:
-            pooled, tokens = x[:, -1], x[:, :-1]
-            pooled = self.ln_final(pooled)
-        else:
-            x = self.ln_final(x)
-            pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x
-
-        if self.text_projection is not None:
-            pooled = pooled @ self.text_projection
-
-        if self.output_tokens:
-            return pooled, tokens
-
-        return pooled
-
-
-class MultimodalTransformer(Transformer):
-    def __init__(
-            self,
-            width: int,
-            layers: int,
-            heads: int,
-            context_length: int = 77,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            output_dim: int = 512,
-    ):
-
-        super().__init__(
-            width=width,
-            layers=layers,
-            heads=heads,
-            mlp_ratio=mlp_ratio,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-        self.context_length = context_length
-        self.cross_attn = nn.ModuleList([
-            ResidualAttentionBlock(
-                width,
-                heads,
-                mlp_ratio,
-                ls_init_value=ls_init_value,
-                act_layer=act_layer,
-                norm_layer=norm_layer,
-                is_cross_attention=True,
-            )
-            for _ in range(layers)
-        ])
-
-        self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
-
-        self.ln_final = norm_layer(width)
-        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
-
-    def init_parameters(self):
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        for block in self.transformer.cross_attn:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def forward(self, image_embs, text_embs):
-        text_embs = text_embs.permute(1, 0, 2)  # NLD -> LNDsq
-        image_embs = image_embs.permute(1, 0, 2)  # NLD -> LND
-        seq_len = text_embs.shape[0]
-
-        for resblock, cross_attn in zip(self.resblocks, self.cross_attn):
-            if self.grad_checkpointing and not torch.jit.is_scripting():
-                # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
-                text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len])
-                text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None)
-            else:
-                text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len])
-                text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs)
-
-        x = text_embs.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x)
-
-        if self.text_projection is not None:
-            x = x @ self.text_projection
-
-        return x
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.grad_checkpointing = enable
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/lineiterator.py b/spaces/Lianjd/stock_dashboard/backtrader/lineiterator.py
deleted file mode 100644
index 3aad26af1d906b3adafb5ec5c9cd334e027761d9..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/lineiterator.py
+++ /dev/null
@@ -1,488 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import collections
-import operator
-import sys
-
-from .utils.py3 import map, range, zip, with_metaclass, string_types
-from .utils import DotDict
-
-from .lineroot import LineRoot, LineSingle
-from .linebuffer import LineActions, LineNum
-from .lineseries import LineSeries, LineSeriesMaker
-from .dataseries import DataSeries
-from . import metabase
-
-
-class MetaLineIterator(LineSeries.__class__):
-    def donew(cls, *args, **kwargs):
-        _obj, args, kwargs = \
-            super(MetaLineIterator, cls).donew(*args, **kwargs)
-
-        # Prepare to hold children that need to be calculated and
-        # influence minperiod - Moved here to support LineNum below
-        _obj._lineiterators = collections.defaultdict(list)
-
-        # Scan args for datas ... if none are found,
-        # use the _owner (to have a clock)
-        mindatas = _obj._mindatas
-        lastarg = 0
-        _obj.datas = []
-        for arg in args:
-            if isinstance(arg, LineRoot):
-                _obj.datas.append(LineSeriesMaker(arg))
-
-            elif not mindatas:
-                break  # found not data and must not be collected
-            else:
-                try:
-                    _obj.datas.append(LineSeriesMaker(LineNum(arg)))
-                except:
-                    # Not a LineNum and is not a LineSeries - bail out
-                    break
-
-            mindatas = max(0, mindatas - 1)
-            lastarg += 1
-
-        newargs = args[lastarg:]
-
-        # If no datas have been passed to an indicator ... use the
-        # main datas of the owner, easing up adding "self.data" ...
-        if not _obj.datas and isinstance(_obj, (IndicatorBase, ObserverBase)):
-            _obj.datas = _obj._owner.datas[0:mindatas]
-
-        # Create a dictionary to be able to check for presence
-        # lists in python use "==" operator when testing for presence with "in"
-        # which doesn't really check for presence but for equality
-        _obj.ddatas = {x: None for x in _obj.datas}
-
-        # For each found data add access member -
-        # for the first data 2 (data and data0)
-        if _obj.datas:
-            _obj.data = data = _obj.datas[0]
-
-            for l, line in enumerate(data.lines):
-                linealias = data._getlinealias(l)
-                if linealias:
-                    setattr(_obj, 'data_%s' % linealias, line)
-                setattr(_obj, 'data_%d' % l, line)
-
-            for d, data in enumerate(_obj.datas):
-                setattr(_obj, 'data%d' % d, data)
-
-                for l, line in enumerate(data.lines):
-                    linealias = data._getlinealias(l)
-                    if linealias:
-                        setattr(_obj, 'data%d_%s' % (d, linealias), line)
-                    setattr(_obj, 'data%d_%d' % (d, l), line)
-
-        # Parameter values have now been set before __init__
-        _obj.dnames = DotDict([(d._name, d)
-                               for d in _obj.datas if getattr(d, '_name', '')])
-
-        return _obj, newargs, kwargs
-
-    def dopreinit(cls, _obj, *args, **kwargs):
-        _obj, args, kwargs = \
-            super(MetaLineIterator, cls).dopreinit(_obj, *args, **kwargs)
-
-        # if no datas were found use, use the _owner (to have a clock)
-        _obj.datas = _obj.datas or [_obj._owner]
-
-        # 1st data source is our ticking clock
-        _obj._clock = _obj.datas[0]
-
-        # To automatically set the period Start by scanning the found datas
-        # No calculation can take place until all datas have yielded "data"
-        # A data could be an indicator and it could take x bars until
-        # something is produced
-        _obj._minperiod = \
-            max([x._minperiod for x in _obj.datas] or [_obj._minperiod])
-
-        # The lines carry at least the same minperiod as
-        # that provided by the datas
-        for line in _obj.lines:
-            line.addminperiod(_obj._minperiod)
-
-        return _obj, args, kwargs
-
-    def dopostinit(cls, _obj, *args, **kwargs):
-        _obj, args, kwargs = \
-            super(MetaLineIterator, cls).dopostinit(_obj, *args, **kwargs)
-
-        # my minperiod is as large as the minperiod of my lines
-        _obj._minperiod = max([x._minperiod for x in _obj.lines])
-
-        # Recalc the period
-        _obj._periodrecalc()
-
-        # Register (my)self as indicator to owner once
-        # _minperiod has been calculated
-        if _obj._owner is not None:
-            _obj._owner.addindicator(_obj)
-
-        return _obj, args, kwargs
-
-
-class LineIterator(with_metaclass(MetaLineIterator, LineSeries)):
-    _nextforce = False  # force cerebro to run in next mode (runonce=False)
-
-    _mindatas = 1
-    _ltype = LineSeries.IndType
-
-    plotinfo = dict(plot=True,
-                    subplot=True,
-                    plotname='',
-                    plotskip=False,
-                    plotabove=False,
-                    plotlinelabels=False,
-                    plotlinevalues=True,
-                    plotvaluetags=True,
-                    plotymargin=0.0,
-                    plotyhlines=[],
-                    plotyticks=[],
-                    plothlines=[],
-                    plotforce=False,
-                    plotmaster=None,)
-
-    def _periodrecalc(self):
-        # last check in case not all lineiterators were assigned to
-        # lines (directly or indirectly after some operations)
-        # An example is Kaufman's Adaptive Moving Average
-        indicators = self._lineiterators[LineIterator.IndType]
-        indperiods = [ind._minperiod for ind in indicators]
-        indminperiod = max(indperiods or [self._minperiod])
-        self.updateminperiod(indminperiod)
-
-    def _stage2(self):
-        super(LineIterator, self)._stage2()
-
-        for data in self.datas:
-            data._stage2()
-
-        for lineiterators in self._lineiterators.values():
-            for lineiterator in lineiterators:
-                lineiterator._stage2()
-
-    def _stage1(self):
-        super(LineIterator, self)._stage1()
-
-        for data in self.datas:
-            data._stage1()
-
-        for lineiterators in self._lineiterators.values():
-            for lineiterator in lineiterators:
-                lineiterator._stage1()
-
-    def getindicators(self):
-        return self._lineiterators[LineIterator.IndType]
-
-    def getindicators_lines(self):
-        return [x for x in self._lineiterators[LineIterator.IndType]
-                if hasattr(x.lines, 'getlinealiases')]
-
-    def getobservers(self):
-        return self._lineiterators[LineIterator.ObsType]
-
-    def addindicator(self, indicator):
-        # store in right queue
-        self._lineiterators[indicator._ltype].append(indicator)
-
-        # use getattr because line buffers don't have this attribute
-        if getattr(indicator, '_nextforce', False):
-            # the indicator needs runonce=False
-            o = self
-            while o is not None:
-                if o._ltype == LineIterator.StratType:
-                    o.cerebro._disable_runonce()
-                    break
-
-                o = o._owner  # move up the hierarchy
-
-    def bindlines(self, owner=None, own=None):
-        if not owner:
-            owner = 0
-
-        if isinstance(owner, string_types):
-            owner = [owner]
-        elif not isinstance(owner, collections.Iterable):
-            owner = [owner]
-
-        if not own:
-            own = range(len(owner))
-
-        if isinstance(own, string_types):
-            own = [own]
-        elif not isinstance(own, collections.Iterable):
-            own = [own]
-
-        for lineowner, lineown in zip(owner, own):
-            if isinstance(lineowner, string_types):
-                lownerref = getattr(self._owner.lines, lineowner)
-            else:
-                lownerref = self._owner.lines[lineowner]
-
-            if isinstance(lineown, string_types):
-                lownref = getattr(self.lines, lineown)
-            else:
-                lownref = self.lines[lineown]
-
-            lownref.addbinding(lownerref)
-
-        return self
-
-    # Alias which may be more readable
-    bind2lines = bindlines
-    bind2line = bind2lines
-
-    def _next(self):
-        clock_len = self._clk_update()
-
-        for indicator in self._lineiterators[LineIterator.IndType]:
-            indicator._next()
-
-        self._notify()
-
-        if self._ltype == LineIterator.StratType:
-            # supporting datas with different lengths
-            minperstatus = self._getminperstatus()
-            if minperstatus < 0:
-                self.next()
-            elif minperstatus == 0:
-                self.nextstart()  # only called for the 1st value
-            else:
-                self.prenext()
-        else:
-            # assume indicators and others operate on same length datas
-            # although the above operation can be generalized
-            if clock_len > self._minperiod:
-                self.next()
-            elif clock_len == self._minperiod:
-                self.nextstart()  # only called for the 1st value
-            elif clock_len:
-                self.prenext()
-
-    def _clk_update(self):
-        clock_len = len(self._clock)
-        if clock_len != len(self):
-            self.forward()
-
-        return clock_len
-
-    def _once(self):
-        self.forward(size=self._clock.buflen())
-
-        for indicator in self._lineiterators[LineIterator.IndType]:
-            indicator._once()
-
-        for observer in self._lineiterators[LineIterator.ObsType]:
-            observer.forward(size=self.buflen())
-
-        for data in self.datas:
-            data.home()
-
-        for indicator in self._lineiterators[LineIterator.IndType]:
-            indicator.home()
-
-        for observer in self._lineiterators[LineIterator.ObsType]:
-            observer.home()
-
-        self.home()
-
-        # These 3 remain empty for a strategy and therefore play no role
-        # because a strategy will always be executed on a next basis
-        # indicators are each called with its min period
-        self.preonce(0, self._minperiod - 1)
-        self.oncestart(self._minperiod - 1, self._minperiod)
-        self.once(self._minperiod, self.buflen())
-
-        for line in self.lines:
-            line.oncebinding()
-
-    def preonce(self, start, end):
-        pass
-
-    def oncestart(self, start, end):
-        self.once(start, end)
-
-    def once(self, start, end):
-        pass
-
-    def prenext(self):
-        '''
-        This method will be called before the minimum period of all
-        datas/indicators have been meet for the strategy to start executing
-        '''
-        pass
-
-    def nextstart(self):
-        '''
-        This method will be called once, exactly when the minimum period for
-        all datas/indicators have been meet. The default behavior is to call
-        next
-        '''
-
-        # Called once for 1st full calculation - defaults to regular next
-        self.next()
-
-    def next(self):
-        '''
-        This method will be called for all remaining data points when the
-        minimum period for all datas/indicators have been meet.
-        '''
-        pass
-
-    def _addnotification(self, *args, **kwargs):
-        pass
-
-    def _notify(self):
-        pass
-
-    def _plotinit(self):
-        pass
-
-    def qbuffer(self, savemem=0):
-        if savemem:
-            for line in self.lines:
-                line.qbuffer()
-
-        # If called, anything under it, must save
-        for obj in self._lineiterators[self.IndType]:
-            obj.qbuffer(savemem=1)
-
-        # Tell datas to adjust buffer to minimum period
-        for data in self.datas:
-            data.minbuffer(self._minperiod)
-
-
-# This 3 subclasses can be used for identification purposes within LineIterator
-# or even outside (like in LineObservers)
-# for the 3 subbranches without generating circular import references
-
-class DataAccessor(LineIterator):
-    PriceClose = DataSeries.Close
-    PriceLow = DataSeries.Low
-    PriceHigh = DataSeries.High
-    PriceOpen = DataSeries.Open
-    PriceVolume = DataSeries.Volume
-    PriceOpenInteres = DataSeries.OpenInterest
-    PriceDateTime = DataSeries.DateTime
-
-
-class IndicatorBase(DataAccessor):
-    pass
-
-
-class ObserverBase(DataAccessor):
-    pass
-
-
-class StrategyBase(DataAccessor):
-    pass
-
-
-# Utility class to couple lines/lineiterators which may have different lengths
-# Will only work when runonce=False is passed to Cerebro
-
-class SingleCoupler(LineActions):
-    def __init__(self, cdata, clock=None):
-        super(SingleCoupler, self).__init__()
-        self._clock = clock if clock is not None else self._owner
-
-        self.cdata = cdata
-        self.dlen = 0
-        self.val = float('NaN')
-
-    def next(self):
-        if len(self.cdata) > self.dlen:
-            self.val = self.cdata[0]
-            self.dlen += 1
-
-        self[0] = self.val
-
-
-class MultiCoupler(LineIterator):
-    _ltype = LineIterator.IndType
-
-    def __init__(self):
-        super(MultiCoupler, self).__init__()
-        self.dlen = 0
-        self.dsize = self.fullsize()  # shorcut for number of lines
-        self.dvals = [float('NaN')] * self.dsize
-
-    def next(self):
-        if len(self.data) > self.dlen:
-            self.dlen += 1
-
-            for i in range(self.dsize):
-                self.dvals[i] = self.data.lines[i][0]
-
-        for i in range(self.dsize):
-            self.lines[i][0] = self.dvals[i]
-
-
-def LinesCoupler(cdata, clock=None, **kwargs):
-    if isinstance(cdata, LineSingle):
-        return SingleCoupler(cdata, clock)  # return for single line
-
-    cdatacls = cdata.__class__  # copy important structures before creation
-    try:
-        LinesCoupler.counter += 1  # counter for unique class name
-    except AttributeError:
-        LinesCoupler.counter = 0
-
-    # Prepare a MultiCoupler subclass
-    nclsname = str('LinesCoupler_%d' % LinesCoupler.counter)
-    ncls = type(nclsname, (MultiCoupler,), {})
-    thismod = sys.modules[LinesCoupler.__module__]
-    setattr(thismod, ncls.__name__, ncls)
-    # Replace lines et al., to get a sensible clone
-    ncls.lines = cdatacls.lines
-    ncls.params = cdatacls.params
-    ncls.plotinfo = cdatacls.plotinfo
-    ncls.plotlines = cdatacls.plotlines
-
-    obj = ncls(cdata, **kwargs)  # instantiate
-    # The clock is set here to avoid it being interpreted as a data by the
-    # LineIterator background scanning code
-    if clock is None:
-        clock = getattr(cdata, '_clock', None)
-        if clock is not None:
-            nclock = getattr(clock, '_clock', None)
-            if nclock is not None:
-                clock = nclock
-            else:
-                nclock = getattr(clock, 'data', None)
-                if nclock is not None:
-                    clock = nclock
-
-        if clock is None:
-            clock = obj._owner
-
-    obj._clock = clock
-    return obj
-
-
-# Add an alias (which seems a lot more sensible for "Single Line" lines
-LineCoupler = LinesCoupler
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/lineroot.py b/spaces/Lianjd/stock_dashboard/backtrader/lineroot.py
deleted file mode 100644
index 4754e125ba521d5f7c9b4f7c040aeca7a6c9a0e8..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/lineroot.py
+++ /dev/null
@@ -1,359 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-'''
-
-.. module:: lineroot
-
-Definition of the base class LineRoot and base classes LineSingle/LineMultiple
-to define interfaces and hierarchy for the real operational classes
-
-.. moduleauthor:: Daniel Rodriguez
-
-'''
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import operator
-
-from .utils.py3 import range, with_metaclass
-
-from . import metabase
-
-
-class MetaLineRoot(metabase.MetaParams):
-    '''
-    Once the object is created (effectively pre-init) the "owner" of this
-    class is sought
-    '''
-
-    def donew(cls, *args, **kwargs):
-        _obj, args, kwargs = super(MetaLineRoot, cls).donew(*args, **kwargs)
-
-        # Find the owner and store it
-        # startlevel = 4 ... to skip intermediate call stacks
-        ownerskip = kwargs.pop('_ownerskip', None)
-        _obj._owner = metabase.findowner(_obj,
-                                         _obj._OwnerCls or LineMultiple,
-                                         skip=ownerskip)
-
-        # Parameter values have now been set before __init__
-        return _obj, args, kwargs
-
-
-class LineRoot(with_metaclass(MetaLineRoot, object)):
-    '''
-    Defines a common base and interfaces for Single and Multiple
-    LineXXX instances
-
-        Period management
-        Iteration management
-        Operation (dual/single operand) Management
-        Rich Comparison operator definition
-    '''
-    _OwnerCls = None
-    _minperiod = 1
-    _opstage = 1
-
-    IndType, StratType, ObsType = range(3)
-
-    def _stage1(self):
-        self._opstage = 1
-
-    def _stage2(self):
-        self._opstage = 2
-
-    def _operation(self, other, operation, r=False, intify=False):
-        if self._opstage == 1:
-            return self._operation_stage1(
-                other, operation, r=r, intify=intify)
-
-        return self._operation_stage2(other, operation, r=r)
-
-    def _operationown(self, operation):
-        if self._opstage == 1:
-            return self._operationown_stage1(operation)
-
-        return self._operationown_stage2(operation)
-
-    def qbuffer(self, savemem=0):
-        '''Change the lines to implement a minimum size qbuffer scheme'''
-        raise NotImplementedError
-
-    def minbuffer(self, size):
-        '''Receive notification of how large the buffer must at least be'''
-        raise NotImplementedError
-
-    def setminperiod(self, minperiod):
-        '''
-        Direct minperiod manipulation. It could be used for example
-        by a strategy
-        to not wait for all indicators to produce a value
-        '''
-        self._minperiod = minperiod
-
-    def updateminperiod(self, minperiod):
-        '''
-        Update the minperiod if needed. The minperiod will have been
-        calculated elsewhere
-        and has to take over if greater that self's
-        '''
-        self._minperiod = max(self._minperiod, minperiod)
-
-    def addminperiod(self, minperiod):
-        '''
-        Add a minperiod to own ... to be defined by subclasses
-        '''
-        raise NotImplementedError
-
-    def incminperiod(self, minperiod):
-        '''
-        Increment the minperiod with no considerations
-        '''
-        raise NotImplementedError
-
-    def prenext(self):
-        '''
-        It will be called during the "minperiod" phase of an iteration.
-        '''
-        pass
-
-    def nextstart(self):
-        '''
-        It will be called when the minperiod phase is over for the 1st
-        post-minperiod value. Only called once and defaults to automatically
-        calling next
-        '''
-        self.next()
-
-    def next(self):
-        '''
-        Called to calculate values when the minperiod is over
-        '''
-        pass
-
-    def preonce(self, start, end):
-        '''
-        It will be called during the "minperiod" phase of a "once" iteration
-        '''
-        pass
-
-    def oncestart(self, start, end):
-        '''
-        It will be called when the minperiod phase is over for the 1st
-        post-minperiod value
-
-        Only called once and defaults to automatically calling once
-        '''
-        self.once(start, end)
-
-    def once(self, start, end):
-        '''
-        Called to calculate values at "once" when the minperiod is over
-        '''
-        pass
-
-    # Arithmetic operators
-    def _makeoperation(self, other, operation, r=False, _ownerskip=None):
-        raise NotImplementedError
-
-    def _makeoperationown(self, operation, _ownerskip=None):
-        raise NotImplementedError
-
-    def _operationown_stage1(self, operation):
-        '''
-        Operation with single operand which is "self"
-        '''
-        return self._makeoperationown(operation, _ownerskip=self)
-
-    def _roperation(self, other, operation, intify=False):
-        '''
-        Relies on self._operation to and passes "r" True to define a
-        reverse operation
-        '''
-        return self._operation(other, operation, r=True, intify=intify)
-
-    def _operation_stage1(self, other, operation, r=False, intify=False):
-        '''
-        Two operands' operation. Scanning of other happens to understand
-        if other must be directly an operand or rather a subitem thereof
-        '''
-        if isinstance(other, LineMultiple):
-            other = other.lines[0]
-
-        return self._makeoperation(other, operation, r, self)
-
-    def _operation_stage2(self, other, operation, r=False):
-        '''
-        Rich Comparison operators. Scans other and returns either an
-        operation with other directly or a subitem from other
-        '''
-        if isinstance(other, LineRoot):
-            other = other[0]
-
-        # operation(float, other) ... expecting other to be a float
-        if r:
-            return operation(other, self[0])
-
-        return operation(self[0], other)
-
-    def _operationown_stage2(self, operation):
-        return operation(self[0])
-
-    def __add__(self, other):
-        return self._operation(other, operator.__add__)
-
-    def __radd__(self, other):
-        return self._roperation(other, operator.__add__)
-
-    def __sub__(self, other):
-        return self._operation(other, operator.__sub__)
-
-    def __rsub__(self, other):
-        return self._roperation(other, operator.__sub__)
-
-    def __mul__(self, other):
-        return self._operation(other, operator.__mul__)
-
-    def __rmul__(self, other):
-        return self._roperation(other, operator.__mul__)
-
-    def __div__(self, other):
-        return self._operation(other, operator.__div__)
-
-    def __rdiv__(self, other):
-        return self._roperation(other, operator.__div__)
-
-    def __floordiv__(self, other):
-        return self._operation(other, operator.__floordiv__)
-
-    def __rfloordiv__(self, other):
-        return self._roperation(other, operator.__floordiv__)
-
-    def __truediv__(self, other):
-        return self._operation(other, operator.__truediv__)
-
-    def __rtruediv__(self, other):
-        return self._roperation(other, operator.__truediv__)
-
-    def __pow__(self, other):
-        return self._operation(other, operator.__pow__)
-
-    def __rpow__(self, other):
-        return self._roperation(other, operator.__pow__)
-
-    def __abs__(self):
-        return self._operationown(operator.__abs__)
-
-    def __neg__(self):
-        return self._operationown(operator.__neg__)
-
-    def __lt__(self, other):
-        return self._operation(other, operator.__lt__)
-
-    def __gt__(self, other):
-        return self._operation(other, operator.__gt__)
-
-    def __le__(self, other):
-        return self._operation(other, operator.__le__)
-
-    def __ge__(self, other):
-        return self._operation(other, operator.__ge__)
-
-    def __eq__(self, other):
-        return self._operation(other, operator.__eq__)
-
-    def __ne__(self, other):
-        return self._operation(other, operator.__ne__)
-
-    def __nonzero__(self):
-        return self._operationown(bool)
-
-    __bool__ = __nonzero__
-
-    # Python 3 forces explicit implementation of hash if
-    # the class has redefined __eq__
-    __hash__ = object.__hash__
-
-
-class LineMultiple(LineRoot):
-    '''
-    Base class for LineXXX instances that hold more than one line
-    '''
-    def reset(self):
-        self._stage1()
-        self.lines.reset()
-
-    def _stage1(self):
-        super(LineMultiple, self)._stage1()
-        for line in self.lines:
-            line._stage1()
-
-    def _stage2(self):
-        super(LineMultiple, self)._stage2()
-        for line in self.lines:
-            line._stage2()
-
-    def addminperiod(self, minperiod):
-        '''
-        The passed minperiod is fed to the lines
-        '''
-        # pass it down to the lines
-        for line in self.lines:
-            line.addminperiod(minperiod)
-
-    def incminperiod(self, minperiod):
-        '''
-        The passed minperiod is fed to the lines
-        '''
-        # pass it down to the lines
-        for line in self.lines:
-            line.incminperiod(minperiod)
-
-    def _makeoperation(self, other, operation, r=False, _ownerskip=None):
-        return self.lines[0]._makeoperation(other, operation, r, _ownerskip)
-
-    def _makeoperationown(self, operation, _ownerskip=None):
-        return self.lines[0]._makeoperationown(operation, _ownerskip)
-
-    def qbuffer(self, savemem=0):
-        for line in self.lines:
-            line.qbuffer(savemem=1)
-
-    def minbuffer(self, size):
-        for line in self.lines:
-            line.minbuffer(size)
-
-
-class LineSingle(LineRoot):
-    '''
-    Base class for LineXXX instances that hold a single line
-    '''
-    def addminperiod(self, minperiod):
-        '''
-        Add the minperiod (substracting the overlapping 1 minimum period)
-        '''
-        self._minperiod += minperiod - 1
-
-    def incminperiod(self, minperiod):
-        '''
-        Increment the minperiod with no considerations
-        '''
-        self._minperiod += minperiod
diff --git a/spaces/Libra7578/Promt-to-Image-diffusions/share_btn.py b/spaces/Libra7578/Promt-to-Image-diffusions/share_btn.py
deleted file mode 100644
index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000
--- a/spaces/Libra7578/Promt-to-Image-diffusions/share_btn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `magic-prompt-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `magic-prompt-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const imgEls = gradioEl.querySelectorAll('#generated-gallery img');
-    const promptTxt = gradioEl.querySelector('#translated textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const inputFile = await getInputImgFile(inputImgEl);
-    files.push(inputFile);
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const urlInputImg = urls.pop();
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-    const htmlImgsMd = htmlImgs.join(`\n`);
-	const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-#### Caption:
-${promptTxt}
-#### Generations:
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgsMd}
-</div>`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/LightChen2333/OpenSLU/common/saver.py b/spaces/LightChen2333/OpenSLU/common/saver.py
deleted file mode 100644
index 82536faeb764ce50bd28db893f693633d35c6310..0000000000000000000000000000000000000000
--- a/spaces/LightChen2333/OpenSLU/common/saver.py
+++ /dev/null
@@ -1,80 +0,0 @@
-'''
-Author: Qiguang Chen
-LastEditors: Qiguang Chen
-Date: 2023-02-12 22:23:58
-LastEditTime: 2023-02-19 14:14:56
-Description: 
-
-'''
-import json
-import os
-import queue
-import shutil
-import torch
-import dill
-from common import utils
-
-
-class Saver():
-    def __init__(self, config, start_time=None) -> None:
-        self.config = config
-        if self.config.get("save_dir"):
-            self.model_save_dir = self.config["save_dir"]
-        else:
-            if not os.path.exists("save/"):
-                os.mkdir("save/")
-            self.model_save_dir = "save/" + start_time
-        if not os.path.exists(self.model_save_dir):
-            os.mkdir(self.model_save_dir)
-        save_mode = config.get("save_mode")
-        self.save_mode = save_mode if save_mode is not None else "save-by-eval"
-        
-        max_save_num = self.config.get("max_save_num")
-        self.max_save_num = max_save_num if max_save_num is not None else 1
-        self.save_pool = queue.Queue(maxsize=max_save_num)
-    
-    def save_tokenizer(self, tokenizer):
-        with open(os.path.join(self.model_save_dir, "tokenizer.pkl"), 'wb') as f:
-            dill.dump(tokenizer, f)
-    
-    def save_label(self, intent_list, slot_list):
-        utils.save_json(os.path.join(self.model_save_dir, "label.json"), {"intent": intent_list, "slot": slot_list})
-    
-    
-    def save_model(self, model, train_state, accelerator=None):
-        step = train_state["step"]
-        if self.max_save_num != 1:
-            
-            model_save_dir =os.path.join(self.model_save_dir, str(step))
-            if self.save_pool.full():
-                delete_dir = self.save_pool.get()
-                shutil.rmtree(delete_dir)
-                self.save_pool.put(model_save_dir)
-            else:
-                self.save_pool.put(model_save_dir)
-            if not os.path.exists(model_save_dir):
-                os.mkdir(model_save_dir)
-        else:
-            model_save_dir = self.model_save_dir
-        if not os.path.exists(model_save_dir):
-            os.mkdir(model_save_dir)
-        if accelerator is None:
-            torch.save(model, os.path.join(model_save_dir, "model.pkl"))
-            torch.save(train_state, os.path.join(model_save_dir, "train_state.pkl"), pickle_module=dill)
-        else:
-            accelerator.wait_for_everyone()
-            unwrapped_model = accelerator.unwrap_model(model)
-            accelerator.save(unwrapped_model, os.path.join(model_save_dir, "model.pkl"))
-            accelerator.save_state(output_dir=model_save_dir)
-    
-    def auto_save_step(self, model, train_state, accelerator=None):
-        step = train_state["step"]
-        if self.save_mode == "save-by-step" and step % self.config.get("save_step")==0 and step != 0:
-            self.save_model(model, train_state, accelerator)
-            return True
-        else:
-            return False
-        
-    
-    def save_output(self, outputs, dataset):
-        outputs.save(self.model_save_dir, dataset)
\ No newline at end of file
diff --git a/spaces/Lilflerkin/WellNexus/main.py b/spaces/Lilflerkin/WellNexus/main.py
deleted file mode 100644
index e87dbb663a978785f4fac01e075c47efe093d694..0000000000000000000000000000000000000000
--- a/spaces/Lilflerkin/WellNexus/main.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Import Dependencies
-import yaml
-from joblib import dump, load
-import pandas as pd
-from sklearn.model_selection import train_test_split, cross_val_score
-from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
-# Naive Bayes Approach
-from sklearn.naive_bayes import MultinomialNB
-# Trees Approach
-from sklearn.tree import DecisionTreeClassifier
-# Ensemble Approach
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
-import seaborn as sn
-import matplotlib.pyplot as plt
-
-
-class DiseasePrediction:
-    # Initialize and Load the Config File
-    def __init__(self, model_name=None):
-        # Load Config File
-        try:
-            with open('./config.yaml', 'r') as f:
-                self.config = yaml.safe_load(f)
-        except Exception as e:
-            print("Error reading Config file...")
-
-        # Verbose
-        self.verbose = self.config['verbose']
-        # Load Training Data
-        self.train_features, self.train_labels, self.train_df = self._load_train_dataset()
-        # Load Test Data
-        self.test_features, self.test_labels, self.test_df = self._load_test_dataset()
-        # Feature Correlation in Training Data
-        self._feature_correlation(data_frame=self.train_df, show_fig=False)
-        # Model Definition
-        self.model_name = model_name
-        # Model Save Path
-        self.model_save_path = self.config['model_save_path']
-
-    # Function to Load Train Dataset
-    def _load_train_dataset(self):
-        df_train = pd.read_csv(self.config['dataset']['training_data_path'])
-        cols = df_train.columns
-        cols = cols[:-2]
-        train_features = df_train[cols]
-        train_labels = df_train['prognosis']
-
-        # Check for data sanity
-        assert (len(train_features.iloc[0]) == 132)
-        assert (len(train_labels) == train_features.shape[0])
-
-        if self.verbose:
-            print("Length of Training Data: ", df_train.shape)
-            print("Training Features: ", train_features.shape)
-            print("Training Labels: ", train_labels.shape)
-        return train_features, train_labels, df_train
-
-    # Function to Load Test Dataset
-    def _load_test_dataset(self):
-        df_test = pd.read_csv(self.config['dataset']['test_data_path'])
-        cols = df_test.columns
-        cols = cols[:-1]
-        test_features = df_test[cols]
-        test_labels = df_test['prognosis']
-
-        # Check for data sanity
-        assert (len(test_features.iloc[0]) == 132)
-        assert (len(test_labels) == test_features.shape[0])
-
-        if self.verbose:
-            print("Length of Test Data: ", df_test.shape)
-            print("Test Features: ", test_features.shape)
-            print("Test Labels: ", test_labels.shape)
-        return test_features, test_labels, df_test
-
-    # Features Correlation
-    def _feature_correlation(self, data_frame=None, show_fig=False):
-        # Get Feature Correlation
-        corr = data_frame.corr()
-        sn.heatmap(corr, square=True, annot=False, cmap="YlGnBu")
-        plt.title("Feature Correlation")
-        plt.tight_layout()
-        if show_fig:
-            plt.show()
-        plt.savefig('feature_correlation.png')
-
-    # Dataset Train Validation Split
-    def _train_val_split(self):
-        X_train, X_val, y_train, y_val = train_test_split(self.train_features, self.train_labels,
-                                                          test_size=self.config['dataset']['validation_size'],
-                                                          random_state=self.config['random_state'])
-
-        if self.verbose:
-            print("Number of Training Features: {0}\tNumber of Training Labels: {1}".format(len(X_train), len(y_train)))
-            print("Number of Validation Features: {0}\tNumber of Validation Labels: {1}".format(len(X_val), len(y_val)))
-        return X_train, y_train, X_val, y_val
-
-    # Model Selection
-    def select_model(self):
-        if self.model_name == 'mnb':
-            self.clf = MultinomialNB()
-        elif self.model_name == 'decision_tree':
-            self.clf = DecisionTreeClassifier(criterion=self.config['model']['decision_tree']['criterion'])
-        elif self.model_name == 'random_forest':
-            self.clf = RandomForestClassifier(n_estimators=self.config['model']['random_forest']['n_estimators'])
-        elif self.model_name == 'gradient_boost':
-            self.clf = GradientBoostingClassifier(n_estimators=self.config['model']['gradient_boost']['n_estimators'],
-                                                  criterion=self.config['model']['gradient_boost']['criterion'])
-        return self.clf
-
-    # ML Model
-    def train_model(self):
-        # Get the Data
-        X_train, y_train, X_val, y_val = self._train_val_split()
-        classifier = self.select_model()
-        # Training the Model
-        classifier = classifier.fit(X_train, y_train)
-        # Trained Model Evaluation on Validation Dataset
-        confidence = classifier.score(X_val, y_val)
-        # Validation Data Prediction
-        y_pred = classifier.predict(X_val)
-        # Model Validation Accuracy
-        accuracy = accuracy_score(y_val, y_pred)
-        # Model Confusion Matrix
-        conf_mat = confusion_matrix(y_val, y_pred)
-        # Model Classification Report
-        clf_report = classification_report(y_val, y_pred)
-        # Model Cross Validation Score
-        score = cross_val_score(classifier, X_val, y_val, cv=3)
-
-        if self.verbose:
-            print('\nTraining Accuracy: ', confidence)
-            print('\nValidation Prediction: ', y_pred)
-            print('\nValidation Accuracy: ', accuracy)
-            print('\nValidation Confusion Matrix: \n', conf_mat)
-            print('\nCross Validation Score: \n', score)
-            print('\nClassification Report: \n', clf_report)
-
-        # Save Trained Model
-        dump(classifier, str(self.model_save_path + self.model_name + ".joblib"))
-
-    # Function to Make Predictions on Test Data
-    def make_prediction(self, saved_model_name=None, test_data=None):
-        try:
-            # Load Trained Model
-            clf = load(str(self.model_save_path + saved_model_name + ".joblib"))
-        except Exception as e:
-            print("Model not found...")
-
-        if test_data is not None:
-            result = clf.predict(test_data)
-            return result
-        else:
-            result = clf.predict(self.test_features)
-        accuracy = accuracy_score(self.test_labels, result)
-        clf_report = classification_report(self.test_labels, result)
-        return accuracy, clf_report
-
-
-if __name__ == "__main__":
-    # Model Currently Training
-    current_model_name = 'decision_tree'
-    # Instantiate the Class
-    dp = DiseasePrediction(model_name=current_model_name)
-    # Train the Model
-    dp.train_model()
-    # Get Model Performance on Test Data
-    test_accuracy, classification_report = dp.make_prediction(saved_model_name=current_model_name)
-    print("Model Test Accuracy: ", test_accuracy)
-    print("Test Data Classification Report: \n", classification_report)
\ No newline at end of file
diff --git a/spaces/LinkSoul/Chinese-LLaVa/README.md b/spaces/LinkSoul/Chinese-LLaVa/README.md
deleted file mode 100644
index eb91474dd3f0a372234d45932bb0bb320f178720..0000000000000000000000000000000000000000
--- a/spaces/LinkSoul/Chinese-LLaVa/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Chinese LLaVA
-emoji: 🏃
-colorFrom: yellow
-colorTo: yellow
-sdk: static
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LittleYuan/My-Real-Bot/inference_realesrgan_video.py b/spaces/LittleYuan/My-Real-Bot/inference_realesrgan_video.py
deleted file mode 100644
index 639b848e6578a2480ee0784e664c7751e325c477..0000000000000000000000000000000000000000
--- a/spaces/LittleYuan/My-Real-Bot/inference_realesrgan_video.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import argparse
-import glob
-import mimetypes
-import os
-import queue
-import shutil
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.logger import AvgTimer
-from tqdm import tqdm
-
-from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-def main():
-    """Inference demo for Real-ESRGAN.
-    It mainly for restoring anime videos.
-
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
-    parser.add_argument(
-        '-n',
-        '--model_name',
-        type=str,
-        default='RealESRGAN_x4plus',
-        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
-              'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
-              'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
-    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
-    parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
-    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
-    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
-    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
-    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
-    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
-    parser.add_argument('--half', action='store_true', help='Use half precision during inference')
-    parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
-    parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
-    parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
-    parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers')
-
-    parser.add_argument(
-        '--alpha_upsampler',
-        type=str,
-        default='realesrgan',
-        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
-    parser.add_argument(
-        '--ext',
-        type=str,
-        default='auto',
-        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
-    args = parser.parse_args()
-
-    # ---------------------- determine models according to model names ---------------------- #
-    args.model_name = args.model_name.split('.')[0]
-    if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']:  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-    elif args.model_name in ['RealESRGAN_x4plus_anime_6B']:  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-        netscale = 4
-    elif args.model_name in ['RealESRGAN_x2plus']:  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2
-    elif args.model_name in [
-            'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
-    ]:  # x2 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
-        netscale = 2
-    elif args.model_name in [
-            'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
-    ]:  # x4 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
-        netscale = 4
-
-    # ---------------------- determine model paths ---------------------- #
-    model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
-    if not os.path.isfile(model_path):
-        model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
-    if not os.path.isfile(model_path):
-        raise ValueError(f'Model {args.model_name} does not exist.')
-
-    # restorer
-    upsampler = RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        model=model,
-        tile=args.tile,
-        tile_pad=args.tile_pad,
-        pre_pad=args.pre_pad,
-        half=args.half)
-
-    if args.face_enhance:  # Use GFPGAN for face enhancement
-        from gfpgan import GFPGANer
-        face_enhancer = GFPGANer(
-            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
-            upscale=args.outscale,
-            arch='clean',
-            channel_multiplier=2,
-            bg_upsampler=upsampler)
-    os.makedirs(args.output, exist_ok=True)
-    # for saving restored frames
-    save_frame_folder = os.path.join(args.output, 'frames_tmpout')
-    os.makedirs(save_frame_folder, exist_ok=True)
-
-    if mimetypes.guess_type(args.input)[0].startswith('video'):  # is a video file
-        video_name = os.path.splitext(os.path.basename(args.input))[0]
-        frame_folder = os.path.join('tmp_frames', video_name)
-        os.makedirs(frame_folder, exist_ok=True)
-        # use ffmpeg to extract frames
-        os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0  {frame_folder}/frame%08d.png')
-        # get image path list
-        paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
-        if args.video:
-            if args.fps is None:
-                # get input video fps
-                import ffmpeg
-                probe = ffmpeg.probe(args.input)
-                video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
-                args.fps = eval(video_streams[0]['avg_frame_rate'])
-    elif mimetypes.guess_type(args.input)[0].startswith('image'):  # is an image file
-        paths = [args.input]
-        video_name = 'video'
-    else:
-        paths = sorted(glob.glob(os.path.join(args.input, '*')))
-        video_name = 'video'
-
-    timer = AvgTimer()
-    timer.start()
-    pbar = tqdm(total=len(paths), unit='frame', desc='inference')
-    # set up prefetch reader
-    reader = PrefetchReader(paths, num_prefetch_queue=4)
-    reader.start()
-
-    que = queue.Queue()
-    consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
-    for consumer in consumers:
-        consumer.start()
-
-    for idx, (path, img) in enumerate(zip(paths, reader)):
-        imgname, extension = os.path.splitext(os.path.basename(path))
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        else:
-            img_mode = None
-
-        try:
-            if args.face_enhance:
-                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-            else:
-                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except RuntimeError as error:
-            print('Error', error)
-            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
-
-        else:
-            if args.ext == 'auto':
-                extension = extension[1:]
-            else:
-                extension = args.ext
-            if img_mode == 'RGBA':  # RGBA images should be saved in png format
-                extension = 'png'
-            save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
-
-            que.put({'output': output, 'save_path': save_path})
-
-        pbar.update(1)
-        torch.cuda.synchronize()
-        timer.record()
-        avg_fps = 1. / (timer.get_avg_time() + 1e-7)
-        pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
-
-    for _ in range(args.consumer):
-        que.put('quit')
-    for consumer in consumers:
-        consumer.join()
-    pbar.close()
-
-    # merge frames to video
-    if args.video:
-        video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
-        if args.audio:
-            os.system(
-                f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
-                f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p  {video_save_path}')
-        else:
-            os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} '
-                      f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
-
-        # delete tmp file
-        shutil.rmtree(save_frame_folder)
-        if os.path.isdir(frame_folder):
-            shutil.rmtree(frame_folder)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/MadhuV28/VideoSumamry/README.md b/spaces/MadhuV28/VideoSumamry/README.md
deleted file mode 100644
index fc6e488fcbd7856a121354730936bf75e37e4b3d..0000000000000000000000000000000000000000
--- a/spaces/MadhuV28/VideoSumamry/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VideoSumamry
-emoji: 🐢
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/commons.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/commons.py
deleted file mode 100644
index d3fa07f65b1681e1f469b04b2fe689b7c174eaaa..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/commons.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    layer = pad_shape[::-1]
-    pad_shape = [item for sublist in layer for item in sublist]
-    return pad_shape
-
-
-def intersperse(lst, item):
-    result = [item] * (len(lst) * 2 + 1)
-    result[1::2] = lst
-    return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    layer = pad_shape[::-1]
-    pad_shape = [item for sublist in layer for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py
deleted file mode 100644
index 2753b3ddee43c7a9fe28d1824db5d786e7e1ad59..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py
+++ /dev/null
@@ -1,297 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from timm.models.layers import DropPath
-
-
-class FeatureResizer(nn.Module):
-    """
-    This class takes as input a set of embeddings of dimension C1 and outputs a set of
-    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
-    """
-
-    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
-        super().__init__()
-        self.do_ln = do_ln
-        # Object feature encoding
-        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
-        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, encoder_features):
-        x = self.fc(encoder_features)
-        if self.do_ln:
-            x = self.layer_norm(x)
-        output = self.dropout(x)
-        return output
-
-
-def l1norm(X, dim, eps=1e-8):
-    """L1-normalize columns of X"""
-    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
-    X = torch.div(X, norm)
-    return X
-
-
-def l2norm(X, dim, eps=1e-8):
-    """L2-normalize columns of X"""
-    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
-    X = torch.div(X, norm)
-    return X
-
-
-def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
-    """
-    query: (n_context, queryL, d)
-    context: (n_context, sourceL, d)
-    """
-    batch_size_q, queryL = query.size(0), query.size(1)
-    batch_size, sourceL = context.size(0), context.size(1)
-
-    # Get attention
-    # --> (batch, d, queryL)
-    queryT = torch.transpose(query, 1, 2)
-
-    # (batch, sourceL, d)(batch, d, queryL)
-    # --> (batch, sourceL, queryL)
-    attn = torch.bmm(context, queryT)
-    if raw_feature_norm == "softmax":
-        # --> (batch*sourceL, queryL)
-        attn = attn.view(batch_size * sourceL, queryL)
-        attn = nn.Softmax()(attn)
-        # --> (batch, sourceL, queryL)
-        attn = attn.view(batch_size, sourceL, queryL)
-    elif raw_feature_norm == "l2norm":
-        attn = l2norm(attn, 2)
-    elif raw_feature_norm == "clipped_l2norm":
-        attn = nn.LeakyReLU(0.1)(attn)
-        attn = l2norm(attn, 2)
-    else:
-        raise ValueError("unknown first norm type:", raw_feature_norm)
-    # --> (batch, queryL, sourceL)
-    attn = torch.transpose(attn, 1, 2).contiguous()
-    # --> (batch*queryL, sourceL)
-    attn = attn.view(batch_size * queryL, sourceL)
-    attn = nn.Softmax()(attn * smooth)
-    # --> (batch, queryL, sourceL)
-    attn = attn.view(batch_size, queryL, sourceL)
-    # --> (batch, sourceL, queryL)
-    attnT = torch.transpose(attn, 1, 2).contiguous()
-
-    # --> (batch, d, sourceL)
-    contextT = torch.transpose(context, 1, 2)
-    # (batch x d x sourceL)(batch x sourceL x queryL)
-    # --> (batch, d, queryL)
-    weightedContext = torch.bmm(contextT, attnT)
-    # --> (batch, queryL, d)
-    weightedContext = torch.transpose(weightedContext, 1, 2)
-
-    return weightedContext, attnT
-
-
-class BiMultiHeadAttention(nn.Module):
-    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
-        super(BiMultiHeadAttention, self).__init__()
-
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim = embed_dim // num_heads
-        self.v_dim = v_dim
-        self.l_dim = l_dim
-
-        assert (
-            self.head_dim * self.num_heads == self.embed_dim
-        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
-        self.scale = self.head_dim ** (-0.5)
-        self.dropout = dropout
-
-        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
-        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
-        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
-        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
-
-        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
-        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
-
-        self.stable_softmax_2d = True
-        self.clamp_min_for_underflow = True
-        self.clamp_max_for_overflow = True
-
-        self._reset_parameters()
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def _reset_parameters(self):
-        nn.init.xavier_uniform_(self.v_proj.weight)
-        self.v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.l_proj.weight)
-        self.l_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.values_v_proj.weight)
-        self.values_v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.values_l_proj.weight)
-        self.values_l_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.out_v_proj.weight)
-        self.out_v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.out_l_proj.weight)
-        self.out_l_proj.bias.data.fill_(0)
-
-    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
-        """_summary_
-
-        Args:
-            v (_type_): bs, n_img, dim
-            l (_type_): bs, n_text, dim
-            attention_mask_v (_type_, optional): _description_. bs, n_img
-            attention_mask_l (_type_, optional): _description_. bs, n_text
-
-        Returns:
-            _type_: _description_
-        """
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-        bsz, tgt_len, _ = v.size()
-
-        query_states = self.v_proj(v) * self.scale
-        key_states = self._shape(self.l_proj(l), -1, bsz)
-        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
-        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_v_states = value_v_states.view(*proj_shape)
-        value_l_states = value_l_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
-            )
-
-        if self.stable_softmax_2d:
-            attn_weights = attn_weights - attn_weights.max()
-
-        if self.clamp_min_for_underflow:
-            attn_weights = torch.clamp(
-                attn_weights, min=-50000
-            )  # Do not increase -50000, data type half has quite limited range
-        if self.clamp_max_for_overflow:
-            attn_weights = torch.clamp(
-                attn_weights, max=50000
-            )  # Do not increase 50000, data type half has quite limited range
-
-        attn_weights_T = attn_weights.transpose(1, 2)
-        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
-        if self.clamp_min_for_underflow:
-            attn_weights_l = torch.clamp(
-                attn_weights_l, min=-50000
-            )  # Do not increase -50000, data type half has quite limited range
-        if self.clamp_max_for_overflow:
-            attn_weights_l = torch.clamp(
-                attn_weights_l, max=50000
-            )  # Do not increase 50000, data type half has quite limited range
-
-        # mask vison for language
-        if attention_mask_v is not None:
-            attention_mask_v = (
-                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
-            )
-            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
-
-        attn_weights_l = attn_weights_l.softmax(dim=-1)
-
-        # mask language for vision
-        if attention_mask_l is not None:
-            attention_mask_l = (
-                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
-            )
-            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
-        attn_weights_v = attn_weights.softmax(dim=-1)
-
-        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
-        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
-
-        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
-        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
-
-        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
-            )
-
-        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
-            )
-
-        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output_v = attn_output_v.transpose(1, 2)
-        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
-        attn_output_l = attn_output_l.transpose(1, 2)
-        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
-
-        attn_output_v = self.out_v_proj(attn_output_v)
-        attn_output_l = self.out_l_proj(attn_output_l)
-
-        return attn_output_v, attn_output_l
-
-
-# Bi-Direction MHA (text->image, image->text)
-class BiAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        v_dim,
-        l_dim,
-        embed_dim,
-        num_heads,
-        dropout=0.1,
-        drop_path=0.0,
-        init_values=1e-4,
-        cfg=None,
-    ):
-        """
-        Inputs:
-            embed_dim - Dimensionality of input and attention feature vectors
-            hidden_dim - Dimensionality of hidden layer in feed-forward network
-                         (usually 2-4x larger than embed_dim)
-            num_heads - Number of heads to use in the Multi-Head Attention block
-            dropout - Amount of dropout to apply in the feed-forward network
-        """
-        super(BiAttentionBlock, self).__init__()
-
-        # pre layer norm
-        self.layer_norm_v = nn.LayerNorm(v_dim)
-        self.layer_norm_l = nn.LayerNorm(l_dim)
-        self.attn = BiMultiHeadAttention(
-            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
-        )
-
-        # add layer scale for training stability
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
-        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
-
-    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
-        v = self.layer_norm_v(v)
-        l = self.layer_norm_l(l)
-        delta_v, delta_l = self.attn(
-            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
-        )
-        # v, l = v + delta_v, l + delta_l
-        v = v + self.drop_path(self.gamma_v * delta_v)
-        l = l + self.drop_path(self.gamma_l * delta_l)
-        return v, l
-
-    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet_blocks.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet_blocks.py
deleted file mode 100644
index ccaa939698c7cb101e5e59a857024e995a54cd79..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/makeaprotagonist/models/unet_blocks.py
+++ /dev/null
@@ -1,625 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
-
-import torch
-from torch import nn
-
-from .attention import Transformer3DModel
-from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
-import ipdb
-
-
-def get_down_block(
-    down_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    temb_channels,
-    add_downsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    downsample_padding=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-    temporal_conv=False,
-):
-    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
-    if down_block_type == "DownBlock3D":
-        return DownBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temporal_conv=temporal_conv
-        )
-    elif down_block_type == "CrossAttnDownBlock3D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
-        return CrossAttnDownBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temporal_conv=temporal_conv
-        )
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_up_block(
-    up_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    prev_output_channel,
-    temb_channels,
-    add_upsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-    temporal_conv=False,
-):
-    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    if up_block_type == "UpBlock3D":
-        return UpBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temporal_conv=temporal_conv
-        )
-    elif up_block_type == "CrossAttnUpBlock3D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
-        return CrossAttnUpBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temporal_conv=temporal_conv
-        )
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-class UNetMidBlock3DCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        upcast_attention=False,
-        temporal_conv=False,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock3D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                temporal_conv=temporal_conv,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if dual_cross_attention:
-                raise NotImplementedError
-            attentions.append(
-                Transformer3DModel(
-                    attn_num_head_channels,
-                    in_channels // attn_num_head_channels,
-                    in_channels=in_channels,
-                    num_layers=1,
-                    cross_attention_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    use_linear_projection=use_linear_projection,
-                    upcast_attention=upcast_attention,
-                )
-            )
-            resnets.append(
-                ResnetBlock3D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    temporal_conv=temporal_conv,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, temb_aux=None, masks=None):
-        hidden_states = self.resnets[0](hidden_states, temb, temb_aux=temb_aux, masks=masks)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
-            hidden_states = resnet(hidden_states, temb, temb_aux=temb_aux, masks=masks)
-
-        return hidden_states
-
-
-class CrossAttnDownBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-        temporal_conv=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock3D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    temporal_conv=temporal_conv
-                )
-            )
-            if dual_cross_attention:
-                raise NotImplementedError
-            attentions.append(
-                Transformer3DModel(
-                    attn_num_head_channels,
-                    out_channels // attn_num_head_channels,
-                    in_channels=out_channels,
-                    num_layers=1,
-                    cross_attention_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    use_linear_projection=use_linear_projection,
-                    only_cross_attention=only_cross_attention,
-                    upcast_attention=upcast_attention,
-                )
-            )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample3D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, adapter_feature=None, temb_aux=None, masks=None):
-        output_states = ()
-
-        # ipdb.set_trace()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb, temb_aux, masks)
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                )[0]
-                # ipdb.set_trace()
-                if adapter_feature is not None:
-                    hidden_states = hidden_states + adapter_feature
-            else:
-                hidden_states = resnet(hidden_states, temb, temb_aux=temb_aux, masks=masks)
-                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
-                if adapter_feature is not None:
-                    hidden_states = hidden_states + adapter_feature
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class DownBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-        temporal_conv=False,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock3D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    temporal_conv=temporal_conv,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample3D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, temb=None, adapter_feature=None, temb_aux=None, masks=None):
-        output_states = ()
-        # ipdb.set_trace()
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb, temb_aux, masks)
-                # ipdb.set_trace()
-                if adapter_feature is not None:
-                    hidden_states = hidden_states + adapter_feature
-
-            else:
-
-                hidden_states = resnet(hidden_states, temb, temb_aux, masks)
-                ## TODO check if it go through this
-                # ipdb.set_trace()
-                if adapter_feature is not None:
-                    hidden_states = hidden_states + adapter_feature
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class CrossAttnUpBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        add_upsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-        temporal_conv=False
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock3D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    temporal_conv=temporal_conv
-                )
-            )
-            if dual_cross_attention:
-                raise NotImplementedError
-            attentions.append(
-                Transformer3DModel(
-                    attn_num_head_channels,
-                    out_channels // attn_num_head_channels,
-                    in_channels=out_channels,
-                    num_layers=1,
-                    cross_attention_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    use_linear_projection=use_linear_projection,
-                    only_cross_attention=only_cross_attention,
-                    upcast_attention=upcast_attention,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        res_hidden_states_tuple,
-        temb=None,
-        encoder_hidden_states=None,
-        upsample_size=None,
-        attention_mask=None,
-        temb_aux=None, 
-        masks=None,
-    ):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb, temb_aux, masks)
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb, temb_aux, masks)
-                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-class UpBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-        temporal_conv=False,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock3D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    temporal_conv=temporal_conv
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, temb_aux=None, masks=None,):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb, temb_aux, masks)
-            else:
-                hidden_states = resnet(hidden_states, temb, temb_aux, masks)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
diff --git a/spaces/Marshalls/testmtd/feature_extraction/utils/smpl2mixamo.py b/spaces/Marshalls/testmtd/feature_extraction/utils/smpl2mixamo.py
deleted file mode 100644
index c46eb467dd7b2dff72e8821fbc7d866f18a73ac2..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/utils/smpl2mixamo.py
+++ /dev/null
@@ -1,102 +0,0 @@
-
-import numpy as np
-import os
-import sys
-module_path = os.path.abspath(os.path.join('PyMO'))
-if sys.path[0] != module_path:
-    sys.path.insert(0,module_path)
-from analysis.pymo.parsers import BVHParser
-from analysis.pymo.preprocessing import *
-from analysis.pymo.viz_tools import *
-from analysis.pymo.writers import *
-
-import pickle
-
-#%% Default skeleton
-
-p = BVHParser()
-default_skel = p.parse("analysis/mixamo.bvh")
-#%%
-
-# scale=1.0
-# # Scale the default sceleton
-# for joint in default_skel.traverse():
-#     offs = np.array(default_skel.skeleton[joint]['offsets'])*scale
-#     default_skel.skeleton[joint]['offsets'] = offs.tolist()
-bvh2expmap = MocapParameterizer('expmap')
-default_exp_map_data = bvh2expmap.fit_transform([default_skel])
-unity_joints_vals = default_exp_map_data[0].values.columns
-
-npfier = Numpyfier()
-npfier.fit_transform(default_exp_map_data)
-
-def angles2smplbvh(out_data, dest_dir, filename, framerate):
-    smpl_bvh = bvh2expmap.inverse_transform(npfier.inverse_transform(out_data))
-    smpl_bvh[0].framerate=float(1.0/framerate)
-
-    writer = BVHWriter()
-
-    with open(f'{dest_dir}/{filename}.bvh','w') as f:
-        writer.write(smpl_bvh[0], f)
-
-
-def convert_smpl(filepath, dest_dir, result_filename, framerate):
-    data = pickle.load(open(filepath, "rb"))
-
-    smpl_angs = data["smpl_poses"].reshape(-1,24*3)
-    smpl_trans = data["smpl_trans"]
-    # print(data)
-    smpl_scale = data["smpl_scaling"]
-
-    smpl_joints = ["Pelvis", "L_Hip", "R_Hip", "Spine1", "L_Knee", "R_Knee", "Spine2", "L_Ankle", "R_Ankle",
-                   "Spine3", "L_Foot", "R_Foot", "Neck", "L_Collar", "R_Collar", "Head", "L_Shoulder", "R_Shoulder",
-                   "L_Elbow", "R_Elbow", "L_Wrist", "R_Wrist", "L_Hand", "R_Hand"]
-
-    unity_joints = [j for j in default_skel.traverse() if j[-3:]!="Nub"]
-
-    smpl_joints_to_unity_joints = {
-        "Pelvis": "Hips",
-        "L_Hip": "LeftUpLeg",
-        "R_Hip": "RightUpLeg",
-        "Spine1": "Spine",
-        "L_Knee": "LeftLeg",
-        "R_Knee": "RightLeg",
-        "L_Ankle": "LeftFoot",
-        "R_Ankle": "RightFoot",
-        "Spine2": "Spine1",
-        "Spine3": "Spine2",
-        "L_Foot": "LeftToeBase",
-        "R_Foot": "RightToeBase",
-        "Neck": "Neck",
-        "Head": "Head",
-        "L_Collar": "LeftShoulder",
-        "R_Collar": "RightShoulder",
-        "L_Shoulder": "LeftArm",
-        "R_Shoulder": "RightArm",
-        "L_Elbow": "LeftForeArm",
-        "R_Elbow": "RightForeArm",
-        "L_Wrist": "LeftHand",
-        "R_Wrist": "RightHand",
-        # "L_Hand": "LeftHand",
-        # "R_Hand": "RightHand",
-    }
-
-    out_data = np.zeros((1, smpl_angs.shape[0], len(unity_joints)*3 + 3))
-    poss = np.zeros((smpl_angs.shape[0], 3))
-    for smpl_joint, unity_joint in smpl_joints_to_unity_joints.items():
-        out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_alpha")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3]
-        out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_beta")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+1]
-        out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_gamma")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+2]
-
-    # relative_scale=188.7
-    # relative_scale=100
-    smpl_scale = 100
-    relative_scale=smpl_scale
-    vertical_offset=-0.6*(smpl_scale/100)
-    out_data[0,:,unity_joints_vals.get_loc("Hips_Xposition")] = smpl_trans[:,0]/relative_scale
-    out_data[0,:,unity_joints_vals.get_loc("Hips_Yposition")] = smpl_trans[:,1]/relative_scale+vertical_offset
-    out_data[0,:,unity_joints_vals.get_loc("Hips_Zposition")] = smpl_trans[:,2]/relative_scale
-
-    angles2smplbvh(out_data, dest_dir, result_filename, framerate)
-
-
diff --git a/spaces/MathysL/AutoGPT4/tests/integration/memory_tests.py b/spaces/MathysL/AutoGPT4/tests/integration/memory_tests.py
deleted file mode 100644
index eead2da1cfa9b8a99592939623955808fc430068..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/tests/integration/memory_tests.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import random
-import string
-import sys
-import unittest
-from pathlib import Path
-
-from autogpt.config import Config
-from autogpt.memory.local import LocalCache
-
-
-class TestLocalCache(unittest.TestCase):
-    def random_string(self, length):
-        return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
-    def setUp(self):
-        cfg = cfg = Config()
-        self.cache = LocalCache(cfg)
-        self.cache.clear()
-
-        # Add example texts to the cache
-        self.example_texts = [
-            "The quick brown fox jumps over the lazy dog",
-            "I love machine learning and natural language processing",
-            "The cake is a lie, but the pie is always true",
-            "ChatGPT is an advanced AI model for conversation",
-        ]
-
-        for text in self.example_texts:
-            self.cache.add(text)
-
-        # Add some random strings to test noise
-        for _ in range(5):
-            self.cache.add(self.random_string(10))
-
-    def test_get_relevant(self):
-        query = "I'm interested in artificial intelligence and NLP"
-        k = 3
-        relevant_texts = self.cache.get_relevant(query, k)
-
-        print(f"Top {k} relevant texts for the query '{query}':")
-        for i, text in enumerate(relevant_texts, start=1):
-            print(f"{i}. {text}")
-
-        self.assertEqual(len(relevant_texts), k)
-        self.assertIn(self.example_texts[1], relevant_texts)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/lovasz_loss.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
deleted file mode 100644
index 6badb67f6d987b59fb07aa97caaaf89896e27a8d..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
-ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
-Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
-
-import annotator.uniformer.mmcv as mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def lovasz_grad(gt_sorted):
-    """Computes gradient of the Lovasz extension w.r.t sorted errors.
-
-    See Alg. 1 in paper.
-    """
-    p = len(gt_sorted)
-    gts = gt_sorted.sum()
-    intersection = gts - gt_sorted.float().cumsum(0)
-    union = gts + (1 - gt_sorted).float().cumsum(0)
-    jaccard = 1. - intersection / union
-    if p > 1:  # cover 1-pixel case
-        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
-    return jaccard
-
-
-def flatten_binary_logits(logits, labels, ignore_index=None):
-    """Flattens predictions in the batch (binary case) Remove labels equal to
-    'ignore_index'."""
-    logits = logits.view(-1)
-    labels = labels.view(-1)
-    if ignore_index is None:
-        return logits, labels
-    valid = (labels != ignore_index)
-    vlogits = logits[valid]
-    vlabels = labels[valid]
-    return vlogits, vlabels
-
-
-def flatten_probs(probs, labels, ignore_index=None):
-    """Flattens predictions in the batch."""
-    if probs.dim() == 3:
-        # assumes output of a sigmoid layer
-        B, H, W = probs.size()
-        probs = probs.view(B, 1, H, W)
-    B, C, H, W = probs.size()
-    probs = probs.permute(0, 2, 3, 1).contiguous().view(-1, C)  # B*H*W, C=P,C
-    labels = labels.view(-1)
-    if ignore_index is None:
-        return probs, labels
-    valid = (labels != ignore_index)
-    vprobs = probs[valid.nonzero().squeeze()]
-    vlabels = labels[valid]
-    return vprobs, vlabels
-
-
-def lovasz_hinge_flat(logits, labels):
-    """Binary Lovasz hinge loss.
-
-    Args:
-        logits (torch.Tensor): [P], logits at each prediction
-            (between -infty and +infty).
-        labels (torch.Tensor): [P], binary ground truth labels (0 or 1).
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if len(labels) == 0:
-        # only void pixels, the gradients should be 0
-        return logits.sum() * 0.
-    signs = 2. * labels.float() - 1.
-    errors = (1. - logits * signs)
-    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
-    perm = perm.data
-    gt_sorted = labels[perm]
-    grad = lovasz_grad(gt_sorted)
-    loss = torch.dot(F.relu(errors_sorted), grad)
-    return loss
-
-
-def lovasz_hinge(logits,
-                 labels,
-                 classes='present',
-                 per_image=False,
-                 class_weight=None,
-                 reduction='mean',
-                 avg_factor=None,
-                 ignore_index=255):
-    """Binary Lovasz hinge loss.
-
-    Args:
-        logits (torch.Tensor): [B, H, W], logits at each pixel
-            (between -infty and +infty).
-        labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1).
-        classes (str | list[int], optional): Placeholder, to be consistent with
-            other loss. Default: None.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        class_weight (list[float], optional): Placeholder, to be consistent
-            with other loss. Default: None.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. This parameter only works when per_image is True.
-            Default: None.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if per_image:
-        loss = [
-            lovasz_hinge_flat(*flatten_binary_logits(
-                logit.unsqueeze(0), label.unsqueeze(0), ignore_index))
-            for logit, label in zip(logits, labels)
-        ]
-        loss = weight_reduce_loss(
-            torch.stack(loss), None, reduction, avg_factor)
-    else:
-        loss = lovasz_hinge_flat(
-            *flatten_binary_logits(logits, labels, ignore_index))
-    return loss
-
-
-def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
-    """Multi-class Lovasz-Softmax loss.
-
-    Args:
-        probs (torch.Tensor): [P, C], class probabilities at each prediction
-            (between 0 and 1).
-        labels (torch.Tensor): [P], ground truth labels (between 0 and C - 1).
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        class_weight (list[float], optional): The weight for each class.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if probs.numel() == 0:
-        # only void pixels, the gradients should be 0
-        return probs * 0.
-    C = probs.size(1)
-    losses = []
-    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
-    for c in class_to_sum:
-        fg = (labels == c).float()  # foreground for class c
-        if (classes == 'present' and fg.sum() == 0):
-            continue
-        if C == 1:
-            if len(classes) > 1:
-                raise ValueError('Sigmoid output possible only with 1 class')
-            class_pred = probs[:, 0]
-        else:
-            class_pred = probs[:, c]
-        errors = (fg - class_pred).abs()
-        errors_sorted, perm = torch.sort(errors, 0, descending=True)
-        perm = perm.data
-        fg_sorted = fg[perm]
-        loss = torch.dot(errors_sorted, lovasz_grad(fg_sorted))
-        if class_weight is not None:
-            loss *= class_weight[c]
-        losses.append(loss)
-    return torch.stack(losses).mean()
-
-
-def lovasz_softmax(probs,
-                   labels,
-                   classes='present',
-                   per_image=False,
-                   class_weight=None,
-                   reduction='mean',
-                   avg_factor=None,
-                   ignore_index=255):
-    """Multi-class Lovasz-Softmax loss.
-
-    Args:
-        probs (torch.Tensor): [B, C, H, W], class probabilities at each
-            prediction (between 0 and 1).
-        labels (torch.Tensor): [B, H, W], ground truth labels (between 0 and
-            C - 1).
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        class_weight (list[float], optional): The weight for each class.
-            Default: None.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. This parameter only works when per_image is True.
-            Default: None.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-
-    if per_image:
-        loss = [
-            lovasz_softmax_flat(
-                *flatten_probs(
-                    prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
-                classes=classes,
-                class_weight=class_weight)
-            for prob, label in zip(probs, labels)
-        ]
-        loss = weight_reduce_loss(
-            torch.stack(loss), None, reduction, avg_factor)
-    else:
-        loss = lovasz_softmax_flat(
-            *flatten_probs(probs, labels, ignore_index),
-            classes=classes,
-            class_weight=class_weight)
-    return loss
-
-
-@LOSSES.register_module()
-class LovaszLoss(nn.Module):
-    """LovaszLoss.
-
-    This loss is proposed in `The Lovasz-Softmax loss: A tractable surrogate
-    for the optimization of the intersection-over-union measure in neural
-    networks <https://arxiv.org/abs/1705.08790>`_.
-
-    Args:
-        loss_type (str, optional): Binary or multi-class loss.
-            Default: 'multi_class'. Options are "binary" and "multi_class".
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-    """
-
-    def __init__(self,
-                 loss_type='multi_class',
-                 classes='present',
-                 per_image=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        super(LovaszLoss, self).__init__()
-        assert loss_type in ('binary', 'multi_class'), "loss_type should be \
-                                                    'binary' or 'multi_class'."
-
-        if loss_type == 'binary':
-            self.cls_criterion = lovasz_hinge
-        else:
-            self.cls_criterion = lovasz_softmax
-        assert classes in ('all', 'present') or mmcv.is_list_of(classes, int)
-        if not per_image:
-            assert reduction == 'none', "reduction should be 'none' when \
-                                                        per_image is False."
-
-        self.classes = classes
-        self.per_image = per_image
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = get_class_weight(class_weight)
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function."""
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-
-        # if multi-class loss, transform logits to probs
-        if self.cls_criterion == lovasz_softmax:
-            cls_score = F.softmax(cls_score, dim=1)
-
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            self.classes,
-            self.per_image,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/MrBodean/VoiceClone/vocoder/audio.py b/spaces/MrBodean/VoiceClone/vocoder/audio.py
deleted file mode 100644
index 116396261e184b9968971bd06fabc6f525e0c2fe..0000000000000000000000000000000000000000
--- a/spaces/MrBodean/VoiceClone/vocoder/audio.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import math
-import numpy as np
-import librosa
-import vocoder.hparams as hp
-from scipy.signal import lfilter
-import soundfile as sf
-
-
-def label_2_float(x, bits) :
-    return 2 * x / (2**bits - 1.) - 1.
-
-
-def float_2_label(x, bits) :
-    assert abs(x).max() <= 1.0
-    x = (x + 1.) * (2**bits - 1) / 2
-    return x.clip(0, 2**bits - 1)
-
-
-def load_wav(path) :
-    return librosa.load(str(path), sr=hp.sample_rate)[0]
-
-
-def save_wav(x, path) :
-    sf.write(path, x.astype(np.float32), hp.sample_rate)
-
-
-def split_signal(x) :
-    unsigned = x + 2**15
-    coarse = unsigned // 256
-    fine = unsigned % 256
-    return coarse, fine
-
-
-def combine_signal(coarse, fine) :
-    return coarse * 256 + fine - 2**15
-
-
-def encode_16bits(x) :
-    return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
-
-
-mel_basis = None
-
-
-def linear_to_mel(spectrogram):
-    global mel_basis
-    if mel_basis is None:
-        mel_basis = build_mel_basis()
-    return np.dot(mel_basis, spectrogram)
-
-
-def build_mel_basis():
-    return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
-
-
-def normalize(S):
-    return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)
-
-
-def denormalize(S):
-    return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db
-
-
-def amp_to_db(x):
-    return 20 * np.log10(np.maximum(1e-5, x))
-
-
-def db_to_amp(x):
-    return np.power(10.0, x * 0.05)
-
-
-def spectrogram(y):
-    D = stft(y)
-    S = amp_to_db(np.abs(D)) - hp.ref_level_db
-    return normalize(S)
-
-
-def melspectrogram(y):
-    D = stft(y)
-    S = amp_to_db(linear_to_mel(np.abs(D)))
-    return normalize(S)
-
-
-def stft(y):
-    return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)
-
-
-def pre_emphasis(x):
-    return lfilter([1, -hp.preemphasis], [1], x)
-
-
-def de_emphasis(x):
-    return lfilter([1], [1, -hp.preemphasis], x)
-
-
-def encode_mu_law(x, mu) :
-    mu = mu - 1
-    fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
-    return np.floor((fx + 1) / 2 * mu + 0.5)
-
-
-def decode_mu_law(y, mu, from_labels=True) :
-    if from_labels: 
-        y = label_2_float(y, math.log2(mu))
-    mu = mu - 1
-    x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
-    return x
-
diff --git a/spaces/MrVicente/RA-BART/README.md b/spaces/MrVicente/RA-BART/README.md
deleted file mode 100644
index 23573d6d6a8f4a39cb892f21082c7eea8fc665f5..0000000000000000000000000000000000000000
--- a/spaces/MrVicente/RA-BART/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RA BART
-emoji: 💻
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/losses/__init__.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/losses/__init__.py
deleted file mode 100644
index 919bad30809b1a4967ecb7edcb206e92637477db..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/losses/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Activations package definition. Subject to change."""
-from official.nlp.modeling.losses.weighted_sparse_categorical_crossentropy import loss as weighted_sparse_categorical_crossentropy_loss
-from official.nlp.modeling.losses.weighted_sparse_categorical_crossentropy import per_example_loss as weighted_sparse_categorical_crossentropy_per_example_loss
diff --git a/spaces/NCTCMumbai/NCTC/models/research/adversarial_logit_pairing/eval.py b/spaces/NCTCMumbai/NCTC/models/research/adversarial_logit_pairing/eval.py
deleted file mode 100644
index 504cc0b0bcf52edff9e7aaa2c0d051079ba521aa..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/adversarial_logit_pairing/eval.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright 2018 Google Inc. 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.
-# ==============================================================================
-
-"""Program which runs evaluation of Imagenet 64x64 and TinyImagenet models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-from absl import app
-from absl import flags
-
-import tensorflow as tf
-
-import adversarial_attack
-import model_lib
-from datasets import dataset_factory
-
-FLAGS = flags.FLAGS
-
-
-flags.DEFINE_string('train_dir', None,
-                    'Training directory. If specified then this program '
-                    'runs in continuous evaluation mode.')
-
-flags.DEFINE_string('checkpoint_path', None,
-                    'Path to the file with checkpoint. If specified then '
-                    'this program evaluates only provided checkpoint one time.')
-
-flags.DEFINE_string('output_file', None,
-                    'Name of output file. Used only in single evaluation mode.')
-
-flags.DEFINE_string('eval_name', 'default', 'Name for eval subdirectory.')
-
-flags.DEFINE_string('master', '', 'Tensorflow master.')
-
-flags.DEFINE_string('model_name', 'resnet_v2_50', 'Name of the model.')
-
-flags.DEFINE_string('adv_method', 'clean',
-                    'Method which is used to generate adversarial examples.')
-
-flags.DEFINE_string('dataset', 'imagenet',
-                    'Dataset: "tiny_imagenet" or "imagenet".')
-
-flags.DEFINE_integer('dataset_image_size', 64,
-                     'Size of the images in the dataset.')
-
-flags.DEFINE_string('hparams', '', 'Hyper parameters.')
-
-flags.DEFINE_string('split_name', 'validation', 'Name of the split.')
-
-flags.DEFINE_float('moving_average_decay', 0.9999,
-                   'The decay to use for the moving average.')
-
-flags.DEFINE_integer('eval_interval_secs', 120,
-                     'The frequency, in seconds, with which evaluation is run.')
-
-flags.DEFINE_integer(
-    'num_examples', -1,
-    'If positive - maximum number of example to use for evaluation.')
-
-flags.DEFINE_bool('eval_once', False,
-                  'If true then evaluate model only once.')
-
-flags.DEFINE_string('trainable_scopes', None,
-                    'If set then it defines list of variable scopes for '
-                    'trainable variables.')
-
-
-def main(_):
-  if not FLAGS.train_dir and not FLAGS.checkpoint_path:
-    print('Either --train_dir or --checkpoint_path flags has to be provided.')
-  if FLAGS.train_dir and FLAGS.checkpoint_path:
-    print('Only one of --train_dir or --checkpoint_path should be provided.')
-  params = model_lib.default_hparams()
-  params.parse(FLAGS.hparams)
-  tf.logging.info('User provided hparams: %s', FLAGS.hparams)
-  tf.logging.info('All hyper parameters: %s', params)
-  batch_size = params.eval_batch_size
-  graph = tf.Graph()
-  with graph.as_default():
-    # dataset
-    dataset, num_examples, num_classes, bounds = dataset_factory.get_dataset(
-        FLAGS.dataset,
-        FLAGS.split_name,
-        batch_size,
-        FLAGS.dataset_image_size,
-        is_training=False)
-    dataset_iterator = dataset.make_one_shot_iterator()
-    images, labels = dataset_iterator.get_next()
-    if FLAGS.num_examples > 0:
-      num_examples = min(num_examples, FLAGS.num_examples)
-
-    # setup model
-    global_step = tf.train.get_or_create_global_step()
-    model_fn_two_args = model_lib.get_model(FLAGS.model_name, num_classes)
-    model_fn = lambda x: model_fn_two_args(x, is_training=False)
-    if not FLAGS.adv_method or FLAGS.adv_method == 'clean':
-      logits = model_fn(images)
-    else:
-      adv_examples = adversarial_attack.generate_adversarial_examples(
-          images, bounds, model_fn, FLAGS.adv_method)
-      logits = model_fn(adv_examples)
-
-    # update trainable variables if fine tuning is used
-    model_lib.filter_trainable_variables(FLAGS.trainable_scopes)
-
-    # Setup the moving averages
-    if FLAGS.moving_average_decay and (FLAGS.moving_average_decay > 0):
-      variable_averages = tf.train.ExponentialMovingAverage(
-          FLAGS.moving_average_decay, global_step)
-      variables_to_restore = variable_averages.variables_to_restore(
-          tf.contrib.framework.get_model_variables())
-      variables_to_restore[global_step.op.name] = global_step
-    else:
-      variables_to_restore = tf.contrib.framework.get_variables_to_restore()
-
-    # Setup evaluation metric
-    with tf.name_scope('Eval'):
-      names_to_values, names_to_updates = (
-          tf.contrib.metrics.aggregate_metric_map({
-              'Accuracy': tf.metrics.accuracy(labels, tf.argmax(logits, 1)),
-              'Top5': tf.metrics.recall_at_k(tf.to_int64(labels), logits, 5)
-          }))
-
-      for name, value in names_to_values.iteritems():
-        tf.summary.scalar(name, value)
-
-    # Run evaluation
-    num_batches = int(num_examples / batch_size)
-    if FLAGS.train_dir:
-      output_dir = os.path.join(FLAGS.train_dir, FLAGS.eval_name)
-      if not tf.gfile.Exists(output_dir):
-        tf.gfile.MakeDirs(output_dir)
-      tf.contrib.training.evaluate_repeatedly(
-          FLAGS.train_dir,
-          master=FLAGS.master,
-          scaffold=tf.train.Scaffold(
-              saver=tf.train.Saver(variables_to_restore)),
-          eval_ops=names_to_updates.values(),
-          eval_interval_secs=FLAGS.eval_interval_secs,
-          hooks=[
-              tf.contrib.training.StopAfterNEvalsHook(num_batches),
-              tf.contrib.training.SummaryAtEndHook(output_dir),
-              tf.train.LoggingTensorHook(names_to_values, at_end=True),
-          ],
-          max_number_of_evaluations=1 if FLAGS.eval_once else None)
-    else:
-      result = tf.contrib.training.evaluate_once(
-          FLAGS.checkpoint_path,
-          master=FLAGS.master,
-          scaffold=tf.train.Scaffold(
-              saver=tf.train.Saver(variables_to_restore)),
-          eval_ops=names_to_updates.values(),
-          final_ops=names_to_values,
-          hooks=[
-              tf.contrib.training.StopAfterNEvalsHook(num_batches),
-              tf.train.LoggingTensorHook(names_to_values, at_end=True),
-          ])
-      if FLAGS.output_file:
-        with tf.gfile.Open(FLAGS.output_file, 'a') as f:
-          f.write('%s,%.3f,%.3f\n'
-                  % (FLAGS.eval_name, result['Accuracy'], result['Top5']))
-
-
-if __name__ == '__main__':
-  app.run(main)
diff --git a/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/README.md b/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/README.md
deleted file mode 100644
index 2cbc4932fb8e502e309da302f88eaeed194205ee..0000000000000000000000000000000000000000
--- a/spaces/Nesip/Aeala-GPT4-x-AlpacaDente2-30b/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Shiny for R template
-emoji: 📚
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-duplicated_from: posit/shiny-for-r-template
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Nick1/rvc-models/config.py b/spaces/Nick1/rvc-models/config.py
deleted file mode 100644
index 6797dd748da4a2ddf57a97fd80ce9776d98ac82e..0000000000000000000000000000000000000000
--- a/spaces/Nick1/rvc-models/config.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import argparse
-import sys
-import torch
-from multiprocessing import cpu_count
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.colab,
-            self.api,
-            self.unsupported
-        ) = self.arg_parse()
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument("--api", action="store_true", help="Launch with api")
-        parser.add_argument("--unsupported", action="store_true", help="Enable unsupported feature")
-        cmd_opts = parser.parse_args()
-
-        return (
-            cmd_opts.colab,
-            cmd_opts.api,
-            cmd_opts.unsupported
-        )
-
-    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-    # check `getattr` and try it for compatibility
-    @staticmethod
-    def has_mps() -> bool:
-        if not torch.backends.mps.is_available():
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("INFO: Found GPU", self.gpu_name, ", force to fp32")
-                self.is_half = False
-            else:
-                print("INFO: Found GPU", self.gpu_name)
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-        elif self.has_mps():
-            print("INFO: No supported Nvidia GPU found, use MPS instead")
-            self.device = "mps"
-            self.is_half = False
-        else:
-            print("INFO: No supported Nvidia GPU found, use CPU instead")
-            self.device = "cpu"
-            self.is_half = False
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
diff --git a/spaces/Nixic/rvc-models/infer_pack/commons.py b/spaces/Nixic/rvc-models/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Nixic/rvc-models/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/latent_depth/latent_depth_src/loss/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/latent_depth/latent_depth_src/loss/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/noising.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/noising.py
deleted file mode 100644
index 2b1cc347203bfbdc9f1cba29e2e36427b7b5be57..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/noising.py
+++ /dev/null
@@ -1,335 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-from fairseq.data import data_utils
-
-
-class WordNoising(object):
-    """Generate a noisy version of a sentence, without changing words themselves."""
-
-    def __init__(self, dictionary, bpe_cont_marker="@@", bpe_end_marker=None):
-        self.dictionary = dictionary
-        self.bpe_end = None
-        if bpe_cont_marker:
-            self.bpe_end = np.array(
-                [
-                    not self.dictionary[i].endswith(bpe_cont_marker)
-                    for i in range(len(self.dictionary))
-                ]
-            )
-        elif bpe_end_marker:
-            self.bpe_end = np.array(
-                [
-                    self.dictionary[i].endswith(bpe_end_marker)
-                    for i in range(len(self.dictionary))
-                ]
-            )
-
-        self.get_word_idx = (
-            self._get_bpe_word_idx if self.bpe_end is not None else self._get_token_idx
-        )
-
-    def noising(self, x, lengths, noising_prob=0.0):
-        raise NotImplementedError()
-
-    def _get_bpe_word_idx(self, x):
-        """
-        Given a list of BPE tokens, for every index in the tokens list,
-        return the index of the word grouping that it belongs to.
-        For example, for input x corresponding to ["how", "are", "y@@", "ou"],
-        return [[0], [1], [2], [2]].
-        """
-        # x: (T x B)
-        bpe_end = self.bpe_end[x]
-
-        if x.size(0) == 1 and x.size(1) == 1:
-            # Special case when we only have one word in x. If x = [[N]],
-            # bpe_end is a scalar (bool) instead of a 2-dim array of bools,
-            # which makes the sum operation below fail.
-            return np.array([[0]])
-
-        # do a reduce front sum to generate word ids
-        word_idx = bpe_end[::-1].cumsum(0)[::-1]
-        word_idx = word_idx.max(0)[None, :] - word_idx
-        return word_idx
-
-    def _get_token_idx(self, x):
-        """
-        This is to extend noising functions to be able to apply to non-bpe
-        tokens, e.g. word or characters.
-        """
-        x = torch.t(x)
-        word_idx = np.array([range(len(x_i)) for x_i in x])
-        return np.transpose(word_idx)
-
-
-class WordDropout(WordNoising):
-    """Randomly drop input words. If not passing blank_idx (default is None),
-    then dropped words will be removed. Otherwise, it will be replaced by the
-    blank_idx."""
-
-    def __init__(
-        self,
-        dictionary,
-        default_dropout_prob=0.1,
-        bpe_cont_marker="@@",
-        bpe_end_marker=None,
-    ):
-        super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
-        self.default_dropout_prob = default_dropout_prob
-
-    def noising(self, x, lengths, dropout_prob=None, blank_idx=None):
-        if dropout_prob is None:
-            dropout_prob = self.default_dropout_prob
-        # x: (T x B), lengths: B
-        if dropout_prob == 0:
-            return x, lengths
-
-        assert 0 < dropout_prob < 1
-
-        # be sure to drop entire words
-        word_idx = self.get_word_idx(x)
-        sentences = []
-        modified_lengths = []
-        for i in range(lengths.size(0)):
-            # Since dropout probabilities need to apply over non-pad tokens,
-            # it is not trivial to generate the keep mask without consider
-            # input lengths; otherwise, this could be done outside the loop
-
-            # We want to drop whole words based on word_idx grouping
-            num_words = max(word_idx[:, i]) + 1
-
-            # ith example: [x0, x1, ..., eos, pad, ..., pad]
-            # We should only generate keep probs for non-EOS tokens. Thus if the
-            # input sentence ends in EOS, the last word idx is not included in
-            # the dropout mask generation and we append True to always keep EOS.
-            # Otherwise, just generate the dropout mask for all word idx
-            # positions.
-            has_eos = x[lengths[i] - 1, i] == self.dictionary.eos()
-            if has_eos:  # has eos?
-                keep = np.random.rand(num_words - 1) >= dropout_prob
-                keep = np.append(keep, [True])  # keep EOS symbol
-            else:
-                keep = np.random.rand(num_words) >= dropout_prob
-
-            words = x[: lengths[i], i].tolist()
-
-            # TODO: speed up the following loop
-            # drop words from the input according to keep
-            new_s = [
-                w if keep[word_idx[j, i]] else blank_idx for j, w in enumerate(words)
-            ]
-            new_s = [w for w in new_s if w is not None]
-            # we need to have at least one word in the sentence (more than the
-            # start / end sentence symbols)
-            if len(new_s) <= 1:
-                # insert at beginning in case the only token left is EOS
-                # EOS should be at end of list.
-                new_s.insert(0, words[np.random.randint(0, len(words))])
-            assert len(new_s) >= 1 and (
-                not has_eos  # Either don't have EOS at end or last token is EOS
-                or (len(new_s) >= 2 and new_s[-1] == self.dictionary.eos())
-            ), "New sentence is invalid."
-            sentences.append(new_s)
-            modified_lengths.append(len(new_s))
-        # re-construct input
-        modified_lengths = torch.LongTensor(modified_lengths)
-        modified_x = torch.LongTensor(
-            modified_lengths.max(), modified_lengths.size(0)
-        ).fill_(self.dictionary.pad())
-        for i in range(modified_lengths.size(0)):
-            modified_x[: modified_lengths[i], i].copy_(torch.LongTensor(sentences[i]))
-
-        return modified_x, modified_lengths
-
-
-class WordShuffle(WordNoising):
-    """Shuffle words by no more than k positions."""
-
-    def __init__(
-        self,
-        dictionary,
-        default_max_shuffle_distance=3,
-        bpe_cont_marker="@@",
-        bpe_end_marker=None,
-    ):
-        super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
-        self.default_max_shuffle_distance = 3
-
-    def noising(self, x, lengths, max_shuffle_distance=None):
-        if max_shuffle_distance is None:
-            max_shuffle_distance = self.default_max_shuffle_distance
-        # x: (T x B), lengths: B
-        if max_shuffle_distance == 0:
-            return x, lengths
-
-        # max_shuffle_distance < 1 will return the same sequence
-        assert max_shuffle_distance > 1
-
-        # define noise word scores
-        noise = np.random.uniform(
-            0,
-            max_shuffle_distance,
-            size=(x.size(0), x.size(1)),
-        )
-        noise[0] = -1  # do not move start sentence symbol
-        # be sure to shuffle entire words
-        word_idx = self.get_word_idx(x)
-        x2 = x.clone()
-        for i in range(lengths.size(0)):
-            length_no_eos = lengths[i]
-            if x[lengths[i] - 1, i] == self.dictionary.eos():
-                length_no_eos = lengths[i] - 1
-            # generate a random permutation
-            scores = word_idx[:length_no_eos, i] + noise[word_idx[:length_no_eos, i], i]
-            # ensure no reordering inside a word
-            scores += 1e-6 * np.arange(length_no_eos.item())
-            permutation = scores.argsort()
-            # shuffle words
-            x2[:length_no_eos, i].copy_(
-                x2[:length_no_eos, i][torch.from_numpy(permutation)]
-            )
-        return x2, lengths
-
-
-class UnsupervisedMTNoising(WordNoising):
-    """
-    Implements the default configuration for noising in UnsupervisedMT
-    (github.com/facebookresearch/UnsupervisedMT)
-    """
-
-    def __init__(
-        self,
-        dictionary,
-        max_word_shuffle_distance,
-        word_dropout_prob,
-        word_blanking_prob,
-        bpe_cont_marker="@@",
-        bpe_end_marker=None,
-    ):
-        super().__init__(dictionary)
-        self.max_word_shuffle_distance = max_word_shuffle_distance
-        self.word_dropout_prob = word_dropout_prob
-        self.word_blanking_prob = word_blanking_prob
-
-        self.word_dropout = WordDropout(
-            dictionary=dictionary,
-            bpe_cont_marker=bpe_cont_marker,
-            bpe_end_marker=bpe_end_marker,
-        )
-        self.word_shuffle = WordShuffle(
-            dictionary=dictionary,
-            bpe_cont_marker=bpe_cont_marker,
-            bpe_end_marker=bpe_end_marker,
-        )
-
-    def noising(self, x, lengths):
-        # 1. Word Shuffle
-        noisy_src_tokens, noisy_src_lengths = self.word_shuffle.noising(
-            x=x,
-            lengths=lengths,
-            max_shuffle_distance=self.max_word_shuffle_distance,
-        )
-        # 2. Word Dropout
-        noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
-            x=noisy_src_tokens,
-            lengths=noisy_src_lengths,
-            dropout_prob=self.word_dropout_prob,
-        )
-        # 3. Word Blanking
-        noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
-            x=noisy_src_tokens,
-            lengths=noisy_src_lengths,
-            dropout_prob=self.word_blanking_prob,
-            blank_idx=self.dictionary.unk(),
-        )
-
-        return noisy_src_tokens
-
-
-class NoisingDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        src_dataset,
-        src_dict,
-        seed,
-        noiser=None,
-        noising_class=UnsupervisedMTNoising,
-        **kwargs
-    ):
-        """
-        Wrap a :class:`~torch.utils.data.Dataset` and apply noise to the
-        samples based on the supplied noising configuration.
-
-        Args:
-            src_dataset (~torch.utils.data.Dataset): dataset to wrap.
-                to build self.src_dataset --
-                a LanguagePairDataset with src dataset as the source dataset and
-                None as the target dataset. Should NOT have padding so that
-                src_lengths are accurately calculated by language_pair_dataset
-                collate function.
-                We use language_pair_dataset here to encapsulate the tgt_dataset
-                so we can re-use the LanguagePairDataset collater to format the
-                batches in the structure that SequenceGenerator expects.
-            src_dict (~fairseq.data.Dictionary): source dictionary
-            seed (int): seed to use when generating random noise
-            noiser (WordNoising): a pre-initialized :class:`WordNoising`
-                instance. If this is None, a new instance will be created using
-                *noising_class* and *kwargs*.
-            noising_class (class, optional): class to use to initialize a
-                default :class:`WordNoising` instance.
-            kwargs (dict, optional): arguments to initialize the default
-                :class:`WordNoising` instance given by *noiser*.
-        """
-        self.src_dataset = src_dataset
-        self.src_dict = src_dict
-        self.seed = seed
-        self.noiser = (
-            noiser
-            if noiser is not None
-            else noising_class(
-                dictionary=src_dict,
-                **kwargs,
-            )
-        )
-        self.sizes = src_dataset.sizes
-
-
-    def __getitem__(self, index):
-        """
-        Returns a single noisy sample. Multiple samples are fed to the collater
-        create a noising dataset batch.
-        """
-        src_tokens = self.src_dataset[index]
-        src_lengths = torch.LongTensor([len(src_tokens)])
-        src_tokens = src_tokens.unsqueeze(0)
-
-        # Transpose src tokens to fit expected shape of x in noising function
-        # (batch size, sequence length) -> (sequence length, batch size)
-        src_tokens_t = torch.t(src_tokens)
-
-        with data_utils.numpy_seed(self.seed + index):
-            noisy_src_tokens = self.noiser.noising(src_tokens_t, src_lengths)
-
-        # Transpose back to expected src_tokens format
-        # (sequence length, 1) -> (1, sequence length)
-        noisy_src_tokens = torch.t(noisy_src_tokens)
-        return noisy_src_tokens[0]
-
-    def __len__(self):
-        """
-        The length of the noising dataset is the length of src.
-        """
-        return len(self.src_dataset)
-
-    @property
-    def supports_prefetch(self):
-        return self.src_dataset.supports_prefetch
-
-    def prefetch(self, indices):
-        if self.src_dataset.supports_prefetch:
-            self.src_dataset.prefetch(indices)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/byte_level_bpe/get_data.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/byte_level_bpe/get_data.sh
deleted file mode 100644
index c3d55d4925a6e6e23d12d293f093c1ae14acf76e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/byte_level_bpe/get_data.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-PY_BIN_ROOT=
-
-# PyPI dependency
-${PY_BIN_ROOT}pip install sentencepiece sacremoses
-
-# Get data
-if [ ! -d "data" ]; then
-  mkdir data
-fi
-
-if [ ! -f "data/fr-en.tgz" ]; then
-  wget https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz -P data
-  tar xvf data/fr-en.tgz -C data
-fi
-${PY_BIN_ROOT}python get_bitext.py --bpe-vocab 16384 --byte-vocab --char-vocab
-for VOCAB_SIZE in 2048 4096; do
-  ${PY_BIN_ROOT}python get_bitext.py --bpe-vocab ${VOCAB_SIZE} --bbpe-vocab ${VOCAB_SIZE}
-done
-rm -r data/fr-en data/fr-en.tgz
-
-# Generate binary dataset
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bpe16384 --joined-dictionary \
-  --workers "$(nproc)" --trainpref data/train.moses.bpe16384 --validpref data/valid.moses.bpe16384 \
-  --testpref data/test.moses.bpe16384
-
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bytes --joined-dictionary \
-  --workers "$(nproc)" --trainpref data/train.moses.bytes --validpref data/valid.moses.bytes \
-  --testpref data/test.moses.bytes
-
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_chars --joined-dictionary \
-  --workers "$(nproc)" --trainpref data/train.moses.chars --validpref data/valid.moses.chars \
-  --testpref data/test.moses.chars
-
-for VOCAB_SIZE in 2048 4096; do
-  for TYPE in bbpe bpe; do
-    ${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir "data/bin_${TYPE}${VOCAB_SIZE}" \
-      --joined-dictionary --workers "$(nproc)" --trainpref "data/train.moses.${TYPE}${VOCAB_SIZE}" \
-      --validpref "data/valid.moses.${TYPE}${VOCAB_SIZE}" --testpref "data/test.moses.${TYPE}${VOCAB_SIZE}"
-  done
-done
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py
deleted file mode 100644
index 7f28c32dd6152f53d6922cdfccfa903e0bdc5829..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-import torch
-from omegaconf import II
-
-from fairseq import metrics, utils
-from fairseq.dataclass import ChoiceEnum
-from fairseq.tasks import register_task
-from fairseq.tasks.translation import TranslationConfig, TranslationTask
-
-from .logsumexp_moe import LogSumExpMoE
-from .mean_pool_gating_network import MeanPoolGatingNetwork
-
-
-METHOD_CHOICES = ChoiceEnum(["sMoElp", "sMoEup", "hMoElp", "hMoEup"])
-
-
-@dataclass
-class TranslationMoEConfig(TranslationConfig):
-    method: METHOD_CHOICES = field(
-        default="hMoEup",
-        metadata={"help": "MoE method"},
-    )
-    num_experts: int = field(
-        default=3,
-        metadata={"help": "number of experts"},
-    )
-    mean_pool_gating_network: bool = field(
-        default=False,
-        metadata={"help": "use a simple mean-pooling gating network"},
-    )
-    mean_pool_gating_network_dropout: float = field(
-        default=0,
-        metadata={"help": "dropout for mean-pooling gating network"},
-    )
-    mean_pool_gating_network_encoder_dim: int = field(
-        default=0,
-        metadata={"help": "encoder output dim for mean-pooling gating network"},
-    )
-    gen_expert: int = field(
-        default=0,
-        metadata={"help": "which expert to use for generation"},
-    )
-    sentence_avg: bool = II("optimization.sentence_avg")
-
-
-@register_task("translation_moe", dataclass=TranslationMoEConfig)
-class TranslationMoETask(TranslationTask):
-    """
-    Translation task for Mixture of Experts (MoE) models.
-
-    See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade"
-    (Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_.
-
-    Args:
-        src_dict (~fairseq.data.Dictionary): dictionary for the source language
-        tgt_dict (~fairseq.data.Dictionary): dictionary for the target language
-
-    .. note::
-
-        The translation task is compatible with :mod:`fairseq-train`,
-        :mod:`fairseq-generate` and :mod:`fairseq-interactive`.
-
-    The translation task provides the following additional command-line
-    arguments:
-
-    .. argparse::
-        :ref: fairseq.tasks.translation_parser
-        :prog:
-    """
-
-    cfg: TranslationMoEConfig
-
-    def __init__(self, cfg: TranslationMoEConfig, src_dict, tgt_dict):
-        if cfg.method == "sMoElp":
-            # soft MoE with learned prior
-            self.uniform_prior = False
-            self.hard_selection = False
-        elif cfg.method == "sMoEup":
-            # soft MoE with uniform prior
-            self.uniform_prior = True
-            self.hard_selection = False
-        elif cfg.method == "hMoElp":
-            # hard MoE with learned prior
-            self.uniform_prior = False
-            self.hard_selection = True
-        elif cfg.method == "hMoEup":
-            # hard MoE with uniform prior
-            self.uniform_prior = True
-            self.hard_selection = True
-
-        # add indicator tokens for each expert
-        for i in range(cfg.num_experts):
-            # add to both dictionaries in case we're sharing embeddings
-            src_dict.add_symbol("<expert_{}>".format(i))
-            tgt_dict.add_symbol("<expert_{}>".format(i))
-
-        super().__init__(cfg, src_dict, tgt_dict)
-
-    def build_model(self, cfg):
-        from fairseq import models
-
-        model = models.build_model(cfg, self)
-        if not self.uniform_prior and not hasattr(model, "gating_network"):
-            if self.cfg.mean_pool_gating_network:
-                if self.cfg.mean_pool_gating_network_encoder_dim > 0:
-                    encoder_dim = self.cfg.mean_pool_gating_network_encoder_dim
-                elif getattr(cfg, "encoder_embed_dim", None):
-                    # assume that encoder_embed_dim is the encoder's output dimension
-                    encoder_dim = cfg.encoder_embed_dim
-                else:
-                    raise ValueError(
-                        "Must specify --mean-pool-gating-network-encoder-dim"
-                    )
-
-                if self.cfg.mean_pool_gating_network_dropout > 0:
-                    dropout = self.cfg.mean_pool_gating_network_dropout
-                elif getattr(cfg, "dropout", None):
-                    dropout = cfg.dropout
-                else:
-                    raise ValueError("Must specify task.mean_pool_gating_network_dropout")
-
-                model.gating_network = MeanPoolGatingNetwork(
-                    encoder_dim,
-                    self.cfg.num_experts,
-                    dropout,
-                )
-            else:
-                raise ValueError(
-                    "translation_moe task with learned prior requires the model to "
-                    "have a gating network; try using --mean-pool-gating-network"
-                )
-        return model
-
-    def expert_index(self, i):
-        return i + self.tgt_dict.index("<expert_0>")
-
-    def _get_loss(self, sample, model, criterion):
-        assert hasattr(
-            criterion, "compute_loss"
-        ), "translation_moe task requires the criterion to implement the compute_loss() method"
-
-        k = self.cfg.num_experts
-        bsz = sample["target"].size(0)
-
-        def get_lprob_y(encoder_out, prev_output_tokens_k):
-            net_output = model.decoder(
-                prev_output_tokens=prev_output_tokens_k,
-                encoder_out=encoder_out,
-            )
-            loss, _ = criterion.compute_loss(model, net_output, sample, reduce=False)
-            loss = loss.view(bsz, -1)
-            return -loss.sum(dim=1, keepdim=True)  # -> B x 1
-
-        def get_lprob_yz(winners=None):
-            encoder_out = model.encoder(
-                src_tokens=sample["net_input"]["src_tokens"],
-                src_lengths=sample["net_input"]["src_lengths"],
-            )
-
-            if winners is None:
-                lprob_y = []
-                for i in range(k):
-                    prev_output_tokens_k = sample["net_input"][
-                        "prev_output_tokens"
-                    ].clone()
-                    assert not prev_output_tokens_k.requires_grad
-                    prev_output_tokens_k[:, 0] = self.expert_index(i)
-                    lprob_y.append(get_lprob_y(encoder_out, prev_output_tokens_k))
-                lprob_y = torch.cat(lprob_y, dim=1)  # -> B x K
-            else:
-                prev_output_tokens_k = sample["net_input"]["prev_output_tokens"].clone()
-                prev_output_tokens_k[:, 0] = self.expert_index(winners)
-                lprob_y = get_lprob_y(encoder_out, prev_output_tokens_k)  # -> B
-
-            if self.uniform_prior:
-                lprob_yz = lprob_y
-            else:
-                lprob_z = model.gating_network(encoder_out)  # B x K
-                if winners is not None:
-                    lprob_z = lprob_z.gather(dim=1, index=winners.unsqueeze(-1))
-                lprob_yz = lprob_y + lprob_z.type_as(lprob_y)  # B x K
-
-            return lprob_yz
-
-        # compute responsibilities without dropout
-        with utils.model_eval(model):  # disable dropout
-            with torch.no_grad():  # disable autograd
-                lprob_yz = get_lprob_yz()  # B x K
-                prob_z_xy = torch.nn.functional.softmax(lprob_yz, dim=1)
-        assert not prob_z_xy.requires_grad
-
-        # compute loss with dropout
-        if self.hard_selection:
-            winners = prob_z_xy.max(dim=1)[1]
-            loss = -get_lprob_yz(winners)
-        else:
-            lprob_yz = get_lprob_yz()  # B x K
-            loss = -LogSumExpMoE.apply(lprob_yz, prob_z_xy, 1)
-
-        loss = loss.sum()
-        sample_size = (
-            sample["target"].size(0) if self.cfg.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": utils.item(loss.data),
-            "ntokens": sample["ntokens"],
-            "nsentences": bsz,
-            "sample_size": sample_size,
-            "posterior": prob_z_xy.float().sum(dim=0).cpu(),
-        }
-        return loss, sample_size, logging_output
-
-    def train_step(
-        self, sample, model, criterion, optimizer, update_num, ignore_grad=False
-    ):
-        model.train()
-        loss, sample_size, logging_output = self._get_loss(sample, model, criterion)
-        if ignore_grad:
-            loss *= 0
-        optimizer.backward(loss)
-        return loss, sample_size, logging_output
-
-    def valid_step(self, sample, model, criterion):
-        model.eval()
-        with torch.no_grad():
-            loss, sample_size, logging_output = self._get_loss(sample, model, criterion)
-        return loss, sample_size, logging_output
-
-    def inference_step(
-        self,
-        generator,
-        models,
-        sample,
-        prefix_tokens=None,
-        expert=None,
-        constraints=None,
-    ):
-        expert = expert or self.cfg.gen_expert
-        with torch.no_grad():
-            return generator.generate(
-                models,
-                sample,
-                prefix_tokens=prefix_tokens,
-                constraints=constraints,
-                bos_token=self.expert_index(expert),
-            )
-
-    def reduce_metrics(self, logging_outputs, criterion):
-        super().reduce_metrics(logging_outputs, criterion)
-        metrics.log_scalar(
-            "posterior",
-            sum(log["posterior"] for log in logging_outputs if "posterior" in log),
-        )
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py
deleted file mode 100644
index 8cc2a7174b765b7ad8808489196e12082a91a2d7..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq.tasks import register_task
-from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
-from fairseq.utils import safe_hasattr
-
-from .loss.latent_depth import LatentLayersKLLoss, LatentLayersSparsityLoss
-
-
-@register_task("multilingual_translation_latent_depth")
-class MultilingualTranslationTaskLatentDepth(MultilingualTranslationTask):
-    """A task for multiple translation with latent depth.
-
-    See `"Deep Transformer with Latent Depth"
-        (Li et al., 2020) <https://arxiv.org/pdf/2009.13102.pdf>`_.
-    """
-
-    @staticmethod
-    def add_args(parser):
-        """Add task-specific arguments to the parser."""
-        # fmt: off
-        MultilingualTranslationTask.add_args(parser)
-        parser.add_argument('--encoder-latent-layer', action='store_true', help='latent layer selection in encoder')
-        parser.add_argument('--decoder-latent-layer', action='store_true', help='latent layer selection in decoder')
-        parser.add_argument('--target-layers', default=-1, type=int,
-                            help='number of effective layers to learn; -1 means no constraint')
-        parser.add_argument('--sparsity-weight', default=0.0, type=float,
-                            help='weight for sparsity loss')
-        parser.add_argument('--share-weight', default=0.0, type=float,
-                            help='weight for sharing loss')
-        parser.add_argument('--soft-update', default=1, type=int,
-                            help='number of updates with soft sampling')
-        parser.add_argument('--anneal-updates', default=1, type=int,
-                            help='number of updates to anneal the KL loss weight')
-        parser.add_argument('--prior', default="uniform", type=str,
-                            help='prior used for computing KL loss')
-        # fmt: on
-
-    def __init__(self, args, dicts, training):
-        super().__init__(args, dicts, training)
-        self.src_langs, self.tgt_langs = zip(
-            *[(lang.split("-")[0], lang.split("-")[1]) for lang in args.lang_pairs]
-        )
-        if self.training and self.encoder_latent_layer:
-            assert self.args.share_encoders
-        if self.training and self.decoder_latent_layer:
-            assert self.args.share_decoders
-        if training or self.encoder_latent_layer or self.decoder_latent_layer:
-            self.lang_pairs = args.lang_pairs
-        else:
-            self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)]
-        self.eval_lang_pairs = self.lang_pairs
-        self.model_lang_pairs = self.lang_pairs
-        if self.training and (self.encoder_latent_layer or self.decoder_latent_layer):
-            self.kl_loss = LatentLayersKLLoss(self.args)
-            self.sparsity_loss = LatentLayersSparsityLoss(self.args)
-
-    def _per_lang_pair_train_loss(
-        self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad
-    ):
-        src, tgt = lang_pair.split("-")
-        if self.encoder_latent_layer:
-            src_lang_idx = self.src_lang_idx_dict[src]
-            model.models[lang_pair].encoder.set_lang_idx(src_lang_idx)
-            model.models[lang_pair].encoder.layer_select.hard_select = (
-                update_num > self.args.soft_update
-            )
-        if self.decoder_latent_layer:
-            tgt_lang_idx = self.tgt_lang_idx_dict[tgt]
-            model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx)
-            model.models[lang_pair].decoder.layer_select.hard_select = (
-                update_num > self.args.soft_update
-            )
-
-        loss, sample_size, logging_output = criterion(
-            model.models[lang_pair], sample[lang_pair]
-        )
-        if self.encoder_latent_layer:
-            none_samples = sum(
-                1 if x is None else 0
-                for x in model.models[lang_pair].encoder.layer_select.layer_samples
-            )
-            if none_samples == 0 or self.args.prior != "agged_posterior":
-                loss += self.kl_loss(
-                    model.models[lang_pair].encoder.layer_select.layer_samples,
-                    src_lang_idx,
-                    update_num,
-                    sample_size,
-                )
-        if self.decoder_latent_layer:
-            none_samples = sum(
-                1 if x is None else 0
-                for x in model.models[lang_pair].decoder.layer_select.layer_samples
-            )
-            if none_samples == 0 or self.args.prior != "agged_posterior":
-                loss += self.kl_loss(
-                    model.models[lang_pair].decoder.layer_select.layer_samples,
-                    tgt_lang_idx,
-                    update_num,
-                    sample_size,
-                )
-        if ignore_grad:
-            loss *= 0
-
-        if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num):
-            # need to retain the graph if sparsity loss needs to be added
-            loss.backward(retain_graph=True)
-        else:
-            optimizer.backward(loss)
-
-        return loss, sample_size, logging_output
-
-    def train_step(
-        self, sample, model, criterion, optimizer, update_num, ignore_grad=False
-    ):
-        agg_loss, agg_sample_size, agg_logging_output = super().train_step(
-            sample, model, criterion, optimizer, update_num, ignore_grad
-        )
-        # compute auxiliary loss from layere sparsity, based on all samples from all languages
-        if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num):
-            sparsity_loss = 0
-            if self.encoder_latent_layer:
-                sparsity_loss += self.sparsity_loss(
-                    next(
-                        iter(model.models.values())
-                    ).encoder.layer_select.layer_samples,
-                    update_num,
-                    agg_sample_size,
-                )
-            if self.decoder_latent_layer:
-                sparsity_loss += self.sparsity_loss(
-                    next(
-                        iter(model.models.values())
-                    ).decoder.layer_select.layer_samples,
-                    update_num,
-                    agg_sample_size,
-                )
-            if sparsity_loss > 0:
-                optimizer.backward(sparsity_loss)
-        return agg_loss, agg_sample_size, agg_logging_output
-
-    def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample):
-        src, tgt = lang_pair.split("-")
-        if self.encoder_latent_layer:
-            src_lang_idx = self.src_lang_idx_dict[src]
-            model.models[lang_pair].encoder.set_lang_idx(src_lang_idx)
-        if self.decoder_latent_layer:
-            tgt_lang_idx = self.tgt_lang_idx_dict[tgt]
-            model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx)
-        loss, sample_size, logging_output = criterion(
-            model.models[lang_pair], sample[lang_pair]
-        )
-        return loss, sample_size, logging_output
-
-    def inference_step(
-        self, generator, models, sample, prefix_tokens=None, constraints=None
-    ):
-        if self.encoder_latent_layer or self.decoder_latent_layer:
-            for model in models:
-                if self.encoder_latent_layer:
-                    assert model.encoder.layer_select is not None
-                    src_lang_idx = self.src_lang_idx_dict[self.args.source_lang]
-                    model.encoder.set_lang_idx(src_lang_idx)
-                if self.decoder_latent_layer:
-                    assert model.decoder.layer_select is not None
-                    tgt_lang_idx = self.tgt_lang_idx_dict[self.args.target_lang]
-                    model.decoder.set_lang_idx(tgt_lang_idx)
-        return super().inference_step(
-            generator, models, sample, prefix_tokens, constraints
-        )
-
-    @property
-    def encoder_latent_layer(self):
-        return (
-            safe_hasattr(self.args, "encoder_latent_layer")
-            and self.args.encoder_latent_layer
-        )
-
-    @property
-    def decoder_latent_layer(self):
-        return (
-            safe_hasattr(self.args, "decoder_latent_layer")
-            and self.args.decoder_latent_layer
-        )
-
-    @property
-    def src_lang_idx_dict(self):
-        return {lang: lang_idx for lang_idx, lang in enumerate(self.src_langs)}
-
-    @property
-    def tgt_lang_idx_dict(self):
-        return {lang: lang_idx for lang_idx, lang in enumerate(self.tgt_langs)}
diff --git a/spaces/OFA-Sys/OFA-vqa/data/mm_data/caption_dataset.py b/spaces/OFA-Sys/OFA-vqa/data/mm_data/caption_dataset.py
deleted file mode 100644
index 2109b19ec0958b5a84429b412d4f62052324147c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/data/mm_data/caption_dataset.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-from io import BytesIO
-
-import logging
-import warnings
-import string
-
-import numpy as np
-import torch
-import base64
-from torchvision import transforms
-
-from PIL import Image, ImageFile
-
-from data import data_utils
-from data.ofa_dataset import OFADataset
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-ImageFile.MAX_IMAGE_PIXELS = None
-Image.MAX_IMAGE_PIXELS = None
-
-logger = logging.getLogger(__name__)
-warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
-
-IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
-
-
-def collate(samples, pad_idx, eos_idx):
-    if len(samples) == 0:
-        return {}
-
-    def merge(key):
-        return data_utils.collate_tokens(
-            [s[key] for s in samples],
-            pad_idx,
-            eos_idx=eos_idx,
-        )
-
-    id = np.array([s["id"] for s in samples])
-    src_tokens = merge("source")
-    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
-
-    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
-    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
-
-    prev_output_tokens = None
-    target = None
-    if samples[0].get("target", None) is not None:
-        target = merge("target")
-        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
-        ntokens = tgt_lengths.sum().item()
-
-        if samples[0].get("prev_output_tokens", None) is not None:
-            prev_output_tokens = merge("prev_output_tokens")
-    else:
-        ntokens = src_lengths.sum().item()
-
-    batch = {
-        "id": id,
-        "nsentences": len(samples),
-        "ntokens": ntokens,
-        "net_input": {
-            "src_tokens": src_tokens,
-            "src_lengths": src_lengths,
-            "patch_images": patch_images,
-            "patch_masks": patch_masks,
-            "prev_output_tokens": prev_output_tokens
-        },
-        "target": target,
-    }
-
-    return batch
-
-
-class CaptionDataset(OFADataset):
-    def __init__(
-        self,
-        split,
-        dataset,
-        bpe,
-        src_dict,
-        tgt_dict=None,
-        max_src_length=128,
-        max_tgt_length=30,
-        patch_image_size=224,
-        imagenet_default_mean_and_std=False,
-        scst=False
-    ):
-        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
-        self.max_src_length = max_src_length
-        self.max_tgt_length = max_tgt_length
-        self.patch_image_size = patch_image_size
-        self.scst = scst
-
-        self.transtab = str.maketrans({key: None for key in string.punctuation})
-
-        if imagenet_default_mean_and_std:
-            mean = IMAGENET_DEFAULT_MEAN
-            std = IMAGENET_DEFAULT_STD
-        else:
-            mean = [0.5, 0.5, 0.5]
-            std = [0.5, 0.5, 0.5]
-
-        self.patch_resize_transform = transforms.Compose([
-            lambda image: image.convert("RGB"),
-            transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=mean, std=std),
-        ])
-
-    def __getitem__(self, index):
-        uniq_id, image, caption = self.dataset[index]
-
-        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
-        patch_image = self.patch_resize_transform(image)
-        patch_mask = torch.tensor([True])
-
-        if self.split == 'train' and not self.scst:
-            caption = caption.translate(self.transtab).strip()
-            caption_token_list = caption.strip().split()
-            tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
-        else:
-            caption = ' '.join(caption.strip().split())
-            caption_list = [cap.translate(self.transtab).strip() for cap in caption.strip().split('&&')]
-            tgt_caption = '&&'.join(caption_list)
-        src_item = self.encode_text(" what does the image describe?")
-        tgt_item = self.encode_text(" {}".format(tgt_caption))
-
-        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
-        target_item = torch.cat([tgt_item, self.eos_item])
-        prev_output_item = torch.cat([self.bos_item, tgt_item])
-
-        example = {
-            "id": uniq_id,
-            "source": src_item,
-            "patch_image": patch_image,
-            "patch_mask": patch_mask,
-            "target": target_item,
-            "prev_output_tokens": prev_output_item
-        }
-        return example
-
-    def collater(self, samples, pad_to_length=None):
-        """Merge a list of samples to form a mini-batch.
-        Args:
-            samples (List[dict]): samples to collate
-        Returns:
-            dict: a mini-batch with the following keys:
-        """
-        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
\ No newline at end of file
diff --git a/spaces/OedoSoldier/chatglm_int4_demo/app.py b/spaces/OedoSoldier/chatglm_int4_demo/app.py
deleted file mode 100644
index 5ee986c0975080640a76e4878f0136f12bd6cc16..0000000000000000000000000000000000000000
--- a/spaces/OedoSoldier/chatglm_int4_demo/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import json
-import datetime
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModel
-
-
-if torch.cuda.is_available():
-    device = 'cuda'
-else:
-    device = 'cpu'
-
-tokenizer = AutoTokenizer.from_pretrained('THUDM/chatglm-6b-int4', trust_remote_code=True)
-
-if device == 'cuda':
-    model = AutoModel.from_pretrained('THUDM/chatglm-6b-int4', trust_remote_code=True).half().cuda()
-else:
-    model = AutoModel.from_pretrained('THUDM/chatglm-6b-int4', trust_remote_code=True).float()
-
-model = model.eval()
-
-
-def parse_text(text):
-    lines = text.split('\n')
-    for i, line in enumerate(lines):
-        if '```' in line:
-            item = line.split('`')[-1]
-            if item:
-                lines[i] = f'<pre><code class="{item}">'
-            else:
-                lines[i] = '</code></pre>'
-        else:
-            if i > 0:
-                line = line.replace('<', '&lt;').replace('>', '&gt;')
-                lines[i] = f'<br/>{line}'
-    return ''.join(lines)
-
-
-def chat_wrapper(query, styled_history, history, max_length, top_p, temperature, memory_limit):
-    if query == '':
-        return [], [], '', *gr_hide()
-    if memory_limit == 0:
-        history = []
-        styled_history = []
-    elif memory_limit > 0:
-        history = history[-memory_limit:]
-        styled_history = styled_history[-memory_limit:]
-    flag = True
-    styled_history_pos = 0
-    for message, history in model.stream_chat(tokenizer, query, history=history,
-                                  max_length=max_length, top_p=top_p, temperature=temperature):
-        if flag:
-            styled_history.append((parse_text(query), parse_text(message)))
-            styled_history_pos = len(styled_history) - 1
-            flag = False
-        else:
-            styled_history[styled_history_pos] = (parse_text(query), parse_text(message))
-        yield styled_history, history, '', *gr_hide()
-
-
-def regenerate_wrapper(styled_history, history, max_length, top_p, temperature, memory_limit):
-    if not history:
-        return [], [], '', *gr_hide()
-
-    styled_history, history, query, _, _, _ = edit_wrapper(styled_history, history)
-    for ret in chat_wrapper(query, styled_history, history, max_length, top_p, temperature, memory_limit):
-        yield ret
-
-
-def edit_wrapper(styled_history, history):
-    if len(history) == 0:
-        return [], [], ''
-    query = history[-1][0]
-    history = history[:-1]
-    styled_history = styled_history[:-1]
-    return styled_history, history, query, *gr_hide()
-
-
-def reset_history():
-    return [], [], '', *gr_hide()
-
-
-def save_history(history):
-    os.makedirs('log', exist_ok=True)
-    dict_list = [{'input': q, 'output': a} for q, a in history]
-    with open(f'log/{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.json', 'w', encoding='utf-8') as f:
-        json.dump(dict_list, f, ensure_ascii=False, indent=2)
-
-
-def save_config(max_length=2048, top_p=0.7, temperature=0.95, memory_limit=-1.0):
-    with open('config.json', 'w') as f:
-        json.dump({'max_length': max_length, 'top_p': top_p, 'temperature': temperature, 'memory_limit': memory_limit}, f, indent=2)
-
-
-def load_history(file, styled_history, history):
-    current_styled_history, current_history = styled_history.copy(), history.copy()
-    try:
-        with open(file.name, 'r', encoding='utf-8') as f:
-            dict_list = json.load(f)
-        history = [(item['input'], item['output']) for item in dict_list]
-        styled_history = [(parse_text(item['input']), parse_text(item['output'])) for item in dict_list]
-    except BaseException:
-        return current_styled_history, current_history, ''
-    return styled_history, history, '', *gr_hide()
-
-
-def gr_show_and_load(history, evt: gr.SelectData):
-    if evt.index[1] == 0:
-        label = f'Editing Q{evt.index[0]}：'
-    else:
-        label = f'Editing Q{evt.index[0]}：'
-    return {'visible': True, '__type__': 'update'}, {'value': history[evt.index[0]][evt.index[1]], 'label': label, '__type__': 'update'}, evt.index
-
-
-def update_history(styled_history, history, log, idx):
-    if log == '':
-        return styled_history, history, {'visible': True, '__type__': 'update'},  {'value': history[idx[0]][idx[1]], '__type__': 'update'}, idx
-
-    def swap_value(lst, idx, value):
-        lst[idx[0]] = tuple(value if j == idx[1] else elem for j, elem in enumerate(lst[idx[0]]))
-        return lst
-    styled_history = swap_value(styled_history, idx, parse_text(log))
-    history = swap_value(history, idx, log)
-    return styled_history, history, *gr_hide()
-
-
-def gr_hide():
-    return {'visible': False, '__type__': 'update'}, {'value': '', 'label': '', '__type__': 'update'}, []
-
-
-with gr.Blocks() as demo:
-    if not os.path.isfile('config.json'):
-        save_config()
-
-    with open('config.json', 'r', encoding='utf-8') as f:
-        configs = json.loads(f.read())
-
-    gr.Markdown('''<h1><center>ChatGLM Demo</center></h1>''')
-
-    with gr.Row():
-        max_length = gr.Slider(minimum=4.0, maximum=4096.0, step=4.0, label='Max Length', value=configs['max_length'])
-        top_p = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, label='Top P', value=configs['top_p'])
-        temperature = gr.Slider(minimum=0.01, maximum=2.0, step=0.01, label='Temperature', value=configs['temperature'])
-        memory_limit = gr.Slider(minimum=-1.0, maximum=20.0, step=1.0, label='Memory Limit', value=configs['memory_limit'])
-    save_conf = gr.Button('保存设置', visible=False)
-
-    gr.Markdown('''<h2>Hint: click on a chat bubble to edit chat history</h2>''')
-
-    state = gr.State([])
-    chatbot = gr.Chatbot(elem_id='chatbot', show_label=False)
-    with gr.Row(visible=False) as edit_log:
-        with gr.Column():
-            log = gr.Textbox()
-            with gr.Row():
-                submit_log = gr.Button('保存')
-                cancel_log = gr.Button('取消')
-    log_idx = gr.State([])
-
-    message = gr.Textbox(placeholder='Input your message', label='Q:')
-
-    with gr.Row():
-        submit = gr.Button('Submit')
-        edit = gr.Button('Edit last question')
-        regen = gr.Button('Re-generate')
-
-    delete = gr.Button('Reset chat')
-
-    with gr.Row(visible=False):
-        save = gr.Button('保存对话（在 `log` 文件夹下）')
-        load = gr.UploadButton('读取对话', file_types=['file'], file_count='single')
-
-    input_list = [message, chatbot, state, max_length, top_p, temperature, memory_limit]
-    output_list = [chatbot, state, message]
-    edit_list = [edit_log, log, log_idx]
-
-    save_conf.click(save_config, inputs=input_list[3:])
-    load.upload(load_history, inputs=[load, chatbot, state], outputs=output_list + edit_list)
-    save.click(save_history, inputs=[state])
-    message.submit(chat_wrapper, inputs=input_list, outputs=output_list + edit_list)
-    submit.click(chat_wrapper, inputs=input_list, outputs=output_list + edit_list)
-    edit.click(edit_wrapper, inputs=input_list[1:3], outputs=output_list + edit_list)
-    regen.click(regenerate_wrapper, inputs=input_list[1:], outputs=output_list + edit_list)
-    delete.click(reset_history, outputs=output_list + edit_list)
-    chatbot.select(gr_show_and_load, inputs=[state], outputs=edit_list)
-    edit_kwargs = {'inputs': [chatbot, state, log, log_idx], 'outputs': [chatbot, state] + edit_list} 
-    log.submit(update_history, **edit_kwargs)
-    submit_log.click(update_history, **edit_kwargs)
-    cancel_log.click(gr_hide, outputs=edit_list)
-
-
-if __name__ == '__main__':
-    demo.queue(concurrency_count=5, max_size=20).launch(debug=True)
diff --git a/spaces/OkamiFeng/Bark-with-Voice-Cloning/util/__init__.py b/spaces/OkamiFeng/Bark-with-Voice-Cloning/util/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Omar7Hany/Conv_Kickstart/app.py b/spaces/Omar7Hany/Conv_Kickstart/app.py
deleted file mode 100644
index 4284460dd57807b6ac26b704a06a13851e91d45b..0000000000000000000000000000000000000000
--- a/spaces/Omar7Hany/Conv_Kickstart/app.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from fastai.tabular.all import *
-import gradio as gr
-from scipy import signal  #For model representation
-from time import sleep
-import matplotlib.pyplot as plt #For response plotting
-import math
-
-
-learn = load_learner('export.pkl')
-
-def predict(SystemParameters):
-    
-    #Obtained from system parameter identification of the plant (MATLAB)
-    gain = 0.051271          
-    timeConstant = 0.028806
-    
-    #Transfer Function
-    num = gain
-    den = [timeConstant, 1.0]
-    sys = signal.TransferFunction(num, den)
-    
-    #Model Discretization
-    dt = 5.0/1000.0   #5ms sampling time (determined on the STM PID code sampling)
-    sysDisc = sys.to_discrete(dt, method = 'gbt', alpha = 0.5) #Discretize by general bilinear transform
-    
-    #Discrete Constants for Iteration-based Simulation
-    b = sysDisc.num
-    a = -sysDisc.den
-
-    SimParams = SystemParameters.split(',')
-
-    setpoint = float(SimParams[0])   #in cm
-    kp = float(SimParams[1])
-    ki = float(SimParams[2])
-    kd = float(SimParams[3])
-    
-    SimulatedDuration = 10    #in seconds
-
-    
-    #Discrete model simulation variables
-    OldOut = 0
-    OldInput = 0
-    CurrentOut = 0
-    CurrentInput = 0
-    
-    #PID Constants
-    error = 0
-    errIntegAccum = 0
-    prevError = 0
-    kiTerm = 0
-    u = 0
-    windup = False
-    
-    #Integrator variable for position representation
-    CurrentOutInteg = 0
-    
-    #Output Storing
-    output = []     #Array to store position
-    time = []       #Array to store timestamp
-    
-    iterations = int(SimulatedDuration/0.005)
-    for  i in range(iterations):
-        
-        error = setpoint-CurrentOutInteg
-    
-        #Integral Windup Test
-        if (error > 0 and u > 0) or (error < 0 and u < 0):
-            if u == 255 or u == -255:
-                windup = True
-            else:
-                windup = False
-    
-        #PID Terms Calculation
-        kpTerm = kp*error
-    
-        if windup == False:
-            errIntegAccum+=((error+prevError)/2)*0.005
-            kiTerm = ki*errIntegAccum
-    
-        kdTerm = kd*((error-prevError)/0.005)
-    
-        u = kpTerm+kiTerm+kdTerm
-    
-        #Output Saturation
-        if u > 255: 
-            u = 255
-    
-        if u < -255:
-            u = -255
-    
-        #Deadzone simulation
-        if (u >= 0 and u < 10) or (u<=0 and u>-10):
-            u = 0
-        else:
-            u-=10
-        
-        CurrentInput = u
-        prevError = error
-        #Simulate discrete model each iteration using the constants extracted from the bilinear transfrom
-        CurrentOut = a[1]*OldOut + b[0]*CurrentInput + b[1]*OldInput  #Update current velocity state
-        CurrentOutInteg+=((CurrentOut+OldOut)/2)*0.005                #Numerically integrate velocity to obtain position
-        OldOut = CurrentOut
-        Oldnput = CurrentInput
-    
-        output.append(CurrentOutInteg)                        #Add response to array
-        time.append(i*0.005)                                  #Add corresponding timestamp to array
-
-    #Response States Extraction
-    SSerror = 0
-    MaxOS = 0
-    RelativeSetTimeError = 0
-
-    #Steady State Error
-    if abs(setpoint - output[iterations-1]) >= 0.1: #0.1 cm is the maximum allowed error
-        SSerror = setpoint-output[iterations-1]
-    else:
-        SSerror = 0
-
-    #Maximum Overshoot Percentage
-    if max(output)-setpoint > 0.1:
-        MaxOS = (100*(max(output)-setpoint))/setpoint
-    else:
-        MaxOS = 0
-
-    #Setpoint-relative Settling Time (%)
-    for i in range(len(time)):
-        if output[i] >= (output[iterations-1]*0.98):  #at 98% of steady state
-            RelativeSetTimeError = time[i]
-    
-    #Predict
-    directTest = {
-        'SSerror': SSerror,
-        'MaxOS%': MaxOS,
-        'SetTimeError': RelativeSetTimeError
-    }
-    
-    test = pd.DataFrame([directTest])
-    row, clas, probs = learn.predict(test.iloc[0])
-    return float(probs)
-
-gr.Interface(fn=predict, inputs="text", outputs="label").launch()
diff --git a/spaces/PBJ/Toxic-Comment-Classification/README.md b/spaces/PBJ/Toxic-Comment-Classification/README.md
deleted file mode 100644
index f09d6d40a2f66930195afad4d341452926e3417b..0000000000000000000000000000000000000000
--- a/spaces/PBJ/Toxic-Comment-Classification/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Toxic Comment Classification
-emoji: 🌖
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-github link for the project :- https://github.com/prabhanjan-jadhav/toxic-comment-classifier/blob/main/toxic-comment-classification.ipynb
diff --git a/spaces/PeepDaSlan9/AutoGPT/README.md b/spaces/PeepDaSlan9/AutoGPT/README.md
deleted file mode 100644
index 5bf09b995f04f7af05d1314906b1b1ff39c20ddc..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AutoGPT
-emoji: 🦾
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: ui/app.py
-pinned: false
-license: mit
-duplicated_from: aliabid94/AutoGPT
----
-
diff --git a/spaces/PeepDaSlan9/Bark-Voice-Cloning/bark/hubert/customtokenizer.py b/spaces/PeepDaSlan9/Bark-Voice-Cloning/bark/hubert/customtokenizer.py
deleted file mode 100644
index d0cbdbf30285c9b707aa5e11eb63dff0902bbb96..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/Bark-Voice-Cloning/bark/hubert/customtokenizer.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""
-Custom tokenizer model.
-Author: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-import json
-import os.path
-from zipfile import ZipFile
-
-import numpy
-import torch
-from torch import nn, optim
-from torch.serialization import MAP_LOCATION
-from tqdm.auto import tqdm
-
-
-class CustomTokenizer(nn.Module):
-    def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
-        super(CustomTokenizer, self).__init__()
-        next_size = input_size
-        if version == 0:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            next_size = hidden_size
-        if version == 1:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            self.intermediate = nn.Linear(hidden_size, 4096)
-            next_size = 4096
-
-        self.fc = nn.Linear(next_size, output_size)
-        self.softmax = nn.LogSoftmax(dim=1)
-        self.optimizer: optim.Optimizer = None
-        self.lossfunc = nn.CrossEntropyLoss()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    def forward(self, x):
-        x, _ = self.lstm(x)
-        if self.version == 1:
-            x = self.intermediate(x)
-        x = self.fc(x)
-        x = self.softmax(x)
-        return x
-
-    @torch.no_grad()
-    def get_token(self, x):
-        """
-        Used to get the token for the first
-        :param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model.
-        :return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model.
-        """
-        return torch.argmax(self(x), dim=1)
-
-    def prepare_training(self):
-        self.optimizer = optim.Adam(self.parameters(), 0.001)
-
-    def train_step(self, x_train, y_train, log_loss=False):
-        # y_train = y_train[:-1]
-        # y_train = y_train[1:]
-
-        optimizer = self.optimizer
-        lossfunc = self.lossfunc
-        # Zero the gradients
-        self.zero_grad()
-
-        # Forward pass
-        y_pred = self(x_train)
-
-        y_train_len = len(y_train)
-        y_pred_len = y_pred.shape[0]
-
-        if y_train_len > y_pred_len:
-            diff = y_train_len - y_pred_len
-            y_train = y_train[diff:]
-        elif y_train_len < y_pred_len:
-            diff = y_pred_len - y_train_len
-            y_pred = y_pred[:-diff, :]
-
-        y_train_hot = torch.zeros(len(y_train), self.output_size)
-        y_train_hot[range(len(y_train)), y_train] = 1
-        y_train_hot = y_train_hot.to('cuda')
-
-        # Calculate the loss
-        loss = lossfunc(y_pred, y_train_hot)
-
-        # Print loss
-        if log_loss:
-            print('Loss', loss.item())
-
-        # Backward pass
-        loss.backward()
-
-        # Update the weights
-        optimizer.step()
-
-    def save(self, path):
-        info_path = '.'.join(os.path.basename(path).split('.')[:-1]) + '/.info'
-        torch.save(self.state_dict(), path)
-        data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version)
-        with ZipFile(path, 'a') as model_zip:
-            model_zip.writestr(info_path, data_from_model.save())
-            model_zip.close()
-
-    @staticmethod
-    def load_from_checkpoint(path, map_location: MAP_LOCATION = None):
-        old = True
-        with ZipFile(path) as model_zip:
-            filesMatch = [file for file in model_zip.namelist() if file.endswith('/.info')]
-            file = filesMatch[0] if filesMatch else None
-            if file:
-                old = False
-                print(f"Loading Custom Hubert Tokenizer {path}")
-                data_from_model = Data.load(model_zip.read(file).decode('utf-8'))
-            model_zip.close()
-        if old:
-            model = CustomTokenizer()
-        else:
-            model = CustomTokenizer(data_from_model.hidden_size, data_from_model.input_size, data_from_model.output_size, data_from_model.version)
-        model.load_state_dict(torch.load(path))
-        if map_location:
-            model = model.to(map_location)
-        return model
-
-
-
-class Data:
-    input_size: int
-    hidden_size: int
-    output_size: int
-    version: int
-
-    def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0):
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    @staticmethod
-    def load(string):
-        data = json.loads(string)
-        return Data(data['input_size'], data['hidden_size'], data['output_size'], data['version'])
-
-    def save(self):
-        data = {
-            'input_size': self.input_size,
-            'hidden_size': self.hidden_size,
-            'output_size': self.output_size,
-            'version': self.version,
-        }
-        return json.dumps(data)
-
-
-def auto_train(data_path, save_path='model.pth', load_model: str | None = None, save_epochs=1, max_epochs=14):
-    data_x, data_y = [], []
-
-    if load_model and os.path.isfile(load_model):
-        print('Loading model from', load_model)
-        model_training = CustomTokenizer.load_from_checkpoint(load_model, 'cuda')
-    else:
-        print('Creating new model.')
-        model_training = CustomTokenizer(version=1).to('cuda')  # Settings for the model to run without lstm
-    save_path = os.path.join(data_path, save_path)
-    base_save_path = '.'.join(save_path.split('.')[:-1])
-
-    sem_string = '_semantic.npy'
-    feat_string = '_semantic_features.npy'
-
-    ready = os.path.join(data_path, 'ready')
-    for input_file in os.listdir(ready):
-        full_path = os.path.join(ready, input_file)
-        if input_file.endswith(sem_string):
-            data_y.append(numpy.load(full_path))
-        elif input_file.endswith(feat_string):
-            data_x.append(numpy.load(full_path))
-    model_training.prepare_training()
-
-    epoch = 1
-    with tqdm(total=((len(data_x) * len(data_y)) / 50) * save_epochs) as pbar1:
-        while epoch <= max_epochs:
-            for i in range(save_epochs):
-                j = 0
-                for x, y in zip(data_x, data_y):
-                    model_training.train_step(torch.tensor(x).to('cuda'), torch.tensor(y).to('cuda'), j % 50 == 0)  # Print loss every 50 steps
-                    j += 1
-                    pbar1.update()
-
-            save_p = save_path
-            save_p_2 = f'{base_save_path}_epoch_{epoch}.pth'
-            model_training.save(save_p)
-            model_training.save(save_p_2)
-            print(f'Epoch {epoch} completed')
-            epoch += 1
-        print(f'Done training for {max_epochs} epochs!')
\ No newline at end of file
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/uper_head.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/uper_head.py
deleted file mode 100644
index 9e1301b706b0d83ed714bbdee8ee24693f150455..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/decode_heads/uper_head.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-from .psp_head import PPM
-
-
-@HEADS.register_module()
-class UPerHead(BaseDecodeHead):
-    """Unified Perceptual Parsing for Scene Understanding.
-
-    This head is the implementation of `UPerNet
-    <https://arxiv.org/abs/1807.10221>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module applied on the last feature. Default: (1, 2, 3, 6).
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
-        super(UPerHead, self).__init__(
-            input_transform='multiple_select', **kwargs)
-        # PSP Module
-        self.psp_modules = PPM(
-            pool_scales,
-            self.in_channels[-1],
-            self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.bottleneck = ConvModule(
-            self.in_channels[-1] + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        # FPN Module
-        self.lateral_convs = nn.ModuleList()
-        self.fpn_convs = nn.ModuleList()
-        for in_channels in self.in_channels[:-1]:  # skip the top layer
-            l_conv = ConvModule(
-                in_channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            fpn_conv = ConvModule(
-                self.channels,
-                self.channels,
-                3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            self.lateral_convs.append(l_conv)
-            self.fpn_convs.append(fpn_conv)
-
-        self.fpn_bottleneck = ConvModule(
-            len(self.in_channels) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def psp_forward(self, inputs):
-        """Forward function of PSP module."""
-        x = inputs[-1]
-        psp_outs = [x]
-        psp_outs.extend(self.psp_modules(x))
-        psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
-
-        return output
-
-    def forward(self, inputs):
-        """Forward function."""
-
-        inputs = self._transform_inputs(inputs)
-
-        # build laterals
-        laterals = [
-            lateral_conv(inputs[i])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-
-        laterals.append(self.psp_forward(inputs))
-
-        # build top-down path
-        used_backbone_levels = len(laterals)
-        for i in range(used_backbone_levels - 1, 0, -1):
-            prev_shape = laterals[i - 1].shape[2:]
-            laterals[i - 1] += resize(
-                laterals[i],
-                size=prev_shape,
-                mode='bilinear',
-                align_corners=self.align_corners)
-
-        # build outputs
-        fpn_outs = [
-            self.fpn_convs[i](laterals[i])
-            for i in range(used_backbone_levels - 1)
-        ]
-        # append psp feature
-        fpn_outs.append(laterals[-1])
-
-        for i in range(used_backbone_levels - 1, 0, -1):
-            fpn_outs[i] = resize(
-                fpn_outs[i],
-                size=fpn_outs[0].shape[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-        fpn_outs = torch.cat(fpn_outs, dim=1)
-        output = self.fpn_bottleneck(fpn_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/Plurigrid/bidirectional/README.md b/spaces/Plurigrid/bidirectional/README.md
deleted file mode 100644
index 306dd2064bf5c6ef02ec1b4b97df9c00dfc87e04..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/bidirectional/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: 'FABRIC: Personalizing Diffusion Models with Iterative Feedback'
-emoji: 🎨
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: dvruette/fabric
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-Demo for Arxiv paper at https://arxiv.org/abs/2307.10159
\ No newline at end of file
diff --git a/spaces/Pranjal2041/GEO-bench/app.py b/spaces/Pranjal2041/GEO-bench/app.py
deleted file mode 100644
index 88b3abf690203932569adfeec9ec11a9c31105b8..0000000000000000000000000000000000000000
--- a/spaces/Pranjal2041/GEO-bench/app.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import gradio as gr
-import pandas as pd
-import os
-import itertools
-from constants import metric_dict, tags, columns
-
-# Download from github and load the data
-
-# TODO: Download every x hours
-def download_data(url = "https://github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/leaderboard.jsonl", path = "leaderboard.jsonl"):
-    ret_code = os.system(f'wget {url} -O {path}_tmp')
-    if ret_code != 0:
-        return ret_code
-    os.system(f'mv {path}_tmp {path}')
-    return 0
-
-def search_leaderboard(df, queries):
-    # Assuming DATA_OVERALL is the DataFrame containing the leaderboard data
-    # filtered_data = df[df["Method"].str.contains(query, case=False, na=False)]
-    temp_pds = []
-    for query in queries:
-        temp_pds.append(df[df["Method"].str.contains(query, case=False, na=False)])
-    return pd.concat(temp_pds).drop_duplicates()
- 
-def search_tags_leaderboard(df, tag_blocks, queries):
-    return search_leaderboard(filter_tags(df, tag_blocks), queries)
-
-def filter_tags(df, tag_blocks):
-    def fuzzy_in(x, y_set):
-        return any(x in z for z in y_set)
-    all_tags_sets = [set(tag.lower() for tag in tag_block) for tag_block in tag_blocks]
-
-    filtered_rows = [i for i, tags in enumerate(complete_dt['tags']) if all('any' in tag_set or any(fuzzy_in(tag.lower(), tag_set) for tag in tags) for tag_set in all_tags_sets)]
-
-    return prepare_complete_dt(df.iloc[filtered_rows])
-
-def prepare_complete_dt(complete_dt):
-    data = []
-    DATA_OVERALL = complete_dt.copy()
-    for Method in set(complete_dt['Method']):
-        data.append([])
-        data[-1].append(Method)
-        for metric in metric_dict:
-            metric_val = metric_dict[metric]
-            data[-1].append(complete_dt[complete_dt['Method'] == Method][metric_val].mean())
-        data[-1].append(complete_dt[complete_dt['Method'] == Method]['source'].iloc[0])
-        DATA_OVERALL = pd.DataFrame(data, columns=columns)
-    try:
-        DATA_OVERALL.sort_values(by=['WordPos Overall'], inplace=True, ascending=False)
-    except: ...
-    return DATA_OVERALL
-
-def format_df_for_leaderboard(df):
-    # The source column needs to be embedded directly into the Method column using appropriate markdown.
-    df['Method'] = df[['source', 'Method']].apply(lambda x: f'<a target="_blank" style="text-decoration: underline; color: #3571d7;" href="{x[0]}">{x[1]}</a>', axis=1)
-    # Convert all float metrics to 1 decimal
-    df_copy = df.copy()
-    for metric in metric_dict:
-        df_copy[metric] = df_copy[metric].apply(lambda x: float(f'{(100*x):.1f}'))
-    # drop the source column
-    return df_copy.drop(columns=['source'])
-
-
-ret_code = 0
-# ret_code = download_data()
-if ret_code != 0:
-    print("Leaderboard Download failed")
-
-complete_dt = pd.read_json('leaderboard.jsonl', lines=True, orient='records')
-DATA_OVERALL = prepare_complete_dt(complete_dt)
-
-
-with gr.Blocks() as demo:
-    
-    demo_content = """
-<style>
-  .badge-container {
-    text-align: center;
-    display: flex;
-    justify-content: center;
-  }
-  .badge {
-    margin: 1px;
-  }
-</style>
-<h1 style="text-align: center;">GEO-Bench Leaderboard</h1>
-<div class="badge-container">
-    <a href="https://pranjal2041.github.io/GEO/" class="badge">
-        <img src="https://img.shields.io/website?down_message=down&style=for-the-badge&up_message=up&url=https%3A%2F%2Fpranjal2041.github.io/GEO/" alt="Website">
-    </a>
-    <a href="https://arxiv.org/abs/2310.18xxx" class="badge">
-        <img src="https://img.shields.io/badge/arXiv-2310.18xxx-red.svg?style=for-the-badge" alt="Arxiv Paper">
-    </a>
-    <a href="https://huggingface.co/datasets/Pranjal2041/geo-bench" class="badge">
-        <img src="https://img.shields.io/badge/Dataset-GEO-%2DBENCH-orange?style=for-the-badge" alt="Dataset">
-    </a>
-    <a href="https://github.com/Pranjal2041/GEO" class="badge">
-        <img src="https://img.shields.io/badge/Github-Code-green?style=for-the-badge" alt="Code">
-    </a>
-</div>
-<p>
-    - For benchmarking content optimization Methods for Generative Engines.<br>
-    - GEO-Bench evaluates Methods for optimizing website content to improve visibility in generative engine responses. Benchmark contains 10K queries across 9 datasets covering diverse domains and intents.<br>
-    - Refer to GEO paper for more <a href="https://arxiv.org/abs/2310.18xxx">details</a>
-</p>
-"""
-
-
-    gr.HTML(demo_content)
-
-
-
-
-    with gr.Tabs():
-
-        with gr.TabItem('Overall 📊'):
-
-            with gr.Row():
-                gr.Markdown('## Overall Leaderboard')
-            
-            with gr.Row():
-                data_overall = gr.components.Dataframe(
-                    format_df_for_leaderboard(DATA_OVERALL),
-                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
-                    type="pandas",
-                    wrap=True,
-                    interactive=False,
-                )
-                # data_overall.
-        
-            with gr.Row():
-                # search_bar = gr.Textbox(type="text", label="Search for a Method:")
-                search_bar = gr.Textbox(
-                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
-                    show_label=False,
-                    elem_id="search-bar",
-                )
-
-                def search_button_click(query):
-                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
-                    return format_df_for_leaderboard(filtered_data)
-                
-        with gr.TabItem('Tag-Wise Results 📊'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## Tag-Wise Results
-                - The following table shows the results for each tag.
-                - The tags are sorted in the order of their performance.
-                - The table is sorted in the order of the overall score.
-                """)
-            with gr.Row():
-
-                search_bar_tag = gr.Textbox(
-                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
-                    show_label=False,
-                    elem_id="search-bar",
-                )
-
-                def search_button_click(query):
-                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
-                    return format_df_for_leaderboard(filtered_data)
-
-            with gr.Row():
-                boxes = dict()
-                with gr.Column(min_width=320):
-                    for tag in list(tags.keys())[:3]:
-                        with gr.Box(elem_id="box-filter"):
-                            boxes[tag] = gr.CheckboxGroup(
-                                label=tag,
-                                choices=tags[tag],
-                                value=tags[tag],
-                                interactive=True,
-                                elem_id=f"filter-{tag}",
-                            )
-                with gr.Column(min_width=320):
-                    for tag in list(tags.keys())[4:]:
-                        with gr.Box(elem_id="box-filter"):
-                            boxes[tag] = gr.CheckboxGroup(
-                                label=tag,
-                                choices=tags[tag],
-                                value=tags[tag],
-                                interactive=True,
-                                elem_id=f"filter-{tag}",
-                            )
-            with gr.Row():
-                tag = list(tags.keys())[3]
-                with gr.Box(elem_id="box-filter"):
-                    boxes[tag] = gr.CheckboxGroup(
-                        label=tag,
-                        choices=tags[tag],
-                        value=tags[tag],
-                        interactive=True,
-                        elem_id=f"filter-{tag}",
-                    )
-            with gr.Row():
-                data_tag_wise = gr.components.Dataframe(
-                    format_df_for_leaderboard(DATA_OVERALL),
-                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
-                    type="pandas",
-                    wrap=True,
-                    interactive=False,
-                )
-            def filter_tag_click(*boxes):
-                return format_df_for_leaderboard(filter_tags(complete_dt, list(boxes)))
-            def search_tag_click(query, *boxes):
-                return format_df_for_leaderboard(search_tags_leaderboard(complete_dt, list(boxes), [x.strip() for x in query.split(',')]))
-            for box in boxes:
-                boxes[box].change(fn=filter_tag_click, inputs=list(boxes.values()), outputs=data_tag_wise)
-                search_bar_tag.submit(fn=search_tag_click, inputs=[search_bar_tag] + list(boxes.values()), outputs=data_tag_wise)
-
-        with gr.TabItem('About GEO-bench 📖'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## About GEO-bench
-                - GEO-bench is a benchmarking platform for content optimization Methods for generative engines.
-                - It is a part of the work released under [GEO](https://arxiv.org/abs/2310.18xxx)
-                - The benchmark comprises of 9 datasets, 7 of which were publicly available, while 2 have been released by us.
-                - Dataset can be downloaded from [here](huggingface.co/datasets/pranjal2041/geo-bench)""")
-
-            with gr.Row():
-
-                # Goal of benchmarking content optimization for generative engines
-                # Contains 10K carefully curated queries
-                # Queries are diverse and cover many domains/intents
-                # Annotated with tags/dimensions like domain, difficulty, etc.
-                # Above list in HTML format
-                gr.HTML(f"""
-                <h3>Key-Highlights of GEO-bench</h3>
-                <ul>
-                    <li>Goal of benchmarking content optimization for generative engines</li>
-                    <li>Contains 10K carefully curated queries</li>
-                    <li>Queries are diverse and cover many domains/intents</li>
-                    <li>Annotated with tags/dimensions like domain, difficulty, etc.</li>
-                </ul>
-                """)
-
-                # Benchmark Link:
-                # gr.Markdown(f"""### Benchmark Link: [GEO-bench](huggingface.co/datasets/pranjal2041/geo-bench)""")
-
-                # Info about tags and other statistics
-                            
-
-        with gr.TabItem('Submit 📝'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## Submit
-                - To submit your Method, please check [here](github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/Readme.md)""")
-
-
-                # Create a form to submit, the response should be sent to a google form
-                            
-        search_bar.submit(fn=search_button_click, inputs=search_bar, outputs=data_overall)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/model.py b/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 533e589a2024f1d7c52093d8c472c3b1b6617e26..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class LinAttnBlock(LinearAttention):
-    """to match AttnBlock usage"""
-    def __init__(self, in_channels):
-        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
-    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        return AttnBlock(in_channels)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
-
-class FirstStagePostProcessor(nn.Module):
-
-    def __init__(self, ch_mult:list, in_channels,
-                 pretrained_model:nn.Module=None,
-                 reshape=False,
-                 n_channels=None,
-                 dropout=0.,
-                 pretrained_config=None):
-        super().__init__()
-        if pretrained_config is None:
-            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.pretrained_model = pretrained_model
-        else:
-            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.instantiate_pretrained(pretrained_config)
-
-        self.do_reshape = reshape
-
-        if n_channels is None:
-            n_channels = self.pretrained_model.encoder.ch
-
-        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
-        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
-                            stride=1,padding=1)
-
-        blocks = []
-        downs = []
-        ch_in = n_channels
-        for m in ch_mult:
-            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
-            ch_in = m * n_channels
-            downs.append(Downsample(ch_in, with_conv=False))
-
-        self.model = nn.ModuleList(blocks)
-        self.downsampler = nn.ModuleList(downs)
-
-
-    def instantiate_pretrained(self, config):
-        model = instantiate_from_config(config)
-        self.pretrained_model = model.eval()
-        # self.pretrained_model.train = False
-        for param in self.pretrained_model.parameters():
-            param.requires_grad = False
-
-
-    @torch.no_grad()
-    def encode_with_pretrained(self,x):
-        c = self.pretrained_model.encode(x)
-        if isinstance(c, DiagonalGaussianDistribution):
-            c = c.mode()
-        return  c
-
-    def forward(self,x):
-        z_fs = self.encode_with_pretrained(x)
-        z = self.proj_norm(z_fs)
-        z = self.proj(z)
-        z = nonlinearity(z)
-
-        for submodel, downmodel in zip(self.model,self.downsampler):
-            z = submodel(z,temb=None)
-            z = downmodel(z)
-
-        if self.do_reshape:
-            z = rearrange(z,'b c h w -> b (h w) c')
-        return z
-
diff --git a/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles/README.md b/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles/README.md
deleted file mode 100644
index 5214d6e21d7fe997684fd81195e01c4f4f8fd84f..0000000000000000000000000000000000000000
--- a/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Whisper Youtube Crosslingual Subtitles
-emoji: 🦀
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-tags:
-  - whisper-event
-  - speech-recognition
----
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/infer/lib/train/process_ckpt.py b/spaces/RMXK/RVC_HFF/infer/lib/train/process_ckpt.py
deleted file mode 100644
index 36d359d5f853452da4e1a696a84b8457b8386c29..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/lib/train/process_ckpt.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import os
-import sys
-import traceback
-from collections import OrderedDict
-
-import torch
-
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-
-
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
-    try:
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        opt["config"] = [
-            hps.data.filter_length // 2 + 1,
-            32,
-            hps.model.inter_channels,
-            hps.model.hidden_channels,
-            hps.model.filter_channels,
-            hps.model.n_heads,
-            hps.model.n_layers,
-            hps.model.kernel_size,
-            hps.model.p_dropout,
-            hps.model.resblock,
-            hps.model.resblock_kernel_sizes,
-            hps.model.resblock_dilation_sizes,
-            hps.model.upsample_rates,
-            hps.model.upsample_initial_channel,
-            hps.model.upsample_kernel_sizes,
-            hps.model.spk_embed_dim,
-            hps.model.gin_channels,
-            hps.data.sampling_rate,
-        ]
-        opt["info"] = "%sepoch" % epoch
-        opt["sr"] = sr
-        opt["f0"] = if_f0
-        opt["version"] = version
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-
-
-def extract_small_model(path, name, sr, if_f0, info, version):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        if "model" in ckpt:
-            ckpt = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        if sr == "40k":
-            opt["config"] = [
-                1025,
-                32,
-                192,
-                192,
-                768,
-                2,
-                6,
-                3,
-                0,
-                "1",
-                [3, 7, 11],
-                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                [10, 10, 2, 2],
-                512,
-                [16, 16, 4, 4],
-                109,
-                256,
-                40000,
-            ]
-        elif sr == "48k":
-            if version == "v1":
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 6, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-            else:
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [12, 10, 2, 2],
-                    512,
-                    [24, 20, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-        elif sr == "32k":
-            if version == "v1":
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 4, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-            else:
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 8, 2, 2],
-                    512,
-                    [20, 16, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-        if info == "":
-            info = "Extracted model."
-        opt["info"] = info
-        opt["version"] = version
-        opt["sr"] = sr
-        opt["f0"] = int(if_f0)
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def change_info(path, info, name):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        ckpt["info"] = info
-        if name == "":
-            name = os.path.basename(path)
-        torch.save(ckpt, "weights/%s" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
-    try:
-
-        def extract(ckpt):
-            a = ckpt["model"]
-            opt = OrderedDict()
-            opt["weight"] = {}
-            for key in a.keys():
-                if "enc_q" in key:
-                    continue
-                opt["weight"][key] = a[key]
-            return opt
-
-        ckpt1 = torch.load(path1, map_location="cpu")
-        ckpt2 = torch.load(path2, map_location="cpu")
-        cfg = ckpt1["config"]
-        if "model" in ckpt1:
-            ckpt1 = extract(ckpt1)
-        else:
-            ckpt1 = ckpt1["weight"]
-        if "model" in ckpt2:
-            ckpt2 = extract(ckpt2)
-        else:
-            ckpt2 = ckpt2["weight"]
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
-            return "Fail to merge the models. The model architectures are not the same."
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt1.keys():
-            # try:
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key][:min_shape0].float())
-                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
-                ).half()
-            else:
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
-                ).half()
-        # except:
-        #     pdb.set_trace()
-        opt["config"] = cfg
-        """
-        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
-        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
-        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
-        """
-        opt["sr"] = sr
-        opt["f0"] = 1 if f0 == i18n("是") else 0
-        opt["version"] = version
-        opt["info"] = info
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
diff --git a/spaces/RMXK/RVC_HFF/lib/uvr5_pack/lib_v5/nets_61968KB.py b/spaces/RMXK/RVC_HFF/lib/uvr5_pack/lib_v5/nets_61968KB.py
deleted file mode 100644
index becbfae85683a13bbb19d3ea6c840da24e61e01e..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/lib/uvr5_pack/lib_v5/nets_61968KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py
deleted file mode 100644
index 5948570178f3e6e79d1ff574241d09d4d8ed78de..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""Filetype information.
-"""
-
-from typing import Tuple
-
-from pip._internal.utils.misc import splitext
-
-WHEEL_EXTENSION = ".whl"
-BZ2_EXTENSIONS: Tuple[str, ...] = (".tar.bz2", ".tbz")
-XZ_EXTENSIONS: Tuple[str, ...] = (
-    ".tar.xz",
-    ".txz",
-    ".tlz",
-    ".tar.lz",
-    ".tar.lzma",
-)
-ZIP_EXTENSIONS: Tuple[str, ...] = (".zip", WHEEL_EXTENSION)
-TAR_EXTENSIONS: Tuple[str, ...] = (".tar.gz", ".tgz", ".tar")
-ARCHIVE_EXTENSIONS = ZIP_EXTENSIONS + BZ2_EXTENSIONS + TAR_EXTENSIONS + XZ_EXTENSIONS
-
-
-def is_archive_file(name: str) -> bool:
-    """Return True if `name` is a considered as an archive file."""
-    ext = splitext(name)[1].lower()
-    if ext in ARCHIVE_EXTENSIONS:
-        return True
-    return False
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/before.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/before.py
deleted file mode 100644
index a72c2c5f70eafdf0229332ccf3c1284b2955ea56..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/before.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    import logging
-
-    from pip._vendor.tenacity import RetryCallState
-
-
-def before_nothing(retry_state: "RetryCallState") -> None:
-    """Before call strategy that does nothing."""
-
-
-def before_log(logger: "logging.Logger", log_level: int) -> typing.Callable[["RetryCallState"], None]:
-    """Before call strategy that logs to some logger the attempt."""
-
-    def log_it(retry_state: "RetryCallState") -> None:
-        logger.log(
-            log_level,
-            f"Starting call to '{_utils.get_callback_name(retry_state.fn)}', "
-            f"this is the {_utils.to_ordinal(retry_state.attempt_number)} time calling it.",
-        )
-
-    return log_it
diff --git a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/convert_to_png.sh b/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/convert_to_png.sh
deleted file mode 100644
index 5b82fff606b4ef60bad32cfef463a601cbfd4586..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/convert_to_png.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-# DELF Extraction script doesn't support .ppm images.
-current_dir=`pwd`
-echo $current_dir
-for dir in `ls hpatches-sequences-release`; do
-    echo $dir
-    cd hpatches-sequences-release/$dir
-    mogrify -format png *.ppm
-    cd $current_dir
-done
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/ocrnet_r50-d8.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/ocrnet_r50-d8.py
deleted file mode 100644
index 615aa3ff703942b6c22b2d6e9642504dd3e41ebd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/ocrnet_r50-d8.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='CascadeEncoderDecoder',
-    num_stages=2,
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=[
-        dict(
-            type='FCNHead',
-            in_channels=1024,
-            in_index=2,
-            channels=256,
-            num_convs=1,
-            concat_input=False,
-            dropout_ratio=0.1,
-            num_classes=19,
-            norm_cfg=norm_cfg,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-        dict(
-            type='OCRHead',
-            in_channels=2048,
-            in_index=3,
-            channels=512,
-            ocr_channels=256,
-            dropout_ratio=0.1,
-            num_classes=19,
-            norm_cfg=norm_cfg,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
-    ],
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/progressbar.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/progressbar.py
deleted file mode 100644
index 0062f670dd94fa9da559ab26ef85517dcf5211c7..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/progressbar.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import sys
-from collections.abc import Iterable
-from multiprocessing import Pool
-from shutil import get_terminal_size
-
-from .timer import Timer
-
-
-class ProgressBar:
-    """A progress bar which can print the progress."""
-
-    def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):
-        self.task_num = task_num
-        self.bar_width = bar_width
-        self.completed = 0
-        self.file = file
-        if start:
-            self.start()
-
-    @property
-    def terminal_width(self):
-        width, _ = get_terminal_size()
-        return width
-
-    def start(self):
-        if self.task_num > 0:
-            self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, '
-                            'elapsed: 0s, ETA:')
-        else:
-            self.file.write('completed: 0, elapsed: 0s')
-        self.file.flush()
-        self.timer = Timer()
-
-    def update(self, num_tasks=1):
-        assert num_tasks > 0
-        self.completed += num_tasks
-        elapsed = self.timer.since_start()
-        if elapsed > 0:
-            fps = self.completed / elapsed
-        else:
-            fps = float('inf')
-        if self.task_num > 0:
-            percentage = self.completed / float(self.task_num)
-            eta = int(elapsed * (1 - percentage) / percentage + 0.5)
-            msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \
-                  f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \
-                  f'ETA: {eta:5}s'
-
-            bar_width = min(self.bar_width,
-                            int(self.terminal_width - len(msg)) + 2,
-                            int(self.terminal_width * 0.6))
-            bar_width = max(2, bar_width)
-            mark_width = int(bar_width * percentage)
-            bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width)
-            self.file.write(msg.format(bar_chars))
-        else:
-            self.file.write(
-                f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,'
-                f' {fps:.1f} tasks/s')
-        self.file.flush()
-
-
-def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs):
-    """Track the progress of tasks execution with a progress bar.
-
-    Tasks are done with a simple for-loop.
-
-    Args:
-        func (callable): The function to be applied to each task.
-        tasks (list or tuple[Iterable, int]): A list of tasks or
-            (tasks, total num).
-        bar_width (int): Width of progress bar.
-
-    Returns:
-        list: The task results.
-    """
-    if isinstance(tasks, tuple):
-        assert len(tasks) == 2
-        assert isinstance(tasks[0], Iterable)
-        assert isinstance(tasks[1], int)
-        task_num = tasks[1]
-        tasks = tasks[0]
-    elif isinstance(tasks, Iterable):
-        task_num = len(tasks)
-    else:
-        raise TypeError(
-            '"tasks" must be an iterable object or a (iterator, int) tuple')
-    prog_bar = ProgressBar(task_num, bar_width, file=file)
-    results = []
-    for task in tasks:
-        results.append(func(task, **kwargs))
-        prog_bar.update()
-    prog_bar.file.write('\n')
-    return results
-
-
-def init_pool(process_num, initializer=None, initargs=None):
-    if initializer is None:
-        return Pool(process_num)
-    elif initargs is None:
-        return Pool(process_num, initializer)
-    else:
-        if not isinstance(initargs, tuple):
-            raise TypeError('"initargs" must be a tuple')
-        return Pool(process_num, initializer, initargs)
-
-
-def track_parallel_progress(func,
-                            tasks,
-                            nproc,
-                            initializer=None,
-                            initargs=None,
-                            bar_width=50,
-                            chunksize=1,
-                            skip_first=False,
-                            keep_order=True,
-                            file=sys.stdout):
-    """Track the progress of parallel task execution with a progress bar.
-
-    The built-in :mod:`multiprocessing` module is used for process pools and
-    tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`.
-
-    Args:
-        func (callable): The function to be applied to each task.
-        tasks (list or tuple[Iterable, int]): A list of tasks or
-            (tasks, total num).
-        nproc (int): Process (worker) number.
-        initializer (None or callable): Refer to :class:`multiprocessing.Pool`
-            for details.
-        initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for
-            details.
-        chunksize (int): Refer to :class:`multiprocessing.Pool` for details.
-        bar_width (int): Width of progress bar.
-        skip_first (bool): Whether to skip the first sample for each worker
-            when estimating fps, since the initialization step may takes
-            longer.
-        keep_order (bool): If True, :func:`Pool.imap` is used, otherwise
-            :func:`Pool.imap_unordered` is used.
-
-    Returns:
-        list: The task results.
-    """
-    if isinstance(tasks, tuple):
-        assert len(tasks) == 2
-        assert isinstance(tasks[0], Iterable)
-        assert isinstance(tasks[1], int)
-        task_num = tasks[1]
-        tasks = tasks[0]
-    elif isinstance(tasks, Iterable):
-        task_num = len(tasks)
-    else:
-        raise TypeError(
-            '"tasks" must be an iterable object or a (iterator, int) tuple')
-    pool = init_pool(nproc, initializer, initargs)
-    start = not skip_first
-    task_num -= nproc * chunksize * int(skip_first)
-    prog_bar = ProgressBar(task_num, bar_width, start, file=file)
-    results = []
-    if keep_order:
-        gen = pool.imap(func, tasks, chunksize)
-    else:
-        gen = pool.imap_unordered(func, tasks, chunksize)
-    for result in gen:
-        results.append(result)
-        if skip_first:
-            if len(results) < nproc * chunksize:
-                continue
-            elif len(results) == nproc * chunksize:
-                prog_bar.start()
-                continue
-        prog_bar.update()
-    prog_bar.file.write('\n')
-    pool.close()
-    pool.join()
-    return results
-
-
-def track_iter_progress(tasks, bar_width=50, file=sys.stdout):
-    """Track the progress of tasks iteration or enumeration with a progress
-    bar.
-
-    Tasks are yielded with a simple for-loop.
-
-    Args:
-        tasks (list or tuple[Iterable, int]): A list of tasks or
-            (tasks, total num).
-        bar_width (int): Width of progress bar.
-
-    Yields:
-        list: The task results.
-    """
-    if isinstance(tasks, tuple):
-        assert len(tasks) == 2
-        assert isinstance(tasks[0], Iterable)
-        assert isinstance(tasks[1], int)
-        task_num = tasks[1]
-        tasks = tasks[0]
-    elif isinstance(tasks, Iterable):
-        task_num = len(tasks)
-    else:
-        raise TypeError(
-            '"tasks" must be an iterable object or a (iterator, int) tuple')
-    prog_bar = ProgressBar(task_num, bar_width, file=file)
-    for task in tasks:
-        yield task
-        prog_bar.update()
-    prog_bar.file.write('\n')
diff --git a/spaces/RomanCast/inspect_mlm/app.py b/spaces/RomanCast/inspect_mlm/app.py
deleted file mode 100644
index a3fa1a0a7f3f5605a3d7b63d23778e815562b83e..0000000000000000000000000000000000000000
--- a/spaces/RomanCast/inspect_mlm/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import datasets
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling
-
-
-ds = datasets.load_dataset("nthngdy/oscar-small", "unshuffled_deduplicated_en", streaming=True, split="train")
-ds = ds.shuffle(buffer_size=1000)
-ds = iter(ds)
-
-model_name = "RomanCast/roberta-en-100k"
-
-model = AutoModelForMaskedLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-collate_fn = DataCollatorForLanguageModeling(tokenizer)
-
-
-with gr.Blocks() as demo:
-    inputs_oscar = gr.TextArea(
-        placeholder="Type a sentence or click the button below to get a random sentence from the English OSCAR corpus",
-        label="Input",
-        num_lines=6,
-        interactive=True,
-    )
-    next_button = gr.Button("Random OSCAR sentence")
-    next_button.click(fn=lambda: next(ds)["text"], outputs=inputs_oscar)
-
-    masked_text = gr.Textbox(label="Masked sentence")
-
-    labels_and_outputs = []
-    with gr.Row():
-        for _ in range(4):
-            with gr.Column():
-                labels_and_outputs.append(gr.Textbox(label="Label"))
-                labels_and_outputs.append(gr.Label(num_top_classes=5, show_label=False))
-    with gr.Row():
-        for _ in range(4):
-            with gr.Column():
-                labels_and_outputs.append(gr.Textbox(label="Label"))
-                labels_and_outputs.append(gr.Label(num_top_classes=5, show_label=False))
-
-    def model_inputs_and_outputs(example):
-        token_ids = tokenizer(example, return_tensors="pt", truncation=True, max_length=128)
-        model_inputs = collate_fn((token_ids,))
-        model_inputs = {k: v[0] for k, v in model_inputs.items()}
-        masked_tokens = tokenizer.batch_decode(model_inputs["input_ids"])[0]
-
-        original_labels = [tokenizer.convert_ids_to_tokens([id])[0] for id in model_inputs["labels"][0] if id != -100]
-
-        out = model(**model_inputs)
-        all_logits = out.logits[model_inputs["labels"] != -100].softmax(-1)
-        all_outputs = [
-            {tokenizer.convert_ids_to_tokens([id])[0]: val.item() for id, val in enumerate(logits)}
-            for logits in all_logits
-        ]
-        out_dict = {masked_text: masked_tokens}
-        for i in range(len(labels_and_outputs) // 2):
-            try:
-                out_dict[labels_and_outputs[2 * i]] = original_labels[i]
-                out_dict[labels_and_outputs[2 * i + 1]] = all_outputs[i]
-            except:
-                out_dict[labels_and_outputs[2 * i]] = ""
-                out_dict[labels_and_outputs[2 * i + 1]] = {}
-        return out_dict
-
-    button = gr.Button("Predict tokens")
-    button.click(fn=model_inputs_and_outputs, inputs=inputs_oscar, outputs=[masked_text] + labels_and_outputs)
-
-
-demo.launch()
diff --git a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/conv2d_gradfix.py b/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index e95e10d0b1d0315a63a76446fd4c5c293c8bbc6d..0000000000000000000000000000000000000000
--- a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-            else: # transpose
-                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------
diff --git a/spaces/SIGGRAPH2022/Text2Human/README.md b/spaces/SIGGRAPH2022/Text2Human/README.md
deleted file mode 100644
index 4022ed94445b9b36774b5fd7875bf4a592bb835c..0000000000000000000000000000000000000000
--- a/spaces/SIGGRAPH2022/Text2Human/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text2Human
-emoji: 🏃
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/SIGGRAPH2022/Text2Human/Text2Human/sample_from_parsing.py b/spaces/SIGGRAPH2022/Text2Human/Text2Human/sample_from_parsing.py
deleted file mode 100644
index 954f389e7e3b320c763e755400ea5fd6aaf8736d..0000000000000000000000000000000000000000
--- a/spaces/SIGGRAPH2022/Text2Human/Text2Human/sample_from_parsing.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import argparse
-import logging
-import os.path as osp
-import random
-
-import torch
-
-from data.segm_attr_dataset import DeepFashionAttrSegmDataset
-from models import create_model
-from utils.logger import get_root_logger
-from utils.options import dict2str, dict_to_nonedict, parse
-from utils.util import make_exp_dirs, set_random_seed
-
-
-def main():
-    # options
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.')
-    args = parser.parse_args()
-    opt = parse(args.opt, is_train=False)
-
-    # mkdir and loggers
-    make_exp_dirs(opt)
-    log_file = osp.join(opt['path']['log'], f"test_{opt['name']}.log")
-    logger = get_root_logger(
-        logger_name='base', log_level=logging.INFO, log_file=log_file)
-    logger.info(dict2str(opt))
-
-    # convert to NoneDict, which returns None for missing keys
-    opt = dict_to_nonedict(opt)
-
-    # random seed
-    seed = opt['manual_seed']
-    if seed is None:
-        seed = random.randint(1, 10000)
-    logger.info(f'Random seed: {seed}')
-    set_random_seed(seed)
-
-    test_dataset = DeepFashionAttrSegmDataset(
-        img_dir=opt['test_img_dir'],
-        segm_dir=opt['segm_dir'],
-        pose_dir=opt['pose_dir'],
-        ann_dir=opt['test_ann_file'])
-    test_loader = torch.utils.data.DataLoader(
-        dataset=test_dataset, batch_size=4, shuffle=False)
-    logger.info(f'Number of test set: {len(test_dataset)}.')
-
-    model = create_model(opt)
-    _ = model.inference(test_loader, opt['path']['results_root'])
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/cleaners.py b/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/cleaners.py
deleted file mode 100644
index c80e113b2b81a66134800dbdaa29c7d96a0152a7..0000000000000000000000000000000000000000
--- a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/cleaners.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import re
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    from text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from text.mandarin import chinese_to_ipa
-    from text.japanese import japanese_to_ipa2
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from text.thai import num_to_thai, latin_to_thai
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from text.shanghainese import shanghainese_to_ipa
-    text = shanghainese_to_ipa(text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from text.mandarin import chinese_to_ipa2
-    from text.japanese import japanese_to_ipa3
-    from text.shanghainese import shanghainese_to_ipa
-    from text.cantonese import cantonese_to_ipa
-    from text.english import english_to_lazy_ipa2
-    from text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                  '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/Sai004/ArticleAPI/README.md b/spaces/Sai004/ArticleAPI/README.md
deleted file mode 100644
index 296c753022cbb34cba021fe71790adbcbd5ce46f..0000000000000000000000000000000000000000
--- a/spaces/Sai004/ArticleAPI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ArticleAPI
-emoji: 📉
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sanjar/kun_uz_test/README.md b/spaces/Sanjar/kun_uz_test/README.md
deleted file mode 100644
index 685d63e12e19be2c27503b9c527158efb69b6950..0000000000000000000000000000000000000000
--- a/spaces/Sanjar/kun_uz_test/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Kun Uz Test
-emoji: 💩
-colorFrom: gray
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py
deleted file mode 100644
index 1be60a1ad9f108fa83bc96f0341bc9c71baf4e76..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# -----------------------------------------------------
-# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
-# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
-# -----------------------------------------------------
-
-import torch.nn as nn
-from models.layers.SE_module import SELayer
-import torch.nn.functional as F
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes, momentum=0.1)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes, momentum=0.1)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * 4, momentum=0.1)
-        if reduction:
-            self.se = SELayer(planes * 4)
-
-        self.reduc = reduction
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
-        out = F.relu(self.bn2(self.conv2(out)), inplace=True)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-        if self.reduc:
-            out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = F.relu(out)
-
-        return out
-
-
-class SEResnet(nn.Module):
-    """ SEResnet """
-
-    def __init__(self, architecture):
-        super(SEResnet, self).__init__()
-        assert architecture in ["resnet50", "resnet101"]
-        self.inplanes = 64
-        self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
-        self.block = Bottleneck
-
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
-                               stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.1, affine=True)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.layer1 = self.make_layer(self.block, 64, self.layers[0])
-        self.layer2 = self.make_layer(
-            self.block, 128, self.layers[1], stride=2)
-        self.layer3 = self.make_layer(
-            self.block, 256, self.layers[2], stride=2)
-
-        self.layer4 = self.make_layer(
-            self.block, 512, self.layers[3], stride=2)
-
-    def forward(self, x):
-        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
-        x = self.layer1(x)  # 256 * h/4 * w/4
-        x = self.layer2(x)  # 512 * h/8 * w/8
-        x = self.layer3(x)  # 1024 * h/16 * w/16
-        x = self.layer4(x)  # 2048 * h/32 * w/32
-        return x
-
-    def stages(self):
-        return [self.layer1, self.layer2, self.layer3, self.layer4]
-
-    def make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion, momentum=0.1),
-            )
-
-        layers = []
-        if downsample is not None:
-            layers.append(block(self.inplanes, planes,
-                                stride, downsample, reduction=True))
-        else:
-            layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
diff --git a/spaces/SeViLA/SeViLA/lavis/models/albef_models/__init__.py b/spaces/SeViLA/SeViLA/lavis/models/albef_models/__init__.py
deleted file mode 100644
index 512729237e32354ec0aca598343320afaf7d4acd..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/albef_models/__init__.py
+++ /dev/null
@@ -1,202 +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 datetime
-import logging
-import os
-import time
-
-import lavis.common.dist_utils as dist_utils
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-from lavis.common.dist_utils import download_cached_file
-from lavis.common.logger import MetricLogger
-from lavis.common.utils import is_url
-from lavis.models.base_model import BaseModel
-from lavis.models.vit import interpolate_pos_embed
-from transformers import BertTokenizer
-
-
-class AlbefBase(BaseModel):
-    @classmethod
-    def init_tokenizer(cls):
-        return BertTokenizer.from_pretrained("bert-base-uncased")
-
-    def load_from_pretrained(self, url_or_filename, rename_text_keys=True):
-        if is_url(url_or_filename):
-            cached_file = download_cached_file(
-                url_or_filename, check_hash=False, progress=True
-            )
-            checkpoint = torch.load(cached_file, map_location="cpu")
-        elif os.path.isfile(url_or_filename):
-            checkpoint = torch.load(url_or_filename, map_location="cpu")
-        else:
-            raise RuntimeError("checkpoint url or path is invalid")
-
-        if "model" in checkpoint:
-            state_dict = checkpoint["model"]
-        else:
-            state_dict = checkpoint
-
-        state_dict["visual_encoder.pos_embed"] = interpolate_pos_embed(
-            state_dict["visual_encoder.pos_embed"], self.visual_encoder
-        )
-        if (
-            "visual_encoder_m.pos_embed" in self.state_dict().keys()
-            and "visual_encoder_m.pos_embed" in state_dict
-        ):
-            state_dict["visual_encoder_m.pos_embed"] = interpolate_pos_embed(
-                state_dict["visual_encoder_m.pos_embed"], self.visual_encoder_m
-            )
-
-        if rename_text_keys:
-            for key in list(state_dict.keys()):
-                if "bert" in key:
-                    new_key = key.replace("bert.", "")
-                    state_dict[new_key] = state_dict[key]
-                    del state_dict[key]
-
-        for key in self.state_dict().keys():
-            if key in state_dict.keys():
-                if state_dict[key].shape != self.state_dict()[key].shape:
-                    del state_dict[key]
-
-        msg = self.load_state_dict(state_dict, strict=False)
-
-        logging.info("Missing keys {}".format(msg.missing_keys))
-        logging.info("load checkpoint from %s" % url_or_filename)
-        return msg
-
-
-def compute_sim_matrix(model, data_loader, **kwargs):
-    k_test = kwargs.pop("k_test")
-
-    metric_logger = MetricLogger(delimiter="  ")
-    header = "Evaluation:"
-
-    logging.info("Computing features for evaluation...")
-    start_time = time.time()
-
-    texts = data_loader.dataset.text
-    num_text = len(texts)
-    text_bs = 256
-    text_ids = []
-    text_embeds = []
-    text_atts = []
-    for i in range(0, num_text, text_bs):
-        text = texts[i : min(num_text, i + text_bs)]
-        text_input = model.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=35,
-            return_tensors="pt",
-        ).to(model.device)
-        text_output = model.text_encoder.forward_text(text_input)
-        text_embed = F.normalize(
-            model.text_proj(text_output.last_hidden_state[:, 0, :])
-        )
-        text_embeds.append(text_embed)
-        text_ids.append(text_input.input_ids)
-        text_atts.append(text_input.attention_mask)
-
-    text_embeds = torch.cat(text_embeds, dim=0)
-    text_ids = torch.cat(text_ids, dim=0)
-    text_atts = torch.cat(text_atts, dim=0)
-    if hasattr(model.tokenizer, "enc_token_id"):
-        text_ids[:, 0] = model.tokenizer.enc_token_id
-
-    image_feats = []
-    image_embeds = []
-    for samples in data_loader:
-        image = samples["image"]
-
-        image = image.to(model.device)
-        image_feat = model.visual_encoder.forward_features(image)
-        image_embed = model.vision_proj(image_feat[:, 0, :])
-        image_embed = F.normalize(image_embed, dim=-1)
-
-        image_feats.append(image_feat.cpu())
-        image_embeds.append(image_embed)
-
-    image_feats = torch.cat(image_feats, dim=0)
-    image_embeds = torch.cat(image_embeds, dim=0)
-
-    sims_matrix = image_embeds @ text_embeds.t()
-    score_matrix_i2t = torch.full(
-        (len(data_loader.dataset.image), len(texts)), -100.0
-    ).to(model.device)
-
-    num_tasks = dist_utils.get_world_size()
-    rank = dist_utils.get_rank()
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-        # topk_sim, topk_idx = sims.topk(k=config["k_test"], dim=0)
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-
-        encoder_output = image_feats[start + i].repeat(k_test, 1, 1).to(model.device)
-        encoder_att = torch.ones(encoder_output.size()[:-1], dtype=torch.long).to(
-            model.device
-        )
-        output = model.text_encoder(
-            text_ids[topk_idx],
-            attention_mask=text_atts[topk_idx],
-            encoder_hidden_states=encoder_output,
-            encoder_attention_mask=encoder_att,
-            return_dict=True,
-        )
-        score = model.itm_head(output.last_hidden_state[:, 0, :])[:, 1]
-        score_matrix_i2t[start + i, topk_idx] = score + topk_sim
-
-    sims_matrix = sims_matrix.t()
-    score_matrix_t2i = torch.full(
-        (len(texts), len(data_loader.dataset.image)), -100.0
-    ).to(model.device)
-
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-        encoder_output = image_feats[topk_idx.cpu()].to(model.device)
-        encoder_att = torch.ones(encoder_output.size()[:-1], dtype=torch.long).to(
-            model.device
-        )
-        output = model.text_encoder(
-            text_ids[start + i].repeat(k_test, 1),
-            attention_mask=text_atts[start + i].repeat(k_test, 1),
-            encoder_hidden_states=encoder_output,
-            encoder_attention_mask=encoder_att,
-            return_dict=True,
-        )
-        score = model.itm_head(output.last_hidden_state[:, 0, :])[:, 1]
-        score_matrix_t2i[start + i, topk_idx] = score + topk_sim
-
-    if dist_utils.is_dist_avail_and_initialized():
-        dist.barrier()
-        torch.distributed.all_reduce(
-            score_matrix_i2t, op=torch.distributed.ReduceOp.SUM
-        )
-        torch.distributed.all_reduce(
-            score_matrix_t2i, op=torch.distributed.ReduceOp.SUM
-        )
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    logging.info("Evaluation time {}".format(total_time_str))
-
-    return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
diff --git a/spaces/ShAnSantosh/Chatbot_Using_Pytorch/nltk_utils.py b/spaces/ShAnSantosh/Chatbot_Using_Pytorch/nltk_utils.py
deleted file mode 100644
index edbabd14da7a5d21dfb0bfe50d26e33e49dd277a..0000000000000000000000000000000000000000
--- a/spaces/ShAnSantosh/Chatbot_Using_Pytorch/nltk_utils.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import numpy as np
-import nltk
-nltk.download('punkt')
-from nltk.stem.porter import PorterStemmer
-stemmer = PorterStemmer()
-
-def tokenize(sentence):
-    """
-    split sentence into array of words/tokens
-    a token can be a word or punctuation character, or number
-    """
-    return nltk.word_tokenize(sentence)
-
-
-def stem(word):
-    """
-    stemming = find the root form of the word
-    examples:
-    words = ["organize", "organizes", "organizing"]
-    words = [stem(w) for w in words]
-    -> ["organ", "organ", "organ"]
-    """
-    return stemmer.stem(word.lower())
-
-
-def bag_of_words(tokenized_sentence, words):
-    """
-    return bag of words array:
-    1 for each known word that exists in the sentence, 0 otherwise
-    example:
-    sentence = ["hello", "how", "are", "you"]
-    words = ["hi", "hello", "I", "you", "bye", "thank", "cool"]
-    bog   = [  0 ,    1 ,    0 ,   1 ,    0 ,    0 ,      0]
-    """
-    # stem each word
-    sentence_words = [stem(word) for word in tokenized_sentence]
-    # initialize bag with 0 for each word
-    bag = np.zeros(len(words), dtype=np.float32)
-    for idx, w in enumerate(words):
-        if w in sentence_words: 
-            bag[idx] = 1
-
-    return bag
\ No newline at end of file
diff --git a/spaces/Sifal/En2Kab/README.md b/spaces/Sifal/En2Kab/README.md
deleted file mode 100644
index ad623c0e3ee23760167163e3d94accf78c9aba09..0000000000000000000000000000000000000000
--- a/spaces/Sifal/En2Kab/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: En2Kab
-emoji: 🏆
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/binary.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/binary.py
deleted file mode 100644
index 52d0cb30a3db51d1b8686001882136d69a1dc0fa..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiofiles/threadpool/binary.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from ..base import AsyncBase, AsyncIndirectBase
-from .utils import (
-    delegate_to_executor,
-    proxy_method_directly,
-    proxy_property_directly,
-)
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "read1",
-    "readinto",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "writable",
-    "write",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly("closed", "raw", "name", "mode")
-class AsyncBufferedIOBase(AsyncBase):
-    """The asyncio executor version of io.BufferedWriter and BufferedIOBase."""
-
-
-@delegate_to_executor("peek")
-class AsyncBufferedReader(AsyncBufferedIOBase):
-    """The asyncio executor version of io.BufferedReader and Random."""
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readall",
-    "readinto",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "writable",
-    "write",
-    "writelines",
-)
-@proxy_method_directly("fileno", "readable")
-@proxy_property_directly("closed", "name", "mode")
-class AsyncFileIO(AsyncBase):
-    """The asyncio executor version of io.FileIO."""
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "read1",
-    "readinto",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "writable",
-    "write",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly("closed", "raw", "name", "mode")
-class AsyncIndirectBufferedIOBase(AsyncIndirectBase):
-    """The indirect asyncio executor version of io.BufferedWriter and BufferedIOBase."""
-
-
-@delegate_to_executor("peek")
-class AsyncIndirectBufferedReader(AsyncIndirectBufferedIOBase):
-    """The indirect asyncio executor version of io.BufferedReader and Random."""
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readall",
-    "readinto",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "writable",
-    "write",
-    "writelines",
-)
-@proxy_method_directly("fileno", "readable")
-@proxy_property_directly("closed", "name", "mode")
-class AsyncIndirectFileIO(AsyncIndirectBase):
-    """The indirect asyncio executor version of io.FileIO."""
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/typedefs.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/typedefs.py
deleted file mode 100644
index 84283d9a4634a4836cd50cabe34efd2ae5915f56..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/typedefs.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import json
-import os
-import sys
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Awaitable,
-    Callable,
-    Iterable,
-    Mapping,
-    Tuple,
-    Union,
-)
-
-from multidict import CIMultiDict, CIMultiDictProxy, MultiDict, MultiDictProxy, istr
-from yarl import URL
-
-# These are for other modules to use (to avoid repeating the conditional import).
-if sys.version_info >= (3, 8):
-    from typing import Final as Final, Protocol as Protocol, TypedDict as TypedDict
-else:
-    from typing_extensions import (  # noqa: F401
-        Final,
-        Protocol as Protocol,
-        TypedDict as TypedDict,
-    )
-
-DEFAULT_JSON_ENCODER = json.dumps
-DEFAULT_JSON_DECODER = json.loads
-
-if TYPE_CHECKING:  # pragma: no cover
-    _CIMultiDict = CIMultiDict[str]
-    _CIMultiDictProxy = CIMultiDictProxy[str]
-    _MultiDict = MultiDict[str]
-    _MultiDictProxy = MultiDictProxy[str]
-    from http.cookies import BaseCookie, Morsel
-
-    from .web import Request, StreamResponse
-else:
-    _CIMultiDict = CIMultiDict
-    _CIMultiDictProxy = CIMultiDictProxy
-    _MultiDict = MultiDict
-    _MultiDictProxy = MultiDictProxy
-
-Byteish = Union[bytes, bytearray, memoryview]
-JSONEncoder = Callable[[Any], str]
-JSONDecoder = Callable[[str], Any]
-LooseHeaders = Union[Mapping[Union[str, istr], str], _CIMultiDict, _CIMultiDictProxy]
-RawHeaders = Tuple[Tuple[bytes, bytes], ...]
-StrOrURL = Union[str, URL]
-
-LooseCookiesMappings = Mapping[str, Union[str, "BaseCookie[str]", "Morsel[Any]"]]
-LooseCookiesIterables = Iterable[
-    Tuple[str, Union[str, "BaseCookie[str]", "Morsel[Any]"]]
-]
-LooseCookies = Union[
-    LooseCookiesMappings,
-    LooseCookiesIterables,
-    "BaseCookie[str]",
-]
-
-Handler = Callable[["Request"], Awaitable["StreamResponse"]]
-
-PathLike = Union[str, "os.PathLike[str]"]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/test_api.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/test_api.py
deleted file mode 100644
index e9405825653276b33ecbc9921b51534ad3382814..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/test_api.py
+++ /dev/null
@@ -1,1365 +0,0 @@
-# type: ignore
-import chromadb
-from chromadb.api.types import QueryResult
-from chromadb.config import Settings
-import chromadb.server.fastapi
-import pytest
-import tempfile
-import numpy as np
-from chromadb.utils.embedding_functions import (
-    DefaultEmbeddingFunction,
-)
-
-
-@pytest.fixture
-def local_persist_api():
-    return chromadb.Client(
-        Settings(
-            chroma_api_impl="local",
-            chroma_db_impl="duckdb+parquet",
-            persist_directory=tempfile.gettempdir() + "/test_server",
-        )
-    )
-
-
-# https://docs.pytest.org/en/6.2.x/fixture.html#fixtures-can-be-requested-more-than-once-per-test-return-values-are-cached
-@pytest.fixture
-def local_persist_api_cache_bust():
-    return chromadb.Client(
-        Settings(
-            chroma_api_impl="local",
-            chroma_db_impl="duckdb+parquet",
-            persist_directory=tempfile.gettempdir() + "/test_server",
-        )
-    )
-
-
-@pytest.mark.parametrize("api_fixture", [local_persist_api])
-def test_persist_index_loading(api_fixture, request):
-    api = request.getfixturevalue("local_persist_api")
-    api.reset()
-    collection = api.create_collection("test")
-    collection.add(ids="id1", documents="hello")
-
-    api.persist()
-    del api
-
-    api2 = request.getfixturevalue("local_persist_api_cache_bust")
-    collection = api2.get_collection("test")
-
-    nn = collection.query(
-        query_texts="hello",
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-
-@pytest.mark.parametrize("api_fixture", [local_persist_api])
-def test_persist_index_loading_embedding_function(api_fixture, request):
-    embedding_function = lambda x: [[1, 2, 3] for _ in range(len(x))]  # noqa E731
-    api = request.getfixturevalue("local_persist_api")
-    api.reset()
-    collection = api.create_collection("test", embedding_function=embedding_function)
-    collection.add(ids="id1", documents="hello")
-
-    api.persist()
-    del api
-
-    api2 = request.getfixturevalue("local_persist_api_cache_bust")
-    collection = api2.get_collection("test", embedding_function=embedding_function)
-
-    nn = collection.query(
-        query_texts="hello",
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-
-@pytest.mark.parametrize("api_fixture", [local_persist_api])
-def test_persist_index_get_or_create_embedding_function(api_fixture, request):
-    embedding_function = lambda x: [[1, 2, 3] for _ in range(len(x))]  # noqa E731
-    api = request.getfixturevalue("local_persist_api")
-    api.reset()
-    collection = api.get_or_create_collection(
-        "test", embedding_function=embedding_function
-    )
-    collection.add(ids="id1", documents="hello")
-
-    api.persist()
-    del api
-
-    api2 = request.getfixturevalue("local_persist_api_cache_bust")
-    collection = api2.get_or_create_collection(
-        "test", embedding_function=embedding_function
-    )
-
-    nn = collection.query(
-        query_texts="hello",
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-    assert nn["ids"] == [["id1"]]
-    assert nn["embeddings"] == [[[1, 2, 3]]]
-    assert nn["documents"] == [["hello"]]
-    assert nn["distances"] == [[0]]
-
-
-@pytest.mark.parametrize("api_fixture", [local_persist_api])
-def test_persist(api_fixture, request):
-    api = request.getfixturevalue(api_fixture.__name__)
-
-    api.reset()
-
-    collection = api.create_collection("testspace")
-
-    collection.add(**batch_records)
-
-    assert collection.count() == 2
-
-    api.persist()
-    del api
-
-    api = request.getfixturevalue(api_fixture.__name__)
-    collection = api.get_collection("testspace")
-    assert collection.count() == 2
-
-    api.delete_collection("testspace")
-    api.persist()
-    del api
-
-    api = request.getfixturevalue(api_fixture.__name__)
-    assert api.list_collections() == []
-
-
-def test_heartbeat(api):
-    assert isinstance(api.heartbeat(), int)
-
-
-batch_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "ids": ["https://example.com/1", "https://example.com/2"],
-}
-
-
-def test_add(api):
-    api.reset()
-
-    collection = api.create_collection("testspace")
-
-    collection.add(**batch_records)
-
-    assert collection.count() == 2
-
-
-def test_get_or_create(api):
-    api.reset()
-
-    collection = api.create_collection("testspace")
-
-    collection.add(**batch_records)
-
-    assert collection.count() == 2
-
-    with pytest.raises(Exception):
-        collection = api.create_collection("testspace")
-
-    collection = api.get_or_create_collection("testspace")
-
-    assert collection.count() == 2
-
-
-minimal_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "ids": ["https://example.com/1", "https://example.com/2"],
-}
-
-
-def test_add_minimal(api):
-    api.reset()
-
-    collection = api.create_collection("testspace")
-
-    collection.add(**minimal_records)
-
-    assert collection.count() == 2
-
-
-def test_get_from_db(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    records = collection.get(include=["embeddings", "documents", "metadatas"])
-    for key in records.keys():
-        assert len(records[key]) == 2
-
-
-def test_reset_db(api):
-    api.reset()
-
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    assert collection.count() == 2
-
-    api.reset()
-    assert len(api.list_collections()) == 0
-
-
-def test_get_nearest_neighbors(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    # assert api.create_index(collection_name="testspace") # default is auto now
-
-    nn = collection.query(
-        query_embeddings=[1.1, 2.3, 3.2],
-        n_results=1,
-        where={},
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-    nn = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2]],
-        n_results=1,
-        where={},
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-    nn = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2], [0.1, 2.3, 4.5]],
-        n_results=1,
-        where={},
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 2
-
-
-def test_delete(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    assert collection.count() == 2
-
-    collection.delete()
-    assert collection.count() == 0
-
-
-def test_delete_with_index(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    assert collection.count() == 2
-    collection.query(query_embeddings=[[1.1, 2.3, 3.2]], n_results=1)
-
-
-def test_count(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    assert collection.count() == 0
-    collection.add(**batch_records)
-    assert collection.count() == 2
-
-
-def test_modify(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.modify(name="testspace2")
-
-    # collection name is modify
-    assert collection.name == "testspace2"
-
-
-def test_modify_error_on_existing_name(api):
-    api.reset()
-
-    api.create_collection("testspace")
-    c2 = api.create_collection("testspace2")
-
-    with pytest.raises(Exception):
-        c2.modify(name="testspace")
-
-
-def test_metadata_cru(api):
-    api.reset()
-    metadata_a = {"a": 1, "b": 2}
-    # Test create metatdata
-    collection = api.create_collection("testspace", metadata=metadata_a)
-    assert collection.metadata is not None
-    assert collection.metadata["a"] == 1
-    assert collection.metadata["b"] == 2
-
-    # Test get metatdata
-    collection = api.get_collection("testspace")
-    assert collection.metadata is not None
-    assert collection.metadata["a"] == 1
-    assert collection.metadata["b"] == 2
-
-    # Test modify metatdata
-    collection.modify(metadata={"a": 2, "c": 3})
-    assert collection.metadata["a"] == 2
-    assert collection.metadata["c"] == 3
-    assert "b" not in collection.metadata
-
-    # Test get after modify metatdata
-    collection = api.get_collection("testspace")
-    assert collection.metadata is not None
-    assert collection.metadata["a"] == 2
-    assert collection.metadata["c"] == 3
-    assert "b" not in collection.metadata
-
-    # Test name exists get_or_create_metadata
-    collection = api.get_or_create_collection("testspace")
-    assert collection.metadata is not None
-    assert collection.metadata["a"] == 2
-    assert collection.metadata["c"] == 3
-
-    # Test name exists create metadata
-    collection = api.get_or_create_collection("testspace2")
-    assert collection.metadata is None
-
-    # Test list collections
-    collections = api.list_collections()
-    for collection in collections:
-        if collection.name == "testspace":
-            assert collection.metadata is not None
-            assert collection.metadata["a"] == 2
-            assert collection.metadata["c"] == 3
-        elif collection.name == "testspace2":
-            assert collection.metadata is None
-
-
-def test_increment_index_on(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    assert collection.count() == 2
-
-    # increment index
-    # collection.create_index(index_type="hnsw", index_params={"M": 16, "efConstruction": 200})
-    nn = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2]],
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-
-def test_increment_index_off(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records, increment_index=False)
-    assert collection.count() == 2
-
-    # incremental index
-    collection.create_index()
-    nn = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2]],
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-
-def skipping_indexing_will_fail(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records, increment_index=False)
-    assert collection.count() == 2
-
-    # incremental index
-    with pytest.raises(Exception) as e:
-        collection.query(query_embeddings=[[1.1, 2.3, 3.2]], n_results=1)
-    assert str(e.value).__contains__("index not found")
-
-
-def test_add_a_collection(api):
-    api.reset()
-    api.create_collection("testspace")
-
-    # get collection does not throw an error
-    collection = api.get_collection("testspace")
-    assert collection.name == "testspace"
-
-    # get collection should throw an error if collection does not exist
-    with pytest.raises(Exception):
-        collection = api.get_collection("testspace2")
-
-
-def test_list_collections(api):
-    api.reset()
-    api.create_collection("testspace")
-    api.create_collection("testspace2")
-
-    # get collection does not throw an error
-    collections = api.list_collections()
-    assert len(collections) == 2
-
-
-def test_reset(api):
-    api.reset()
-    api.create_collection("testspace")
-    api.create_collection("testspace2")
-
-    # get collection does not throw an error
-    collections = api.list_collections()
-    assert len(collections) == 2
-
-    api.reset()
-    collections = api.list_collections()
-    assert len(collections) == 0
-
-
-def test_peek(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**batch_records)
-    assert collection.count() == 2
-
-    # peek
-    peek = collection.peek()
-    for key in peek.keys():
-        assert len(peek[key]) == 2
-
-
-# TEST METADATA AND METADATA FILTERING
-# region
-
-metadata_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "ids": ["id1", "id2"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001},
-        {"int_value": 2},
-    ],
-}
-
-
-def test_metadata_add_get_int_float(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    items = collection.get(ids=["id1", "id2"])
-    assert items["metadatas"][0]["int_value"] == 1
-    assert items["metadatas"][0]["float_value"] == 1.001
-    assert items["metadatas"][1]["int_value"] == 2
-    assert type(items["metadatas"][0]["int_value"]) == int
-    assert type(items["metadatas"][0]["float_value"]) == float
-
-
-def test_metadata_add_query_int_float(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    items: QueryResult = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2]], n_results=1
-    )
-    assert items["metadatas"] is not None
-    assert items["metadatas"][0][0]["int_value"] == 1
-    assert items["metadatas"][0][0]["float_value"] == 1.001
-    assert type(items["metadatas"][0][0]["int_value"]) == int
-    assert type(items["metadatas"][0][0]["float_value"]) == float
-
-
-def test_metadata_get_where_string(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    items = collection.get(where={"string_value": "one"})
-    assert items["metadatas"][0]["int_value"] == 1
-    assert items["metadatas"][0]["string_value"] == "one"
-
-
-def test_metadata_get_where_int(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    items = collection.get(where={"int_value": 1})
-    assert items["metadatas"][0]["int_value"] == 1
-    assert items["metadatas"][0]["string_value"] == "one"
-
-
-def test_metadata_get_where_float(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    items = collection.get(where={"float_value": 1.001})
-    assert items["metadatas"][0]["int_value"] == 1
-    assert items["metadatas"][0]["string_value"] == "one"
-    assert items["metadatas"][0]["float_value"] == 1.001
-
-
-def test_metadata_update_get_int_float(api):
-    api.reset()
-    collection = api.create_collection("test_int")
-    collection.add(**metadata_records)
-
-    collection.update(
-        ids=["id1"],
-        metadatas=[{"int_value": 2, "string_value": "two", "float_value": 2.002}],
-    )
-    items = collection.get(ids=["id1"])
-    assert items["metadatas"][0]["int_value"] == 2
-    assert items["metadatas"][0]["string_value"] == "two"
-    assert items["metadatas"][0]["float_value"] == 2.002
-
-
-bad_metadata_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "ids": ["id1", "id2"],
-    "metadatas": [{"value": {"nested": "5"}}, {"value": [1, 2, 3]}],
-}
-
-
-def test_metadata_validation_add(api):
-    api.reset()
-    collection = api.create_collection("test_metadata_validation")
-    with pytest.raises(ValueError, match="metadata"):
-        collection.add(**bad_metadata_records)
-
-
-def test_metadata_validation_update(api):
-    api.reset()
-    collection = api.create_collection("test_metadata_validation")
-    collection.add(**metadata_records)
-    with pytest.raises(ValueError, match="metadata"):
-        collection.update(ids=["id1"], metadatas={"value": {"nested": "5"}})
-
-
-def test_where_validation_get(api):
-    api.reset()
-    collection = api.create_collection("test_where_validation")
-    with pytest.raises(ValueError, match="where"):
-        collection.get(where={"value": {"nested": "5"}})
-
-
-def test_where_validation_query(api):
-    api.reset()
-    collection = api.create_collection("test_where_validation")
-    with pytest.raises(ValueError, match="where"):
-        collection.query(query_embeddings=[0, 0, 0], where={"value": {"nested": "5"}})
-
-
-operator_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "ids": ["id1", "id2"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001},
-        {"int_value": 2, "float_value": 2.002, "string_value": "two"},
-    ],
-}
-
-
-def test_where_lt(api):
-    api.reset()
-    collection = api.create_collection("test_where_lt")
-    collection.add(**operator_records)
-    items = collection.get(where={"int_value": {"$lt": 2}})
-    assert len(items["metadatas"]) == 1
-
-
-def test_where_lte(api):
-    api.reset()
-    collection = api.create_collection("test_where_lte")
-    collection.add(**operator_records)
-    items = collection.get(where={"int_value": {"$lte": 2.0}})
-    assert len(items["metadatas"]) == 2
-
-
-def test_where_gt(api):
-    api.reset()
-    collection = api.create_collection("test_where_lte")
-    collection.add(**operator_records)
-    items = collection.get(where={"float_value": {"$gt": -1.4}})
-    assert len(items["metadatas"]) == 2
-
-
-def test_where_gte(api):
-    api.reset()
-    collection = api.create_collection("test_where_lte")
-    collection.add(**operator_records)
-    items = collection.get(where={"float_value": {"$gte": 2.002}})
-    assert len(items["metadatas"]) == 1
-
-
-def test_where_ne_string(api):
-    api.reset()
-    collection = api.create_collection("test_where_lte")
-    collection.add(**operator_records)
-    items = collection.get(where={"string_value": {"$ne": "two"}})
-    assert len(items["metadatas"]) == 1
-
-
-def test_where_ne_eq_number(api):
-    api.reset()
-    collection = api.create_collection("test_where_lte")
-    collection.add(**operator_records)
-    items = collection.get(where={"int_value": {"$ne": 1}})
-    assert len(items["metadatas"]) == 1
-    items = collection.get(where={"float_value": {"$eq": 2.002}})
-    assert len(items["metadatas"]) == 1
-
-
-def test_where_valid_operators(api):
-    api.reset()
-    collection = api.create_collection("test_where_valid_operators")
-    collection.add(**operator_records)
-    with pytest.raises(ValueError):
-        collection.get(where={"int_value": {"$invalid": 2}})
-
-    with pytest.raises(ValueError):
-        collection.get(where={"int_value": {"$lt": "2"}})
-
-    with pytest.raises(ValueError):
-        collection.get(where={"int_value": {"$lt": 2, "$gt": 1}})
-
-    # Test invalid $and, $or
-    with pytest.raises(ValueError):
-        collection.get(where={"$and": {"int_value": {"$lt": 2}}})
-
-    with pytest.raises(ValueError):
-        collection.get(
-            where={"int_value": {"$lt": 2}, "$or": {"int_value": {"$gt": 1}}}
-        )
-
-    with pytest.raises(ValueError):
-        collection.get(
-            where={"$gt": [{"int_value": {"$lt": 2}}, {"int_value": {"$gt": 1}}]}
-        )
-
-    with pytest.raises(ValueError):
-        collection.get(where={"$or": [{"int_value": {"$lt": 2}}]})
-
-    with pytest.raises(ValueError):
-        collection.get(where={"$or": []})
-
-    with pytest.raises(ValueError):
-        collection.get(where={"a": {"$contains": "test"}})
-
-    with pytest.raises(ValueError):
-        collection.get(
-            where={
-                "$or": [
-                    {"a": {"$contains": "first"}},  # invalid
-                    {"$contains": "second"},  # valid
-                ]
-            }
-        )
-
-
-# TODO: Define the dimensionality of these embeddingds in terms of the default record
-bad_dimensionality_records = {
-    "embeddings": [[1.1, 2.3, 3.2, 4.5], [1.2, 2.24, 3.2, 4.5]],
-    "ids": ["id1", "id2"],
-}
-
-bad_dimensionality_query = {
-    "query_embeddings": [[1.1, 2.3, 3.2, 4.5], [1.2, 2.24, 3.2, 4.5]],
-}
-
-bad_number_of_results_query = {
-    "query_embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "n_results": 100,
-}
-
-
-def test_dimensionality_validation_add(api):
-    api.reset()
-    collection = api.create_collection("test_dimensionality_validation")
-    collection.add(**minimal_records)
-
-    with pytest.raises(Exception) as e:
-        collection.add(**bad_dimensionality_records)
-    assert "dimensionality" in str(e.value)
-
-
-def test_dimensionality_validation_query(api):
-    api.reset()
-    collection = api.create_collection("test_dimensionality_validation_query")
-    collection.add(**minimal_records)
-
-    with pytest.raises(Exception) as e:
-        collection.query(**bad_dimensionality_query)
-    assert "dimensionality" in str(e.value)
-
-
-def test_query_document_valid_operators(api):
-    api.reset()
-    collection = api.create_collection("test_where_valid_operators")
-    collection.add(**operator_records)
-    with pytest.raises(ValueError, match="where document"):
-        collection.get(where_document={"$lt": {"$nested": 2}})
-
-    with pytest.raises(ValueError, match="where document"):
-        collection.query(query_embeddings=[0, 0, 0], where_document={"$contains": 2})
-
-    with pytest.raises(ValueError, match="where document"):
-        collection.get(where_document={"$contains": []})
-
-    # Test invalid $and, $or
-    with pytest.raises(ValueError):
-        collection.get(where_document={"$and": {"$unsupported": "doc"}})
-
-    with pytest.raises(ValueError):
-        collection.get(
-            where_document={"$or": [{"$unsupported": "doc"}, {"$unsupported": "doc"}]}
-        )
-
-    with pytest.raises(ValueError):
-        collection.get(where_document={"$or": [{"$contains": "doc"}]})
-
-    with pytest.raises(ValueError):
-        collection.get(where_document={"$or": []})
-
-    with pytest.raises(ValueError):
-        collection.get(
-            where_document={
-                "$or": [{"$and": [{"$contains": "doc"}]}, {"$contains": "doc"}]
-            }
-        )
-
-
-contains_records = {
-    "embeddings": [[1.1, 2.3, 3.2], [1.2, 2.24, 3.2]],
-    "documents": ["this is doc1 and it's great!", "doc2 is also great!"],
-    "ids": ["id1", "id2"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001},
-        {"int_value": 2, "float_value": 2.002, "string_value": "two"},
-    ],
-}
-
-
-def test_get_where_document(api):
-    api.reset()
-    collection = api.create_collection("test_get_where_document")
-    collection.add(**contains_records)
-
-    items = collection.get(where_document={"$contains": "doc1"})
-    assert len(items["metadatas"]) == 1
-
-    items = collection.get(where_document={"$contains": "great"})
-    assert len(items["metadatas"]) == 2
-
-    items = collection.get(where_document={"$contains": "bad"})
-    assert len(items["metadatas"]) == 0
-
-
-def test_query_where_document(api):
-    api.reset()
-    collection = api.create_collection("test_query_where_document")
-    collection.add(**contains_records)
-
-    items = collection.query(
-        query_embeddings=[1, 0, 0], where_document={"$contains": "doc1"}, n_results=1
-    )
-    assert len(items["metadatas"][0]) == 1
-
-    items = collection.query(
-        query_embeddings=[0, 0, 0], where_document={"$contains": "great"}, n_results=2
-    )
-    assert len(items["metadatas"][0]) == 2
-
-    with pytest.raises(Exception) as e:
-        items = collection.query(
-            query_embeddings=[0, 0, 0], where_document={"$contains": "bad"}, n_results=1
-        )
-        assert "datapoints" in str(e.value)
-
-
-def test_delete_where_document(api):
-    api.reset()
-    collection = api.create_collection("test_delete_where_document")
-    collection.add(**contains_records)
-
-    collection.delete(where_document={"$contains": "doc1"})
-    assert collection.count() == 1
-
-    collection.delete(where_document={"$contains": "bad"})
-    assert collection.count() == 1
-
-    collection.delete(where_document={"$contains": "great"})
-    assert collection.count() == 0
-
-
-logical_operator_records = {
-    "embeddings": [
-        [1.1, 2.3, 3.2],
-        [1.2, 2.24, 3.2],
-        [1.3, 2.25, 3.2],
-        [1.4, 2.26, 3.2],
-    ],
-    "ids": ["id1", "id2", "id3", "id4"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001, "is": "doc"},
-        {"int_value": 2, "float_value": 2.002, "string_value": "two", "is": "doc"},
-        {"int_value": 3, "float_value": 3.003, "string_value": "three", "is": "doc"},
-        {"int_value": 4, "float_value": 4.004, "string_value": "four", "is": "doc"},
-    ],
-    "documents": [
-        "this document is first and great",
-        "this document is second and great",
-        "this document is third and great",
-        "this document is fourth and great",
-    ],
-}
-
-
-def test_where_logical_operators(api):
-    api.reset()
-    collection = api.create_collection("test_logical_operators")
-    collection.add(**logical_operator_records)
-
-    items = collection.get(
-        where={
-            "$and": [
-                {"$or": [{"int_value": {"$gte": 3}}, {"float_value": {"$lt": 1.9}}]},
-                {"is": "doc"},
-            ]
-        }
-    )
-    assert len(items["metadatas"]) == 3
-
-    items = collection.get(
-        where={
-            "$or": [
-                {
-                    "$and": [
-                        {"int_value": {"$eq": 3}},
-                        {"string_value": {"$eq": "three"}},
-                    ]
-                },
-                {
-                    "$and": [
-                        {"int_value": {"$eq": 4}},
-                        {"string_value": {"$eq": "four"}},
-                    ]
-                },
-            ]
-        }
-    )
-    assert len(items["metadatas"]) == 2
-
-    items = collection.get(
-        where={
-            "$and": [
-                {
-                    "$or": [
-                        {"int_value": {"$eq": 1}},
-                        {"string_value": {"$eq": "two"}},
-                    ]
-                },
-                {
-                    "$or": [
-                        {"int_value": {"$eq": 2}},
-                        {"string_value": {"$eq": "one"}},
-                    ]
-                },
-            ]
-        }
-    )
-    assert len(items["metadatas"]) == 2
-
-
-def test_where_document_logical_operators(api):
-    api.reset()
-    collection = api.create_collection("test_document_logical_operators")
-    collection.add(**logical_operator_records)
-
-    items = collection.get(
-        where_document={
-            "$and": [
-                {"$contains": "first"},
-                {"$contains": "doc"},
-            ]
-        }
-    )
-    assert len(items["metadatas"]) == 1
-
-    items = collection.get(
-        where_document={
-            "$or": [
-                {"$contains": "first"},
-                {"$contains": "second"},
-            ]
-        }
-    )
-    assert len(items["metadatas"]) == 2
-
-    items = collection.get(
-        where_document={
-            "$or": [
-                {"$contains": "first"},
-                {"$contains": "second"},
-            ]
-        },
-        where={
-            "int_value": {"$ne": 2},
-        },
-    )
-    assert len(items["metadatas"]) == 1
-
-
-# endregion
-
-records = {
-    "embeddings": [[0, 0, 0], [1.2, 2.24, 3.2]],
-    "ids": ["id1", "id2"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001},
-        {"int_value": 2},
-    ],
-    "documents": ["this document is first", "this document is second"],
-}
-
-
-def test_query_include(api):
-    api.reset()
-    collection = api.create_collection("test_query_include")
-    collection.add(**records)
-
-    items = collection.query(
-        query_embeddings=[0, 0, 0],
-        include=["metadatas", "documents", "distances"],
-        n_results=1,
-    )
-    assert items["embeddings"] is None
-    assert items["ids"][0][0] == "id1"
-    assert items["metadatas"][0][0]["int_value"] == 1
-
-    items = collection.query(
-        query_embeddings=[0, 0, 0],
-        include=["embeddings", "documents", "distances"],
-        n_results=1,
-    )
-    assert items["metadatas"] is None
-    assert items["ids"][0][0] == "id1"
-
-    items = collection.query(
-        query_embeddings=[[0, 0, 0], [1, 2, 1.2]],
-        include=[],
-        n_results=2,
-    )
-    assert items["documents"] is None
-    assert items["metadatas"] is None
-    assert items["embeddings"] is None
-    assert items["distances"] is None
-    assert items["ids"][0][0] == "id1"
-    assert items["ids"][0][1] == "id2"
-
-
-def test_get_include(api):
-    api.reset()
-    collection = api.create_collection("test_get_include")
-    collection.add(**records)
-
-    items = collection.get(include=["metadatas", "documents"], where={"int_value": 1})
-    assert items["embeddings"] is None
-    assert items["ids"][0] == "id1"
-    assert items["metadatas"][0]["int_value"] == 1
-    assert items["documents"][0] == "this document is first"
-
-    items = collection.get(include=["embeddings", "documents"])
-    assert items["metadatas"] is None
-    assert items["ids"][0] == "id1"
-    assert items["embeddings"][1][0] == 1.2
-
-    items = collection.get(include=[])
-    assert items["documents"] is None
-    assert items["metadatas"] is None
-    assert items["embeddings"] is None
-    assert items["ids"][0] == "id1"
-
-    with pytest.raises(ValueError, match="include"):
-        items = collection.get(include=["metadatas", "undefined"])
-
-    with pytest.raises(ValueError, match="include"):
-        items = collection.get(include=None)
-
-
-# make sure query results are returned in the right order
-
-
-def test_query_order(api):
-    api.reset()
-    collection = api.create_collection("test_query_order")
-    collection.add(**records)
-
-    items = collection.query(
-        query_embeddings=[1.2, 2.24, 3.2],
-        include=["metadatas", "documents", "distances"],
-        n_results=2,
-    )
-
-    assert items["documents"][0][0] == "this document is second"
-    assert items["documents"][0][1] == "this document is first"
-
-
-# test to make sure add, get, delete error on invalid id input
-
-
-def test_invalid_id(api):
-    api.reset()
-    collection = api.create_collection("test_invalid_id")
-    # Add with non-string id
-    with pytest.raises(ValueError) as e:
-        collection.add(embeddings=[0, 0, 0], ids=[1], metadatas=[{}])
-    assert "ID" in str(e.value)
-
-    # Get with non-list id
-    with pytest.raises(ValueError) as e:
-        collection.get(ids=1)
-    assert "ID" in str(e.value)
-
-    # Delete with malformed ids
-    with pytest.raises(ValueError) as e:
-        collection.delete(ids=["valid", 0])
-    assert "ID" in str(e.value)
-
-
-def test_index_params(api):
-    # first standard add
-    api.reset()
-    collection = api.create_collection(name="test_index_params")
-    collection.add(**records)
-    items = collection.query(
-        query_embeddings=[0.6, 1.12, 1.6],
-        n_results=1,
-    )
-    assert items["distances"][0][0] > 4
-
-    # cosine
-    api.reset()
-    collection = api.create_collection(
-        name="test_index_params",
-        metadata={"hnsw:space": "cosine", "hnsw:construction_ef": 20, "hnsw:M": 5},
-    )
-    collection.add(**records)
-    items = collection.query(
-        query_embeddings=[0.6, 1.12, 1.6],
-        n_results=1,
-    )
-    assert items["distances"][0][0] > 0
-    assert items["distances"][0][0] < 1
-
-    # ip
-    api.reset()
-    collection = api.create_collection(
-        name="test_index_params", metadata={"hnsw:space": "ip"}
-    )
-    collection.add(**records)
-    items = collection.query(
-        query_embeddings=[0.6, 1.12, 1.6],
-        n_results=1,
-    )
-    assert items["distances"][0][0] < -5
-
-
-def test_invalid_index_params(api):
-    api.reset()
-
-    with pytest.raises(Exception):
-        collection = api.create_collection(
-            name="test_index_params", metadata={"hnsw:foobar": "blarg"}
-        )
-        collection.add(**records)
-
-    with pytest.raises(Exception):
-        collection = api.create_collection(
-            name="test_index_params", metadata={"hnsw:space": "foobar"}
-        )
-        collection.add(**records)
-
-
-def test_persist_index_loading_params(api, request):
-    api = request.getfixturevalue("local_persist_api")
-    api.reset()
-    collection = api.create_collection("test", metadata={"hnsw:space": "ip"})
-    collection.add(ids="id1", documents="hello")
-
-    api.persist()
-    del api
-
-    api2 = request.getfixturevalue("local_persist_api_cache_bust")
-    collection = api2.get_collection("test")
-
-    assert collection.metadata["hnsw:space"] == "ip"
-
-    nn = collection.query(
-        query_texts="hello",
-        n_results=1,
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in nn.keys():
-        assert len(nn[key]) == 1
-
-
-def test_add_large(api):
-    api.reset()
-
-    collection = api.create_collection("testspace")
-
-    # Test adding a large number of records
-    large_records = np.random.rand(2000, 512).astype(np.float32).tolist()
-
-    collection.add(
-        embeddings=large_records,
-        ids=[f"http://example.com/{i}" for i in range(len(large_records))],
-    )
-
-    assert collection.count() == len(large_records)
-
-
-# test get_version
-def test_get_version(api):
-    api.reset()
-    version = api.get_version()
-
-    # assert version matches the pattern x.y.z
-    import re
-
-    assert re.match(r"\d+\.\d+\.\d+", version)
-
-
-# test delete_collection
-def test_delete_collection(api):
-    api.reset()
-    collection = api.create_collection("test_delete_collection")
-    collection.add(**records)
-
-    assert len(api.list_collections()) == 1
-    api.delete_collection("test_delete_collection")
-    assert len(api.list_collections()) == 0
-
-
-# test default embedding function
-def test_default_embedding():
-    embedding_function = DefaultEmbeddingFunction()
-    docs = ["this is a test" for _ in range(64)]
-    embeddings = embedding_function(docs)
-    assert len(embeddings) == 64
-
-
-def test_multiple_collections(api):
-    embeddings1 = np.random.rand(10, 512).astype(np.float32).tolist()
-    embeddings2 = np.random.rand(10, 512).astype(np.float32).tolist()
-    ids1 = [f"http://example.com/1/{i}" for i in range(len(embeddings1))]
-    ids2 = [f"http://example.com/2/{i}" for i in range(len(embeddings2))]
-
-    api.reset()
-    coll1 = api.create_collection("coll1")
-    coll1.add(embeddings=embeddings1, ids=ids1)
-
-    coll2 = api.create_collection("coll2")
-    coll2.add(embeddings=embeddings2, ids=ids2)
-
-    assert len(api.list_collections()) == 2
-    assert coll1.count() == len(embeddings1)
-    assert coll2.count() == len(embeddings2)
-
-    results1 = coll1.query(query_embeddings=embeddings1[0], n_results=1)
-    results2 = coll2.query(query_embeddings=embeddings2[0], n_results=1)
-
-    assert results1["ids"][0][0] == ids1[0]
-    assert results2["ids"][0][0] == ids2[0]
-
-
-def test_update_query(api):
-    api.reset()
-    collection = api.create_collection("test_update_query")
-    collection.add(**records)
-
-    updated_records = {
-        "ids": [records["ids"][0]],
-        "embeddings": [[0.1, 0.2, 0.3]],
-        "documents": ["updated document"],
-        "metadatas": [{"foo": "bar"}],
-    }
-
-    collection.update(**updated_records)
-
-    # test query
-    results = collection.query(
-        query_embeddings=updated_records["embeddings"],
-        n_results=1,
-        include=["embeddings", "documents", "metadatas"],
-    )
-    assert len(results["ids"][0]) == 1
-    assert results["ids"][0][0] == updated_records["ids"][0]
-    assert results["documents"][0][0] == updated_records["documents"][0]
-    assert results["metadatas"][0][0]["foo"] == "bar"
-    assert results["embeddings"][0][0] == updated_records["embeddings"][0]
-
-
-def test_get_nearest_neighbors_where_n_results_more_than_element(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**records)
-
-    results1 = collection.query(
-        query_embeddings=[[1.1, 2.3, 3.2]],
-        n_results=5,
-        where={},
-        include=["embeddings", "documents", "metadatas", "distances"],
-    )
-    for key in results1.keys():
-        assert len(results1[key][0]) == 2
-
-
-def test_invalid_n_results_param(api):
-    api.reset()
-    collection = api.create_collection("testspace")
-    collection.add(**records)
-    with pytest.raises(TypeError) as exc:
-        collection.query(
-            query_embeddings=[[1.1, 2.3, 3.2]],
-            n_results=-1,
-            where={},
-            include=["embeddings", "documents", "metadatas", "distances"],
-        )
-    assert "Number of requested results -1, cannot be negative, or zero." in str(
-        exc.value
-    )
-    assert exc.type == TypeError
-
-    with pytest.raises(ValueError) as exc:
-        collection.query(
-            query_embeddings=[[1.1, 2.3, 3.2]],
-            n_results="one",
-            where={},
-            include=["embeddings", "documents", "metadatas", "distances"],
-        )
-    assert "int" in str(exc.value)
-    assert exc.type == ValueError
-
-
-initial_records = {
-    "embeddings": [[0, 0, 0], [1.2, 2.24, 3.2], [2.2, 3.24, 4.2]],
-    "ids": ["id1", "id2", "id3"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one", "float_value": 1.001},
-        {"int_value": 2},
-        {"string_value": "three"},
-    ],
-    "documents": [
-        "this document is first",
-        "this document is second",
-        "this document is third",
-    ],
-}
-
-new_records = {
-    "embeddings": [[3.0, 3.0, 1.1], [3.2, 4.24, 5.2]],
-    "ids": ["id1", "id4"],
-    "metadatas": [
-        {"int_value": 1, "string_value": "one_of_one", "float_value": 1.001},
-        {"int_value": 4},
-    ],
-    "documents": [
-        "this document is even more first",
-        "this document is new and fourth",
-    ],
-}
-
-
-def test_upsert(api):
-    api.reset()
-    collection = api.create_collection("test")
-
-    collection.add(**initial_records)
-    assert collection.count() == 3
-
-    collection.upsert(**new_records)
-    assert collection.count() == 4
-
-    get_result = collection.get(
-        include=["embeddings", "metadatas", "documents"], ids=new_records["ids"][0]
-    )
-    assert get_result["embeddings"][0] == new_records["embeddings"][0]
-    assert get_result["metadatas"][0] == new_records["metadatas"][0]
-    assert get_result["documents"][0] == new_records["documents"][0]
-
-    query_result = collection.query(
-        query_embeddings=get_result["embeddings"],
-        n_results=1,
-        include=["embeddings", "metadatas", "documents"],
-    )
-    assert query_result["embeddings"][0][0] == new_records["embeddings"][0]
-    assert query_result["metadatas"][0][0] == new_records["metadatas"][0]
-    assert query_result["documents"][0][0] == new_records["documents"][0]
-
-    collection.delete(ids=initial_records["ids"][2])
-    collection.upsert(
-        ids=initial_records["ids"][2],
-        embeddings=[[1.1, 0.99, 2.21]],
-        metadatas=[{"string_value": "a new string value"}],
-    )
-    assert collection.count() == 4
-
-    get_result = collection.get(
-        include=["embeddings", "metadatas", "documents"], ids=["id3"]
-    )
-    assert get_result["embeddings"][0] == [1.1, 0.99, 2.21]
-    assert get_result["metadatas"][0] == {"string_value": "a new string value"}
-    assert get_result["documents"][0] is None
-
-
-# test to make sure add, query, update, upsert error on invalid embeddings input
-
-
-def test_invalid_embeddings(api):
-    api.reset()
-    collection = api.create_collection("test_invalid_embeddings")
-
-    # Add with string embeddings
-    invalid_records = {
-        "embeddings": [["0", "0", "0"], ["1.2", "2.24", "3.2"]],
-        "ids": ["id1", "id2"],
-    }
-    with pytest.raises(ValueError) as e:
-        collection.add(**invalid_records)
-    assert "embedding" in str(e.value)
-
-    # Query with invalid embeddings
-    with pytest.raises(ValueError) as e:
-        collection.query(
-            query_embeddings=[["1.1", "2.3", "3.2"]],
-            n_results=1,
-        )
-    assert "embedding" in str(e.value)
-
-    # Update with invalid embeddings
-    invalid_records = {
-        "embeddings": [[[0], [0], [0]], [[1.2], [2.24], [3.2]]],
-        "ids": ["id1", "id2"],
-    }
-    with pytest.raises(ValueError) as e:
-        collection.update(**invalid_records)
-    assert "embedding" in str(e.value)
-
-    # Upsert with invalid embeddings
-    invalid_records = {
-        "embeddings": [[[1.1, 2.3, 3.2]], [[1.2, 2.24, 3.2]]],
-        "ids": ["id1", "id2"],
-    }
-    with pytest.raises(ValueError) as e:
-        collection.upsert(**invalid_records)
-    assert "embedding" in str(e.value)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/__main__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/__main__.py
deleted file mode 100644
index 24c2b7faaf7c0a493ab5decead8bbd4c7c16509d..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/__main__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-import sys
-
-
-if __name__ == "__main__":
-    # debugpy can also be invoked directly rather than via -m. In this case, the first
-    # entry on sys.path is the one added automatically by Python for the directory
-    # containing this file. This means that import debugpy will not work, since we need
-    # the parent directory of debugpy/ to be in sys.path, rather than debugpy/ itself.
-    #
-    # The other issue is that many other absolute imports will break, because they
-    # will be resolved relative to debugpy/ - e.g. `import debugger` will then try
-    # to import debugpy/debugger.py.
-    #
-    # To fix both, we need to replace the automatically added entry such that it points
-    # at parent directory of debugpy/ instead of debugpy/ itself, import debugpy with that
-    # in sys.path, and then remove the first entry entry altogether, so that it doesn't
-    # affect any further imports we might do. For example, suppose the user did:
-    #
-    #   python /foo/bar/debugpy ...
-    #
-    # At the beginning of this script, sys.path will contain "/foo/bar/debugpy" as the
-    # first entry. What we want is to replace it with "/foo/bar', then import debugpy
-    # with that in effect, and then remove the replaced entry before any more
-    # code runs. The imported debugpy module will remain in sys.modules, and thus all
-    # future imports of it or its submodules will resolve accordingly.
-    if "debugpy" not in sys.modules:
-        # Do not use dirname() to walk up - this can be a relative path, e.g. ".".
-        sys.path[0] = sys.path[0] + "/../"
-        import debugpy  # noqa
-
-        del sys.path[0]
-
-    from debugpy.server import cli
-
-    cli.main()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/any_url.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/any_url.py
deleted file mode 100644
index 6d930aa53f3e84492bbfc0687c369f8a737281af..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/any_url.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os
-import urllib
-import urllib.parse
-import urllib.request
-from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
-
-import numpy as np
-from pydantic import AnyUrl as BaseAnyUrl
-from pydantic import errors, parse_obj_as
-
-from docarray.typing.abstract_type import AbstractType
-from docarray.typing.proto_register import _register_proto
-
-if TYPE_CHECKING:
-    from pydantic import BaseConfig
-    from pydantic.fields import ModelField
-    from pydantic.networks import Parts
-
-    from docarray.proto import NodeProto
-
-T = TypeVar('T', bound='AnyUrl')
-
-
-@_register_proto(proto_type_name='any_url')
-class AnyUrl(BaseAnyUrl, AbstractType):
-    host_required = (
-        False  # turn off host requirement to allow passing of local paths as URL
-    )
-
-    def _to_node_protobuf(self) -> 'NodeProto':
-        """Convert Document into a NodeProto protobuf message. This function should
-        be called when the Document is nested into another Document that need to
-        be converted into a protobuf
-
-        :return: the nested item protobuf message
-        """
-        from docarray.proto import NodeProto
-
-        return NodeProto(text=str(self), type=self._proto_type_name)
-
-    @classmethod
-    def validate(
-        cls: Type[T],
-        value: Union[T, np.ndarray, Any],
-        field: 'ModelField',
-        config: 'BaseConfig',
-    ) -> T:
-        import os
-
-        abs_path: Union[T, np.ndarray, Any]
-        if (
-            isinstance(value, str)
-            and not value.startswith('http')
-            and not os.path.isabs(value)
-        ):
-            input_is_relative_path = True
-            abs_path = os.path.abspath(value)
-        else:
-            input_is_relative_path = False
-            abs_path = value
-
-        url = super().validate(abs_path, field, config)  # basic url validation
-
-        if input_is_relative_path:
-            return cls(str(value), scheme=None)
-        else:
-            return cls(str(url), scheme=None)
-
-    @classmethod
-    def validate_parts(cls, parts: 'Parts', validate_port: bool = True) -> 'Parts':
-        """
-        A method used to validate parts of a URL.
-        Our URLs should be able to function both in local and remote settings.
-        Therefore, we allow missing `scheme`, making it possible to pass a file
-        path without prefix.
-        If `scheme` is missing, we assume it is a local file path.
-        """
-        scheme = parts['scheme']
-        if scheme is None:
-            # allow missing scheme, unlike pydantic
-            pass
-
-        elif cls.allowed_schemes and scheme.lower() not in cls.allowed_schemes:
-            raise errors.UrlSchemePermittedError(set(cls.allowed_schemes))
-
-        if validate_port:
-            cls._validate_port(parts['port'])
-
-        user = parts['user']
-        if cls.user_required and user is None:
-            raise errors.UrlUserInfoError()
-
-        return parts
-
-    @classmethod
-    def build(
-        cls,
-        *,
-        scheme: str,
-        user: Optional[str] = None,
-        password: Optional[str] = None,
-        host: str,
-        port: Optional[str] = None,
-        path: Optional[str] = None,
-        query: Optional[str] = None,
-        fragment: Optional[str] = None,
-        **_kwargs: str,
-    ) -> str:
-        """
-        Build a URL from its parts.
-        The only difference from the pydantic implementation is that we allow
-        missing `scheme`, making it possible to pass a file path without prefix.
-        """
-
-        # allow missing scheme, unlike pydantic
-        scheme_ = scheme if scheme is not None else ''
-        url = super().build(
-            scheme=scheme_,
-            user=user,
-            password=password,
-            host=host,
-            port=port,
-            path=path,
-            query=query,
-            fragment=fragment,
-            **_kwargs,
-        )
-        if scheme is None and url.startswith('://'):
-            # remove the `://` prefix, since scheme is missing
-            url = url[3:]
-        return url
-
-    @classmethod
-    def from_protobuf(cls: Type[T], pb_msg: 'str') -> T:
-        """
-        Read url from a proto msg.
-        :param pb_msg:
-        :return: url
-        """
-        return parse_obj_as(cls, pb_msg)
-
-    def load_bytes(self, timeout: Optional[float] = None) -> bytes:
-        """Convert url to bytes. This will either load or download the file and save
-        it into a bytes object.
-        :param timeout: timeout for urlopen. Only relevant if URI is not local
-        :return: bytes.
-        """
-        if urllib.parse.urlparse(self).scheme in {'http', 'https', 'data'}:
-            req = urllib.request.Request(self, headers={'User-Agent': 'Mozilla/5.0'})
-            urlopen_kwargs = {'timeout': timeout} if timeout is not None else {}
-            with urllib.request.urlopen(req, **urlopen_kwargs) as fp:  # type: ignore
-                return fp.read()
-        elif os.path.exists(self):
-            with open(self, 'rb') as fp:
-                return fp.read()
-        else:
-            raise FileNotFoundError(f'`{self}` is not a URL or a valid local path')
diff --git a/spaces/Suniilkumaar/SwapMukham/face_parsing/model.py b/spaces/Suniilkumaar/SwapMukham/face_parsing/model.py
deleted file mode 100644
index 5119e751c3ae18e4dc1eecde7bfcb5bf9c62fb92..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/SwapMukham/face_parsing/model.py
+++ /dev/null
@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from .resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan,
-                out_chan,
-                kernel_size = ks,
-                stride = stride,
-                padding = padding,
-                bias = False)
-        self.bn = nn.BatchNorm2d(out_chan)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
-    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.conv_out(x)
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(ContextPath, self).__init__()
-        self.resnet = Resnet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-        self.init_weight()
-
-    def forward(self, x):
-        H0, W0 = x.size()[2:]
-        feat8, feat16, feat32 = self.resnet(x)
-        H8, W8 = feat8.size()[2:]
-        H16, W16 = feat16.size()[2:]
-        H32, W32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(SpatialPath, self).__init__()
-        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
-        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv1(x)
-        feat = self.conv2(feat)
-        feat = self.conv3(feat)
-        feat = self.conv_out(feat)
-        return feat
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan,
-                out_chan//4,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.conv2 = nn.Conv2d(out_chan//4,
-                out_chan,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
-    def __init__(self, n_classes, *args, **kwargs):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        ## here self.sp is deleted
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, n_classes)
-        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
-        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
-        self.init_weight()
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
-        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        feat_out = self.conv_out(feat_fuse)
-        feat_out16 = self.conv_out16(feat_cp8)
-        feat_out32 = self.conv_out32(feat_cp16)
-
-        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
-        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
-        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
-        return feat_out, feat_out16, feat_out32
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
-        for name, child in self.named_children():
-            child_wd_params, child_nowd_params = child.get_params()
-            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
-                lr_mul_wd_params += child_wd_params
-                lr_mul_nowd_params += child_nowd_params
-            else:
-                wd_params += child_wd_params
-                nowd_params += child_nowd_params
-        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
-    net = BiSeNet(19)
-    net.cuda()
-    net.eval()
-    in_ten = torch.randn(16, 3, 640, 480).cuda()
-    out, out16, out32 = net(in_ten)
-    print(out.shape)
-
-    net.get_params()
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/utils.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/utils.py
deleted file mode 100644
index c5befb8e56ece50b5fecfd007b26f8a29124c0bd..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
-    """Get hostname and username.
-
-    Return empty string if exception raised, e.g. ``getpass.getuser()`` will
-    lead to error in docker container
-    """
-    host = ''
-    try:
-        host = f'{getuser()}@{gethostname()}'
-    except Exception as e:
-        warnings.warn(f'Host or user not found: {str(e)}')
-    finally:
-        return host
-
-
-def get_time_str():
-    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
-    """Initialize an object from dict.
-
-    The dict must contain the key "type", which indicates the object type, it
-    can be either a string or type, such as "list" or ``list``. Remaining
-    fields are treated as the arguments for constructing the object.
-
-    Args:
-        info (dict): Object types and arguments.
-        parent (:class:`module`): Module which may containing expected object
-            classes.
-        default_args (dict, optional): Default arguments for initializing the
-            object.
-
-    Returns:
-        any type: Object built from the dict.
-    """
-    assert isinstance(info, dict) and 'type' in info
-    assert isinstance(default_args, dict) or default_args is None
-    args = info.copy()
-    obj_type = args.pop('type')
-    if mmcv.is_str(obj_type):
-        if parent is not None:
-            obj_type = getattr(parent, obj_type)
-        else:
-            obj_type = sys.modules[obj_type]
-    elif not isinstance(obj_type, type):
-        raise TypeError('type must be a str or valid type, but '
-                        f'got {type(obj_type)}')
-    if default_args is not None:
-        for name, value in default_args.items():
-            args.setdefault(name, value)
-    return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-        rank_shift (bool): Whether to add rank number to the random seed to
-            have different random seed in different threads. Default: False.
-    """
-    if use_rank_shift:
-        rank, _ = mmcv.runner.get_dist_info()
-        seed += rank
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
diff --git a/spaces/TH5314/newbing/src/components/chat-message.tsx b/spaces/TH5314/newbing/src/components/chat-message.tsx
deleted file mode 100644
index bf272d8d7005cfd06c53bd213e09ea217e803549..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/components/chat-message.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
-  message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
-  useEffect(() => {
-    if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
-      window.scrollBy(0, 200)
-    }
-  }, [message.text])
-
-  return message.text ? (
-    <div
-      className={cn('text-message', message.author)}
-      {...props}
-    >
-      <div className="text-message-content">
-        <MemoizedReactMarkdown
-          linkTarget="_blank"
-          className="prose break-words dark:prose-invert prose-p:leading-relaxed prose-pre:p-0"
-          remarkPlugins={[remarkGfm, remarkMath, supersub, remarkBreaks]}
-          components={{
-            img(obj) {
-              try {
-                const uri = new URL(obj.src!)
-                const w = uri.searchParams.get('w')
-                const h = uri.searchParams.get('h')
-                if (w && h) {
-                  uri.searchParams.delete('w')
-                  uri.searchParams.delete('h')
-                  return <a style={{ float: 'left', maxWidth: '50%' }} href={uri.toString()} target="_blank" rel="noopener noreferrer"><img src={obj.src} alt={obj.alt} width={w!} height={h!}/></a>
-                }
-              } catch (e) {
-              }
-              return <img src={obj.src} alt={obj.alt} title={obj.title} />
-            },
-            p({ children }) {
-              return <p className="mb-2">{children}</p>
-            },
-            code({ node, inline, className, children, ...props }) {
-              if (children.length) {
-                if (children[0] == '▍') {
-                  return (
-                    <span className="mt-1 animate-pulse cursor-default">▍</span>
-                  )
-                }
-
-                children[0] = (children[0] as string).replace('`▍`', '▍')
-              }
-
-              const match = /language-(\w+)/.exec(className || '')
-
-              if (inline) {
-                return (
-                  <code className={className} {...props}>
-                    {children}
-                  </code>
-                )
-              }
-
-              return (
-                <CodeBlock
-                  key={Math.random()}
-                  language={(match && match[1]) || ''}
-                  value={String(children).replace(/\n$/, '')}
-                  {...props}
-                />
-              )
-            }
-          }}
-        >
-          {message.text}
-        </MemoizedReactMarkdown>
-      </div>
-      <div className="text-message-footer">
-        {message.author === 'bot' && <LearnMore sourceAttributions={message.sourceAttributions} />}
-        {message.author === 'bot' && <TurnCounter throttling={message.throttling} />}
-      </div>
-    </div>
-  ) : null
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/filetypes.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/filetypes.py
deleted file mode 100644
index 5948570178f3e6e79d1ff574241d09d4d8ed78de..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/filetypes.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""Filetype information.
-"""
-
-from typing import Tuple
-
-from pip._internal.utils.misc import splitext
-
-WHEEL_EXTENSION = ".whl"
-BZ2_EXTENSIONS: Tuple[str, ...] = (".tar.bz2", ".tbz")
-XZ_EXTENSIONS: Tuple[str, ...] = (
-    ".tar.xz",
-    ".txz",
-    ".tlz",
-    ".tar.lz",
-    ".tar.lzma",
-)
-ZIP_EXTENSIONS: Tuple[str, ...] = (".zip", WHEEL_EXTENSION)
-TAR_EXTENSIONS: Tuple[str, ...] = (".tar.gz", ".tgz", ".tar")
-ARCHIVE_EXTENSIONS = ZIP_EXTENSIONS + BZ2_EXTENSIONS + TAR_EXTENSIONS + XZ_EXTENSIONS
-
-
-def is_archive_file(name: str) -> bool:
-    """Return True if `name` is a considered as an archive file."""
-    ext = splitext(name)[1].lower()
-    if ext in ARCHIVE_EXTENSIONS:
-        return True
-    return False
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcharsetprober.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcharsetprober.py
deleted file mode 100644
index 666307e8fe0608c69f2b6578a49794e1e20a139a..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcharsetprober.py
+++ /dev/null
@@ -1,95 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#   Proofpoint, Inc.
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import Optional, Union
-
-from .chardistribution import CharDistributionAnalysis
-from .charsetprober import CharSetProber
-from .codingstatemachine import CodingStateMachine
-from .enums import LanguageFilter, MachineState, ProbingState
-
-
-class MultiByteCharSetProber(CharSetProber):
-    """
-    MultiByteCharSetProber
-    """
-
-    def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None:
-        super().__init__(lang_filter=lang_filter)
-        self.distribution_analyzer: Optional[CharDistributionAnalysis] = None
-        self.coding_sm: Optional[CodingStateMachine] = None
-        self._last_char = bytearray(b"\0\0")
-
-    def reset(self) -> None:
-        super().reset()
-        if self.coding_sm:
-            self.coding_sm.reset()
-        if self.distribution_analyzer:
-            self.distribution_analyzer.reset()
-        self._last_char = bytearray(b"\0\0")
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
-        assert self.coding_sm is not None
-        assert self.distribution_analyzer is not None
-
-        for i, byte in enumerate(byte_str):
-            coding_state = self.coding_sm.next_state(byte)
-            if coding_state == MachineState.ERROR:
-                self.logger.debug(
-                    "%s %s prober hit error at byte %s",
-                    self.charset_name,
-                    self.language,
-                    i,
-                )
-                self._state = ProbingState.NOT_ME
-                break
-            if coding_state == MachineState.ITS_ME:
-                self._state = ProbingState.FOUND_IT
-                break
-            if coding_state == MachineState.START:
-                char_len = self.coding_sm.get_current_charlen()
-                if i == 0:
-                    self._last_char[1] = byte
-                    self.distribution_analyzer.feed(self._last_char, char_len)
-                else:
-                    self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
-
-        self._last_char[0] = byte_str[-1]
-
-        if self.state == ProbingState.DETECTING:
-            if self.distribution_analyzer.got_enough_data() and (
-                self.get_confidence() > self.SHORTCUT_THRESHOLD
-            ):
-                self._state = ProbingState.FOUND_IT
-
-        return self.state
-
-    def get_confidence(self) -> float:
-        assert self.distribution_analyzer is not None
-        return self.distribution_analyzer.get_confidence()
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tools/train_net.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tools/train_net.py
deleted file mode 100644
index 6ebf5f60a2197633fa418e83aadfedb824537b8e..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tools/train_net.py
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-A main training script.
-
-This scripts reads a given config file and runs the training or evaluation.
-It is an entry point that is made to train standard models in detectron2.
-
-In order to let one script support training of many models,
-this script contains logic that are specific to these built-in models and therefore
-may not be suitable for your own project.
-For example, your research project perhaps only needs a single "evaluator".
-
-Therefore, we recommend you to use detectron2 as an library and take
-this file as an example of how to use the library.
-You may want to write your own script with your datasets and other customizations.
-"""
-
-import logging
-import os
-from collections import OrderedDict
-import torch
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
-from detectron2.evaluation import (
-    CityscapesInstanceEvaluator,
-    CityscapesSemSegEvaluator,
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-    LVISEvaluator,
-    PascalVOCDetectionEvaluator,
-    SemSegEvaluator,
-    verify_results,
-)
-from detectron2.modeling import GeneralizedRCNNWithTTA
-
-
-def build_evaluator(cfg, dataset_name, output_folder=None):
-    """
-    Create evaluator(s) for a given dataset.
-    This uses the special metadata "evaluator_type" associated with each builtin dataset.
-    For your own dataset, you can simply create an evaluator manually in your
-    script and do not have to worry about the hacky if-else logic here.
-    """
-    if output_folder is None:
-        output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-    evaluator_list = []
-    evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-    if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
-        evaluator_list.append(
-            SemSegEvaluator(
-                dataset_name,
-                distributed=True,
-                output_dir=output_folder,
-            )
-        )
-    if evaluator_type in ["coco", "coco_panoptic_seg"]:
-        evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
-    if evaluator_type == "coco_panoptic_seg":
-        evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-    if evaluator_type == "cityscapes_instance":
-        assert (
-            torch.cuda.device_count() > comm.get_rank()
-        ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesInstanceEvaluator(dataset_name)
-    if evaluator_type == "cityscapes_sem_seg":
-        assert (
-            torch.cuda.device_count() > comm.get_rank()
-        ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesSemSegEvaluator(dataset_name)
-    elif evaluator_type == "pascal_voc":
-        return PascalVOCDetectionEvaluator(dataset_name)
-    elif evaluator_type == "lvis":
-        return LVISEvaluator(dataset_name, output_dir=output_folder)
-    if len(evaluator_list) == 0:
-        raise NotImplementedError(
-            "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type)
-        )
-    elif len(evaluator_list) == 1:
-        return evaluator_list[0]
-    return DatasetEvaluators(evaluator_list)
-
-
-class Trainer(DefaultTrainer):
-    """
-    We use the "DefaultTrainer" which contains pre-defined default logic for
-    standard training workflow. They may not work for you, especially if you
-    are working on a new research project. In that case you can write your
-    own training loop. You can use "tools/plain_train_net.py" as an example.
-    """
-
-    @classmethod
-    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
-        return build_evaluator(cfg, dataset_name, output_folder)
-
-    @classmethod
-    def test_with_TTA(cls, cfg, model):
-        logger = logging.getLogger("detectron2.trainer")
-        # In the end of training, run an evaluation with TTA
-        # Only support some R-CNN models.
-        logger.info("Running inference with test-time augmentation ...")
-        model = GeneralizedRCNNWithTTA(cfg, model)
-        evaluators = [
-            cls.build_evaluator(
-                cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
-            )
-            for name in cfg.DATASETS.TEST
-        ]
-        res = cls.test(cfg, model, evaluators)
-        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
-        return res
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    if args.eval_only:
-        model = Trainer.build_model(cfg)
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        res = Trainer.test(cfg, model)
-        if cfg.TEST.AUG.ENABLED:
-            res.update(Trainer.test_with_TTA(cfg, model))
-        if comm.is_main_process():
-            verify_results(cfg, res)
-        return res
-
-    """
-    If you'd like to do anything fancier than the standard training logic,
-    consider writing your own training loop (see plain_train_net.py) or
-    subclassing the trainer.
-    """
-    trainer = Trainer(cfg)
-    trainer.resume_or_load(resume=args.resume)
-    if cfg.TEST.AUG.ENABLED:
-        trainer.register_hooks(
-            [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
-        )
-    return trainer.train()
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )
diff --git a/spaces/TencentARC/VLog/models/kts_model.py b/spaces/TencentARC/VLog/models/kts_model.py
deleted file mode 100644
index 778c75a62403ddf4a0c971426d302e54475c8494..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/kts_model.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch
-import numpy as np
-from models.kts_src.kts_utils import cpd_auto, l2_normalize_np_array
-
-class VideoSegmentor:
-    def __init__(self, alpha, beta):
-        self.alpha = alpha
-        self.beta = beta
-        
-    def __call__(self, video_features, video_length):
-        K = l2_normalize_np_array(video_features)
-        K = np.dot(K, K.T)
-        clip_num = K.shape[0]
-        max_seg_num = clip_num // self.alpha
-        
-        cps, _ = cpd_auto(K, max_seg_num - 1, 0)
-        seg_num = len(cps) + 1
-
-        seg_points = [x * self.beta for x in cps]
-        seg_points = np.insert(seg_points, 0, 0)
-        seg_points = np.append(seg_points, video_length)
-    
-        seg_windows = [[int(seg_points[i]), int(seg_points[i+1])] for i in range(seg_num)]
-        return seg_windows
diff --git a/spaces/ThirdEyeData/Image-Blur-Prediction/app.py b/spaces/ThirdEyeData/Image-Blur-Prediction/app.py
deleted file mode 100644
index 415da477a3983d46f1f90d8ce3abc81afa6b3723..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Image-Blur-Prediction/app.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore", category=UserWarning)
-import streamlit as st
-import os
-
-from PIL import Image
-import tempfile
-from tempfile import NamedTemporaryFile
-from io import BytesIO
-
-import pickle
-import cv2
-import numpy as np
-from sklearn.ensemble import RandomForestClassifier
-
-st.title("Image Blur Prediction System")
-
-st.write("""Image Bluriness Prediction Model allows users to analyze the bluriness of images. 
-            It utilizes a pre-trained random forest classifier model to predict whether an image is blurry or not. 
-            The application provides two options for image selection: 
-            users can either upload their own image or choose from a set of sample images. 
-            Once an image is selected, the application calculates the Variance of Laplacian (VoL) score, 
-            a metric used to measure image bluriness. The classifier model then predicts whether the image is blurry or not based 
-            on the VoL score. The prediction result and the VoL score are displayed to the user. 
-            The application also includes a sidebar that showcases sample images for quick testing.""")
-
-# Load the saved random forest classifier model
-with open('image_blur_model.pkl', 'rb') as f:
-    clf = pickle.load(f)
-    
-# For sample images as a sidebar
-images = ["test2.jpg","test1.jpg","test4.jpg","test5.jpg","test6.jpg","download1.jpg","download2.jpg","sample1.jpg",
-          "download3.jpg","download4.jpg","download.png","img1.jpg","img17.jpg"]
-with st.sidebar:
-    st.write("Choose an image")
-    st.image(images)
-
-
-# Function to predict bluriness
-def predict_bluriness(image):
-    # Convert the image to grayscale and compute the VoL metric
-    gray = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
-    vol = cv2.Laplacian(gray, cv2.CV_64F).var()
-
-    # Make a prediction using the loaded model
-    prediction = clf.predict([[vol]])
-
-    # Return the prediction result and VoL value
-    return prediction, vol
-
-
-# CSS code for changing color of the button
-st.markdown("""
-    <style>
-    .stButton button {
-        background-color: #668f45;                        
-        color: white;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-# File uploader
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-# Predict button
-if st.button("Click to Predict"):
-    image = None
-
-    # Read the uploaded image if available
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-    
-    # Perform prediction if image is available
-    if image is not None:
-        # Perform prediction
-        prediction, vol = predict_bluriness(image)
-
-        # Display prediction result and VoL value
-        st.write("**Prediction:**", "The image is not blurry." if prediction == 1 else "The image is blurry.")
-        st.write("**Variance of Laplacian Score:**", vol)
-
-
-st.write("""
-**For the detailed Description of the Model, Project and Technologies used, please go through our Documentation**  
-""")
-
-url = 'https://huggingface.co/spaces/ThirdEyeData/Image-Blur-Prediction/blob/main/Documentation.md'
-
-st.markdown(f'''
-<a href={url}><button style="background-color: #668f45;">Click to Read Model's Documentation</button></a>
-''',
-unsafe_allow_html=True)
diff --git a/spaces/Toritto/Genshin-impact-IA-project-v1/lib/infer_pack/models.py b/spaces/Toritto/Genshin-impact-IA-project-v1/lib/infer_pack/models.py
deleted file mode 100644
index 3665d03bc0514a6ed07d3372ea24717dae1e0a65..0000000000000000000000000000000000000000
--- a/spaces/Toritto/Genshin-impact-IA-project-v1/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1142 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Truepic/watermarked-content-credentials/README.md b/spaces/Truepic/watermarked-content-credentials/README.md
deleted file mode 100644
index 3bcac55934aa20be0d3aa8d97a899021681f0626..0000000000000000000000000000000000000000
--- a/spaces/Truepic/watermarked-content-credentials/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Watermarked Content Credentials
-emoji: 📊
-colorFrom: pink
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TushDeMort/yolo/utils/general.py b/spaces/TushDeMort/yolo/utils/general.py
deleted file mode 100644
index decdcc64ecd72927bc6c185683977854e593711d..0000000000000000000000000000000000000000
--- a/spaces/TushDeMort/yolo/utils/general.py
+++ /dev/null
@@ -1,892 +0,0 @@
-# YOLOR general utils
-
-import glob
-import logging
-import math
-import os
-import platform
-import random
-import re
-import subprocess
-import time
-from pathlib import Path
-
-import cv2
-import numpy as np
-import pandas as pd
-import torch
-import torchvision
-import yaml
-
-from utils.google_utils import gsutil_getsize
-from utils.metrics import fitness
-from utils.torch_utils import init_torch_seeds
-
-# Settings
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads
-
-
-def set_logging(rank=-1):
-    logging.basicConfig(
-        format="%(message)s",
-        level=logging.INFO if rank in [-1, 0] else logging.WARN)
-
-
-def init_seeds(seed=0):
-    # Initialize random number generator (RNG) seeds
-    random.seed(seed)
-    np.random.seed(seed)
-    init_torch_seeds(seed)
-
-
-def get_latest_run(search_dir='.'):
-    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
-    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
-    return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def isdocker():
-    # Is environment a Docker container
-    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()
-
-
-def emojis(str=''):
-    # Return platform-dependent emoji-safe version of string
-    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def check_online():
-    # Check internet connectivity
-    import socket
-    try:
-        socket.create_connection(("1.1.1.1", 443), 5)  # check host accesability
-        return True
-    except OSError:
-        return False
-
-
-def check_git_status():
-    # Recommend 'git pull' if code is out of date
-    print(colorstr('github: '), end='')
-    try:
-        assert Path('.git').exists(), 'skipping check (not a git repository)'
-        assert not isdocker(), 'skipping check (Docker image)'
-        assert check_online(), 'skipping check (offline)'
-
-        cmd = 'git fetch && git config --get remote.origin.url'
-        url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git')  # github repo url
-        branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
-        n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
-        if n > 0:
-            s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \
-                f"Use 'git pull' to update or 'git clone {url}' to download latest."
-        else:
-            s = f'up to date with {url} ✅'
-        print(emojis(s))  # emoji-safe
-    except Exception as e:
-        print(e)
-
-
-def check_requirements(requirements='requirements.txt', exclude=()):
-    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
-    import pkg_resources as pkg
-    prefix = colorstr('red', 'bold', 'requirements:')
-    if isinstance(requirements, (str, Path)):  # requirements.txt file
-        file = Path(requirements)
-        if not file.exists():
-            print(f"{prefix} {file.resolve()} not found, check failed.")
-            return
-        requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
-    else:  # list or tuple of packages
-        requirements = [x for x in requirements if x not in exclude]
-
-    n = 0  # number of packages updates
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
-            n += 1
-            print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...")
-            print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode())
-
-    if n:  # if packages updated
-        source = file.resolve() if 'file' in locals() else requirements
-        s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-            f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-        print(emojis(s))  # emoji-safe
-
-
-def check_img_size(img_size, s=32):
-    # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
-    if new_size != img_size:
-        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
-    return new_size
-
-
-def check_imshow():
-    # Check if environment supports image displays
-    try:
-        assert not isdocker(), 'cv2.imshow() is disabled in Docker environments'
-        cv2.imshow('test', np.zeros((1, 1, 3)))
-        cv2.waitKey(1)
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-        return True
-    except Exception as e:
-        print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
-        return False
-
-
-def check_file(file):
-    # Search for file if not found
-    if Path(file).is_file() or file == '':
-        return file
-    else:
-        files = glob.glob('./**/' + file, recursive=True)  # find file
-        assert len(files), f'File Not Found: {file}'  # assert file was found
-        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
-        return files[0]  # return file
-
-
-def check_dataset(dict):
-    # Download dataset if not found locally
-    val, s = dict.get('val'), dict.get('download')
-    if val and len(val):
-        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
-        if not all(x.exists() for x in val):
-            print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
-            if s and len(s):  # download script
-                print('Downloading %s ...' % s)
-                if s.startswith('http') and s.endswith('.zip'):  # URL
-                    f = Path(s).name  # filename
-                    torch.hub.download_url_to_file(s, f)
-                    r = os.system('unzip -q %s -d ../ && rm %s' % (f, f))  # unzip
-                else:  # bash script
-                    r = os.system(s)
-                print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure'))  # analyze return value
-            else:
-                raise Exception('Dataset not found.')
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
-    # Cleans a string by replacing special characters with underscore _
-    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
-    # lambda function for sinusoidal ramp from y1 to y2
-    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
-    colors = {'black': '\033[30m',  # basic colors
-              'red': '\033[31m',
-              'green': '\033[32m',
-              'yellow': '\033[33m',
-              'blue': '\033[34m',
-              'magenta': '\033[35m',
-              'cyan': '\033[36m',
-              'white': '\033[37m',
-              'bright_black': '\033[90m',  # bright colors
-              'bright_red': '\033[91m',
-              'bright_green': '\033[92m',
-              'bright_yellow': '\033[93m',
-              'bright_blue': '\033[94m',
-              'bright_magenta': '\033[95m',
-              'bright_cyan': '\033[96m',
-              'bright_white': '\033[97m',
-              'end': '\033[0m',  # misc
-              'bold': '\033[1m',
-              'underline': '\033[4m'}
-    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-def labels_to_class_weights(labels, nc=80):
-    # Get class weights (inverse frequency) from training labels
-    if labels[0] is None:  # no labels loaded
-        return torch.Tensor()
-
-    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(np.int32)  # labels = [class xywh]
-    weights = np.bincount(classes, minlength=nc)  # occurrences per class
-
-    # Prepend gridpoint count (for uCE training)
-    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
-    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
-
-    weights[weights == 0] = 1  # replace empty bins with 1
-    weights = 1 / weights  # number of targets per class
-    weights /= weights.sum()  # normalize
-    return torch.from_numpy(weights)
-
-
-def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
-    # Produces image weights based on class_weights and image contents
-    class_counts = np.array([np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels])
-    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
-    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
-    return image_weights
-
-
-def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
-    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
-    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
-    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
-    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
-         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-    return x
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
-    return y
-
-
-def xywh2xyxy(x):
-    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
-    return y
-
-
-def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
-    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
-    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
-    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
-    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
-    return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
-    # Convert normalized segments into pixel segments, shape (n,2)
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * x[:, 0] + padw  # top left x
-    y[:, 1] = h * x[:, 1] + padh  # top left y
-    return y
-
-
-def segment2box(segment, width=640, height=640):
-    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
-    x, y = segment.T  # segment xy
-    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
-    x, y, = x[inside], y[inside]
-    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
-
-
-def segments2boxes(segments):
-    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
-    boxes = []
-    for s in segments:
-        x, y = s.T  # segment xy
-        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
-    return xyxy2xywh(np.array(boxes))  # cls, xywh
-
-
-def resample_segments(segments, n=1000):
-    # Up-sample an (n,2) segment
-    for i, s in enumerate(segments):
-        s = np.concatenate((s, s[0:1, :]), axis=0)
-        x = np.linspace(0, len(s) - 1, n)
-        xp = np.arange(len(s))
-        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
-    return segments
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2]] -= pad[0]  # x padding
-    coords[:, [1, 3]] -= pad[1]  # y padding
-    coords[:, :4] /= gain
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, img_shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    boxes[:, 0].clamp_(0, img_shape[1])  # x1
-    boxes[:, 1].clamp_(0, img_shape[0])  # y1
-    boxes[:, 2].clamp_(0, img_shape[1])  # x2
-    boxes[:, 3].clamp_(0, img_shape[0])  # y2
-
-
-def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
-    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
-    box2 = box2.T
-
-    # Get the coordinates of bounding boxes
-    if x1y1x2y2:  # x1, y1, x2, y2 = box1
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-    else:  # transform from xywh to xyxy
-        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
-        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
-        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
-        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
-
-    # Intersection area
-    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
-            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
-
-    # Union Area
-    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    iou = inter / union
-
-    if GIoU or DIoU or CIoU:
-        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
-        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
-        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
-            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
-                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
-            if DIoU:
-                return iou - rho2 / c2  # DIoU
-            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
-                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2)
-                with torch.no_grad():
-                    alpha = v / (v - iou + (1 + eps))
-                return iou - (rho2 / c2 + v * alpha)  # CIoU
-        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-            c_area = cw * ch + eps  # convex area
-            return iou - (c_area - union) / c_area  # GIoU
-    else:
-        return iou  # IoU
-
-
-
-
-def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9):
-    # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4
-    box2 = box2.T
-
-    # Get the coordinates of bounding boxes
-    if x1y1x2y2:  # x1, y1, x2, y2 = box1
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-    else:  # transform from xywh to xyxy
-        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
-        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
-        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
-        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
-
-    # Intersection area
-    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
-            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
-
-    # Union Area
-    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    # change iou into pow(iou+eps)
-    # iou = inter / union
-    iou = torch.pow(inter/union + eps, alpha)
-    # beta = 2 * alpha
-    if GIoU or DIoU or CIoU:
-        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
-        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
-        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            c2 = (cw ** 2 + ch ** 2) ** alpha + eps  # convex diagonal
-            rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2)
-            rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2)
-            rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha  # center distance
-            if DIoU:
-                return iou - rho2 / c2  # DIoU
-            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
-                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-                with torch.no_grad():
-                    alpha_ciou = v / ((1 + eps) - inter / union + v)
-                # return iou - (rho2 / c2 + v * alpha_ciou)  # CIoU
-                return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha))  # CIoU
-        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-            # c_area = cw * ch + eps  # convex area
-            # return iou - (c_area - union) / c_area  # GIoU
-            c_area = torch.max(cw * ch + eps, union) # convex area
-            return iou - torch.pow((c_area - union) / c_area + eps, alpha)  # GIoU
-    else:
-        return iou # torch.log(iou+eps) or iou
-
-
-def box_iou(box1, box2):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        # box = 4xn
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-
-    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
-
-
-def wh_iou(wh1, wh2):
-    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
-    wh1 = wh1[:, None]  # [N,1,2]
-    wh2 = wh2[None]  # [1,M,2]
-    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
-    return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
-
-
-def box_giou(box1, box2):
-    """
-    Return generalized intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values
-        for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        # box = 4xn
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-    
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    areai = whi[:, :, 0] * whi[:, :, 1]
-
-    return iou - (areai - union) / areai
-
-
-def box_ciou(box1, box2, eps: float = 1e-7):
-    """
-    Return complete intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-        eps (float, optional): small number to prevent division by zero. Default: 1e-7
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values
-        for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        # box = 4xn
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-    
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps
-
-    # centers of boxes
-    x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2
-    y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2
-    x_g = (box2[:, 0] + box2[:, 2]) / 2
-    y_g = (box2[:, 1] + box2[:, 3]) / 2
-    # The distance between boxes' centers squared.
-    centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2
-
-    w_pred = box1[:, None, 2] - box1[:, None, 0]
-    h_pred = box1[:, None, 3] - box1[:, None, 1]
-
-    w_gt = box2[:, 2] - box2[:, 0]
-    h_gt = box2[:, 3] - box2[:, 1]
-
-    v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
-    with torch.no_grad():
-        alpha = v / (1 - iou + v + eps)
-    return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v
-
-
-def box_diou(box1, box2, eps: float = 1e-7):
-    """
-    Return distance intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-        eps (float, optional): small number to prevent division by zero. Default: 1e-7
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values
-        for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        # box = 4xn
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-    
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps
-
-    # centers of boxes
-    x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2
-    y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2
-    x_g = (box2[:, 0] + box2[:, 2]) / 2
-    y_g = (box2[:, 1] + box2[:, 3]) / 2
-    # The distance between boxes' centers squared.
-    centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2
-
-    # The distance IoU is the IoU penalized by a normalized
-    # distance between boxes' centers squared.
-    return iou - (centers_distance_squared / diagonal_distance_squared)
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=()):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
-    max_det = 300  # maximum number of detections per image
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        if nc == 1:
-            x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
-                                 # so there is no need to multiplicate.
-        else:
-            x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Apply finite constraint
-        # if not torch.isfinite(x).all():
-        #     x = x[torch.isfinite(x).all(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if i.shape[0] > max_det:  # limit detections
-            i = i[:max_det]
-        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=(), kpt_label=False, nc=None, nkpt=None):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-    if nc is None:
-        nc = prediction.shape[2] - 5  if not kpt_label else prediction.shape[2] - 56 # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
-    max_det = 300  # maximum number of detections per image
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0,6), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:5+nc] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            if not kpt_label:
-                conf, j = x[:, 5:].max(1, keepdim=True)
-                x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-            else:
-                kpts = x[:, 6:]
-                conf, j = x[:, 5:6].max(1, keepdim=True)
-                x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres]
-
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Apply finite constraint
-        # if not torch.isfinite(x).all():
-        #     x = x[torch.isfinite(x).all(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if i.shape[0] > max_det:  # limit detections
-            i = i[:max_det]
-        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
-    # Strip optimizer from 'f' to finalize training, optionally save as 's'
-    x = torch.load(f, map_location=torch.device('cpu'))
-    if x.get('ema'):
-        x['model'] = x['ema']  # replace model with ema
-    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
-        x[k] = None
-    x['epoch'] = -1
-    x['model'].half()  # to FP16
-    for p in x['model'].parameters():
-        p.requires_grad = False
-    torch.save(x, s or f)
-    mb = os.path.getsize(s or f) / 1E6  # filesize
-    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
-    # Print mutation results to evolve.txt (for use with train.py --evolve)
-    a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
-    b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
-    c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
-    print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
-
-    if bucket:
-        url = 'gs://%s/evolve.txt' % bucket
-        if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
-            os.system('gsutil cp %s .' % url)  # download evolve.txt if larger than local
-
-    with open('evolve.txt', 'a') as f:  # append result
-        f.write(c + b + '\n')
-    x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0)  # load unique rows
-    x = x[np.argsort(-fitness(x))]  # sort
-    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
-
-    # Save yaml
-    for i, k in enumerate(hyp.keys()):
-        hyp[k] = float(x[0, i + 7])
-    with open(yaml_file, 'w') as f:
-        results = tuple(x[0, :7])
-        c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
-        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
-        yaml.dump(hyp, f, sort_keys=False)
-
-    if bucket:
-        os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket))  # upload
-
-
-def apply_classifier(x, model, img, im0):
-    # applies a second stage classifier to yolo outputs
-    im0 = [im0] if isinstance(im0, np.ndarray) else im0
-    for i, d in enumerate(x):  # per image
-        if d is not None and len(d):
-            d = d.clone()
-
-            # Reshape and pad cutouts
-            b = xyxy2xywh(d[:, :4])  # boxes
-            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
-            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
-            d[:, :4] = xywh2xyxy(b).long()
-
-            # Rescale boxes from img_size to im0 size
-            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
-            # Classes
-            pred_cls1 = d[:, 5].long()
-            ims = []
-            for j, a in enumerate(d):  # per item
-                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
-                im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('test%i.jpg' % j, cutout)
-
-                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
-                im /= 255.0  # 0 - 255 to 0.0 - 1.0
-                ims.append(im)
-
-            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
-            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
-
-    return x
-
-
-def increment_path(path, exist_ok=True, sep=''):
-    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
-    path = Path(path)  # os-agnostic
-    if (path.exists() and exist_ok) or (not path.exists()):
-        return str(path)
-    else:
-        dirs = glob.glob(f"{path}{sep}*")  # similar paths
-        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
-        i = [int(m.groups()[0]) for m in matches if m]  # indices
-        n = max(i) + 1 if i else 2  # increment number
-        return f"{path}{sep}{n}"  # update path
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/DeepAi.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/DeepAi.py
deleted file mode 100644
index 02b08120ec8ef50c91c9237047a4f36c822a7bfc..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/DeepAi.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-import json
-import random
-import hashlib
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://deepai.org'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    def md5(text: str) -> str:
-        return hashlib.md5(text.encode()).hexdigest()[::-1]
-
-
-    def get_api_key(user_agent: str) -> str:
-        part1 = str(random.randint(0, 10**11))
-        part2 = md5(user_agent + md5(user_agent + md5(user_agent + part1 + "x")))
-        
-        return f"tryit-{part1}-{part2}"
-
-    user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
-
-    headers = {
-        "api-key": get_api_key(user_agent),
-        "user-agent": user_agent
-    }
-
-    files = {
-        "chat_style": (None, "chat"),
-        "chatHistory": (None, json.dumps(messages))
-    }
-
-    r = requests.post("https://api.deepai.org/chat_response", headers=headers, files=files, stream=True)
-
-    for chunk in r.iter_content(chunk_size=None):
-        r.raise_for_status()
-        yield chunk.decode()
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/common/utils.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/common/utils.py
deleted file mode 100644
index a3069cd10ce986a1ec249490fa813cae9254bd0d..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/common/utils.py
+++ /dev/null
@@ -1,424 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import io
-import json
-import logging
-import os
-import pickle
-import re
-import shutil
-import urllib
-import urllib.error
-import urllib.request
-from typing import Optional
-from urllib.parse import urlparse
-
-import numpy as np
-import pandas as pd
-import yaml
-from iopath.common.download import download
-from iopath.common.file_io import file_lock, g_pathmgr
-from minigpt4.common.registry import registry
-from torch.utils.model_zoo import tqdm
-from torchvision.datasets.utils import (
-    check_integrity,
-    download_file_from_google_drive,
-    extract_archive,
-)
-
-
-def now():
-    from datetime import datetime
-
-    return datetime.now().strftime("%Y%m%d%H%M")[:-1]
-
-
-def is_url(url_or_filename):
-    parsed = urlparse(url_or_filename)
-    return parsed.scheme in ("http", "https")
-
-
-def get_cache_path(rel_path):
-    return os.path.expanduser(os.path.join(registry.get_path("cache_root"), rel_path))
-
-
-def get_abs_path(rel_path):
-    return os.path.join(registry.get_path("library_root"), rel_path)
-
-
-def load_json(filename):
-    with open(filename, "r") as f:
-        return json.load(f)
-
-
-# The following are adapted from torchvision and vissl
-# torchvision: https://github.com/pytorch/vision
-# vissl: https://github.com/facebookresearch/vissl/blob/main/vissl/utils/download.py
-
-
-def makedir(dir_path):
-    """
-    Create the directory if it does not exist.
-    """
-    is_success = False
-    try:
-        if not g_pathmgr.exists(dir_path):
-            g_pathmgr.mkdirs(dir_path)
-        is_success = True
-    except BaseException:
-        print(f"Error creating directory: {dir_path}")
-    return is_success
-
-
-def get_redirected_url(url: str):
-    """
-    Given a URL, returns the URL it redirects to or the
-    original URL in case of no indirection
-    """
-    import requests
-
-    with requests.Session() as session:
-        with session.get(url, stream=True, allow_redirects=True) as response:
-            if response.history:
-                return response.url
-            else:
-                return url
-
-
-def to_google_drive_download_url(view_url: str) -> str:
-    """
-    Utility function to transform a view URL of google drive
-    to a download URL for google drive
-    Example input:
-        https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp/view
-    Example output:
-        https://drive.google.com/uc?export=download&id=137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp
-    """
-    splits = view_url.split("/")
-    assert splits[-1] == "view"
-    file_id = splits[-2]
-    return f"https://drive.google.com/uc?export=download&id={file_id}"
-
-
-def download_google_drive_url(url: str, output_path: str, output_file_name: str):
-    """
-    Download a file from google drive
-    Downloading an URL from google drive requires confirmation when
-    the file of the size is too big (google drive notifies that
-    anti-viral checks cannot be performed on such files)
-    """
-    import requests
-
-    with requests.Session() as session:
-
-        # First get the confirmation token and append it to the URL
-        with session.get(url, stream=True, allow_redirects=True) as response:
-            for k, v in response.cookies.items():
-                if k.startswith("download_warning"):
-                    url = url + "&confirm=" + v
-
-        # Then download the content of the file
-        with session.get(url, stream=True, verify=True) as response:
-            makedir(output_path)
-            path = os.path.join(output_path, output_file_name)
-            total_size = int(response.headers.get("Content-length", 0))
-            with open(path, "wb") as file:
-                from tqdm import tqdm
-
-                with tqdm(total=total_size) as progress_bar:
-                    for block in response.iter_content(
-                        chunk_size=io.DEFAULT_BUFFER_SIZE
-                    ):
-                        file.write(block)
-                        progress_bar.update(len(block))
-
-
-def _get_google_drive_file_id(url: str) -> Optional[str]:
-    parts = urlparse(url)
-
-    if re.match(r"(drive|docs)[.]google[.]com", parts.netloc) is None:
-        return None
-
-    match = re.match(r"/file/d/(?P<id>[^/]*)", parts.path)
-    if match is None:
-        return None
-
-    return match.group("id")
-
-
-def _urlretrieve(url: str, filename: str, chunk_size: int = 1024) -> None:
-    with open(filename, "wb") as fh:
-        with urllib.request.urlopen(
-            urllib.request.Request(url, headers={"User-Agent": "vissl"})
-        ) as response:
-            with tqdm(total=response.length) as pbar:
-                for chunk in iter(lambda: response.read(chunk_size), ""):
-                    if not chunk:
-                        break
-                    pbar.update(chunk_size)
-                    fh.write(chunk)
-
-
-def download_url(
-    url: str,
-    root: str,
-    filename: Optional[str] = None,
-    md5: Optional[str] = None,
-) -> None:
-    """Download a file from a url and place it in root.
-    Args:
-        url (str): URL to download file from
-        root (str): Directory to place downloaded file in
-        filename (str, optional): Name to save the file under.
-                                  If None, use the basename of the URL.
-        md5 (str, optional): MD5 checksum of the download. If None, do not check
-    """
-    root = os.path.expanduser(root)
-    if not filename:
-        filename = os.path.basename(url)
-    fpath = os.path.join(root, filename)
-
-    makedir(root)
-
-    # check if file is already present locally
-    if check_integrity(fpath, md5):
-        print("Using downloaded and verified file: " + fpath)
-        return
-
-    # expand redirect chain if needed
-    url = get_redirected_url(url)
-
-    # check if file is located on Google Drive
-    file_id = _get_google_drive_file_id(url)
-    if file_id is not None:
-        return download_file_from_google_drive(file_id, root, filename, md5)
-
-    # download the file
-    try:
-        print("Downloading " + url + " to " + fpath)
-        _urlretrieve(url, fpath)
-    except (urllib.error.URLError, IOError) as e:  # type: ignore[attr-defined]
-        if url[:5] == "https":
-            url = url.replace("https:", "http:")
-            print(
-                "Failed download. Trying https -> http instead."
-                " Downloading " + url + " to " + fpath
-            )
-            _urlretrieve(url, fpath)
-        else:
-            raise e
-
-    # check integrity of downloaded file
-    if not check_integrity(fpath, md5):
-        raise RuntimeError("File not found or corrupted.")
-
-
-def download_and_extract_archive(
-    url: str,
-    download_root: str,
-    extract_root: Optional[str] = None,
-    filename: Optional[str] = None,
-    md5: Optional[str] = None,
-    remove_finished: bool = False,
-) -> None:
-    download_root = os.path.expanduser(download_root)
-    if extract_root is None:
-        extract_root = download_root
-    if not filename:
-        filename = os.path.basename(url)
-
-    download_url(url, download_root, filename, md5)
-
-    archive = os.path.join(download_root, filename)
-    print("Extracting {} to {}".format(archive, extract_root))
-    extract_archive(archive, extract_root, remove_finished)
-
-
-def cache_url(url: str, cache_dir: str) -> str:
-    """
-    This implementation downloads the remote resource and caches it locally.
-    The resource will only be downloaded if not previously requested.
-    """
-    parsed_url = urlparse(url)
-    dirname = os.path.join(cache_dir, os.path.dirname(parsed_url.path.lstrip("/")))
-    makedir(dirname)
-    filename = url.split("/")[-1]
-    cached = os.path.join(dirname, filename)
-    with file_lock(cached):
-        if not os.path.isfile(cached):
-            logging.info(f"Downloading {url} to {cached} ...")
-            cached = download(url, dirname, filename=filename)
-    logging.info(f"URL {url} cached in {cached}")
-    return cached
-
-
-# TODO (prigoyal): convert this into RAII-style API
-def create_file_symlink(file1, file2):
-    """
-    Simply create the symlinks for a given file1 to file2.
-    Useful during model checkpointing to symlinks to the
-    latest successful checkpoint.
-    """
-    try:
-        if g_pathmgr.exists(file2):
-            g_pathmgr.rm(file2)
-        g_pathmgr.symlink(file1, file2)
-    except Exception as e:
-        logging.info(f"Could NOT create symlink. Error: {e}")
-
-
-def save_file(data, filename, append_to_json=True, verbose=True):
-    """
-    Common i/o utility to handle saving data to various file formats.
-    Supported:
-        .pkl, .pickle, .npy, .json
-    Specifically for .json, users have the option to either append (default)
-    or rewrite by passing in Boolean value to append_to_json.
-    """
-    if verbose:
-        logging.info(f"Saving data to file: {filename}")
-    file_ext = os.path.splitext(filename)[1]
-    if file_ext in [".pkl", ".pickle"]:
-        with g_pathmgr.open(filename, "wb") as fopen:
-            pickle.dump(data, fopen, pickle.HIGHEST_PROTOCOL)
-    elif file_ext == ".npy":
-        with g_pathmgr.open(filename, "wb") as fopen:
-            np.save(fopen, data)
-    elif file_ext == ".json":
-        if append_to_json:
-            with g_pathmgr.open(filename, "a") as fopen:
-                fopen.write(json.dumps(data, sort_keys=True) + "\n")
-                fopen.flush()
-        else:
-            with g_pathmgr.open(filename, "w") as fopen:
-                fopen.write(json.dumps(data, sort_keys=True) + "\n")
-                fopen.flush()
-    elif file_ext == ".yaml":
-        with g_pathmgr.open(filename, "w") as fopen:
-            dump = yaml.dump(data)
-            fopen.write(dump)
-            fopen.flush()
-    else:
-        raise Exception(f"Saving {file_ext} is not supported yet")
-
-    if verbose:
-        logging.info(f"Saved data to file: {filename}")
-
-
-def load_file(filename, mmap_mode=None, verbose=True, allow_pickle=False):
-    """
-    Common i/o utility to handle loading data from various file formats.
-    Supported:
-        .pkl, .pickle, .npy, .json
-    For the npy files, we support reading the files in mmap_mode.
-    If the mmap_mode of reading is not successful, we load data without the
-    mmap_mode.
-    """
-    if verbose:
-        logging.info(f"Loading data from file: {filename}")
-
-    file_ext = os.path.splitext(filename)[1]
-    if file_ext == ".txt":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = fopen.readlines()
-    elif file_ext in [".pkl", ".pickle"]:
-        with g_pathmgr.open(filename, "rb") as fopen:
-            data = pickle.load(fopen, encoding="latin1")
-    elif file_ext == ".npy":
-        if mmap_mode:
-            try:
-                with g_pathmgr.open(filename, "rb") as fopen:
-                    data = np.load(
-                        fopen,
-                        allow_pickle=allow_pickle,
-                        encoding="latin1",
-                        mmap_mode=mmap_mode,
-                    )
-            except ValueError as e:
-                logging.info(
-                    f"Could not mmap {filename}: {e}. Trying without g_pathmgr"
-                )
-                data = np.load(
-                    filename,
-                    allow_pickle=allow_pickle,
-                    encoding="latin1",
-                    mmap_mode=mmap_mode,
-                )
-                logging.info("Successfully loaded without g_pathmgr")
-            except Exception:
-                logging.info("Could not mmap without g_pathmgr. Trying without mmap")
-                with g_pathmgr.open(filename, "rb") as fopen:
-                    data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1")
-        else:
-            with g_pathmgr.open(filename, "rb") as fopen:
-                data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1")
-    elif file_ext == ".json":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = json.load(fopen)
-    elif file_ext == ".yaml":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = yaml.load(fopen, Loader=yaml.FullLoader)
-    elif file_ext == ".csv":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = pd.read_csv(fopen)
-    else:
-        raise Exception(f"Reading from {file_ext} is not supported yet")
-    return data
-
-
-def abspath(resource_path: str):
-    """
-    Make a path absolute, but take into account prefixes like
-    "http://" or "manifold://"
-    """
-    regex = re.compile(r"^\w+://")
-    if regex.match(resource_path) is None:
-        return os.path.abspath(resource_path)
-    else:
-        return resource_path
-
-
-def makedir(dir_path):
-    """
-    Create the directory if it does not exist.
-    """
-    is_success = False
-    try:
-        if not g_pathmgr.exists(dir_path):
-            g_pathmgr.mkdirs(dir_path)
-        is_success = True
-    except BaseException:
-        logging.info(f"Error creating directory: {dir_path}")
-    return is_success
-
-
-def is_url(input_url):
-    """
-    Check if an input string is a url. look for http(s):// and ignoring the case
-    """
-    is_url = re.match(r"^(?:http)s?://", input_url, re.IGNORECASE) is not None
-    return is_url
-
-
-def cleanup_dir(dir):
-    """
-    Utility for deleting a directory. Useful for cleaning the storage space
-    that contains various training artifacts like checkpoints, data etc.
-    """
-    if os.path.exists(dir):
-        logging.info(f"Deleting directory: {dir}")
-        shutil.rmtree(dir)
-    logging.info(f"Deleted contents of directory: {dir}")
-
-
-def get_file_size(filename):
-    """
-    Given a file, get the size of file in MB
-    """
-    size_in_mb = os.path.getsize(filename) / float(1024**2)
-    return size_in_mb
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/models/blip2.py b/spaces/Vision-CAIR/minigpt4/minigpt4/models/blip2.py
deleted file mode 100644
index bb04c4d95f102266d9f97ddd98050d65f5be7ff2..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/models/blip2.py
+++ /dev/null
@@ -1,221 +0,0 @@
-"""
- Copyright (c) 2023, 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 contextlib
-import logging
-import os
-import time
-import datetime
-
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-import torch.nn.functional as F
-
-import minigpt4.common.dist_utils as dist_utils
-from minigpt4.common.dist_utils import download_cached_file
-from minigpt4.common.utils import is_url
-from minigpt4.common.logger import MetricLogger
-from minigpt4.models.base_model import BaseModel
-from minigpt4.models.Qformer import BertConfig, BertLMHeadModel
-from minigpt4.models.eva_vit import create_eva_vit_g
-from transformers import BertTokenizer
-
-
-class Blip2Base(BaseModel):
-    @classmethod
-    def init_tokenizer(cls):
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-        tokenizer.add_special_tokens({"bos_token": "[DEC]"})
-        return tokenizer
-
-    def maybe_autocast(self, dtype=torch.float16):
-        # if on cpu, don't use autocast
-        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
-        enable_autocast = self.device != torch.device("cpu")
-
-        if enable_autocast:
-            return torch.cuda.amp.autocast(dtype=dtype)
-        else:
-            return contextlib.nullcontext()
-
-    @classmethod
-    def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
-        encoder_config = BertConfig.from_pretrained("bert-base-uncased")
-        encoder_config.encoder_width = vision_width
-        # insert cross-attention layer every other block
-        encoder_config.add_cross_attention = True
-        encoder_config.cross_attention_freq = cross_attention_freq
-        encoder_config.query_length = num_query_token
-        Qformer = BertLMHeadModel(config=encoder_config)
-        query_tokens = nn.Parameter(
-            torch.zeros(1, num_query_token, encoder_config.hidden_size)
-        )
-        query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
-        return Qformer, query_tokens
-
-    @classmethod
-    def init_vision_encoder(
-        cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
-    ):
-        assert model_name == "eva_clip_g", "vit model must be eva_clip_g for current version of MiniGPT-4"
-        visual_encoder = create_eva_vit_g(
-            img_size, drop_path_rate, use_grad_checkpoint, precision
-        )
-
-        ln_vision = LayerNorm(visual_encoder.num_features)
-        return visual_encoder, ln_vision
-
-    def load_from_pretrained(self, url_or_filename):
-        if is_url(url_or_filename):
-            cached_file = download_cached_file(
-                url_or_filename, check_hash=False, progress=True
-            )
-            checkpoint = torch.load(cached_file, map_location="cpu")
-        elif os.path.isfile(url_or_filename):
-            checkpoint = torch.load(url_or_filename, map_location="cpu")
-        else:
-            raise RuntimeError("checkpoint url or path is invalid")
-
-        state_dict = checkpoint["model"]
-
-        msg = self.load_state_dict(state_dict, strict=False)
-
-        # logging.info("Missing keys {}".format(msg.missing_keys))
-        logging.info("load checkpoint from %s" % url_or_filename)
-
-        return msg
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-def compute_sim_matrix(model, data_loader, **kwargs):
-    k_test = kwargs.pop("k_test")
-
-    metric_logger = MetricLogger(delimiter="  ")
-    header = "Evaluation:"
-
-    logging.info("Computing features for evaluation...")
-    start_time = time.time()
-
-    texts = data_loader.dataset.text
-    num_text = len(texts)
-    text_bs = 256
-    text_ids = []
-    text_embeds = []
-    text_atts = []
-    for i in range(0, num_text, text_bs):
-        text = texts[i : min(num_text, i + text_bs)]
-        text_input = model.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=35,
-            return_tensors="pt",
-        ).to(model.device)
-        text_feat = model.forward_text(text_input)
-        text_embed = F.normalize(model.text_proj(text_feat))
-        text_embeds.append(text_embed)
-        text_ids.append(text_input.input_ids)
-        text_atts.append(text_input.attention_mask)
-
-    text_embeds = torch.cat(text_embeds, dim=0)
-    text_ids = torch.cat(text_ids, dim=0)
-    text_atts = torch.cat(text_atts, dim=0)
-
-    vit_feats = []
-    image_embeds = []
-    for samples in data_loader:
-        image = samples["image"]
-
-        image = image.to(model.device)
-        image_feat, vit_feat = model.forward_image(image)
-        image_embed = model.vision_proj(image_feat)
-        image_embed = F.normalize(image_embed, dim=-1)
-
-        vit_feats.append(vit_feat.cpu())
-        image_embeds.append(image_embed)
-
-    vit_feats = torch.cat(vit_feats, dim=0)
-    image_embeds = torch.cat(image_embeds, dim=0)
-
-    sims_matrix = []
-    for image_embed in image_embeds:
-        sim_q2t = image_embed @ text_embeds.t()
-        sim_i2t, _ = sim_q2t.max(0)
-        sims_matrix.append(sim_i2t)
-    sims_matrix = torch.stack(sims_matrix, dim=0)
-
-    score_matrix_i2t = torch.full(
-        (len(data_loader.dataset.image), len(texts)), -100.0
-    ).to(model.device)
-
-    num_tasks = dist_utils.get_world_size()
-    rank = dist_utils.get_rank()
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-        image_inputs = vit_feats[start + i].repeat(k_test, 1, 1).to(model.device)
-        score = model.compute_itm(
-            image_inputs=image_inputs,
-            text_ids=text_ids[topk_idx],
-            text_atts=text_atts[topk_idx],
-        ).float()
-        score_matrix_i2t[start + i, topk_idx] = score + topk_sim
-
-    sims_matrix = sims_matrix.t()
-    score_matrix_t2i = torch.full(
-        (len(texts), len(data_loader.dataset.image)), -100.0
-    ).to(model.device)
-
-    step = sims_matrix.size(0) // num_tasks + 1
-    start = rank * step
-    end = min(sims_matrix.size(0), start + step)
-
-    for i, sims in enumerate(
-        metric_logger.log_every(sims_matrix[start:end], 50, header)
-    ):
-        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
-        image_inputs = vit_feats[topk_idx.cpu()].to(model.device)
-        score = model.compute_itm(
-            image_inputs=image_inputs,
-            text_ids=text_ids[start + i].repeat(k_test, 1),
-            text_atts=text_atts[start + i].repeat(k_test, 1),
-        ).float()
-        score_matrix_t2i[start + i, topk_idx] = score + topk_sim
-
-    if dist_utils.is_dist_avail_and_initialized():
-        dist.barrier()
-        torch.distributed.all_reduce(
-            score_matrix_i2t, op=torch.distributed.ReduceOp.SUM
-        )
-        torch.distributed.all_reduce(
-            score_matrix_t2i, op=torch.distributed.ReduceOp.SUM
-        )
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    logging.info("Evaluation time {}".format(total_time_str))
-
-    return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/models/builders.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/models/builders.py
deleted file mode 100644
index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/models/builders.py
+++ /dev/null
@@ -1,218 +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.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-import warnings
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
-    CodebooksPatternProvider,
-    DelayedPatternProvider,
-    ParallelPatternProvider,
-    UnrolledPatternProvider,
-    VALLEPattern,
-    MusicLMPattern,
-)
-from ..modules.conditioners import (
-    BaseConditioner,
-    ConditioningProvider,
-    LUTConditioner,
-    T5Conditioner,
-    ConditionFuser,
-    ChromaStemConditioner,
-)
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-
-
-def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
-    klass = {
-        'no_quant': qt.DummyQuantizer,
-        'rvq': qt.ResidualVectorQuantizer
-    }[quantizer]
-    kwargs = dict_from_config(getattr(cfg, quantizer))
-    if quantizer != 'no_quant':
-        kwargs['dimension'] = dimension
-    return klass(**kwargs)
-
-
-def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
-    if encoder_name == 'seanet':
-        kwargs = dict_from_config(getattr(cfg, 'seanet'))
-        encoder_override_kwargs = kwargs.pop('encoder')
-        decoder_override_kwargs = kwargs.pop('decoder')
-        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
-        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
-        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
-        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
-        return encoder, decoder
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
-    """Instantiate a compression model.
-    """
-    if cfg.compression_model == 'encodec':
-        kwargs = dict_from_config(getattr(cfg, 'encodec'))
-        encoder_name = kwargs.pop('autoencoder')
-        quantizer_name = kwargs.pop('quantizer')
-        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
-        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
-        frame_rate = kwargs['sample_rate'] // encoder.hop_length
-        renormalize = kwargs.pop('renormalize', None)
-        renorm = kwargs.pop('renorm')
-        if renormalize is None:
-            renormalize = renorm is not None
-            warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
-        return EncodecModel(encoder, decoder, quantizer,
-                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
-    """Instantiate a transformer LM.
-    """
-    if cfg.lm_model == 'transformer_lm':
-        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
-        n_q = kwargs['n_q']
-        q_modeling = kwargs.pop('q_modeling', None)
-        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
-        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
-        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
-        cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
-        fuser = get_condition_fuser(cfg)
-        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
-        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programatically
-            kwargs['cross_attention'] = True
-        if codebooks_pattern_cfg.modeling is None:
-            assert q_modeling is not None, \
-                'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
-            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
-                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
-            )
-        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
-        return LMModel(
-            pattern_provider=pattern_provider,
-            condition_provider=condition_provider,
-            fuser=fuser,
-            cfg_dropout=cfg_prob,
-            cfg_coef=cfg_coef,
-            attribute_dropout=attribute_dropout,
-            dtype=getattr(torch, cfg.dtype),
-            device=cfg.device,
-            **kwargs
-        ).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected LM model {cfg.lm_model}')
-
-
-def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
-    """Instantiate a conditioning model.
-    """
-    device = cfg.device
-    duration = cfg.dataset.segment_duration
-    cfg = getattr(cfg, "conditioners")
-    cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
-    conditioners: tp.Dict[str, BaseConditioner] = {}
-    with omegaconf.open_dict(cfg):
-        condition_provider_args = cfg.pop('args', {})
-    for cond, cond_cfg in cfg.items():
-        model_type = cond_cfg["model"]
-        model_args = cond_cfg[model_type]
-        if model_type == "t5":
-            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
-        elif model_type == "lut":
-            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
-        elif model_type == "chroma_stem":
-            model_args.pop('cache_path', None)
-            conditioners[str(cond)] = ChromaStemConditioner(
-                output_dim=output_dim,
-                duration=duration,
-                device=device,
-                **model_args
-            )
-        else:
-            raise ValueError(f"unrecognized conditioning model: {model_type}")
-    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
-    return conditioner
-
-
-def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
-    """Instantiate a condition fuser object.
-    """
-    fuser_cfg = getattr(cfg, "fuser")
-    fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
-    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
-    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
-    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
-    return fuser
-
-
-def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
-    """Instantiate a codebooks pattern provider object.
-    """
-    pattern_providers = {
-        'parallel': ParallelPatternProvider,
-        'delay': DelayedPatternProvider,
-        'unroll': UnrolledPatternProvider,
-        'valle': VALLEPattern,
-        'musiclm': MusicLMPattern,
-    }
-    name = cfg.modeling
-    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
-    klass = pattern_providers[name]
-    return klass(n_q, **kwargs)
-
-
-def get_debug_compression_model(device='cpu'):
-    """Instantiate a debug compression model to be used for unit tests.
-    """
-    seanet_kwargs = {
-        'n_filters': 4,
-        'n_residual_layers': 1,
-        'dimension': 32,
-        'ratios': [10, 8, 16]  # 25 Hz at 32kHz
-    }
-    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
-    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
-    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
-    init_x = torch.randn(8, 32, 128)
-    quantizer(init_x, 1)  # initialize kmeans etc.
-    compression_model = EncodecModel(
-        encoder, decoder, quantizer,
-        frame_rate=25, sample_rate=32000, channels=1).to(device)
-    return compression_model.eval()
-
-
-def get_debug_lm_model(device='cpu'):
-    """Instantiate a debug LM to be used for unit tests.
-    """
-    pattern = DelayedPatternProvider(n_q=4)
-    dim = 16
-    providers = {
-        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
-    }
-    condition_provider = ConditioningProvider(providers)
-    fuser = ConditionFuser(
-        {'cross': ['description'], 'prepend': [],
-         'sum': [], 'input_interpolate': []})
-    lm = LMModel(
-        pattern, condition_provider, fuser,
-        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
-        cross_attention=True, causal=True)
-    return lm.to(device).eval()
diff --git a/spaces/XingHe0127/Chatbot/assets/custom.js b/spaces/XingHe0127/Chatbot/assets/custom.js
deleted file mode 100644
index b8071034f3618c541e3f4169c7fc6d6593d56f44..0000000000000000000000000000000000000000
--- a/spaces/XingHe0127/Chatbot/assets/custom.js
+++ /dev/null
@@ -1,224 +0,0 @@
-
-// custom javascript here
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-var user_input_ta;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var apSwitch = null;
-
-var ga = document.getElementsByTagName("gradio-app");
-var targetNode = ga[0];
-var isInIframe = (window.self !== window.top);
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
-    for (var i = 0; i < mutations.length; i++) {
-        if (mutations[i].addedNodes.length) {  
-            gradioContainer = document.querySelector(".gradio-container");
-            user_input_tb = document.getElementById('user_input_tb');
-            userInfoDiv = document.getElementById("user_info");
-            appTitleDiv = document.getElementById("app_title");
-            chatbot = document.querySelector('#chuanhu_chatbot');
-            apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-
-            if (gradioContainer && apSwitch) {  // gradioCainter 加载出来了没?
-                adjustDarkMode();
-            }
-            if (user_input_tb) {  // user_input_tb 加载出来了没?
-                selectHistory();
-            }
-            if (userInfoDiv && appTitleDiv) {  // userInfoDiv 和 appTitleDiv 加载出来了没?
-                setTimeout(showOrHideUserInfo(), 2000);
-            }
-            if (chatbot) {  // chatbot 加载出来了没?
-                setChatbotHeight()
-            }
-        }
-    }
-}
-
-function selectHistory() {
-    user_input_ta = user_input_tb.querySelector("textarea");
-    if (user_input_ta) {
-        observer.disconnect(); // 停止监听
-        // 在 textarea 上监听 keydown 事件
-        user_input_ta.addEventListener("keydown", function (event) {
-            var value = user_input_ta.value.trim();
-            // 判断按下的是否为方向键
-            if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
-                // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
-                if (value && key_down_history.indexOf(value) === -1)
-                    return;
-                // 对于需要响应的动作，阻止默认行为。
-                event.preventDefault();
-                var length = key_down_history.length;
-                if (length === 0) {
-                    currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
-                    return;
-                }
-                if (currentIndex === -1) {
-                    currentIndex = length;
-                }
-                if (event.code === 'ArrowUp' && currentIndex > 0) {
-                    currentIndex--;
-                    user_input_ta.value = key_down_history[currentIndex];
-                } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
-                    currentIndex++;
-                    user_input_ta.value = key_down_history[currentIndex];
-                }
-                user_input_ta.selectionStart = user_input_ta.value.length;
-                user_input_ta.selectionEnd = user_input_ta.value.length;
-                const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
-                user_input_ta.dispatchEvent(input_event);
-            } else if (event.code === "Enter") {
-                if (value) {
-                    currentIndex = -1;
-                    if (key_down_history.indexOf(value) === -1) {
-                        key_down_history.push(value);
-                        if (key_down_history.length > MAX_HISTORY_LENGTH) {
-                            key_down_history.shift();
-                        }
-                    }
-                }
-            }
-        });
-    }
-}
-
-function toggleUserInfoVisibility(shouldHide) {
-    if (userInfoDiv) {
-        if (shouldHide) {
-            userInfoDiv.classList.add("hideK");
-        } else {
-            userInfoDiv.classList.remove("hideK");
-        }
-    }
-}
-function showOrHideUserInfo() {
-    var sendBtn = document.getElementById("submit_btn");
-
-    // Bind mouse/touch events to show/hide user info
-    appTitleDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    userInfoDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    sendBtn.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-
-    appTitleDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    userInfoDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    sendBtn.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-
-    appTitleDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    userInfoDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    sendBtn.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-
-    appTitleDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); 
-    };
-    userInfoDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); 
-    };
-    sendBtn.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); // Delay 1 second to hide user info
-    };
-
-    // Hide user info after 2 second
-    setTimeout(function () {
-        toggleUserInfoVisibility(true);
-    }, 2000);
-}
-
-function toggleDarkMode(isEnabled) {
-    if (isEnabled) {
-        gradioContainer.classList.add("dark");
-        document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
-    } else {
-        gradioContainer.classList.remove("dark");
-        document.body.style.backgroundColor = "";
-    }
-}
-function adjustDarkMode() {
-    const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
-
-    // 根据当前颜色模式设置初始状态
-    apSwitch.checked = darkModeQuery.matches;
-    toggleDarkMode(darkModeQuery.matches);
-    // 监听颜色模式变化
-    darkModeQuery.addEventListener("change", (e) => {
-        apSwitch.checked = e.matches;
-        toggleDarkMode(e.matches);
-    });
-    // apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-    apSwitch.addEventListener("change", (e) => {
-        toggleDarkMode(e.target.checked);
-    });
-}
-
-function setChatbotHeight() {
-    const screenWidth = window.innerWidth;
-    const statusDisplay = document.querySelector('#status_display');
-    const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
-    const wrap = chatbot.querySelector('.wrap');
-    const vh = window.innerHeight * 0.01;
-    document.documentElement.style.setProperty('--vh', `${vh}px`);
-    if (isInIframe) {
-        chatbot.style.height = `700px`;
-        wrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
-    } else {
-        if (screenWidth <= 320) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else if (screenWidth <= 499) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        }
-    }
-}
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
-    gradioLoaded(mutations);
-});
-observer.observe(targetNode, { childList: true, subtree: true });
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
-    isInIframe = (window.self !== window.top);
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', setChatbotHeight);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
\ No newline at end of file
diff --git a/spaces/XzJosh/Ava2-Bert-VITS2/server.py b/spaces/XzJosh/Ava2-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava2-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    print([f"{p}{t}" for p, t in zip(phone, tone)])
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
-    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
-    with torch.no_grad():
-        x_tst=phones.to(dev).unsqueeze(0)
-        tones=tones.to(dev).unsqueeze(0)
-        lang_ids=lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        return audio
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-def wav2(i, o, format):
-    inp = avopen(i, 'rb')
-    out = avopen(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
-
-    for p in ostream.encode(None): out.mux(p)
-
-    out.close()
-    inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
-    if request.method == 'GET':
-        try:
-            speaker = request.args.get('speaker')
-            text = request.args.get('text').replace("/n","")
-            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-            noise = float(request.args.get("noise", 0.5))
-            noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.2))
-            if length >= 2:
-                return "Too big length"
-            if len(text) >=200:
-                return "Too long text"
-            fmt = request.args.get("format", "wav")
-            if None in (speaker, text):
-                return "Missing Parameter"
-            if fmt not in ("mp3", "wav", "ogg"):
-                return "Invalid Format"
-        except:
-            return "Invalid Parameter"
-
-        with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
-        with BytesIO() as wav:
-            wavfile.write(wav, hps.data.sampling_rate, audio)
-            torch.cuda.empty_cache()
-            if fmt == "wav":
-                return Response(wav.getvalue(), mimetype="audio/wav")
-            wav.seek(0, 0)
-            with BytesIO() as ofp:
-                wav2(wav, ofp, fmt)
-                return Response(
-                    ofp.getvalue(),
-                    mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-                )
diff --git a/spaces/XzJosh/Carol-Bert-VITS2/mel_processing.py b/spaces/XzJosh/Carol-Bert-VITS2/mel_processing.py
deleted file mode 100644
index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Carol-Bert-VITS2/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/grit/modeling/text/text_decoder.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/grit/modeling/text/text_decoder.py
deleted file mode 100644
index 071baa7a52d21d7132cc492f070cba066d17aa43..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/grit/modeling/text/text_decoder.py
+++ /dev/null
@@ -1,672 +0,0 @@
-# Modified by Jialian Wu from
-# https://github.com/microsoft/GenerativeImage2Text/blob/main/generativeimage2text/layers/decoder.py
-# and https://github.com/kdexd/virtex
-from torch import nn
-import torch
-import functools
-from torch.nn import functional as F
-import warnings
-
-
-class TextualHead(nn.Module):
-    def __init__(self,
-                 visual_feature_size: int, vocab_size: int, hidden_size: int):
-        super().__init__()
-        self.visual_feature_size = visual_feature_size
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-
-    @property
-    def textual_feature_size(self):
-        return self.hidden_size
-
-
-class WordAndPositionalEmbedding(nn.Module):
-    def __init__(
-        self,
-        vocab_size: int,
-        hidden_size: int,
-        dropout: float = 0.0,
-        max_caption_length: int = 30,
-        padding_idx: int = 0,
-    ):
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.padding_idx = padding_idx
-
-        #self.words = nn.Embedding(vocab_size, hidden_size, padding_idx=padding_idx)
-        self.words = nn.Embedding(vocab_size, hidden_size)
-
-        # We provide no "padding index" for positional embeddings. We zero out
-        # the positional embeddings of padded positions as a post-processing.
-        self.positions = nn.Embedding(max_caption_length, hidden_size)
-        self.layer_norm = nn.LayerNorm(
-            hidden_size, eps=1e-8, elementwise_affine=True
-        )
-        self.dropout = nn.Dropout(p=dropout)
-
-    def forward(self, tokens: torch.Tensor):
-        position_indices = self._create_position_indices(tokens)
-
-        # shape: (batch_size, max_caption_length, hidden_size)
-        word_embeddings = self.words(tokens)
-        position_embeddings = self.positions(position_indices)
-
-        # shape: (batch_size, max_caption_length, hidden_size)
-        embeddings = self.layer_norm(word_embeddings + position_embeddings)
-        embeddings = self.dropout(embeddings)
-
-        return embeddings
-
-    @functools.lru_cache(maxsize=128)
-    def _create_position_indices(self, tokens: torch.Tensor):
-
-        # Create position indices of the same size as token indices.
-        batch_size, max_caption_length = tokens.size()
-        positions = torch.arange(
-            max_caption_length, dtype=tokens.dtype, device=tokens.device
-        )
-        # shape: (batch_size, max_caption_length)
-        positions = positions.unsqueeze(0).expand(batch_size, max_caption_length)
-        return positions
-
-
-class BertEncoderAsDecoder(nn.Module):
-    def __init__(self, encoder):
-        super().__init__()
-        self.encoder = encoder
-
-    def forward(self, tgt, memory,
-                tgt_mask=None,
-                tgt_key_padding_mask=None,
-                memory_key_padding_mask=None,
-                tgt_bi_valid_mask=None,
-                encoder_history_states=None,
-                ):
-        assert tgt_key_padding_mask is None, 'not supported'
-        assert tgt_mask.dim() == 2
-        assert tgt_mask.shape[0] == tgt_mask.shape[1]
-        # tgt_mask should always be 0/negative infinity
-        tgt = tgt.transpose(0, 1)
-        memory = memory.transpose(0, 1)
-
-        hidden_states = torch.cat((memory, tgt), dim=1)
-        num_tgt = tgt.shape[1]
-        num_memory = memory.shape[1]
-        device = tgt.device
-        dtype = tgt.dtype
-        top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
-        top_right = torch.full((num_memory, num_tgt), float('-inf'), device=tgt.device, dtype=dtype,)
-        bottom_left = torch.zeros((num_tgt, num_memory), dtype=dtype, device=tgt_mask.device,)
-        left = torch.cat((top_left, bottom_left), dim=0)
-        right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0)
-
-        full_attention_mask = torch.cat((left, right), dim=1)[None, :]
-
-        if memory_key_padding_mask is None:
-            memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
-        # if it is False, it means valid. That is, it is not a padding
-        assert memory_key_padding_mask.dtype == torch.bool
-        zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype)
-        zero_negative_infinity[memory_key_padding_mask] = float('-inf')
-        full_attention_mask = full_attention_mask.expand((memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + num_tgt))
-        full_attention_mask = full_attention_mask.clone()
-        origin_left = full_attention_mask[:, :, :num_memory]
-        update = zero_negative_infinity[:, None, :]
-        full_attention_mask[:, :, :num_memory] = origin_left + update
-
-        if tgt_bi_valid_mask is not None:
-            # verify the correctness
-            bs = full_attention_mask.shape[0]
-            # during inference, tgt_bi_valid_mask's length is not changed, but
-            # num_tgt can be increased
-            max_valid_target = tgt_bi_valid_mask.shape[1]
-            mask = tgt_bi_valid_mask[:, None, :].expand((bs, num_memory+num_tgt, max_valid_target))
-            full_attention_mask[:, :, num_memory:(num_memory+max_valid_target)][mask] = 0
-
-        # add axis for multi-head
-        full_attention_mask = full_attention_mask[:, None, :, :]
-
-        if encoder_history_states is None:
-            result = self.encoder(
-                hidden_states=hidden_states,
-                attention_mask=full_attention_mask,
-                encoder_history_states=encoder_history_states,
-            )
-            result = list(result)
-            result[0] = result[0][:, num_memory:].transpose(0, 1)
-            if self.encoder.output_hidden_states:
-                return result[0], result[1]
-            else:
-                # make it back-compatible
-                return result[0]
-        else:
-            encoder_out = self.encoder(
-                hidden_states=hidden_states[:, -1:],
-                attention_mask=full_attention_mask[:, :, -1:],
-                encoder_history_states=encoder_history_states,
-            )
-            result = encoder_out[0].transpose(0, 1)
-            if self.encoder.output_hidden_states:
-                return result, encoder_out[1]
-            else:
-                return result
-
-
-def create_transformer(decoder_type, norm_type,
-                   textual_feature_size,
-                   attention_heads,
-                   feedforward_size,
-                   dropout,
-                   num_layers,
-                   output_hidden_states=False,
-                   use_mlp_wrapper=None,
-                   use_act_checkpoint=True,
-                   ):
-    assert norm_type in ['post', 'pre']
-    if decoder_type is None:
-        LayerClass = (
-            nn.TransformerDecoderLayer
-            if norm_type == "post"
-            else PreNormTransformerDecoderLayer
-        )
-        _layer = LayerClass(
-            textual_feature_size,
-            attention_heads,
-            dim_feedforward=feedforward_size,
-            dropout=dropout,
-            activation="gelu",
-        )
-        return nn.TransformerDecoder(_layer, num_layers)
-    elif decoder_type == 'bert_en':
-        from .modeling_bert import BertConfig, BertEncoder
-        config = BertConfig(
-            vocab_size_or_config_json_file=30522,
-            hidden_size=textual_feature_size,
-            num_hidden_layers=num_layers,
-            num_attention_heads=attention_heads,
-            intermediate_size=feedforward_size,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.1,
-            attention_probs_dropout_prob=0.1,
-            layer_norm_eps=1e-12,
-        )
-        config.pre_norm = (norm_type == 'pre')
-        config.use_mlp_wrapper = use_mlp_wrapper
-        config.output_hidden_states = output_hidden_states
-        encoder = BertEncoder(config, use_act_checkpoint=use_act_checkpoint)
-        return BertEncoderAsDecoder(encoder)
-
-
-class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
-    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
-                tgt_key_padding_mask=None, memory_key_padding_mask=None):
-        # fmt: off
-        # We use the members (modules) from super-class, just the order of
-        # operations is changed here. First layernorm, then attention.
-        tgt2 = self.norm1(tgt)
-        tgt2, _ = self.self_attn(
-            tgt2, tgt2, tgt2, attn_mask=tgt_mask,
-            key_padding_mask=tgt_key_padding_mask
-        )
-        tgt = tgt + self.dropout1(tgt2)
-
-        # Layernorm first, then decoder attention.
-        tgt2 = self.norm2(tgt)
-        tgt2, _ = self.multihead_attn(
-            tgt2, memory, memory, attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask
-        )
-        tgt = tgt + self.dropout2(tgt2)
-
-        # Layernorm first, then transformation through feedforward network.
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-
-class TransformerDecoderTextualHead(TextualHead):
-    def __init__(
-        self,
-        object_feature_size: int,
-        vocab_size: int,
-        hidden_size: int,
-        num_layers: int,
-        attention_heads: int,
-        feedforward_size: int,
-        dropout: float = 0.1,
-        norm_type: str = "post",
-        mask_future_positions: bool = True,
-        max_caption_length: int = 1024,
-        padding_idx: int = 0,
-        decoder_type=None,
-        not_tie_weight=None,
-        output_hidden_states=None,
-        use_mlp_wrapper=None,
-        use_act_checkpoint=True,
-    ):
-        super().__init__(object_feature_size, vocab_size, hidden_size)
-        self.num_layers = num_layers
-        self.attention_heads = attention_heads
-        self.feedforward_size = feedforward_size
-        self.dropout = dropout
-        assert mask_future_positions
-        self.padding_idx = padding_idx
-
-        self.object_feature_projection = nn.Sequential(
-            nn.Linear(object_feature_size, self.textual_feature_size),
-            nn.LayerNorm(self.textual_feature_size))
-
-        self.embedding = WordAndPositionalEmbedding(
-            self.vocab_size,
-            self.textual_feature_size,
-            dropout=dropout,
-            max_caption_length=max_caption_length,
-            padding_idx=padding_idx,
-        )
-        self.transformer = create_transformer(
-            decoder_type=decoder_type,
-            norm_type=norm_type,
-            textual_feature_size=self.textual_feature_size,
-            attention_heads=self.attention_heads,
-            feedforward_size=self.feedforward_size,
-            dropout=dropout,
-            num_layers=self.num_layers,
-            output_hidden_states=output_hidden_states,
-            use_mlp_wrapper=use_mlp_wrapper,
-            use_act_checkpoint=use_act_checkpoint,
-        )
-        self.apply(self._init_weights)
-
-        # Create an output linear layer and tie the input and output word
-        # embeddings to reduce parametejs.
-        self.output = nn.Linear(self.textual_feature_size, vocab_size)
-        if not not_tie_weight:
-            self.output.weight = self.embedding.words.weight
-
-    @staticmethod
-    def _init_weights(module):
-        """Initialize weights like BERT - N(0.0, 0.02), bias = 0."""
-
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-        elif isinstance(module, nn.MultiheadAttention):
-            module.in_proj_weight.data.normal_(mean=0.0, std=0.02)
-            module.out_proj.weight.data.normal_(mean=0.0, std=0.02)
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def forward(
-        self,
-        hidden_states,
-        text_tokens,
-    ):
-        projected_object_features = self.object_feature_projection(hidden_states) if hidden_states is not None else None
-        batch_size, max_text_length = text_tokens.size()
-        text_embeddings = self.embedding(text_tokens)
-
-        # An additive mask for masking the future (one direction).
-        uni_mask_zero_neg = self._generate_future_mask(
-            max_text_length, text_embeddings.dtype, text_embeddings.device
-        )
-
-        # We transpose the first two dimensions of tokens embeddings and visual
-        # features, as required by decoder.
-        text_embeddings = text_embeddings.transpose(0, 1)
-
-        projected_object_features = projected_object_features.transpose(0, 1)
-
-        # if transformer here is the pytorch/decoder, there is no chance, the
-        # output is always tensor
-        trans_out = self.transformer(
-            text_embeddings,
-            projected_object_features,
-            tgt_mask=uni_mask_zero_neg,
-        )
-        if isinstance(trans_out, tuple):
-            textual_features = trans_out[0]
-        else:
-            assert isinstance(trans_out, torch.Tensor)
-            textual_features = trans_out
-        # Undo the transpose and bring batch to dim 0.
-        # shape: (batch_size, max_caption_length, hidden_size)
-        textual_features = textual_features.transpose(0, 1)
-
-        # shape: (batch_size, max_caption_length, vocab_size)
-        output_logits = self.output(textual_features)
-        if isinstance(trans_out, tuple):
-            return output_logits, trans_out[1]
-        else:
-            return output_logits
-
-    def _generate_future_mask(
-        self, size: int, dtype: torch.dtype, device: torch.device
-    ):
-        # Default mask is for forward direction. Flip for backward direction.
-        mask = torch.triu(
-            torch.ones(size, size, device=device, dtype=dtype), diagonal=1
-        )
-        mask = mask.masked_fill(mask == 1, float("-inf"))
-        return mask
-
-
-class AutoRegressiveBeamSearch(object):
-    def __init__(
-        self,
-        end_token_id: int,
-        max_steps: int = 50,
-        beam_size: int = 5,
-        objectdet=True,
-        per_node_beam_size: int = 2,
-    ):
-        self._eos_index = end_token_id
-        self.max_steps = max_steps
-        self.beam_size = beam_size
-        self.objectdet = objectdet
-        self.per_node_beam_size = per_node_beam_size or beam_size
-
-    def search(self, begin_tokens, step):
-        if self.beam_size > 1 and self.objectdet:
-            only_return_best = False
-        else:
-            only_return_best = True
-
-        batch_size = begin_tokens.size()[0]
-
-        predictions = begin_tokens.unsqueeze(1).expand((batch_size, self.beam_size, begin_tokens.shape[-1]))
-        # Calculate the first timestep. This is done outside the main loop
-        # because we are going from a single decoder input (the output from the
-        # encoder) to the top `beam_size` decoder outputs. On the other hand,
-        # within the main loop we are going from the `beam_size` elements of the
-        # beam to `beam_size`^2 candidates from which we will select the top
-        # `beam_size` elements for the next iteration.
-        # shape: (batch_size, num_classes)
-        start_class_logits = step(begin_tokens)
-
-        # Convert logits to logprobs.
-        # shape: (batch_size * beam_size, vocab_size)
-        start_class_logprobs = F.log_softmax(start_class_logits, dim=1)
-
-        num_classes = start_class_logprobs.size()[1]
-
-        # shape: (batch_size, beam_size), (batch_size, beam_size)
-        start_top_logprobs, start_predicted_classes = start_class_logprobs.topk(
-            self.beam_size
-        )
-
-        if (
-            self.beam_size == 1
-            and (start_predicted_classes == self._eos_index).all()
-        ):
-            warnings.warn(
-                "Empty object description predicted. You may want to increase beam"
-                "size or ensure your step function is working properly.",
-                RuntimeWarning,
-            )
-            if only_return_best:
-                return start_predicted_classes, start_top_logprobs
-            else:
-                return start_predicted_classes.unsqueeze(-1), start_top_logprobs
-
-        # The log probs for the last time step.
-        # shape: (batch_size, beam_size)
-        last_logprobs = start_top_logprobs
-
-        # shape: (batch_size, beam_size, sequence_length)
-        predictions = torch.cat([predictions, start_predicted_classes.unsqueeze(-1)], dim=-1)
-
-        # Log probability tensor that mandates that the end token is selected.
-        # shape: (batch_size * beam_size, num_classes)
-        logprobs_after_end = start_class_logprobs.new_full(
-            (batch_size * self.beam_size, num_classes), float("-inf")
-        )
-        logprobs_after_end[:, self._eos_index] = 0.0
-
-        logits_after_end = start_class_logprobs.new_full(
-            (batch_size * self.beam_size, num_classes), float("-inf")
-        )
-        logits_after_end[:, self._eos_index] = 0
-
-        while predictions.shape[-1] < self.max_steps:
-            # shape: (batch_size * beam_size,)
-            last_predictions = predictions[:, :, -1].reshape(batch_size * self.beam_size)
-
-            # If every predicted token from the last step is `self._eos_index`,
-            # then we can stop early.
-            if (last_predictions == self._eos_index).all():
-                break
-
-            predictions_so_far = predictions.view(
-                batch_size * self.beam_size, -1
-            )
-            # shape: (batch_size * beam_size, num_classes)
-            class_logits = step(predictions_so_far)
-
-            # Set logprobs of last predicted tokens as high negative value to avoid
-            # repetition in description.
-            class_logits = class_logits.scatter(1, predictions_so_far[:, -1].view((-1, 1)), -10000)
-
-            # shape: (batch_size * beam_size, num_classes)
-            last_predictions_expanded = last_predictions.unsqueeze(-1).expand(
-                batch_size * self.beam_size, num_classes
-            )
-
-            # Here we are finding any beams where we predicted the end token in
-            # the previous timestep and replacing the distribution with a
-            # one-hot distribution, forcing the beam to predict the end token
-            # this timestep as well.
-            class_logits = torch.where(
-                last_predictions_expanded == self._eos_index,
-                logits_after_end,
-                class_logits,
-            )
-
-            # Convert logits to logprobs.
-            # shape: (batch_size * beam_size, vocab_size)
-            class_logprobs = F.log_softmax(class_logits, dim=1)
-
-            # shape (both): (batch_size * beam_size, per_node_beam_size)
-            top_logprobs, predicted_classes = class_logprobs.topk(
-                self.per_node_beam_size
-            )
-
-            # Here we expand the last log probs to `(batch_size * beam_size,
-            # per_node_beam_size)` so that we can add them to the current log
-            # probs for this timestep. This lets us maintain the log
-            # probability of each element on the beam.
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            expanded_last_logprobs = (
-                last_logprobs.unsqueeze(2)
-                .expand(batch_size, self.beam_size, self.per_node_beam_size)
-                .reshape(batch_size * self.beam_size, self.per_node_beam_size)
-            )
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            summed_top_logprobs = top_logprobs + expanded_last_logprobs
-
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_summed = summed_top_logprobs.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_predicted_classes = predicted_classes.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # Append the predictions to the current beam.
-            reshaped_beam = (
-                predictions.view(batch_size * self.beam_size, 1, -1)
-                .repeat(1, self.per_node_beam_size, 1)
-                .reshape(batch_size, self.beam_size * self.per_node_beam_size, -1)
-            )
-            # batch_size, (beam_size * per_node_beach_size), #token
-            reshaped_beam = torch.cat([reshaped_beam, reshaped_predicted_classes.unsqueeze(-1)], dim=-1)
-
-            # Keep only the top `beam_size` beam indices.
-            # shape: (batch_size, beam_size), (batch_size, beam_size)
-            restricted_beam_logprobs, restricted_beam_indices = reshaped_summed.topk(
-                self.beam_size
-            )
-            predictions = reshaped_beam.gather(
-                1, restricted_beam_indices.unsqueeze(-1).repeat(1,1,reshaped_beam.shape[-1])
-            )
-
-            # shape: (batch_size, beam_size)
-            last_logprobs = restricted_beam_logprobs
-
-        if not torch.isfinite(last_logprobs).all():
-            warnings.warn(
-                "Infinite log probs encountered. Some final descriptions may not "
-                "make sense. This can happen when the beam size is larger than"
-                " the number of valid (non-zero probability) transitions that "
-                "the step function produces.",
-                RuntimeWarning,
-            )
-
-        # Optionally select best beam and its logprobs.
-        if only_return_best:
-            # shape: (batch_size, sequence_length)
-            predictions = predictions[:, 0, :]
-            last_logprobs = last_logprobs[:, 0]
-        num_valid = (predictions != self._eos_index).sum(dim=-1)
-        num_valid += (predictions == self._eos_index).sum(dim=-1) > 0
-        num_valid = num_valid - begin_tokens.shape[1]
-        num_valid = num_valid.clip(min=1)
-
-        last_logprobs = last_logprobs / num_valid
-
-        return predictions, last_logprobs
-
-
-class GRiTTextDecoder(nn.Module):
-    def __init__(
-        self,
-        transformer,
-        begin_token_id=101,
-        beamsearch_decode=None,
-        loss_type=None,
-        tokenizer=None,
-    ):
-        super().__init__()
-        self.textual = transformer
-        self.padding_idx = self.textual.padding_idx
-
-        self.begin_token_id = begin_token_id
-        self.beamsearch_decode = beamsearch_decode
-        self.tokenizer = tokenizer
-
-        if loss_type is None:
-            self.loss = nn.CrossEntropyLoss(ignore_index=self.padding_idx)
-        elif loss_type == 'smooth':
-            self.loss = SmoothLabelCrossEntropyLoss(ignore_index=self.padding_idx)
-        else:
-            raise NotImplementedError(loss_type)
-
-    def forward(self, batch):
-        object_features = batch['object_features']
-
-        if self.training:
-            caption_token_input = batch["text_tokens"]
-
-            output_logits = self.textual(
-                object_features,
-                caption_token_input,
-            )
-
-            if 'need_predict' in batch:
-                # in place should also be good, but we do not choose that for
-                # safety as we may use it in prediction results in future
-                target = batch["text_tokens"].clone()
-                target[batch['need_predict'] == 0] = self.padding_idx
-            else:
-                target = batch["text_tokens"]
-
-            feat = output_logits[:, :-1].contiguous()
-            target = target[:, 1:].contiguous()
-            feat = feat.view(-1, self.textual.vocab_size)
-            target = target.view(-1)
-
-            valid_mask = target != self.padding_idx
-            target = target[valid_mask]
-            feat = feat[valid_mask]
-            loss = self.loss(feat, target)
-
-            return loss
-        else:
-            output_dict = self.infer(object_features)
-        return output_dict
-
-    def infer(self, object_features):
-        batch_size = object_features.size(0)
-        begin_tokens = object_features.new_full(
-            (batch_size, 1), self.begin_token_id
-        ).long()
-
-        decoding_step = functools.partial(
-            self.decoding_step, object_features
-        )
-
-        object_description_tokens, logprobs = self.beamsearch_decode.search(
-            begin_tokens, decoding_step
-        )
-
-        output_dict = {
-            'predictions': object_description_tokens,
-            'logprobs': logprobs,
-        }
-
-        return output_dict
-
-    def decoding_step(self, object_features, partial_text):
-        batch_size = object_features.shape[0]
-        beam_size = int(partial_text.size(0) / batch_size)
-        if beam_size > 1:
-            batch_size, num_token, channels = object_features.size()
-            object_features = object_features.unsqueeze(1).repeat(1, beam_size, 1, 1)
-            object_features = object_features.view(
-                batch_size * beam_size, num_token, channels
-            )
-
-        text_lengths = torch.ones_like(partial_text)
-        if len(text_lengths.size()) != 2:
-            partial_text = partial_text.unsqueeze(1)
-
-        # shape: (batch_size * beam_size, partial_caption_length, vocab_size)
-        logits = self.textual(
-            object_features,
-            partial_text,
-        )
-
-        return logits[:, -1, :].float()
-
-
-class SmoothLabelCrossEntropyLoss(nn.Module):
-    def __init__(self, eps=0.1, log_prefix='', ignore_index=None):
-        super().__init__()
-        self.eps = eps
-        self.log_soft = nn.LogSoftmax(dim=1)
-        self.kl = nn.KLDivLoss(reduction='none')
-
-        self.iter = 0
-        self.max_loss = 0
-        self.min_loss = 0
-        self.log_prefix = log_prefix
-        self.ignore_index = ignore_index
-
-    def forward(self, feature, target):
-        feature = feature.float()
-        if self.ignore_index is not None:
-            valid_mask = target != self.ignore_index
-            target = target[valid_mask]
-            feature = feature[valid_mask]
-        assert target.numel() > 0
-        self.iter += 1
-        eps = self.eps
-        n_class = feature.size(1)
-        one_hot = torch.zeros_like(feature).scatter(1, target.view(-1, 1), 1)
-        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
-        log_prb = self.log_soft(feature)
-        loss = self.kl(log_prb, one_hot)
-        return loss.sum(dim=1).mean()
-
diff --git a/spaces/YuAnthony/Audio-Caption/tools/.ipynb_checkpoints/printing-checkpoint.py b/spaces/YuAnthony/Audio-Caption/tools/.ipynb_checkpoints/printing-checkpoint.py
deleted file mode 100644
index 93049018127d3baadd364321f56c8a0fb87417fa..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/tools/.ipynb_checkpoints/printing-checkpoint.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from sys import stdout
-from pathlib import Path
-from pprint import PrettyPrinter
-
-from loguru import logger
-from loguru._handler import StrRecord
-from _io import TextIOWrapper
-
-__author__ = 'Konstantinos Drossos -- Tampere University'
-__docformat__ = 'reStructuredText'
-__all__ = ['get_pretty_printer',
-           'init_loggers']
-
-
-def _rotation_logger(x: StrRecord,
-                     _: TextIOWrapper) \
-        -> bool:
-    """Callable to determine the rotation of files in logger.
-
-    :param x: Str to be logged.
-    :type x: loguru._handler.StrRecord
-    :param _: File used for logging.
-    :type _: _io.TextIOWrapper
-    :return: Shall we switch to a new file?
-    :rtype: bool
-    """
-    return 'Captions start' in x
-
-
-def get_pretty_printer():
-    """Gets the pprint.
-
-    :return: Pretty printer.
-    :rtype: pprint.PrettyPrinter
-    """
-    return PrettyPrinter(indent=4, width=100)
-
-
-def init_loggers(verbose, settings):
-    """Initializes the logging process.
-
-    :param verbose: Be verbose?
-    :type verbose: bool
-    :param settings: Settings to use.
-    :type settings: dict
-    """
-    logger.remove()
-
-    for indent in range(3):
-        log_string = '{level} | [{time}] {name} -- {space}{message}'.format(
-            level='{level}',
-            time='{time:HH:mm:ss}',
-            name='{name}',
-            message='{message}',
-            space=' ' * (indent*2))
-        logger.add(
-            stdout,
-            format=log_string,
-            level='INFO',
-            filter=lambda record, i=indent:
-            record['extra']['indent'] == i and not record['extra']['is_caption'])
-
-    logging_path = Path(settings['root_dirs']['outputs'],
-                        settings['logging']['logger_dir'])
-
-    log_file_main = f'{settings["logging"]["caption_logger_file"]}'
-
-    logging_file = logging_path.joinpath(log_file_main)
-
-    logger.add(str(logging_file), format='{message}', level='INFO',
-               filter=lambda record: record['extra']['is_caption'],
-               rotation=_rotation_logger)
-
-    logging_path.mkdir(parents=True, exist_ok=True)
-
-    if not verbose:
-        logger.disable()
-
-
-# EOF
diff --git a/spaces/abhishek/first-order-motion-model/sync_batchnorm/batchnorm.py b/spaces/abhishek/first-order-motion-model/sync_batchnorm/batchnorm.py
deleted file mode 100644
index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000
--- a/spaces/abhishek/first-order-motion-model/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dementions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
-        # Always using same "device order" makes the ReduceAdd operation faster.
-        # Thanks to:: Tete Xiao (http://tetexiao.com/)
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
-        return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/cc_attention.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/cc_attention.py
deleted file mode 100644
index 9207aa95e6730bd9b3362dee612059a5f0ce1c5e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/cc_attention.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
-
-
-def NEG_INF_DIAG(n, device):
-    """Returns a diagonal matrix of size [n, n].
-
-    The diagonal are all "-inf". This is for avoiding calculating the
-    overlapped element in the Criss-Cross twice.
-    """
-    return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
-
-
-@PLUGIN_LAYERS.register_module()
-class CrissCrossAttention(nn.Module):
-    """Criss-Cross Attention Module.
-
-    .. note::
-        Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
-        to a pure PyTorch and equivalent implementation. For more
-        details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
-
-        Speed comparison for one forward pass
-
-        - Input size: [2,512,97,97]
-        - Device: 1 NVIDIA GeForce RTX 2080 Ti
-
-        +-----------------------+---------------+------------+---------------+
-        |                       |PyTorch version|CUDA version|Relative speed |
-        +=======================+===============+============+===============+
-        |with torch.no_grad()   |0.00554402 s   |0.0299619 s |5.4x           |
-        +-----------------------+---------------+------------+---------------+
-        |no with torch.no_grad()|0.00562803 s   |0.0301349 s |5.4x           |
-        +-----------------------+---------------+------------+---------------+
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-    """
-
-    def __init__(self, in_channels):
-        super().__init__()
-        self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
-        self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
-        self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
-        self.gamma = Scale(0.)
-        self.in_channels = in_channels
-
-    def forward(self, x):
-        """forward function of Criss-Cross Attention.
-
-        Args:
-            x (Tensor): Input feature. \
-                shape (batch_size, in_channels, height, width)
-        Returns:
-            Tensor: Output of the layer, with shape of \
-            (batch_size, in_channels, height, width)
-        """
-        B, C, H, W = x.size()
-        query = self.query_conv(x)
-        key = self.key_conv(x)
-        value = self.value_conv(x)
-        energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
-            H, query.device)
-        energy_H = energy_H.transpose(1, 2)
-        energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
-        attn = F.softmax(
-            torch.cat([energy_H, energy_W], dim=-1), dim=-1)  # [B,H,W,(H+W)]
-        out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
-        out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
-
-        out = self.gamma(out) + x
-        out = out.contiguous()
-
-        return out
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(in_channels={self.in_channels})'
-        return s
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py
deleted file mode 100644
index 687cdc58c0336c92b1e4f9a410ba67ebaab2bc7a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class DvcliveLoggerHook(LoggerHook):
-    """Class to log metrics with dvclive.
-
-    It requires `dvclive`_ to be installed.
-
-    Args:
-        path (str): Directory where dvclive will write TSV log files.
-        interval (int): Logging interval (every k iterations).
-            Default 10.
-        ignore_last (bool): Ignore the log of last iterations in each epoch
-            if less than `interval`.
-            Default: True.
-        reset_flag (bool): Whether to clear the output buffer after logging.
-            Default: True.
-        by_epoch (bool): Whether EpochBasedRunner is used.
-            Default: True.
-
-    .. _dvclive:
-        https://dvc.org/doc/dvclive
-    """
-
-    def __init__(self,
-                 path,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=True,
-                 by_epoch=True):
-
-        super(DvcliveLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
-        self.path = path
-        self.import_dvclive()
-
-    def import_dvclive(self):
-        try:
-            import dvclive
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install dvclive" to install dvclive')
-        self.dvclive = dvclive
-
-    @master_only
-    def before_run(self, runner):
-        self.dvclive.init(self.path)
-
-    @master_only
-    def log(self, runner):
-        tags = self.get_loggable_tags(runner)
-        if tags:
-            for k, v in tags.items():
-                self.dvclive.log(k, v, step=self.get_iter(runner))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/fast_rcnn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/fast_rcnn.py
deleted file mode 100644
index 3d6e242767b927ed37198b6bc7862abecef99a33..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/fast_rcnn.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class FastRCNN(TwoStageDetector):
-    """Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
-
-    def __init__(self,
-                 backbone,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-        super(FastRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-
-    def forward_test(self, imgs, img_metas, proposals, **kwargs):
-        """
-        Args:
-            imgs (List[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch.
-            img_metas (List[List[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch.
-            proposals (List[List[Tensor]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. The Tensor should have a shape Px4, where
-                P is the number of proposals.
-        """
-        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(imgs)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(imgs)}) '
-                             f'!= num of image meta ({len(img_metas)})')
-
-        if num_augs == 1:
-            return self.simple_test(imgs[0], img_metas[0], proposals[0],
-                                    **kwargs)
-        else:
-            # TODO: support test-time augmentation
-            assert NotImplementedError
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gdiplus.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gdiplus.py
deleted file mode 100644
index 235e1a76377bc910c12211ef1673fb7e0fcc88a3..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gdiplus.py
+++ /dev/null
@@ -1,311 +0,0 @@
-from pyglet.libs.win32.com import pIUnknown
-from pyglet.image import *
-from pyglet.image.codecs import *
-from pyglet.libs.win32.constants import *
-from pyglet.libs.win32.types import *
-from pyglet.libs.win32 import _kernel32 as kernel32
-from pyglet.libs.win32 import _ole32 as ole32
-
-
-gdiplus = windll.gdiplus
-
-REAL = c_float
-
-PixelFormat1bppIndexed    = 196865
-PixelFormat4bppIndexed    = 197634
-PixelFormat8bppIndexed    = 198659
-PixelFormat16bppGrayScale = 1052676
-PixelFormat16bppRGB555    = 135173
-PixelFormat16bppRGB565    = 135174
-PixelFormat16bppARGB1555  = 397319
-PixelFormat24bppRGB       = 137224
-PixelFormat32bppRGB       = 139273
-PixelFormat32bppARGB      = 2498570
-PixelFormat32bppPARGB     = 925707
-PixelFormat48bppRGB       = 1060876
-PixelFormat64bppARGB      = 3424269
-PixelFormat64bppPARGB     = 29622286
-PixelFormatMax            = 15
-
-ImageLockModeRead = 1
-ImageLockModeWrite = 2
-ImageLockModeUserInputBuf = 4
-
-PropertyTagFrameDelay = 0x5100
-
-
-class GdiplusStartupInput(Structure):
-    _fields_ = [
-        ('GdiplusVersion', c_uint32),
-        ('DebugEventCallback', c_void_p),
-        ('SuppressBackgroundThread', BOOL),
-        ('SuppressExternalCodecs', BOOL)
-    ]
-
-
-class GdiplusStartupOutput(Structure):
-    _fields = [
-        ('NotificationHookProc', c_void_p),
-        ('NotificationUnhookProc', c_void_p)
-    ]
-
-
-class BitmapData(Structure):
-    _fields_ = [
-        ('Width', c_uint),
-        ('Height', c_uint),
-        ('Stride', c_int),
-        ('PixelFormat', c_int),
-        ('Scan0', POINTER(c_byte)),
-        ('Reserved', POINTER(c_uint))
-    ]
-
-
-class Rect(Structure):
-    _fields_ = [
-        ('X', c_int),
-        ('Y', c_int),
-        ('Width', c_int),
-        ('Height', c_int)
-    ]
-
-
-class PropertyItem(Structure):
-    _fields_ = [
-        ('id', c_uint),
-        ('length', c_ulong),
-        ('type', c_short),
-        ('value', c_void_p)
-    ]
-
-
-INT_PTR = POINTER(INT)
-UINT_PTR = POINTER(UINT)
-
-
-gdiplus.GdipBitmapLockBits.restype = c_int
-gdiplus.GdipBitmapLockBits.argtypes = [c_void_p, c_void_p, UINT, c_int, c_void_p]
-gdiplus.GdipBitmapUnlockBits.restype = c_int
-gdiplus.GdipBitmapUnlockBits.argtypes = [c_void_p, c_void_p]
-gdiplus.GdipCloneStringFormat.restype = c_int
-gdiplus.GdipCloneStringFormat.argtypes = [c_void_p, c_void_p]
-gdiplus.GdipCreateBitmapFromScan0.restype = c_int
-gdiplus.GdipCreateBitmapFromScan0.argtypes = [c_int, c_int, c_int, c_int, POINTER(BYTE), c_void_p]
-gdiplus.GdipCreateBitmapFromStream.restype = c_int
-gdiplus.GdipCreateBitmapFromStream.argtypes = [c_void_p, c_void_p]
-gdiplus.GdipCreateFont.restype = c_int
-gdiplus.GdipCreateFont.argtypes = [c_void_p, REAL, INT, c_int, c_void_p]
-gdiplus.GdipCreateFontFamilyFromName.restype = c_int
-gdiplus.GdipCreateFontFamilyFromName.argtypes = [c_wchar_p, c_void_p, c_void_p]
-gdiplus.GdipCreateMatrix.restype = None
-gdiplus.GdipCreateMatrix.argtypes = [c_void_p]
-gdiplus.GdipCreateSolidFill.restype = c_int
-gdiplus.GdipCreateSolidFill.argtypes = [c_int, c_void_p]  # ARGB
-gdiplus.GdipDisposeImage.restype = c_int
-gdiplus.GdipDisposeImage.argtypes = [c_void_p]
-gdiplus.GdipDrawString.restype = c_int
-gdiplus.GdipDrawString.argtypes = [c_void_p, c_wchar_p, c_int, c_void_p, c_void_p, c_void_p, c_void_p]
-gdiplus.GdipGetFamilyName.restype = c_int
-gdiplus.GdipGetFamilyName.argtypes = [LONG_PTR, c_wchar_p, c_wchar]
-gdiplus.GdipFlush.restype = c_int
-gdiplus.GdipFlush.argtypes = [c_void_p, c_int]
-gdiplus.GdipGetFontCollectionFamilyCount.restype = c_int
-gdiplus.GdipGetFontCollectionFamilyCount.argtypes = [c_void_p, INT_PTR]
-gdiplus.GdipGetFontCollectionFamilyList.restype = c_int
-gdiplus.GdipGetFontCollectionFamilyList.argtypes = [c_void_p, INT, c_void_p, INT_PTR]
-gdiplus.GdipGetImageDimension.restype = c_int
-gdiplus.GdipGetImageDimension.argtypes = [c_void_p, POINTER(REAL), POINTER(REAL)]
-gdiplus.GdipGetImageGraphicsContext.restype = c_int
-gdiplus.GdipGetImageGraphicsContext.argtypes = [c_void_p, c_void_p]
-gdiplus.GdipGetImagePixelFormat.restype = c_int
-gdiplus.GdipGetImagePixelFormat.argtypes = [c_void_p, c_void_p]
-gdiplus.GdipGetPropertyItem.restype = c_int
-gdiplus.GdipGetPropertyItem.argtypes = [c_void_p, c_uint, c_uint, c_void_p]
-gdiplus.GdipGetPropertyItemSize.restype = c_int
-gdiplus.GdipGetPropertyItemSize.argtypes = [c_void_p, c_uint, UINT_PTR]
-gdiplus.GdipGraphicsClear.restype = c_int
-gdiplus.GdipGraphicsClear.argtypes = [c_void_p, c_int]  # ARGB
-gdiplus.GdipImageGetFrameCount.restype = c_int
-gdiplus.GdipImageGetFrameCount.argtypes = [c_void_p, c_void_p, UINT_PTR]
-gdiplus.GdipImageGetFrameDimensionsCount.restype = c_int
-gdiplus.GdipImageGetFrameDimensionsCount.argtypes = [c_void_p, UINT_PTR]
-gdiplus.GdipImageGetFrameDimensionsList.restype = c_int
-gdiplus.GdipImageGetFrameDimensionsList.argtypes = [c_void_p, c_void_p, UINT]
-gdiplus.GdipImageSelectActiveFrame.restype = c_int
-gdiplus.GdipImageSelectActiveFrame.argtypes = [c_void_p, c_void_p, UINT]
-gdiplus.GdipMeasureString.restype = c_int
-gdiplus.GdipMeasureString.argtypes = [c_void_p, c_wchar_p, c_int, c_void_p, c_void_p, c_void_p, c_void_p, INT_PTR, INT_PTR]
-gdiplus.GdipNewPrivateFontCollection.restype = c_int
-gdiplus.GdipNewPrivateFontCollection.argtypes = [c_void_p]
-gdiplus.GdipPrivateAddMemoryFont.restype = c_int
-gdiplus.GdipPrivateAddMemoryFont.argtypes = [c_void_p, c_void_p, c_int]
-gdiplus.GdipSetPageUnit.restype = c_int
-gdiplus.GdipSetPageUnit.argtypes = [c_void_p, c_int]
-gdiplus.GdipSetStringFormatFlags.restype = c_int
-gdiplus.GdipSetStringFormatFlags.argtypes = [c_void_p, c_int]
-gdiplus.GdipSetTextRenderingHint.restype = c_int
-gdiplus.GdipSetTextRenderingHint.argtypes = [c_void_p, c_int]
-gdiplus.GdipStringFormatGetGenericTypographic.restype = c_int
-gdiplus.GdipStringFormatGetGenericTypographic.argtypes = [c_void_p]
-gdiplus.GdiplusShutdown.restype = None
-gdiplus.GdiplusShutdown.argtypes = [POINTER(ULONG)]
-gdiplus.GdiplusStartup.restype = c_int
-gdiplus.GdiplusStartup.argtypes = [c_void_p, c_void_p, c_void_p]
-
-
-class GDIPlusDecoder(ImageDecoder):
-    def get_file_extensions(self):
-        return ['.bmp', '.gif', '.jpg', '.jpeg', '.exif', '.png', '.tif', '.tiff']
-
-    def get_animation_file_extensions(self):
-        # TIFF also supported as a multi-page image; but that's not really an
-        # animation, is it?
-        return ['.gif']
-
-    def _load_bitmap(self, filename, file):
-        data = file.read()
-
-        # Create a HGLOBAL with image data
-        hglob = kernel32.GlobalAlloc(GMEM_MOVEABLE, len(data))
-        ptr = kernel32.GlobalLock(hglob)
-        memmove(ptr, data, len(data))
-        kernel32.GlobalUnlock(hglob)
-
-        # Create IStream for the HGLOBAL
-        self.stream = pIUnknown()
-        ole32.CreateStreamOnHGlobal(hglob, True, byref(self.stream))
-
-        # Load image from stream
-        bitmap = c_void_p()
-        status = gdiplus.GdipCreateBitmapFromStream(self.stream, byref(bitmap))
-        if status != 0:
-            self.stream.Release()
-            raise ImageDecodeException('GDI+ cannot load %r' % (filename or file))
-
-        return bitmap
-
-    @staticmethod
-    def _get_image(bitmap):
-        # Get size of image (Bitmap subclasses Image)
-        width = REAL()
-        height = REAL()
-        gdiplus.GdipGetImageDimension(bitmap, byref(width), byref(height))
-        width = int(width.value)
-        height = int(height.value)
-
-        # Get image pixel format
-        pf = c_int()
-        gdiplus.GdipGetImagePixelFormat(bitmap, byref(pf))
-        pf = pf.value
-
-        # Reverse from what's documented because of Intel little-endianness.
-        fmt = 'BGRA'
-        if pf == PixelFormat24bppRGB:
-            fmt = 'BGR'
-        elif pf == PixelFormat32bppRGB:
-            pass
-        elif pf == PixelFormat32bppARGB:
-            pass
-        elif pf in (PixelFormat16bppARGB1555, PixelFormat32bppPARGB,
-                    PixelFormat64bppARGB, PixelFormat64bppPARGB):
-            pf = PixelFormat32bppARGB
-        else:
-            fmt = 'BGR'
-            pf = PixelFormat24bppRGB
-
-        # Lock pixel data in best format
-        rect = Rect()
-        rect.X = 0
-        rect.Y = 0
-        rect.Width = width
-        rect.Height = height
-        bitmap_data = BitmapData()
-        gdiplus.GdipBitmapLockBits(bitmap, byref(rect), ImageLockModeRead, pf, byref(bitmap_data))
-        
-        # Create buffer for RawImage
-        buffer = create_string_buffer(bitmap_data.Stride * height)
-        memmove(buffer, bitmap_data.Scan0, len(buffer))
-        
-        # Unlock data
-        gdiplus.GdipBitmapUnlockBits(bitmap, byref(bitmap_data))
-
-        return ImageData(width, height, fmt, buffer, -bitmap_data.Stride)
-
-    def _delete_bitmap(self, bitmap):
-        # Release image and stream
-        gdiplus.GdipDisposeImage(bitmap)
-        self.stream.Release()
-
-    def decode(self, filename, file):
-        if not file:
-            file = open(filename, 'rb')
-        bitmap = self._load_bitmap(filename, file)
-        image = self._get_image(bitmap)
-        self._delete_bitmap(bitmap)
-        return image
-
-    def decode_animation(self, filename, file):
-        if not file:
-            file = open(filename, 'rb')
-        bitmap = self._load_bitmap(filename, file)
-        
-        dimension_count = c_uint()
-        gdiplus.GdipImageGetFrameDimensionsCount(bitmap, byref(dimension_count))
-        if dimension_count.value < 1:
-            self._delete_bitmap(bitmap)
-            raise ImageDecodeException('Image has no frame dimensions')
-        
-        # XXX Make sure this dimension is time?
-        dimensions = (c_void_p * dimension_count.value)()
-        gdiplus.GdipImageGetFrameDimensionsList(bitmap, dimensions, dimension_count.value)
-
-        frame_count = c_uint()
-        gdiplus.GdipImageGetFrameCount(bitmap, dimensions, byref(frame_count))
-
-        prop_id = PropertyTagFrameDelay
-        prop_size = c_uint()
-        gdiplus.GdipGetPropertyItemSize(bitmap, prop_id, byref(prop_size))
-
-        prop_buffer = c_buffer(prop_size.value)
-        prop_item = cast(prop_buffer, POINTER(PropertyItem)).contents 
-        gdiplus.GdipGetPropertyItem(bitmap, prop_id, prop_size.value, prop_buffer)
-
-        n_delays = prop_item.length // sizeof(c_long)
-        delays = cast(prop_item.value, POINTER(c_long * n_delays)).contents
-
-        frames = []
-        
-        for i in range(frame_count.value):
-            gdiplus.GdipImageSelectActiveFrame(bitmap, dimensions, i)
-            image = self._get_image(bitmap)
-
-            delay = delays[i]
-            if delay <= 1:
-                delay = 10
-            frames.append(AnimationFrame(image, delay/100.))
-
-        self._delete_bitmap(bitmap)
-
-        return Animation(frames)
-
-
-def get_decoders():
-    return [GDIPlusDecoder()]
-
-
-def get_encoders():
-    return []
-
-
-def init():
-    token = c_ulong()
-    startup_in = GdiplusStartupInput()
-    startup_in.GdiplusVersion = 1
-    startup_out = GdiplusStartupOutput()
-    gdiplus.GdiplusStartup(byref(token), byref(startup_in), byref(startup_out))
-
-    # Shutdown later?
-    # gdiplus.GdiplusShutdown(token)
-
-
-init()
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/math.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/math.py
deleted file mode 100644
index 664780899740cd5e2ebb47a699b98dfd127e80bf..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/math.py
+++ /dev/null
@@ -1,1038 +0,0 @@
-"""Matrix and Vector math.
-
-This module provides Vector and Matrix objects, including Vec2, Vec3,
-Vec4, Mat3, and Mat4. Most common matrix and vector operations are
-supported. Helper methods are included for rotating, scaling, and
-transforming. The :py:class:`~pyglet.matrix.Mat4` includes class methods
-for creating orthographic and perspective projection matrixes.
-
-Matrices behave just like they do in GLSL: they are specified in column-major
-order and multiply on the left of vectors, which are treated as columns.
-
-:note: For performance, Matrixes subclass the `tuple` type. They
-    are therefore immutable - all operations return a new object;
-    the object is not updated in-place.
-"""
-
-from __future__ import annotations
-
-import math as _math
-import typing as _typing
-import warnings as _warnings
-
-from operator import mul as _mul
-from collections.abc import Iterable as _Iterable
-from collections.abc import Iterator as _Iterator
-
-
-number = _typing.Union[float, int]
-Mat4T = _typing.TypeVar("Mat4T", bound="Mat4")
-
-
-def clamp(num: float, min_val: float, max_val: float) -> float:
-    return max(min(num, max_val), min_val)
-
-
-class Vec2:
-    __slots__ = 'x', 'y'
-
-    """A two-dimensional vector represented as an X Y coordinate pair."""
-
-    def __init__(self, x: number = 0.0, y: number = 0.0) -> None:
-        self.x = x
-        self.y = y
-
-    def __iter__(self) -> _Iterator[float]:
-        yield self.x
-        yield self.y
-
-    @_typing.overload
-    def __getitem__(self, item: int) -> float:
-        ...
-
-    @_typing.overload
-    def __getitem__(self, item: slice) -> tuple[float, ...]:
-        ...
-
-    def __getitem__(self, item):
-        return (self.x, self.y)[item]
-
-    def __setitem__(self, key, value):
-        if type(key) is slice:
-            for i, attr in enumerate(['x', 'y'][key]):
-                setattr(self, attr, value[i])
-        else:
-            setattr(self, ['x', 'y'][key], value)
-
-    def __len__(self) -> int:
-        return 2
-
-    def __add__(self, other: Vec2) -> Vec2:
-        return Vec2(self.x + other.x, self.y + other.y)
-
-    def __sub__(self, other: Vec2) -> Vec2:
-        return Vec2(self.x - other.x, self.y - other.y)
-
-    def __mul__(self, scalar: number) -> Vec2:
-        return Vec2(self.x * scalar, self.y * scalar)
-
-    def __truediv__(self, scalar: number) -> Vec2:
-        return Vec2(self.x / scalar, self.y / scalar)
-
-    def __floordiv__(self, scalar: number) -> Vec2:
-        return Vec2(self.x // scalar, self.y // scalar)
-
-    def __abs__(self) -> float:
-        return _math.sqrt(self.x ** 2 + self.y ** 2)
-
-    def __neg__(self) -> Vec2:
-        return Vec2(-self.x, -self.y)
-
-    def __round__(self, ndigits: int | None = None) -> Vec2:
-        return Vec2(*(round(v, ndigits) for v in self))
-
-    def __radd__(self, other: Vec2 | int) -> Vec2:
-        """Reverse add. Required for functionality with sum()
-        """
-        if other == 0:
-            return self
-        else:
-            return self.__add__(_typing.cast(Vec2, other))
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, Vec2) and self.x == other.x and self.y == other.y
-
-    def __ne__(self, other: object) -> bool:
-        return not isinstance(other, Vec2) or self.x != other.x or self.y != other.y
-
-    @staticmethod
-    def from_polar(mag: float, angle: float) -> Vec2:
-        """Create a new vector from the given polar coordinates.
-
-        :parameters:
-            `mag`   : int or float :
-                The magnitude of the vector.
-            `angle` : int or float :
-                The angle of the vector in radians.
-
-        :returns: A new vector with the given angle and magnitude.
-        :rtype: Vec2
-        """
-        return Vec2(mag * _math.cos(angle), mag * _math.sin(angle))
-
-    def from_magnitude(self, magnitude: float) -> Vec2:
-        """Create a new Vector of the given magnitude by normalizing,
-        then scaling the vector. The heading remains unchanged.
-
-        :parameters:
-            `magnitude` : int or float :
-                The magnitude of the new vector.
-
-        :returns: A new vector with the magnitude.
-        :rtype: Vec2
-        """
-        return self.normalize() * magnitude
-
-    def from_heading(self, heading: float) -> Vec2:
-        """Create a new vector of the same magnitude with the given heading. I.e. Rotate the vector to the heading.
-
-        :parameters:
-            `heading` : int or float :
-                The angle of the new vector in radians.
-
-        :returns: A new vector with the given heading.
-        :rtype: Vec2
-        """
-        mag = self.__abs__()
-        return Vec2(mag * _math.cos(heading), mag * _math.sin(heading))
-
-    @property
-    def heading(self) -> float:
-        """The angle of the vector in radians.
-
-        :type: float
-        """
-        return _math.atan2(self.y, self.x)
-
-    @property
-    def mag(self) -> float:
-        """The magnitude, or length of the vector. The distance between the coordinates and the origin.
-
-        Alias of abs(self).
-
-        :type: float
-        """
-        return self.__abs__()
-
-    def limit(self, maximum: float) -> Vec2:
-        """Limit the magnitude of the vector to the value used for the max parameter.
-
-        :parameters:
-            `maximum`  : int or float :
-                The maximum magnitude for the vector.
-
-        :returns: Either self or a new vector with the maximum magnitude.
-        :rtype: Vec2
-        """
-        if self.x ** 2 + self.y ** 2 > maximum * maximum:
-            return self.from_magnitude(maximum)
-        return self
-
-    def lerp(self, other: Vec2, alpha: float) -> Vec2:
-        """Create a new Vec2 linearly interpolated between this vector and another Vec2.
-
-        :parameters:
-            `other`  : Vec2 :
-                The vector to linearly interpolate with.
-            `alpha` : float or int :
-                The amount of interpolation.
-                Some value between 0.0 (this vector) and 1.0 (other vector).
-                0.5 is halfway inbetween.
-
-        :returns: A new interpolated vector.
-        :rtype: Vec2
-        """
-        return Vec2(self.x + (alpha * (other.x - self.x)),
-                    self.y + (alpha * (other.y - self.y)))
-
-    def reflect(self, normal: Vec2) -> Vec2:
-        """Create a new Vec2 reflected (ricochet) from the given normal."""
-        return self - normal * 2 * normal.dot(self)
-
-    def rotate(self, angle: float) -> Vec2:
-        """Create a new Vector rotated by the angle. The magnitude remains unchanged.
-
-        :parameters:
-            `angle` : int or float :
-                The angle to rotate by
-
-        :returns: A new rotated vector of the same magnitude.
-        :rtype: Vec2
-        """
-        s = _math.sin(angle)
-        c = _math.cos(angle)
-        return Vec2(c * self.x - s * self.y, s * self.x + c * self.y)
-
-    def distance(self, other: Vec2) -> float:
-        """Calculate the distance between this vector and another 2D vector."""
-        return _math.sqrt(((other.x - self.x) ** 2) + ((other.y - self.y) ** 2))
-
-    def normalize(self) -> Vec2:
-        """Normalize the vector to have a magnitude of 1. i.e. make it a unit vector.
-
-        :returns: A unit vector with the same heading.
-        :rtype: Vec2
-        """
-        d = self.__abs__()
-        if d:
-            return Vec2(self.x / d, self.y / d)
-        return self
-
-    def clamp(self, min_val: float, max_val: float) -> Vec2:
-        """Restrict the value of the X and Y components of the vector to be within the given values.
-
-        :parameters:
-            `min_val` : int or float :
-                The minimum value
-            `max_val` : int or float :
-                The maximum value
-
-        :returns: A new vector with clamped X and Y components.
-        :rtype: Vec2
-        """
-        return Vec2(clamp(self.x, min_val, max_val), clamp(self.y, min_val, max_val))
-
-    def dot(self, other: Vec2) -> float:
-        """Calculate the dot product of this vector and another 2D vector.
-
-        :parameters:
-            `other`  : Vec2 :
-                The other vector.
-
-        :returns: The dot product of the two vectors.
-        :rtype: float
-        """
-        return self.x * other.x + self.y * other.y
-
-    def __getattr__(self, attrs: str) -> Vec2 | Vec3 | Vec4:
-        try:
-            # Allow swizzled getting of attrs
-            vec_class = {2: Vec2, 3: Vec3, 4: Vec4}[len(attrs)]
-            return vec_class(*(self['xy'.index(c)] for c in attrs))
-        except Exception:
-            raise AttributeError(
-                f"'{self.__class__.__name__}' object has no attribute '{attrs}'"
-            ) from None
-
-    def __repr__(self) -> str:
-        return f"Vec2({self.x}, {self.y})"
-
-
-class Vec3:
-    __slots__ = 'x', 'y', 'z'
-
-    """A three-dimensional vector represented as X Y Z coordinates."""
-
-    def __init__(self, x: number = 0.0, y: number = 0.0, z: number = 0.0) -> None:
-        self.x = x
-        self.y = y
-        self.z = z
-
-    def __iter__(self) -> _Iterator[float]:
-        yield self.x
-        yield self.y
-        yield self.z
-
-    @_typing.overload
-    def __getitem__(self, item: int) -> float:
-        ...
-
-    @_typing.overload
-    def __getitem__(self, item: slice) -> tuple[float, ...]:
-        ...
-
-    def __getitem__(self, item):
-        return (self.x, self.y, self.z)[item]
-
-    def __setitem__(self, key, value):
-        if type(key) is slice:
-            for i, attr in enumerate(['x', 'y', 'z'][key]):
-                setattr(self, attr, value[i])
-        else:
-            setattr(self, ['x', 'y', 'z'][key], value)
-
-    def __len__(self) -> int:
-        return 3
-
-    @property
-    def mag(self) -> float:
-        """The magnitude, or length of the vector. The distance between the coordinates and the origin.
-
-        Alias of abs(self).
-
-        :type: float
-        """
-        return self.__abs__()
-
-    def __add__(self, other: Vec3) -> Vec3:
-        return Vec3(self.x + other.x, self.y + other.y, self.z + other.z)
-
-    def __sub__(self, other: Vec3) -> Vec3:
-        return Vec3(self.x - other.x, self.y - other.y, self.z - other.z)
-
-    def __mul__(self, scalar: number) -> Vec3:
-        return Vec3(self.x * scalar, self.y * scalar, self.z * scalar)
-
-    def __truediv__(self, scalar: number) -> Vec3:
-        return Vec3(self.x / scalar, self.y / scalar, self.z / scalar)
-
-    def __floordiv__(self, scalar: number) -> Vec3:
-        return Vec3(self.x // scalar, self.y // scalar, self.z // scalar)
-
-    def __abs__(self) -> float:
-        return _math.sqrt(self.x ** 2 + self.y ** 2 + self.z ** 2)
-
-    def __neg__(self) -> Vec3:
-        return Vec3(-self.x, -self.y, -self.z)
-
-    def __round__(self, ndigits: int | None = None) -> Vec3:
-        return Vec3(*(round(v, ndigits) for v in self))
-
-    def __radd__(self, other: Vec3 | int) -> Vec3:
-        """Reverse add. Required for functionality with sum()"""
-        if other == 0:
-            return self
-        else:
-            return self.__add__(_typing.cast(Vec3, other))
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, Vec3) and self.x == other.x and self.y == other.y and self.z == other.z
-
-    def __ne__(self, other: object) -> bool:
-        return not isinstance(other, Vec3) or self.x != other.x or self.y != other.y or self.z != other.z
-
-    def from_magnitude(self, magnitude: float) -> Vec3:
-        """Create a new Vector of the given magnitude by normalizing,
-        then scaling the vector. The rotation remains unchanged.
-
-        :parameters:
-            `magnitude` : int or float :
-                The magnitude of the new vector.
-
-        :returns: A new vector with the magnitude.
-        :rtype: Vec3
-        """
-        return self.normalize() * magnitude
-
-    def limit(self, maximum: float) -> Vec3:
-        """Limit the magnitude of the vector to the value used for the max parameter.
-
-        :parameters:
-            `maximum`  : int or float :
-                The maximum magnitude for the vector.
-
-        :returns: Either self or a new vector with the maximum magnitude.
-        :rtype: Vec3
-        """
-        if self.x ** 2 + self.y ** 2 + self.z ** 2 > maximum * maximum * maximum:
-            return self.from_magnitude(maximum)
-        return self
-
-    def cross(self, other: Vec3) -> Vec3:
-        """Calculate the cross product of this vector and another 3D vector.
-
-        :parameters:
-            `other`  : Vec3 :
-                The other vector.
-
-        :returns: The cross product of the two vectors.
-        :rtype: float
-        """
-        return Vec3((self.y * other.z) - (self.z * other.y),
-                    (self.z * other.x) - (self.x * other.z),
-                    (self.x * other.y) - (self.y * other.x))
-
-    def dot(self, other: Vec3) -> float:
-        """Calculate the dot product of this vector and another 3D vector.
-
-        :parameters:
-            `other`  : Vec3 :
-                The other vector.
-
-        :returns: The dot product of the two vectors.
-        :rtype: float
-        """
-        return self.x * other.x + self.y * other.y + self.z * other.z
-
-    def lerp(self, other: Vec3, alpha: float) -> Vec3:
-        """Create a new Vec3 linearly interpolated between this vector and another Vec3.
-
-        :parameters:
-            `other`  : Vec3 :
-                The vector to linearly interpolate with.
-            `alpha` : float or int :
-                The amount of interpolation.
-                Some value between 0.0 (this vector) and 1.0 (other vector).
-                0.5 is halfway inbetween.
-
-        :returns: A new interpolated vector.
-        :rtype: Vec3
-        """
-        return Vec3(self.x + (alpha * (other.x - self.x)),
-                    self.y + (alpha * (other.y - self.y)),
-                    self.z + (alpha * (other.z - self.z)))
-
-    def distance(self, other: Vec3) -> float:
-        """Calculate the distance between this vector and another 3D vector.
-
-        :parameters:
-            `other`  : Vec3 :
-                The other vector
-
-        :returns: The distance between the two vectors.
-        :rtype: float
-        """
-        return _math.sqrt(((other.x - self.x) ** 2) +
-                          ((other.y - self.y) ** 2) +
-                          ((other.z - self.z) ** 2))
-
-    def normalize(self) -> Vec3:
-        """Normalize the vector to have a magnitude of 1. i.e. make it a unit vector.
-
-        :returns: A unit vector with the same rotation.
-        :rtype: Vec3
-        """
-        try:
-            d = self.__abs__()
-            return Vec3(self.x / d, self.y / d, self.z / d)
-        except ZeroDivisionError:
-            return self
-
-    def clamp(self, min_val: float, max_val: float) -> Vec3:
-        """Restrict the value of the X,  Y and Z components of the vector to be within the given values.
-
-        :parameters:
-            `min_val` : int or float :
-                The minimum value
-            `max_val` : int or float :
-                The maximum value
-
-        :returns: A new vector with clamped X, Y and Z components.
-        :rtype: Vec3
-        """
-        return Vec3(clamp(self.x, min_val, max_val),
-                    clamp(self.y, min_val, max_val),
-                    clamp(self.z, min_val, max_val))
-
-    def __getattr__(self, attrs: str) -> Vec2 | Vec3 | Vec4:
-        try:
-            # Allow swizzled getting of attrs
-            vec_class = {2: Vec2, 3: Vec3, 4: Vec4}[len(attrs)]
-            return vec_class(*(self['xyz'.index(c)] for c in attrs))
-        except Exception:
-            raise AttributeError(
-                f"'{self.__class__.__name__}' object has no attribute '{attrs}'"
-            ) from None
-
-    def __repr__(self) -> str:
-        return f"Vec3({self.x}, {self.y}, {self.z})"
-
-
-class Vec4:
-    __slots__ = 'x', 'y', 'z', 'w'
-
-    """A four-dimensional vector represented as X Y Z W coordinates."""
-
-    def __init__(self, x: number = 0.0, y: number = 0.0, z: number = 0.0, w: number = 0.0) -> None:
-        self.x = x
-        self.y = y
-        self.z = z
-        self.w = w
-
-    def __iter__(self) -> _Iterator[float]:
-        yield self.x
-        yield self.y
-        yield self.z
-        yield self.w
-
-    @_typing.overload
-    def __getitem__(self, item: int) -> float:
-        ...
-
-    @_typing.overload
-    def __getitem__(self, item: slice) -> tuple[float, ...]:
-        ...
-
-    def __getitem__(self, item):
-        return (self.x, self.y, self.z, self.w)[item]
-
-    def __setitem__(self, key, value):
-        if type(key) is slice:
-            for i, attr in enumerate(['x', 'y', 'z', 'w'][key]):
-                setattr(self, attr, value[i])
-        else:
-            setattr(self, ['x', 'y', 'z', 'w'][key], value)
-
-    def __len__(self) -> int:
-        return 4
-
-    def __add__(self, other: Vec4) -> Vec4:
-        return Vec4(self.x + other.x, self.y + other.y, self.z + other.z, self.w + other.w)
-
-    def __sub__(self, other: Vec4) -> Vec4:
-        return Vec4(self.x - other.x, self.y - other.y, self.z - other.z, self.w - other.w)
-
-    def __mul__(self, scalar: number) -> Vec4:
-        return Vec4(self.x * scalar, self.y * scalar, self.z * scalar, self.w * scalar)
-
-    def __truediv__(self, scalar: number) -> Vec4:
-        return Vec4(self.x / scalar, self.y / scalar, self.z / scalar, self.w / scalar)
-
-    def __floordiv__(self, scalar: number) -> Vec4:
-        return Vec4(self.x // scalar, self.y // scalar, self.z // scalar, self.w // scalar)
-
-    def __abs__(self) -> float:
-        return _math.sqrt(self.x ** 2 + self.y ** 2 + self.z ** 2 + self.w ** 2)
-
-    def __neg__(self) -> Vec4:
-        return Vec4(-self.x, -self.y, -self.z, -self.w)
-
-    def __round__(self, ndigits: int | None = None) -> Vec4:
-        return Vec4(*(round(v, ndigits) for v in self))
-
-    def __radd__(self, other: Vec4 | int) -> Vec4:
-        if other == 0:
-            return self
-        else:
-            return self.__add__(_typing.cast(Vec4, other))
-
-    def __eq__(self, other: object) -> bool:
-        return (
-                isinstance(other, Vec4)
-                and self.x == other.x
-                and self.y == other.y
-                and self.z == other.z
-                and self.w == other.w
-        )
-
-    def __ne__(self, other: object) -> bool:
-        return (
-                not isinstance(other, Vec4)
-                or self.x != other.x
-                or self.y != other.y
-                or self.z != other.z
-                or self.w != other.w
-        )
-
-    def lerp(self, other: Vec4, alpha: float) -> Vec4:
-        """Create a new Vec4 linearly interpolated between this one and another Vec4.
-
-        :parameters:
-            `other`  : Vec4 :
-                The vector to linearly interpolate with.
-            `alpha` : float or int :
-                The amount of interpolation.
-                Some value between 0.0 (this vector) and 1.0 (other vector).
-                0.5 is halfway inbetween.
-
-        :returns: A new interpolated vector.
-        :rtype: Vec4
-        """
-        return Vec4(self.x + (alpha * (other.x - self.x)),
-                    self.y + (alpha * (other.y - self.y)),
-                    self.z + (alpha * (other.z - self.z)),
-                    self.w + (alpha * (other.w - self.w)))
-
-    def distance(self, other: Vec4) -> float:
-        return _math.sqrt(((other.x - self.x) ** 2) +
-                          ((other.y - self.y) ** 2) +
-                          ((other.z - self.z) ** 2) +
-                          ((other.w - self.w) ** 2))
-
-    def normalize(self) -> Vec4:
-        """Normalize the vector to have a magnitude of 1. i.e. make it a unit vector."""
-        d = self.__abs__()
-        if d:
-            return Vec4(self.x / d, self.y / d, self.z / d, self.w / d)
-        return self
-
-    def clamp(self, min_val: float, max_val: float) -> Vec4:
-        return Vec4(clamp(self.x, min_val, max_val),
-                    clamp(self.y, min_val, max_val),
-                    clamp(self.z, min_val, max_val),
-                    clamp(self.w, min_val, max_val))
-
-    def dot(self, other: Vec4) -> float:
-        return self.x * other.x + self.y * other.y + self.z * other.z + self.w * other.w
-
-    def __getattr__(self, attrs: str) -> Vec2 | Vec3 | Vec4:
-        try:
-            # Allow swizzled getting of attrs
-            vec_class = {2: Vec2, 3: Vec3, 4: Vec4}[len(attrs)]
-            return vec_class(*(self['xyzw'.index(c)] for c in attrs))
-        except Exception:
-            raise AttributeError(
-                f"'{self.__class__.__name__}' object has no attribute '{attrs}'"
-            ) from None
-
-    def __repr__(self) -> str:
-        return f"Vec4({self.x}, {self.y}, {self.z}, {self.w})"
-
-
-class Mat3(tuple):
-    """A 3x3 Matrix class
-
-    `Mat3` is an immutable 3x3 Matrix, including most common
-    operators. Matrix multiplication must be performed using
-    the "@" operator.
-    """
-
-    def __new__(cls, values: _Iterable[float] = (1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) -> Mat3:
-        """Create a 3x3 Matrix
-
-        A Mat3 can be created with a list or tuple of 9 values.
-        If no values are provided, an "identity matrix" will be created
-        (1.0 on the main diagonal). Matrix objects are immutable, so
-        all operations return a new Mat3 object.
-
-        :Parameters:
-            `values` : tuple of float or int
-                A tuple or list containing 9 floats or ints.
-        """
-        new = super().__new__(Mat3, values)
-        assert len(new) == 9, "A 3x3 Matrix requires 9 values"
-        return new
-
-    def scale(self, sx: float, sy: float) -> Mat3:
-        return self @ Mat3((1.0 / sx, 0.0, 0.0, 0.0, 1.0 / sy, 0.0, 0.0, 0.0, 1.0))
-
-    def translate(self, tx: float, ty: float) -> Mat3:
-        return self @ Mat3((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, -tx, ty, 1.0))
-
-    def rotate(self, phi: float) -> Mat3:
-        s = _math.sin(_math.radians(phi))
-        c = _math.cos(_math.radians(phi))
-        return self @ Mat3((c, s, 0.0, -s, c, 0.0, 0.0, 0.0, 1.0))
-
-    def shear(self, sx: float, sy: float) -> Mat3:
-        return self @ Mat3((1.0, sy, 0.0, sx, 1.0, 0.0, 0.0, 0.0, 1.0))
-
-    def __add__(self, other: Mat3) -> Mat3:
-        if not isinstance(other, Mat3):
-            raise TypeError("Can only add to other Mat3 types")
-        return Mat3(s + o for s, o in zip(self, other))
-
-    def __sub__(self, other: Mat3) -> Mat3:
-        if not isinstance(other, Mat3):
-            raise TypeError("Can only subtract from other Mat3 types")
-        return Mat3(s - o for s, o in zip(self, other))
-
-    def __pos__(self) -> Mat3:
-        return self
-
-    def __neg__(self) -> Mat3:
-        return Mat3(-v for v in self)
-
-    def __round__(self, ndigits: int | None = None) -> Mat3:
-        return Mat3(round(v, ndigits) for v in self)
-
-    def __mul__(self, other: object) -> _typing.NoReturn:
-        raise NotImplementedError("Please use the @ operator for Matrix multiplication.")
-
-    @_typing.overload
-    def __matmul__(self, other: Vec3) -> Vec3:
-        ...
-
-    @_typing.overload
-    def __matmul__(self, other: Mat3) -> Mat3:
-        ...
-
-    def __matmul__(self, other):
-        if isinstance(other, Vec3):
-            # Rows:
-            r0 = self[0::3]
-            r1 = self[1::3]
-            r2 = self[2::3]
-            return Vec3(sum(map(_mul, r0, other)),
-                        sum(map(_mul, r1, other)),
-                        sum(map(_mul, r2, other)))
-
-        if not isinstance(other, Mat3):
-            raise TypeError("Can only multiply with Mat3 or Vec3 types")
-
-        # Rows:
-        r0 = self[0::3]
-        r1 = self[1::3]
-        r2 = self[2::3]
-        # Columns:
-        c0 = other[0:3]
-        c1 = other[3:6]
-        c2 = other[6:9]
-
-        # Multiply and sum rows * columns:
-        return Mat3((sum(map(_mul, c0, r0)), sum(map(_mul, c0, r1)), sum(map(_mul, c0, r2)),
-                     sum(map(_mul, c1, r0)), sum(map(_mul, c1, r1)), sum(map(_mul, c1, r2)),
-                     sum(map(_mul, c2, r0)), sum(map(_mul, c2, r1)), sum(map(_mul, c2, r2))))
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}{self[0:3]}\n    {self[3:6]}\n    {self[6:9]}"
-
-
-class Mat4(tuple):
-    """A 4x4 Matrix class
-
-    `Mat4` is an immutable 4x4 Matrix, including most common
-    operators. Matrix multiplication must be performed using
-    the "@" operator.
-    Class methods are available for creating orthogonal
-    and perspective projections matrixes.
-    """
-
-    def __new__(cls, values: _Iterable[float] = (1.0, 0.0, 0.0, 0.0,
-                                                 0.0, 1.0, 0.0, 0.0,
-                                                 0.0, 0.0, 1.0, 0.0,
-                                                 0.0, 0.0, 0.0, 1.0,)) -> Mat4:
-        """Create a 4x4 Matrix
-
-        A Matrix can be created with a list or tuple of 16 values.
-        If no values are provided, an "identity matrix" will be created
-        (1.0 on the main diagonal). Matrix objects are immutable, so
-        all operations return a new Mat4 object.
-
-        :Parameters:
-            `values` : tuple of float or int
-                A tuple or list containing 16 floats or ints.
-        """
-
-        new = super().__new__(Mat4, values)
-        assert len(new) == 16, "A 4x4 Matrix requires 16 values"
-        return new
-
-    @classmethod
-    def orthogonal_projection(
-        cls: type[Mat4T],
-        left: float,
-        right: float,
-        bottom: float,
-        top: float,
-        z_near: float,
-        z_far: float
-    ) -> Mat4T:
-        """Create a Mat4 orthographic projection matrix for use with OpenGL.
-
-        This matrix doesn't actually perform the projection; it transforms the
-        space so that OpenGL's vertex processing performs it.
-        """
-        width = right - left
-        height = top - bottom
-        depth = z_far - z_near
-
-        sx = 2.0 / width
-        sy = 2.0 / height
-        sz = 2.0 / -depth
-
-        tx = -(right + left) / width
-        ty = -(top + bottom) / height
-        tz = -(z_far + z_near) / depth
-
-        return cls((sx, 0.0, 0.0, 0.0,
-                    0.0, sy, 0.0, 0.0,
-                    0.0, 0.0, sz, 0.0,
-                    tx, ty, tz, 1.0))
-
-    @classmethod
-    def perspective_projection(
-        cls: type[Mat4T],
-        aspect: float,
-        z_near: float,
-        z_far: float,
-        fov: float = 60
-    ) -> Mat4T:
-        """
-        Create a Mat4 perspective projection matrix for use with OpenGL.
-
-        This matrix doesn't actually perform the projection; it transforms the
-        space so that OpenGL's vertex processing performs it.
-
-        :Parameters:
-            `aspect` : The aspect ratio as a `float`
-            `z_near` : The near plane as a `float`
-            `z_far` : The far plane as a `float`
-            `fov` : Field of view in degrees as a `float`
-        """
-        xy_max = z_near * _math.tan(fov * _math.pi / 360)
-        y_min = -xy_max
-        x_min = -xy_max
-
-        width = xy_max - x_min
-        height = xy_max - y_min
-        depth = z_far - z_near
-        q = -(z_far + z_near) / depth
-        qn = -2 * z_far * z_near / depth
-
-        w = 2 * z_near / width
-        w = w / aspect
-        h = 2 * z_near / height
-
-        return cls((w, 0, 0, 0,
-                    0, h, 0, 0,
-                    0, 0, q, -1,
-                    0, 0, qn, 0))
-
-    @classmethod
-    def from_rotation(cls, angle: float, vector: Vec3) -> Mat4:
-        """Create a rotation matrix from an angle and Vec3.
-
-        :Parameters:
-            `angle` : A `float` :
-                The angle as a float.
-            `vector` : A `Vec3`, or 3 component tuple of float or int :
-                Vec3 or tuple with x, y and z translation values
-        """
-        return cls().rotate(angle, vector)
-
-    @classmethod
-    def from_scale(cls: type[Mat4T], vector: Vec3) -> Mat4T:
-        """Create a scale matrix from a Vec3.
-
-        :Parameters:
-            `vector` : A `Vec3`, or 3 component tuple of float or int
-                Vec3 or tuple with x, y and z scale values
-        """
-        return cls((vector[0], 0.0, 0.0, 0.0,
-                    0.0, vector[1], 0.0, 0.0,
-                    0.0, 0.0, vector[2], 0.0,
-                    0.0, 0.0, 0.0, 1.0))
-
-    @classmethod
-    def from_translation(cls: type[Mat4T], vector: Vec3) -> Mat4T:
-        """Create a translation matrix from a Vec3.
-
-        :Parameters:
-            `vector` : A `Vec3`, or 3 component tuple of float or int
-                Vec3 or tuple with x, y and z translation values
-        """
-        return cls((1.0, 0.0, 0.0, 0.0,
-                    0.0, 1.0, 0.0, 0.0,
-                    0.0, 0.0, 1.0, 0.0,
-                    vector[0], vector[1], vector[2], 1.0))
-
-    @classmethod
-    def look_at(cls: type[Mat4T], position: Vec3, target: Vec3, up: Vec3):
-        f = (target - position).normalize()
-        u = up.normalize()
-        s = f.cross(u).normalize()
-        u = s.cross(f)
-
-        return cls([s.x, u.x, -f.x, 0.0,
-                    s.y, u.y, -f.y, 0.0,
-                    s.z, u.z, -f.z, 0.0,
-                    -s.dot(position), -u.dot(position), f.dot(position), 1.0])
-
-    def row(self, index: int) -> tuple:
-        """Get a specific row as a tuple."""
-        return self[index::4]
-
-    def column(self, index: int) -> tuple:
-        """Get a specific column as a tuple."""
-        return self[index * 4: index * 4 + 4]
-
-    def rotate(self, angle: float, vector: Vec3) -> Mat4:
-        """Get a rotation Matrix on x, y, or z axis."""
-        if not all(abs(n) <= 1 for n in vector):
-            raise ValueError("vector must be normalized (<=1)")
-        x, y, z = vector
-        c = _math.cos(angle)
-        s = _math.sin(angle)
-        t = 1 - c
-        temp_x, temp_y, temp_z = t * x, t * y, t * z
-
-        ra = c + temp_x * x
-        rb = 0 + temp_x * y + s * z
-        rc = 0 + temp_x * z - s * y
-        re = 0 + temp_y * x - s * z
-        rf = c + temp_y * y
-        rg = 0 + temp_y * z + s * x
-        ri = 0 + temp_z * x + s * y
-        rj = 0 + temp_z * y - s * x
-        rk = c + temp_z * z
-
-        # ra, rb, rc, --
-        # re, rf, rg, --
-        # ri, rj, rk, --
-        # --, --, --, --
-
-        return Mat4(self) @ Mat4((ra, rb, rc, 0, re, rf, rg, 0, ri, rj, rk, 0, 0, 0, 0, 1))
-
-    def scale(self, vector: Vec3) -> Mat4:
-        """Get a scale Matrix on x, y, or z axis."""
-        temp = list(self)
-        temp[0] *= vector[0]
-        temp[5] *= vector[1]
-        temp[10] *= vector[2]
-        return Mat4(temp)
-
-    def translate(self, vector: Vec3) -> Mat4:
-        """Get a translation Matrix along x, y, and z axis."""
-        return self @ Mat4((1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, *vector, 1))
-
-    def transpose(self) -> Mat4:
-        """Get a transpose of this Matrix."""
-        return Mat4(self[0::4] + self[1::4] + self[2::4] + self[3::4])
-
-    def __add__(self, other: Mat4) -> Mat4:
-        if not isinstance(other, Mat4):
-            raise TypeError("Can only add to other Mat4 types")
-        return Mat4(s + o for s, o in zip(self, other))
-
-    def __sub__(self, other: Mat4) -> Mat4:
-        if not isinstance(other, Mat4):
-            raise TypeError("Can only subtract from other Mat4 types")
-        return Mat4(s - o for s, o in zip(self, other))
-
-    def __pos__(self) -> Mat4:
-        return self
-
-    def __neg__(self) -> Mat4:
-        return Mat4(-v for v in self)
-
-    def __invert__(self) -> Mat4:
-        a = self[10] * self[15] - self[11] * self[14]
-        b = self[9] * self[15] - self[11] * self[13]
-        c = self[9] * self[14] - self[10] * self[13]
-        d = self[8] * self[15] - self[11] * self[12]
-        e = self[8] * self[14] - self[10] * self[12]
-        f = self[8] * self[13] - self[9] * self[12]
-        g = self[6] * self[15] - self[7] * self[14]
-        h = self[5] * self[15] - self[7] * self[13]
-        i = self[5] * self[14] - self[6] * self[13]
-        j = self[6] * self[11] - self[7] * self[10]
-        k = self[5] * self[11] - self[7] * self[9]
-        l = self[5] * self[10] - self[6] * self[9]
-        m = self[4] * self[15] - self[7] * self[12]
-        n = self[4] * self[14] - self[6] * self[12]
-        o = self[4] * self[11] - self[7] * self[8]
-        p = self[4] * self[10] - self[6] * self[8]
-        q = self[4] * self[13] - self[5] * self[12]
-        r = self[4] * self[9] - self[5] * self[8]
-
-        det = (self[0] * (self[5] * a - self[6] * b + self[7] * c)
-               - self[1] * (self[4] * a - self[6] * d + self[7] * e)
-               + self[2] * (self[4] * b - self[5] * d + self[7] * f)
-               - self[3] * (self[4] * c - self[5] * e + self[6] * f))
-
-        if det == 0:
-            _warnings.warn("Unable to calculate inverse of singular Matrix")
-            return self
-
-        pdet = 1 / det
-        ndet = -pdet
-
-        return Mat4((pdet * (self[5] * a - self[6] * b + self[7] * c),
-                     ndet * (self[1] * a - self[2] * b + self[3] * c),
-                     pdet * (self[1] * g - self[2] * h + self[3] * i),
-                     ndet * (self[1] * j - self[2] * k + self[3] * l),
-                     ndet * (self[4] * a - self[6] * d + self[7] * e),
-                     pdet * (self[0] * a - self[2] * d + self[3] * e),
-                     ndet * (self[0] * g - self[2] * m + self[3] * n),
-                     pdet * (self[0] * j - self[2] * o + self[3] * p),
-                     pdet * (self[4] * b - self[5] * d + self[7] * f),
-                     ndet * (self[0] * b - self[1] * d + self[3] * f),
-                     pdet * (self[0] * h - self[1] * m + self[3] * q),
-                     ndet * (self[0] * k - self[1] * o + self[3] * r),
-                     ndet * (self[4] * c - self[5] * e + self[6] * f),
-                     pdet * (self[0] * c - self[1] * e + self[2] * f),
-                     ndet * (self[0] * i - self[1] * n + self[2] * q),
-                     pdet * (self[0] * l - self[1] * p + self[2] * r)))
-
-    def __round__(self, ndigits: int | None = None) -> Mat4:
-        return Mat4(round(v, ndigits) for v in self)
-
-    def __mul__(self, other: int) -> _typing.NoReturn:
-        raise NotImplementedError("Please use the @ operator for Matrix multiplication.")
-
-    @_typing.overload
-    def __matmul__(self, other: Vec4) -> Vec4:
-        ...
-
-    @_typing.overload
-    def __matmul__(self, other: Mat4) -> Mat4:
-        ...
-
-    def __matmul__(self, other):
-        if isinstance(other, Vec4):
-            # Rows:
-            r0 = self[0::4]
-            r1 = self[1::4]
-            r2 = self[2::4]
-            r3 = self[3::4]
-            return Vec4(sum(map(_mul, r0, other)),
-                        sum(map(_mul, r1, other)),
-                        sum(map(_mul, r2, other)),
-                        sum(map(_mul, r3, other)))
-
-        if not isinstance(other, Mat4):
-            raise TypeError("Can only multiply with Mat4 or Vec4 types")
-        # Rows:
-        r0 = self[0::4]
-        r1 = self[1::4]
-        r2 = self[2::4]
-        r3 = self[3::4]
-        # Columns:
-        c0 = other[0:4]
-        c1 = other[4:8]
-        c2 = other[8:12]
-        c3 = other[12:16]
-
-        # Multiply and sum rows * columns:
-        return Mat4((sum(map(_mul, c0, r0)), sum(map(_mul, c0, r1)), sum(map(_mul, c0, r2)), sum(map(_mul, c0, r3)),
-                     sum(map(_mul, c1, r0)), sum(map(_mul, c1, r1)), sum(map(_mul, c1, r2)), sum(map(_mul, c1, r3)),
-                     sum(map(_mul, c2, r0)), sum(map(_mul, c2, r1)), sum(map(_mul, c2, r2)), sum(map(_mul, c2, r3)),
-                     sum(map(_mul, c3, r0)), sum(map(_mul, c3, r1)), sum(map(_mul, c3, r2)), sum(map(_mul, c3, r3))))
-
-    # def __getitem__(self, item):
-    #     row = [slice(0, 4), slice(4, 8), slice(8, 12), slice(12, 16)][item]
-    #     return super().__getitem__(row)
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}{self[0:4]}\n    {self[4:8]}\n    {self[8:12]}\n    {self[12:16]}"
diff --git a/spaces/akhaliq/Detic/detic/data/datasets/objects365.py b/spaces/akhaliq/Detic/detic/data/datasets/objects365.py
deleted file mode 100644
index b98128738b43a71d24ac1c22554631f78b80d664..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/detic/data/datasets/objects365.py
+++ /dev/null
@@ -1,770 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.data.datasets.register_coco import register_coco_instances
-import os
-
-# categories_v2 = [
-#     {'id': 1, 'name': 'Person'},
-#     {'id': 2, 'name': 'Sneakers'},
-#     {'id': 3, 'name': 'Chair'},
-#     {'id': 4, 'name': 'Other Shoes'},
-#     {'id': 5, 'name': 'Hat'},
-#     {'id': 6, 'name': 'Car'},
-#     {'id': 7, 'name': 'Lamp'},
-#     {'id': 8, 'name': 'Glasses'},
-#     {'id': 9, 'name': 'Bottle'},
-#     {'id': 10, 'name': 'Desk'},
-#     {'id': 11, 'name': 'Cup'},
-#     {'id': 12, 'name': 'Street Lights'},
-#     {'id': 13, 'name': 'Cabinet/shelf'},
-#     {'id': 14, 'name': 'Handbag/Satchel'},
-#     {'id': 15, 'name': 'Bracelet'},
-#     {'id': 16, 'name': 'Plate'},
-#     {'id': 17, 'name': 'Picture/Frame'},
-#     {'id': 18, 'name': 'Helmet'},
-#     {'id': 19, 'name': 'Book'},
-#     {'id': 20, 'name': 'Gloves'},
-#     {'id': 21, 'name': 'Storage box'},
-#     {'id': 22, 'name': 'Boat'},
-#     {'id': 23, 'name': 'Leather Shoes'},
-#     {'id': 24, 'name': 'Flower'},
-#     {'id': 25, 'name': 'Bench'},
-#     {'id': 26, 'name': 'Potted Plant'},
-#     {'id': 27, 'name': 'Bowl/Basin'},
-#     {'id': 28, 'name': 'Flag'},
-#     {'id': 29, 'name': 'Pillow'},
-#     {'id': 30, 'name': 'Boots'},
-#     {'id': 31, 'name': 'Vase'},
-#     {'id': 32, 'name': 'Microphone'},
-#     {'id': 33, 'name': 'Necklace'},
-#     {'id': 34, 'name': 'Ring'},
-#     {'id': 35, 'name': 'SUV'},
-#     {'id': 36, 'name': 'Wine Glass'},
-#     {'id': 37, 'name': 'Belt'},
-#     {'id': 38, 'name': 'Moniter/TV'},
-#     {'id': 39, 'name': 'Backpack'},
-#     {'id': 40, 'name': 'Umbrella'},
-#     {'id': 41, 'name': 'Traffic Light'},
-#     {'id': 42, 'name': 'Speaker'},
-#     {'id': 43, 'name': 'Watch'},
-#     {'id': 44, 'name': 'Tie'},
-#     {'id': 45, 'name': 'Trash bin Can'},
-#     {'id': 46, 'name': 'Slippers'},
-#     {'id': 47, 'name': 'Bicycle'},
-#     {'id': 48, 'name': 'Stool'},
-#     {'id': 49, 'name': 'Barrel/bucket'},
-#     {'id': 50, 'name': 'Van'},
-#     {'id': 51, 'name': 'Couch'},
-#     {'id': 52, 'name': 'Sandals'},
-#     {'id': 53, 'name': 'Bakset'},
-#     {'id': 54, 'name': 'Drum'},
-#     {'id': 55, 'name': 'Pen/Pencil'},
-#     {'id': 56, 'name': 'Bus'},
-#     {'id': 57, 'name': 'Wild Bird'},
-#     {'id': 58, 'name': 'High Heels'},
-#     {'id': 59, 'name': 'Motorcycle'},
-#     {'id': 60, 'name': 'Guitar'},
-#     {'id': 61, 'name': 'Carpet'},
-#     {'id': 62, 'name': 'Cell Phone'},
-#     {'id': 63, 'name': 'Bread'},
-#     {'id': 64, 'name': 'Camera'},
-#     {'id': 65, 'name': 'Canned'},
-#     {'id': 66, 'name': 'Truck'},
-#     {'id': 67, 'name': 'Traffic cone'},
-#     {'id': 68, 'name': 'Cymbal'},
-#     {'id': 69, 'name': 'Lifesaver'},
-#     {'id': 70, 'name': 'Towel'},
-#     {'id': 71, 'name': 'Stuffed Toy'},
-#     {'id': 72, 'name': 'Candle'},
-#     {'id': 73, 'name': 'Sailboat'},
-#     {'id': 74, 'name': 'Laptop'},
-#     {'id': 75, 'name': 'Awning'},
-#     {'id': 76, 'name': 'Bed'},
-#     {'id': 77, 'name': 'Faucet'},
-#     {'id': 78, 'name': 'Tent'},
-#     {'id': 79, 'name': 'Horse'},
-#     {'id': 80, 'name': 'Mirror'},
-#     {'id': 81, 'name': 'Power outlet'},
-#     {'id': 82, 'name': 'Sink'},
-#     {'id': 83, 'name': 'Apple'},
-#     {'id': 84, 'name': 'Air Conditioner'},
-#     {'id': 85, 'name': 'Knife'},
-#     {'id': 86, 'name': 'Hockey Stick'},
-#     {'id': 87, 'name': 'Paddle'},
-#     {'id': 88, 'name': 'Pickup Truck'},
-#     {'id': 89, 'name': 'Fork'},
-#     {'id': 90, 'name': 'Traffic Sign'},
-#     {'id': 91, 'name': 'Ballon'},
-#     {'id': 92, 'name': 'Tripod'},
-#     {'id': 93, 'name': 'Dog'},
-#     {'id': 94, 'name': 'Spoon'},
-#     {'id': 95, 'name': 'Clock'},
-#     {'id': 96, 'name': 'Pot'},
-#     {'id': 97, 'name': 'Cow'},
-#     {'id': 98, 'name': 'Cake'},
-#     {'id': 99, 'name': 'Dinning Table'},
-#     {'id': 100, 'name': 'Sheep'},
-#     {'id': 101, 'name': 'Hanger'},
-#     {'id': 102, 'name': 'Blackboard/Whiteboard'},
-#     {'id': 103, 'name': 'Napkin'},
-#     {'id': 104, 'name': 'Other Fish'},
-#     {'id': 105, 'name': 'Orange/Tangerine'},
-#     {'id': 106, 'name': 'Toiletry'},
-#     {'id': 107, 'name': 'Keyboard'},
-#     {'id': 108, 'name': 'Tomato'},
-#     {'id': 109, 'name': 'Lantern'},
-#     {'id': 110, 'name': 'Machinery Vehicle'},
-#     {'id': 111, 'name': 'Fan'},
-#     {'id': 112, 'name': 'Green Vegetables'},
-#     {'id': 113, 'name': 'Banana'},
-#     {'id': 114, 'name': 'Baseball Glove'},
-#     {'id': 115, 'name': 'Airplane'},
-#     {'id': 116, 'name': 'Mouse'},
-#     {'id': 117, 'name': 'Train'},
-#     {'id': 118, 'name': 'Pumpkin'},
-#     {'id': 119, 'name': 'Soccer'},
-#     {'id': 120, 'name': 'Skiboard'},
-#     {'id': 121, 'name': 'Luggage'},
-#     {'id': 122, 'name': 'Nightstand'},
-#     {'id': 123, 'name': 'Tea pot'},
-#     {'id': 124, 'name': 'Telephone'},
-#     {'id': 125, 'name': 'Trolley'},
-#     {'id': 126, 'name': 'Head Phone'},
-#     {'id': 127, 'name': 'Sports Car'},
-#     {'id': 128, 'name': 'Stop Sign'},
-#     {'id': 129, 'name': 'Dessert'},
-#     {'id': 130, 'name': 'Scooter'},
-#     {'id': 131, 'name': 'Stroller'},
-#     {'id': 132, 'name': 'Crane'},
-#     {'id': 133, 'name': 'Remote'},
-#     {'id': 134, 'name': 'Refrigerator'},
-#     {'id': 135, 'name': 'Oven'},
-#     {'id': 136, 'name': 'Lemon'},
-#     {'id': 137, 'name': 'Duck'},
-#     {'id': 138, 'name': 'Baseball Bat'},
-#     {'id': 139, 'name': 'Surveillance Camera'},
-#     {'id': 140, 'name': 'Cat'},
-#     {'id': 141, 'name': 'Jug'},
-#     {'id': 142, 'name': 'Broccoli'},
-#     {'id': 143, 'name': 'Piano'},
-#     {'id': 144, 'name': 'Pizza'},
-#     {'id': 145, 'name': 'Elephant'},
-#     {'id': 146, 'name': 'Skateboard'},
-#     {'id': 147, 'name': 'Surfboard'},
-#     {'id': 148, 'name': 'Gun'},
-#     {'id': 149, 'name': 'Skating and Skiing shoes'},
-#     {'id': 150, 'name': 'Gas stove'},
-#     {'id': 151, 'name': 'Donut'},
-#     {'id': 152, 'name': 'Bow Tie'},
-#     {'id': 153, 'name': 'Carrot'},
-#     {'id': 154, 'name': 'Toilet'},
-#     {'id': 155, 'name': 'Kite'},
-#     {'id': 156, 'name': 'Strawberry'},
-#     {'id': 157, 'name': 'Other Balls'},
-#     {'id': 158, 'name': 'Shovel'},
-#     {'id': 159, 'name': 'Pepper'},
-#     {'id': 160, 'name': 'Computer Box'},
-#     {'id': 161, 'name': 'Toilet Paper'},
-#     {'id': 162, 'name': 'Cleaning Products'},
-#     {'id': 163, 'name': 'Chopsticks'},
-#     {'id': 164, 'name': 'Microwave'},
-#     {'id': 165, 'name': 'Pigeon'},
-#     {'id': 166, 'name': 'Baseball'},
-#     {'id': 167, 'name': 'Cutting/chopping Board'},
-#     {'id': 168, 'name': 'Coffee Table'},
-#     {'id': 169, 'name': 'Side Table'},
-#     {'id': 170, 'name': 'Scissors'},
-#     {'id': 171, 'name': 'Marker'},
-#     {'id': 172, 'name': 'Pie'},
-#     {'id': 173, 'name': 'Ladder'},
-#     {'id': 174, 'name': 'Snowboard'},
-#     {'id': 175, 'name': 'Cookies'},
-#     {'id': 176, 'name': 'Radiator'},
-#     {'id': 177, 'name': 'Fire Hydrant'},
-#     {'id': 178, 'name': 'Basketball'},
-#     {'id': 179, 'name': 'Zebra'},
-#     {'id': 180, 'name': 'Grape'},
-#     {'id': 181, 'name': 'Giraffe'},
-#     {'id': 182, 'name': 'Potato'},
-#     {'id': 183, 'name': 'Sausage'},
-#     {'id': 184, 'name': 'Tricycle'},
-#     {'id': 185, 'name': 'Violin'},
-#     {'id': 186, 'name': 'Egg'},
-#     {'id': 187, 'name': 'Fire Extinguisher'},
-#     {'id': 188, 'name': 'Candy'},
-#     {'id': 189, 'name': 'Fire Truck'},
-#     {'id': 190, 'name': 'Billards'},
-#     {'id': 191, 'name': 'Converter'},
-#     {'id': 192, 'name': 'Bathtub'},
-#     {'id': 193, 'name': 'Wheelchair'},
-#     {'id': 194, 'name': 'Golf Club'},
-#     {'id': 195, 'name': 'Briefcase'},
-#     {'id': 196, 'name': 'Cucumber'},
-#     {'id': 197, 'name': 'Cigar/Cigarette '},
-#     {'id': 198, 'name': 'Paint Brush'},
-#     {'id': 199, 'name': 'Pear'},
-#     {'id': 200, 'name': 'Heavy Truck'},
-#     {'id': 201, 'name': 'Hamburger'},
-#     {'id': 202, 'name': 'Extractor'},
-#     {'id': 203, 'name': 'Extention Cord'},
-#     {'id': 204, 'name': 'Tong'},
-#     {'id': 205, 'name': 'Tennis Racket'},
-#     {'id': 206, 'name': 'Folder'},
-#     {'id': 207, 'name': 'American Football'},
-#     {'id': 208, 'name': 'earphone'},
-#     {'id': 209, 'name': 'Mask'},
-#     {'id': 210, 'name': 'Kettle'},
-#     {'id': 211, 'name': 'Tennis'},
-#     {'id': 212, 'name': 'Ship'},
-#     {'id': 213, 'name': 'Swing'},
-#     {'id': 214, 'name': 'Coffee Machine'},
-#     {'id': 215, 'name': 'Slide'},
-#     {'id': 216, 'name': 'Carriage'},
-#     {'id': 217, 'name': 'Onion'},
-#     {'id': 218, 'name': 'Green beans'},
-#     {'id': 219, 'name': 'Projector'},
-#     {'id': 220, 'name': 'Frisbee'},
-#     {'id': 221, 'name': 'Washing Machine/Drying Machine'},
-#     {'id': 222, 'name': 'Chicken'},
-#     {'id': 223, 'name': 'Printer'},
-#     {'id': 224, 'name': 'Watermelon'},
-#     {'id': 225, 'name': 'Saxophone'},
-#     {'id': 226, 'name': 'Tissue'},
-#     {'id': 227, 'name': 'Toothbrush'},
-#     {'id': 228, 'name': 'Ice cream'},
-#     {'id': 229, 'name': 'Hotair ballon'},
-#     {'id': 230, 'name': 'Cello'},
-#     {'id': 231, 'name': 'French Fries'},
-#     {'id': 232, 'name': 'Scale'},
-#     {'id': 233, 'name': 'Trophy'},
-#     {'id': 234, 'name': 'Cabbage'},
-#     {'id': 235, 'name': 'Hot dog'},
-#     {'id': 236, 'name': 'Blender'},
-#     {'id': 237, 'name': 'Peach'},
-#     {'id': 238, 'name': 'Rice'},
-#     {'id': 239, 'name': 'Wallet/Purse'},
-#     {'id': 240, 'name': 'Volleyball'},
-#     {'id': 241, 'name': 'Deer'},
-#     {'id': 242, 'name': 'Goose'},
-#     {'id': 243, 'name': 'Tape'},
-#     {'id': 244, 'name': 'Tablet'},
-#     {'id': 245, 'name': 'Cosmetics'},
-#     {'id': 246, 'name': 'Trumpet'},
-#     {'id': 247, 'name': 'Pineapple'},
-#     {'id': 248, 'name': 'Golf Ball'},
-#     {'id': 249, 'name': 'Ambulance'},
-#     {'id': 250, 'name': 'Parking meter'},
-#     {'id': 251, 'name': 'Mango'},
-#     {'id': 252, 'name': 'Key'},
-#     {'id': 253, 'name': 'Hurdle'},
-#     {'id': 254, 'name': 'Fishing Rod'},
-#     {'id': 255, 'name': 'Medal'},
-#     {'id': 256, 'name': 'Flute'},
-#     {'id': 257, 'name': 'Brush'},
-#     {'id': 258, 'name': 'Penguin'},
-#     {'id': 259, 'name': 'Megaphone'},
-#     {'id': 260, 'name': 'Corn'},
-#     {'id': 261, 'name': 'Lettuce'},
-#     {'id': 262, 'name': 'Garlic'},
-#     {'id': 263, 'name': 'Swan'},
-#     {'id': 264, 'name': 'Helicopter'},
-#     {'id': 265, 'name': 'Green Onion'},
-#     {'id': 266, 'name': 'Sandwich'},
-#     {'id': 267, 'name': 'Nuts'},
-#     {'id': 268, 'name': 'Speed Limit Sign'},
-#     {'id': 269, 'name': 'Induction Cooker'},
-#     {'id': 270, 'name': 'Broom'},
-#     {'id': 271, 'name': 'Trombone'},
-#     {'id': 272, 'name': 'Plum'},
-#     {'id': 273, 'name': 'Rickshaw'},
-#     {'id': 274, 'name': 'Goldfish'},
-#     {'id': 275, 'name': 'Kiwi fruit'},
-#     {'id': 276, 'name': 'Router/modem'},
-#     {'id': 277, 'name': 'Poker Card'},
-#     {'id': 278, 'name': 'Toaster'},
-#     {'id': 279, 'name': 'Shrimp'},
-#     {'id': 280, 'name': 'Sushi'},
-#     {'id': 281, 'name': 'Cheese'},
-#     {'id': 282, 'name': 'Notepaper'},
-#     {'id': 283, 'name': 'Cherry'},
-#     {'id': 284, 'name': 'Pliers'},
-#     {'id': 285, 'name': 'CD'},
-#     {'id': 286, 'name': 'Pasta'},
-#     {'id': 287, 'name': 'Hammer'},
-#     {'id': 288, 'name': 'Cue'},
-#     {'id': 289, 'name': 'Avocado'},
-#     {'id': 290, 'name': 'Hamimelon'},
-#     {'id': 291, 'name': 'Flask'},
-#     {'id': 292, 'name': 'Mushroon'},
-#     {'id': 293, 'name': 'Screwdriver'},
-#     {'id': 294, 'name': 'Soap'},
-#     {'id': 295, 'name': 'Recorder'},
-#     {'id': 296, 'name': 'Bear'},
-#     {'id': 297, 'name': 'Eggplant'},
-#     {'id': 298, 'name': 'Board Eraser'},
-#     {'id': 299, 'name': 'Coconut'},
-#     {'id': 300, 'name': 'Tape Measur/ Ruler'},
-#     {'id': 301, 'name': 'Pig'},
-#     {'id': 302, 'name': 'Showerhead'},
-#     {'id': 303, 'name': 'Globe'},
-#     {'id': 304, 'name': 'Chips'},
-#     {'id': 305, 'name': 'Steak'},
-#     {'id': 306, 'name': 'Crosswalk Sign'},
-#     {'id': 307, 'name': 'Stapler'},
-#     {'id': 308, 'name': 'Campel'},
-#     {'id': 309, 'name': 'Formula 1 '},
-#     {'id': 310, 'name': 'Pomegranate'},
-#     {'id': 311, 'name': 'Dishwasher'},
-#     {'id': 312, 'name': 'Crab'},
-#     {'id': 313, 'name': 'Hoverboard'},
-#     {'id': 314, 'name': 'Meat ball'},
-#     {'id': 315, 'name': 'Rice Cooker'},
-#     {'id': 316, 'name': 'Tuba'},
-#     {'id': 317, 'name': 'Calculator'},
-#     {'id': 318, 'name': 'Papaya'},
-#     {'id': 319, 'name': 'Antelope'},
-#     {'id': 320, 'name': 'Parrot'},
-#     {'id': 321, 'name': 'Seal'},
-#     {'id': 322, 'name': 'Buttefly'},
-#     {'id': 323, 'name': 'Dumbbell'},
-#     {'id': 324, 'name': 'Donkey'},
-#     {'id': 325, 'name': 'Lion'},
-#     {'id': 326, 'name': 'Urinal'},
-#     {'id': 327, 'name': 'Dolphin'},
-#     {'id': 328, 'name': 'Electric Drill'},
-#     {'id': 329, 'name': 'Hair Dryer'},
-#     {'id': 330, 'name': 'Egg tart'},
-#     {'id': 331, 'name': 'Jellyfish'},
-#     {'id': 332, 'name': 'Treadmill'},
-#     {'id': 333, 'name': 'Lighter'},
-#     {'id': 334, 'name': 'Grapefruit'},
-#     {'id': 335, 'name': 'Game board'},
-#     {'id': 336, 'name': 'Mop'},
-#     {'id': 337, 'name': 'Radish'},
-#     {'id': 338, 'name': 'Baozi'},
-#     {'id': 339, 'name': 'Target'},
-#     {'id': 340, 'name': 'French'},
-#     {'id': 341, 'name': 'Spring Rolls'},
-#     {'id': 342, 'name': 'Monkey'},
-#     {'id': 343, 'name': 'Rabbit'},
-#     {'id': 344, 'name': 'Pencil Case'},
-#     {'id': 345, 'name': 'Yak'},
-#     {'id': 346, 'name': 'Red Cabbage'},
-#     {'id': 347, 'name': 'Binoculars'},
-#     {'id': 348, 'name': 'Asparagus'},
-#     {'id': 349, 'name': 'Barbell'},
-#     {'id': 350, 'name': 'Scallop'},
-#     {'id': 351, 'name': 'Noddles'},
-#     {'id': 352, 'name': 'Comb'},
-#     {'id': 353, 'name': 'Dumpling'},
-#     {'id': 354, 'name': 'Oyster'},
-#     {'id': 355, 'name': 'Table Teniis paddle'},
-#     {'id': 356, 'name': 'Cosmetics Brush/Eyeliner Pencil'},
-#     {'id': 357, 'name': 'Chainsaw'},
-#     {'id': 358, 'name': 'Eraser'},
-#     {'id': 359, 'name': 'Lobster'},
-#     {'id': 360, 'name': 'Durian'},
-#     {'id': 361, 'name': 'Okra'},
-#     {'id': 362, 'name': 'Lipstick'},
-#     {'id': 363, 'name': 'Cosmetics Mirror'},
-#     {'id': 364, 'name': 'Curling'},
-#     {'id': 365, 'name': 'Table Tennis '},
-# ]
-
-'''
-The official Objects365 category names contains typos.
-Below is a manual fix.
-'''
-categories_v2_fix = [
-  {'id': 1, 'name': 'Person'},
-  {'id': 2, 'name': 'Sneakers'},
-  {'id': 3, 'name': 'Chair'},
-  {'id': 4, 'name': 'Other Shoes'},
-  {'id': 5, 'name': 'Hat'},
-  {'id': 6, 'name': 'Car'},
-  {'id': 7, 'name': 'Lamp'},
-  {'id': 8, 'name': 'Glasses'},
-  {'id': 9, 'name': 'Bottle'},
-  {'id': 10, 'name': 'Desk'},
-  {'id': 11, 'name': 'Cup'},
-  {'id': 12, 'name': 'Street Lights'},
-  {'id': 13, 'name': 'Cabinet/shelf'},
-  {'id': 14, 'name': 'Handbag/Satchel'},
-  {'id': 15, 'name': 'Bracelet'},
-  {'id': 16, 'name': 'Plate'},
-  {'id': 17, 'name': 'Picture/Frame'},
-  {'id': 18, 'name': 'Helmet'},
-  {'id': 19, 'name': 'Book'},
-  {'id': 20, 'name': 'Gloves'},
-  {'id': 21, 'name': 'Storage box'},
-  {'id': 22, 'name': 'Boat'},
-  {'id': 23, 'name': 'Leather Shoes'},
-  {'id': 24, 'name': 'Flower'},
-  {'id': 25, 'name': 'Bench'},
-  {'id': 26, 'name': 'Potted Plant'},
-  {'id': 27, 'name': 'Bowl/Basin'},
-  {'id': 28, 'name': 'Flag'},
-  {'id': 29, 'name': 'Pillow'},
-  {'id': 30, 'name': 'Boots'},
-  {'id': 31, 'name': 'Vase'},
-  {'id': 32, 'name': 'Microphone'},
-  {'id': 33, 'name': 'Necklace'},
-  {'id': 34, 'name': 'Ring'},
-  {'id': 35, 'name': 'SUV'},
-  {'id': 36, 'name': 'Wine Glass'},
-  {'id': 37, 'name': 'Belt'},
-  {'id': 38, 'name': 'Monitor/TV'},
-  {'id': 39, 'name': 'Backpack'},
-  {'id': 40, 'name': 'Umbrella'},
-  {'id': 41, 'name': 'Traffic Light'},
-  {'id': 42, 'name': 'Speaker'},
-  {'id': 43, 'name': 'Watch'},
-  {'id': 44, 'name': 'Tie'},
-  {'id': 45, 'name': 'Trash bin Can'},
-  {'id': 46, 'name': 'Slippers'},
-  {'id': 47, 'name': 'Bicycle'},
-  {'id': 48, 'name': 'Stool'},
-  {'id': 49, 'name': 'Barrel/bucket'},
-  {'id': 50, 'name': 'Van'},
-  {'id': 51, 'name': 'Couch'},
-  {'id': 52, 'name': 'Sandals'},
-  {'id': 53, 'name': 'Basket'},
-  {'id': 54, 'name': 'Drum'},
-  {'id': 55, 'name': 'Pen/Pencil'},
-  {'id': 56, 'name': 'Bus'},
-  {'id': 57, 'name': 'Wild Bird'},
-  {'id': 58, 'name': 'High Heels'},
-  {'id': 59, 'name': 'Motorcycle'},
-  {'id': 60, 'name': 'Guitar'},
-  {'id': 61, 'name': 'Carpet'},
-  {'id': 62, 'name': 'Cell Phone'},
-  {'id': 63, 'name': 'Bread'},
-  {'id': 64, 'name': 'Camera'},
-  {'id': 65, 'name': 'Canned'},
-  {'id': 66, 'name': 'Truck'},
-  {'id': 67, 'name': 'Traffic cone'},
-  {'id': 68, 'name': 'Cymbal'},
-  {'id': 69, 'name': 'Lifesaver'},
-  {'id': 70, 'name': 'Towel'},
-  {'id': 71, 'name': 'Stuffed Toy'},
-  {'id': 72, 'name': 'Candle'},
-  {'id': 73, 'name': 'Sailboat'},
-  {'id': 74, 'name': 'Laptop'},
-  {'id': 75, 'name': 'Awning'},
-  {'id': 76, 'name': 'Bed'},
-  {'id': 77, 'name': 'Faucet'},
-  {'id': 78, 'name': 'Tent'},
-  {'id': 79, 'name': 'Horse'},
-  {'id': 80, 'name': 'Mirror'},
-  {'id': 81, 'name': 'Power outlet'},
-  {'id': 82, 'name': 'Sink'},
-  {'id': 83, 'name': 'Apple'},
-  {'id': 84, 'name': 'Air Conditioner'},
-  {'id': 85, 'name': 'Knife'},
-  {'id': 86, 'name': 'Hockey Stick'},
-  {'id': 87, 'name': 'Paddle'},
-  {'id': 88, 'name': 'Pickup Truck'},
-  {'id': 89, 'name': 'Fork'},
-  {'id': 90, 'name': 'Traffic Sign'},
-  {'id': 91, 'name': 'Ballon'},
-  {'id': 92, 'name': 'Tripod'},
-  {'id': 93, 'name': 'Dog'},
-  {'id': 94, 'name': 'Spoon'},
-  {'id': 95, 'name': 'Clock'},
-  {'id': 96, 'name': 'Pot'},
-  {'id': 97, 'name': 'Cow'},
-  {'id': 98, 'name': 'Cake'},
-  {'id': 99, 'name': 'Dining Table'},
-  {'id': 100, 'name': 'Sheep'},
-  {'id': 101, 'name': 'Hanger'},
-  {'id': 102, 'name': 'Blackboard/Whiteboard'},
-  {'id': 103, 'name': 'Napkin'},
-  {'id': 104, 'name': 'Other Fish'},
-  {'id': 105, 'name': 'Orange/Tangerine'},
-  {'id': 106, 'name': 'Toiletry'},
-  {'id': 107, 'name': 'Keyboard'},
-  {'id': 108, 'name': 'Tomato'},
-  {'id': 109, 'name': 'Lantern'},
-  {'id': 110, 'name': 'Machinery Vehicle'},
-  {'id': 111, 'name': 'Fan'},
-  {'id': 112, 'name': 'Green Vegetables'},
-  {'id': 113, 'name': 'Banana'},
-  {'id': 114, 'name': 'Baseball Glove'},
-  {'id': 115, 'name': 'Airplane'},
-  {'id': 116, 'name': 'Mouse'},
-  {'id': 117, 'name': 'Train'},
-  {'id': 118, 'name': 'Pumpkin'},
-  {'id': 119, 'name': 'Soccer'},
-  {'id': 120, 'name': 'Skiboard'},
-  {'id': 121, 'name': 'Luggage'},
-  {'id': 122, 'name': 'Nightstand'},
-  {'id': 123, 'name': 'Teapot'},
-  {'id': 124, 'name': 'Telephone'},
-  {'id': 125, 'name': 'Trolley'},
-  {'id': 126, 'name': 'Head Phone'},
-  {'id': 127, 'name': 'Sports Car'},
-  {'id': 128, 'name': 'Stop Sign'},
-  {'id': 129, 'name': 'Dessert'},
-  {'id': 130, 'name': 'Scooter'},
-  {'id': 131, 'name': 'Stroller'},
-  {'id': 132, 'name': 'Crane'},
-  {'id': 133, 'name': 'Remote'},
-  {'id': 134, 'name': 'Refrigerator'},
-  {'id': 135, 'name': 'Oven'},
-  {'id': 136, 'name': 'Lemon'},
-  {'id': 137, 'name': 'Duck'},
-  {'id': 138, 'name': 'Baseball Bat'},
-  {'id': 139, 'name': 'Surveillance Camera'},
-  {'id': 140, 'name': 'Cat'},
-  {'id': 141, 'name': 'Jug'},
-  {'id': 142, 'name': 'Broccoli'},
-  {'id': 143, 'name': 'Piano'},
-  {'id': 144, 'name': 'Pizza'},
-  {'id': 145, 'name': 'Elephant'},
-  {'id': 146, 'name': 'Skateboard'},
-  {'id': 147, 'name': 'Surfboard'},
-  {'id': 148, 'name': 'Gun'},
-  {'id': 149, 'name': 'Skating and Skiing shoes'},
-  {'id': 150, 'name': 'Gas stove'},
-  {'id': 151, 'name': 'Donut'},
-  {'id': 152, 'name': 'Bow Tie'},
-  {'id': 153, 'name': 'Carrot'},
-  {'id': 154, 'name': 'Toilet'},
-  {'id': 155, 'name': 'Kite'},
-  {'id': 156, 'name': 'Strawberry'},
-  {'id': 157, 'name': 'Other Balls'},
-  {'id': 158, 'name': 'Shovel'},
-  {'id': 159, 'name': 'Pepper'},
-  {'id': 160, 'name': 'Computer Box'},
-  {'id': 161, 'name': 'Toilet Paper'},
-  {'id': 162, 'name': 'Cleaning Products'},
-  {'id': 163, 'name': 'Chopsticks'},
-  {'id': 164, 'name': 'Microwave'},
-  {'id': 165, 'name': 'Pigeon'},
-  {'id': 166, 'name': 'Baseball'},
-  {'id': 167, 'name': 'Cutting/chopping Board'},
-  {'id': 168, 'name': 'Coffee Table'},
-  {'id': 169, 'name': 'Side Table'},
-  {'id': 170, 'name': 'Scissors'},
-  {'id': 171, 'name': 'Marker'},
-  {'id': 172, 'name': 'Pie'},
-  {'id': 173, 'name': 'Ladder'},
-  {'id': 174, 'name': 'Snowboard'},
-  {'id': 175, 'name': 'Cookies'},
-  {'id': 176, 'name': 'Radiator'},
-  {'id': 177, 'name': 'Fire Hydrant'},
-  {'id': 178, 'name': 'Basketball'},
-  {'id': 179, 'name': 'Zebra'},
-  {'id': 180, 'name': 'Grape'},
-  {'id': 181, 'name': 'Giraffe'},
-  {'id': 182, 'name': 'Potato'},
-  {'id': 183, 'name': 'Sausage'},
-  {'id': 184, 'name': 'Tricycle'},
-  {'id': 185, 'name': 'Violin'},
-  {'id': 186, 'name': 'Egg'},
-  {'id': 187, 'name': 'Fire Extinguisher'},
-  {'id': 188, 'name': 'Candy'},
-  {'id': 189, 'name': 'Fire Truck'},
-  {'id': 190, 'name': 'Billards'},
-  {'id': 191, 'name': 'Converter'},
-  {'id': 192, 'name': 'Bathtub'},
-  {'id': 193, 'name': 'Wheelchair'},
-  {'id': 194, 'name': 'Golf Club'},
-  {'id': 195, 'name': 'Briefcase'},
-  {'id': 196, 'name': 'Cucumber'},
-  {'id': 197, 'name': 'Cigar/Cigarette '},
-  {'id': 198, 'name': 'Paint Brush'},
-  {'id': 199, 'name': 'Pear'},
-  {'id': 200, 'name': 'Heavy Truck'},
-  {'id': 201, 'name': 'Hamburger'},
-  {'id': 202, 'name': 'Extractor'},
-  {'id': 203, 'name': 'Extension Cord'},
-  {'id': 204, 'name': 'Tong'},
-  {'id': 205, 'name': 'Tennis Racket'},
-  {'id': 206, 'name': 'Folder'},
-  {'id': 207, 'name': 'American Football'},
-  {'id': 208, 'name': 'earphone'},
-  {'id': 209, 'name': 'Mask'},
-  {'id': 210, 'name': 'Kettle'},
-  {'id': 211, 'name': 'Tennis'},
-  {'id': 212, 'name': 'Ship'},
-  {'id': 213, 'name': 'Swing'},
-  {'id': 214, 'name': 'Coffee Machine'},
-  {'id': 215, 'name': 'Slide'},
-  {'id': 216, 'name': 'Carriage'},
-  {'id': 217, 'name': 'Onion'},
-  {'id': 218, 'name': 'Green beans'},
-  {'id': 219, 'name': 'Projector'},
-  {'id': 220, 'name': 'Frisbee'},
-  {'id': 221, 'name': 'Washing Machine/Drying Machine'},
-  {'id': 222, 'name': 'Chicken'},
-  {'id': 223, 'name': 'Printer'},
-  {'id': 224, 'name': 'Watermelon'},
-  {'id': 225, 'name': 'Saxophone'},
-  {'id': 226, 'name': 'Tissue'},
-  {'id': 227, 'name': 'Toothbrush'},
-  {'id': 228, 'name': 'Ice cream'},
-  {'id': 229, 'name': 'Hot air balloon'},
-  {'id': 230, 'name': 'Cello'},
-  {'id': 231, 'name': 'French Fries'},
-  {'id': 232, 'name': 'Scale'},
-  {'id': 233, 'name': 'Trophy'},
-  {'id': 234, 'name': 'Cabbage'},
-  {'id': 235, 'name': 'Hot dog'},
-  {'id': 236, 'name': 'Blender'},
-  {'id': 237, 'name': 'Peach'},
-  {'id': 238, 'name': 'Rice'},
-  {'id': 239, 'name': 'Wallet/Purse'},
-  {'id': 240, 'name': 'Volleyball'},
-  {'id': 241, 'name': 'Deer'},
-  {'id': 242, 'name': 'Goose'},
-  {'id': 243, 'name': 'Tape'},
-  {'id': 244, 'name': 'Tablet'},
-  {'id': 245, 'name': 'Cosmetics'},
-  {'id': 246, 'name': 'Trumpet'},
-  {'id': 247, 'name': 'Pineapple'},
-  {'id': 248, 'name': 'Golf Ball'},
-  {'id': 249, 'name': 'Ambulance'},
-  {'id': 250, 'name': 'Parking meter'},
-  {'id': 251, 'name': 'Mango'},
-  {'id': 252, 'name': 'Key'},
-  {'id': 253, 'name': 'Hurdle'},
-  {'id': 254, 'name': 'Fishing Rod'},
-  {'id': 255, 'name': 'Medal'},
-  {'id': 256, 'name': 'Flute'},
-  {'id': 257, 'name': 'Brush'},
-  {'id': 258, 'name': 'Penguin'},
-  {'id': 259, 'name': 'Megaphone'},
-  {'id': 260, 'name': 'Corn'},
-  {'id': 261, 'name': 'Lettuce'},
-  {'id': 262, 'name': 'Garlic'},
-  {'id': 263, 'name': 'Swan'},
-  {'id': 264, 'name': 'Helicopter'},
-  {'id': 265, 'name': 'Green Onion'},
-  {'id': 266, 'name': 'Sandwich'},
-  {'id': 267, 'name': 'Nuts'},
-  {'id': 268, 'name': 'Speed Limit Sign'},
-  {'id': 269, 'name': 'Induction Cooker'},
-  {'id': 270, 'name': 'Broom'},
-  {'id': 271, 'name': 'Trombone'},
-  {'id': 272, 'name': 'Plum'},
-  {'id': 273, 'name': 'Rickshaw'},
-  {'id': 274, 'name': 'Goldfish'},
-  {'id': 275, 'name': 'Kiwi fruit'},
-  {'id': 276, 'name': 'Router/modem'},
-  {'id': 277, 'name': 'Poker Card'},
-  {'id': 278, 'name': 'Toaster'},
-  {'id': 279, 'name': 'Shrimp'},
-  {'id': 280, 'name': 'Sushi'},
-  {'id': 281, 'name': 'Cheese'},
-  {'id': 282, 'name': 'Notepaper'},
-  {'id': 283, 'name': 'Cherry'},
-  {'id': 284, 'name': 'Pliers'},
-  {'id': 285, 'name': 'CD'},
-  {'id': 286, 'name': 'Pasta'},
-  {'id': 287, 'name': 'Hammer'},
-  {'id': 288, 'name': 'Cue'},
-  {'id': 289, 'name': 'Avocado'},
-  {'id': 290, 'name': 'Hami melon'},
-  {'id': 291, 'name': 'Flask'},
-  {'id': 292, 'name': 'Mushroom'},
-  {'id': 293, 'name': 'Screwdriver'},
-  {'id': 294, 'name': 'Soap'},
-  {'id': 295, 'name': 'Recorder'},
-  {'id': 296, 'name': 'Bear'},
-  {'id': 297, 'name': 'Eggplant'},
-  {'id': 298, 'name': 'Board Eraser'},
-  {'id': 299, 'name': 'Coconut'},
-  {'id': 300, 'name': 'Tape Measure/ Ruler'},
-  {'id': 301, 'name': 'Pig'},
-  {'id': 302, 'name': 'Showerhead'},
-  {'id': 303, 'name': 'Globe'},
-  {'id': 304, 'name': 'Chips'},
-  {'id': 305, 'name': 'Steak'},
-  {'id': 306, 'name': 'Crosswalk Sign'},
-  {'id': 307, 'name': 'Stapler'},
-  {'id': 308, 'name': 'Camel'},
-  {'id': 309, 'name': 'Formula 1 '},
-  {'id': 310, 'name': 'Pomegranate'},
-  {'id': 311, 'name': 'Dishwasher'},
-  {'id': 312, 'name': 'Crab'},
-  {'id': 313, 'name': 'Hoverboard'},
-  {'id': 314, 'name': 'Meatball'},
-  {'id': 315, 'name': 'Rice Cooker'},
-  {'id': 316, 'name': 'Tuba'},
-  {'id': 317, 'name': 'Calculator'},
-  {'id': 318, 'name': 'Papaya'},
-  {'id': 319, 'name': 'Antelope'},
-  {'id': 320, 'name': 'Parrot'},
-  {'id': 321, 'name': 'Seal'},
-  {'id': 322, 'name': 'Butterfly'},
-  {'id': 323, 'name': 'Dumbbell'},
-  {'id': 324, 'name': 'Donkey'},
-  {'id': 325, 'name': 'Lion'},
-  {'id': 326, 'name': 'Urinal'},
-  {'id': 327, 'name': 'Dolphin'},
-  {'id': 328, 'name': 'Electric Drill'},
-  {'id': 329, 'name': 'Hair Dryer'},
-  {'id': 330, 'name': 'Egg tart'},
-  {'id': 331, 'name': 'Jellyfish'},
-  {'id': 332, 'name': 'Treadmill'},
-  {'id': 333, 'name': 'Lighter'},
-  {'id': 334, 'name': 'Grapefruit'},
-  {'id': 335, 'name': 'Game board'},
-  {'id': 336, 'name': 'Mop'},
-  {'id': 337, 'name': 'Radish'},
-  {'id': 338, 'name': 'Baozi'},
-  {'id': 339, 'name': 'Target'},
-  {'id': 340, 'name': 'French'},
-  {'id': 341, 'name': 'Spring Rolls'},
-  {'id': 342, 'name': 'Monkey'},
-  {'id': 343, 'name': 'Rabbit'},
-  {'id': 344, 'name': 'Pencil Case'},
-  {'id': 345, 'name': 'Yak'},
-  {'id': 346, 'name': 'Red Cabbage'},
-  {'id': 347, 'name': 'Binoculars'},
-  {'id': 348, 'name': 'Asparagus'},
-  {'id': 349, 'name': 'Barbell'},
-  {'id': 350, 'name': 'Scallop'},
-  {'id': 351, 'name': 'Noddles'},
-  {'id': 352, 'name': 'Comb'},
-  {'id': 353, 'name': 'Dumpling'},
-  {'id': 354, 'name': 'Oyster'},
-  {'id': 355, 'name': 'Table Tennis paddle'},
-  {'id': 356, 'name': 'Cosmetics Brush/Eyeliner Pencil'},
-  {'id': 357, 'name': 'Chainsaw'},
-  {'id': 358, 'name': 'Eraser'},
-  {'id': 359, 'name': 'Lobster'},
-  {'id': 360, 'name': 'Durian'},
-  {'id': 361, 'name': 'Okra'},
-  {'id': 362, 'name': 'Lipstick'},
-  {'id': 363, 'name': 'Cosmetics Mirror'},
-  {'id': 364, 'name': 'Curling'},
-  {'id': 365, 'name': 'Table Tennis '},
-]
-
-
-def _get_builtin_metadata():
-    id_to_name = {x['id']: x['name'] for x in categories_v2_fix}
-    thing_dataset_id_to_contiguous_id = {
-        x['id']: i for i, x in enumerate(
-            sorted(categories_v2_fix, key=lambda x: x['id']))}
-    thing_classes = [id_to_name[k] for k in sorted(id_to_name)]
-    return {
-        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
-        "thing_classes": thing_classes}
-
-
-_PREDEFINED_SPLITS_OBJECTS365 = {
-    "objects365_v2_train": ("objects365/train", "objects365/annotations/zhiyuan_objv2_train_fixname_fixmiss.json"),
-    # 80,000 images, 1,240,587 annotations
-    "objects365_v2_val": ("objects365/val", "objects365/annotations/zhiyuan_objv2_val_fixname.json"),
-    "objects365_v2_val_rare": ("objects365/val", "objects365/annotations/zhiyuan_objv2_val_fixname_rare.json"),
-}
-
-for key, (image_root, json_file) in _PREDEFINED_SPLITS_OBJECTS365.items():
-    register_coco_instances(
-        key,
-        _get_builtin_metadata(),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
\ No newline at end of file
diff --git a/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/replicate.py b/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/replicate.py
deleted file mode 100644
index b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/replicate.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : replicate.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import functools
-
-from torch.nn.parallel.data_parallel import DataParallel
-
-__all__ = [
-    'CallbackContext',
-    'execute_replication_callbacks',
-    'DataParallelWithCallback',
-    'patch_replication_callback'
-]
-
-
-class CallbackContext(object):
-    pass
-
-
-def execute_replication_callbacks(modules):
-    """
-    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
-
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Note that, as all modules are isomorphism, we assign each sub-module with a context
-    (shared among multiple copies of this module on different devices).
-    Through this context, different copies can share some information.
-
-    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
-    of any slave copies.
-    """
-    master_copy = modules[0]
-    nr_modules = len(list(master_copy.modules()))
-    ctxs = [CallbackContext() for _ in range(nr_modules)]
-
-    for i, module in enumerate(modules):
-        for j, m in enumerate(module.modules()):
-            if hasattr(m, '__data_parallel_replicate__'):
-                m.__data_parallel_replicate__(ctxs[j], i)
-
-
-class DataParallelWithCallback(DataParallel):
-    """
-    Data Parallel with a replication callback.
-
-    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
-    original `replicate` function.
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-        # sync_bn.__data_parallel_replicate__ will be invoked.
-    """
-
-    def replicate(self, module, device_ids):
-        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-
-def patch_replication_callback(data_parallel):
-    """
-    Monkey-patch an existing `DataParallel` object. Add the replication callback.
-    Useful when you have customized `DataParallel` implementation.
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-        > patch_replication_callback(sync_bn)
-        # this is equivalent to
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-    """
-
-    assert isinstance(data_parallel, DataParallel)
-
-    old_replicate = data_parallel.replicate
-
-    @functools.wraps(old_replicate)
-    def new_replicate(module, device_ids):
-        modules = old_replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-    data_parallel.replicate = new_replicate
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/base.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/base.py
deleted file mode 100644
index 1a5a781cb3e3ca196ddebdb17768b59026646bbb..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/base.py
+++ /dev/null
@@ -1,546 +0,0 @@
-import csv
-import email.message
-import json
-import logging
-import pathlib
-import re
-import zipfile
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    Collection,
-    Container,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
-
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.specifiers import InvalidSpecifier, SpecifierSet
-from pip._vendor.packaging.utils import NormalizedName
-from pip._vendor.packaging.version import LegacyVersion, Version
-
-from pip._internal.exceptions import NoneMetadataError
-from pip._internal.locations import site_packages, user_site
-from pip._internal.models.direct_url import (
-    DIRECT_URL_METADATA_NAME,
-    DirectUrl,
-    DirectUrlValidationError,
-)
-from pip._internal.utils.compat import stdlib_pkgs  # TODO: Move definition here.
-from pip._internal.utils.egg_link import (
-    egg_link_path_from_location,
-    egg_link_path_from_sys_path,
-)
-from pip._internal.utils.misc import is_local, normalize_path
-from pip._internal.utils.urls import url_to_path
-
-if TYPE_CHECKING:
-    from typing import Protocol
-else:
-    Protocol = object
-
-DistributionVersion = Union[LegacyVersion, Version]
-
-InfoPath = Union[str, pathlib.PurePosixPath]
-
-logger = logging.getLogger(__name__)
-
-
-class BaseEntryPoint(Protocol):
-    @property
-    def name(self) -> str:
-        raise NotImplementedError()
-
-    @property
-    def value(self) -> str:
-        raise NotImplementedError()
-
-    @property
-    def group(self) -> str:
-        raise NotImplementedError()
-
-
-def _convert_installed_files_path(
-    entry: Tuple[str, ...],
-    info: Tuple[str, ...],
-) -> str:
-    """Convert a legacy installed-files.txt path into modern RECORD path.
-
-    The legacy format stores paths relative to the info directory, while the
-    modern format stores paths relative to the package root, e.g. the
-    site-packages directory.
-
-    :param entry: Path parts of the installed-files.txt entry.
-    :param info: Path parts of the egg-info directory relative to package root.
-    :returns: The converted entry.
-
-    For best compatibility with symlinks, this does not use ``abspath()`` or
-    ``Path.resolve()``, but tries to work with path parts:
-
-    1. While ``entry`` starts with ``..``, remove the equal amounts of parts
-       from ``info``; if ``info`` is empty, start appending ``..`` instead.
-    2. Join the two directly.
-    """
-    while entry and entry[0] == "..":
-        if not info or info[-1] == "..":
-            info += ("..",)
-        else:
-            info = info[:-1]
-        entry = entry[1:]
-    return str(pathlib.Path(*info, *entry))
-
-
-class BaseDistribution(Protocol):
-    def __repr__(self) -> str:
-        return f"{self.raw_name} {self.version} ({self.location})"
-
-    def __str__(self) -> str:
-        return f"{self.raw_name} {self.version}"
-
-    @property
-    def location(self) -> Optional[str]:
-        """Where the distribution is loaded from.
-
-        A string value is not necessarily a filesystem path, since distributions
-        can be loaded from other sources, e.g. arbitrary zip archives. ``None``
-        means the distribution is created in-memory.
-
-        Do not canonicalize this value with e.g. ``pathlib.Path.resolve()``. If
-        this is a symbolic link, we want to preserve the relative path between
-        it and files in the distribution.
-        """
-        raise NotImplementedError()
-
-    @property
-    def editable_project_location(self) -> Optional[str]:
-        """The project location for editable distributions.
-
-        This is the directory where pyproject.toml or setup.py is located.
-        None if the distribution is not installed in editable mode.
-        """
-        # TODO: this property is relatively costly to compute, memoize it ?
-        direct_url = self.direct_url
-        if direct_url:
-            if direct_url.is_local_editable():
-                return url_to_path(direct_url.url)
-        else:
-            # Search for an .egg-link file by walking sys.path, as it was
-            # done before by dist_is_editable().
-            egg_link_path = egg_link_path_from_sys_path(self.raw_name)
-            if egg_link_path:
-                # TODO: get project location from second line of egg_link file
-                #       (https://github.com/pypa/pip/issues/10243)
-                return self.location
-        return None
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        """The distribution's "installed" location.
-
-        This should generally be a ``site-packages`` directory. This is
-        usually ``dist.location``, except for legacy develop-installed packages,
-        where ``dist.location`` is the source code location, and this is where
-        the ``.egg-link`` file is.
-
-        The returned location is normalized (in particular, with symlinks removed).
-        """
-        egg_link = egg_link_path_from_location(self.raw_name)
-        if egg_link:
-            location = egg_link
-        elif self.location:
-            location = self.location
-        else:
-            return None
-        return normalize_path(location)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        """Location of the .[egg|dist]-info directory or file.
-
-        Similarly to ``location``, a string value is not necessarily a
-        filesystem path. ``None`` means the distribution is created in-memory.
-
-        For a modern .dist-info installation on disk, this should be something
-        like ``{location}/{raw_name}-{version}.dist-info``.
-
-        Do not canonicalize this value with e.g. ``pathlib.Path.resolve()``. If
-        this is a symbolic link, we want to preserve the relative path between
-        it and other files in the distribution.
-        """
-        raise NotImplementedError()
-
-    @property
-    def installed_by_distutils(self) -> bool:
-        """Whether this distribution is installed with legacy distutils format.
-
-        A distribution installed with "raw" distutils not patched by setuptools
-        uses one single file at ``info_location`` to store metadata. We need to
-        treat this specially on uninstallation.
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        return pathlib.Path(info_location).is_file()
-
-    @property
-    def installed_as_egg(self) -> bool:
-        """Whether this distribution is installed as an egg.
-
-        This usually indicates the distribution was installed by (older versions
-        of) easy_install.
-        """
-        location = self.location
-        if not location:
-            return False
-        return location.endswith(".egg")
-
-    @property
-    def installed_with_setuptools_egg_info(self) -> bool:
-        """Whether this distribution is installed with the ``.egg-info`` format.
-
-        This usually indicates the distribution was installed with setuptools
-        with an old pip version or with ``single-version-externally-managed``.
-
-        Note that this ensure the metadata store is a directory. distutils can
-        also installs an ``.egg-info``, but as a file, not a directory. This
-        property is *False* for that case. Also see ``installed_by_distutils``.
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        if not info_location.endswith(".egg-info"):
-            return False
-        return pathlib.Path(info_location).is_dir()
-
-    @property
-    def installed_with_dist_info(self) -> bool:
-        """Whether this distribution is installed with the "modern format".
-
-        This indicates a "modern" installation, e.g. storing metadata in the
-        ``.dist-info`` directory. This applies to installations made by
-        setuptools (but through pip, not directly), or anything using the
-        standardized build backend interface (PEP 517).
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        if not info_location.endswith(".dist-info"):
-            return False
-        return pathlib.Path(info_location).is_dir()
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        raise NotImplementedError()
-
-    @property
-    def version(self) -> DistributionVersion:
-        raise NotImplementedError()
-
-    @property
-    def setuptools_filename(self) -> str:
-        """Convert a project name to its setuptools-compatible filename.
-
-        This is a copy of ``pkg_resources.to_filename()`` for compatibility.
-        """
-        return self.raw_name.replace("-", "_")
-
-    @property
-    def direct_url(self) -> Optional[DirectUrl]:
-        """Obtain a DirectUrl from this distribution.
-
-        Returns None if the distribution has no `direct_url.json` metadata,
-        or if `direct_url.json` is invalid.
-        """
-        try:
-            content = self.read_text(DIRECT_URL_METADATA_NAME)
-        except FileNotFoundError:
-            return None
-        try:
-            return DirectUrl.from_json(content)
-        except (
-            UnicodeDecodeError,
-            json.JSONDecodeError,
-            DirectUrlValidationError,
-        ) as e:
-            logger.warning(
-                "Error parsing %s for %s: %s",
-                DIRECT_URL_METADATA_NAME,
-                self.canonical_name,
-                e,
-            )
-            return None
-
-    @property
-    def installer(self) -> str:
-        try:
-            installer_text = self.read_text("INSTALLER")
-        except (OSError, ValueError, NoneMetadataError):
-            return ""  # Fail silently if the installer file cannot be read.
-        for line in installer_text.splitlines():
-            cleaned_line = line.strip()
-            if cleaned_line:
-                return cleaned_line
-        return ""
-
-    @property
-    def editable(self) -> bool:
-        return bool(self.editable_project_location)
-
-    @property
-    def local(self) -> bool:
-        """If distribution is installed in the current virtual environment.
-
-        Always True if we're not in a virtualenv.
-        """
-        if self.installed_location is None:
-            return False
-        return is_local(self.installed_location)
-
-    @property
-    def in_usersite(self) -> bool:
-        if self.installed_location is None or user_site is None:
-            return False
-        return self.installed_location.startswith(normalize_path(user_site))
-
-    @property
-    def in_site_packages(self) -> bool:
-        if self.installed_location is None or site_packages is None:
-            return False
-        return self.installed_location.startswith(normalize_path(site_packages))
-
-    def is_file(self, path: InfoPath) -> bool:
-        """Check whether an entry in the info directory is a file."""
-        raise NotImplementedError()
-
-    def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
-        """Iterate through a directory in the info directory.
-
-        Each item yielded would be a path relative to the info directory.
-
-        :raise FileNotFoundError: If ``name`` does not exist in the directory.
-        :raise NotADirectoryError: If ``name`` does not point to a directory.
-        """
-        raise NotImplementedError()
-
-    def read_text(self, path: InfoPath) -> str:
-        """Read a file in the info directory.
-
-        :raise FileNotFoundError: If ``name`` does not exist in the directory.
-        :raise NoneMetadataError: If ``name`` exists in the info directory, but
-            cannot be read.
-        """
-        raise NotImplementedError()
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        raise NotImplementedError()
-
-    @property
-    def metadata(self) -> email.message.Message:
-        """Metadata of distribution parsed from e.g. METADATA or PKG-INFO.
-
-        This should return an empty message if the metadata file is unavailable.
-
-        :raises NoneMetadataError: If the metadata file is available, but does
-            not contain valid metadata.
-        """
-        raise NotImplementedError()
-
-    @property
-    def metadata_version(self) -> Optional[str]:
-        """Value of "Metadata-Version:" in distribution metadata, if available."""
-        return self.metadata.get("Metadata-Version")
-
-    @property
-    def raw_name(self) -> str:
-        """Value of "Name:" in distribution metadata."""
-        # The metadata should NEVER be missing the Name: key, but if it somehow
-        # does, fall back to the known canonical name.
-        return self.metadata.get("Name", self.canonical_name)
-
-    @property
-    def requires_python(self) -> SpecifierSet:
-        """Value of "Requires-Python:" in distribution metadata.
-
-        If the key does not exist or contains an invalid value, an empty
-        SpecifierSet should be returned.
-        """
-        value = self.metadata.get("Requires-Python")
-        if value is None:
-            return SpecifierSet()
-        try:
-            # Convert to str to satisfy the type checker; this can be a Header object.
-            spec = SpecifierSet(str(value))
-        except InvalidSpecifier as e:
-            message = "Package %r has an invalid Requires-Python: %s"
-            logger.warning(message, self.raw_name, e)
-            return SpecifierSet()
-        return spec
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        """Dependencies of this distribution.
-
-        For modern .dist-info distributions, this is the collection of
-        "Requires-Dist:" entries in distribution metadata.
-        """
-        raise NotImplementedError()
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        """Extras provided by this distribution.
-
-        For modern .dist-info distributions, this is the collection of
-        "Provides-Extra:" entries in distribution metadata.
-        """
-        raise NotImplementedError()
-
-    def _iter_declared_entries_from_record(self) -> Optional[Iterator[str]]:
-        try:
-            text = self.read_text("RECORD")
-        except FileNotFoundError:
-            return None
-        # This extra Path-str cast normalizes entries.
-        return (str(pathlib.Path(row[0])) for row in csv.reader(text.splitlines()))
-
-    def _iter_declared_entries_from_legacy(self) -> Optional[Iterator[str]]:
-        try:
-            text = self.read_text("installed-files.txt")
-        except FileNotFoundError:
-            return None
-        paths = (p for p in text.splitlines(keepends=False) if p)
-        root = self.location
-        info = self.info_location
-        if root is None or info is None:
-            return paths
-        try:
-            info_rel = pathlib.Path(info).relative_to(root)
-        except ValueError:  # info is not relative to root.
-            return paths
-        if not info_rel.parts:  # info *is* root.
-            return paths
-        return (
-            _convert_installed_files_path(pathlib.Path(p).parts, info_rel.parts)
-            for p in paths
-        )
-
-    def iter_declared_entries(self) -> Optional[Iterator[str]]:
-        """Iterate through file entires declared in this distribution.
-
-        For modern .dist-info distributions, this is the files listed in the
-        ``RECORD`` metadata file. For legacy setuptools distributions, this
-        comes from ``installed-files.txt``, with entries normalized to be
-        compatible with the format used by ``RECORD``.
-
-        :return: An iterator for listed entries, or None if the distribution
-            contains neither ``RECORD`` nor ``installed-files.txt``.
-        """
-        return (
-            self._iter_declared_entries_from_record()
-            or self._iter_declared_entries_from_legacy()
-        )
-
-
-class BaseEnvironment:
-    """An environment containing distributions to introspect."""
-
-    @classmethod
-    def default(cls) -> "BaseEnvironment":
-        raise NotImplementedError()
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> "BaseEnvironment":
-        raise NotImplementedError()
-
-    def get_distribution(self, name: str) -> Optional["BaseDistribution"]:
-        """Given a requirement name, return the installed distributions.
-
-        The name may not be normalized. The implementation must canonicalize
-        it for lookup.
-        """
-        raise NotImplementedError()
-
-    def _iter_distributions(self) -> Iterator["BaseDistribution"]:
-        """Iterate through installed distributions.
-
-        This function should be implemented by subclass, but never called
-        directly. Use the public ``iter_distribution()`` instead, which
-        implements additional logic to make sure the distributions are valid.
-        """
-        raise NotImplementedError()
-
-    def iter_distributions(self) -> Iterator["BaseDistribution"]:
-        """Iterate through installed distributions."""
-        for dist in self._iter_distributions():
-            # Make sure the distribution actually comes from a valid Python
-            # packaging distribution. Pip's AdjacentTempDirectory leaves folders
-            # e.g. ``~atplotlib.dist-info`` if cleanup was interrupted. The
-            # valid project name pattern is taken from PEP 508.
-            project_name_valid = re.match(
-                r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$",
-                dist.canonical_name,
-                flags=re.IGNORECASE,
-            )
-            if not project_name_valid:
-                logger.warning(
-                    "Ignoring invalid distribution %s (%s)",
-                    dist.canonical_name,
-                    dist.location,
-                )
-                continue
-            yield dist
-
-    def iter_installed_distributions(
-        self,
-        local_only: bool = True,
-        skip: Container[str] = stdlib_pkgs,
-        include_editables: bool = True,
-        editables_only: bool = False,
-        user_only: bool = False,
-    ) -> Iterator[BaseDistribution]:
-        """Return a list of installed distributions.
-
-        :param local_only: If True (default), only return installations
-        local to the current virtualenv, if in a virtualenv.
-        :param skip: An iterable of canonicalized project names to ignore;
-            defaults to ``stdlib_pkgs``.
-        :param include_editables: If False, don't report editables.
-        :param editables_only: If True, only report editables.
-        :param user_only: If True, only report installations in the user
-        site directory.
-        """
-        it = self.iter_distributions()
-        if local_only:
-            it = (d for d in it if d.local)
-        if not include_editables:
-            it = (d for d in it if not d.editable)
-        if editables_only:
-            it = (d for d in it if d.editable)
-        if user_only:
-            it = (d for d in it if d.in_usersite)
-        return (d for d in it if d.canonical_name not in skip)
-
-
-class Wheel(Protocol):
-    location: str
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        raise NotImplementedError()
-
-
-class FilesystemWheel(Wheel):
-    def __init__(self, location: str) -> None:
-        self.location = location
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        return zipfile.ZipFile(self.location, allowZip64=True)
-
-
-class MemoryWheel(Wheel):
-    def __init__(self, location: str, stream: IO[bytes]) -> None:
-        self.location = location
-        self.stream = stream
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        return zipfile.ZipFile(self.stream, allowZip64=True)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatter.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatter.py
deleted file mode 100644
index b585562b58874d0452a67aeb3677237afa435937..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatter.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""
-    pygments.formatter
-    ~~~~~~~~~~~~~~~~~~
-
-    Base formatter class.
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import codecs
-
-from pip._vendor.pygments.util import get_bool_opt
-from pip._vendor.pygments.styles import get_style_by_name
-
-__all__ = ['Formatter']
-
-
-def _lookup_style(style):
-    if isinstance(style, str):
-        return get_style_by_name(style)
-    return style
-
-
-class Formatter:
-    """
-    Converts a token stream to text.
-
-    Options accepted:
-
-    ``style``
-        The style to use, can be a string or a Style subclass
-        (default: "default"). Not used by e.g. the
-        TerminalFormatter.
-    ``full``
-        Tells the formatter to output a "full" document, i.e.
-        a complete self-contained document. This doesn't have
-        any effect for some formatters (default: false).
-    ``title``
-        If ``full`` is true, the title that should be used to
-        caption the document (default: '').
-    ``encoding``
-        If given, must be an encoding name. This will be used to
-        convert the Unicode token strings to byte strings in the
-        output. If it is "" or None, Unicode strings will be written
-        to the output file, which most file-like objects do not
-        support (default: None).
-    ``outencoding``
-        Overrides ``encoding`` if given.
-    """
-
-    #: Name of the formatter
-    name = None
-
-    #: Shortcuts for the formatter
-    aliases = []
-
-    #: fn match rules
-    filenames = []
-
-    #: If True, this formatter outputs Unicode strings when no encoding
-    #: option is given.
-    unicodeoutput = True
-
-    def __init__(self, **options):
-        self.style = _lookup_style(options.get('style', 'default'))
-        self.full = get_bool_opt(options, 'full', False)
-        self.title = options.get('title', '')
-        self.encoding = options.get('encoding', None) or None
-        if self.encoding in ('guess', 'chardet'):
-            # can happen for e.g. pygmentize -O encoding=guess
-            self.encoding = 'utf-8'
-        self.encoding = options.get('outencoding') or self.encoding
-        self.options = options
-
-    def get_style_defs(self, arg=''):
-        """
-        Return the style definitions for the current style as a string.
-
-        ``arg`` is an additional argument whose meaning depends on the
-        formatter used. Note that ``arg`` can also be a list or tuple
-        for some formatters like the html formatter.
-        """
-        return ''
-
-    def format(self, tokensource, outfile):
-        """
-        Format ``tokensource``, an iterable of ``(tokentype, tokenstring)``
-        tuples and write it into ``outfile``.
-        """
-        if self.encoding:
-            # wrap the outfile in a StreamWriter
-            outfile = codecs.lookup(self.encoding)[3](outfile)
-        return self.format_unencoded(tokensource, outfile)
diff --git a/spaces/aliabd/SummerTime/model/query_based/bm25_model.py b/spaces/aliabd/SummerTime/model/query_based/bm25_model.py
deleted file mode 100644
index d5fc06bbebfe0d75eecd0ee239f7e56f4fc2ef17..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/model/query_based/bm25_model.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from .base_query_based_model import QueryBasedSummModel
-from model.base_model import SummModel
-from model.single_doc import TextRankModel
-from typing import List
-
-from gensim.summarization.bm25 import BM25
-from nltk import word_tokenize
-
-
-class BM25SummModel(QueryBasedSummModel):
-
-    # static variables
-    model_name = "BM25"
-    is_extractive = True  # only represents the retrieval part
-    is_neural = False  # only represents the retrieval part
-    is_query_based = True
-
-    def __init__(
-        self,
-        trained_domain: str = None,
-        max_input_length: int = None,
-        max_output_length: int = None,
-        model_backend: SummModel = TextRankModel,
-        retrieval_ratio: float = 0.5,
-        preprocess: bool = True,
-        **kwargs
-    ):
-        super(BM25SummModel, self).__init__(
-            trained_domain=trained_domain,
-            max_input_length=max_input_length,
-            max_output_length=max_output_length,
-            model_backend=model_backend,
-            retrieval_ratio=retrieval_ratio,
-            preprocess=preprocess,
-            **kwargs
-        )
-
-    def _retrieve(self, instance: List[str], query: List[str], n_best):
-        bm25 = BM25(word_tokenize(s) for s in instance)
-        scores = bm25.get_scores(query)
-        best_sent_ind = sorted(
-            range(len(scores)), key=lambda i: scores[i], reverse=True
-        )[:n_best]
-        top_n_sent = [instance[ind] for ind in sorted(best_sent_ind)]
-        return top_n_sent
diff --git a/spaces/aliabid94/AutoGPT/autogpt/chat.py b/spaces/aliabid94/AutoGPT/autogpt/chat.py
deleted file mode 100644
index 1f6bca96eb216c667656b50f131006b83c681065..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/chat.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import time
-
-from openai.error import RateLimitError
-
-from autogpt import token_counter
-from autogpt.config import Config
-from autogpt.llm_utils import create_chat_completion
-from autogpt.logs import logger
-
-cfg = Config()
-
-
-def create_chat_message(role, content):
-    """
-    Create a chat message with the given role and content.
-
-    Args:
-    role (str): The role of the message sender, e.g., "system", "user", or "assistant".
-    content (str): The content of the message.
-
-    Returns:
-    dict: A dictionary containing the role and content of the message.
-    """
-    return {"role": role, "content": content}
-
-
-def generate_context(prompt, relevant_memory, full_message_history, model):
-    current_context = [
-        create_chat_message("system", prompt),
-        create_chat_message(
-            "system", f"The current time and date is {time.strftime('%c')}"
-        ),
-        create_chat_message(
-            "system",
-            f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
-        ),
-    ]
-
-    # Add messages from the full message history until we reach the token limit
-    next_message_to_add_index = len(full_message_history) - 1
-    insertion_index = len(current_context)
-    # Count the currently used tokens
-    current_tokens_used = token_counter.count_message_tokens(current_context, model)
-    return (
-        next_message_to_add_index,
-        current_tokens_used,
-        insertion_index,
-        current_context,
-    )
-
-
-# TODO: Change debug from hardcode to argument
-def chat_with_ai(
-    prompt, user_input, full_message_history, permanent_memory, token_limit
-):
-    """Interact with the OpenAI API, sending the prompt, user input, message history,
-    and permanent memory."""
-    while True:
-        try:
-            """
-            Interact with the OpenAI API, sending the prompt, user input,
-                message history, and permanent memory.
-
-            Args:
-                prompt (str): The prompt explaining the rules to the AI.
-                user_input (str): The input from the user.
-                full_message_history (list): The list of all messages sent between the
-                    user and the AI.
-                permanent_memory (Obj): The memory object containing the permanent
-                  memory.
-                token_limit (int): The maximum number of tokens allowed in the API call.
-
-            Returns:
-            str: The AI's response.
-            """
-            model = cfg.fast_llm_model  # TODO: Change model from hardcode to argument
-            # Reserve 1000 tokens for the response
-
-            logger.debug(f"Token limit: {token_limit}")
-            send_token_limit = token_limit - 1000
-
-            relevant_memory = (
-                ""
-                if len(full_message_history) == 0
-                else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
-            )
-
-            logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
-
-            (
-                next_message_to_add_index,
-                current_tokens_used,
-                insertion_index,
-                current_context,
-            ) = generate_context(prompt, relevant_memory, full_message_history, model)
-
-            while current_tokens_used > 2500:
-                # remove memories until we are under 2500 tokens
-                relevant_memory = relevant_memory[:-1]
-                (
-                    next_message_to_add_index,
-                    current_tokens_used,
-                    insertion_index,
-                    current_context,
-                ) = generate_context(
-                    prompt, relevant_memory, full_message_history, model
-                )
-
-            current_tokens_used += token_counter.count_message_tokens(
-                [create_chat_message("user", user_input)], model
-            )  # Account for user input (appended later)
-
-            while next_message_to_add_index >= 0:
-                # print (f"CURRENT TOKENS USED: {current_tokens_used}")
-                message_to_add = full_message_history[next_message_to_add_index]
-
-                tokens_to_add = token_counter.count_message_tokens(
-                    [message_to_add], model
-                )
-                if current_tokens_used + tokens_to_add > send_token_limit:
-                    break
-
-                # Add the most recent message to the start of the current context,
-                #  after the two system prompts.
-                current_context.insert(
-                    insertion_index, full_message_history[next_message_to_add_index]
-                )
-
-                # Count the currently used tokens
-                current_tokens_used += tokens_to_add
-
-                # Move to the next most recent message in the full message history
-                next_message_to_add_index -= 1
-
-            # Append user input, the length of this is accounted for above
-            current_context.extend([create_chat_message("user", user_input)])
-
-            # Calculate remaining tokens
-            tokens_remaining = token_limit - current_tokens_used
-            # assert tokens_remaining >= 0, "Tokens remaining is negative.
-            # This should never happen, please submit a bug report at
-            #  https://www.github.com/Torantulino/Auto-GPT"
-
-            # Debug print the current context
-            logger.debug(f"Token limit: {token_limit}")
-            logger.debug(f"Send Token Count: {current_tokens_used}")
-            logger.debug(f"Tokens remaining for response: {tokens_remaining}")
-            logger.debug("------------ CONTEXT SENT TO AI ---------------")
-            for message in current_context:
-                # Skip printing the prompt
-                if message["role"] == "system" and message["content"] == prompt:
-                    continue
-                logger.debug(f"{message['role'].capitalize()}: {message['content']}")
-                logger.debug("")
-            logger.debug("----------- END OF CONTEXT ----------------")
-
-            # TODO: use a model defined elsewhere, so that model can contain
-            # temperature and other settings we care about
-            assistant_reply = create_chat_completion(
-                model=model,
-                messages=current_context,
-                max_tokens=tokens_remaining,
-            )
-
-            # Update full message history
-            full_message_history.append(create_chat_message("user", user_input))
-            full_message_history.append(
-                create_chat_message("assistant", assistant_reply)
-            )
-
-            return assistant_reply
-        except RateLimitError:
-            # TODO: When we switch to langchain, this is built in
-            print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
-            time.sleep(10)
diff --git a/spaces/aliabid94/AutoGPT/autogpt/speech/__init__.py b/spaces/aliabid94/AutoGPT/autogpt/speech/__init__.py
deleted file mode 100644
index 2ff0d2bf48dc356bf810cb5a2063d6774e5fec6e..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/speech/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""This module contains the speech recognition and speech synthesis functions."""
-from autogpt.speech.say import say_text
-
-__all__ = ["say_text"]
diff --git a/spaces/allknowingroger/Image-Models-Test129/app.py b/spaces/allknowingroger/Image-Models-Test129/app.py
deleted file mode 100644
index 7d1c404e1f858335e6a210e6c29b555b1b59dfff..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test129/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "LinoyTsaban/huggy_v19",
-    "deepgoyal19/lora_tb",
-    "hariharasudhan/paper-suki",
-    "Tapas256/Lens",
-    "Yntec/ReVAnimated768",
-    "jasonxxr666/lora-trained-xl-colab-v3.1",
-    "matgu23/swzdry",
-    "gorilla-ai/jenlaw",
-    "Yacong/guitar-lora-trained-xl",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/almn-uhc/Sentiment-Analysis-Streamlit/app.py b/spaces/almn-uhc/Sentiment-Analysis-Streamlit/app.py
deleted file mode 100644
index 772611111046a64c2e69a9809c981b5dee34ecc7..0000000000000000000000000000000000000000
--- a/spaces/almn-uhc/Sentiment-Analysis-Streamlit/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-# Load the sentiment analysis model from Hugging Face
-classifier = pipeline('sentiment-analysis')
-
-# Create a Streamlit app
-st.title('Sentiment Analysis with Hugging Face')
-st.write('Enter some text and we will predict its sentiment!')
-
-# Add a text input box for the user to enter text
-text_input = st.text_input('Enter text here')
-
-# When the user submits text, run the sentiment analysis model on it
-if st.button('Submit'):
-    # Predict the sentiment of the text using the Hugging Face model
-    sentiment = classifier(text_input)[0]['label']
-    
-    # Display the sentiment prediction to the user
-    st.write(f'Sentiment: {sentiment}')
\ No newline at end of file
diff --git a/spaces/alsalemi/pv-segment-01/train.py b/spaces/alsalemi/pv-segment-01/train.py
deleted file mode 100644
index dea483c5f7537ded5da48bd523be8394928714e4..0000000000000000000000000000000000000000
--- a/spaces/alsalemi/pv-segment-01/train.py
+++ /dev/null
@@ -1,308 +0,0 @@
-r"""PyTorch Detection Training.
-
-To run in a multi-gpu environment, use the distributed launcher::
-
-    python -m torch.distributed.launch --nproc_per_node=$NGPU --use_env \
-        train.py ... --world-size $NGPU
-
-The default hyperparameters are tuned for training on 8 gpus and 2 images per gpu.
-    --lr 0.02 --batch-size 2 --world-size 8
-If you use different number of gpus, the learning rate should be changed to 0.02/8*$NGPU.
-
-On top of that, for training Faster/Mask R-CNN, the default hyperparameters are
-    --epochs 26 --lr-steps 16 22 --aspect-ratio-group-factor 3
-
-Also, if you train Keypoint R-CNN, the default hyperparameters are
-    --epochs 46 --lr-steps 36 43 --aspect-ratio-group-factor 3
-Because the number of images is smaller in the person keypoint subset of COCO,
-the number of epochs should be adapted so that we have the same number of iterations.
-"""
-import datetime
-import os
-import time
-
-import presets
-import torch
-import torch.utils.data
-import torchvision
-import torchvision.models.detection
-import torchvision.models.detection.mask_rcnn
-import utils
-from coco_utils import get_coco, get_coco_kp
-from engine import evaluate, train_one_epoch
-from group_by_aspect_ratio import create_aspect_ratio_groups, GroupedBatchSampler
-from torchvision.transforms import InterpolationMode
-from transforms import SimpleCopyPaste
-
-
-def copypaste_collate_fn(batch):
-    copypaste = SimpleCopyPaste(blending=True, resize_interpolation=InterpolationMode.BILINEAR)
-    return copypaste(*utils.collate_fn(batch))
-
-
-def get_dataset(name, image_set, transform, data_path):
-    paths = {"coco": (data_path, get_coco, 91), "coco_kp": (data_path, get_coco_kp, 2)}
-    p, ds_fn, num_classes = paths[name]
-
-    ds = ds_fn(p, image_set=image_set, transforms=transform)
-    return ds, num_classes
-
-
-def get_transform(train, args):
-    if train:
-        return presets.DetectionPresetTrain(data_augmentation=args.data_augmentation)
-    elif args.weights and args.test_only:
-        weights = torchvision.models.get_weight(args.weights)
-        trans = weights.transforms()
-        return lambda img, target: (trans(img), target)
-    else:
-        return presets.DetectionPresetEval()
-
-
-def get_args_parser(add_help=True):
-    import argparse
-
-    parser = argparse.ArgumentParser(description="PyTorch Detection Training", add_help=add_help)
-
-    parser.add_argument("--data-path", default="/datasets01/COCO/022719/", type=str, help="dataset path")
-    parser.add_argument("--dataset", default="coco", type=str, help="dataset name")
-    parser.add_argument("--model", default="maskrcnn_resnet50_fpn", type=str, help="model name")
-    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
-    parser.add_argument(
-        "-b", "--batch-size", default=2, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
-    )
-    parser.add_argument("--epochs", default=26, type=int, metavar="N", help="number of total epochs to run")
-    parser.add_argument(
-        "-j", "--workers", default=4, type=int, metavar="N", help="number of data loading workers (default: 4)"
-    )
-    parser.add_argument("--opt", default="sgd", type=str, help="optimizer")
-    parser.add_argument(
-        "--lr",
-        default=0.02,
-        type=float,
-        help="initial learning rate, 0.02 is the default value for training on 8 gpus and 2 images_per_gpu",
-    )
-    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
-    parser.add_argument(
-        "--wd",
-        "--weight-decay",
-        default=1e-4,
-        type=float,
-        metavar="W",
-        help="weight decay (default: 1e-4)",
-        dest="weight_decay",
-    )
-    parser.add_argument(
-        "--norm-weight-decay",
-        default=None,
-        type=float,
-        help="weight decay for Normalization layers (default: None, same value as --wd)",
-    )
-    parser.add_argument(
-        "--lr-scheduler", default="multisteplr", type=str, help="name of lr scheduler (default: multisteplr)"
-    )
-    parser.add_argument(
-        "--lr-step-size", default=8, type=int, help="decrease lr every step-size epochs (multisteplr scheduler only)"
-    )
-    parser.add_argument(
-        "--lr-steps",
-        default=[16, 22],
-        nargs="+",
-        type=int,
-        help="decrease lr every step-size epochs (multisteplr scheduler only)",
-    )
-    parser.add_argument(
-        "--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma (multisteplr scheduler only)"
-    )
-    parser.add_argument("--print-freq", default=20, type=int, help="print frequency")
-    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
-    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
-    parser.add_argument("--start_epoch", default=0, type=int, help="start epoch")
-    parser.add_argument("--aspect-ratio-group-factor", default=3, type=int)
-    parser.add_argument("--rpn-score-thresh", default=None, type=float, help="rpn score threshold for faster-rcnn")
-    parser.add_argument(
-        "--trainable-backbone-layers", default=None, type=int, help="number of trainable layers of backbone"
-    )
-    parser.add_argument(
-        "--data-augmentation", default="hflip", type=str, help="data augmentation policy (default: hflip)"
-    )
-    parser.add_argument(
-        "--sync-bn",
-        dest="sync_bn",
-        help="Use sync batch norm",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--test-only",
-        dest="test_only",
-        help="Only test the model",
-        action="store_true",
-    )
-
-    parser.add_argument(
-        "--use-deterministic-algorithms", action="store_true", help="Forces the use of deterministic algorithms only."
-    )
-
-    # distributed training parameters
-    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
-    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
-    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
-    parser.add_argument("--weights-backbone", default=None, type=str, help="the backbone weights enum name to load")
-
-    # Mixed precision training parameters
-    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
-
-    # Use CopyPaste augmentation training parameter
-    parser.add_argument(
-        "--use-copypaste",
-        action="store_true",
-        help="Use CopyPaste data augmentation. Works only with data-augmentation='lsj'.",
-    )
-
-    return parser
-
-
-def main(args):
-    if args.output_dir:
-        utils.mkdir(args.output_dir)
-
-    utils.init_distributed_mode(args)
-    print(args)
-
-    device = torch.device(args.device)
-
-    if args.use_deterministic_algorithms:
-        torch.use_deterministic_algorithms(True)
-
-    # Data loading code
-    print("Loading data")
-
-    dataset, num_classes = get_dataset(args.dataset, "train", get_transform(True, args), args.data_path)
-    dataset_test, _ = get_dataset(args.dataset, "val", get_transform(False, args), args.data_path)
-
-    print("Creating data loaders")
-    if args.distributed:
-        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
-        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test, shuffle=False)
-    else:
-        train_sampler = torch.utils.data.RandomSampler(dataset)
-        test_sampler = torch.utils.data.SequentialSampler(dataset_test)
-
-    if args.aspect_ratio_group_factor >= 0:
-        group_ids = create_aspect_ratio_groups(dataset, k=args.aspect_ratio_group_factor)
-        train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, args.batch_size)
-    else:
-        train_batch_sampler = torch.utils.data.BatchSampler(train_sampler, args.batch_size, drop_last=True)
-
-    train_collate_fn = utils.collate_fn
-    if args.use_copypaste:
-        if args.data_augmentation != "lsj":
-            raise RuntimeError("SimpleCopyPaste algorithm currently only supports the 'lsj' data augmentation policies")
-
-        train_collate_fn = copypaste_collate_fn
-
-    data_loader = torch.utils.data.DataLoader(
-        dataset, batch_sampler=train_batch_sampler, num_workers=args.workers, collate_fn=train_collate_fn
-    )
-
-    data_loader_test = torch.utils.data.DataLoader(
-        dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
-    )
-
-    print("Creating model")
-    kwargs = {"trainable_backbone_layers": args.trainable_backbone_layers}
-    if args.data_augmentation in ["multiscale", "lsj"]:
-        kwargs["_skip_resize"] = True
-    if "rcnn" in args.model:
-        if args.rpn_score_thresh is not None:
-            kwargs["rpn_score_thresh"] = args.rpn_score_thresh
-    model = torchvision.models.get_model(
-        args.model, weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, **kwargs
-    )
-    model.to(device)
-    if args.distributed and args.sync_bn:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
-
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module
-
-    if args.norm_weight_decay is None:
-        parameters = [p for p in model.parameters() if p.requires_grad]
-    else:
-        param_groups = torchvision.ops._utils.split_normalization_params(model)
-        wd_groups = [args.norm_weight_decay, args.weight_decay]
-        parameters = [{"params": p, "weight_decay": w} for p, w in zip(param_groups, wd_groups) if p]
-
-    opt_name = args.opt.lower()
-    if opt_name.startswith("sgd"):
-        optimizer = torch.optim.SGD(
-            parameters,
-            lr=args.lr,
-            momentum=args.momentum,
-            weight_decay=args.weight_decay,
-            nesterov="nesterov" in opt_name,
-        )
-    elif opt_name == "adamw":
-        optimizer = torch.optim.AdamW(parameters, lr=args.lr, weight_decay=args.weight_decay)
-    else:
-        raise RuntimeError(f"Invalid optimizer {args.opt}. Only SGD and AdamW are supported.")
-
-    scaler = torch.cuda.amp.GradScaler() if args.amp else None
-
-    args.lr_scheduler = args.lr_scheduler.lower()
-    if args.lr_scheduler == "multisteplr":
-        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
-    elif args.lr_scheduler == "cosineannealinglr":
-        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
-    else:
-        raise RuntimeError(
-            f"Invalid lr scheduler '{args.lr_scheduler}'. Only MultiStepLR and CosineAnnealingLR are supported."
-        )
-
-    if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
-        model_without_ddp.load_state_dict(checkpoint["model"])
-        optimizer.load_state_dict(checkpoint["optimizer"])
-        lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
-        args.start_epoch = checkpoint["epoch"] + 1
-        if args.amp:
-            scaler.load_state_dict(checkpoint["scaler"])
-
-    if args.test_only:
-        torch.backends.cudnn.deterministic = True
-        evaluate(model, data_loader_test, device=device)
-        return
-
-    print("Start training")
-    start_time = time.time()
-    for epoch in range(args.start_epoch, args.epochs):
-        if args.distributed:
-            train_sampler.set_epoch(epoch)
-        train_one_epoch(model, optimizer, data_loader, device, epoch, args.print_freq, scaler)
-        lr_scheduler.step()
-        if args.output_dir:
-            checkpoint = {
-                "model": model_without_ddp.state_dict(),
-                "optimizer": optimizer.state_dict(),
-                "lr_scheduler": lr_scheduler.state_dict(),
-                "args": args,
-                "epoch": epoch,
-            }
-            if args.amp:
-                checkpoint["scaler"] = scaler.state_dict()
-            utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
-            utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))
-
-        # evaluate after every epoch
-        evaluate(model, data_loader_test, device=device)
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print(f"Training time {total_time_str}")
-
-
-if __name__ == "__main__":
-    args = get_args_parser().parse_args()
-    main(args)
diff --git a/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/README.md b/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/README.md
deleted file mode 100644
index aa6fe1c97d676cf02353da15e2ceaa2c779239ad..0000000000000000000000000000000000000000
--- a/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Adi The ArxivGPT With Voice and featuring answers
-emoji: 💻
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/my/utils/ticker.py b/spaces/amankishore/sjc/my/utils/ticker.py
deleted file mode 100644
index ac19520075aaa8fa27b9a9baf57a7478e4cc83d5..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/my/utils/ticker.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from datetime import date, time, datetime, timedelta
-from time import sleep
-
-
-class IntervalTicker():
-    def __init__(self, interval=60):
-        self.interval = timedelta(seconds=interval)
-        self.last_tick = datetime.now()
-        self.now = self.last_tick
-
-    def tick(self):
-        self.now = datetime.now()
-        if (self.now - self.last_tick) > self.interval:
-            self.last_tick = self.now
-            return True
-
-    def tick_str(self):
-        return self.now.isoformat(timespec='seconds')
diff --git a/spaces/amasgari06/ChatGPT4/README.md b/spaces/amasgari06/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/amasgari06/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/amirhnikzad/MLSG_01/app.py b/spaces/amirhnikzad/MLSG_01/app.py
deleted file mode 100644
index 99de811c8e0356db55008fb97f38e1f128730cc2..0000000000000000000000000000000000000000
--- a/spaces/amirhnikzad/MLSG_01/app.py
+++ /dev/null
@@ -1,334 +0,0 @@
-
-#Import Libraries
-import spacy
-nlp = spacy.load("en_core_web_sm")
-from transformer_srl import dataset_readers, models, predictors
-predictor = predictors.SrlTransformersPredictor.from_path("srl_bert_base_conll2012.tar.gz","transformer_srl")
-import re
-import nltk
-nltk.download('stopwords')
-from nltk.corpus import stopwords
-stop_words = set(stopwords.words('english'))
-import numpy as np
-import pandas as pd
-import networkx as nx
-from IPython.core.display import display, HTML
-from pyvis.network import Network
-from somajo import SoMaJo
-import requests
-import json
-import gradio as gr
-import matplotlib
-import matplotlib.pyplot as plt
-from statistics import mean
-from statistics import stdev
-
-import math
-
-
-#Semantic Graph Function
-def semantic_graph(text_input, srl_model, flow, predication, action, setting, cause, manner, negation,
-                   orientation):
-#Define dictionary to convert pronouns to their root
-    pronouns_dic = {'i': 'i', 'I': 'i', 'he': 'he', 'she': 'she', 'you': 'you', 'we': 'we', 'they': 'they', 'me': 'i',
-                    'my': 'i', 'mine': 'i', 'your': 'you', 'yours': 'you', 'him': 'he', 'his': 'he', 'her': 'she',
-                    'hers': 'she', 'us': 'we', 'ours': 'we', 'our': 'we', 'their': 'they', 'theirs': 'they',
-                    'them': 'they', 'its': 'it', 'it': 'it', "'em": 'they', 'myself': 'i', 'that': 'that',
-                    'this': 'this', 'those': 'those', 'these': 'these'}
-    nlp = spacy.load("en_core_web_sm")
-    text = [text_input]
-#Parse text into sentences:
-    sentence_df = pd.DataFrame()
-    tokenizer = SoMaJo("en_PTB", split_camel_case=True)
-    somajo_sentences = tokenizer.tokenize_text(text)
-    n_sent = 1
-    for sentence in somajo_sentences:
-        each_sentence = []
-        for token in sentence:
-            each_sentence.append(token.text)
-        content_sent = ' '.join(each_sentence)
-        instance = pd.DataFrame({'sentence_id': [n_sent], 'content': [content_sent]})
-        sentence_df = sentence_df.append(instance, ignore_index=True)
-        n_sent += 1
-#Define semantic Roles:
-    arguments = ['ARG0', 'ARG1', 'ARG2', 'ARG3', 'ARG4' ,'ARG5']
-    spatiotemporal = ['ARGM-LOC', 'ARGM-DIR', 'ARGM-GOL', 'ARGM-TMP']
-    causal = ['ARGM-PRP', 'ARGM-CAU']
-    how = ['ARGM-MNR', 'ARGM-COM', 'ARGM-EXT', 'ARGM-ADV', 'ARGM-ADJ', 'ARGM-PRD']
-    meta = ['ARGM-DIS', 'ARGM-MOD', 'ARGM-REC']
-    neg = ['ARGM-NEG']
-    columns = ['sentence_id', 'pred_id', 'frame', 'lemma'] + arguments + spatiotemporal + causal + how + meta + neg
-    srl_df = pd.DataFrame(columns=columns)
-    pred_id = 1
-#Branch for SRL models:
-    if srl_model == 'Transformer_SRL':
-        for index, row in sentence_df.iterrows():
-            srl = predictor.predict((row['content']))
-            for each_verb in srl['verbs']:
-                srl_elements = re.findall(r'\[.*?\]', each_verb['description'])
-                instance = {}
-                instance['sentence_id'] = row['sentence_id']
-                instance['frame'] = each_verb['frame']
-                instance['lemma'] = each_verb['lemma']
-                instance['pred_id'] = pred_id
-                any_element = False
-                for element in srl_elements:
-                    if element.split(':')[0][1:] != each_verb['frame']:
-                        any_element = True
-                        srl_content = element[1:-1].split(':')
-                        # print(srl_content)
-                        instance[srl_content[0].strip()] = srl_content[1].strip()
-                if any_element == True:
-                    srl_df = srl_df.append(instance, ignore_index=True)
-                    pred_id += 1
-        srl_df['Pred'] = srl_df['lemma']
-
-    elif srl_model == 'VerbAtlas':
-        URL = "https://verbatlas.org/api/model"
-        for index, row in sentence_df.iterrows():
-            try:
-                r = requests.post(url=URL, json=[{"text": row['content'], "lang": "EN"}])
-                all_srl = json.loads(r.text)
-                for item in all_srl:
-                    token_register = {}
-                    for token in item['tokens']:
-                        token_register[token['index']] = token['rawText']
-                    for head in item['annotations']:
-                        any_element = False
-                        head_index = head['tokenIndex']
-                        english_prop = head['englishPropbank']
-                        prop_head = english_prop['frameName']
-                        prop_roles = english_prop['roles']
-                        instance = {}
-                        instance['sentence_id'] = row['sentence_id']
-                        instance['frame'] = prop_head
-                        instance['pred_id'] = pred_id
-                        for role in prop_roles:
-                            cat = role['role']
-                            span_0 = role['span'][0]
-                            span_1 = role['span'][1]
-                            token_list = []
-                            for i in range(span_0, span_1):
-                                token_list.append(token_register[i])
-                            instance[cat] = ' '.join(token_list)
-                            any_element = True
-                        if any_element == True:
-                            srl_df = srl_df.append(instance, ignore_index=True)
-                            pred_id += 1
-            except:
-                pass
-        srl_df['Pred'] = srl_df['frame']
-    
-    semantic_df = srl_df.copy()
-#Extract roles:
-    roles = semantic_df.drop(['sentence_id', 'pred_id', 'frame', 'lemma', 'Pred'], axis=1).columns.to_list()
-#Iterate on roles and clean:
-    for index, row in semantic_df.iterrows():
-        for role in roles:
-            try:
-                arg_content = row[role].split(' ')
-                filtered_content = []
-                for token in arg_content:
-                    if nlp(token)[0].pos_ == 'PRON':
-                        try:
-                            filtered_content.append(pronouns_dic[token.lower()])  # ,
-                        except:
-                            pass
-                    elif nlp(token)[0].pos_ not in ['ADP', 'PART', 'PUNCT', 'DET', 'SPACE']:
-                        filtered_content.append(token)
-                semantic_df.at[index, role] = ' '.join(filtered_content)
-            except:
-                pass
-#Define Graph:
-    G_sem = nx.MultiDiGraph()
-#Iterate on semantic df and add nodes and edges to the semantic graph:
-    for index, row in semantic_df.iterrows():
-        if index < len(semantic_df) - 1 and len(semantic_df) > 1:
-            a = semantic_df.at[index, 'Pred'].lower()
-            b = semantic_df.at[index + 1, 'Pred'].lower()
-            if a not in G_sem:
-                G_sem.add_node(a, category='Predication', color='salmon', label=a)
-            if b not in G_sem:
-                G_sem.add_node(b, category='Predication', color='salmon', label=b)
-            G_sem.add_edge(a, b, turn=index + 0.5, color='brown', category='Flow')
-        elif len(semantic_df) == 1:
-            a = semantic_df.at[index, 'Pred'].lower()
-            G_sem.add_node(a, category='Predication', color='salmon', label=a)
-        a = semantic_df.at[index, 'Pred'].lower()
-        for arg in arguments:
-            if type(row[arg]) != float and row[arg] != '':
-                c = row[arg].lower()
-                G_sem.add_node(c, category='Argument', color='lightblue', label=c)
-                G_sem.add_edge(a, c, turn=index, category='Predication', color='salmon')
-        if type(row['ARG0']) != float:
-            c = row['ARG0'].lower()
-            if type(row['ARG1']) != float and row['ARG1'] != '':
-                d = row['ARG1'].lower()
-                G_sem.add_edge(c, d, turn=index, category='Argument', color='lightblue')
-            if type(row['ARG2']) != float and row['ARG2'] != '':
-                d = row['ARG2'].lower()
-                G_sem.add_edge(c, d, turn=index, category='Argument', color='lightblue')
-            #if type(row['ARG3']) != float:
-                #d = row['ARG3'].lower()
-                #G_sem.add_edge(c, d, turn=index, category='Argument', color='lightblue')
-            #if type(row['ARG4']) != float:
-                #d = row['ARG4'].lower()
-                #G_sem.add_edge(c, d, turn=index, category='Argument', color='lightblue')
-        for argm in spatiotemporal:
-            if type(row[argm]) != float and row[arg] != '':
-                d = row[argm].lower()
-                G_sem.add_node(d, category='Setting', color='lightgreen', label=d)
-                G_sem.add_edge(d, a, turn=index, category='Setting', color='lightgreen')
-        for argm in causal:
-            if type(row[argm]) != float and row[arg] != '':
-                d = row[argm].lower()
-                G_sem.add_node(d, category='Cause', color='gold', label=d)
-                G_sem.add_edge(d, a, turn=index, category='Cause', color='gold')
-        for argm in how:
-            if type(row[argm]) != float and row[arg] != '':
-                d = row[argm].lower()
-                G_sem.add_node(d, category='Manner', color='grey', label=d)
-                G_sem.add_edge(d, a, turn=index, category='Manner', color='grey')
-        for argm in meta:
-            if type(row[argm]) != float and row[arg] != '':
-                d = row[argm].lower()
-                G_sem.add_node(d, category='Orientation', color='aqua', label=d)
-                G_sem.add_edge(d, a, turn=index, category='Orientation', color='aqua')
-        for argm in neg:
-            if type(row[argm]) != float and row[arg] != '':
-                d = row[argm].lower()
-                G_sem.add_node(d, category='Negation', color='black', label=d)
-                G_sem.add_edge(d, a, turn=index, category='Negation', color='black')
-
-    cat_list = ['Flow', 'Predication', 'Argument', 'Setting', 'Cause', 'Manner', 'Negation', 'Orientation']
-    categories = [flow, predication, action, setting, cause, manner, negation, orientation]
-    network_oi = []
-    for i in range(0, len(categories)):
-        if categories[i] == True:
-            network_oi.append(cat_list[i])
-
-    # G_filtered_cat = G_sem.subgraph([n for n,v in G_sem.nodes(data=True) if v['cat'] in network_oi])
-    G_filtered_cat = nx.MultiDiGraph(
-        [(u, v, {'turn': e['turn'], 'category': e['category'], 'color': e['color']}) for u, v, e in G_sem.edges(data=True) if
-         e['category'] in network_oi])
-    for v in G_filtered_cat.nodes:
-        G_filtered_cat.nodes[v]['category'] = G_sem.nodes[v]['category']
-        G_filtered_cat.nodes[v]['title'] = G_sem.nodes[v]['category']
-        G_filtered_cat.nodes[v]['color'] = G_sem.nodes[v]['color']
-#Visualize static graph:
-    color_map = []
-    for each_node in G_filtered_cat.nodes(data=True):
-        color_map.append(each_node[1]['color'])
-    color_map2 = []
-    for each_edge in G_filtered_cat.edges(data=True):
-      color_map2.append(each_edge[2]['color'])
-    legend_colors = pd.DataFrame(columns=['x','y','category','color'])
-    for each_node in G_filtered_cat.nodes(data=True):
-      instance = {}
-      instance['color']=(each_node[1]['color'])
-      instance['category']=(each_node[1]['category'])
-      legend_colors = legend_colors.append(instance, ignore_index=True)
-    legend_colors=legend_colors.drop_duplicates()
-    legend_colors2 = pd.DataFrame(columns=['x','y','category','color'])
-    for each_edge in G_filtered_cat.edges(data=True):
-        instance = {}
-        instance['color']=(each_edge[2]['color'])
-        instance['category']=(each_edge[2]['category']) 
-        legend_colors2 = legend_colors2.append(instance, ignore_index=True)
-    legend_colors2=legend_colors2.drop_duplicates()
-
-    fig, ax = plt.subplots(figsize=(8,8))                       
-    for index, row in legend_colors.iterrows():
-        plt.scatter(row['x'],row['y'], c=row['color'], label=row['category'])
-    legend = plt.legend(bbox_to_anchor=(1.05, 1.0),loc=1, title='Nodes')
-    #nx.draw_kamada_kawai(G_filtered_cat,node_color=color_map,edge_color=color_map2, with_labels=True)
-    pos = nx.spring_layout(G_filtered_cat, k=5/math.sqrt(G_filtered_cat.order()))
-    nx.draw(G_filtered_cat, pos=pos,node_color=color_map,edge_color=color_map2, with_labels=True)
-    plt.tight_layout()
-    plt.show()
-
-#Visualize interactive graph:
-    net = Network("600px", "600px", notebook=True, directed=True, cdn_resources="remote", filter_menu=True, select_menu=True) 
-    net.from_nx(G_filtered_cat, show_edge_weights=False)
-    net.show_buttons(filter_=['physics'])
-    net.show('network.html')
-    #display(HTML('network.html'))
-#Compute Graph metrics:
-    G_stat = nx.DiGraph(G_filtered_cat)
-    nn = G_stat.number_of_nodes()
-    ne = G_stat.number_of_edges()
-    try:
-        diameter = nx.diameter(nx.to_undirected(G_stat))
-    except:
-        diameter = np.nan
-    try:
-        aspl = nx.average_shortest_path_length(nx.to_undirected(G_stat))
-    except:
-        aspl=np.nan
-    try:
-        ad = sum([d for (n, d) in nx.degree(G_stat)]) / float(G_stat.number_of_nodes())
-    except:
-        ad = 0
-    try:
-        awd = sum([d for (n, d) in nx.degree(G_stat,weight='weight')]) / float(G_stat.number_of_nodes())
-    except:
-        awd = 0
-    gd = nx.density(G_stat)
-    try:
-        cc = nx.average_clustering(nx.Graph(G_stat))
-    except:
-        cc = np.nan
-    ncc = len(sorted(nx.connected_components(nx.Graph(G_stat)), key=len, reverse=True))
-    try:
-        lcc = len(max(nx.connected_components(nx.Graph(G_stat)), key=len))
-    except:
-        lcc = 0
-    try:
-        lscc  = len(max(nx.strongly_connected_components(G_stat), key=len))
-    except:
-        lscc = 0
-    ncc_list = []
-    lcc_list = []
-    lscc_list =[]
-    for seed in range(0,1000):
-        G_rand = nx.gnm_random_graph(nn, ne, seed=seed,directed=True)
-        try:
-            ncc_list.append(len(sorted(nx.connected_components(nx.Graph(G_rand)), key=len, reverse=True)))
-        except:
-            pass
-        try:
-            lcc_list.append(len(max(nx.connected_components(nx.Graph(G_rand)), key=len)))
-        except:
-            pass
-        try:
-            lscc_list.append(len(max(nx.strongly_connected_components(G_rand), key=len)))
-        except:
-            pass
-    try:
-        nccz = (ncc-mean(ncc_list))/stdev(ncc_list)
-    except:
-        nccz = np.nan
-    try:
-        lccz = (lcc-mean(lcc_list))/stdev(lcc_list)
-    except:
-        lccz = np.nan
-    try:
-        lsccz = (lscc-mean(lscc_list))/stdev(lscc_list)
-    except:
-        lsccz = np.nan
-    cols = ['#Nodes', '#Edges','Diameter','ASPL','AD','AWD','Density','CC','NCC', 'LCC', 'LSCC', 'NCCZ', 'LCCZ', 'LSCCZ']
-    df_stats = pd.DataFrame(data=[[nn,ne,diameter,aspl,ad,awd,gd,cc,ncc,lcc,lscc,nccz,lccz,lsccz]], columns=cols) 
-    
-    return fig, df_stats, 'network.html'    #, net.generate_html('network.html')
-
-#Build gradio interface
-demo = gr.Interface(
-    fn=semantic_graph, title = 'Multi-Layered Semantic Speech Graph - Tang Lab',
-    inputs=[gr.Textbox(label='Insert speech sample:', placeholder= "Type or paste..."), gr.Radio(["Transformer_SRL", "VerbAtlas"], label='Select Semantic Role Labeling model and semantic layers:'), 
-            gr.Checkbox(label='Flow of Predicates'), gr.Checkbox(label='Predication'), gr.Checkbox(label='Action'),
-            gr.Checkbox(label='Setting'), gr.Checkbox(label='Cause'), gr.Checkbox(label='Manner'), 
-            gr.Checkbox(label='Negation'), gr.Checkbox(label='Orientation')],
-    examples = [['The dog is chasing the cat.', 'VerbAtlas', False, True, True, False, False, False, False, False]],
-    outputs=[ gr.Plot(label='Graph Representation', scroll_to_output=True), gr.Dataframe(label='Graph Metrics'), gr.File(label='Interactive Graph')] #['A large hall, numerous guests, whom we were receiving. Among them was Irma. I at once took her to one side, as though to answer her letter and to reproach her for not having accepted my solution yet.','Transformer_SRL', False, True, True, True, False, False, False, False]],
-)
-demo.launch() #share=True
\ No newline at end of file
diff --git a/spaces/anakin87/who-killed-laura-palmer/crawler/tpcrawler/tpcrawler/__init__.py b/spaces/anakin87/who-killed-laura-palmer/crawler/tpcrawler/tpcrawler/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/antonovmaxim/text-generation-webui-space/extensions/superbooga/chromadb.py b/spaces/antonovmaxim/text-generation-webui-space/extensions/superbooga/chromadb.py
deleted file mode 100644
index 52f4854bdbaaa9d0fcde18ea395c50e216b06b8e..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/extensions/superbooga/chromadb.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import logging
-
-import posthog
-import torch
-from sentence_transformers import SentenceTransformer
-
-import chromadb
-from chromadb.config import Settings
-
-logging.info('Intercepting all calls to posthog :)')
-posthog.capture = lambda *args, **kwargs: None
-
-
-class Collecter():
-    def __init__(self):
-        pass
-
-    def add(self, texts: list[str]):
-        pass
-
-    def get(self, search_strings: list[str], n_results: int) -> list[str]:
-        pass
-
-    def clear(self):
-        pass
-
-
-class Embedder():
-    def __init__(self):
-        pass
-
-    def embed(self, text: str) -> list[torch.Tensor]:
-        pass
-
-
-class ChromaCollector(Collecter):
-    def __init__(self, embedder: Embedder):
-        super().__init__()
-        self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
-        self.embedder = embedder
-        self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
-        self.ids = []
-
-    def add(self, texts: list[str]):
-        self.ids = [f"id{i}" for i in range(len(texts))]
-        self.collection.add(documents=texts, ids=self.ids)
-
-    def get_documents_and_ids(self, search_strings: list[str], n_results: int):
-        n_results = min(len(self.ids), n_results)
-        result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])
-        documents = result['documents'][0]
-        ids = list(map(lambda x: int(x[2:]), result['ids'][0]))
-        return documents, ids
-
-    # Get chunks by similarity
-    def get(self, search_strings: list[str], n_results: int) -> list[str]:
-        documents, _ = self.get_documents_and_ids(search_strings, n_results)
-        return documents
-
-    # Get ids by similarity
-    def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
-        _ , ids = self.get_documents_and_ids(search_strings, n_results)
-        return ids
-
-    # Get chunks by similarity and then sort by insertion order
-    def get_sorted(self, search_strings: list[str], n_results: int) -> list[str]:
-        documents, ids = self.get_documents_and_ids(search_strings, n_results)
-        return [x for _, x in sorted(zip(ids, documents))]
-
-    # Get ids by similarity and then sort by insertion order
-    def get_ids_sorted(self, search_strings: list[str], n_results: int) -> list[str]:
-        _ , ids = self.get_documents_and_ids(search_strings, n_results)
-        return sorted(ids)
-
-    def clear(self):
-        self.collection.delete(ids=self.ids)
-
-
-class SentenceTransformerEmbedder(Embedder):
-    def __init__(self) -> None:
-        self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
-        self.embed = self.model.encode
-
-
-def make_collector():
-    global embedder
-    return ChromaCollector(embedder)
-
-
-def add_chunks_to_collector(chunks, collector):
-    collector.clear()
-    collector.add(chunks)
-
-
-embedder = SentenceTransformerEmbedder()
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/deforum_controlnet_hardcode.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/deforum_controlnet_hardcode.py
deleted file mode 100644
index 1446f77634a54c294eb1327786ae33c1ee7b4dcd..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/deforum_controlnet_hardcode.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# TODO HACK FIXME HARDCODE — as using the scripts doesn't seem to work for some reason
-deforum_latest_network = None
-deforum_latest_params = (None, 'placeholder to trigger the model loading')
-deforum_input_image = None
-from scripts.processor import unload_hed, unload_mlsd, unload_midas, unload_leres, unload_pidinet, unload_openpose, unload_uniformer, HWC3
-import modules.shared as shared
-import modules.devices as devices
-import modules.processing as processing
-from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img
-import numpy as np
-from scripts.controlnet import update_cn_models, cn_models, cn_models_names
-import os
-import modules.scripts as scrpts
-import torch
-from scripts.cldm import PlugableControlModel
-from scripts.adapter import PlugableAdapter
-from scripts.utils import load_state_dict
-from torchvision.transforms import Resize, InterpolationMode, CenterCrop, Compose
-from einops import rearrange
-cn_models_dir = os.path.join(scrpts.basedir(), "models")
-default_conf_adapter = os.path.join(cn_models_dir, "sketch_adapter_v14.yaml")
-default_conf = os.path.join(cn_models_dir, "cldm_v15.yaml")
-unloadable = {
-    "hed": unload_hed,
-    "fake_scribble": unload_hed,
-    "mlsd": unload_mlsd,
-    "depth": unload_midas,
-    "depth_leres": unload_leres,
-    "normal_map": unload_midas,
-    "pidinet": unload_pidinet,
-    "openpose": unload_openpose,
-    "openpose_hand": unload_openpose,
-    "segmentation": unload_uniformer,
-}
-deforum_latest_model_hash = ""
-
-def restore_networks(unet):
-    global deforum_latest_network
-    global deforum_latest_params
-    if deforum_latest_network is not None:
-        print("restoring last networks")
-        deforum_input_image = None
-        deforum_latest_network.restore(unet)
-        deforum_latest_network = None
-
-    last_module = deforum_latest_params[0]
-    if last_module is not None:
-        unloadable.get(last_module, lambda:None)()
-
-def process(p, *args):
-
-    global deforum_latest_network
-    global deforum_latest_params
-    global deforum_input_image
-    global deforum_latest_model_hash
-    
-    unet = p.sd_model.model.diffusion_model
-
-    enabled, module, model, weight, image, scribble_mode, \
-        resize_mode, rgbbgr_mode, lowvram, pres, pthr_a, pthr_b, guidance_strength = args
-
-    if not enabled:
-        restore_networks(unet)
-        return
-
-    models_changed = deforum_latest_params[1] != model \
-        or deforum_latest_model_hash != p.sd_model.sd_model_hash or deforum_latest_network == None \
-        or (deforum_latest_network is not None and deforum_latest_network.lowvram != lowvram)
-
-    deforum_latest_params = (module, model)
-    deforum_latest_model_hash = p.sd_model.sd_model_hash
-    if models_changed:
-        restore_networks(unet)
-        model_path = cn_models.get(model, None)
-
-        if model_path is None:
-            raise RuntimeError(f"model not found: {model}")
-
-        # trim '"' at start/end
-        if model_path.startswith("\"") and model_path.endswith("\""):
-            model_path = model_path[1:-1]
-
-        if not os.path.exists(model_path):
-            raise ValueError(f"file not found: {model_path}")
-
-        print(f"Loading preprocessor: {module}, model: {model}")
-        state_dict = load_state_dict(model_path)
-        network_module = PlugableControlModel
-        network_config = shared.opts.data.get("control_net_model_config", default_conf)
-        if any([k.startswith("body.") for k, v in state_dict.items()]):
-            # adapter model     
-            network_module = PlugableAdapter
-            network_config = shared.opts.data.get("control_net_model_adapter_config", default_conf_adapter)
-
-        network = network_module(
-            state_dict=state_dict, 
-            config_path=network_config, 
-            weight=weight, 
-            lowvram=lowvram,
-            base_model=unet,
-        )
-        network.to(p.sd_model.device, dtype=p.sd_model.dtype)
-        network.hook(unet, p.sd_model)
-
-        print(f"ControlNet model {model} loaded.")
-        deforum_latest_network = network
-        
-    if image is not None:
-        deforum_input_image = HWC3(image['image'])
-        if 'mask' in image and image['mask'] is not None and not ((image['mask'][:, :, 0]==0).all() or (image['mask'][:, :, 0]==255).all()):
-            print("using mask as input")
-            deforum_input_image = HWC3(image['mask'][:, :, 0])
-            scribble_mode = True
-    else:
-        # use img2img init_image as default
-        deforum_input_image = getattr(p, "init_images", [None])[0]
-        if deforum_input_image is None:
-            raise ValueError('controlnet is enabled but no input image is given')
-        deforum_input_image = HWC3(np.asarray(deforum_input_image))
-            
-    if scribble_mode:
-        detected_map = np.zeros_like(deforum_input_image, dtype=np.uint8)
-        detected_map[np.min(deforum_input_image, axis=2) < 127] = 255
-        deforum_input_image = detected_map
-    
-    from scripts.processor import canny, midas, midas_normal, leres, hed, mlsd, openpose, pidinet, simple_scribble, fake_scribble, uniformer
-    
-    preprocessor = {
-        "none": lambda x, *args, **kwargs: x,
-        "canny": canny,
-        "depth": midas,
-        "depth_leres": leres,
-        "hed": hed,
-        "mlsd": mlsd,
-        "normal_map": midas_normal,
-        "openpose": openpose,
-        # "openpose_hand": openpose_hand,
-        "pidinet": pidinet,
-        "scribble": simple_scribble,
-        "fake_scribble": fake_scribble,
-        "segmentation": uniformer,
-    }
-            
-    preprocessor = preprocessor[deforum_latest_params[0]]
-    h, w, bsz = p.height, p.width, p.batch_size
-    if pres > 64:
-        detected_map = preprocessor(deforum_input_image, res=pres, thr_a=pthr_a, thr_b=pthr_b)
-    else:
-        detected_map = preprocessor(deforum_input_image)
-    detected_map = HWC3(detected_map)
-    
-    if module == "normal_map" or rgbbgr_mode:
-        control = torch.from_numpy(detected_map[:, :, ::-1].copy()).float().to(devices.get_device_for("controlnet")) / 255.0
-    else:
-        control = torch.from_numpy(detected_map.copy()).float().to(devices.get_device_for("controlnet")) / 255.0
-    
-    control = rearrange(control, 'h w c -> c h w')
-    detected_map = rearrange(torch.from_numpy(detected_map), 'h w c -> c h w')
-    if resize_mode == "Scale to Fit (Inner Fit)":
-        transform = Compose([
-            Resize(h if h<w else w, interpolation=InterpolationMode.BICUBIC),
-            CenterCrop(size=(h, w))
-        ]) 
-        control = transform(control)
-        detected_map = transform(detected_map)
-    elif resize_mode == "Envelope (Outer Fit)":
-        transform = Compose([
-            Resize(h if h>w else w, interpolation=InterpolationMode.BICUBIC),
-            CenterCrop(size=(h, w))
-        ]) 
-        control = transform(control)
-        detected_map = transform(detected_map)
-    else:
-        control = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(control)
-        detected_map = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(detected_map)
-        
-    # for log use
-    detected_map = rearrange(detected_map, 'c h w -> h w c').numpy().astype(np.uint8)
-        
-    # control = torch.stack([control for _ in range(bsz)], dim=0)
-    deforum_latest_network.notify(control, weight, guidance_strength)
-
-    if shared.opts.data.get("control_net_skip_img2img_processing") and hasattr(p, "init_images"):
-        swap_img2img_pipeline(p)
-
-def swap_img2img_pipeline(p: processing.StableDiffusionProcessingImg2Img):
-    p.__class__ = processing.StableDiffusionProcessingTxt2Img
-    dummy = processing.StableDiffusionProcessingTxt2Img()
-    for k,v in dummy.__dict__.items():
-        if hasattr(p, k):
-            continue
-        setattr(p, k, v)
-
diff --git a/spaces/arbml/Ashaar/poetry_diacritizer/modules/attention.py b/spaces/arbml/Ashaar/poetry_diacritizer/modules/attention.py
deleted file mode 100644
index ae916b43783efa55f2f29e7df79dc4d2dfffbc1b..0000000000000000000000000000000000000000
--- a/spaces/arbml/Ashaar/poetry_diacritizer/modules/attention.py
+++ /dev/null
@@ -1,199 +0,0 @@
-from typing import Optional
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from poetry_diacritizer.options import AttentionType
-
-
-class BahdanauAttention(nn.Module):
-    def __init__(self, dim):
-        super(BahdanauAttention, self).__init__()
-        self.query_layer = nn.Linear(dim, dim, bias=False)
-        self.tanh = nn.Tanh()
-        self.v = nn.Linear(dim, 1, bias=False)
-
-    def forward(self, query: torch.Tensor, keys: torch.Tensor):
-        """
-        Args:
-            query: (B, 1, dim) or (batch, dim)
-            processed_memory: (batch, max_time, dim)
-        """
-        if query.dim() == 2:
-            # insert time-axis for broadcasting
-            query = query.unsqueeze(1)
-        # (batch, 1, dim)
-        query = self.query_layer(query)
-
-        # (batch, max_time, 1)
-        alignment = self.v(self.tanh(query + keys))
-
-        # (batch, max_time)
-        return alignment.squeeze(-1)
-
-
-class LocationSensitive(nn.Module):
-    def __init__(self, dim):
-        super(LocationSensitive, self).__init__()
-        self.query_layer = nn.Linear(dim, dim, bias=False)
-        self.v = nn.Linear(dim, 1, bias=True)
-        self.location_layer = nn.Linear(32, dim, bias=False)
-        padding = int((31 - 1) / 2)
-        self.location_conv = torch.nn.Conv1d(
-            1, 32, kernel_size=31, stride=1, padding=padding, dilation=1, bias=False
-        )
-
-        self.score_mask_value = -float("inf")
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        keys: torch.Tensor,
-        prev_alignments: torch.Tensor,
-    ):
-        # keys = keys.permute(1,0,2)
-        query = self.query_layer(query)
-        if query.dim() == 2:
-            # insert time-axis for broadcasting
-            query = query.unsqueeze(1)
-            # -> [batch_size, 1, attention_dim]
-
-        alignments = prev_alignments.unsqueeze(1)
-
-        # location features [batch_size, max_time, filters]
-        filters = self.location_conv(alignments)
-        location_features = self.location_layer(filters.transpose(1, 2))
-
-        alignments = self.v(torch.tanh(query + location_features + keys))
-        return alignments.squeeze(-1)
-
-
-class AttentionWrapper(nn.Module):
-    def __init__(
-        self,
-        attention_type: AttentionType = AttentionType.LocationSensitive,
-        attention_units: int = 256,
-        score_mask_value=-float("inf"),
-    ):
-        super().__init__()
-        self.score_mask_value = score_mask_value
-        self.attention_type = attention_type
-
-        if attention_type == AttentionType.LocationSensitive:
-            self.attention_mechanism = LocationSensitive(attention_units)
-        elif attention_type == AttentionType.Content_Based:
-            self.attention_mechanism = BahdanauAttention(attention_units)
-        else:
-            raise Exception("The attention type is not known")
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        keys: torch.Tensor,
-        values: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        prev_alignment: Optional[torch.Tensor] = None,
-    ):
-
-        # Alignment
-        # (batch, max_time)
-        if self.attention_type == AttentionType.Content_Based:
-            alignment = self.attention_mechanism(query, keys)
-        else:
-            alignment = self.attention_mechanism(query, keys, prev_alignment)
-
-        # Attention context vector
-
-        if mask is not None:
-            alignment.data.masked_fill_(mask, self.score_mask_value)
-
-        alignment = F.softmax(alignment, dim=1)
-        attention = torch.bmm(alignment.unsqueeze(1), values)
-        attention = attention.squeeze(1)
-
-        return attention, alignment
-
-
-class MultiHeadAttentionLayer(nn.Module):
-    def __init__(self, hid_dim: int, n_heads: int, dropout: float = 0.0):
-        super().__init__()
-
-        assert hid_dim % n_heads == 0
-
-        self.hid_dim = hid_dim
-        self.n_heads = n_heads
-        self.head_dim = hid_dim // n_heads
-
-        self.fc_q = nn.Linear(hid_dim, hid_dim)
-        self.fc_k = nn.Linear(hid_dim, hid_dim)
-        self.fc_v = nn.Linear(hid_dim, hid_dim)
-
-        self.fc_o = nn.Linear(hid_dim * 2, hid_dim)
-
-        if dropout != 0.0:
-            self.dropout = nn.Dropout(dropout)
-
-        self.use_dropout = dropout != 0.0
-
-        device = next(self.parameters()).device
-
-        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)
-
-    def forward(self, query, key, value, mask=None):
-
-        batch_size = query.shape[0]
-
-        # query = [batch size, query len, hid dim]
-        # key = [batch size, key len, hid dim]
-        # value = [batch size, value len, hid dim]
-
-        Q = self.fc_q(query)
-        K = self.fc_k(key)
-        V = self.fc_v(value)
-
-        # Q = [batch size, query len, hid dim]
-        # K = [batch size, key len, hid dim]
-        # V = [batch size, value len, hid dim]
-
-        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
-        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
-        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
-
-        # Q = [batch size, n heads, query len, head dim]
-        # K = [batch size, n heads, key len, head dim]
-        # V = [batch size, n heads, value len, head dim]
-
-        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale
-
-        # energy = [batch size, n heads, query len, key len]
-
-        if mask is not None:
-            energy = energy.masked_fill(mask == 0, -float("inf"))
-
-        attention = torch.softmax(energy, dim=-1)
-
-        # attention = [batch size, n heads, query len, key len]
-
-        if self.use_dropout:
-            context_vector = torch.matmul(self.dropout(attention), V)
-        else:
-            context_vector = torch.matmul(attention, V)
-
-        # x = [batch size, n heads, query len, head dim]
-
-        context_vector = context_vector.permute(0, 2, 1, 3).contiguous()
-
-        # x = [batch size, query len, n heads, head dim]
-
-        context_vector = context_vector.view(batch_size, -1, self.hid_dim)
-
-        x = torch.cat((query, context_vector), dim=-1)
-
-        # x = [batch size, query len, hid dim * 2]
-
-        x = self.fc_o(x)
-
-        # x = [batch size, query len, hid dim]
-
-        return x, attention
diff --git a/spaces/arslan-ahmed/talk-to-arslan/README.md b/spaces/arslan-ahmed/talk-to-arslan/README.md
deleted file mode 100644
index 3b9f8e43a187e51a97ca4315e0553b72c676567a..0000000000000000000000000000000000000000
--- a/spaces/arslan-ahmed/talk-to-arslan/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Talk To Arslan
-emoji: 📚
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-<!-- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference -->
-
-# Talk To Your Docs
-
-## For users
-You can use this app via Hugging Face where it is already deployed:<br/>
-https://huggingface.co/spaces/arslan-ahmed/talk-to-your-docs
-
-
-## For developers
-Source code:<br/>
-https://huggingface.co/spaces/arslan-ahmed/talk-to-your-docs/tree/main
-
-## Create your personal bot (let people talk to your docs)
-You can develop and deploy your own personal chatbot (similar to https://huggingface.co/spaces/arslan-ahmed/talk-to-arslan), with the following three commands:
-
-
-docker pull arslan2k12/ttyd_base (https://hub.docker.com/r/arslan2k12/ttyd_base) <br/>
-docker pull arslan2k12/arslanbot (https://hub.docker.com/r/arslan2k12/arslanbot)<br/>
-docker run --rm -d -p 7860:7860 --env-file ./.env arslan2k12/ttyd_arslanbot
-
-
-Contents of `.env` file:
-```
-TTYD_MODE=personalBot_John
-#replace John with your name - use only alphabets, no special characters
-
-GDRIVE_FOLDER_URL=https://drive.google.com/drive/folders/1ce1n1kleS1FOotdcu5joXeSRu_xnHjDt
-# replace with your Google Drive folder URL that has all your knowledge base files (.pdf, .docs, .txt) - make sure this folder is publicly accessible (everyone with the link)
-
-OPENAI_API_KEY=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
-# your OpenAI API Key taken from https://platform.openai.com/account/api-keys
-```
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/comet_chart.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/comet_chart.py
deleted file mode 100644
index e0749a0abb09e653f78d5813343433d22b083c8c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/comet_chart.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""
-Comet Chart Example
-----------------------------
-Inspired by `Zan Armstrong's comet chart <https://www.zanarmstrong.com/infovisresearch>`_
-this plot uses ``mark_trail`` to visualize change of grouped data over time.
-A more elaborate example and explanation of creating comet charts in Altair
-is shown in `this blogpost <https://medium.com/de-dataverbinders/comet-charts-in-python-visualizing-statistical-mix-effects-and-simpsons-paradox-with-altair-6cd51fb58b7c>`_.
-"""
-# category: other charts
-
-import altair as alt
-import vega_datasets
-
-(
-    alt.Chart(vega_datasets.data.barley.url)
-    .transform_pivot("year", value="yield", groupby=["variety", "site"])
-    .transform_fold(["1931", "1932"], as_=["year", "yield"])
-    .transform_calculate(calculate="datum['1932'] - datum['1931']", as_="delta")
-    .mark_trail()
-    .encode(
-        x=alt.X('year:O', title=None), 
-        y=alt.Y('variety:N', title='Variety'),
-        size=alt.Size('yield:Q', scale=alt.Scale(range=[0, 12]), legend=alt.Legend(values=[20, 60], title='Barley Yield (bushels/acre)')),
-        color=alt.Color('delta:Q', scale=alt.Scale(domainMid=0), legend=alt.Legend(title='Yield Delta (%)')),
-        tooltip=alt.Tooltip(['year:O', 'yield:Q']),
-        column=alt.Column('site:N', title='Site')
-
-    )
-    .configure_view(stroke=None)
-    .configure_legend(orient='bottom', direction='horizontal')
-    .properties(title='Barley Yield comparison between 1932 and 1931')
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/world_projections.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/world_projections.py
deleted file mode 100644
index 970a106b66dd7191d9444fee52c31af3ca77d153..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/world_projections.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-World Projections
------------------
-This example shows a map of the countries of the world using four available
-geographic projections. For more details on the projections available in
-Altair, see https://vega.github.io/vega-lite/docs/projection.html
-"""
-# category: maps
-import altair as alt
-from vega_datasets import data
-
-source = alt.topo_feature(data.world_110m.url, 'countries')
-
-base = alt.Chart(source).mark_geoshape(
-    fill='#666666',
-    stroke='white'
-).properties(
-    width=300,
-    height=180
-)
-
-projections = ['equirectangular', 'mercator', 'orthographic', 'gnomonic']
-charts = [base.project(proj).properties(title=proj)
-          for proj in projections]
-
-alt.concat(*charts, columns=2)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_subprocesses.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_subprocesses.py
deleted file mode 100644
index 43fa6b6f400d777a8409d44c94ab9b1b7b4e728e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_subprocesses.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from io import BytesIO
-from os import PathLike
-from subprocess import DEVNULL, PIPE, CalledProcessError, CompletedProcess
-from typing import (
-    IO,
-    Any,
-    AsyncIterable,
-    List,
-    Mapping,
-    Optional,
-    Sequence,
-    Union,
-    cast,
-)
-
-from ..abc import Process
-from ._eventloop import get_asynclib
-from ._tasks import create_task_group
-
-
-async def run_process(
-    command: Union[str, bytes, Sequence[Union[str, bytes]]],
-    *,
-    input: Optional[bytes] = None,
-    stdout: Union[int, IO[Any], None] = PIPE,
-    stderr: Union[int, IO[Any], None] = PIPE,
-    check: bool = True,
-    cwd: Union[str, bytes, "PathLike[str]", None] = None,
-    env: Optional[Mapping[str, str]] = None,
-    start_new_session: bool = False,
-) -> "CompletedProcess[bytes]":
-    """
-    Run an external command in a subprocess and wait until it completes.
-
-    .. seealso:: :func:`subprocess.run`
-
-    :param command: either a string to pass to the shell, or an iterable of strings containing the
-        executable name or path and its arguments
-    :param input: bytes passed to the standard input of the subprocess
-    :param stdout: either :data:`subprocess.PIPE` or :data:`subprocess.DEVNULL`
-    :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL` or
-        :data:`subprocess.STDOUT`
-    :param check: if ``True``, raise :exc:`~subprocess.CalledProcessError` if the process
-        terminates with a return code other than 0
-    :param cwd: If not ``None``, change the working directory to this before running the command
-    :param env: if not ``None``, this mapping replaces the inherited environment variables from the
-        parent process
-    :param start_new_session: if ``true`` the setsid() system call will be made in the child
-        process prior to the execution of the subprocess. (POSIX only)
-    :return: an object representing the completed process
-    :raises ~subprocess.CalledProcessError: if ``check`` is ``True`` and the process exits with a
-        nonzero return code
-
-    """
-
-    async def drain_stream(stream: AsyncIterable[bytes], index: int) -> None:
-        buffer = BytesIO()
-        async for chunk in stream:
-            buffer.write(chunk)
-
-        stream_contents[index] = buffer.getvalue()
-
-    async with await open_process(
-        command,
-        stdin=PIPE if input else DEVNULL,
-        stdout=stdout,
-        stderr=stderr,
-        cwd=cwd,
-        env=env,
-        start_new_session=start_new_session,
-    ) as process:
-        stream_contents: List[Optional[bytes]] = [None, None]
-        try:
-            async with create_task_group() as tg:
-                if process.stdout:
-                    tg.start_soon(drain_stream, process.stdout, 0)
-                if process.stderr:
-                    tg.start_soon(drain_stream, process.stderr, 1)
-                if process.stdin and input:
-                    await process.stdin.send(input)
-                    await process.stdin.aclose()
-
-                await process.wait()
-        except BaseException:
-            process.kill()
-            raise
-
-    output, errors = stream_contents
-    if check and process.returncode != 0:
-        raise CalledProcessError(cast(int, process.returncode), command, output, errors)
-
-    return CompletedProcess(command, cast(int, process.returncode), output, errors)
-
-
-async def open_process(
-    command: Union[str, bytes, Sequence[Union[str, bytes]]],
-    *,
-    stdin: Union[int, IO[Any], None] = PIPE,
-    stdout: Union[int, IO[Any], None] = PIPE,
-    stderr: Union[int, IO[Any], None] = PIPE,
-    cwd: Union[str, bytes, "PathLike[str]", None] = None,
-    env: Optional[Mapping[str, str]] = None,
-    start_new_session: bool = False,
-) -> Process:
-    """
-    Start an external command in a subprocess.
-
-    .. seealso:: :class:`subprocess.Popen`
-
-    :param command: either a string to pass to the shell, or an iterable of strings containing the
-        executable name or path and its arguments
-    :param stdin: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`, a
-        file-like object, or ``None``
-    :param stdout: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`,
-        a file-like object, or ``None``
-    :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`,
-        :data:`subprocess.STDOUT`, a file-like object, or ``None``
-    :param cwd: If not ``None``, the working directory is changed before executing
-    :param env: If env is not ``None``, it must be a mapping that defines the environment
-        variables for the new process
-    :param start_new_session: if ``true`` the setsid() system call will be made in the child
-        process prior to the execution of the subprocess. (POSIX only)
-    :return: an asynchronous process object
-
-    """
-    shell = isinstance(command, str)
-    return await get_asynclib().open_process(
-        command,
-        shell=shell,
-        stdin=stdin,
-        stdout=stdout,
-        stderr=stderr,
-        cwd=cwd,
-        env=env,
-        start_new_session=start_new_session,
-    )
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/endtask.md b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/endtask.md
deleted file mode 100644
index 7690955327283ded4b37857a4a7b78463e0eb0f8..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/endtask.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Zero-shot Transfer and Finetuning
-
-(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.)
-All finetuning datasets (specifically `processors`) are defined in `mmpt.processors.dsprocessor`.
-Given the complexity of different types of finetuning tasks, each task may have their own meta/video/text/aligner processors and `mmpt/evaluators/{Predictor,Metric}`.
-
-### Tasks
-
-Currently, we support 5 end datasets: `MSRVTT`, `Youcook`, `COIN`, `Crosstask` and `DiDeMo` with the following tasks:  
-text-video retrieval: `MSRVTT`, `Youcook`, `DiDeMo`;   
-video captioning: `Youcook`;  
-Video Question and Answering: `MSRVTT-QA`.  
-
-To add your own dataset, you can specify the corresponding processors and config them in the `dataset` field of a config file, such as `projects/task/vtt.yaml`.
-
-### Zero-shot Transfer (no Training)
-Zero-shot transfer will run the pre-trained model (e.g., VideoCLIP) directly on testing data. Configs with pattern: `projects/task/*_zs_*.yaml` are dedicated for zero-shot transfer.
-
-### Fine-tuning
-
-The training of a downstream task is similar to pretraining, execept you may need to specify the `restore_file` in `fairseq.checkpoint` and reset optimizers, see `projects/task/ft.yaml` that is included by `projects/task/vtt.yaml`.
-
-We typically do finetuning on 2 gpus (`local_small`).
-
-### Testing
-For each finetuning dataset, you may need to specify a testing config, similar to `projects/task/test_vtt.yaml`.  
-
-We define `mmpt.evaluators.Predictor` for different types of prediction. For example, `MSRVTT` and `Youcook` are video-retrieval tasks and expecting to use `RetrievalPredictor`. You may need to define your new type of predictors and specify that in `predictor` field of a testing config.
-
-Each task may also have their own metric for evaluation. This can be created in `mmpt.evaluators.Metric` and specified in the `metric` field of a testing config.
-
-Launching a testing is as simple as training by specifying the path of a testing config:
-```python locallaunch.py projects/mfmmlm/test_vtt.yaml```
-Testing will be launched locally by default since prediction is computationally less expensive.
-
-### Third-party Libraries
-We list the following finetuning tasks that require third-party libraries.
-
-Youcook captioning: `https://github.com/Maluuba/nlg-eval`  
-
-CrossTask: `https://github.com/DmZhukov/CrossTask`'s `dp` under `third-party/CrossTask` (`python setup.py build_ext --inplace`)
diff --git a/spaces/asd998877/TsGpt/README.md b/spaces/asd998877/TsGpt/README.md
deleted file mode 100644
index 7128e29689e35d059c9cc0a5050910fbd34873cd..0000000000000000000000000000000000000000
--- a/spaces/asd998877/TsGpt/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/attention-refocusing/Attention-refocusing/example_component.py b/spaces/attention-refocusing/Attention-refocusing/example_component.py
deleted file mode 100644
index 19fceb0d8abb853da6d66901201c0784930be8fe..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/example_component.py
+++ /dev/null
@@ -1,805 +0,0 @@
-"""
-Defines helper methods useful for loading and caching Interface examples.
-"""
-from __future__ import annotations
-
-import ast
-import csv
-import inspect
-import os
-import subprocess
-import tempfile
-import threading
-import warnings
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Tuple
-
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import PIL
-import PIL.Image
-
-from gradio import components, processing_utils, routes, utils
-from gradio.context import Context
-from gradio.documentation import document, set_documentation_group
-from gradio.flagging import CSVLogger
-
-if TYPE_CHECKING:  # Only import for type checking (to avoid circular imports).
-    from gradio.components import IOComponent
-
-CACHED_FOLDER = "gradio_cached_examples"
-LOG_FILE = "log.csv"
-
-set_documentation_group("helpers")
-
-
-def create_examples(
-    examples: List[Any] | List[List[Any]] | str,
-    inputs: IOComponent | List[IOComponent],
-    outputs: IOComponent | List[IOComponent] | None = None,
-    fn: Callable | None = None,
-    cache_examples: bool = False,
-    examples_per_page: int = 10,
-    _api_mode: bool = False,
-    label: str | None = None,
-    elem_id: str | None = None,
-    run_on_click: bool = False,
-    preprocess: bool = True,
-    postprocess: bool = True,
-    batch: bool = False,
-):
-    """Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component."""
-    examples_obj = Examples(
-        examples=examples,
-        inputs=inputs,
-        outputs=outputs,
-        fn=fn,
-        cache_examples=cache_examples,
-        examples_per_page=examples_per_page,
-        _api_mode=_api_mode,
-        label=label,
-        elem_id=elem_id,
-        run_on_click=run_on_click,
-        preprocess=preprocess,
-        postprocess=postprocess,
-        batch=batch,
-        _initiated_directly=False,
-    )
-    utils.synchronize_async(examples_obj.create)
-    return examples_obj
-
-
-class Examples:
-    """
-    This class is a wrapper over the Dataset component and can be used to create Examples
-    for Blocks / Interfaces. Populates the Dataset component with examples and
-    assigns event listener so that clicking on an example populates the input/output
-    components. Optionally handles example caching for fast inference.
-
-    Demos: blocks_inputs, fake_gan
-    Guides: more_on_examples_and_flagging, using_hugging_face_integrations, image_classification_in_pytorch, image_classification_in_tensorflow, image_classification_with_vision_transformers, create_your_own_friends_with_a_gan
-    """
-
-    def __init__(
-        self,
-        examples: List[Any] | List[List[Any]] | str,
-        inputs: IOComponent | List[IOComponent],
-        outputs: IOComponent | List[IOComponent] | None = None,
-        fn: Callable | None = None,
-        cache_examples: bool = False,
-        examples_per_page: int = 10,
-        _api_mode: bool = False,
-        label: str | None = "Examples",
-        elem_id: str | None = None,
-        run_on_click: bool = False,
-        preprocess: bool = True,
-        postprocess: bool = True,
-        batch: bool = False,
-        _initiated_directly: bool = True,
-    ):
-        """
-        Parameters:
-            examples: example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.
-            inputs: the component or list of components corresponding to the examples
-            outputs: optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache` is True.
-            fn: optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache` is True.
-            cache_examples: if True, caches examples for fast runtime. If True, then `fn` and `outputs` need to be provided
-            examples_per_page: how many examples to show per page.
-            label: the label to use for the examples component (by default, "Examples")
-            elem_id: an optional string that is assigned as the id of this component in the HTML DOM.
-            run_on_click: if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True.
-            preprocess: if True, preprocesses the example input before running the prediction function and caching the output. Only applies if cache_examples is True.
-            postprocess: if True, postprocesses the example output after running the prediction function and before caching. Only applies if cache_examples is True.
-            batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. Used only if cache_examples is True.
-        """
-        if _initiated_directly:
-            warnings.warn(
-                "Please use gr.Examples(...) instead of gr.examples.Examples(...) to create the Examples.",
-            )
-
-        if cache_examples and (fn is None or outputs is None):
-            raise ValueError("If caching examples, `fn` and `outputs` must be provided")
-
-        if not isinstance(inputs, list):
-            inputs = [inputs]
-        if outputs and not isinstance(outputs, list):
-            outputs = [outputs]
-
-        working_directory = Path().absolute()
-
-        if examples is None:
-            raise ValueError("The parameter `examples` cannot be None")
-        elif isinstance(examples, list) and (
-            len(examples) == 0 or isinstance(examples[0], list)
-        ):
-            pass
-        elif (
-            isinstance(examples, list) and len(inputs) == 1
-        ):  # If there is only one input component, examples can be provided as a regular list instead of a list of lists
-            examples = [[e] for e in examples]
-        elif isinstance(examples, str):
-            if not Path(examples).exists():
-                raise FileNotFoundError(
-                    "Could not find examples directory: " + examples
-                )
-            working_directory = examples
-            if not (Path(examples) / LOG_FILE).exists():
-                if len(inputs) == 1:
-                    examples = [[e] for e in os.listdir(examples)]
-                else:
-                    raise FileNotFoundError(
-                        "Could not find log file (required for multiple inputs): "
-                        + LOG_FILE
-                    )
-            else:
-                with open(Path(examples) / LOG_FILE) as logs:
-                    examples = list(csv.reader(logs))
-                    examples = [
-                        examples[i][: len(inputs)] for i in range(1, len(examples))
-                    ]  # remove header and unnecessary columns
-
-        else:
-            raise ValueError(
-                "The parameter `examples` must either be a string directory or a list"
-                "(if there is only 1 input component) or (more generally), a nested "
-                "list, where each sublist represents a set of inputs."
-            )
-
-        input_has_examples = [False] * len(inputs)
-        for example in examples:
-            for idx, example_for_input in enumerate(example):
-                if not (example_for_input is None):
-                    try:
-                        input_has_examples[idx] = True
-                    except IndexError:
-                        pass  # If there are more example components than inputs, ignore. This can sometimes be intentional (e.g. loading from a log file where outputs and timestamps are also logged)
-
-        inputs_with_examples = [
-            inp for (inp, keep) in zip(inputs, input_has_examples) if keep
-        ]
-        non_none_examples = [
-            [ex for (ex, keep) in zip(example, input_has_examples) if keep]
-            for example in examples
-        ]
-
-        self.examples = examples
-        self.non_none_examples = non_none_examples
-        self.inputs = inputs
-        self.inputs_with_examples = inputs_with_examples
-        self.outputs = outputs
-        self.fn = fn
-        self.cache_examples = cache_examples
-        self._api_mode = _api_mode
-        self.preprocess = preprocess
-        self.postprocess = postprocess
-        self.batch = batch
-
-        with utils.set_directory(working_directory):
-            self.processed_examples = [
-                [
-                    component.postprocess(sample)
-                    for component, sample in zip(inputs, example)
-                ]
-                for example in examples
-            ]
-        self.non_none_processed_examples = [
-            [ex for (ex, keep) in zip(example, input_has_examples) if keep]
-            for example in self.processed_examples
-        ]
-        if cache_examples:
-            for example in self.examples:
-                if len([ex for ex in example if ex is not None]) != len(self.inputs):
-                    warnings.warn(
-                        "Examples are being cached but not all input components have "
-                        "example values. This may result in an exception being thrown by "
-                        "your function. If you do get an error while caching examples, make "
-                        "sure all of your inputs have example values for all of your examples "
-                        "or you provide default values for those particular parameters in your function."
-                    )
-                    break
-
-        with utils.set_directory(working_directory):
-            self.dataset = components.Dataset(
-                components=inputs_with_examples,
-                samples=non_none_examples,
-                type="index",
-                label=label,
-                samples_per_page=examples_per_page,
-                elem_id=elem_id,
-            )
-
-        self.cached_folder = Path(CACHED_FOLDER) / str(self.dataset._id)
-        self.cached_file = Path(self.cached_folder) / "log.csv"
-        self.cache_examples = cache_examples
-        self.run_on_click = run_on_click
-
-    async def create(self) -> None:
-        """Caches the examples if self.cache_examples is True and creates the Dataset
-        component to hold the examples"""
-
-        async def load_example(example_id):
-            # import pdb; pdb.set_trace()
-            if self.cache_examples:
-                processed_example = self.non_none_processed_examples[
-                    example_id
-                ] + await self.load_from_cache(example_id)
-            else:
-                processed_example = self.non_none_processed_examples[example_id]
-            return utils.resolve_singleton(processed_example)
-
-        if Context.root_block:
-            if self.cache_examples and self.outputs:
-                targets = self.inputs_with_examples + self.outputs
-            else:
-                targets = self.inputs_with_examples
-            self.dataset.click(
-                load_example,
-                inputs=[self.dataset],
-                outputs=targets,  # type: ignore
-                postprocess=False,
-                queue=False,
-            )
-            self.dataset.click(
-                self.fn,
-                inputs=[self.dataset],
-                outputs=targets,  # type: ignore
-                postprocess=False,
-                queue=False,
-            )
-            # if self.run_on_click and not self.cache_examples:
-            #     if self.fn is None:
-            #         raise ValueError("Cannot run_on_click if no function is provided")
-            #     self.dataset.click(
-            #         self.fn,
-            #         inputs=self.inputs,  # type: ignore
-            #         outputs=self.outputs,  # type: ignore
-            #     )
-
-        if self.cache_examples:
-            await self.cache()
-
-    async def cache(self) -> None:
-        """
-        Caches all of the examples so that their predictions can be shown immediately.
-        """
-        if Path(self.cached_file).exists():
-            print(
-                f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache."
-            )
-        else:
-            if Context.root_block is None:
-                raise ValueError("Cannot cache examples if not in a Blocks context")
-
-            print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'")
-            cache_logger = CSVLogger()
-
-            # create a fake dependency to process the examples and get the predictions
-            dependency = Context.root_block.set_event_trigger(
-                event_name="fake_event",
-                fn=self.fn,
-                inputs=self.inputs_with_examples,  # type: ignore
-                outputs=self.outputs,  # type: ignore
-                preprocess=self.preprocess and not self._api_mode,
-                postprocess=self.postprocess and not self._api_mode,
-                batch=self.batch,
-            )
-
-            fn_index = Context.root_block.dependencies.index(dependency)
-            assert self.outputs is not None
-            cache_logger.setup(self.outputs, self.cached_folder)
-            for example_id, _ in enumerate(self.examples):
-                processed_input = self.processed_examples[example_id]
-                if self.batch:
-                    processed_input = [[value] for value in processed_input]
-                prediction = await Context.root_block.process_api(
-                    fn_index=fn_index, inputs=processed_input, request=None, state={}
-                )
-                output = prediction["data"]
-                if self.batch:
-                    output = [value[0] for value in output]
-                cache_logger.flag(output)
-            # Remove the "fake_event" to prevent bugs in loading interfaces from spaces
-            Context.root_block.dependencies.remove(dependency)
-            Context.root_block.fns.pop(fn_index)
-
-    async def load_from_cache(self, example_id: int) -> List[Any]:
-        """Loads a particular cached example for the interface.
-        Parameters:
-            example_id: The id of the example to process (zero-indexed).
-        """
-        # import pdb; pdb.set_trace()
-        with open(self.cached_file, encoding="utf-8") as cache:
-            examples = list(csv.reader(cache))
-        example = examples[example_id + 1]  # +1 to adjust for header
-        output = []
-        assert self.outputs is not None
-        for component, value in zip(self.outputs, example):
-            try:
-                value_as_dict = ast.literal_eval(value)
-                assert utils.is_update(value_as_dict)
-                output.append(value_as_dict)
-            except (ValueError, TypeError, SyntaxError, AssertionError):
-                output.append(component.serialize(value, self.cached_folder))
-        return output
-
-
-class TrackedIterable:
-    def __init__(
-        self,
-        iterable: Iterable | None,
-        index: int | None,
-        length: int | None,
-        desc: str | None,
-        unit: str | None,
-        _tqdm=None,
-        progress: float | None = None,
-    ) -> None:
-        self.iterable = iterable
-        self.index = index
-        self.length = length
-        self.desc = desc
-        self.unit = unit
-        self._tqdm = _tqdm
-        self.progress = progress
-
-
-@document("__call__", "tqdm")
-class Progress(Iterable):
-    """
-    The Progress class provides a custom progress tracker that is used in a function signature.
-    To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance.
-    The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable.
-    The Progress tracker is currently only available with `queue()`.
-    Example:
-        import gradio as gr
-        import time
-        def my_function(x, progress=gr.Progress()):
-            progress(0, desc="Starting...")
-            time.sleep(1)
-            for i in progress.tqdm(range(100)):
-                time.sleep(0.1)
-            return x
-        gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()
-    Demos: progress
-    """
-
-    def __init__(
-        self,
-        track_tqdm: bool = False,
-        _callback: Callable | None = None,  # for internal use only
-        _event_id: str | None = None,
-    ):
-        """
-        Parameters:
-            track_tqdm: If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function.
-        """
-        self.track_tqdm = track_tqdm
-        self._callback = _callback
-        self._event_id = _event_id
-        self.iterables: List[TrackedIterable] = []
-
-    def __len__(self):
-        return self.iterables[-1].length
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        """
-        Updates progress tracker with next item in iterable.
-        """
-        if self._callback:
-            current_iterable = self.iterables[-1]
-            while (
-                not hasattr(current_iterable.iterable, "__next__")
-                and len(self.iterables) > 0
-            ):
-                current_iterable = self.iterables.pop()
-            self._callback(
-                event_id=self._event_id,
-                iterables=self.iterables,
-            )
-            assert current_iterable.index is not None, "Index not set."
-            current_iterable.index += 1
-            try:
-                return next(current_iterable.iterable)  # type: ignore
-            except StopIteration:
-                self.iterables.pop()
-                raise StopIteration
-        else:
-            return self
-
-    def __call__(
-        self,
-        progress: float | Tuple[int, int | None] | None,
-        desc: str | None = None,
-        total: int | None = None,
-        unit: str = "steps",
-        _tqdm=None,
-    ):
-        """
-        Updates progress tracker with progress and message text.
-        Parameters:
-            progress: If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar.
-            desc: description to display.
-            total: estimated total number of steps.
-            unit: unit of iterations.
-        """
-        if self._callback:
-            if isinstance(progress, tuple):
-                index, total = progress
-                progress = None
-            else:
-                index = None
-            self._callback(
-                event_id=self._event_id,
-                iterables=self.iterables
-                + [TrackedIterable(None, index, total, desc, unit, _tqdm, progress)],
-            )
-        else:
-            return progress
-
-    def tqdm(
-        self,
-        iterable: Iterable | None,
-        desc: str | None = None,
-        total: int | None = None,
-        unit: str = "steps",
-        _tqdm=None,
-        *args,
-        **kwargs,
-    ):
-        """
-        Attaches progress tracker to iterable, like tqdm.
-        Parameters:
-            iterable: iterable to attach progress tracker to.
-            desc: description to display.
-            total: estimated total number of steps.
-            unit: unit of iterations.
-        """
-        if self._callback:
-            if iterable is None:
-                new_iterable = TrackedIterable(None, 0, total, desc, unit, _tqdm)
-                self.iterables.append(new_iterable)
-                self._callback(event_id=self._event_id, iterables=self.iterables)
-                return self
-            length = len(iterable) if hasattr(iterable, "__len__") else None  # type: ignore
-            self.iterables.append(
-                TrackedIterable(iter(iterable), 0, length, desc, unit, _tqdm)
-            )
-        return self
-
-    def update(self, n=1):
-        """
-        Increases latest iterable with specified number of steps.
-        Parameters:
-            n: number of steps completed.
-        """
-        if self._callback and len(self.iterables) > 0:
-            current_iterable = self.iterables[-1]
-            assert current_iterable.index is not None, "Index not set."
-            current_iterable.index += n
-            self._callback(
-                event_id=self._event_id,
-                iterables=self.iterables,
-            )
-        else:
-            return
-
-    def close(self, _tqdm):
-        """
-        Removes iterable with given _tqdm.
-        """
-        if self._callback:
-            for i in range(len(self.iterables)):
-                if id(self.iterables[i]._tqdm) == id(_tqdm):
-                    self.iterables.pop(i)
-                    break
-            self._callback(
-                event_id=self._event_id,
-                iterables=self.iterables,
-            )
-        else:
-            return
-
-
-def create_tracker(root_blocks, event_id, fn, track_tqdm):
-
-    progress = Progress(_callback=root_blocks._queue.set_progress, _event_id=event_id)
-    if not track_tqdm:
-        return progress, fn
-
-    try:
-        _tqdm = __import__("tqdm")
-    except ModuleNotFoundError:
-        return progress, fn
-    if not hasattr(root_blocks, "_progress_tracker_per_thread"):
-        root_blocks._progress_tracker_per_thread = {}
-
-    def init_tqdm(self, iterable=None, desc=None, *args, **kwargs):
-        self._progress = root_blocks._progress_tracker_per_thread.get(
-            threading.get_ident()
-        )
-        if self._progress is not None:
-            self._progress.event_id = event_id
-            self._progress.tqdm(iterable, desc, _tqdm=self, *args, **kwargs)
-            kwargs["file"] = open(os.devnull, "w")
-        self.__init__orig__(iterable, desc, *args, **kwargs)
-
-    def iter_tqdm(self):
-        if self._progress is not None:
-            return self._progress
-        else:
-            return self.__iter__orig__()
-
-    def update_tqdm(self, n=1):
-        if self._progress is not None:
-            self._progress.update(n)
-        return self.__update__orig__(n)
-
-    def close_tqdm(self):
-        if self._progress is not None:
-            self._progress.close(self)
-        return self.__close__orig__()
-
-    def exit_tqdm(self, exc_type, exc_value, traceback):
-        if self._progress is not None:
-            self._progress.close(self)
-        return self.__exit__orig__(exc_type, exc_value, traceback)
-
-    if not hasattr(_tqdm.tqdm, "__init__orig__"):
-        _tqdm.tqdm.__init__orig__ = _tqdm.tqdm.__init__
-    _tqdm.tqdm.__init__ = init_tqdm
-    if not hasattr(_tqdm.tqdm, "__update__orig__"):
-        _tqdm.tqdm.__update__orig__ = _tqdm.tqdm.update
-    _tqdm.tqdm.update = update_tqdm
-    if not hasattr(_tqdm.tqdm, "__close__orig__"):
-        _tqdm.tqdm.__close__orig__ = _tqdm.tqdm.close
-    _tqdm.tqdm.close = close_tqdm
-    if not hasattr(_tqdm.tqdm, "__exit__orig__"):
-        _tqdm.tqdm.__exit__orig__ = _tqdm.tqdm.__exit__
-    _tqdm.tqdm.__exit__ = exit_tqdm
-    if not hasattr(_tqdm.tqdm, "__iter__orig__"):
-        _tqdm.tqdm.__iter__orig__ = _tqdm.tqdm.__iter__
-    _tqdm.tqdm.__iter__ = iter_tqdm
-    if hasattr(_tqdm, "auto") and hasattr(_tqdm.auto, "tqdm"):
-        _tqdm.auto.tqdm = _tqdm.tqdm
-
-    def tracked_fn(*args):
-        thread_id = threading.get_ident()
-        root_blocks._progress_tracker_per_thread[thread_id] = progress
-        response = fn(*args)
-        del root_blocks._progress_tracker_per_thread[thread_id]
-        return response
-
-    return progress, tracked_fn
-
-
-def special_args(
-    fn: Callable,
-    inputs: List[Any] | None = None,
-    request: routes.Request | None = None,
-):
-    """
-    Checks if function has special arguments Request (via annotation) or Progress (via default value).
-    If inputs is provided, these values will be loaded into the inputs array.
-    Parameters:
-        block_fn: function to check.
-        inputs: array to load special arguments into.
-        request: request to load into inputs.
-    Returns:
-        updated inputs, request index, progress index
-    """
-    signature = inspect.signature(fn)
-    positional_args = []
-    for i, param in enumerate(signature.parameters.values()):
-        if param.kind not in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD):
-            break
-        positional_args.append(param)
-    progress_index = None
-    for i, param in enumerate(positional_args):
-        if isinstance(param.default, Progress):
-            progress_index = i
-            if inputs is not None:
-                inputs.insert(i, param.default)
-        elif param.annotation == routes.Request:
-            if inputs is not None:
-                inputs.insert(i, request)
-    if inputs is not None:
-        while len(inputs) < len(positional_args):
-            i = len(inputs)
-            param = positional_args[i]
-            if param.default == param.empty:
-                warnings.warn("Unexpected argument. Filling with None.")
-                inputs.append(None)
-            else:
-                inputs.append(param.default)
-    return inputs or [], progress_index
-
-
-@document()
-def update(**kwargs) -> dict:
-    """
-    Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component.
-    This is a shorthand for using the update method on a component.
-    For example, rather than using gr.Number.update(...) you can just use gr.update(...).
-    Note that your editor's autocompletion will suggest proper parameters
-    if you use the update method on the component.
-    Demos: blocks_essay, blocks_update, blocks_essay_update
-
-    Parameters:
-        kwargs: Key-word arguments used to update the component's properties.
-    Example:
-        # Blocks Example
-        import gradio as gr
-        with gr.Blocks() as demo:
-            radio = gr.Radio([1, 2, 4], label="Set the value of the number")
-            number = gr.Number(value=2, interactive=True)
-            radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number)
-        demo.launch()
-
-        # Interface example
-        import gradio as gr
-        def change_textbox(choice):
-          if choice == "short":
-              return gr.Textbox.update(lines=2, visible=True)
-          elif choice == "long":
-              return gr.Textbox.update(lines=8, visible=True)
-          else:
-              return gr.Textbox.update(visible=False)
-        gr.Interface(
-          change_textbox,
-          gr.Radio(
-              ["short", "long", "none"], label="What kind of essay would you like to write?"
-          ),
-          gr.Textbox(lines=2),
-          live=True,
-        ).launch()
-    """
-    kwargs["__type__"] = "generic_update"
-    return kwargs
-
-
-def skip() -> dict:
-    return update()
-
-
-@document()
-def make_waveform(
-    audio: str | Tuple[int, np.ndarray],
-    *,
-    bg_color: str = "#f3f4f6",
-    bg_image: str | None = None,
-    fg_alpha: float = 0.75,
-    bars_color: str | Tuple[str, str] = ("#fbbf24", "#ea580c"),
-    bar_count: int = 50,
-    bar_width: float = 0.6,
-):
-    """
-    Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component.
-    Parameters:
-        audio: Audio file path or tuple of (sample_rate, audio_data)
-        bg_color: Background color of waveform (ignored if bg_image is provided)
-        bg_image: Background image of waveform
-        fg_alpha: Opacity of foreground waveform
-        bars_color: Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient
-        bar_count: Number of bars in waveform
-        bar_width: Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc.
-    Returns:
-        A filepath to the output video.
-    """
-    if isinstance(audio, str):
-        audio_file = audio
-        audio = processing_utils.audio_from_file(audio)
-    else:
-        tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-        processing_utils.audio_to_file(audio[0], audio[1], tmp_wav.name)
-        audio_file = tmp_wav.name
-    duration = round(len(audio[1]) / audio[0], 4)
-
-    # Helper methods to create waveform
-    def hex_to_RGB(hex_str):
-        return [int(hex_str[i : i + 2], 16) for i in range(1, 6, 2)]
-
-    def get_color_gradient(c1, c2, n):
-        assert n > 1
-        c1_rgb = np.array(hex_to_RGB(c1)) / 255
-        c2_rgb = np.array(hex_to_RGB(c2)) / 255
-        mix_pcts = [x / (n - 1) for x in range(n)]
-        rgb_colors = [((1 - mix) * c1_rgb + (mix * c2_rgb)) for mix in mix_pcts]
-        return [
-            "#" + "".join([format(int(round(val * 255)), "02x") for val in item])
-            for item in rgb_colors
-        ]
-
-    # Reshape audio to have a fixed number of bars
-    samples = audio[1]
-    if len(samples.shape) > 1:
-        samples = np.mean(samples, 1)
-    bins_to_pad = bar_count - (len(samples) % bar_count)
-    samples = np.pad(samples, [(0, bins_to_pad)])
-    samples = np.reshape(samples, (bar_count, -1))
-    samples = np.abs(samples)
-    samples = np.max(samples, 1)
-
-    matplotlib.use("Agg")
-    plt.clf()
-    # Plot waveform
-    color = (
-        bars_color
-        if isinstance(bars_color, str)
-        else get_color_gradient(bars_color[0], bars_color[1], bar_count)
-    )
-    plt.bar(
-        np.arange(0, bar_count),
-        samples * 2,
-        bottom=(-1 * samples),
-        width=bar_width,
-        color=color,
-    )
-    plt.axis("off")
-    plt.margins(x=0)
-    tmp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    savefig_kwargs: Dict[str, Any] = {"bbox_inches": "tight"}
-    if bg_image is not None:
-        savefig_kwargs["transparent"] = True
-    else:
-        savefig_kwargs["facecolor"] = bg_color
-    plt.savefig(tmp_img.name, **savefig_kwargs)
-    waveform_img = PIL.Image.open(tmp_img.name)
-    waveform_img = waveform_img.resize((1000, 200))
-
-    # Composite waveform with background image
-    if bg_image is not None:
-        waveform_array = np.array(waveform_img)
-        waveform_array[:, :, 3] = waveform_array[:, :, 3] * fg_alpha
-        waveform_img = PIL.Image.fromarray(waveform_array)
-
-        bg_img = PIL.Image.open(bg_image)
-        waveform_width, waveform_height = waveform_img.size
-        bg_width, bg_height = bg_img.size
-        if waveform_width != bg_width:
-            bg_img = bg_img.resize(
-                (waveform_width, 2 * int(bg_height * waveform_width / bg_width / 2))
-            )
-            bg_width, bg_height = bg_img.size
-        composite_height = max(bg_height, waveform_height)
-        composite = PIL.Image.new("RGBA", (waveform_width, composite_height), "#FFFFFF")
-        composite.paste(bg_img, (0, composite_height - bg_height))
-        composite.paste(
-            waveform_img, (0, composite_height - waveform_height), waveform_img
-        )
-        composite.save(tmp_img.name)
-        img_width, img_height = composite.size
-    else:
-        img_width, img_height = waveform_img.size
-        waveform_img.save(tmp_img.name)
-
-    # Convert waveform to video with ffmpeg
-    output_mp4 = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-
-    ffmpeg_cmd = f"""ffmpeg -loop 1 -i {tmp_img.name} -i {audio_file} -vf "color=c=#FFFFFF77:s={img_width}x{img_height}[bar];[0][bar]overlay=-w+(w/{duration})*t:H-h:shortest=1" -t {duration} -y {output_mp4.name}"""
-
-    subprocess.call(ffmpeg_cmd, shell=True)
-    return output_mp4.name
diff --git a/spaces/avivdm1/AutoGPT/tests/test_json_parser.py b/spaces/avivdm1/AutoGPT/tests/test_json_parser.py
deleted file mode 100644
index 41c90a6f66c0b0468f1443de80033cc4f268eca0..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/tests/test_json_parser.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.json_utils.json_fix_llm import fix_and_parse_json
-
-
-class TestParseJson(unittest.TestCase):
-    def test_valid_json(self):
-        # Test that a valid JSON string is parsed correctly
-        json_str = '{"name": "John", "age": 30, "city": "New York"}'
-        obj = fix_and_parse_json(json_str)
-        self.assertEqual(obj, {"name": "John", "age": 30, "city": "New York"})
-
-    def test_invalid_json_minor(self):
-        # Test that an invalid JSON string can be fixed with gpt
-        json_str = '{"name": "John", "age": 30, "city": "New York",}'
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_major_with_gpt(self):
-        # Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_major_without_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
-        # Assert that this raises an exception:
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_leading_sentence_with_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = """I suggest we start by browsing the repository to find any issues that we can fix.
-
-{
-    "command": {
-        "name": "browse_website",
-        "args":{
-            "url": "https://github.com/Torantulino/Auto-GPT"
-        }
-    },
-    "thoughts":
-    {
-        "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-        "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-        "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-        "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-        "speak": "I will start browsing the repository to find any issues we can fix."
-    }
-}"""
-        good_obj = {
-            "command": {
-                "name": "browse_website",
-                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            },
-            "thoughts": {
-                "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-                "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-                "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-                "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-                "speak": "I will start browsing the repository to find any issues we can fix.",
-            },
-        }
-        # Assert that this raises an exception:
-        self.assertEqual(
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
-        )
-
-    def test_invalid_json_leading_sentence_with_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = """I will first need to browse the repository (https://github.com/Torantulino/Auto-GPT) and identify any potential bugs that need fixing. I will use the "browse_website" command for this.
-
-{
-    "command": {
-        "name": "browse_website",
-        "args":{
-            "url": "https://github.com/Torantulino/Auto-GPT"
-        }
-    },
-    "thoughts":
-    {
-        "text": "Browsing the repository to identify potential bugs",
-        "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-        "plan": "- Analyze the repository for potential bugs and areas of improvement",
-        "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-        "speak": "I am browsing the repository to identify potential bugs."
-    }
-}"""
-        good_obj = {
-            "command": {
-                "name": "browse_website",
-                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            },
-            "thoughts": {
-                "text": "Browsing the repository to identify potential bugs",
-                "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-                "plan": "- Analyze the repository for potential bugs and areas of improvement",
-                "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-                "speak": "I am browsing the repository to identify potential bugs.",
-            },
-        }
-        # Assert that this raises an exception:
-        self.assertEqual(
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/avivdm1/AutoGPT/tests/unit/test_chat.py b/spaces/avivdm1/AutoGPT/tests/unit/test_chat.py
deleted file mode 100644
index 774f4103762c28d5a02e89c14b224fae0bc0756a..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/tests/unit/test_chat.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Generated by CodiumAI
-import time
-import unittest
-from unittest.mock import patch
-
-from autogpt.chat import create_chat_message, generate_context
-
-
-class TestChat(unittest.TestCase):
-    # Tests that the function returns a dictionary with the correct keys and values when valid strings are provided for role and content.
-    def test_happy_path_role_content(self):
-        result = create_chat_message("system", "Hello, world!")
-        self.assertEqual(result, {"role": "system", "content": "Hello, world!"})
-
-    # Tests that the function returns a dictionary with the correct keys and values when empty strings are provided for role and content.
-    def test_empty_role_content(self):
-        result = create_chat_message("", "")
-        self.assertEqual(result, {"role": "", "content": ""})
-
-    # Tests the behavior of the generate_context function when all input parameters are empty.
-    @patch("time.strftime")
-    def test_generate_context_empty_inputs(self, mock_strftime):
-        # Mock the time.strftime function to return a fixed value
-        mock_strftime.return_value = "Sat Apr 15 00:00:00 2023"
-        # Arrange
-        prompt = ""
-        relevant_memory = ""
-        full_message_history = []
-        model = "gpt-3.5-turbo-0301"
-
-        # Act
-        result = generate_context(prompt, relevant_memory, full_message_history, model)
-
-        # Assert
-        expected_result = (
-            -1,
-            47,
-            3,
-            [
-                {"role": "system", "content": ""},
-                {
-                    "role": "system",
-                    "content": f"The current time and date is {time.strftime('%c')}",
-                },
-                {
-                    "role": "system",
-                    "content": f"This reminds you of these events from your past:\n\n\n",
-                },
-            ],
-        )
-        self.assertEqual(result, expected_result)
-
-    # Tests that the function successfully generates a current_context given valid inputs.
-    def test_generate_context_valid_inputs(self):
-        # Given
-        prompt = "What is your favorite color?"
-        relevant_memory = "You once painted your room blue."
-        full_message_history = [
-            create_chat_message("user", "Hi there!"),
-            create_chat_message("assistant", "Hello! How can I assist you today?"),
-            create_chat_message("user", "Can you tell me a joke?"),
-            create_chat_message(
-                "assistant",
-                "Why did the tomato turn red? Because it saw the salad dressing!",
-            ),
-            create_chat_message("user", "Haha, that's funny."),
-        ]
-        model = "gpt-3.5-turbo-0301"
-
-        # When
-        result = generate_context(prompt, relevant_memory, full_message_history, model)
-
-        # Then
-        self.assertIsInstance(result[0], int)
-        self.assertIsInstance(result[1], int)
-        self.assertIsInstance(result[2], int)
-        self.assertIsInstance(result[3], list)
-        self.assertGreaterEqual(result[0], 0)
-        self.assertGreaterEqual(result[1], 0)
-        self.assertGreaterEqual(result[2], 0)
-        self.assertGreaterEqual(
-            len(result[3]), 3
-        )  # current_context should have at least 3 messages
-        self.assertLessEqual(
-            result[1], 2048
-        )  # token limit for GPT-3.5-turbo-0301 is 2048 tokens
diff --git a/spaces/awacke1/NVidia.Raytrace.Mirror.HTML5.ThreeJS/index.html b/spaces/awacke1/NVidia.Raytrace.Mirror.HTML5.ThreeJS/index.html
deleted file mode 100644
index 4933dbae663f4b1b657d7c5f5c2b3060ae25af4f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/NVidia.Raytrace.Mirror.HTML5.ThreeJS/index.html
+++ /dev/null
@@ -1,109 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <title>Minnesota Map</title>
-  <script src="https://aframe.io/releases/1.2.0/aframe.min.js"></script>
-  <script src="https://unpkg.com/aframe-environment-component/dist/aframe-environment-component.min.js"></script>
-  <script src="https://unpkg.com/@aframe/physics-system"></script>
-  <style>
-    #canvas {
-      height: 500px;
-      width: 800px;
-    }
-    .button {
-      background-color: #222;
-      border: none;
-      color: white;
-      font-size: 16px;
-      padding: 10px 16px;
-      text-align: center;
-      text-decoration: none;
-      display: inline-block;
-      margin: 4px 2px;
-      cursor: pointer;
-    }
-    .link {
-      color: #8CEEEF;
-    }
-  </style>
-</head>
-<body>
-  <a-scene physics="debug: true;">
-    <a-entity environment="preset: forest"></a-entity>
-
-    <!-- Define the polygons for Minnesota -->
-    <a-entity position="0 0 -5">
-      <a-entity position="-2.5 0 0">
-        <a-polygon fill="#2D6C4E" vertices="0 0, 1 0, 0.5 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0.5 1, 1 0, 1 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0 0, 0 1, 0.5 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0.5 0, 1 0, 0.5 1" static-body></a-polygon>
-      </a-entity>
-      <a-entity position="2.5 0 0">
-        <a-polygon fill="#2D6C4E" vertices="0 0, 1 0, 0.5 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0 0, 0 1, 0.5 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0.5 0, 1 0, 1 1" static-body></a-polygon>
-        <a-polygon fill="#2D6C4E" vertices="0.5 0, 0.5 1, 1 1" static-body></a-polygon>
-      </a-entity>
-    </a-entity>
-
-    <!-- Define the camera and buttons -->
-    <a-entity id="player" position="0 50 0" kinematic-body>
-      <a-entity id="camera" camera look-controls kinematic-body></a-entity>
-      <a-entity id="zoomIn" class="button" position="-2.5 0 0" rotation="0 0 180" onclick="zoomIn()">+</a-entity>
-      <a-entity id="zoomOut" class="button" position="2.5 0 0" onclick="zoomOut()">-</a-entity>
-</a-entity><!-- Define the script to update the video texture -->
-<script>
-  const video = document.querySelector('#video');
-  const canvas = document.querySelector('#videoTextureCanvas');
-  const ctx = canvas.getContext('2d');
-
-  function updateVideoTexture() {
-    ctx.drawImage(video, 0, 0, canvas.width, canvas.height);
-    const texture = document.querySelector('#videoTexture');
-    texture.needsUpdate = true;
-  }
-
-  video.addEventListener('play', function() {
-    setInterval(updateVideoTexture, 16);
-  });
-</script>
-
-<script>
-  // Get the camera entity
-  const player = document.querySelector('#player');
-  const camera = document.querySelector('#camera');
-
-  // Define the zoom in and zoom out functions
-  function zoomIn() {
-    camera.setAttribute('position', {x: camera.getAttribute('position').x, y: camera.getAttribute('position').y - 5, z: camera.getAttribute('position').z});
-  }
-
-  function zoomOut() {
-    camera.setAttribute('position', {x: camera.getAttribute('position').x, y: camera.getAttribute('position').y + 5, z: camera.getAttribute('position').z});
-  }
-
-  // Define the gravity and physics for the player
-  player.addEventListener('componentinitialized', function (evt) {
-    if (evt.detail.name !== 'kinematic-body') { return; }
-    const physics = player.getAttribute('physics');
-    physics.debug = false;
-    physics.driver = 'physics';
-    physics.debug = true;
-    physics.debugInterval = 500;
-    physics.debugTiming = 'interval';
-    physics.falling = true;
-    physics.fallingAcceleration = 30;
-    physics.gliding = true;
-    physics.glidingFactor = 0.2;
-    player.setAttribute('physics', physics);
-    player.body.linearDamping = 0.9;
-    player.body.angularDamping = 0.9;
-    player.body.gravity.y = -30;
-    player.body.mass = 80;
-  });
-</script>
-  </a-scene>
-</body>
-</html>
diff --git a/spaces/awacke1/QuoteBotForQuotesMeditation/README.md b/spaces/awacke1/QuoteBotForQuotesMeditation/README.md
deleted file mode 100644
index c452340420a837d994ad61984aa36011dbd1ef41..0000000000000000000000000000000000000000
--- a/spaces/awacke1/QuoteBotForQuotesMeditation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: QuoteBotForQuotesMeditation
-emoji: 🏢
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/SpeechStoryReadAloud/app.py b/spaces/awacke1/SpeechStoryReadAloud/app.py
deleted file mode 100644
index 905b8b63b77e356fd73bd7f786eb0c958a630604..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SpeechStoryReadAloud/app.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import streamlit as st
-import firebase_admin
-import datetime
-import gradio as gr
-import numpy as np
-import tempfile
-
-from firebase_admin import credentials
-from firebase_admin import firestore
-from transformers import pipeline
-from typing import Optional
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-from gradio import inputs
-from gradio.inputs import Textbox
-from gradio import outputs
-
-#Persistence via Cloud Store
-@st.experimental_singleton
-def get_db_firestore():
-    cred = credentials.Certificate('test.json')
-    firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',})
-    db = firestore.client()
-    return db
-db = get_db_firestore()
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-
-#STT Models
-MODEL_NAMES = [
-    "en/ljspeech/tacotron2-DDC",
-    "en/ljspeech/glow-tts",
-    "en/ljspeech/speedy-speech-wn",
-    "en/ljspeech/vits",
-    #"en/sam/tacotron-DDC",
-    #"fr/mai/tacotron2-DDC",
-    #"de/thorsten/tacotron2-DCA",
-]
-MODELS = {}
-manager = ModelManager()
-for MODEL_NAME in MODEL_NAMES:
-    print(f"downloading {MODEL_NAME}")
-    model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}")
-    vocoder_name: Optional[str] = model_item["default_vocoder"]
-    vocoder_path = None
-    vocoder_config_path = None
-    if vocoder_name is not None:
-        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
-
-    synthesizer = Synthesizer(
-        model_path, config_path, None, vocoder_path, vocoder_config_path,
-    )
-    MODELS[MODEL_NAME] = synthesizer
-
-GEN_NAMES = [
-    "huggingface/EleutherAI/gpt-neo-2.7B",
-    "huggingface/EleutherAI/gpt-j-6B",
-    "huggingface/gpt2-large"
-]
-
-
-#ASR
-def transcribe(audio):
-    text = asr(audio)["text"]
-    return text
-
-#Sentiment Classifier
-classifier = pipeline("text-classification")
-
-# GPT-J: Story Generation Pipeline
-story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")
-
-
-#STT
-def speech_to_text(speech):
-    text = asr(speech)["text"]
-    return text
-    
-#TTSentiment
-def text_to_sentiment(text):
-    sentiment = classifier(text)[0]["label"]
-    return sentiment 
-
-#Save
-def upsert(text):
-    date_time =str(datetime.datetime.today())
-    doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
-    doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/TTS-STT-Blocks/', u'last': text, u'born': date_time,})
-    saved = select('TTS-STT', date_time)
-    # check it here:  https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
-    return saved
-      
-#OpenLast
-def select(collection, document):
-    doc_ref = db.collection(collection).document(document)
-    doc = doc_ref.get()
-    docid = ("The id is: ", doc.id)
-    contents = ("The contents are: ", doc.to_dict())
-    return contents
-
-#OpenAll   
-def selectall(text):
-    docs = db.collection('Text2SpeechSentimentSave').stream()
-    doclist=''
-    for doc in docs:
-        r=(f'{doc.id} => {doc.to_dict()}')
-        doclist += r
-    return doclist 
-
-#TTS
-def tts(text: str, model_name: str):
-    print(text, model_name)
-    synthesizer = MODELS.get(model_name, None)
-    if synthesizer is None:
-        raise NameError("model not found")
-    wavs = synthesizer.tts(text)
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        synthesizer.save_wav(wavs, fp)
-        return fp.name
-        
-
-#Blocks Rock It
-demo = gr.Blocks()
-with demo:
-    #UI
-    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
-    text = gr.Textbox()
-    label = gr.Label()
-    saved = gr.Textbox()
-    savedAll = gr.Textbox()
-    TTSchoice = gr.inputs.Radio( label="Pick a TTS Model", choices=MODEL_NAMES,   )
-    audio = gr.Audio(label="Output", interactive=False)
-    
-    
-    #Buttons
-    b1 = gr.Button("Recognize Speech")
-    b2 = gr.Button("Classify Sentiment")
-    b3 = gr.Button("Save Speech to Text")
-    b4 = gr.Button("Retrieve All")
-    b5 = gr.Button("Read It Back Aloud")
-
-    #Event Model Chains
-    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    b2.click(text_to_sentiment, inputs=text, outputs=label)
-    b3.click(upsert, inputs=text, outputs=saved)
-    b4.click(selectall, inputs=text, outputs=savedAll)
-    b5.click(tts,  inputs=[text,TTSchoice], outputs=audio)
-
-# Lets Do It
-demo.launch(share=True)
-
-title = "Story Generators"
-examples = [
-    ["At which point do we invent Love?"],
-    ["Love is a capacity more than consciousness is universal."],
-    ["See the grace of god in eachother."],
-    ["Love is a capacity more than consciousness is universal."],
-    ["Love is generativity when there is more energy than what they need for equilibrium."],
-    ["Collections of people have agency and mass having agency at the mesoscopic level"],
-    ["Having a deep human connection is an interface problem to solve."],
-    ["Having a collective creates agency since we build trust in eachother."]
-]
\ No newline at end of file
diff --git a/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota/app.py b/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota/app.py
deleted file mode 100644
index 9ba84f964d335ef657b24adb5e8b93b5fac0e14a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota/app.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import streamlit as st
-import graphviz as gv
-from graphviz import Graph
-
-# Define the cluster relations graph using gvmap
-g = Graph(format='svg')
-g.graph_attr['bgcolor'] = '#FFFFFF'
-g.graph_attr['outputorder'] = 'edgesfirst'
-g.graph_attr['size'] = '10,10'
-g.node_attr['style'] = 'filled'
-g.node_attr['shape'] = 'box'
-g.node_attr['fillcolor'] = '#FFDAB9'
-
-with g.subgraph(name='cluster_MN') as c:
-    c.graph_attr['bgcolor'] = '#ADD8E6'
-    c.node_attr['color'] = '#000000'
-    c.node_attr['fontcolor'] = '#000000'
-    c.attr(label='Minnesota', fontsize='24')
-    c.node('Allina Health', URL='https://www.allinahealth.org/', target='_blank', tooltip='Allina Health: Abbott Northwestern Hospital')
-    c.node('Fairview Health Services', URL='https://www.fairview.org/', target='_blank', tooltip='Fairview Health Services: University of Minnesota Medical Center')
-    c.node('HealthPartners', URL='https://www.healthpartners.com/', target='_blank', tooltip='HealthPartners: Regions Hospital')
-    c.node('Mayo Clinic', URL='https://www.mayoclinic.org/', target='_blank', tooltip='Mayo Clinic: Saint Marys Hospital')
-    c.node('CentraCare', URL='https://www.centracare.com/', target='_blank', tooltip='CentraCare: St. Cloud Hospital')
-    c.node('Essentia Health', URL='https://www.essentiahealth.org/', target='_blank', tooltip='Essentia Health: St. Mary’s Medical Center')
-    c.node('Hennepin Healthcare', URL='https://www.hennepinhealthcare.org/', target='_blank', tooltip='Hennepin Healthcare: HCMC')
-    c.node('Children\'s Minnesota', URL='https://www.childrensmn.org/', target='_blank', tooltip='Children\'s Minnesota: Children\'s Hospitals and Clinics of Minnesota')
-    c.node('Sanford Health', URL='https://www.sanfordhealth.org/', target='_blank', tooltip='Sanford Health: Sanford Bemidji Medical Center')
-    c.node('St. Luke\'s Hospital', URL='https://www.slhduluth.com/', target='_blank', tooltip='St. Luke\'s Hospital: St. Luke\'s Hospital')
-
-# Render the graph using streamlit
-st.graphviz_chart(g)
-
-
-import folium
-from streamlit_folium import folium_static
-
-# Define hospitals data
-hospitals = [('Allina Health', 'Abbott Northwestern Hospital', 44.957071, -93.262609),
-             ('Fairview Health Services', 'University of Minnesota Medical Center', 44.973944, -93.230865),
-             ('HealthPartners', 'Regions Hospital', 44.948451, -93.093207),
-             ('Mayo Clinic', 'Saint Marys Hospital', 44.020716, -92.467034),
-             ('CentraCare', 'St. Cloud Hospital', 45.570253, -94.168973),
-             ('Essentia Health', 'St. Mary’s Medical Center', 46.802551, -92.099605),
-             ('Hennepin Healthcare', 'HCMC', 44.973946, -93.261186),
-             ("Children's Minnesota", "Children's Hospitals and Clinics of Minnesota", 44.946371, -93.233535),
-             ('Sanford Health', 'Sanford Bemidji Medical Center', 47.496691, -94.864009),
-             ("St. Luke's Hospital", "St. Luke's Hospital", 46.774196, -92.104049)]
-
-# Create a map centered on Minnesota
-m = folium.Map(location=[46.729553, -94.6859], zoom_start=7)
-
-# Add markers for each hospital
-for hospital in hospitals:
-    folium.Marker(
-        location=[hospital[2], hospital[3]],
-        popup=f'{hospital[1]}<br>{hospital[2]},{hospital[3]}'
-    ).add_to(m)
-
-# Display the map in Streamlit
-folium_static(m)
-
-
-
-
-
diff --git a/spaces/awinml/falcon-7b-instruct-api/README.md b/spaces/awinml/falcon-7b-instruct-api/README.md
deleted file mode 100644
index b0d28a3d6e381b25e5bf7e16d1bea651ff4bc7a4..0000000000000000000000000000000000000000
--- a/spaces/awinml/falcon-7b-instruct-api/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Falcon 7b Instruct Api
-emoji: 💻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aziz28/fernet-app/README.md b/spaces/aziz28/fernet-app/README.md
deleted file mode 100644
index 89a7f256462263444a0b2fe5aaef1bccb3376fce..0000000000000000000000000000000000000000
--- a/spaces/aziz28/fernet-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fernet App
-emoji: 🐨
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: fernet.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/pretrain/meta.py b/spaces/azusarang/so-vits-svc-models-ba_P/pretrain/meta.py
deleted file mode 100644
index cc35dd3c0dfe8436e7d635f2db507cedca75ed49..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/pretrain/meta.py
+++ /dev/null
@@ -1,31 +0,0 @@
-def download_dict():
-    return {
-        "vec768l12": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "vec256l9": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "hubertsoft": {
-            "url": "https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt",
-            "output": "./pretrain/hubert-soft-0d54a1f4.pt"
-        },
-        "whisper-ppg": {
-            "url": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
-            "output": "./pretrain/medium.pt"
-        }
-    }
-
-
-def get_speech_encoder(config_path="configs/config.json"):
-    import json
-
-    with open(config_path, "r") as f:
-        data = f.read()
-        config = json.loads(data)
-        speech_encoder = config["model"]["speech_encoder"]
-        dict = download_dict()
-
-        return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/bsdfs/BlinnExponentToRoughnessNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/bsdfs/BlinnExponentToRoughnessNode.js
deleted file mode 100644
index d5d429085ef2841553c97a0410e6880481fb4f3f..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/bsdfs/BlinnExponentToRoughnessNode.js
+++ /dev/null
@@ -1,50 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { TempNode } from '../core/TempNode.js';
-import { BlinnShininessExponentNode } from './BlinnShininessExponentNode.js';
-
-function BlinnExponentToRoughnessNode( blinnExponent ) {
-
-	TempNode.call( this, 'f' );
-
-	this.blinnExponent = blinnExponent || new BlinnShininessExponentNode();
-
-}
-
-BlinnExponentToRoughnessNode.prototype = Object.create( TempNode.prototype );
-BlinnExponentToRoughnessNode.prototype.constructor = BlinnExponentToRoughnessNode;
-BlinnExponentToRoughnessNode.prototype.nodeType = "BlinnExponentToRoughness";
-
-BlinnExponentToRoughnessNode.prototype.generate = function ( builder, output ) {
-
-	return builder.format( 'BlinnExponentToGGXRoughness( ' + this.blinnExponent.build( builder, 'f' ) + ' )', this.type, output );
-
-};
-
-BlinnExponentToRoughnessNode.prototype.copy = function ( source ) {
-
-	TempNode.prototype.copy.call( this, source );
-
-	this.blinnExponent = source.blinnExponent;
-
-};
-
-BlinnExponentToRoughnessNode.prototype.toJSON = function ( meta ) {
-
-	var data = this.getJSONNode( meta );
-
-	if ( ! data ) {
-
-		data = this.createJSONNode( meta );
-
-		data.blinnExponent = this.blinnExponent;
-
-	}
-
-	return data;
-
-};
-
-export { BlinnExponentToRoughnessNode };
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135601.py b/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135601.py
deleted file mode 100644
index b40252ae5dff9d33162d5c013c234236a1463d52..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135601.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#-*- coding : utf-8-*-
-import base64
-from subprocess import STDOUT 
-import streamlit as st
-import pandas as pd 
-import camelot as cam # extracting tables from PDFs 
-
-st.title("PDF Table Extractor")
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-background = st.selectbox("表格线条是否透明",(False,True))
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-    page_number = st.text_input("请填写表格所在PDF页码，eg: 3,1-3,2-end,all", value = 1)
-    tables_all= cam.read_pdf("input.pdf", pages=page_number, process_background=background)
-    result_all = pd.ExcelWriter("result.xlsx", engine='xlsxwriter') 
-    for i in range(0,len(tables_all)):
-        table = tables_all[i].df
-        sheetname = str(i)
-        table.to_excel(result_all, sheetname,index=False) 
-    result_all.save()
-    with open(result_all,'rb') as f:
-       st.download_button('抽取完成, 点击下载！', f,file_name="result.xlsx",mime="application/vnd.ms-excel")
-    
\ No newline at end of file
diff --git a/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/examples/cpp_example.cpp b/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/examples/cpp_example.cpp
deleted file mode 100644
index 8f3ff5c52783cf1e9d6ec6a8a6017f857c372c0e..0000000000000000000000000000000000000000
--- a/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/examples/cpp_example.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <iostream>
-#include <opencv2/imgcodecs.hpp>
-#include <opencv2/highgui.hpp>
-
-#include "masked_image.h"
-#include "nnf.h"
-#include "inpaint.h"
-
-int main() {
-    auto source = cv::imread("./images/forest_pruned.bmp", cv::IMREAD_COLOR);
-
-    auto mask = cv::Mat(source.size(), CV_8UC1);
-    mask = cv::Scalar::all(0);
-    for (int i = 0; i < source.size().height; ++i) {
-        for (int j = 0; j < source.size().width; ++j) {
-            auto source_ptr = source.ptr<unsigned char>(i, j);
-            if (source_ptr[0] == 255 && source_ptr[1] == 255 && source_ptr[2] == 255) {
-                mask.at<unsigned char>(i, j) = 1;
-            }
-        }
-    }
-
-    auto metric = PatchSSDDistanceMetric(3);
-    auto result = Inpainting(source, mask, &metric).run(true, true);
-    // cv::imwrite("./images/forest_recovered.bmp", result);
-    // cv::imshow("Result", result);
-    // cv::waitKey();
-
-    return 0;
-}
-
diff --git a/spaces/bingbing520/ChatGPT/README.md b/spaces/bingbing520/ChatGPT/README.md
deleted file mode 100644
index 7128e29689e35d059c9cc0a5050910fbd34873cd..0000000000000000000000000000000000000000
--- a/spaces/bingbing520/ChatGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Anu Script Manager 6.0 Free Download Free Version LINK.md b/spaces/bioriAsaeru/text-to-voice/Anu Script Manager 6.0 Free Download Free Version LINK.md
deleted file mode 100644
index f4d34707163d0e2287ce6087f739beb1f3cc1a43..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Anu Script Manager 6.0 Free Download Free Version LINK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/Download The Odyssey Full Movie 1997 25 How to Enjoy the Best Version of Homers Poem.md b/spaces/bioriAsaeru/text-to-voice/Download The Odyssey Full Movie 1997 25 How to Enjoy the Best Version of Homers Poem.md
deleted file mode 100644
index 8092b95d4ae0908d56c7c2ac6d2246617487f3dc..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Download The Odyssey Full Movie 1997 25 How to Enjoy the Best Version of Homers Poem.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p><i>Star Wars</i> was originally slated for release on Christmas 1976; however, its production delays pushed the film's release to mid-1977.[120] Editor John Jympson began cutting the film together while Lucas was still filming in Tunisia; as Lucas noted, the editor was in an "impossible position" because Lucas had not explained any of the film's material to him. When Lucas watched Jympson's rough cut for the first time, he disliked what he saw. J. W. Rinzler wrote that "Jympson's selection of takes was questionable, and he seemed to be having trouble doing match-cuts." Lucas was prepared to give Jympson more time; Jympson disliked Lucas's working style.[121] As production went on, Lucas still disapproved of Jympson's cut and fired him halfway through the film's production. He commented: "Unfortunately it didn't work out. It's very hard when you are hiring people to know if they are going to mesh with you and if you are going to get what you want. In the end, I don't think he fully understood the movie and what I was trying to do. I shoot in a very peculiar way, in a documentary style, and it takes a lot of hard editing to make it work."[122] After attempting to persuade Jympson to cut the film his way, Lucas replaced him with Paul Hirsch, Richard Chew, and his then-wife, Marcia Lucas, who was also cutting the film <i>New York, New York</i> (1977) with Lucas's friend Martin Scorsese. Richard Chew considered the film to have been cut in a slow, by-the-book manner: scenes were played out in master shots that flowed into close-up coverage. He found that the pace was dictated by the actors instead of the cuts. Hirsch and Chew worked on two reels simultaneously.[5]</p>
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-<p>Clocking in at roughly three hours, 1997's The Odyssey can be viewed as either one long movie or a two-part mini-series. A terrifically truthful adaptation of the Greek myth of Odysseus, the film recounts his ten-year journey home after the events of the Trojan War.</p>
-<p>As with many examples of ancient Greek mythology, the tale of Odysseus is long, complex, and full of allegory, and the 1997 film adaptation pulls no punches, including almost every aspect of the original story. It's certainly a lengthy watch, but it's so detail-rich that it could almost pass as a sort of documentary.</p> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Frank S Budnick Applied Mathematics Pdf Solution Manual.md b/spaces/bioriAsaeru/text-to-voice/Frank S Budnick Applied Mathematics Pdf Solution Manual.md
deleted file mode 100644
index 07c03cc539f94a10132d6d7b9e71ae9188ce0921..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Frank S Budnick Applied Mathematics Pdf Solution Manual.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>frank s budnick applied mathematics pdf solution manual</h2><br /><p><b><b>Download File</b> &#10002; <a href="https://urloso.com/2uyQaB">https://urloso.com/2uyQaB</a></b></p><br /><br />
-
-frank s budnick applied mathematics pdf solution manual
-
-After a few revisions to the implementation of the “orientable surfaces” classification in the work of Peter Scott, the question of homotopy type of a closed, orientable surface was settled in the 1975 book of Frank S.Budnick and Richard F.Williams. Their results are crucial for the solution to the question of the classification of manifolds, as well as the classification of homotopy equivalent closed, smooth manifolds up to dimension 7. Their solution is a beautiful proof that, in a sense, the two manifolds are homeomorphic.
-
-In the case of manifolds, it may be possible to distinguish between “nearly homotopy equivalent” manifolds that are known not to be homeomorphic, by techniques that involve Morse theory. These techniques are due to R.H.Bing (1930), C.S.Chen (1939), M.H.Freedman (1976), M.A.Hirsch (1976), A.Nicolau (1977), J.C.Oxtoby (1977), R.C.Rubins (1977), M.A.Sharkovskii (1979), J.M.Swallow (1981), and A.A.Tumanov (1989). Some of these authors are known to have studied manifolds which are not diffeomorphic to each other.
-
-Two quasi-isometric manifolds are said to be nearly homotopy equivalent if they have the same homotopy type up to finite aspherical summands. (This is due to Gromov [1986].)
-
-The following two theorems of Budnick and Williams provide an efficient criterion for this. (As we will see, even this criterion is best known for the case where $X$ is a compact manifold.)
-
-Let $X$ and $Y$ be closed, smooth manifolds. Then $X$ is nearly homotopy equivalent to $Y$ if and only if $X$ is homotopy equivalent to a submanifold of $Y$.
-
-Let $X$ and $Y$ be closed, smooth manifolds. Then $X$ is nearly homotopy equivalent to $Y$ if and only if $X$ is homotopy equivalent to a compact smooth submanifold of $Y$ having the same homotopy type as $Y$.
-
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diff --git a/spaces/bioriAsaeru/text-to-voice/Hate Story 3 Man 3 In Hindi 720p Torrent.md b/spaces/bioriAsaeru/text-to-voice/Hate Story 3 Man 3 In Hindi 720p Torrent.md
deleted file mode 100644
index 5c2856054f29d501b2fd53b0f8ad9842eaebb34d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Hate Story 3 Man 3 In Hindi 720p Torrent.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/blanchon/gaussian-splatting-kit/services/utils/read_write_model.py b/spaces/blanchon/gaussian-splatting-kit/services/utils/read_write_model.py
deleted file mode 100644
index 9598cdaeb1886a234302839c85f16e9d61469932..0000000000000000000000000000000000000000
--- a/spaces/blanchon/gaussian-splatting-kit/services/utils/read_write_model.py
+++ /dev/null
@@ -1,514 +0,0 @@
-# This file is adapted from
-# https://github.com/colmap/colmap/blob/bf3e19140f491c3042bfd85b7192ef7d249808ec/scripts/python/read_write_model.py
-# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-#     * Redistributions of source code must retain the above copyright
-#       notice, this list of conditions and the following disclaimer.
-#
-#     * Redistributions in binary form must reproduce the above copyright
-#       notice, this list of conditions and the following disclaimer in the
-#       documentation and/or other materials provided with the distribution.
-#
-#     * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
-#       its contributors may be used to endorse or promote products derived
-#       from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-#
-# Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
-#  type: ignore
-from __future__ import annotations
-
-import argparse
-import collections
-import os
-import struct
-from pathlib import Path
-from typing import Mapping
-
-import numpy as np
-
-CameraModel = collections.namedtuple("CameraModel", ["model_id", "model_name", "num_params"])
-Camera = collections.namedtuple("Camera", ["id", "model", "width", "height", "params"])
-BaseImage = collections.namedtuple("Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
-Point3D = collections.namedtuple("Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
-
-
-class Image(BaseImage):
-    def qvec2rotmat(self):
-        return qvec2rotmat(self.qvec)
-
-
-CAMERA_MODELS = {
-    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
-    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
-    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
-    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
-    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
-    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
-    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
-    CameraModel(model_id=7, model_name="FOV", num_params=5),
-    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
-    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
-    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12),
-}
-CAMERA_MODEL_IDS = {camera_model.model_id: camera_model for camera_model in CAMERA_MODELS}
-CAMERA_MODEL_NAMES = {camera_model.model_name: camera_model for camera_model in CAMERA_MODELS}
-
-
-def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
-    """
-    Read and unpack the next bytes from a binary file.
-
-    :param fid:
-    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
-    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
-    :param endian_character: Any of {@, =, <, >, !}
-    :return: Tuple of read and unpacked values.
-    """
-    data = fid.read(num_bytes)
-    return struct.unpack(endian_character + format_char_sequence, data)
-
-
-def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
-    """
-    Pack and write to a binary file.
-
-    :param fid:
-    :param data: data to send, if multiple elements are sent at the same time,
-    they should be encapsuled either in a list or a tuple
-    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
-    should be the same length as the data list or tuple
-    :param endian_character: Any of {@, =, <, >, !}
-    """
-    if isinstance(data, (list, tuple)):
-        bytes = struct.pack(endian_character + format_char_sequence, *data)
-    else:
-        bytes = struct.pack(endian_character + format_char_sequence, data)
-    fid.write(bytes)
-
-
-def read_cameras_text(path: Path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasText(const std::string& path)
-        void Reconstruction::ReadCamerasText(const std::string& path)
-    """
-    cameras = {}
-    with open(path) as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                camera_id = int(elems[0])
-                model = elems[1]
-                width = int(elems[2])
-                height = int(elems[3])
-                params = np.array(tuple(map(float, elems[4:])))
-                cameras[camera_id] = Camera(id=camera_id, model=model, width=width, height=height, params=params)
-    return cameras
-
-
-def read_cameras_binary(path_to_model_file: Path) -> Mapping[int, Camera]:
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasBinary(const std::string& path)
-        void Reconstruction::ReadCamerasBinary(const std::string& path)
-    """
-    cameras = {}
-    with path_to_model_file.open("rb") as fid:
-        num_cameras = read_next_bytes(fid, 8, "Q")[0]
-        for _ in range(num_cameras):
-            camera_properties = read_next_bytes(fid, num_bytes=24, format_char_sequence="iiQQ")
-            camera_id = camera_properties[0]
-            model_id = camera_properties[1]
-            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
-            width = camera_properties[2]
-            height = camera_properties[3]
-            num_params = CAMERA_MODEL_IDS[model_id].num_params
-            params = read_next_bytes(fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params)
-            cameras[camera_id] = Camera(
-                id=camera_id, model=model_name, width=width, height=height, params=np.array(params)
-            )
-        assert len(cameras) == num_cameras
-    return cameras
-
-
-def write_cameras_text(cameras, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasText(const std::string& path)
-        void Reconstruction::ReadCamerasText(const std::string& path)
-    """
-    HEADER = (
-        "# Camera list with one line of data per camera:\n"
-        + "#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n"
-        + f"# Number of cameras: {len(cameras)}\n"
-    )
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, cam in cameras.items():
-            to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
-            line = " ".join([str(elem) for elem in to_write])
-            fid.write(line + "\n")
-
-
-def write_cameras_binary(cameras, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasBinary(const std::string& path)
-        void Reconstruction::ReadCamerasBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(cameras), "Q")
-        for _, cam in cameras.items():
-            model_id = CAMERA_MODEL_NAMES[cam.model].model_id
-            camera_properties = [cam.id, model_id, cam.width, cam.height]
-            write_next_bytes(fid, camera_properties, "iiQQ")
-            for p in cam.params:
-                write_next_bytes(fid, float(p), "d")
-    return cameras
-
-
-def read_images_text(path: Path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesText(const std::string& path)
-        void Reconstruction::WriteImagesText(const std::string& path)
-    """
-    images = {}
-    with open(path) as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                image_id = int(elems[0])
-                qvec = np.array(tuple(map(float, elems[1:5])))
-                tvec = np.array(tuple(map(float, elems[5:8])))
-                camera_id = int(elems[8])
-                image_name = elems[9]
-                elems = fid.readline().split()
-                xys = np.column_stack([tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))])
-                point3D_ids = np.array(tuple(map(int, elems[2::3])))
-                images[image_id] = Image(
-                    id=image_id,
-                    qvec=qvec,
-                    tvec=tvec,
-                    camera_id=camera_id,
-                    name=image_name,
-                    xys=xys,
-                    point3D_ids=point3D_ids,
-                )
-    return images
-
-
-def read_images_binary(path_to_model_file: Path) -> Mapping[int, Image]:
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesBinary(const std::string& path)
-        void Reconstruction::WriteImagesBinary(const std::string& path)
-    """
-    images = {}
-    with open(path_to_model_file, "rb") as fid:
-        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
-        for _ in range(num_reg_images):
-            binary_image_properties = read_next_bytes(fid, num_bytes=64, format_char_sequence="idddddddi")
-            image_id = binary_image_properties[0]
-            qvec = np.array(binary_image_properties[1:5])
-            tvec = np.array(binary_image_properties[5:8])
-            camera_id = binary_image_properties[8]
-            image_name = ""
-            current_char = read_next_bytes(fid, 1, "c")[0]
-            while current_char != b"\x00":  # look for the ASCII 0 entry
-                image_name += current_char.decode("utf-8")
-                current_char = read_next_bytes(fid, 1, "c")[0]
-            num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[0]
-            x_y_id_s = read_next_bytes(fid, num_bytes=24 * num_points2D, format_char_sequence="ddq" * num_points2D)
-            xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))])
-            point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
-            images[image_id] = Image(
-                id=image_id,
-                qvec=qvec,
-                tvec=tvec,
-                camera_id=camera_id,
-                name=image_name,
-                xys=xys,
-                point3D_ids=point3D_ids,
-            )
-    return images
-
-
-def write_images_text(images, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesText(const std::string& path)
-        void Reconstruction::WriteImagesText(const std::string& path)
-    """
-    if len(images) == 0:
-        mean_observations = 0
-    else:
-        mean_observations = sum((len(img.point3D_ids) for _, img in images.items())) / len(images)
-    HEADER = (
-        "# Image list with two lines of data per image:\n"
-        + "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n"
-        + "#   POINTS2D[] as (X, Y, POINT3D_ID)\n"
-        + f"# Number of images: {len(images)}, mean observations per image: {mean_observations}\n"
-    )
-
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, img in images.items():
-            image_header = [img.id, *img.qvec, *img.tvec, img.camera_id, img.name]
-            first_line = " ".join(map(str, image_header))
-            fid.write(first_line + "\n")
-
-            points_strings = []
-            for xy, point3D_id in zip(img.xys, img.point3D_ids):
-                points_strings.append(" ".join(map(str, [*xy, point3D_id])))
-            fid.write(" ".join(points_strings) + "\n")
-
-
-def write_images_binary(images, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesBinary(const std::string& path)
-        void Reconstruction::WriteImagesBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(images), "Q")
-        for _, img in images.items():
-            write_next_bytes(fid, img.id, "i")
-            write_next_bytes(fid, img.qvec.tolist(), "dddd")
-            write_next_bytes(fid, img.tvec.tolist(), "ddd")
-            write_next_bytes(fid, img.camera_id, "i")
-            for char in img.name:
-                write_next_bytes(fid, char.encode("utf-8"), "c")
-            write_next_bytes(fid, b"\x00", "c")
-            write_next_bytes(fid, len(img.point3D_ids), "Q")
-            for xy, p3d_id in zip(img.xys, img.point3D_ids):
-                write_next_bytes(fid, [*xy, p3d_id], "ddq")
-
-
-def read_points3D_text(path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DText(const std::string& path)
-        void Reconstruction::WritePoints3DText(const std::string& path)
-    """
-    points3D = {}
-    with open(path) as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                point3D_id = int(elems[0])
-                xyz = np.array(tuple(map(float, elems[1:4])))
-                rgb = np.array(tuple(map(int, elems[4:7])))
-                error = float(elems[7])
-                image_ids = np.array(tuple(map(int, elems[8::2])))
-                point2D_idxs = np.array(tuple(map(int, elems[9::2])))
-                points3D[point3D_id] = Point3D(
-                    id=point3D_id, xyz=xyz, rgb=rgb, error=error, image_ids=image_ids, point2D_idxs=point2D_idxs
-                )
-    return points3D
-
-
-def read_points3D_binary(path_to_model_file: Path) -> Mapping[int, Point3D]:
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DBinary(const std::string& path)
-        void Reconstruction::WritePoints3DBinary(const std::string& path)
-    """
-    points3D = {}
-    with open(path_to_model_file, "rb") as fid:
-        num_points = read_next_bytes(fid, 8, "Q")[0]
-        for _ in range(num_points):
-            binary_point_line_properties = read_next_bytes(fid, num_bytes=43, format_char_sequence="QdddBBBd")
-            point3D_id = binary_point_line_properties[0]
-            xyz = np.array(binary_point_line_properties[1:4])
-            rgb = np.array(binary_point_line_properties[4:7])
-            error = np.array(binary_point_line_properties[7])
-            track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[0]
-            track_elems = read_next_bytes(fid, num_bytes=8 * track_length, format_char_sequence="ii" * track_length)
-            image_ids = np.array(tuple(map(int, track_elems[0::2])))
-            point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
-            points3D[point3D_id] = Point3D(
-                id=point3D_id, xyz=xyz, rgb=rgb, error=error, image_ids=image_ids, point2D_idxs=point2D_idxs
-            )
-    return points3D
-
-
-def write_points3D_text(points3D, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DText(const std::string& path)
-        void Reconstruction::WritePoints3DText(const std::string& path)
-    """
-    if len(points3D) == 0:
-        mean_track_length = 0
-    else:
-        mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items())) / len(points3D)
-    HEADER = (
-        "# 3D point list with one line of data per point:\n"
-        + "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
-        + f"# Number of points: {len(points3D)}, mean track length: {mean_track_length}\n"
-    )
-
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, pt in points3D.items():
-            point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
-            fid.write(" ".join(map(str, point_header)) + " ")
-            track_strings = []
-            for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
-                track_strings.append(" ".join(map(str, [image_id, point2D])))
-            fid.write(" ".join(track_strings) + "\n")
-
-
-def write_points3D_binary(points3D, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DBinary(const std::string& path)
-        void Reconstruction::WritePoints3DBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(points3D), "Q")
-        for _, pt in points3D.items():
-            write_next_bytes(fid, pt.id, "Q")
-            write_next_bytes(fid, pt.xyz.tolist(), "ddd")
-            write_next_bytes(fid, pt.rgb.tolist(), "BBB")
-            write_next_bytes(fid, pt.error, "d")
-            track_length = pt.image_ids.shape[0]
-            write_next_bytes(fid, track_length, "Q")
-            for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
-                write_next_bytes(fid, [image_id, point2D_id], "ii")
-
-
-def detect_model_format(path: Path, ext: str) -> bool:
-    parts = ["cameras", "images", "points3D"]
-    if all([(path / p).with_suffix(ext) for p in parts]):
-        print("Detected model format: '" + ext + "'")
-        return True
-
-    return False
-
-
-def read_model(path: Path, ext: str = ""):
-    # try to detect the extension automatically
-    if ext == "":
-        if detect_model_format(path, ".bin"):
-            ext = ".bin"
-        elif detect_model_format(path, ".txt"):
-            ext = ".txt"
-        else:
-            print("Provide model format: '.bin' or '.txt'")
-            return
-
-    if ext == ".txt":
-        cameras = read_cameras_text((path / "cameras").with_suffix(ext))
-        images = read_images_text((path / "images").with_suffix(ext))
-        points3D = read_points3D_text((path / "points3D").with_suffix(ext))
-    else:
-        cameras = read_cameras_binary((path / "cameras").with_suffix(ext))
-        images = read_images_binary((path / "images").with_suffix(ext))
-        points3D = read_points3D_binary((path / "points3D").with_suffix(ext))
-    return cameras, images, points3D
-
-
-def write_model(cameras, images, points3D, path, ext=".bin"):
-    if ext == ".txt":
-        write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
-        write_images_text(images, os.path.join(path, "images" + ext))
-        write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
-    else:
-        write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
-        write_images_binary(images, os.path.join(path, "images" + ext))
-        write_points3D_binary(points3D, os.path.join(path, "points3D") + ext)
-    return cameras, images, points3D
-
-
-def qvec2rotmat(qvec):
-    return np.array(
-        [
-            [
-                1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
-                2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
-                2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
-            ],
-            [
-                2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
-                1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
-                2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
-            ],
-            [
-                2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
-                2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
-                1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
-            ],
-        ]
-    )
-
-
-def rotmat2qvec(R):
-    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
-    K = (
-        np.array(
-            [
-                [Rxx - Ryy - Rzz, 0, 0, 0],
-                [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
-                [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
-                [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz],
-            ]
-        )
-        / 3.0
-    )
-    eigvals, eigvecs = np.linalg.eigh(K)
-    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
-    if qvec[0] < 0:
-        qvec *= -1
-    return qvec
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Read and write COLMAP binary and text models")
-    parser.add_argument("--input_model", help="path to input model folder")
-    parser.add_argument("--input_format", choices=[".bin", ".txt"], help="input model format", default="")
-    parser.add_argument("--output_model", help="path to output model folder")
-    parser.add_argument("--output_format", choices=[".bin", ".txt"], help="output model format", default=".txt")
-    args = parser.parse_args()
-
-    cameras, images, points3D = read_model(path=args.input_model, ext=args.input_format)
-
-    print("num_cameras:", len(cameras))
-    print("num_images:", len(images))
-    print("num_points3D:", len(points3D))
-
-    if args.output_model is not None:
-        write_model(cameras, images, points3D, path=args.output_model, ext=args.output_format)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/botlik100/kaki/config.py b/spaces/botlik100/kaki/config.py
deleted file mode 100644
index 5b72235b58b65ac629f49bcc4aad032b5b59d8d4..0000000000000000000000000000000000000000
--- a/spaces/botlik100/kaki/config.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import argparse
-import sys
-import torch
-import json
-from multiprocessing import cpu_count
-
-global usefp16
-usefp16 = False
-
-
-def use_fp32_config():
-    usefp16 = False
-    device_capability = 0
-    if torch.cuda.is_available():
-        device = torch.device("cuda:0")  # Assuming you have only one GPU (index 0).
-        device_capability = torch.cuda.get_device_capability(device)[0]
-        if device_capability >= 7:
-            usefp16 = True
-            for config_file in ["32k.json", "40k.json", "48k.json"]:
-                with open(f"configs/{config_file}", "r") as d:
-                    data = json.load(d)
-
-                if "train" in data and "fp16_run" in data["train"]:
-                    data["train"]["fp16_run"] = True
-
-                with open(f"configs/{config_file}", "w") as d:
-                    json.dump(data, d, indent=4)
-
-                print(f"Set fp16_run to true in {config_file}")
-
-            with open(
-                "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
-            ) as f:
-                strr = f.read()
-
-            strr = strr.replace("3.0", "3.7")
-
-            with open(
-                "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
-            ) as f:
-                f.write(strr)
-        else:
-            for config_file in ["32k.json", "40k.json", "48k.json"]:
-                with open(f"configs/{config_file}", "r") as f:
-                    data = json.load(f)
-
-                if "train" in data and "fp16_run" in data["train"]:
-                    data["train"]["fp16_run"] = False
-
-                with open(f"configs/{config_file}", "w") as d:
-                    json.dump(data, d, indent=4)
-
-                print(f"Set fp16_run to false in {config_file}")
-
-            with open(
-                "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
-            ) as f:
-                strr = f.read()
-
-            strr = strr.replace("3.7", "3.0")
-
-            with open(
-                "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
-            ) as f:
-                f.write(strr)
-    else:
-        print(
-            "CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed."
-        )
-    return (usefp16, device_capability)
-
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.python_cmd,
-            self.listen_port,
-            self.iscolab,
-            self.noparallel,
-            self.noautoopen,
-            self.paperspace,
-            self.is_cli,
-        ) = self.arg_parse()
-
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        exe = sys.executable or "python"
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--port", type=int, default=7865, help="Listen port")
-        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument(
-            "--noparallel", action="store_true", help="Disable parallel processing"
-        )
-        parser.add_argument(
-            "--noautoopen",
-            action="store_true",
-            help="Do not open in browser automatically",
-        )
-        parser.add_argument(  # Fork Feature. Paperspace integration for web UI
-            "--paperspace",
-            action="store_true",
-            help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
-        )
-        parser.add_argument(  # Fork Feature. Embed a CLI into the infer-web.py
-            "--is_cli",
-            action="store_true",
-            help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
-        )
-        cmd_opts = parser.parse_args()
-
-        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
-        return (
-            cmd_opts.pycmd,
-            cmd_opts.port,
-            cmd_opts.colab,
-            cmd_opts.noparallel,
-            cmd_opts.noautoopen,
-            cmd_opts.paperspace,
-            cmd_opts.is_cli,
-        )
-
-    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-    # check `getattr` and try it for compatibility
-    @staticmethod
-    def has_mps() -> bool:
-        if not torch.backends.mps.is_available():
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("Found GPU", self.gpu_name, ", force to fp32")
-                self.is_half = False
-            else:
-                print("Found GPU", self.gpu_name)
-                use_fp32_config()
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif self.has_mps():
-            print("No supported Nvidia GPU found, use MPS instead")
-            self.device = "mps"
-            self.is_half = False
-            use_fp32_config()
-        else:
-            print("No supported Nvidia GPU found, use CPU instead")
-            self.device = "cpu"
-            self.is_half = False
-            use_fp32_config()
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py
deleted file mode 100644
index 6433d5dec75c3d6141252af144b61d8999077bb7..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from typing import Any
-import torch
-from torch.nn import functional as F
-
-from detectron2.structures import BitMasks, Boxes, BoxMode
-
-from .base import IntTupleBox, make_int_box
-from .to_mask import ImageSizeType
-
-
-def resample_coarse_segm_tensor_to_bbox(coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox):
-    """
-    Resample coarse segmentation tensor to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        coarse_segm: float tensor of shape [1, K, Hout, Wout]
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    x, y, w, h = box_xywh_abs
-    w = max(int(w), 1)
-    h = max(int(h), 1)
-    labels = F.interpolate(coarse_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
-    return labels
-
-
-def resample_fine_and_coarse_segm_tensors_to_bbox(
-    fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox
-):
-    """
-    Resample fine and coarse segmentation tensors to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        fine_segm: float tensor of shape [1, C, Hout, Wout]
-        coarse_segm: float tensor of shape [1, K, Hout, Wout]
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    x, y, w, h = box_xywh_abs
-    w = max(int(w), 1)
-    h = max(int(h), 1)
-    # coarse segmentation
-    coarse_segm_bbox = F.interpolate(
-        coarse_segm,
-        (h, w),
-        mode="bilinear",
-        align_corners=False,
-    ).argmax(dim=1)
-    # combined coarse and fine segmentation
-    labels = (
-        F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
-        * (coarse_segm_bbox > 0).long()
-    )
-    return labels
-
-
-def resample_fine_and_coarse_segm_to_bbox(predictor_output: Any, box_xywh_abs: IntTupleBox):
-    """
-    Resample fine and coarse segmentation outputs from a predictor to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        predictor_output: DensePose predictor output that contains segmentation
-            results to be resampled
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    return resample_fine_and_coarse_segm_tensors_to_bbox(
-        predictor_output.fine_segm,
-        predictor_output.coarse_segm,
-        box_xywh_abs,
-    )
-
-
-def predictor_output_with_coarse_segm_to_mask(
-    predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
-) -> BitMasks:
-    """
-    Convert predictor output with coarse and fine segmentation to a mask.
-    Assumes that predictor output has the following attributes:
-     - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
-         unnormalized scores for N instances; D is the number of coarse
-         segmentation labels, H and W is the resolution of the estimate
-
-    Args:
-        predictor_output: DensePose predictor output to be converted to mask
-        boxes (Boxes): bounding boxes that correspond to the DensePose
-            predictor outputs
-        image_size_hw (tuple [int, int]): image height Himg and width Wimg
-    Return:
-        BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
-        a mask of the size of the image for each instance
-    """
-    H, W = image_size_hw
-    boxes_xyxy_abs = boxes.tensor.clone()
-    boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    N = len(boxes_xywh_abs)
-    masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
-    for i in range(len(boxes_xywh_abs)):
-        box_xywh = make_int_box(boxes_xywh_abs[i])
-        box_mask = resample_coarse_segm_tensor_to_bbox(predictor_output[i].coarse_segm, box_xywh)
-        x, y, w, h = box_xywh
-        masks[i, y : y + h, x : x + w] = box_mask
-
-    return BitMasks(masks)
-
-
-def predictor_output_with_fine_and_coarse_segm_to_mask(
-    predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
-) -> BitMasks:
-    """
-    Convert predictor output with coarse and fine segmentation to a mask.
-    Assumes that predictor output has the following attributes:
-     - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
-         unnormalized scores for N instances; D is the number of coarse
-         segmentation labels, H and W is the resolution of the estimate
-     - fine_segm (tensor of size [N, C, H, W]): fine segmentation
-         unnormalized scores for N instances; C is the number of fine
-         segmentation labels, H and W is the resolution of the estimate
-
-    Args:
-        predictor_output: DensePose predictor output to be converted to mask
-        boxes (Boxes): bounding boxes that correspond to the DensePose
-            predictor outputs
-        image_size_hw (tuple [int, int]): image height Himg and width Wimg
-    Return:
-        BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
-        a mask of the size of the image for each instance
-    """
-    H, W = image_size_hw
-    boxes_xyxy_abs = boxes.tensor.clone()
-    boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    N = len(boxes_xywh_abs)
-    masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
-    for i in range(len(boxes_xywh_abs)):
-        box_xywh = make_int_box(boxes_xywh_abs[i])
-        labels_i = resample_fine_and_coarse_segm_to_bbox(predictor_output[i], box_xywh)
-        x, y, w, h = box_xywh
-        masks[i, y : y + h, x : x + w] = labels_i > 0
-    return BitMasks(masks)
diff --git a/spaces/bwconrad/anime-character-classification/detect.py b/spaces/bwconrad/anime-character-classification/detect.py
deleted file mode 100644
index f4e70032e3c5c41948bcd86b34bc345e32581639..0000000000000000000000000000000000000000
--- a/spaces/bwconrad/anime-character-classification/detect.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-
-
-def detect(image_pil, cascade_file="lbpcascade_animeface.xml"):
-    cascade = cv2.CascadeClassifier(cascade_file)
-
-    image = np.array(image_pil)
-    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    gray = cv2.equalizeHist(gray)
-
-    faces = cascade.detectMultiScale(
-        gray,
-        scaleFactor=1.1,
-        minNeighbors=5,
-        minSize=(24, 24),
-    )
-
-    # No faces found
-    if len(faces) == 0:
-        return image_pil
-
-    x, y, w, h = faces[0]
-    face = image[y : y + h, x : x + w]
-
-    return Image.fromarray(face)
\ No newline at end of file
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/raft/core/extractor.py b/spaces/caffeinum/VToonify/vtoonify/model/raft/core/extractor.py
deleted file mode 100644
index 9a9c759d1243d4694e8656c2f6f8a37e53edd009..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/raft/core/extractor.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
-        super(ResidualBlock, self).__init__()
-  
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
-        self.relu = nn.ReLU(inplace=True)
-
-        num_groups = planes // 8
-
-        if norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            if not stride == 1:
-                self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-        
-        elif norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(planes)
-            self.norm2 = nn.BatchNorm2d(planes)
-            if not stride == 1:
-                self.norm3 = nn.BatchNorm2d(planes)
-        
-        elif norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(planes)
-            self.norm2 = nn.InstanceNorm2d(planes)
-            if not stride == 1:
-                self.norm3 = nn.InstanceNorm2d(planes)
-
-        elif norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-            self.norm2 = nn.Sequential()
-            if not stride == 1:
-                self.norm3 = nn.Sequential()
-
-        if stride == 1:
-            self.downsample = None
-        
-        else:    
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
-
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x+y)
-
-
-
-class BottleneckBlock(nn.Module):
-    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
-        super(BottleneckBlock, self).__init__()
-  
-        self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
-        self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
-        self.relu = nn.ReLU(inplace=True)
-
-        num_groups = planes // 8
-
-        if norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
-            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
-            self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-            if not stride == 1:
-                self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
-        
-        elif norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(planes//4)
-            self.norm2 = nn.BatchNorm2d(planes//4)
-            self.norm3 = nn.BatchNorm2d(planes)
-            if not stride == 1:
-                self.norm4 = nn.BatchNorm2d(planes)
-        
-        elif norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(planes//4)
-            self.norm2 = nn.InstanceNorm2d(planes//4)
-            self.norm3 = nn.InstanceNorm2d(planes)
-            if not stride == 1:
-                self.norm4 = nn.InstanceNorm2d(planes)
-
-        elif norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-            self.norm2 = nn.Sequential()
-            self.norm3 = nn.Sequential()
-            if not stride == 1:
-                self.norm4 = nn.Sequential()
-
-        if stride == 1:
-            self.downsample = None
-        
-        else:    
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
-
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-        y = self.relu(self.norm3(self.conv3(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x+y)
-
-class BasicEncoder(nn.Module):
-    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
-        super(BasicEncoder, self).__init__()
-        self.norm_fn = norm_fn
-
-        if self.norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
-            
-        elif self.norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(64)
-
-        elif self.norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(64)
-
-        elif self.norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.in_planes = 64
-        self.layer1 = self._make_layer(64,  stride=1)
-        self.layer2 = self._make_layer(96, stride=2)
-        self.layer3 = self._make_layer(128, stride=2)
-
-        # output convolution
-        self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
-
-        self.dropout = None
-        if dropout > 0:
-            self.dropout = nn.Dropout2d(p=dropout)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
-                if m.weight is not None:
-                    nn.init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, dim, stride=1):
-        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
-        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
-        layers = (layer1, layer2)
-        
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-
-    def forward(self, x):
-
-        # if input is list, combine batch dimension
-        is_list = isinstance(x, tuple) or isinstance(x, list)
-        if is_list:
-            batch_dim = x[0].shape[0]
-            x = torch.cat(x, dim=0)
-
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-
-        x = self.conv2(x)
-
-        if self.training and self.dropout is not None:
-            x = self.dropout(x)
-
-        if is_list:
-            x = torch.split(x, [batch_dim, batch_dim], dim=0)
-
-        return x
-
-
-class SmallEncoder(nn.Module):
-    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
-        super(SmallEncoder, self).__init__()
-        self.norm_fn = norm_fn
-
-        if self.norm_fn == 'group':
-            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
-            
-        elif self.norm_fn == 'batch':
-            self.norm1 = nn.BatchNorm2d(32)
-
-        elif self.norm_fn == 'instance':
-            self.norm1 = nn.InstanceNorm2d(32)
-
-        elif self.norm_fn == 'none':
-            self.norm1 = nn.Sequential()
-
-        self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.in_planes = 32
-        self.layer1 = self._make_layer(32,  stride=1)
-        self.layer2 = self._make_layer(64, stride=2)
-        self.layer3 = self._make_layer(96, stride=2)
-
-        self.dropout = None
-        if dropout > 0:
-            self.dropout = nn.Dropout2d(p=dropout)
-        
-        self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
-                if m.weight is not None:
-                    nn.init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, dim, stride=1):
-        layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
-        layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
-        layers = (layer1, layer2)
-    
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-
-    def forward(self, x):
-
-        # if input is list, combine batch dimension
-        is_list = isinstance(x, tuple) or isinstance(x, list)
-        if is_list:
-            batch_dim = x[0].shape[0]
-            x = torch.cat(x, dim=0)
-
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.conv2(x)
-
-        if self.training and self.dropout is not None:
-            x = self.dropout(x)
-
-        if is_list:
-            x = torch.split(x, [batch_dim, batch_dim], dim=0)
-
-        return x
diff --git a/spaces/cbensimon/streamlit-query-params/app.py b/spaces/cbensimon/streamlit-query-params/app.py
deleted file mode 100644
index 8e113b504a7a63412af4e89802fc9cf82f0e7a50..0000000000000000000000000000000000000000
--- a/spaces/cbensimon/streamlit-query-params/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-
-import streamlit as st
-
-
-st.write(st.experimental_get_query_params())
-
-st.experimental_set_query_params(test='ok')
diff --git a/spaces/chansung/LLM-As-Chatbot/models/camel.py b/spaces/chansung/LLM-As-Chatbot/models/camel.py
deleted file mode 100644
index 5188c54ceb1ecf263bd35e1f4baaea2438b66e7a..0000000000000000000000000000000000000000
--- a/spaces/chansung/LLM-As-Chatbot/models/camel.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from optimum.bettertransformer import BetterTransformer
-
-def load_model(
-    base, 
-    finetuned, 
-    mode_cpu,
-    mode_mps,
-    mode_full_gpu,
-    mode_8bit,
-    mode_4bit,
-    force_download_ckpt
-):
-    tokenizer = AutoTokenizer.from_pretrained(base)
-    tokenizer.padding_side = "left"
-
-    if mode_cpu:
-        print("cpu mode")
-        model = AutoModelForCausalLM.from_pretrained(
-            base, 
-            device_map={"": "cpu"}, 
-            use_safetensors=False
-        )
-            
-    elif mode_mps:
-        print("mps mode")
-        model = AutoModelForCausalLM.from_pretrained(
-            base,
-            device_map={"": "mps"},
-            torch_dtype=torch.float16,
-            use_safetensors=False
-        )
-            
-    else:
-        print("gpu mode")
-        print(f"8bit = {mode_8bit}, 4bit = {mode_4bit}")
-        model = AutoModelForCausalLM.from_pretrained(
-            base,
-            load_in_8bit=mode_8bit,
-            load_in_4bit=mode_4bit,
-            device_map="auto",
-            torch_dtype=torch.float16,
-            use_safetensors=False
-        )
-
-        if not mode_8bit and not mode_4bit:
-            model.half()
-
-    model = BetterTransformer.transform(model)
-    return model, tokenizer
\ No newline at end of file
diff --git a/spaces/chasemcdo/hf_localai/pkg/whisper/whisper.go b/spaces/chasemcdo/hf_localai/pkg/whisper/whisper.go
deleted file mode 100644
index 41c4587212addf5e99370cc84f17b1f9f320a620..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/pkg/whisper/whisper.go
+++ /dev/null
@@ -1,90 +0,0 @@
-package whisper
-
-import (
-	"fmt"
-	"os"
-	"os/exec"
-	"path/filepath"
-
-	"github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper"
-	wav "github.com/go-audio/wav"
-)
-
-func sh(c string) (string, error) {
-	cmd := exec.Command("/bin/sh", "-c", c)
-	cmd.Env = os.Environ()
-	o, err := cmd.CombinedOutput()
-	return string(o), err
-}
-
-// AudioToWav converts audio to wav for transcribe. It bashes out to ffmpeg
-// TODO: use https://github.com/mccoyst/ogg?
-func audioToWav(src, dst string) error {
-	out, err := sh(fmt.Sprintf("ffmpeg -i %s -format s16le -ar 16000 -ac 1 -acodec pcm_s16le %s", src, dst))
-	if err != nil {
-		return fmt.Errorf("error: %w out: %s", err, out)
-	}
-
-	return nil
-}
-
-func Transcript(model whisper.Model, audiopath, language string, threads uint) (string, error) {
-
-	dir, err := os.MkdirTemp("", "whisper")
-	if err != nil {
-		return "", err
-	}
-	defer os.RemoveAll(dir)
-
-	convertedPath := filepath.Join(dir, "converted.wav")
-
-	if err := audioToWav(audiopath, convertedPath); err != nil {
-		return "", err
-	}
-
-	// Open samples
-	fh, err := os.Open(convertedPath)
-	if err != nil {
-		return "", err
-	}
-	defer fh.Close()
-
-	// Read samples
-	d := wav.NewDecoder(fh)
-	buf, err := d.FullPCMBuffer()
-	if err != nil {
-		return "", err
-	}
-
-	data := buf.AsFloat32Buffer().Data
-
-	// Process samples
-	context, err := model.NewContext()
-	if err != nil {
-		return "", err
-
-	}
-
-	context.SetThreads(threads)
-
-	if language != "" {
-		context.SetLanguage(language)
-	} else {
-		context.SetLanguage("auto")
-	}
-
-	if err := context.Process(data, nil); err != nil {
-		return "", err
-	}
-
-	text := ""
-	for {
-		segment, err := context.NextSegment()
-		if err != nil {
-			break
-		}
-		text += segment.Text
-	}
-
-	return text, nil
-}
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/commands/download.py b/spaces/chendl/compositional_test/transformers/src/transformers/commands/download.py
deleted file mode 100644
index 3c224555dfd56e033d9ec3589fd6baf6f4b5478a..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/commands/download.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# Copyright 2020 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.
-
-from argparse import ArgumentParser
-
-from . import BaseTransformersCLICommand
-
-
-def download_command_factory(args):
-    return DownloadCommand(args.model, args.cache_dir, args.force)
-
-
-class DownloadCommand(BaseTransformersCLICommand):
-    @staticmethod
-    def register_subcommand(parser: ArgumentParser):
-        download_parser = parser.add_parser("download")
-        download_parser.add_argument(
-            "--cache-dir", type=str, default=None, help="Path to location to store the models"
-        )
-        download_parser.add_argument(
-            "--force", action="store_true", help="Force the model to be download even if already in cache-dir"
-        )
-        download_parser.add_argument("model", type=str, help="Name of the model to download")
-        download_parser.set_defaults(func=download_command_factory)
-
-    def __init__(self, model: str, cache: str, force: bool):
-        self._model = model
-        self._cache = cache
-        self._force = force
-
-    def run(self):
-        from ..models.auto import AutoModel, AutoTokenizer
-
-        AutoModel.from_pretrained(self._model, cache_dir=self._cache, force_download=self._force)
-        AutoTokenizer.from_pretrained(self._model, cache_dir=self._cache, force_download=self._force)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/number_types.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/number_types.py
deleted file mode 100644
index 47942ffb2c2381fdd64161eaa56aefdccf221eaf..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/number_types.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright 2014 Google Inc. 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 collections
-import struct
-
-from . import packer
-from .compat import import_numpy, NumpyRequiredForThisFeature
-
-np = import_numpy()
-
-# For reference, see:
-# https://docs.python.org/2/library/ctypes.html#ctypes-fundamental-data-types-2
-
-# These classes could be collections.namedtuple instances, but those are new
-# in 2.6 and we want to work towards 2.5 compatability.
-
-class BoolFlags(object):
-    bytewidth = 1
-    min_val = False
-    max_val = True
-    py_type = bool
-    name = "bool"
-    packer_type = packer.boolean
-
-
-class Uint8Flags(object):
-    bytewidth = 1
-    min_val = 0
-    max_val = (2**8) - 1
-    py_type = int
-    name = "uint8"
-    packer_type = packer.uint8
-
-
-class Uint16Flags(object):
-    bytewidth = 2
-    min_val = 0
-    max_val = (2**16) - 1
-    py_type = int
-    name = "uint16"
-    packer_type = packer.uint16
-
-
-class Uint32Flags(object):
-    bytewidth = 4
-    min_val = 0
-    max_val = (2**32) - 1
-    py_type = int
-    name = "uint32"
-    packer_type = packer.uint32
-
-
-class Uint64Flags(object):
-    bytewidth = 8
-    min_val = 0
-    max_val = (2**64) - 1
-    py_type = int
-    name = "uint64"
-    packer_type = packer.uint64
-
-
-class Int8Flags(object):
-    bytewidth = 1
-    min_val = -(2**7)
-    max_val = (2**7) - 1
-    py_type = int
-    name = "int8"
-    packer_type = packer.int8
-
-
-class Int16Flags(object):
-    bytewidth = 2
-    min_val = -(2**15)
-    max_val = (2**15) - 1
-    py_type = int
-    name = "int16"
-    packer_type = packer.int16
-
-
-class Int32Flags(object):
-    bytewidth = 4
-    min_val = -(2**31)
-    max_val = (2**31) - 1
-    py_type = int
-    name = "int32"
-    packer_type = packer.int32
-
-
-class Int64Flags(object):
-    bytewidth = 8
-    min_val = -(2**63)
-    max_val = (2**63) - 1
-    py_type = int
-    name = "int64"
-    packer_type = packer.int64
-
-
-class Float32Flags(object):
-    bytewidth = 4
-    min_val = None
-    max_val = None
-    py_type = float
-    name = "float32"
-    packer_type = packer.float32
-
-
-class Float64Flags(object):
-    bytewidth = 8
-    min_val = None
-    max_val = None
-    py_type = float
-    name = "float64"
-    packer_type = packer.float64
-
-
-class SOffsetTFlags(Int32Flags):
-    pass
-
-
-class UOffsetTFlags(Uint32Flags):
-    pass
-
-
-class VOffsetTFlags(Uint16Flags):
-    pass
-
-
-def valid_number(n, flags):
-    if flags.min_val is None and flags.max_val is None:
-        return True
-    return flags.min_val <= n <= flags.max_val
-
-
-def enforce_number(n, flags):
-    if flags.min_val is None and flags.max_val is None:
-        return
-    if not flags.min_val <= n <= flags.max_val:
-        raise TypeError("bad number %s for type %s" % (str(n), flags.name))
-
-
-def float32_to_uint32(n):
-    packed = struct.pack("<1f", n)
-    (converted,) = struct.unpack("<1L", packed)
-    return converted
-
-
-def uint32_to_float32(n):
-    packed = struct.pack("<1L", n)
-    (unpacked,) = struct.unpack("<1f", packed)
-    return unpacked
-
-
-def float64_to_uint64(n):
-    packed = struct.pack("<1d", n)
-    (converted,) = struct.unpack("<1Q", packed)
-    return converted
-
-
-def uint64_to_float64(n):
-    packed = struct.pack("<1Q", n)
-    (unpacked,) = struct.unpack("<1d", packed)
-    return unpacked
-
-
-def to_numpy_type(number_type):
-    if np is not None:
-        return np.dtype(number_type.name).newbyteorder('<')
-    else:
-        raise NumpyRequiredForThisFeature('Numpy was not found.')
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_h_e_a.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_h_e_a.py
deleted file mode 100644
index c293fafcb040ed5fe661c28278309bae3a419b2b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_h_e_a.py
+++ /dev/null
@@ -1,134 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from fontTools.misc.fixedTools import (
-    ensureVersionIsLong as fi2ve,
-    versionToFixed as ve2fi,
-)
-from . import DefaultTable
-import math
-
-
-hheaFormat = """
-		>  # big endian
-		tableVersion:           L
-		ascent:                 h
-		descent:                h
-		lineGap:                h
-		advanceWidthMax:        H
-		minLeftSideBearing:     h
-		minRightSideBearing:    h
-		xMaxExtent:             h
-		caretSlopeRise:         h
-		caretSlopeRun:          h
-		caretOffset:            h
-		reserved0:              h
-		reserved1:              h
-		reserved2:              h
-		reserved3:              h
-		metricDataFormat:       h
-		numberOfHMetrics:       H
-"""
-
-
-class table__h_h_e_a(DefaultTable.DefaultTable):
-
-    # Note: Keep in sync with table__v_h_e_a
-
-    dependencies = ["hmtx", "glyf", "CFF ", "CFF2"]
-
-    # OpenType spec renamed these, add aliases for compatibility
-    @property
-    def ascender(self):
-        return self.ascent
-
-    @ascender.setter
-    def ascender(self, value):
-        self.ascent = value
-
-    @property
-    def descender(self):
-        return self.descent
-
-    @descender.setter
-    def descender(self, value):
-        self.descent = value
-
-    def decompile(self, data, ttFont):
-        sstruct.unpack(hheaFormat, data, self)
-
-    def compile(self, ttFont):
-        if ttFont.recalcBBoxes and (
-            ttFont.isLoaded("glyf")
-            or ttFont.isLoaded("CFF ")
-            or ttFont.isLoaded("CFF2")
-        ):
-            self.recalc(ttFont)
-        self.tableVersion = fi2ve(self.tableVersion)
-        return sstruct.pack(hheaFormat, self)
-
-    def recalc(self, ttFont):
-        if "hmtx" in ttFont:
-            hmtxTable = ttFont["hmtx"]
-            self.advanceWidthMax = max(adv for adv, _ in hmtxTable.metrics.values())
-
-        boundsWidthDict = {}
-        if "glyf" in ttFont:
-            glyfTable = ttFont["glyf"]
-            for name in ttFont.getGlyphOrder():
-                g = glyfTable[name]
-                if g.numberOfContours == 0:
-                    continue
-                if g.numberOfContours < 0 and not hasattr(g, "xMax"):
-                    # Composite glyph without extents set.
-                    # Calculate those.
-                    g.recalcBounds(glyfTable)
-                boundsWidthDict[name] = g.xMax - g.xMin
-        elif "CFF " in ttFont or "CFF2" in ttFont:
-            if "CFF " in ttFont:
-                topDict = ttFont["CFF "].cff.topDictIndex[0]
-            else:
-                topDict = ttFont["CFF2"].cff.topDictIndex[0]
-            charStrings = topDict.CharStrings
-            for name in ttFont.getGlyphOrder():
-                cs = charStrings[name]
-                bounds = cs.calcBounds(charStrings)
-                if bounds is not None:
-                    boundsWidthDict[name] = int(
-                        math.ceil(bounds[2]) - math.floor(bounds[0])
-                    )
-
-        if boundsWidthDict:
-            minLeftSideBearing = float("inf")
-            minRightSideBearing = float("inf")
-            xMaxExtent = -float("inf")
-            for name, boundsWidth in boundsWidthDict.items():
-                advanceWidth, lsb = hmtxTable[name]
-                rsb = advanceWidth - lsb - boundsWidth
-                extent = lsb + boundsWidth
-                minLeftSideBearing = min(minLeftSideBearing, lsb)
-                minRightSideBearing = min(minRightSideBearing, rsb)
-                xMaxExtent = max(xMaxExtent, extent)
-            self.minLeftSideBearing = minLeftSideBearing
-            self.minRightSideBearing = minRightSideBearing
-            self.xMaxExtent = xMaxExtent
-
-        else:  # No glyph has outlines.
-            self.minLeftSideBearing = 0
-            self.minRightSideBearing = 0
-            self.xMaxExtent = 0
-
-    def toXML(self, writer, ttFont):
-        formatstring, names, fixes = sstruct.getformat(hheaFormat)
-        for name in names:
-            value = getattr(self, name)
-            if name == "tableVersion":
-                value = fi2ve(value)
-                value = "0x%08x" % value
-            writer.simpletag(name, value=value)
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "tableVersion":
-            setattr(self, name, ve2fi(attrs["value"]))
-            return
-        setattr(self, name, safeEval(attrs["value"]))
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_n_a_m_e.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_n_a_m_e.py
deleted file mode 100644
index 152e4f268dca40bf2596833154b64e114289c78a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_n_a_m_e.py
+++ /dev/null
@@ -1,1213 +0,0 @@
-# -*- coding: utf-8 -*-
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import (
-    bytechr,
-    byteord,
-    bytesjoin,
-    strjoin,
-    tobytes,
-    tostr,
-    safeEval,
-)
-from fontTools.misc.encodingTools import getEncoding
-from fontTools.ttLib import newTable
-from fontTools.ttLib.ttVisitor import TTVisitor
-from fontTools import ttLib
-import fontTools.ttLib.tables.otTables as otTables
-from fontTools.ttLib.tables import C_P_A_L_
-from . import DefaultTable
-import struct
-import logging
-
-
-log = logging.getLogger(__name__)
-
-nameRecordFormat = """
-		>	# big endian
-		platformID:	H
-		platEncID:	H
-		langID:		H
-		nameID:		H
-		length:		H
-		offset:		H
-"""
-
-nameRecordSize = sstruct.calcsize(nameRecordFormat)
-
-
-class table__n_a_m_e(DefaultTable.DefaultTable):
-    dependencies = ["ltag"]
-
-    def decompile(self, data, ttFont):
-        format, n, stringOffset = struct.unpack(b">HHH", data[:6])
-        expectedStringOffset = 6 + n * nameRecordSize
-        if stringOffset != expectedStringOffset:
-            log.error(
-                "'name' table stringOffset incorrect. Expected: %s; Actual: %s",
-                expectedStringOffset,
-                stringOffset,
-            )
-        stringData = data[stringOffset:]
-        data = data[6:]
-        self.names = []
-        for i in range(n):
-            if len(data) < 12:
-                log.error("skipping malformed name record #%d", i)
-                continue
-            name, data = sstruct.unpack2(nameRecordFormat, data, NameRecord())
-            name.string = stringData[name.offset : name.offset + name.length]
-            if name.offset + name.length > len(stringData):
-                log.error("skipping malformed name record #%d", i)
-                continue
-            assert len(name.string) == name.length
-            # if (name.platEncID, name.platformID) in ((0, 0), (1, 3)):
-            # 	if len(name.string) % 2:
-            # 		print "2-byte string doesn't have even length!"
-            # 		print name.__dict__
-            del name.offset, name.length
-            self.names.append(name)
-
-    def compile(self, ttFont):
-        if not hasattr(self, "names"):
-            # only happens when there are NO name table entries read
-            # from the TTX file
-            self.names = []
-        names = self.names
-        names.sort()  # sort according to the spec; see NameRecord.__lt__()
-        stringData = b""
-        format = 0
-        n = len(names)
-        stringOffset = 6 + n * sstruct.calcsize(nameRecordFormat)
-        data = struct.pack(b">HHH", format, n, stringOffset)
-        lastoffset = 0
-        done = {}  # remember the data so we can reuse the "pointers"
-        for name in names:
-            string = name.toBytes()
-            if string in done:
-                name.offset, name.length = done[string]
-            else:
-                name.offset, name.length = done[string] = len(stringData), len(string)
-                stringData = bytesjoin([stringData, string])
-            data = data + sstruct.pack(nameRecordFormat, name)
-        return data + stringData
-
-    def toXML(self, writer, ttFont):
-        for name in self.names:
-            name.toXML(writer, ttFont)
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name != "namerecord":
-            return  # ignore unknown tags
-        if not hasattr(self, "names"):
-            self.names = []
-        name = NameRecord()
-        self.names.append(name)
-        name.fromXML(name, attrs, content, ttFont)
-
-    def getName(self, nameID, platformID, platEncID, langID=None):
-        for namerecord in self.names:
-            if (
-                namerecord.nameID == nameID
-                and namerecord.platformID == platformID
-                and namerecord.platEncID == platEncID
-            ):
-                if langID is None or namerecord.langID == langID:
-                    return namerecord
-        return None  # not found
-
-    def getDebugName(self, nameID):
-        englishName = someName = None
-        for name in self.names:
-            if name.nameID != nameID:
-                continue
-            try:
-                unistr = name.toUnicode()
-            except UnicodeDecodeError:
-                continue
-
-            someName = unistr
-            if (name.platformID, name.langID) in ((1, 0), (3, 0x409)):
-                englishName = unistr
-                break
-        if englishName:
-            return englishName
-        elif someName:
-            return someName
-        else:
-            return None
-
-    def getFirstDebugName(self, nameIDs):
-        for nameID in nameIDs:
-            name = self.getDebugName(nameID)
-            if name is not None:
-                return name
-        return None
-
-    def getBestFamilyName(self):
-        # 21 = WWS Family Name
-        # 16 = Typographic Family Name
-        # 1 = Family Name
-        return self.getFirstDebugName((21, 16, 1))
-
-    def getBestSubFamilyName(self):
-        # 22 = WWS SubFamily Name
-        # 17 = Typographic SubFamily Name
-        # 2 = SubFamily Name
-        return self.getFirstDebugName((22, 17, 2))
-
-    def getBestFullName(self):
-        # 4 = Full Name
-        # 6 = PostScript Name
-        for nameIDs in ((21, 22), (16, 17), (1, 2), (4,), (6,)):
-            if len(nameIDs) == 2:
-                name_fam = self.getDebugName(nameIDs[0])
-                name_subfam = self.getDebugName(nameIDs[1])
-                if None in [name_fam, name_subfam]:
-                    continue  # if any is None, skip
-                name = f"{name_fam} {name_subfam}"
-                if name_subfam.lower() == "regular":
-                    name = f"{name_fam}"
-                return name
-            else:
-                name = self.getDebugName(nameIDs[0])
-                if name is not None:
-                    return name
-        return None
-
-    def setName(self, string, nameID, platformID, platEncID, langID):
-        """Set the 'string' for the name record identified by 'nameID', 'platformID',
-        'platEncID' and 'langID'. If a record with that nameID doesn't exist, create it
-        and append to the name table.
-
-        'string' can be of type `str` (`unicode` in PY2) or `bytes`. In the latter case,
-        it is assumed to be already encoded with the correct plaform-specific encoding
-        identified by the (platformID, platEncID, langID) triplet. A warning is issued
-        to prevent unexpected results.
-        """
-        if not hasattr(self, "names"):
-            self.names = []
-        if not isinstance(string, str):
-            if isinstance(string, bytes):
-                log.warning(
-                    "name string is bytes, ensure it's correctly encoded: %r", string
-                )
-            else:
-                raise TypeError(
-                    "expected unicode or bytes, found %s: %r"
-                    % (type(string).__name__, string)
-                )
-        namerecord = self.getName(nameID, platformID, platEncID, langID)
-        if namerecord:
-            namerecord.string = string
-        else:
-            self.names.append(makeName(string, nameID, platformID, platEncID, langID))
-
-    def removeNames(self, nameID=None, platformID=None, platEncID=None, langID=None):
-        """Remove any name records identified by the given combination of 'nameID',
-        'platformID', 'platEncID' and 'langID'.
-        """
-        args = {
-            argName: argValue
-            for argName, argValue in (
-                ("nameID", nameID),
-                ("platformID", platformID),
-                ("platEncID", platEncID),
-                ("langID", langID),
-            )
-            if argValue is not None
-        }
-        if not args:
-            # no arguments, nothing to do
-            return
-        self.names = [
-            rec
-            for rec in self.names
-            if any(
-                argValue != getattr(rec, argName) for argName, argValue in args.items()
-            )
-        ]
-
-    @staticmethod
-    def removeUnusedNames(ttFont):
-        """Remove any name records which are not in NameID range 0-255 and not utilized
-        within the font itself."""
-        visitor = NameRecordVisitor()
-        visitor.visit(ttFont)
-        toDelete = set()
-        for record in ttFont["name"].names:
-            # Name IDs 26 to 255, inclusive, are reserved for future standard names.
-            # https://learn.microsoft.com/en-us/typography/opentype/spec/name#name-ids
-            if record.nameID < 256:
-                continue
-            if record.nameID not in visitor.seen:
-                toDelete.add(record.nameID)
-
-        if not toDelete:
-            return
-        log.info(f"Deleting name records with NameIDs {toDelete}")
-        for nameID in toDelete:
-            ttFont["name"].removeNames(nameID)
-        return toDelete
-
-    def _findUnusedNameID(self, minNameID=256):
-        """Finds an unused name id.
-
-        The nameID is assigned in the range between 'minNameID' and 32767 (inclusive),
-        following the last nameID in the name table.
-        """
-        names = getattr(self, "names", [])
-        nameID = 1 + max([n.nameID for n in names] + [minNameID - 1])
-        if nameID > 32767:
-            raise ValueError("nameID must be less than 32768")
-        return nameID
-
-    def findMultilingualName(self, names, windows=True, mac=True, minNameID=0):
-        """Return the name ID of an existing multilingual name that
-        matches the 'names' dictionary, or None if not found.
-
-        'names' is a dictionary with the name in multiple languages,
-        such as {'en': 'Pale', 'de': 'Blaß', 'de-CH': 'Blass'}.
-        The keys can be arbitrary IETF BCP 47 language codes;
-        the values are Unicode strings.
-
-        If 'windows' is True, the returned name ID is guaranteed
-        exist for all requested languages for platformID=3 and
-        platEncID=1.
-        If 'mac' is True, the returned name ID is guaranteed to exist
-        for all requested languages for platformID=1 and platEncID=0.
-
-        The returned name ID will not be less than the 'minNameID'
-        argument.
-        """
-        # Gather the set of requested
-        #   (string, platformID, platEncID, langID)
-        # tuples
-        reqNameSet = set()
-        for lang, name in sorted(names.items()):
-            if windows:
-                windowsName = _makeWindowsName(name, None, lang)
-                if windowsName is not None:
-                    reqNameSet.add(
-                        (
-                            windowsName.string,
-                            windowsName.platformID,
-                            windowsName.platEncID,
-                            windowsName.langID,
-                        )
-                    )
-            if mac:
-                macName = _makeMacName(name, None, lang)
-                if macName is not None:
-                    reqNameSet.add(
-                        (
-                            macName.string,
-                            macName.platformID,
-                            macName.platEncID,
-                            macName.langID,
-                        )
-                    )
-
-        # Collect matching name IDs
-        matchingNames = dict()
-        for name in self.names:
-            try:
-                key = (name.toUnicode(), name.platformID, name.platEncID, name.langID)
-            except UnicodeDecodeError:
-                continue
-            if key in reqNameSet and name.nameID >= minNameID:
-                nameSet = matchingNames.setdefault(name.nameID, set())
-                nameSet.add(key)
-
-        # Return the first name ID that defines all requested strings
-        for nameID, nameSet in sorted(matchingNames.items()):
-            if nameSet == reqNameSet:
-                return nameID
-
-        return None  # not found
-
-    def addMultilingualName(
-        self, names, ttFont=None, nameID=None, windows=True, mac=True, minNameID=0
-    ):
-        """Add a multilingual name, returning its name ID
-
-        'names' is a dictionary with the name in multiple languages,
-        such as {'en': 'Pale', 'de': 'Blaß', 'de-CH': 'Blass'}.
-        The keys can be arbitrary IETF BCP 47 language codes;
-        the values are Unicode strings.
-
-        'ttFont' is the TTFont to which the names are added, or None.
-        If present, the font's 'ltag' table can get populated
-        to store exotic language codes, which allows encoding
-        names that otherwise cannot get encoded at all.
-
-        'nameID' is the name ID to be used, or None to let the library
-        find an existing set of name records that match, or pick an
-        unused name ID.
-
-        If 'windows' is True, a platformID=3 name record will be added.
-        If 'mac' is True, a platformID=1 name record will be added.
-
-        If the 'nameID' argument is None, the created nameID will not
-        be less than the 'minNameID' argument.
-        """
-        if not hasattr(self, "names"):
-            self.names = []
-        if nameID is None:
-            # Reuse nameID if possible
-            nameID = self.findMultilingualName(
-                names, windows=windows, mac=mac, minNameID=minNameID
-            )
-            if nameID is not None:
-                return nameID
-            nameID = self._findUnusedNameID()
-        # TODO: Should minimize BCP 47 language codes.
-        # https://github.com/fonttools/fonttools/issues/930
-        for lang, name in sorted(names.items()):
-            if windows:
-                windowsName = _makeWindowsName(name, nameID, lang)
-                if windowsName is not None:
-                    self.names.append(windowsName)
-                else:
-                    # We cannot not make a Windows name: make sure we add a
-                    # Mac name as a fallback. This can happen for exotic
-                    # BCP47 language tags that have no Windows language code.
-                    mac = True
-            if mac:
-                macName = _makeMacName(name, nameID, lang, ttFont)
-                if macName is not None:
-                    self.names.append(macName)
-        return nameID
-
-    def addName(self, string, platforms=((1, 0, 0), (3, 1, 0x409)), minNameID=255):
-        """Add a new name record containing 'string' for each (platformID, platEncID,
-        langID) tuple specified in the 'platforms' list.
-
-        The nameID is assigned in the range between 'minNameID'+1 and 32767 (inclusive),
-        following the last nameID in the name table.
-        If no 'platforms' are specified, two English name records are added, one for the
-        Macintosh (platformID=0), and one for the Windows platform (3).
-
-        The 'string' must be a Unicode string, so it can be encoded with different,
-        platform-specific encodings.
-
-        Return the new nameID.
-        """
-        assert (
-            len(platforms) > 0
-        ), "'platforms' must contain at least one (platformID, platEncID, langID) tuple"
-        if not hasattr(self, "names"):
-            self.names = []
-        if not isinstance(string, str):
-            raise TypeError(
-                "expected str, found %s: %r" % (type(string).__name__, string)
-            )
-        nameID = self._findUnusedNameID(minNameID + 1)
-        for platformID, platEncID, langID in platforms:
-            self.names.append(makeName(string, nameID, platformID, platEncID, langID))
-        return nameID
-
-
-def makeName(string, nameID, platformID, platEncID, langID):
-    name = NameRecord()
-    name.string, name.nameID, name.platformID, name.platEncID, name.langID = (
-        string,
-        nameID,
-        platformID,
-        platEncID,
-        langID,
-    )
-    return name
-
-
-def _makeWindowsName(name, nameID, language):
-    """Create a NameRecord for the Microsoft Windows platform
-
-    'language' is an arbitrary IETF BCP 47 language identifier such
-    as 'en', 'de-CH', 'de-AT-1901', or 'fa-Latn'. If Microsoft Windows
-    does not support the desired language, the result will be None.
-    Future versions of fonttools might return a NameRecord for the
-    OpenType 'name' table format 1, but this is not implemented yet.
-    """
-    langID = _WINDOWS_LANGUAGE_CODES.get(language.lower())
-    if langID is not None:
-        return makeName(name, nameID, 3, 1, langID)
-    else:
-        log.warning(
-            "cannot add Windows name in language %s "
-            "because fonttools does not yet support "
-            "name table format 1" % language
-        )
-        return None
-
-
-def _makeMacName(name, nameID, language, font=None):
-    """Create a NameRecord for Apple platforms
-
-    'language' is an arbitrary IETF BCP 47 language identifier such
-    as 'en', 'de-CH', 'de-AT-1901', or 'fa-Latn'. When possible, we
-    create a Macintosh NameRecord that is understood by old applications
-    (platform ID 1 and an old-style Macintosh language enum). If this
-    is not possible, we create a Unicode NameRecord (platform ID 0)
-    whose language points to the font’s 'ltag' table. The latter
-    can encode any string in any language, but legacy applications
-    might not recognize the format (in which case they will ignore
-    those names).
-
-    'font' should be the TTFont for which you want to create a name.
-    If 'font' is None, we only return NameRecords for legacy Macintosh;
-    in that case, the result will be None for names that need to
-    be encoded with an 'ltag' table.
-
-    See the section “The language identifier” in Apple’s specification:
-    https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6name.html
-    """
-    macLang = _MAC_LANGUAGE_CODES.get(language.lower())
-    macScript = _MAC_LANGUAGE_TO_SCRIPT.get(macLang)
-    if macLang is not None and macScript is not None:
-        encoding = getEncoding(1, macScript, macLang, default="ascii")
-        # Check if we can actually encode this name. If we can't,
-        # for example because we have no support for the legacy
-        # encoding, or because the name string contains Unicode
-        # characters that the legacy encoding cannot represent,
-        # we fall back to encoding the name in Unicode and put
-        # the language tag into the ltag table.
-        try:
-            _ = tobytes(name, encoding, errors="strict")
-            return makeName(name, nameID, 1, macScript, macLang)
-        except UnicodeEncodeError:
-            pass
-    if font is not None:
-        ltag = font.tables.get("ltag")
-        if ltag is None:
-            ltag = font["ltag"] = newTable("ltag")
-        # 0 = Unicode; 4 = “Unicode 2.0 or later semantics (non-BMP characters allowed)”
-        # “The preferred platform-specific code for Unicode would be 3 or 4.”
-        # https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6name.html
-        return makeName(name, nameID, 0, 4, ltag.addTag(language))
-    else:
-        log.warning(
-            "cannot store language %s into 'ltag' table "
-            "without having access to the TTFont object" % language
-        )
-        return None
-
-
-class NameRecord(object):
-    def getEncoding(self, default="ascii"):
-        """Returns the Python encoding name for this name entry based on its platformID,
-        platEncID, and langID.  If encoding for these values is not known, by default
-        'ascii' is returned.  That can be overriden by passing a value to the default
-        argument.
-        """
-        return getEncoding(self.platformID, self.platEncID, self.langID, default)
-
-    def encodingIsUnicodeCompatible(self):
-        return self.getEncoding(None) in ["utf_16_be", "ucs2be", "ascii", "latin1"]
-
-    def __str__(self):
-        return self.toStr(errors="backslashreplace")
-
-    def isUnicode(self):
-        return self.platformID == 0 or (
-            self.platformID == 3 and self.platEncID in [0, 1, 10]
-        )
-
-    def toUnicode(self, errors="strict"):
-        """
-        If self.string is a Unicode string, return it; otherwise try decoding the
-        bytes in self.string to a Unicode string using the encoding of this
-        entry as returned by self.getEncoding(); Note that  self.getEncoding()
-        returns 'ascii' if the encoding is unknown to the library.
-
-        Certain heuristics are performed to recover data from bytes that are
-        ill-formed in the chosen encoding, or that otherwise look misencoded
-        (mostly around bad UTF-16BE encoded bytes, or bytes that look like UTF-16BE
-        but marked otherwise).  If the bytes are ill-formed and the heuristics fail,
-        the error is handled according to the errors parameter to this function, which is
-        passed to the underlying decode() function; by default it throws a
-        UnicodeDecodeError exception.
-
-        Note: The mentioned heuristics mean that roundtripping a font to XML and back
-        to binary might recover some misencoded data whereas just loading the font
-        and saving it back will not change them.
-        """
-
-        def isascii(b):
-            return (b >= 0x20 and b <= 0x7E) or b in [0x09, 0x0A, 0x0D]
-
-        encoding = self.getEncoding()
-        string = self.string
-
-        if (
-            isinstance(string, bytes)
-            and encoding == "utf_16_be"
-            and len(string) % 2 == 1
-        ):
-            # Recover badly encoded UTF-16 strings that have an odd number of bytes:
-            # - If the last byte is zero, drop it.  Otherwise,
-            # - If all the odd bytes are zero and all the even bytes are ASCII,
-            #   prepend one zero byte.  Otherwise,
-            # - If first byte is zero and all other bytes are ASCII, insert zero
-            #   bytes between consecutive ASCII bytes.
-            #
-            # (Yes, I've seen all of these in the wild... sigh)
-            if byteord(string[-1]) == 0:
-                string = string[:-1]
-            elif all(
-                byteord(b) == 0 if i % 2 else isascii(byteord(b))
-                for i, b in enumerate(string)
-            ):
-                string = b"\0" + string
-            elif byteord(string[0]) == 0 and all(
-                isascii(byteord(b)) for b in string[1:]
-            ):
-                string = bytesjoin(b"\0" + bytechr(byteord(b)) for b in string[1:])
-
-        string = tostr(string, encoding=encoding, errors=errors)
-
-        # If decoded strings still looks like UTF-16BE, it suggests a double-encoding.
-        # Fix it up.
-        if all(
-            ord(c) == 0 if i % 2 == 0 else isascii(ord(c)) for i, c in enumerate(string)
-        ):
-            # If string claims to be Mac encoding, but looks like UTF-16BE with ASCII text,
-            # narrow it down.
-            string = "".join(c for c in string[1::2])
-
-        return string
-
-    def toBytes(self, errors="strict"):
-        """If self.string is a bytes object, return it; otherwise try encoding
-        the Unicode string in self.string to bytes using the encoding of this
-        entry as returned by self.getEncoding(); Note that self.getEncoding()
-        returns 'ascii' if the encoding is unknown to the library.
-
-        If the Unicode string cannot be encoded to bytes in the chosen encoding,
-        the error is handled according to the errors parameter to this function,
-        which is passed to the underlying encode() function; by default it throws a
-        UnicodeEncodeError exception.
-        """
-        return tobytes(self.string, encoding=self.getEncoding(), errors=errors)
-
-    toStr = toUnicode
-
-    def toXML(self, writer, ttFont):
-        try:
-            unistr = self.toUnicode()
-        except UnicodeDecodeError:
-            unistr = None
-        attrs = [
-            ("nameID", self.nameID),
-            ("platformID", self.platformID),
-            ("platEncID", self.platEncID),
-            ("langID", hex(self.langID)),
-        ]
-
-        if unistr is None or not self.encodingIsUnicodeCompatible():
-            attrs.append(("unicode", unistr is not None))
-
-        writer.begintag("namerecord", attrs)
-        writer.newline()
-        if unistr is not None:
-            writer.write(unistr)
-        else:
-            writer.write8bit(self.string)
-        writer.newline()
-        writer.endtag("namerecord")
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        self.nameID = safeEval(attrs["nameID"])
-        self.platformID = safeEval(attrs["platformID"])
-        self.platEncID = safeEval(attrs["platEncID"])
-        self.langID = safeEval(attrs["langID"])
-        s = strjoin(content).strip()
-        encoding = self.getEncoding()
-        if self.encodingIsUnicodeCompatible() or safeEval(
-            attrs.get("unicode", "False")
-        ):
-            self.string = s.encode(encoding)
-        else:
-            # This is the inverse of write8bit...
-            self.string = s.encode("latin1")
-
-    def __lt__(self, other):
-        if type(self) != type(other):
-            return NotImplemented
-
-        try:
-            selfTuple = (
-                self.platformID,
-                self.platEncID,
-                self.langID,
-                self.nameID,
-            )
-            otherTuple = (
-                other.platformID,
-                other.platEncID,
-                other.langID,
-                other.nameID,
-            )
-        except AttributeError:
-            # This can only happen for
-            # 1) an object that is not a NameRecord, or
-            # 2) an unlikely incomplete NameRecord object which has not been
-            #    fully populated
-            return NotImplemented
-
-        try:
-            # Include the actual NameRecord string in the comparison tuples
-            selfTuple = selfTuple + (self.toBytes(),)
-            otherTuple = otherTuple + (other.toBytes(),)
-        except UnicodeEncodeError as e:
-            # toBytes caused an encoding error in either of the two, so content
-            # to sorting based on IDs only
-            log.error("NameRecord sorting failed to encode: %s" % e)
-
-        # Implemented so that list.sort() sorts according to the spec by using
-        # the order of the tuple items and their comparison
-        return selfTuple < otherTuple
-
-    def __repr__(self):
-        return "<NameRecord NameID=%d; PlatformID=%d; LanguageID=%d>" % (
-            self.nameID,
-            self.platformID,
-            self.langID,
-        )
-
-
-# Windows language ID → IETF BCP-47 language tag
-#
-# While Microsoft indicates a region/country for all its language
-# IDs, we follow Unicode practice by omitting “most likely subtags”
-# as per Unicode CLDR. For example, English is simply “en” and not
-# “en-Latn” because according to Unicode, the default script
-# for English is Latin.
-#
-# http://www.unicode.org/cldr/charts/latest/supplemental/likely_subtags.html
-# http://www.iana.org/assignments/language-subtag-registry/language-subtag-registry
-_WINDOWS_LANGUAGES = {
-    0x0436: "af",
-    0x041C: "sq",
-    0x0484: "gsw",
-    0x045E: "am",
-    0x1401: "ar-DZ",
-    0x3C01: "ar-BH",
-    0x0C01: "ar",
-    0x0801: "ar-IQ",
-    0x2C01: "ar-JO",
-    0x3401: "ar-KW",
-    0x3001: "ar-LB",
-    0x1001: "ar-LY",
-    0x1801: "ary",
-    0x2001: "ar-OM",
-    0x4001: "ar-QA",
-    0x0401: "ar-SA",
-    0x2801: "ar-SY",
-    0x1C01: "aeb",
-    0x3801: "ar-AE",
-    0x2401: "ar-YE",
-    0x042B: "hy",
-    0x044D: "as",
-    0x082C: "az-Cyrl",
-    0x042C: "az",
-    0x046D: "ba",
-    0x042D: "eu",
-    0x0423: "be",
-    0x0845: "bn",
-    0x0445: "bn-IN",
-    0x201A: "bs-Cyrl",
-    0x141A: "bs",
-    0x047E: "br",
-    0x0402: "bg",
-    0x0403: "ca",
-    0x0C04: "zh-HK",
-    0x1404: "zh-MO",
-    0x0804: "zh",
-    0x1004: "zh-SG",
-    0x0404: "zh-TW",
-    0x0483: "co",
-    0x041A: "hr",
-    0x101A: "hr-BA",
-    0x0405: "cs",
-    0x0406: "da",
-    0x048C: "prs",
-    0x0465: "dv",
-    0x0813: "nl-BE",
-    0x0413: "nl",
-    0x0C09: "en-AU",
-    0x2809: "en-BZ",
-    0x1009: "en-CA",
-    0x2409: "en-029",
-    0x4009: "en-IN",
-    0x1809: "en-IE",
-    0x2009: "en-JM",
-    0x4409: "en-MY",
-    0x1409: "en-NZ",
-    0x3409: "en-PH",
-    0x4809: "en-SG",
-    0x1C09: "en-ZA",
-    0x2C09: "en-TT",
-    0x0809: "en-GB",
-    0x0409: "en",
-    0x3009: "en-ZW",
-    0x0425: "et",
-    0x0438: "fo",
-    0x0464: "fil",
-    0x040B: "fi",
-    0x080C: "fr-BE",
-    0x0C0C: "fr-CA",
-    0x040C: "fr",
-    0x140C: "fr-LU",
-    0x180C: "fr-MC",
-    0x100C: "fr-CH",
-    0x0462: "fy",
-    0x0456: "gl",
-    0x0437: "ka",
-    0x0C07: "de-AT",
-    0x0407: "de",
-    0x1407: "de-LI",
-    0x1007: "de-LU",
-    0x0807: "de-CH",
-    0x0408: "el",
-    0x046F: "kl",
-    0x0447: "gu",
-    0x0468: "ha",
-    0x040D: "he",
-    0x0439: "hi",
-    0x040E: "hu",
-    0x040F: "is",
-    0x0470: "ig",
-    0x0421: "id",
-    0x045D: "iu",
-    0x085D: "iu-Latn",
-    0x083C: "ga",
-    0x0434: "xh",
-    0x0435: "zu",
-    0x0410: "it",
-    0x0810: "it-CH",
-    0x0411: "ja",
-    0x044B: "kn",
-    0x043F: "kk",
-    0x0453: "km",
-    0x0486: "quc",
-    0x0487: "rw",
-    0x0441: "sw",
-    0x0457: "kok",
-    0x0412: "ko",
-    0x0440: "ky",
-    0x0454: "lo",
-    0x0426: "lv",
-    0x0427: "lt",
-    0x082E: "dsb",
-    0x046E: "lb",
-    0x042F: "mk",
-    0x083E: "ms-BN",
-    0x043E: "ms",
-    0x044C: "ml",
-    0x043A: "mt",
-    0x0481: "mi",
-    0x047A: "arn",
-    0x044E: "mr",
-    0x047C: "moh",
-    0x0450: "mn",
-    0x0850: "mn-CN",
-    0x0461: "ne",
-    0x0414: "nb",
-    0x0814: "nn",
-    0x0482: "oc",
-    0x0448: "or",
-    0x0463: "ps",
-    0x0415: "pl",
-    0x0416: "pt",
-    0x0816: "pt-PT",
-    0x0446: "pa",
-    0x046B: "qu-BO",
-    0x086B: "qu-EC",
-    0x0C6B: "qu",
-    0x0418: "ro",
-    0x0417: "rm",
-    0x0419: "ru",
-    0x243B: "smn",
-    0x103B: "smj-NO",
-    0x143B: "smj",
-    0x0C3B: "se-FI",
-    0x043B: "se",
-    0x083B: "se-SE",
-    0x203B: "sms",
-    0x183B: "sma-NO",
-    0x1C3B: "sms",
-    0x044F: "sa",
-    0x1C1A: "sr-Cyrl-BA",
-    0x0C1A: "sr",
-    0x181A: "sr-Latn-BA",
-    0x081A: "sr-Latn",
-    0x046C: "nso",
-    0x0432: "tn",
-    0x045B: "si",
-    0x041B: "sk",
-    0x0424: "sl",
-    0x2C0A: "es-AR",
-    0x400A: "es-BO",
-    0x340A: "es-CL",
-    0x240A: "es-CO",
-    0x140A: "es-CR",
-    0x1C0A: "es-DO",
-    0x300A: "es-EC",
-    0x440A: "es-SV",
-    0x100A: "es-GT",
-    0x480A: "es-HN",
-    0x080A: "es-MX",
-    0x4C0A: "es-NI",
-    0x180A: "es-PA",
-    0x3C0A: "es-PY",
-    0x280A: "es-PE",
-    0x500A: "es-PR",
-    # Microsoft has defined two different language codes for
-    # “Spanish with modern sorting” and “Spanish with traditional
-    # sorting”. This makes sense for collation APIs, and it would be
-    # possible to express this in BCP 47 language tags via Unicode
-    # extensions (eg., “es-u-co-trad” is “Spanish with traditional
-    # sorting”). However, for storing names in fonts, this distinction
-    # does not make sense, so we use “es” in both cases.
-    0x0C0A: "es",
-    0x040A: "es",
-    0x540A: "es-US",
-    0x380A: "es-UY",
-    0x200A: "es-VE",
-    0x081D: "sv-FI",
-    0x041D: "sv",
-    0x045A: "syr",
-    0x0428: "tg",
-    0x085F: "tzm",
-    0x0449: "ta",
-    0x0444: "tt",
-    0x044A: "te",
-    0x041E: "th",
-    0x0451: "bo",
-    0x041F: "tr",
-    0x0442: "tk",
-    0x0480: "ug",
-    0x0422: "uk",
-    0x042E: "hsb",
-    0x0420: "ur",
-    0x0843: "uz-Cyrl",
-    0x0443: "uz",
-    0x042A: "vi",
-    0x0452: "cy",
-    0x0488: "wo",
-    0x0485: "sah",
-    0x0478: "ii",
-    0x046A: "yo",
-}
-
-
-_MAC_LANGUAGES = {
-    0: "en",
-    1: "fr",
-    2: "de",
-    3: "it",
-    4: "nl",
-    5: "sv",
-    6: "es",
-    7: "da",
-    8: "pt",
-    9: "no",
-    10: "he",
-    11: "ja",
-    12: "ar",
-    13: "fi",
-    14: "el",
-    15: "is",
-    16: "mt",
-    17: "tr",
-    18: "hr",
-    19: "zh-Hant",
-    20: "ur",
-    21: "hi",
-    22: "th",
-    23: "ko",
-    24: "lt",
-    25: "pl",
-    26: "hu",
-    27: "es",
-    28: "lv",
-    29: "se",
-    30: "fo",
-    31: "fa",
-    32: "ru",
-    33: "zh",
-    34: "nl-BE",
-    35: "ga",
-    36: "sq",
-    37: "ro",
-    38: "cz",
-    39: "sk",
-    40: "sl",
-    41: "yi",
-    42: "sr",
-    43: "mk",
-    44: "bg",
-    45: "uk",
-    46: "be",
-    47: "uz",
-    48: "kk",
-    49: "az-Cyrl",
-    50: "az-Arab",
-    51: "hy",
-    52: "ka",
-    53: "mo",
-    54: "ky",
-    55: "tg",
-    56: "tk",
-    57: "mn-CN",
-    58: "mn",
-    59: "ps",
-    60: "ks",
-    61: "ku",
-    62: "sd",
-    63: "bo",
-    64: "ne",
-    65: "sa",
-    66: "mr",
-    67: "bn",
-    68: "as",
-    69: "gu",
-    70: "pa",
-    71: "or",
-    72: "ml",
-    73: "kn",
-    74: "ta",
-    75: "te",
-    76: "si",
-    77: "my",
-    78: "km",
-    79: "lo",
-    80: "vi",
-    81: "id",
-    82: "tl",
-    83: "ms",
-    84: "ms-Arab",
-    85: "am",
-    86: "ti",
-    87: "om",
-    88: "so",
-    89: "sw",
-    90: "rw",
-    91: "rn",
-    92: "ny",
-    93: "mg",
-    94: "eo",
-    128: "cy",
-    129: "eu",
-    130: "ca",
-    131: "la",
-    132: "qu",
-    133: "gn",
-    134: "ay",
-    135: "tt",
-    136: "ug",
-    137: "dz",
-    138: "jv",
-    139: "su",
-    140: "gl",
-    141: "af",
-    142: "br",
-    143: "iu",
-    144: "gd",
-    145: "gv",
-    146: "ga",
-    147: "to",
-    148: "el-polyton",
-    149: "kl",
-    150: "az",
-    151: "nn",
-}
-
-
-_WINDOWS_LANGUAGE_CODES = {
-    lang.lower(): code for code, lang in _WINDOWS_LANGUAGES.items()
-}
-_MAC_LANGUAGE_CODES = {lang.lower(): code for code, lang in _MAC_LANGUAGES.items()}
-
-
-# MacOS language ID → MacOS script ID
-#
-# Note that the script ID is not sufficient to determine what encoding
-# to use in TrueType files. For some languages, MacOS used a modification
-# of a mainstream script. For example, an Icelandic name would be stored
-# with smRoman in the TrueType naming table, but the actual encoding
-# is a special Icelandic version of the normal Macintosh Roman encoding.
-# As another example, Inuktitut uses an 8-bit encoding for Canadian Aboriginal
-# Syllables but MacOS had run out of available script codes, so this was
-# done as a (pretty radical) “modification” of Ethiopic.
-#
-# http://unicode.org/Public/MAPPINGS/VENDORS/APPLE/Readme.txt
-_MAC_LANGUAGE_TO_SCRIPT = {
-    0: 0,  # langEnglish → smRoman
-    1: 0,  # langFrench → smRoman
-    2: 0,  # langGerman → smRoman
-    3: 0,  # langItalian → smRoman
-    4: 0,  # langDutch → smRoman
-    5: 0,  # langSwedish → smRoman
-    6: 0,  # langSpanish → smRoman
-    7: 0,  # langDanish → smRoman
-    8: 0,  # langPortuguese → smRoman
-    9: 0,  # langNorwegian → smRoman
-    10: 5,  # langHebrew → smHebrew
-    11: 1,  # langJapanese → smJapanese
-    12: 4,  # langArabic → smArabic
-    13: 0,  # langFinnish → smRoman
-    14: 6,  # langGreek → smGreek
-    15: 0,  # langIcelandic → smRoman (modified)
-    16: 0,  # langMaltese → smRoman
-    17: 0,  # langTurkish → smRoman (modified)
-    18: 0,  # langCroatian → smRoman (modified)
-    19: 2,  # langTradChinese → smTradChinese
-    20: 4,  # langUrdu → smArabic
-    21: 9,  # langHindi → smDevanagari
-    22: 21,  # langThai → smThai
-    23: 3,  # langKorean → smKorean
-    24: 29,  # langLithuanian → smCentralEuroRoman
-    25: 29,  # langPolish → smCentralEuroRoman
-    26: 29,  # langHungarian → smCentralEuroRoman
-    27: 29,  # langEstonian → smCentralEuroRoman
-    28: 29,  # langLatvian → smCentralEuroRoman
-    29: 0,  # langSami → smRoman
-    30: 0,  # langFaroese → smRoman (modified)
-    31: 4,  # langFarsi → smArabic (modified)
-    32: 7,  # langRussian → smCyrillic
-    33: 25,  # langSimpChinese → smSimpChinese
-    34: 0,  # langFlemish → smRoman
-    35: 0,  # langIrishGaelic → smRoman (modified)
-    36: 0,  # langAlbanian → smRoman
-    37: 0,  # langRomanian → smRoman (modified)
-    38: 29,  # langCzech → smCentralEuroRoman
-    39: 29,  # langSlovak → smCentralEuroRoman
-    40: 0,  # langSlovenian → smRoman (modified)
-    41: 5,  # langYiddish → smHebrew
-    42: 7,  # langSerbian → smCyrillic
-    43: 7,  # langMacedonian → smCyrillic
-    44: 7,  # langBulgarian → smCyrillic
-    45: 7,  # langUkrainian → smCyrillic (modified)
-    46: 7,  # langByelorussian → smCyrillic
-    47: 7,  # langUzbek → smCyrillic
-    48: 7,  # langKazakh → smCyrillic
-    49: 7,  # langAzerbaijani → smCyrillic
-    50: 4,  # langAzerbaijanAr → smArabic
-    51: 24,  # langArmenian → smArmenian
-    52: 23,  # langGeorgian → smGeorgian
-    53: 7,  # langMoldavian → smCyrillic
-    54: 7,  # langKirghiz → smCyrillic
-    55: 7,  # langTajiki → smCyrillic
-    56: 7,  # langTurkmen → smCyrillic
-    57: 27,  # langMongolian → smMongolian
-    58: 7,  # langMongolianCyr → smCyrillic
-    59: 4,  # langPashto → smArabic
-    60: 4,  # langKurdish → smArabic
-    61: 4,  # langKashmiri → smArabic
-    62: 4,  # langSindhi → smArabic
-    63: 26,  # langTibetan → smTibetan
-    64: 9,  # langNepali → smDevanagari
-    65: 9,  # langSanskrit → smDevanagari
-    66: 9,  # langMarathi → smDevanagari
-    67: 13,  # langBengali → smBengali
-    68: 13,  # langAssamese → smBengali
-    69: 11,  # langGujarati → smGujarati
-    70: 10,  # langPunjabi → smGurmukhi
-    71: 12,  # langOriya → smOriya
-    72: 17,  # langMalayalam → smMalayalam
-    73: 16,  # langKannada → smKannada
-    74: 14,  # langTamil → smTamil
-    75: 15,  # langTelugu → smTelugu
-    76: 18,  # langSinhalese → smSinhalese
-    77: 19,  # langBurmese → smBurmese
-    78: 20,  # langKhmer → smKhmer
-    79: 22,  # langLao → smLao
-    80: 30,  # langVietnamese → smVietnamese
-    81: 0,  # langIndonesian → smRoman
-    82: 0,  # langTagalog → smRoman
-    83: 0,  # langMalayRoman → smRoman
-    84: 4,  # langMalayArabic → smArabic
-    85: 28,  # langAmharic → smEthiopic
-    86: 28,  # langTigrinya → smEthiopic
-    87: 28,  # langOromo → smEthiopic
-    88: 0,  # langSomali → smRoman
-    89: 0,  # langSwahili → smRoman
-    90: 0,  # langKinyarwanda → smRoman
-    91: 0,  # langRundi → smRoman
-    92: 0,  # langNyanja → smRoman
-    93: 0,  # langMalagasy → smRoman
-    94: 0,  # langEsperanto → smRoman
-    128: 0,  # langWelsh → smRoman (modified)
-    129: 0,  # langBasque → smRoman
-    130: 0,  # langCatalan → smRoman
-    131: 0,  # langLatin → smRoman
-    132: 0,  # langQuechua → smRoman
-    133: 0,  # langGuarani → smRoman
-    134: 0,  # langAymara → smRoman
-    135: 7,  # langTatar → smCyrillic
-    136: 4,  # langUighur → smArabic
-    137: 26,  # langDzongkha → smTibetan
-    138: 0,  # langJavaneseRom → smRoman
-    139: 0,  # langSundaneseRom → smRoman
-    140: 0,  # langGalician → smRoman
-    141: 0,  # langAfrikaans → smRoman
-    142: 0,  # langBreton → smRoman (modified)
-    143: 28,  # langInuktitut → smEthiopic (modified)
-    144: 0,  # langScottishGaelic → smRoman (modified)
-    145: 0,  # langManxGaelic → smRoman (modified)
-    146: 0,  # langIrishGaelicScript → smRoman (modified)
-    147: 0,  # langTongan → smRoman
-    148: 6,  # langGreekAncient → smRoman
-    149: 0,  # langGreenlandic → smRoman
-    150: 0,  # langAzerbaijanRoman → smRoman
-    151: 0,  # langNynorsk → smRoman
-}
-
-
-class NameRecordVisitor(TTVisitor):
-    def __init__(self):
-        self.seen = set()
-
-
-@NameRecordVisitor.register_attrs(
-    (
-        (otTables.FeatureParamsSize, ("SubfamilyID", "SubfamilyNameID")),
-        (otTables.FeatureParamsStylisticSet, ("UINameID",)),
-        (
-            otTables.FeatureParamsCharacterVariants,
-            (
-                "FeatUILabelNameID",
-                "FeatUITooltipTextNameID",
-                "SampleTextNameID",
-                "FirstParamUILabelNameID",
-            ),
-        ),
-        (otTables.STAT, ("ElidedFallbackNameID",)),
-        (otTables.AxisRecord, ("AxisNameID",)),
-        (otTables.AxisValue, ("ValueNameID",)),
-        (otTables.FeatureName, ("FeatureNameID",)),
-        (otTables.Setting, ("SettingNameID",)),
-    )
-)
-def visit(visitor, obj, attr, value):
-    visitor.seen.add(value)
-
-
-@NameRecordVisitor.register(ttLib.getTableClass("fvar"))
-def visit(visitor, obj):
-    for inst in obj.instances:
-        if inst.postscriptNameID != 0xFFFF:
-            visitor.seen.add(inst.postscriptNameID)
-        visitor.seen.add(inst.subfamilyNameID)
-
-    for axis in obj.axes:
-        visitor.seen.add(axis.axisNameID)
-
-
-@NameRecordVisitor.register(ttLib.getTableClass("CPAL"))
-def visit(visitor, obj):
-    if obj.version == 1:
-        visitor.seen.update(obj.paletteLabels)
-        visitor.seen.update(obj.paletteEntryLabels)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__main__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__main__.py
deleted file mode 100644
index 64ffff2b9fdf58d8a557de7c1ae631b5c6fb4b6f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import sys
-from fontTools.varLib.instancer import main
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/clarin-pl/datasets-explorer/clarin_datasets/nkjp_pos_dataset.py b/spaces/clarin-pl/datasets-explorer/clarin_datasets/nkjp_pos_dataset.py
deleted file mode 100644
index 7679b81e58d7b1fc170c8f276c67e7e3ce312ea9..0000000000000000000000000000000000000000
--- a/spaces/clarin-pl/datasets-explorer/clarin_datasets/nkjp_pos_dataset.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-from datasets import load_dataset
-from sklearn.manifold import TSNE
-import streamlit as st
-
-from clarin_datasets.dataset_to_show import DatasetToShow
-from clarin_datasets.utils import (
-    PLOT_COLOR_PALETTE,
-    embed_sentence
-)
-
-
-class NkjpPosDataset(DatasetToShow):
-    def __init__(self):
-        DatasetToShow.__init__(self)
-        self.data_dict_named = None
-        self.dataset_name = "clarin-pl/nkjp-pos"
-        self.description = [
-            f"""
-            Dataset link: https://huggingface.co/datasets/{self.dataset_name}
-            
-            NKJP-POS is a part the National Corpus of Polish (Narodowy Korpus Języka Polskiego). 
-            Its objective is part-of-speech tagging, e.g. nouns, verbs, adjectives, adverbs, etc. During the creation of 
-            corpus, texts of were annotated by humans from various sources, covering many domains and genres. 
-            """,
-            "Tasks (input, output and metrics)",
-            """
-            Part-of-speech tagging (POS tagging) - tagging words in text with their corresponding part of speech.
-
-            Input ('tokens' column): sequence of tokens
-            
-            Output ('pos_tags' column): sequence of predicted tokens’ classes (35 possible classes, described in detail in the annotation guidelines)
-            
-            Measurements: F1-score (seqeval)
-            
-            Example:
-            
-            Input: ['Zarejestruj', 'się', 'jako', 'bezrobotny', '.']
-            
-            Input (translated by DeepL): Register as unemployed.
-            
-            Output: ['impt', 'qub', 'conj', 'subst', 'interp']
-            """,
-        ]
-
-    def load_data(self):
-        raw_dataset = load_dataset(self.dataset_name)
-        self.data_dict = {
-            subset: raw_dataset[subset].to_pandas() for subset in self.subsets
-        }
-        self.data_dict_named = {}
-        for subset in self.subsets:
-            references = raw_dataset[subset]["pos_tags"]
-            references_named = [
-                [
-                    raw_dataset[subset].features["pos_tags"].feature.names[label]
-                    for label in labels
-                ]
-                for labels in references
-            ]
-            self.data_dict_named[subset] = pd.DataFrame(
-                {
-                    "tokens": self.data_dict[subset]["tokens"],
-                    "tags": references_named,
-                }
-            )
-
-    def show_dataset(self):
-        header = st.container()
-        description = st.container()
-        dataframe_head = st.container()
-        class_distribution = st.container()
-        tsne_projection = st.container()
-
-        with header:
-            st.title(self.dataset_name)
-
-        with description:
-            st.header("Dataset description")
-            st.write(self.description[0])
-            st.subheader(self.description[1])
-            st.write(self.description[2])
-
-        with dataframe_head:
-            st.header("First 10 observations of the chosen subset")
-            subset_to_show = st.selectbox(
-                label="Select subset to see", options=self.subsets
-            )
-            df_to_show = (
-                self.data_dict[subset_to_show].head(10).drop("id", axis="columns")
-            )
-            st.dataframe(df_to_show)
-            st.text_area(label="LaTeX code", value=df_to_show.style.to_latex())
-
-        class_distribution_dict = {}
-        for subset in self.subsets:
-            all_labels_from_subset = self.data_dict_named[subset]["tags"].tolist()
-            all_labels_from_subset = [
-                x for subarray in all_labels_from_subset for x in subarray
-            ]
-            all_labels_from_subset = pd.Series(all_labels_from_subset)
-            class_distribution_dict[subset] = (
-                all_labels_from_subset.value_counts(normalize=True)
-                .sort_index()
-                .reset_index()
-                .rename({"index": "class", 0: subset}, axis="columns")
-            )
-
-        class_distribution_df = pd.merge(
-            class_distribution_dict["train"],
-            class_distribution_dict["test"],
-            on="class",
-        )
-
-        with class_distribution:
-            st.header("Class distribution in each subset")
-            st.dataframe(class_distribution_df)
-            st.text_area(
-                label="LaTeX code", value=class_distribution_df.style.to_latex()
-            )
-        SHOW_TSNE_PROJECTION = False
-        if SHOW_TSNE_PROJECTION:
-            with tsne_projection:
-                st.header("t-SNE projection of the dataset")
-                subset_to_project = st.selectbox(
-                    label="Select subset to project", options=self.subsets
-                )
-                tokens_unzipped = self.data_dict_named[subset_to_project]["tokens"].tolist()
-                tokens_unzipped = np.array([x for subarray in tokens_unzipped for x in subarray])
-                labels_unzipped = self.data_dict_named[subset_to_project]["tags"].tolist()
-                labels_unzipped = np.array([x for subarray in labels_unzipped for x in subarray])
-                df_unzipped = pd.DataFrame(
-                    {
-                        "tokens": tokens_unzipped,
-                        "tags": labels_unzipped,
-                    }
-                )
-                tokens_unzipped = df_unzipped["tokens"].values
-                labels_unzipped = df_unzipped["tags"].values
-                mapping_dict = {name: number for number, name in enumerate(set(labels_unzipped))}
-                labels_as_ints = [mapping_dict[label] for label in labels_unzipped]
-                embedded_tokens = np.array(
-                    [embed_sentence(x) for x in tokens_unzipped]
-                )
-                reducer = TSNE(
-                    n_components=2
-                )
-                transformed_embeddings = reducer.fit_transform(embedded_tokens)
-                fig, ax = plt.subplots()
-                ax.scatter(
-                    x=transformed_embeddings[:, 0],
-                    y=transformed_embeddings[:, 1],
-                    c=[
-                         PLOT_COLOR_PALETTE[i]
-                         for i in labels_as_ints
-                    ],
-                )
-                st.pyplot(fig)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/ast.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/ast.py
deleted file mode 100644
index 17c6cc3fbe494a076d2b59f4664ab9fe56ecd20f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/ast.py
+++ /dev/null
@@ -1,2134 +0,0 @@
-from fontTools.feaLib.error import FeatureLibError
-from fontTools.feaLib.location import FeatureLibLocation
-from fontTools.misc.encodingTools import getEncoding
-from fontTools.misc.textTools import byteord, tobytes
-from collections import OrderedDict
-import itertools
-
-SHIFT = " " * 4
-
-__all__ = [
-    "Element",
-    "FeatureFile",
-    "Comment",
-    "GlyphName",
-    "GlyphClass",
-    "GlyphClassName",
-    "MarkClassName",
-    "AnonymousBlock",
-    "Block",
-    "FeatureBlock",
-    "NestedBlock",
-    "LookupBlock",
-    "GlyphClassDefinition",
-    "GlyphClassDefStatement",
-    "MarkClass",
-    "MarkClassDefinition",
-    "AlternateSubstStatement",
-    "Anchor",
-    "AnchorDefinition",
-    "AttachStatement",
-    "AxisValueLocationStatement",
-    "BaseAxis",
-    "CVParametersNameStatement",
-    "ChainContextPosStatement",
-    "ChainContextSubstStatement",
-    "CharacterStatement",
-    "ConditionsetStatement",
-    "CursivePosStatement",
-    "ElidedFallbackName",
-    "ElidedFallbackNameID",
-    "Expression",
-    "FeatureNameStatement",
-    "FeatureReferenceStatement",
-    "FontRevisionStatement",
-    "HheaField",
-    "IgnorePosStatement",
-    "IgnoreSubstStatement",
-    "IncludeStatement",
-    "LanguageStatement",
-    "LanguageSystemStatement",
-    "LigatureCaretByIndexStatement",
-    "LigatureCaretByPosStatement",
-    "LigatureSubstStatement",
-    "LookupFlagStatement",
-    "LookupReferenceStatement",
-    "MarkBasePosStatement",
-    "MarkLigPosStatement",
-    "MarkMarkPosStatement",
-    "MultipleSubstStatement",
-    "NameRecord",
-    "OS2Field",
-    "PairPosStatement",
-    "ReverseChainSingleSubstStatement",
-    "ScriptStatement",
-    "SinglePosStatement",
-    "SingleSubstStatement",
-    "SizeParameters",
-    "Statement",
-    "STATAxisValueStatement",
-    "STATDesignAxisStatement",
-    "STATNameStatement",
-    "SubtableStatement",
-    "TableBlock",
-    "ValueRecord",
-    "ValueRecordDefinition",
-    "VheaField",
-]
-
-
-def deviceToString(device):
-    if device is None:
-        return "<device NULL>"
-    else:
-        return "<device %s>" % ", ".join("%d %d" % t for t in device)
-
-
-fea_keywords = set(
-    [
-        "anchor",
-        "anchordef",
-        "anon",
-        "anonymous",
-        "by",
-        "contour",
-        "cursive",
-        "device",
-        "enum",
-        "enumerate",
-        "excludedflt",
-        "exclude_dflt",
-        "feature",
-        "from",
-        "ignore",
-        "ignorebaseglyphs",
-        "ignoreligatures",
-        "ignoremarks",
-        "include",
-        "includedflt",
-        "include_dflt",
-        "language",
-        "languagesystem",
-        "lookup",
-        "lookupflag",
-        "mark",
-        "markattachmenttype",
-        "markclass",
-        "nameid",
-        "null",
-        "parameters",
-        "pos",
-        "position",
-        "required",
-        "righttoleft",
-        "reversesub",
-        "rsub",
-        "script",
-        "sub",
-        "substitute",
-        "subtable",
-        "table",
-        "usemarkfilteringset",
-        "useextension",
-        "valuerecorddef",
-        "base",
-        "gdef",
-        "head",
-        "hhea",
-        "name",
-        "vhea",
-        "vmtx",
-    ]
-)
-
-
-def asFea(g):
-    if hasattr(g, "asFea"):
-        return g.asFea()
-    elif isinstance(g, tuple) and len(g) == 2:
-        return asFea(g[0]) + " - " + asFea(g[1])  # a range
-    elif g.lower() in fea_keywords:
-        return "\\" + g
-    else:
-        return g
-
-
-class Element(object):
-    """A base class representing "something" in a feature file."""
-
-    def __init__(self, location=None):
-        #: location of this element as a `FeatureLibLocation` object.
-        if location and not isinstance(location, FeatureLibLocation):
-            location = FeatureLibLocation(*location)
-        self.location = location
-
-    def build(self, builder):
-        pass
-
-    def asFea(self, indent=""):
-        """Returns this element as a string of feature code. For block-type
-        elements (such as :class:`FeatureBlock`), the `indent` string is
-        added to the start of each line in the output."""
-        raise NotImplementedError
-
-    def __str__(self):
-        return self.asFea()
-
-
-class Statement(Element):
-    pass
-
-
-class Expression(Element):
-    pass
-
-
-class Comment(Element):
-    """A comment in a feature file."""
-
-    def __init__(self, text, location=None):
-        super(Comment, self).__init__(location)
-        #: Text of the comment
-        self.text = text
-
-    def asFea(self, indent=""):
-        return self.text
-
-
-class NullGlyph(Expression):
-    """The NULL glyph, used in glyph deletion substitutions."""
-
-    def __init__(self, location=None):
-        Expression.__init__(self, location)
-        #: The name itself as a string
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return ()
-
-    def asFea(self, indent=""):
-        return "NULL"
-
-
-class GlyphName(Expression):
-    """A single glyph name, such as ``cedilla``."""
-
-    def __init__(self, glyph, location=None):
-        Expression.__init__(self, location)
-        #: The name itself as a string
-        self.glyph = glyph
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return (self.glyph,)
-
-    def asFea(self, indent=""):
-        return asFea(self.glyph)
-
-
-class GlyphClass(Expression):
-    """A glyph class, such as ``[acute cedilla grave]``."""
-
-    def __init__(self, glyphs=None, location=None):
-        Expression.__init__(self, location)
-        #: The list of glyphs in this class, as :class:`GlyphName` objects.
-        self.glyphs = glyphs if glyphs is not None else []
-        self.original = []
-        self.curr = 0
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs)
-
-    def asFea(self, indent=""):
-        if len(self.original):
-            if self.curr < len(self.glyphs):
-                self.original.extend(self.glyphs[self.curr :])
-                self.curr = len(self.glyphs)
-            return "[" + " ".join(map(asFea, self.original)) + "]"
-        else:
-            return "[" + " ".join(map(asFea, self.glyphs)) + "]"
-
-    def extend(self, glyphs):
-        """Add a list of :class:`GlyphName` objects to the class."""
-        self.glyphs.extend(glyphs)
-
-    def append(self, glyph):
-        """Add a single :class:`GlyphName` object to the class."""
-        self.glyphs.append(glyph)
-
-    def add_range(self, start, end, glyphs):
-        """Add a range (e.g. ``A-Z``) to the class. ``start`` and ``end``
-        are either :class:`GlyphName` objects or strings representing the
-        start and end glyphs in the class, and ``glyphs`` is the full list of
-        :class:`GlyphName` objects in the range."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append((start, end))
-        self.glyphs.extend(glyphs)
-        self.curr = len(self.glyphs)
-
-    def add_cid_range(self, start, end, glyphs):
-        """Add a range to the class by glyph ID. ``start`` and ``end`` are the
-        initial and final IDs, and ``glyphs`` is the full list of
-        :class:`GlyphName` objects in the range."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append(("\\{}".format(start), "\\{}".format(end)))
-        self.glyphs.extend(glyphs)
-        self.curr = len(self.glyphs)
-
-    def add_class(self, gc):
-        """Add glyphs from the given :class:`GlyphClassName` object to the
-        class."""
-        if self.curr < len(self.glyphs):
-            self.original.extend(self.glyphs[self.curr :])
-        self.original.append(gc)
-        self.glyphs.extend(gc.glyphSet())
-        self.curr = len(self.glyphs)
-
-
-class GlyphClassName(Expression):
-    """A glyph class name, such as ``@FRENCH_MARKS``. This must be instantiated
-    with a :class:`GlyphClassDefinition` object."""
-
-    def __init__(self, glyphclass, location=None):
-        Expression.__init__(self, location)
-        assert isinstance(glyphclass, GlyphClassDefinition)
-        self.glyphclass = glyphclass
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphclass.glyphSet())
-
-    def asFea(self, indent=""):
-        return "@" + self.glyphclass.name
-
-
-class MarkClassName(Expression):
-    """A mark class name, such as ``@FRENCH_MARKS`` defined with ``markClass``.
-    This must be instantiated with a :class:`MarkClass` object."""
-
-    def __init__(self, markClass, location=None):
-        Expression.__init__(self, location)
-        assert isinstance(markClass, MarkClass)
-        self.markClass = markClass
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return self.markClass.glyphSet()
-
-    def asFea(self, indent=""):
-        return "@" + self.markClass.name
-
-
-class AnonymousBlock(Statement):
-    """An anonymous data block."""
-
-    def __init__(self, tag, content, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag  #: string containing the block's "tag"
-        self.content = content  #: block data as string
-
-    def asFea(self, indent=""):
-        res = "anon {} {{\n".format(self.tag)
-        res += self.content
-        res += "}} {};\n\n".format(self.tag)
-        return res
-
-
-class Block(Statement):
-    """A block of statements: feature, lookup, etc."""
-
-    def __init__(self, location=None):
-        Statement.__init__(self, location)
-        self.statements = []  #: Statements contained in the block
-
-    def build(self, builder):
-        """When handed a 'builder' object of comparable interface to
-        :class:`fontTools.feaLib.builder`, walks the statements in this
-        block, calling the builder callbacks."""
-        for s in self.statements:
-            s.build(builder)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        return (
-            indent
-            + ("\n" + indent).join([s.asFea(indent=indent) for s in self.statements])
-            + "\n"
-        )
-
-
-class FeatureFile(Block):
-    """The top-level element of the syntax tree, containing the whole feature
-    file in its ``statements`` attribute."""
-
-    def __init__(self):
-        Block.__init__(self, location=None)
-        self.markClasses = {}  # name --> ast.MarkClass
-
-    def asFea(self, indent=""):
-        return "\n".join(s.asFea(indent=indent) for s in self.statements)
-
-
-class FeatureBlock(Block):
-    """A named feature block."""
-
-    def __init__(self, name, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.use_extension = name, use_extension
-
-    def build(self, builder):
-        """Call the ``start_feature`` callback on the builder object, visit
-        all the statements in this feature, and then call ``end_feature``."""
-        # TODO(sascha): Handle use_extension.
-        builder.start_feature(self.location, self.name)
-        # language exclude_dflt statements modify builder.features_
-        # limit them to this block with temporary builder.features_
-        features = builder.features_
-        builder.features_ = {}
-        Block.build(self, builder)
-        for key, value in builder.features_.items():
-            features.setdefault(key, []).extend(value)
-        builder.features_ = features
-        builder.end_feature()
-
-    def asFea(self, indent=""):
-        res = indent + "feature %s " % self.name.strip()
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += indent + "} %s;\n" % self.name.strip()
-        return res
-
-
-class NestedBlock(Block):
-    """A block inside another block, for example when found inside a
-    ``cvParameters`` block."""
-
-    def __init__(self, tag, block_name, location=None):
-        Block.__init__(self, location)
-        self.tag = tag
-        self.block_name = block_name
-
-    def build(self, builder):
-        Block.build(self, builder)
-        if self.block_name == "ParamUILabelNameID":
-            builder.add_to_cv_num_named_params(self.tag)
-
-    def asFea(self, indent=""):
-        res = "{}{} {{\n".format(indent, self.block_name)
-        res += Block.asFea(self, indent=indent)
-        res += "{}}};\n".format(indent)
-        return res
-
-
-class LookupBlock(Block):
-    """A named lookup, containing ``statements``."""
-
-    def __init__(self, name, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.use_extension = name, use_extension
-
-    def build(self, builder):
-        # TODO(sascha): Handle use_extension.
-        builder.start_lookup_block(self.location, self.name)
-        Block.build(self, builder)
-        builder.end_lookup_block()
-
-    def asFea(self, indent=""):
-        res = "lookup {} ".format(self.name)
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += "{}}} {};\n".format(indent, self.name)
-        return res
-
-
-class TableBlock(Block):
-    """A ``table ... { }`` block."""
-
-    def __init__(self, name, location=None):
-        Block.__init__(self, location)
-        self.name = name
-
-    def asFea(self, indent=""):
-        res = "table {} {{\n".format(self.name.strip())
-        res += super(TableBlock, self).asFea(indent=indent)
-        res += "}} {};\n".format(self.name.strip())
-        return res
-
-
-class GlyphClassDefinition(Statement):
-    """Example: ``@UPPERCASE = [A-Z];``."""
-
-    def __init__(self, name, glyphs, location=None):
-        Statement.__init__(self, location)
-        self.name = name  #: class name as a string, without initial ``@``
-        self.glyphs = glyphs  #: a :class:`GlyphClass` object
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs.glyphSet())
-
-    def asFea(self, indent=""):
-        return "@" + self.name + " = " + self.glyphs.asFea() + ";"
-
-
-class GlyphClassDefStatement(Statement):
-    """Example: ``GlyphClassDef @UPPERCASE, [B], [C], [D];``. The parameters
-    must be either :class:`GlyphClass` or :class:`GlyphClassName` objects, or
-    ``None``."""
-
-    def __init__(
-        self, baseGlyphs, markGlyphs, ligatureGlyphs, componentGlyphs, location=None
-    ):
-        Statement.__init__(self, location)
-        self.baseGlyphs, self.markGlyphs = (baseGlyphs, markGlyphs)
-        self.ligatureGlyphs = ligatureGlyphs
-        self.componentGlyphs = componentGlyphs
-
-    def build(self, builder):
-        """Calls the builder's ``add_glyphClassDef`` callback."""
-        base = self.baseGlyphs.glyphSet() if self.baseGlyphs else tuple()
-        liga = self.ligatureGlyphs.glyphSet() if self.ligatureGlyphs else tuple()
-        mark = self.markGlyphs.glyphSet() if self.markGlyphs else tuple()
-        comp = self.componentGlyphs.glyphSet() if self.componentGlyphs else tuple()
-        builder.add_glyphClassDef(self.location, base, liga, mark, comp)
-
-    def asFea(self, indent=""):
-        return "GlyphClassDef {}, {}, {}, {};".format(
-            self.baseGlyphs.asFea() if self.baseGlyphs else "",
-            self.ligatureGlyphs.asFea() if self.ligatureGlyphs else "",
-            self.markGlyphs.asFea() if self.markGlyphs else "",
-            self.componentGlyphs.asFea() if self.componentGlyphs else "",
-        )
-
-
-class MarkClass(object):
-    """One `or more` ``markClass`` statements for the same mark class.
-
-    While glyph classes can be defined only once, the feature file format
-    allows expanding mark classes with multiple definitions, each using
-    different glyphs and anchors. The following are two ``MarkClassDefinitions``
-    for the same ``MarkClass``::
-
-        markClass [acute grave] <anchor 350 800> @FRENCH_ACCENTS;
-        markClass [cedilla] <anchor 350 -200> @FRENCH_ACCENTS;
-
-    The ``MarkClass`` object is therefore just a container for a list of
-    :class:`MarkClassDefinition` statements.
-    """
-
-    def __init__(self, name):
-        self.name = name
-        self.definitions = []
-        self.glyphs = OrderedDict()  # glyph --> ast.MarkClassDefinitions
-
-    def addDefinition(self, definition):
-        """Add a :class:`MarkClassDefinition` statement to this mark class."""
-        assert isinstance(definition, MarkClassDefinition)
-        self.definitions.append(definition)
-        for glyph in definition.glyphSet():
-            if glyph in self.glyphs:
-                otherLoc = self.glyphs[glyph].location
-                if otherLoc is None:
-                    end = ""
-                else:
-                    end = f" at {otherLoc}"
-                raise FeatureLibError(
-                    "Glyph %s already defined%s" % (glyph, end), definition.location
-                )
-            self.glyphs[glyph] = definition
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return tuple(self.glyphs.keys())
-
-    def asFea(self, indent=""):
-        res = "\n".join(d.asFea() for d in self.definitions)
-        return res
-
-
-class MarkClassDefinition(Statement):
-    """A single ``markClass`` statement. The ``markClass`` should be a
-    :class:`MarkClass` object, the ``anchor`` an :class:`Anchor` object,
-    and the ``glyphs`` parameter should be a `glyph-containing object`_ .
-
-    Example:
-
-        .. code:: python
-
-            mc = MarkClass("FRENCH_ACCENTS")
-            mc.addDefinition( MarkClassDefinition(mc, Anchor(350, 800),
-                GlyphClass([ GlyphName("acute"), GlyphName("grave") ])
-            ) )
-            mc.addDefinition( MarkClassDefinition(mc, Anchor(350, -200),
-                GlyphClass([ GlyphName("cedilla") ])
-            ) )
-
-            mc.asFea()
-            # markClass [acute grave] <anchor 350 800> @FRENCH_ACCENTS;
-            # markClass [cedilla] <anchor 350 -200> @FRENCH_ACCENTS;
-
-    """
-
-    def __init__(self, markClass, anchor, glyphs, location=None):
-        Statement.__init__(self, location)
-        assert isinstance(markClass, MarkClass)
-        assert isinstance(anchor, Anchor) and isinstance(glyphs, Expression)
-        self.markClass, self.anchor, self.glyphs = markClass, anchor, glyphs
-
-    def glyphSet(self):
-        """The glyphs in this class as a tuple of :class:`GlyphName` objects."""
-        return self.glyphs.glyphSet()
-
-    def asFea(self, indent=""):
-        return "markClass {} {} @{};".format(
-            self.glyphs.asFea(), self.anchor.asFea(), self.markClass.name
-        )
-
-
-class AlternateSubstStatement(Statement):
-    """A ``sub ... from ...`` statement.
-
-    ``prefix``, ``glyph``, ``suffix`` and ``replacement`` should be lists of
-    `glyph-containing objects`_. ``glyph`` should be a `one element list`."""
-
-    def __init__(self, prefix, glyph, suffix, replacement, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyph, self.suffix = (prefix, glyph, suffix)
-        self.replacement = replacement
-
-    def build(self, builder):
-        """Calls the builder's ``add_alternate_subst`` callback."""
-        glyph = self.glyph.glyphSet()
-        assert len(glyph) == 1, glyph
-        glyph = list(glyph)[0]
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        replacement = self.replacement.glyphSet()
-        builder.add_alternate_subst(self.location, prefix, glyph, suffix, replacement)
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix):
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += asFea(self.glyph) + "'"  # even though we really only use 1
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += asFea(self.glyph)
-        res += " from "
-        res += asFea(self.replacement)
-        res += ";"
-        return res
-
-
-class Anchor(Expression):
-    """An ``Anchor`` element, used inside a ``pos`` rule.
-
-    If a ``name`` is given, this will be used in preference to the coordinates.
-    Other values should be integer.
-    """
-
-    def __init__(
-        self,
-        x,
-        y,
-        name=None,
-        contourpoint=None,
-        xDeviceTable=None,
-        yDeviceTable=None,
-        location=None,
-    ):
-        Expression.__init__(self, location)
-        self.name = name
-        self.x, self.y, self.contourpoint = x, y, contourpoint
-        self.xDeviceTable, self.yDeviceTable = xDeviceTable, yDeviceTable
-
-    def asFea(self, indent=""):
-        if self.name is not None:
-            return "<anchor {}>".format(self.name)
-        res = "<anchor {} {}".format(self.x, self.y)
-        if self.contourpoint:
-            res += " contourpoint {}".format(self.contourpoint)
-        if self.xDeviceTable or self.yDeviceTable:
-            res += " "
-            res += deviceToString(self.xDeviceTable)
-            res += " "
-            res += deviceToString(self.yDeviceTable)
-        res += ">"
-        return res
-
-
-class AnchorDefinition(Statement):
-    """A named anchor definition. (2.e.viii). ``name`` should be a string."""
-
-    def __init__(self, name, x, y, contourpoint=None, location=None):
-        Statement.__init__(self, location)
-        self.name, self.x, self.y, self.contourpoint = name, x, y, contourpoint
-
-    def asFea(self, indent=""):
-        res = "anchorDef {} {}".format(self.x, self.y)
-        if self.contourpoint:
-            res += " contourpoint {}".format(self.contourpoint)
-        res += " {};".format(self.name)
-        return res
-
-
-class AttachStatement(Statement):
-    """A ``GDEF`` table ``Attach`` statement."""
-
-    def __init__(self, glyphs, contourPoints, location=None):
-        Statement.__init__(self, location)
-        self.glyphs = glyphs  #: A `glyph-containing object`_
-        self.contourPoints = contourPoints  #: A list of integer contour points
-
-    def build(self, builder):
-        """Calls the builder's ``add_attach_points`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_attach_points(self.location, glyphs, self.contourPoints)
-
-    def asFea(self, indent=""):
-        return "Attach {} {};".format(
-            self.glyphs.asFea(), " ".join(str(c) for c in self.contourPoints)
-        )
-
-
-class ChainContextPosStatement(Statement):
-    r"""A chained contextual positioning statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_ .
-
-    ``lookups`` should be a list of elements representing what lookups
-    to apply at each glyph position. Each element should be a
-    :class:`LookupBlock` to apply a single chaining lookup at the given
-    position, a list of :class:`LookupBlock`\ s to apply multiple
-    lookups, or ``None`` to apply no lookup. The length of the outer
-    list should equal the length of ``glyphs``; the inner lists can be
-    of variable length."""
-
-    def __init__(self, prefix, glyphs, suffix, lookups, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = prefix, glyphs, suffix
-        self.lookups = list(lookups)
-        for i, lookup in enumerate(lookups):
-            if lookup:
-                try:
-                    (_ for _ in lookup)
-                except TypeError:
-                    self.lookups[i] = [lookup]
-
-    def build(self, builder):
-        """Calls the builder's ``add_chain_context_pos`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_chain_context_pos(
-            self.location, prefix, glyphs, suffix, self.lookups
-        )
-
-    def asFea(self, indent=""):
-        res = "pos "
-        if (
-            len(self.prefix)
-            or len(self.suffix)
-            or any([x is not None for x in self.lookups])
-        ):
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            for i, g in enumerate(self.glyphs):
-                res += g.asFea() + "'"
-                if self.lookups[i]:
-                    for lu in self.lookups[i]:
-                        res += " lookup " + lu.name
-                if i < len(self.glyphs) - 1:
-                    res += " "
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(map(asFea, self.glyph))
-        res += ";"
-        return res
-
-
-class ChainContextSubstStatement(Statement):
-    r"""A chained contextual substitution statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_ .
-
-    ``lookups`` should be a list of elements representing what lookups
-    to apply at each glyph position. Each element should be a
-    :class:`LookupBlock` to apply a single chaining lookup at the given
-    position, a list of :class:`LookupBlock`\ s to apply multiple
-    lookups, or ``None`` to apply no lookup. The length of the outer
-    list should equal the length of ``glyphs``; the inner lists can be
-    of variable length."""
-
-    def __init__(self, prefix, glyphs, suffix, lookups, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = prefix, glyphs, suffix
-        self.lookups = list(lookups)
-        for i, lookup in enumerate(lookups):
-            if lookup:
-                try:
-                    (_ for _ in lookup)
-                except TypeError:
-                    self.lookups[i] = [lookup]
-
-    def build(self, builder):
-        """Calls the builder's ``add_chain_context_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_chain_context_subst(
-            self.location, prefix, glyphs, suffix, self.lookups
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if (
-            len(self.prefix)
-            or len(self.suffix)
-            or any([x is not None for x in self.lookups])
-        ):
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            for i, g in enumerate(self.glyphs):
-                res += g.asFea() + "'"
-                if self.lookups[i]:
-                    for lu in self.lookups[i]:
-                        res += " lookup " + lu.name
-                if i < len(self.glyphs) - 1:
-                    res += " "
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(map(asFea, self.glyph))
-        res += ";"
-        return res
-
-
-class CursivePosStatement(Statement):
-    """A cursive positioning statement. Entry and exit anchors can either
-    be :class:`Anchor` objects or ``None``."""
-
-    def __init__(self, glyphclass, entryAnchor, exitAnchor, location=None):
-        Statement.__init__(self, location)
-        self.glyphclass = glyphclass
-        self.entryAnchor, self.exitAnchor = entryAnchor, exitAnchor
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cursive_pos`` callback."""
-        builder.add_cursive_pos(
-            self.location, self.glyphclass.glyphSet(), self.entryAnchor, self.exitAnchor
-        )
-
-    def asFea(self, indent=""):
-        entry = self.entryAnchor.asFea() if self.entryAnchor else "<anchor NULL>"
-        exit = self.exitAnchor.asFea() if self.exitAnchor else "<anchor NULL>"
-        return "pos cursive {} {} {};".format(self.glyphclass.asFea(), entry, exit)
-
-
-class FeatureReferenceStatement(Statement):
-    """Example: ``feature salt;``"""
-
-    def __init__(self, featureName, location=None):
-        Statement.__init__(self, location)
-        self.location, self.featureName = (location, featureName)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_feature_reference`` callback."""
-        builder.add_feature_reference(self.location, self.featureName)
-
-    def asFea(self, indent=""):
-        return "feature {};".format(self.featureName)
-
-
-class IgnorePosStatement(Statement):
-    """An ``ignore pos`` statement, containing `one or more` contexts to ignore.
-
-    ``chainContexts`` should be a list of ``(prefix, glyphs, suffix)`` tuples,
-    with each of ``prefix``, ``glyphs`` and ``suffix`` being
-    `glyph-containing objects`_ ."""
-
-    def __init__(self, chainContexts, location=None):
-        Statement.__init__(self, location)
-        self.chainContexts = chainContexts
-
-    def build(self, builder):
-        """Calls the builder object's ``add_chain_context_pos`` callback on each
-        rule context."""
-        for prefix, glyphs, suffix in self.chainContexts:
-            prefix = [p.glyphSet() for p in prefix]
-            glyphs = [g.glyphSet() for g in glyphs]
-            suffix = [s.glyphSet() for s in suffix]
-            builder.add_chain_context_pos(self.location, prefix, glyphs, suffix, [])
-
-    def asFea(self, indent=""):
-        contexts = []
-        for prefix, glyphs, suffix in self.chainContexts:
-            res = ""
-            if len(prefix) or len(suffix):
-                if len(prefix):
-                    res += " ".join(map(asFea, prefix)) + " "
-                res += " ".join(g.asFea() + "'" for g in glyphs)
-                if len(suffix):
-                    res += " " + " ".join(map(asFea, suffix))
-            else:
-                res += " ".join(map(asFea, glyphs))
-            contexts.append(res)
-        return "ignore pos " + ", ".join(contexts) + ";"
-
-
-class IgnoreSubstStatement(Statement):
-    """An ``ignore sub`` statement, containing `one or more` contexts to ignore.
-
-    ``chainContexts`` should be a list of ``(prefix, glyphs, suffix)`` tuples,
-    with each of ``prefix``, ``glyphs`` and ``suffix`` being
-    `glyph-containing objects`_ ."""
-
-    def __init__(self, chainContexts, location=None):
-        Statement.__init__(self, location)
-        self.chainContexts = chainContexts
-
-    def build(self, builder):
-        """Calls the builder object's ``add_chain_context_subst`` callback on
-        each rule context."""
-        for prefix, glyphs, suffix in self.chainContexts:
-            prefix = [p.glyphSet() for p in prefix]
-            glyphs = [g.glyphSet() for g in glyphs]
-            suffix = [s.glyphSet() for s in suffix]
-            builder.add_chain_context_subst(self.location, prefix, glyphs, suffix, [])
-
-    def asFea(self, indent=""):
-        contexts = []
-        for prefix, glyphs, suffix in self.chainContexts:
-            res = ""
-            if len(prefix):
-                res += " ".join(map(asFea, prefix)) + " "
-            res += " ".join(g.asFea() + "'" for g in glyphs)
-            if len(suffix):
-                res += " " + " ".join(map(asFea, suffix))
-            contexts.append(res)
-        return "ignore sub " + ", ".join(contexts) + ";"
-
-
-class IncludeStatement(Statement):
-    """An ``include()`` statement."""
-
-    def __init__(self, filename, location=None):
-        super(IncludeStatement, self).__init__(location)
-        self.filename = filename  #: String containing name of file to include
-
-    def build(self):
-        # TODO: consider lazy-loading the including parser/lexer?
-        raise FeatureLibError(
-            "Building an include statement is not implemented yet. "
-            "Instead, use Parser(..., followIncludes=True) for building.",
-            self.location,
-        )
-
-    def asFea(self, indent=""):
-        return indent + "include(%s);" % self.filename
-
-
-class LanguageStatement(Statement):
-    """A ``language`` statement within a feature."""
-
-    def __init__(self, language, include_default=True, required=False, location=None):
-        Statement.__init__(self, location)
-        assert len(language) == 4
-        self.language = language  #: A four-character language tag
-        self.include_default = include_default  #: If false, "exclude_dflt"
-        self.required = required
-
-    def build(self, builder):
-        """Call the builder object's ``set_language`` callback."""
-        builder.set_language(
-            location=self.location,
-            language=self.language,
-            include_default=self.include_default,
-            required=self.required,
-        )
-
-    def asFea(self, indent=""):
-        res = "language {}".format(self.language.strip())
-        if not self.include_default:
-            res += " exclude_dflt"
-        if self.required:
-            res += " required"
-        res += ";"
-        return res
-
-
-class LanguageSystemStatement(Statement):
-    """A top-level ``languagesystem`` statement."""
-
-    def __init__(self, script, language, location=None):
-        Statement.__init__(self, location)
-        self.script, self.language = (script, language)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_language_system`` callback."""
-        builder.add_language_system(self.location, self.script, self.language)
-
-    def asFea(self, indent=""):
-        return "languagesystem {} {};".format(self.script, self.language.strip())
-
-
-class FontRevisionStatement(Statement):
-    """A ``head`` table ``FontRevision`` statement. ``revision`` should be a
-    number, and will be formatted to three significant decimal places."""
-
-    def __init__(self, revision, location=None):
-        Statement.__init__(self, location)
-        self.revision = revision
-
-    def build(self, builder):
-        builder.set_font_revision(self.location, self.revision)
-
-    def asFea(self, indent=""):
-        return "FontRevision {:.3f};".format(self.revision)
-
-
-class LigatureCaretByIndexStatement(Statement):
-    """A ``GDEF`` table ``LigatureCaretByIndex`` statement. ``glyphs`` should be
-    a `glyph-containing object`_, and ``carets`` should be a list of integers."""
-
-    def __init__(self, glyphs, carets, location=None):
-        Statement.__init__(self, location)
-        self.glyphs, self.carets = (glyphs, carets)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_ligatureCaretByIndex_`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_ligatureCaretByIndex_(self.location, glyphs, set(self.carets))
-
-    def asFea(self, indent=""):
-        return "LigatureCaretByIndex {} {};".format(
-            self.glyphs.asFea(), " ".join(str(x) for x in self.carets)
-        )
-
-
-class LigatureCaretByPosStatement(Statement):
-    """A ``GDEF`` table ``LigatureCaretByPos`` statement. ``glyphs`` should be
-    a `glyph-containing object`_, and ``carets`` should be a list of integers."""
-
-    def __init__(self, glyphs, carets, location=None):
-        Statement.__init__(self, location)
-        self.glyphs, self.carets = (glyphs, carets)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_ligatureCaretByPos_`` callback."""
-        glyphs = self.glyphs.glyphSet()
-        builder.add_ligatureCaretByPos_(self.location, glyphs, set(self.carets))
-
-    def asFea(self, indent=""):
-        return "LigatureCaretByPos {} {};".format(
-            self.glyphs.asFea(), " ".join(str(x) for x in self.carets)
-        )
-
-
-class LigatureSubstStatement(Statement):
-    """A chained contextual substitution statement.
-
-    ``prefix``, ``glyphs``, and ``suffix`` should be lists of
-    `glyph-containing objects`_; ``replacement`` should be a single
-    `glyph-containing object`_.
-
-    If ``forceChain`` is True, this is expressed as a chaining rule
-    (e.g. ``sub f' i' by f_i``) even when no context is given."""
-
-    def __init__(self, prefix, glyphs, suffix, replacement, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.glyphs, self.suffix = (prefix, glyphs, suffix)
-        self.replacement, self.forceChain = replacement, forceChain
-
-    def build(self, builder):
-        prefix = [p.glyphSet() for p in self.prefix]
-        glyphs = [g.glyphSet() for g in self.glyphs]
-        suffix = [s.glyphSet() for s in self.suffix]
-        builder.add_ligature_subst(
-            self.location, prefix, glyphs, suffix, self.replacement, self.forceChain
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(g.asFea() for g in self.prefix) + " "
-            res += " ".join(g.asFea() + "'" for g in self.glyphs)
-            if len(self.suffix):
-                res += " " + " ".join(g.asFea() for g in self.suffix)
-        else:
-            res += " ".join(g.asFea() for g in self.glyphs)
-        res += " by "
-        res += asFea(self.replacement)
-        res += ";"
-        return res
-
-
-class LookupFlagStatement(Statement):
-    """A ``lookupflag`` statement. The ``value`` should be an integer value
-    representing the flags in use, but not including the ``markAttachment``
-    class and ``markFilteringSet`` values, which must be specified as
-    glyph-containing objects."""
-
-    def __init__(
-        self, value=0, markAttachment=None, markFilteringSet=None, location=None
-    ):
-        Statement.__init__(self, location)
-        self.value = value
-        self.markAttachment = markAttachment
-        self.markFilteringSet = markFilteringSet
-
-    def build(self, builder):
-        """Calls the builder object's ``set_lookup_flag`` callback."""
-        markAttach = None
-        if self.markAttachment is not None:
-            markAttach = self.markAttachment.glyphSet()
-        markFilter = None
-        if self.markFilteringSet is not None:
-            markFilter = self.markFilteringSet.glyphSet()
-        builder.set_lookup_flag(self.location, self.value, markAttach, markFilter)
-
-    def asFea(self, indent=""):
-        res = []
-        flags = ["RightToLeft", "IgnoreBaseGlyphs", "IgnoreLigatures", "IgnoreMarks"]
-        curr = 1
-        for i in range(len(flags)):
-            if self.value & curr != 0:
-                res.append(flags[i])
-            curr = curr << 1
-        if self.markAttachment is not None:
-            res.append("MarkAttachmentType {}".format(self.markAttachment.asFea()))
-        if self.markFilteringSet is not None:
-            res.append("UseMarkFilteringSet {}".format(self.markFilteringSet.asFea()))
-        if not res:
-            res = ["0"]
-        return "lookupflag {};".format(" ".join(res))
-
-
-class LookupReferenceStatement(Statement):
-    """Represents a ``lookup ...;`` statement to include a lookup in a feature.
-
-    The ``lookup`` should be a :class:`LookupBlock` object."""
-
-    def __init__(self, lookup, location=None):
-        Statement.__init__(self, location)
-        self.location, self.lookup = (location, lookup)
-
-    def build(self, builder):
-        """Calls the builder object's ``add_lookup_call`` callback."""
-        builder.add_lookup_call(self.lookup.name)
-
-    def asFea(self, indent=""):
-        return "lookup {};".format(self.lookup.name)
-
-
-class MarkBasePosStatement(Statement):
-    """A mark-to-base positioning rule. The ``base`` should be a
-    `glyph-containing object`_. The ``marks`` should be a list of
-    (:class:`Anchor`, :class:`MarkClass`) tuples."""
-
-    def __init__(self, base, marks, location=None):
-        Statement.__init__(self, location)
-        self.base, self.marks = base, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_base_pos`` callback."""
-        builder.add_mark_base_pos(self.location, self.base.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos base {}".format(self.base.asFea())
-        for a, m in self.marks:
-            res += "\n" + indent + SHIFT + "{} mark @{}".format(a.asFea(), m.name)
-        res += ";"
-        return res
-
-
-class MarkLigPosStatement(Statement):
-    """A mark-to-ligature positioning rule. The ``ligatures`` must be a
-    `glyph-containing object`_. The ``marks`` should be a list of lists: each
-    element in the top-level list represents a component glyph, and is made
-    up of a list of (:class:`Anchor`, :class:`MarkClass`) tuples representing
-    mark attachment points for that position.
-
-    Example::
-
-        m1 = MarkClass("TOP_MARKS")
-        m2 = MarkClass("BOTTOM_MARKS")
-        # ... add definitions to mark classes...
-
-        glyph = GlyphName("lam_meem_jeem")
-        marks = [
-            [ (Anchor(625,1800), m1) ], # Attachments on 1st component (lam)
-            [ (Anchor(376,-378), m2) ], # Attachments on 2nd component (meem)
-            [ ]                         # No attachments on the jeem
-        ]
-        mlp = MarkLigPosStatement(glyph, marks)
-
-        mlp.asFea()
-        # pos ligature lam_meem_jeem <anchor 625 1800> mark @TOP_MARKS
-        # ligComponent <anchor 376 -378> mark @BOTTOM_MARKS;
-
-    """
-
-    def __init__(self, ligatures, marks, location=None):
-        Statement.__init__(self, location)
-        self.ligatures, self.marks = ligatures, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_lig_pos`` callback."""
-        builder.add_mark_lig_pos(self.location, self.ligatures.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos ligature {}".format(self.ligatures.asFea())
-        ligs = []
-        for l in self.marks:
-            temp = ""
-            if l is None or not len(l):
-                temp = "\n" + indent + SHIFT * 2 + "<anchor NULL>"
-            else:
-                for a, m in l:
-                    temp += (
-                        "\n"
-                        + indent
-                        + SHIFT * 2
-                        + "{} mark @{}".format(a.asFea(), m.name)
-                    )
-            ligs.append(temp)
-        res += ("\n" + indent + SHIFT + "ligComponent").join(ligs)
-        res += ";"
-        return res
-
-
-class MarkMarkPosStatement(Statement):
-    """A mark-to-mark positioning rule. The ``baseMarks`` must be a
-    `glyph-containing object`_. The ``marks`` should be a list of
-    (:class:`Anchor`, :class:`MarkClass`) tuples."""
-
-    def __init__(self, baseMarks, marks, location=None):
-        Statement.__init__(self, location)
-        self.baseMarks, self.marks = baseMarks, marks
-
-    def build(self, builder):
-        """Calls the builder object's ``add_mark_mark_pos`` callback."""
-        builder.add_mark_mark_pos(self.location, self.baseMarks.glyphSet(), self.marks)
-
-    def asFea(self, indent=""):
-        res = "pos mark {}".format(self.baseMarks.asFea())
-        for a, m in self.marks:
-            res += "\n" + indent + SHIFT + "{} mark @{}".format(a.asFea(), m.name)
-        res += ";"
-        return res
-
-
-class MultipleSubstStatement(Statement):
-    """A multiple substitution statement.
-
-    Args:
-        prefix: a list of `glyph-containing objects`_.
-        glyph: a single glyph-containing object.
-        suffix: a list of glyph-containing objects.
-        replacement: a list of glyph-containing objects.
-        forceChain: If true, the statement is expressed as a chaining rule
-            (e.g. ``sub f' i' by f_i``) even when no context is given.
-    """
-
-    def __init__(
-        self, prefix, glyph, suffix, replacement, forceChain=False, location=None
-    ):
-        Statement.__init__(self, location)
-        self.prefix, self.glyph, self.suffix = prefix, glyph, suffix
-        self.replacement = replacement
-        self.forceChain = forceChain
-
-    def build(self, builder):
-        """Calls the builder object's ``add_multiple_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        if hasattr(self.glyph, "glyphSet"):
-            originals = self.glyph.glyphSet()
-        else:
-            originals = [self.glyph]
-        count = len(originals)
-        replaces = []
-        for r in self.replacement:
-            if hasattr(r, "glyphSet"):
-                replace = r.glyphSet()
-            else:
-                replace = [r]
-            if len(replace) == 1 and len(replace) != count:
-                replace = replace * count
-            replaces.append(replace)
-        replaces = list(zip(*replaces))
-
-        seen_originals = set()
-        for i, original in enumerate(originals):
-            if original not in seen_originals:
-                seen_originals.add(original)
-                builder.add_multiple_subst(
-                    self.location,
-                    prefix,
-                    original,
-                    suffix,
-                    replaces and replaces[i] or (),
-                    self.forceChain,
-                )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += asFea(self.glyph) + "'"
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += asFea(self.glyph)
-        replacement = self.replacement or [NullGlyph()]
-        res += " by "
-        res += " ".join(map(asFea, replacement))
-        res += ";"
-        return res
-
-
-class PairPosStatement(Statement):
-    """A pair positioning statement.
-
-    ``glyphs1`` and ``glyphs2`` should be `glyph-containing objects`_.
-    ``valuerecord1`` should be a :class:`ValueRecord` object;
-    ``valuerecord2`` should be either a :class:`ValueRecord` object or ``None``.
-    If ``enumerated`` is true, then this is expressed as an
-    `enumerated pair <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#6.b.ii>`_.
-    """
-
-    def __init__(
-        self,
-        glyphs1,
-        valuerecord1,
-        glyphs2,
-        valuerecord2,
-        enumerated=False,
-        location=None,
-    ):
-        Statement.__init__(self, location)
-        self.enumerated = enumerated
-        self.glyphs1, self.valuerecord1 = glyphs1, valuerecord1
-        self.glyphs2, self.valuerecord2 = glyphs2, valuerecord2
-
-    def build(self, builder):
-        """Calls a callback on the builder object:
-
-        * If the rule is enumerated, calls ``add_specific_pair_pos`` on each
-          combination of first and second glyphs.
-        * If the glyphs are both single :class:`GlyphName` objects, calls
-          ``add_specific_pair_pos``.
-        * Else, calls ``add_class_pair_pos``.
-        """
-        if self.enumerated:
-            g = [self.glyphs1.glyphSet(), self.glyphs2.glyphSet()]
-            seen_pair = False
-            for glyph1, glyph2 in itertools.product(*g):
-                seen_pair = True
-                builder.add_specific_pair_pos(
-                    self.location, glyph1, self.valuerecord1, glyph2, self.valuerecord2
-                )
-            if not seen_pair:
-                raise FeatureLibError(
-                    "Empty glyph class in positioning rule", self.location
-                )
-            return
-
-        is_specific = isinstance(self.glyphs1, GlyphName) and isinstance(
-            self.glyphs2, GlyphName
-        )
-        if is_specific:
-            builder.add_specific_pair_pos(
-                self.location,
-                self.glyphs1.glyph,
-                self.valuerecord1,
-                self.glyphs2.glyph,
-                self.valuerecord2,
-            )
-        else:
-            builder.add_class_pair_pos(
-                self.location,
-                self.glyphs1.glyphSet(),
-                self.valuerecord1,
-                self.glyphs2.glyphSet(),
-                self.valuerecord2,
-            )
-
-    def asFea(self, indent=""):
-        res = "enum " if self.enumerated else ""
-        if self.valuerecord2:
-            res += "pos {} {} {} {};".format(
-                self.glyphs1.asFea(),
-                self.valuerecord1.asFea(),
-                self.glyphs2.asFea(),
-                self.valuerecord2.asFea(),
-            )
-        else:
-            res += "pos {} {} {};".format(
-                self.glyphs1.asFea(), self.glyphs2.asFea(), self.valuerecord1.asFea()
-            )
-        return res
-
-
-class ReverseChainSingleSubstStatement(Statement):
-    """A reverse chaining substitution statement. You don't see those every day.
-
-    Note the unusual argument order: ``suffix`` comes `before` ``glyphs``.
-    ``old_prefix``, ``old_suffix``, ``glyphs`` and ``replacements`` should be
-    lists of `glyph-containing objects`_. ``glyphs`` and ``replacements`` should
-    be one-item lists.
-    """
-
-    def __init__(self, old_prefix, old_suffix, glyphs, replacements, location=None):
-        Statement.__init__(self, location)
-        self.old_prefix, self.old_suffix = old_prefix, old_suffix
-        self.glyphs = glyphs
-        self.replacements = replacements
-
-    def build(self, builder):
-        prefix = [p.glyphSet() for p in self.old_prefix]
-        suffix = [s.glyphSet() for s in self.old_suffix]
-        originals = self.glyphs[0].glyphSet()
-        replaces = self.replacements[0].glyphSet()
-        if len(replaces) == 1:
-            replaces = replaces * len(originals)
-        builder.add_reverse_chain_single_subst(
-            self.location, prefix, suffix, dict(zip(originals, replaces))
-        )
-
-    def asFea(self, indent=""):
-        res = "rsub "
-        if len(self.old_prefix) or len(self.old_suffix):
-            if len(self.old_prefix):
-                res += " ".join(asFea(g) for g in self.old_prefix) + " "
-            res += " ".join(asFea(g) + "'" for g in self.glyphs)
-            if len(self.old_suffix):
-                res += " " + " ".join(asFea(g) for g in self.old_suffix)
-        else:
-            res += " ".join(map(asFea, self.glyphs))
-        res += " by {};".format(" ".join(asFea(g) for g in self.replacements))
-        return res
-
-
-class SingleSubstStatement(Statement):
-    """A single substitution statement.
-
-    Note the unusual argument order: ``prefix`` and suffix come `after`
-    the replacement ``glyphs``. ``prefix``, ``suffix``, ``glyphs`` and
-    ``replace`` should be lists of `glyph-containing objects`_. ``glyphs`` and
-    ``replace`` should be one-item lists.
-    """
-
-    def __init__(self, glyphs, replace, prefix, suffix, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.prefix, self.suffix = prefix, suffix
-        self.forceChain = forceChain
-        self.glyphs = glyphs
-        self.replacements = replace
-
-    def build(self, builder):
-        """Calls the builder object's ``add_single_subst`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        originals = self.glyphs[0].glyphSet()
-        replaces = self.replacements[0].glyphSet()
-        if len(replaces) == 1:
-            replaces = replaces * len(originals)
-        builder.add_single_subst(
-            self.location,
-            prefix,
-            suffix,
-            OrderedDict(zip(originals, replaces)),
-            self.forceChain,
-        )
-
-    def asFea(self, indent=""):
-        res = "sub "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(asFea(g) for g in self.prefix) + " "
-            res += " ".join(asFea(g) + "'" for g in self.glyphs)
-            if len(self.suffix):
-                res += " " + " ".join(asFea(g) for g in self.suffix)
-        else:
-            res += " ".join(asFea(g) for g in self.glyphs)
-        res += " by {};".format(" ".join(asFea(g) for g in self.replacements))
-        return res
-
-
-class ScriptStatement(Statement):
-    """A ``script`` statement."""
-
-    def __init__(self, script, location=None):
-        Statement.__init__(self, location)
-        self.script = script  #: the script code
-
-    def build(self, builder):
-        """Calls the builder's ``set_script`` callback."""
-        builder.set_script(self.location, self.script)
-
-    def asFea(self, indent=""):
-        return "script {};".format(self.script.strip())
-
-
-class SinglePosStatement(Statement):
-    """A single position statement. ``prefix`` and ``suffix`` should be
-    lists of `glyph-containing objects`_.
-
-    ``pos`` should be a one-element list containing a (`glyph-containing object`_,
-    :class:`ValueRecord`) tuple."""
-
-    def __init__(self, pos, prefix, suffix, forceChain, location=None):
-        Statement.__init__(self, location)
-        self.pos, self.prefix, self.suffix = pos, prefix, suffix
-        self.forceChain = forceChain
-
-    def build(self, builder):
-        """Calls the builder object's ``add_single_pos`` callback."""
-        prefix = [p.glyphSet() for p in self.prefix]
-        suffix = [s.glyphSet() for s in self.suffix]
-        pos = [(g.glyphSet(), value) for g, value in self.pos]
-        builder.add_single_pos(self.location, prefix, suffix, pos, self.forceChain)
-
-    def asFea(self, indent=""):
-        res = "pos "
-        if len(self.prefix) or len(self.suffix) or self.forceChain:
-            if len(self.prefix):
-                res += " ".join(map(asFea, self.prefix)) + " "
-            res += " ".join(
-                [
-                    asFea(x[0]) + "'" + ((" " + x[1].asFea()) if x[1] else "")
-                    for x in self.pos
-                ]
-            )
-            if len(self.suffix):
-                res += " " + " ".join(map(asFea, self.suffix))
-        else:
-            res += " ".join(
-                [asFea(x[0]) + " " + (x[1].asFea() if x[1] else "") for x in self.pos]
-            )
-        res += ";"
-        return res
-
-
-class SubtableStatement(Statement):
-    """Represents a subtable break."""
-
-    def __init__(self, location=None):
-        Statement.__init__(self, location)
-
-    def build(self, builder):
-        """Calls the builder objects's ``add_subtable_break`` callback."""
-        builder.add_subtable_break(self.location)
-
-    def asFea(self, indent=""):
-        return "subtable;"
-
-
-class ValueRecord(Expression):
-    """Represents a value record."""
-
-    def __init__(
-        self,
-        xPlacement=None,
-        yPlacement=None,
-        xAdvance=None,
-        yAdvance=None,
-        xPlaDevice=None,
-        yPlaDevice=None,
-        xAdvDevice=None,
-        yAdvDevice=None,
-        vertical=False,
-        location=None,
-    ):
-        Expression.__init__(self, location)
-        self.xPlacement, self.yPlacement = (xPlacement, yPlacement)
-        self.xAdvance, self.yAdvance = (xAdvance, yAdvance)
-        self.xPlaDevice, self.yPlaDevice = (xPlaDevice, yPlaDevice)
-        self.xAdvDevice, self.yAdvDevice = (xAdvDevice, yAdvDevice)
-        self.vertical = vertical
-
-    def __eq__(self, other):
-        return (
-            self.xPlacement == other.xPlacement
-            and self.yPlacement == other.yPlacement
-            and self.xAdvance == other.xAdvance
-            and self.yAdvance == other.yAdvance
-            and self.xPlaDevice == other.xPlaDevice
-            and self.xAdvDevice == other.xAdvDevice
-        )
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return (
-            hash(self.xPlacement)
-            ^ hash(self.yPlacement)
-            ^ hash(self.xAdvance)
-            ^ hash(self.yAdvance)
-            ^ hash(self.xPlaDevice)
-            ^ hash(self.yPlaDevice)
-            ^ hash(self.xAdvDevice)
-            ^ hash(self.yAdvDevice)
-        )
-
-    def asFea(self, indent=""):
-        if not self:
-            return "<NULL>"
-
-        x, y = self.xPlacement, self.yPlacement
-        xAdvance, yAdvance = self.xAdvance, self.yAdvance
-        xPlaDevice, yPlaDevice = self.xPlaDevice, self.yPlaDevice
-        xAdvDevice, yAdvDevice = self.xAdvDevice, self.yAdvDevice
-        vertical = self.vertical
-
-        # Try format A, if possible.
-        if x is None and y is None:
-            if xAdvance is None and vertical:
-                return str(yAdvance)
-            elif yAdvance is None and not vertical:
-                return str(xAdvance)
-
-        # Make any remaining None value 0 to avoid generating invalid records.
-        x = x or 0
-        y = y or 0
-        xAdvance = xAdvance or 0
-        yAdvance = yAdvance or 0
-
-        # Try format B, if possible.
-        if (
-            xPlaDevice is None
-            and yPlaDevice is None
-            and xAdvDevice is None
-            and yAdvDevice is None
-        ):
-            return "<%s %s %s %s>" % (x, y, xAdvance, yAdvance)
-
-        # Last resort is format C.
-        return "<%s %s %s %s %s %s %s %s>" % (
-            x,
-            y,
-            xAdvance,
-            yAdvance,
-            deviceToString(xPlaDevice),
-            deviceToString(yPlaDevice),
-            deviceToString(xAdvDevice),
-            deviceToString(yAdvDevice),
-        )
-
-    def __bool__(self):
-        return any(
-            getattr(self, v) is not None
-            for v in [
-                "xPlacement",
-                "yPlacement",
-                "xAdvance",
-                "yAdvance",
-                "xPlaDevice",
-                "yPlaDevice",
-                "xAdvDevice",
-                "yAdvDevice",
-            ]
-        )
-
-    __nonzero__ = __bool__
-
-
-class ValueRecordDefinition(Statement):
-    """Represents a named value record definition."""
-
-    def __init__(self, name, value, location=None):
-        Statement.__init__(self, location)
-        self.name = name  #: Value record name as string
-        self.value = value  #: :class:`ValueRecord` object
-
-    def asFea(self, indent=""):
-        return "valueRecordDef {} {};".format(self.value.asFea(), self.name)
-
-
-def simplify_name_attributes(pid, eid, lid):
-    if pid == 3 and eid == 1 and lid == 1033:
-        return ""
-    elif pid == 1 and eid == 0 and lid == 0:
-        return "1"
-    else:
-        return "{} {} {}".format(pid, eid, lid)
-
-
-class NameRecord(Statement):
-    """Represents a name record. (`Section 9.e. <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.e>`_)"""
-
-    def __init__(self, nameID, platformID, platEncID, langID, string, location=None):
-        Statement.__init__(self, location)
-        self.nameID = nameID  #: Name ID as integer (e.g. 9 for designer's name)
-        self.platformID = platformID  #: Platform ID as integer
-        self.platEncID = platEncID  #: Platform encoding ID as integer
-        self.langID = langID  #: Language ID as integer
-        self.string = string  #: Name record value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_name_record`` callback."""
-        builder.add_name_record(
-            self.location,
-            self.nameID,
-            self.platformID,
-            self.platEncID,
-            self.langID,
-            self.string,
-        )
-
-    def asFea(self, indent=""):
-        def escape(c, escape_pattern):
-            # Also escape U+0022 QUOTATION MARK and U+005C REVERSE SOLIDUS
-            if c >= 0x20 and c <= 0x7E and c not in (0x22, 0x5C):
-                return chr(c)
-            else:
-                return escape_pattern % c
-
-        encoding = getEncoding(self.platformID, self.platEncID, self.langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", self.location)
-        s = tobytes(self.string, encoding=encoding)
-        if encoding == "utf_16_be":
-            escaped_string = "".join(
-                [
-                    escape(byteord(s[i]) * 256 + byteord(s[i + 1]), r"\%04x")
-                    for i in range(0, len(s), 2)
-                ]
-            )
-        else:
-            escaped_string = "".join([escape(byteord(b), r"\%02x") for b in s])
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'nameid {} {}"{}";'.format(self.nameID, plat, escaped_string)
-
-
-class FeatureNameStatement(NameRecord):
-    """Represents a ``sizemenuname`` or ``name`` statement."""
-
-    def build(self, builder):
-        """Calls the builder object's ``add_featureName`` callback."""
-        NameRecord.build(self, builder)
-        builder.add_featureName(self.nameID)
-
-    def asFea(self, indent=""):
-        if self.nameID == "size":
-            tag = "sizemenuname"
-        else:
-            tag = "name"
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return '{} {}"{}";'.format(tag, plat, self.string)
-
-
-class STATNameStatement(NameRecord):
-    """Represents a STAT table ``name`` statement."""
-
-    def asFea(self, indent=""):
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'name {}"{}";'.format(plat, self.string)
-
-
-class SizeParameters(Statement):
-    """A ``parameters`` statement."""
-
-    def __init__(self, DesignSize, SubfamilyID, RangeStart, RangeEnd, location=None):
-        Statement.__init__(self, location)
-        self.DesignSize = DesignSize
-        self.SubfamilyID = SubfamilyID
-        self.RangeStart = RangeStart
-        self.RangeEnd = RangeEnd
-
-    def build(self, builder):
-        """Calls the builder object's ``set_size_parameters`` callback."""
-        builder.set_size_parameters(
-            self.location,
-            self.DesignSize,
-            self.SubfamilyID,
-            self.RangeStart,
-            self.RangeEnd,
-        )
-
-    def asFea(self, indent=""):
-        res = "parameters {:.1f} {}".format(self.DesignSize, self.SubfamilyID)
-        if self.RangeStart != 0 or self.RangeEnd != 0:
-            res += " {} {}".format(int(self.RangeStart * 10), int(self.RangeEnd * 10))
-        return res + ";"
-
-
-class CVParametersNameStatement(NameRecord):
-    """Represent a name statement inside a ``cvParameters`` block."""
-
-    def __init__(
-        self, nameID, platformID, platEncID, langID, string, block_name, location=None
-    ):
-        NameRecord.__init__(
-            self, nameID, platformID, platEncID, langID, string, location=location
-        )
-        self.block_name = block_name
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cv_parameter`` callback."""
-        item = ""
-        if self.block_name == "ParamUILabelNameID":
-            item = "_{}".format(builder.cv_num_named_params_.get(self.nameID, 0))
-        builder.add_cv_parameter(self.nameID)
-        self.nameID = (self.nameID, self.block_name + item)
-        NameRecord.build(self, builder)
-
-    def asFea(self, indent=""):
-        plat = simplify_name_attributes(self.platformID, self.platEncID, self.langID)
-        if plat != "":
-            plat += " "
-        return 'name {}"{}";'.format(plat, self.string)
-
-
-class CharacterStatement(Statement):
-    """
-    Statement used in cvParameters blocks of Character Variant features (cvXX).
-    The Unicode value may be written with either decimal or hexadecimal
-    notation. The value must be preceded by '0x' if it is a hexadecimal value.
-    The largest Unicode value allowed is 0xFFFFFF.
-    """
-
-    def __init__(self, character, tag, location=None):
-        Statement.__init__(self, location)
-        self.character = character
-        self.tag = tag
-
-    def build(self, builder):
-        """Calls the builder object's ``add_cv_character`` callback."""
-        builder.add_cv_character(self.character, self.tag)
-
-    def asFea(self, indent=""):
-        return "Character {:#x};".format(self.character)
-
-
-class BaseAxis(Statement):
-    """An axis definition, being either a ``VertAxis.BaseTagList/BaseScriptList``
-    pair or a ``HorizAxis.BaseTagList/BaseScriptList`` pair."""
-
-    def __init__(self, bases, scripts, vertical, location=None):
-        Statement.__init__(self, location)
-        self.bases = bases  #: A list of baseline tag names as strings
-        self.scripts = scripts  #: A list of script record tuplets (script tag, default baseline tag, base coordinate)
-        self.vertical = vertical  #: Boolean; VertAxis if True, HorizAxis if False
-
-    def build(self, builder):
-        """Calls the builder object's ``set_base_axis`` callback."""
-        builder.set_base_axis(self.bases, self.scripts, self.vertical)
-
-    def asFea(self, indent=""):
-        direction = "Vert" if self.vertical else "Horiz"
-        scripts = [
-            "{} {} {}".format(a[0], a[1], " ".join(map(str, a[2])))
-            for a in self.scripts
-        ]
-        return "{}Axis.BaseTagList {};\n{}{}Axis.BaseScriptList {};".format(
-            direction, " ".join(self.bases), indent, direction, ", ".join(scripts)
-        )
-
-
-class OS2Field(Statement):
-    """An entry in the ``OS/2`` table. Most ``values`` should be numbers or
-    strings, apart from when the key is ``UnicodeRange``, ``CodePageRange``
-    or ``Panose``, in which case it should be an array of integers."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_os2_field`` callback."""
-        builder.add_os2_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        def intarr2str(x):
-            return " ".join(map(str, x))
-
-        numbers = (
-            "FSType",
-            "TypoAscender",
-            "TypoDescender",
-            "TypoLineGap",
-            "winAscent",
-            "winDescent",
-            "XHeight",
-            "CapHeight",
-            "WeightClass",
-            "WidthClass",
-            "LowerOpSize",
-            "UpperOpSize",
-        )
-        ranges = ("UnicodeRange", "CodePageRange")
-        keywords = dict([(x.lower(), [x, str]) for x in numbers])
-        keywords.update([(x.lower(), [x, intarr2str]) for x in ranges])
-        keywords["panose"] = ["Panose", intarr2str]
-        keywords["vendor"] = ["Vendor", lambda y: '"{}"'.format(y)]
-        if self.key in keywords:
-            return "{} {};".format(
-                keywords[self.key][0], keywords[self.key][1](self.value)
-            )
-        return ""  # should raise exception
-
-
-class HheaField(Statement):
-    """An entry in the ``hhea`` table."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_hhea_field`` callback."""
-        builder.add_hhea_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        fields = ("CaretOffset", "Ascender", "Descender", "LineGap")
-        keywords = dict([(x.lower(), x) for x in fields])
-        return "{} {};".format(keywords[self.key], self.value)
-
-
-class VheaField(Statement):
-    """An entry in the ``vhea`` table."""
-
-    def __init__(self, key, value, location=None):
-        Statement.__init__(self, location)
-        self.key = key
-        self.value = value
-
-    def build(self, builder):
-        """Calls the builder object's ``add_vhea_field`` callback."""
-        builder.add_vhea_field(self.key, self.value)
-
-    def asFea(self, indent=""):
-        fields = ("VertTypoAscender", "VertTypoDescender", "VertTypoLineGap")
-        keywords = dict([(x.lower(), x) for x in fields])
-        return "{} {};".format(keywords[self.key], self.value)
-
-
-class STATDesignAxisStatement(Statement):
-    """A STAT table Design Axis
-
-    Args:
-        tag (str): a 4 letter axis tag
-        axisOrder (int): an int
-        names (list): a list of :class:`STATNameStatement` objects
-    """
-
-    def __init__(self, tag, axisOrder, names, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag
-        self.axisOrder = axisOrder
-        self.names = names
-        self.location = location
-
-    def build(self, builder):
-        builder.addDesignAxis(self, self.location)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        res = f"DesignAxis {self.tag} {self.axisOrder} {{ \n"
-        res += ("\n" + indent).join([s.asFea(indent=indent) for s in self.names]) + "\n"
-        res += "};"
-        return res
-
-
-class ElidedFallbackName(Statement):
-    """STAT table ElidedFallbackName
-
-    Args:
-        names: a list of :class:`STATNameStatement` objects
-    """
-
-    def __init__(self, names, location=None):
-        Statement.__init__(self, location)
-        self.names = names
-        self.location = location
-
-    def build(self, builder):
-        builder.setElidedFallbackName(self.names, self.location)
-
-    def asFea(self, indent=""):
-        indent += SHIFT
-        res = "ElidedFallbackName { \n"
-        res += ("\n" + indent).join([s.asFea(indent=indent) for s in self.names]) + "\n"
-        res += "};"
-        return res
-
-
-class ElidedFallbackNameID(Statement):
-    """STAT table ElidedFallbackNameID
-
-    Args:
-        value: an int pointing to an existing name table name ID
-    """
-
-    def __init__(self, value, location=None):
-        Statement.__init__(self, location)
-        self.value = value
-        self.location = location
-
-    def build(self, builder):
-        builder.setElidedFallbackName(self.value, self.location)
-
-    def asFea(self, indent=""):
-        return f"ElidedFallbackNameID {self.value};"
-
-
-class STATAxisValueStatement(Statement):
-    """A STAT table Axis Value Record
-
-    Args:
-        names (list): a list of :class:`STATNameStatement` objects
-        locations (list): a list of :class:`AxisValueLocationStatement` objects
-        flags (int): an int
-    """
-
-    def __init__(self, names, locations, flags, location=None):
-        Statement.__init__(self, location)
-        self.names = names
-        self.locations = locations
-        self.flags = flags
-
-    def build(self, builder):
-        builder.addAxisValueRecord(self, self.location)
-
-    def asFea(self, indent=""):
-        res = "AxisValue {\n"
-        for location in self.locations:
-            res += location.asFea()
-
-        for nameRecord in self.names:
-            res += nameRecord.asFea()
-            res += "\n"
-
-        if self.flags:
-            flags = ["OlderSiblingFontAttribute", "ElidableAxisValueName"]
-            flagStrings = []
-            curr = 1
-            for i in range(len(flags)):
-                if self.flags & curr != 0:
-                    flagStrings.append(flags[i])
-                curr = curr << 1
-            res += f"flag {' '.join(flagStrings)};\n"
-        res += "};"
-        return res
-
-
-class AxisValueLocationStatement(Statement):
-    """
-    A STAT table Axis Value Location
-
-    Args:
-        tag (str): a 4 letter axis tag
-        values (list): a list of ints and/or floats
-    """
-
-    def __init__(self, tag, values, location=None):
-        Statement.__init__(self, location)
-        self.tag = tag
-        self.values = values
-
-    def asFea(self, res=""):
-        res += f"location {self.tag} "
-        res += f"{' '.join(str(i) for i in self.values)};\n"
-        return res
-
-
-class ConditionsetStatement(Statement):
-    """
-    A variable layout conditionset
-
-    Args:
-        name (str): the name of this conditionset
-        conditions (dict): a dictionary mapping axis tags to a
-            tuple of (min,max) userspace coordinates.
-    """
-
-    def __init__(self, name, conditions, location=None):
-        Statement.__init__(self, location)
-        self.name = name
-        self.conditions = conditions
-
-    def build(self, builder):
-        builder.add_conditionset(self.location, self.name, self.conditions)
-
-    def asFea(self, res="", indent=""):
-        res += indent + f"conditionset {self.name} " + "{\n"
-        for tag, (minvalue, maxvalue) in self.conditions.items():
-            res += indent + SHIFT + f"{tag} {minvalue} {maxvalue};\n"
-        res += indent + "}" + f" {self.name};\n"
-        return res
-
-
-class VariationBlock(Block):
-    """A variation feature block, applicable in a given set of conditions."""
-
-    def __init__(self, name, conditionset, use_extension=False, location=None):
-        Block.__init__(self, location)
-        self.name, self.conditionset, self.use_extension = (
-            name,
-            conditionset,
-            use_extension,
-        )
-
-    def build(self, builder):
-        """Call the ``start_feature`` callback on the builder object, visit
-        all the statements in this feature, and then call ``end_feature``."""
-        builder.start_feature(self.location, self.name)
-        if (
-            self.conditionset != "NULL"
-            and self.conditionset not in builder.conditionsets_
-        ):
-            raise FeatureLibError(
-                f"variation block used undefined conditionset {self.conditionset}",
-                self.location,
-            )
-
-        # language exclude_dflt statements modify builder.features_
-        # limit them to this block with temporary builder.features_
-        features = builder.features_
-        builder.features_ = {}
-        Block.build(self, builder)
-        for key, value in builder.features_.items():
-            items = builder.feature_variations_.setdefault(key, {}).setdefault(
-                self.conditionset, []
-            )
-            items.extend(value)
-            if key not in features:
-                features[key] = []  # Ensure we make a feature record
-        builder.features_ = features
-        builder.end_feature()
-
-    def asFea(self, indent=""):
-        res = indent + "variation %s " % self.name.strip()
-        res += self.conditionset + " "
-        if self.use_extension:
-            res += "useExtension "
-        res += "{\n"
-        res += Block.asFea(self, indent=indent)
-        res += indent + "} %s;\n" % self.name.strip()
-        return res
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/dictTools.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/dictTools.py
deleted file mode 100644
index 259613b27048c458980986167d429847d270691f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/dictTools.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Misc dict tools."""
-
-
-__all__ = ["hashdict"]
-
-# https://stackoverflow.com/questions/1151658/python-hashable-dicts
-class hashdict(dict):
-    """
-    hashable dict implementation, suitable for use as a key into
-    other dicts.
-
-        >>> h1 = hashdict({"apples": 1, "bananas":2})
-        >>> h2 = hashdict({"bananas": 3, "mangoes": 5})
-        >>> h1+h2
-        hashdict(apples=1, bananas=3, mangoes=5)
-        >>> d1 = {}
-        >>> d1[h1] = "salad"
-        >>> d1[h1]
-        'salad'
-        >>> d1[h2]
-        Traceback (most recent call last):
-        ...
-        KeyError: hashdict(bananas=3, mangoes=5)
-
-    based on answers from
-       http://stackoverflow.com/questions/1151658/python-hashable-dicts
-
-    """
-
-    def __key(self):
-        return tuple(sorted(self.items()))
-
-    def __repr__(self):
-        return "{0}({1})".format(
-            self.__class__.__name__,
-            ", ".join("{0}={1}".format(str(i[0]), repr(i[1])) for i in self.__key()),
-        )
-
-    def __hash__(self):
-        return hash(self.__key())
-
-    def __setitem__(self, key, value):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def __delitem__(self, key):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def clear(self):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def pop(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def popitem(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def setdefault(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def update(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    # update is not ok because it mutates the object
-    # __add__ is ok because it creates a new object
-    # while the new object is under construction, it's ok to mutate it
-    def __add__(self, right):
-        result = hashdict(self)
-        dict.update(result, right)
-        return result
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_D_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_D_.py
deleted file mode 100644
index 536ff2f98a0abb8b27fe6da44199534a32fd0c3e..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_D_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_D_(table_T_S_I_V_):
-    pass
diff --git a/spaces/codeparrot/code-generation-models/utils/summary.md b/spaces/codeparrot/code-generation-models/utils/summary.md
deleted file mode 100644
index a4cd11bf17d166f0bb35fc3e9b782de825e2b585..0000000000000000000000000000000000000000
--- a/spaces/codeparrot/code-generation-models/utils/summary.md
+++ /dev/null
@@ -1,7 +0,0 @@
-This is an **interactive** blog that provides an overview of open-source language models for code generation. This post presents:
-* code datasets
-* model architecture
-* model evaluation 
-
-We also give examples and tips to use the 🤗 Hub for this task. 
-At the end of this blog, you will find a **demo** to test and compare code generation across multi-billion parameter code models directly in the browser! Here's a small teaser ✨:
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Traffic Rider Mod APK for Windows 7.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Traffic Rider Mod APK for Windows 7.md
deleted file mode 100644
index e995613f2949ab2c64130b3b145046f280e6464e..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Traffic Rider Mod APK for Windows 7.md	
+++ /dev/null
@@ -1,112 +0,0 @@
-<br />
-<h1><b>Traffic Rider Mod APK for Windows 7: How to Download and Play</b></h1>
- <p>Do you love racing games? Do you want to experience the thrill of riding a motorcycle on realistic roads? Do you want to unlock all the bikes and customize them as you wish? If you answered yes to any of these questions, then you should try <b>Traffic Rider</b>, one of the most popular and addictive racing games for Android devices.</p>
- <p>But what if you don't have an Android device or you want to play Traffic Rider on a bigger screen? Don't worry, there is a way to enjoy this game on your Windows 7 PC or laptop. All you need is an Android emulator and a modded version of Traffic Rider, which will give you unlimited money, gold, and access to all the bikes in the game. In this article, we will show you how to download and install Traffic Rider Mod APK on your Windows 7 PC or laptop using Gameloop, one of the best Android emulators for gaming. But first, let's take a look at what Traffic Rider is and what features it offers.</p>
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- <h2><b>What is Traffic Rider?</b></h2>
- <p>Traffic Rider is a racing game developed by Soner Kara, the same team behind Traffic Racer. The game was released in 2016 and has since gained over 100 million downloads on Google Play Store. The game is rated 4.4 out of 5 stars by more than 7 million users. The game is also available on iOS and Windows Phone platforms.</p>
- <p>Traffic Rider is a first-person motorcycle racing game that puts you in the shoes of a rider who has to race through traffic and complete various missions. The game has realistic graphics and sound effects that make you feel like you are actually riding a bike on the road. The game also has multiple game modes and missions that challenge your skills and keep you entertained for hours.</p>
- <h3><b>Features of Traffic Rider</b></h3>
- <p>Traffic Rider has many features that make it one of the best racing games for Android devices. Here are some of them:</p>
- <h4><b>Realistic graphics and sound effects</b></h4>
- <p>Traffic Rider has stunning 3D graphics that show every detail of the bikes, roads, traffic, and environment. The game also has realistic sound effects that include engine noises, horn sounds, brake sounds, and more. The game also supports day and night variations, weather effects, and different camera angles. You can even see your rider's hands on the handlebars and feel the vibration of the bike as you accelerate or brake.</p>
- <h4><b>Multiple game modes and missions</b></h4>
- <p>Traffic Rider has four game modes that offer different challenges and objectives. They are:</p>
- <ul>
-<li>Career mode: This is the main mode of the game, where you have to complete over 70 missions with different goals and difficulties. You can earn money and gold by completing missions and use them to buy or upgrade bikes. You can also unlock new locations and tracks as you progress in the career mode.</li>
-<li>Endless mode: This is a mode where you can ride as long as you can without crashing or running out of time. You can choose from four different types of roads: one-way, two-way, time trial, and free ride. You can also adjust the traffic density and speed to suit your preference.</li>
-<li>Time trial mode: This is a mode where you have to race against the clock and reach checkpoints before time runs out. You can choose from three different difficulty levels: easy, medium, and hard. You can also compete with other players online and see your rank on the global leaderboards.</li>
-<li>Free ride mode: This is a mode where you can ride freely without any rules or objectives. You can choose any bike, road, traffic, weather, and time of day you want. You can also explore the map and discover hidden places and secrets.</li>
-</ul>
- <h4><b>Customizable bikes and riders</b></h4>
- <p>Traffic Rider has over 40 bikes that you can choose from, ranging from scooters to super bikes. Each bike has its own characteristics, such as speed, acceleration, handling, braking, and stability. You can also customize your bike's color, wheels, exhausts, mirrors, lights, and more. You can also change your rider's appearance, such as helmet, gloves, jacket, pants, boots, and more.</p>
- <h4><b>Online leaderboards and achievements</b></h4>
- <p>Traffic Rider has online leaderboards that show your rank among other players around the world. You can compare your scores and stats with other players in different categories, such as career mode, endless mode, time trial mode, free ride mode, distance traveled, highest speed reached, longest wheelie performed, etc. You can also earn achievements by completing various tasks and challenges in the game.</p>
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- <h2><b>What is Traffic Rider Mod APK?</b></h2>
- <p>Traffic Rider Mod APK is a modified version of Traffic Rider that gives you some extra benefits that are not available in the original version of the game. These benefits include:</p>
- <h3><b>Benefits of Traffic Rider Mod APK</b></h3>
- <p>Traffic Rider Mod APK has many benefits that make it more fun and enjoyable to play than the original version of the game. Here are some of them:</p>
- <h4>< b>Unlimited money and gold</b></h4>
- <p>Traffic Rider Mod APK gives you unlimited money and gold that you can use to buy or upgrade any bike you want. You don't have to worry about running out of cash or grinding for hours to earn enough money to buy your favorite bike. You can also use the money and gold to customize your bike and rider as you wish.</p>
- <h4><b>All bikes unlocked and upgraded</b></h4>
- <p>Traffic Rider Mod APK unlocks all the bikes in the game and upgrades them to the maximum level. You don't have to complete missions or reach certain levels to unlock new bikes. You can choose any bike you want from the start and enjoy its full potential. You can also switch between different bikes anytime you want without losing your progress.</p>
- <h4><b>No ads and no root required</b></h4>
- <p>Traffic Rider Mod APK removes all the ads that interrupt your gameplay and annoy you. You don't have to watch any videos or banners to get extra rewards or bonuses. You can enjoy the game without any distractions or interruptions. Moreover, Traffic Rider Mod APK does not require root access to work on your device. You don't have to risk damaging your device or voiding your warranty to install the modded version of the game.</p>
- <h2><b>How to Download and Install Traffic Rider Mod APK on Windows 7?</b></h2>
- <p>Now that you know what Traffic Rider Mod APK is and what benefits it offers, you might be wondering how to download and install it on your Windows 7 PC or laptop. Well, it's not as hard as you might think. All you need is an Android emulator and a Traffic Rider Mod APK file. Here are the requirements and steps for downloading and installing Traffic Rider Mod APK on Windows 7:</p>
- <h3><b>Requirements for Traffic Rider Mod APK on Windows 7</b></h3>
- <p>Before you start downloading and installing Traffic Rider Mod APK on Windows 7, make sure you have the following requirements:</p>
- <h4><b>Windows 7 PC or laptop</b></h4>
- <p>Obviously, you need a Windows 7 PC or laptop to play Traffic Rider Mod APK on it. The game should run smoothly on most devices, but it's recommended that you have at least 2 GB of RAM and 4 GB of free disk space for optimal performance.</p>
- <h4><b>Android emulator (Gameloop)</b></h4>
- <p>An Android emulator is a software that allows you to run Android apps and games on your PC or laptop. There are many Android emulators available online, but we recommend using Gameloop, which is one of the best Android emulators for gaming. Gameloop is developed by Tencent, the same company behind PUBG Mobile and Call of Duty Mobile. Gameloop has a user-friendly interface, high compatibility, fast performance, and low system requirements. You can download Gameloop from its official website for free.</p>
- <h4><b>Traffic Rider Mod APK file (from Uptodown)</b></h4>
- <p>A Traffic Rider Mod APK file is a modified version of Traffic Rider that gives you unlimited money, gold, and access to all the bikes in the game. You can download Traffic Rider Mod APK file from Uptodown, which is a trusted website that provides safe and reliable downloads of apps and games. Uptodown also has a virus scanner that checks every file before uploading it to its servers. You can download Traffic Rider Mod APK file from Uptodown for free.</p>
- <h3><b>Steps for Downloading and Installing Traffic Rider Mod APK on Windows 7</b></h3>
- <p>Once you have the requirements ready, you can follow these steps to download and install Traffic Rider Mod APK on Windows 7:</p>
- <h4><b>Step 1: Download and install Gameloop on your Windows 7 PC or laptop.</b></h4>
- <p>Go to the official website of Gameloop and click on the "Download" button. A setup file will be downloaded to your device. Run the setup file and follow the instructions to install Gameloop on your PC or laptop. It may take a few minutes depending on your internet speed and system configuration.</p>
- <h4><b>Step 2: Download Traffic Rider Mod APK file from Uptodown.</b></h4>
- <p>Go to the Uptodown website and search for "Traffic Rider Mod APK". You will see a list of results with different versions of the modded game. Choose the latest version (or any version that suits your preference) and click on the "Download" button. A modded apk file will be downloaded to your device.</p>
- <h4><b>Step 3: Open Gameloop and click on "My Games". Then click on "Add an app" and select Traffic Rider Mod APK file from your downloads folder.</b></h4>
- <p>After installing Gameloop, open it and click on the "My Games" tab. You will see a list of games that are available on Gameloop. To add Traffic Rider Mod APK to the list, click on the "Add an app" button at the bottom right corner of the screen. A file explorer window will open. Navigate to your downloads folder and select the Traffic Rider Mod APK file that you downloaded from Uptodown. Click on "Open" to add the file to Gameloop.</p>
- <h4><b>Step 4: Wait for Gameloop to install Traffic Rider Mod APK on your PC or laptop. Then click on "Play" to launch the game. Enjoy!</b></h4>
- <p>Once you add the Traffic Rider Mod APK file to Gameloop, it will start installing the game on your PC or laptop. This may take a few seconds or minutes depending on your device and internet speed. You can see the progress of the installation on the screen. When the installation is complete, you will see a "Play" button next to the game icon. Click on it to launch the game and start playing. You can use your keyboard and mouse to control the game, or you can connect a gamepad or a joystick if you prefer. Enjoy!</p>
- <h2><b>Conclusion</b></h2>
- <p>Traffic Rider is a fun and exciting racing game that lets you ride a motorcycle on realistic roads and complete various missions. The game has amazing graphics and sound effects that make you feel like you are actually riding a bike. The game also has many features that keep you entertained for hours, such as multiple game modes, customizable bikes and riders, online leaderboards and achievements, and more.</p>
- <p>However, if you want to enjoy the game even more, you should try Traffic Rider Mod APK, which gives you unlimited money, gold, and access to all the bikes in the game. You can download and install Traffic Rider Mod APK on your Windows 7 PC or laptop using Gameloop, which is one of the best Android emulators for gaming. Gameloop is easy to use, fast, and compatible with most devices. You can also play other Android games on your PC or laptop using Gameloop.</p>
- <p>So what are you waiting for? Download Traffic Rider Mod APK and Gameloop today and experience the thrill of riding a bike on your PC or laptop. Have fun!</p>
- <h2><b>FAQs</b></h2>
- <p>Here are some frequently asked questions about Traffic Rider Mod APK and Gameloop:</p>
- <h4><b>Q: Is Traffic Rider Mod APK safe to download and install?</b></h4>
- <p>A: Yes, Traffic Rider Mod APK is safe to download and install, as long as you get it from a trusted website like Uptodown. Uptodown has a virus scanner that checks every file before uploading it to its servers. You can also scan the file yourself using any antivirus software before installing it.</p>
- <h4><b>Q: Is Gameloop safe to use?</b></h4>
- <p>A: Yes, Gameloop is safe to use, as it is developed by Tencent, a reputable company that also makes popular games like PUBG Mobile and Call of Duty Mobile. Gameloop does not contain any malware or spyware that can harm your device or compromise your privacy.</p>
- <h4><b>Q: Do I need root access to use Traffic Rider Mod APK?</b></h4>
- <p>A: No, you do not need root access to use Traffic Rider Mod APK. The modded version of the game works fine without root access. However, if you have root access, you can use some extra features like backup and restore.</p>
- <h4><b>Q: Can I play Traffic Rider Mod APK online with other players?</b></h4>
- <p>A: Yes, you can play Traffic Rider Mod APK online with other players in time trial mode and compete with them on the global leaderboards. However, you may not be able to play with players who are using the original version of the game, as they may have different versions or updates.</p>
- <h4><b>Q: Can I update Traffic Rider Mod APK?</b></h4>
- <p>A: Yes, you can update Traffic Rider Mod APK whenever there is a new version available. However, you may have to download and install the new version manually from Uptodown or any other website that provides it. You may also lose your progress or data if you update the game without backing it up first.</p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to Download Lion King Game and Have Fun with Simba and Friends.md b/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to Download Lion King Game and Have Fun with Simba and Friends.md
deleted file mode 100644
index c4706c49a3b3c67618b3949c422f780139f80d19..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to Download Lion King Game and Have Fun with Simba and Friends.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
-<h1>How to Download Lion King Game for PC and Mobile</h1>
-<p>If you are a fan of The Lion King, the classic animated movie from Disney, you might be interested in playing some of the games based on it. The Lion King is a franchise that has spawned several video games over the years, featuring the characters and stories from the movie. Whether you want to relive the adventures of Simba, the lion cub who becomes the king of the Pride Lands, or explore the world of Africa with his friends Timon, Pumbaa, Nala, and others, there is a game for you.</p>
-<p>Playing Lion King games can be a lot of fun and entertainment for people of all ages. You can enjoy the stunning graphics, the catchy music, the exciting gameplay, and the nostalgic feeling of watching your favorite movie come to life. You can also learn more about the culture and wildlife of Africa, as well as the themes of courage, friendship, and responsibility that The Lion King teaches.</p>
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-<p>In this article, we will show you how to download Lion King game for different platforms, such as PC and mobile. We will also give you some information about each game, such as what it is about, what features it has, and how to play it. So, if you are ready to join Simba and his friends in their epic journey, read on!</p>
- <h2>Download Lion King Game for PC</h2>
-<p>If you want to play Lion King games on your computer, you have two options. You can either buy and download Disney Classic Games: Aladdin and The Lion King, which is a collection of two remastered games from the 90s, or you can download The Lion King (1994), which is the original game from SNES. Here are the details of each option:</p>
- <h3>Disney Classic Games: Aladdin and The Lion King</h3>
-<p>This is a package of two games that were released in 1993 and 1994 for various consoles, such as Sega Genesis, Super Nintendo Entertainment System (SNES), Game Boy, and others. These games are based on the movies Aladdin and The Lion King, and they feature platforming, action, adventure, and puzzle elements. You can play as Aladdin or Simba, depending on which game you choose, and follow their stories from the movies.</p>
-<p>The games have been enhanced with new features, such as 1080p graphics, adjustable screen aspect ratios, custom filter options, in-game rewind, interactive game viewer, save feature, multiple game versions, display options, updated visuals, and more. You can also access bonus content, such as behind-the-scenes interviews, art galleries, music players, etc.</p <p>To buy and download this game, you need to have a Steam account and a compatible PC. Steam is a digital distribution platform that allows you to buy, download, and play games online. You can create a free account on Steam's website and download the Steam client on your PC. Once you have done that, you can search for Disney Classic Games: Aladdin and The Lion King on the Steam store and purchase it for $29.99. After that, you can download and install the game on your PC and start playing.</p>
-<p>To play this game on PC, you need to use a keyboard or a controller. You can customize the controls according to your preference in the settings menu. You can also choose which game version you want to play, such as Sega Genesis, SNES, Game Boy, etc. You can switch between the games by pressing the Escape key and selecting the game from the menu. You can also access the bonus content from the menu.</p>
- <h3>The Lion King (1994)</h3>
-<p>This is the original game that was released in 1994 for SNES and other consoles. It is also based on the movie The Lion King, and it features platforming, action, adventure, and puzzle elements. You can play as Simba, from his childhood to his adulthood, and follow his story from the movie. You can explore different levels, such as Pride Rock, The Elephant Graveyard, The Stampede, Hakuna Matata, Simba's Destiny, Be Prepared, Simba's Return, and Pride Rock Battle.</p>
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-<p>The game has 2D graphics, retro music, and challenging gameplay. You can run, jump, roar, swing, roll, and fight your way through the levels. You can also collect bugs for health and lives, and find hidden items and secrets.</p>
-<p>To download this game, you need to visit Arcade Spot's website, which is a free online gaming site that offers classic games. You can search for The Lion King (1994) on the site and click on the game icon. You will see a description of the game and a button that says "Play Game". Click on that button and wait for the game to load on your browser.</p>
-<p>To play this game on PC, you need to use a keyboard or a controller. The default controls are as follows:</p>
-<table>
-<tr><th>Key</th><th>Action</th></tr>
-<tr><td>Enter</td><td>Start Game / Pause</td></tr>
-<tr><td>Space</td><td>Select</td></tr>
-<tr><td>Arrow Keys</td><td>Move</td></tr>
-<tr><td>A</td><td>Jump</td></tr>
-<tr><td>S</td><td>Roar / Roll / Throw</td></tr>
-<tr><td>D</td><td>Run / Swing</td></tr>
-</table>
-<p>You can also change the controls in the settings menu by pressing F12.</p>
- <h2>Download Lion King Game for Mobile</h2>
-<p>If you want to play Lion King games on your smartphone or tablet, you have two options. You can either download The Lion King: Simba's Mighty Adventure, which is a 3D platformer game from 2000, or you can download The Lion Guard: Protector of the Pridelands, which is a 2D side-scrolling game from 2016. Here are the details of each option:</p>
- <h3>The Lion King: Simba's Mighty Adventure</h3>
-<p>This is a game that was released in 2000 for PlayStation and Game Boy Color. It is based on both The Lion King and The Lion King II: Simba's Pride movies, and it features platforming, action, adventure, and puzzle elements. You can play as Simba or Kiara, depending on which movie you choose, and follow their stories from the movies.</p>
-<p>The game has 3D graphics, original music, voice acting, and cinematic cutscenes. You can explore different levels, such as Pride Rock, The Elephant Graveyard, The Stampede, Hakuna Matata, The Outlands, Upendi, etc. You can also collect coins for extra lives, find hidden items and secrets.</p>
-<p>To download this game for mobile devices, you need to have an Android or iOS device with enough storage space. You also need to have an emulator app that can run PlayStation or Game Boy Color games on your device. An emulator app is a software that mimics the functions of another device or system. For example, you can use ePSXe for Android or RetroArch for iOS to emulate PlayStation games on your mobile device.</p>
-<p>Once you have an emulator app installed on your device, you need to download the ROM file of The Lion King: Simba's Mighty Adventure from a reliable source, such as CoolROM or EmuParadise. A ROM file is a digital copy of a game that can be played on an emulator. You need to make sure that the ROM file is compatible with the emulator app and your device. You also need to follow the legal and ethical guidelines of using ROM files, such as owning the original game or deleting the file after 24 hours.</p>
-<p>Once you have the ROM file downloaded on your device, you need to open the emulator app and locate the file from your device's storage. Then, you can load the file and start playing the game on your mobile device. You can use the touch screen or a controller to play the game. You can also adjust the settings of the emulator app, such as graphics, sound, controls, etc.</p>
- <h3>The Lion Guard: Protector of the Pridelands</h3>
-<p>This is a game that was released in 2016 for Android and iOS devices. It is based on The Lion Guard, which is a spin-off TV series from The Lion King franchise. The game features platforming, action, adventure, and puzzle elements. You can play as Kion, the son of Simba and Nala, and the leader of the Lion Guard, which is a team of animals that protect the Pride Lands from threats.</p>
-<p>The game has 2D graphics, original music, voice acting, and animated cutscenes. You can explore different levels, such as The Outlands, The Savannah, The Canyons, etc. You can also switch between different members of the Lion Guard, such as Bunga, Fuli, Beshte, and Ono, and use their unique abilities to overcome obstacles and enemies. You can also collect tokens for extra lives, find hidden items and secrets.</p>
-<p>To download this game for mobile devices, you need to have an Android or iOS device with enough storage space and internet connection. You also need to have a Google Play Store or App Store account that allows you to download apps on your device. You can search for The Lion Guard: Protector of the Pridelands on the Google Play Store or App Store and install it for free on your device. After that, you can open the game and start playing.</p>
-<p>To play this game on mobile devices, you need to use the touch screen or a controller. You can tap on the screen to move, jump, attack, or interact with objects. You can also swipe on the screen to switch between characters or use their abilities. You can also access the menu by tapping on the pause button.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Lion King game for different platforms, such as PC and mobile. We have also given you some information about each game, such as what it is about, what features it has, and how to play it. We hope that you have found this article helpful and informative.</p>
-<p>Playing Lion King games can be a great way to enjoy your favorite movie in a new way. You can experience the story of Simba and his friends from different perspectives and in different formats. You can also challenge yourself with various levels of difficulty and discover new things along the way.</p>
-<p>So, what are you waiting for? Go ahead and download Lion King game for your preferred platform and have fun! And don't forget to share your feedback with us in the comments section below!</p>
- <h2>FAQs</h2>
-<h4>What are some of the best Lion King games?</h4>
-<p>Some of the best Lion King games are Disney Classic Games: Aladdin and The Lion King, The Lion King (1994), The Lion King: Simba's Mighty Adventure, The Lion Guard: Protector of the Pridelands, The Lion King 1½ (2003), The Lion King II: Simba's Pride - Active Play (1998), etc.</p>
-<h4>How can I play Lion King games online without downloading?</h4>
-<p>You can play some of the Lion King games online without downloading by using online gaming sites that offer flash or HTML5 games. Some examples are Arcade Spot, Crazy Games, Poki, etc. However, these sites may not have all the features or quality of the original games.</p>
-<h4>Are there any cheats or hacks for Lion King games?</h4>
-<p>There are some cheats or hacks for some of the Lion King games that can help you unlock levels, get extra lives, skip cutscenes, etc. However, these cheats or hacks may not work on all versions or platforms of the games. They may also affect your gameplay experience or cause glitches or errors.</p>
-<h4>What are the system requirements for playing Lion King games on PC?</h4>
-<p>The system requirements for playing Lion King games on PC may vary depending on which game you want to play. For example, for Disney Classic Games: Aladdin and The Lion King, you need to have Windows 7 (SP1), 8.1, or 10, a 2.0 GHz processor, 2 GB of RAM, 3 GB of available space, and a DirectX 11 compatible video card. For The Lion King (1994), you need to have Windows XP, Vista, 7, 8, or 10, a 1.8 GHz processor, 512 MB of RAM, 9 MB of available space, and a DirectX 9 compatible video card.</p>
-<h4>How can I watch The Lion King movie online?</h4>
-<p>You can watch The Lion King movie online by using streaming services that offer the movie in their catalog. Some examples are Disney+, Netflix, Amazon Prime Video, Hulu, etc. However, these services may require a subscription fee or a rental fee to access the movie. You can also buy or rent the movie from digital platforms such as iTunes, Google Play, YouTube, etc.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Tlchargez Jeux Sniper 3D  Le jeu de tir qui vous met dans la peau dun sniper professionnel.md b/spaces/congsaPfin/Manga-OCR/logs/Tlchargez Jeux Sniper 3D  Le jeu de tir qui vous met dans la peau dun sniper professionnel.md
deleted file mode 100644
index 3ddad3f3d0d84675e5bf2a410257dd78a79528d7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Tlchargez Jeux Sniper 3D  Le jeu de tir qui vous met dans la peau dun sniper professionnel.md	
+++ /dev/null
@@ -1,149 +0,0 @@
-
-<h1>Download Jeux Sniper 3D: How to Enjoy the Ultimate Shooting Game on Your Device</h1>
- <p>If you are looking for a thrilling and action-packed shooting game that will test your sniper skills, then you should try Jeux Sniper 3D. This is a free-to-play gun game that puts you in the shoes of a deadly assassin who has to eliminate high-profile targets in various locations. In this article, we will tell you what Jeux Sniper 3D is, why you should play it, and how to download it on your device. We will also give you some tips and tricks on how to play the game and become the ultimate shooter.</p>
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- <h2>What is Jeux Sniper 3D?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Jeux Sniper 3D is a 3D shooting game that was developed by Fun Games For Free, a popular game studio that also created games like Block Craft 3D and Castle Crush. The game was released in 2014 and has since gained over 500 million downloads and 16 million reviews on Google Play Store. The game is also available on other platforms like Y8.com and Steam.</p>
- <p>The game features over 850+ thrilling missions and 13 different worlds that you can explore as a sniper. You can unlock a vast arsenal of sniper rifles, assault rifles, and other powerful guns that you can upgrade and customize. You can also enjoy realistic graphics, intuitive controls, and realistic ballistics that will make you feel like a real shooter. The game also offers both offline and online modes, so you can play it anytime and anywhere.</p>
- <h3>Why you should play Jeux Sniper 3D</h3>
- <p>There are many reasons why you should play Jeux Sniper 3D, but here are some of the main ones:</p>
- <ul>
-<li>It's fun and addictive: Jeux Sniper 3D is a game that will keep you hooked for hours as you complete various challenging missions and show off your shooting skills. You will never get bored as there are always new targets to eliminate and new guns to unlock.</li>
-<li>It's free and accessible: Jeux Sniper 3D is a free-to-play game that does not require any subscription or payment to enjoy. You can also play it offline without any internet connection or data. You can also choose from different platforms and devices to play it on.</li>
-<li>It's immersive and realistic: Jeux Sniper 3D is a game that will immerse you in a realistic sniping experience that will make you feel like a professional assassin. You will have to consider factors like wind direction, distance, and gravity when aiming your shots. You will also have to deal with different weather conditions, such as rain, fog, and snow.</li>
-</ul>
- <h2>How to download Jeux Sniper 3D</h2>
- <h3>Download from Google Play Store</h3>
- <h4>Steps to download and install the game</h4>
- <p>If you want to download Jeux Sniper 3D from Google Play Store, here are the steps that you need to follow:</p>
- <ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Jeux Sniper 3D" or "Sniper 3D：Gun Shooting Games" in the search bar.</li>
-<li>Select the game from the list of results and tap on " Install" or "Get" to start the download process.</li>
-<li>Wait for the download to finish and then tap on "Open" to launch the game.</li>
-<li>Enjoy playing Jeux Sniper 3D on your device.</li>
-</ol>
- <h4>Benefits of downloading from Google Play Store</h4>
- <p>Downloading Jeux Sniper 3D from Google Play Store has some benefits, such as:</p>
- <ul>
-<li>You can get the latest version of the game and enjoy new features and updates.</li>
-<li>You can access the game's ratings and reviews and see what other players think about it.</li>
-<li>You can get support and help from the game's developers and community.</li>
-</ul>
- <h3>Download from Y8.com</h3>
- <h4>Steps to play the game online</h4>
- <p>If you want to play Jeux Sniper 3D online without downloading it, you can do so on Y8.com, a website that offers free online games. Here are the steps that you need to follow:</p>
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- <ol>
-<li>Open your web browser and go to <a href="">Y8.com</a>.</li>
-<li>Search for "Jeux Sniper 3D" or "Sniper 3D" in the search bar.</li>
-<li>Select the game from the list of results and click on "Play Game" to start playing.</li>
-<li>Enjoy playing Jeux Sniper 3D online on your browser.</li>
-</ol>
- <h4>Benefits of playing online</h4>
- <p>Playing Jeux Sniper 3D online has some benefits, such as:</p>
- <ul>
-<li>You don't need to download or install anything on your device, saving space and time.</li>
-<li>You can play the game on any device that has a web browser, such as a laptop, tablet, or smartphone.</li>
-<li>You can play the game anytime and anywhere as long as you have an internet connection.</li>
-</ul>
- <h3>Download from Steam</h3>
- <h4>Steps to download and install the game</h4>
- <p>If you want to download Jeux Sniper 3D from Steam, a digital distribution platform for PC games, here are the steps that you need to follow:</p>
- <ol>
-<li>Open the Steam app on your PC or go to <a href="">Steam's website</a>.</li>
-<li>Search for "Jeux Sniper 3D" or "Sniper 3D: Fun Free Online FPS Shooting Game" in the search bar.</li>
-<li>Select the game from the list of results and click on "Add to Cart" to purchase it. The game costs $9.99 USD.</li>
-<li>After purchasing the game, go to your library and click on "Install" to start the download process.</li>
-<li>Wait for the download to finish and then click on "Play" to launch the game.</li>
-<li>Enjoy playing Jeux Sniper 3D on your PC.</li>
-</ol>
- <h4>Benefits of downloading from Steam</h4>
- <p>Downloading Jeux Sniper 3D from Steam has some benefits, such as:</p>
- <ul>
-<li>You can get a high-quality version of the game with enhanced graphics and performance.</li>
-<li>You can access Steam's features and services, such as achievements, cloud saves, multiplayer, chat, and more.</li>
-<li>You can get discounts and deals on the game during Steam's sales and events.</li>
-</ul>
- <h2>How to play Jeux Sniper 3D</h2>
- <h3>Basic controls and gameplay</h3>
- <p>The basic controls and gameplay of Jeux Sniper 3D are simple and easy to learn. Here are some of them:</p>
- <ul>
-<li>To aim your sniper rifle, use your mouse or touch screen to move the crosshair over your target.</li>
-<li>To zoom in or out, use the scroll wheel or pinch gesture on your device.</li>
-<li>To shoot, press the left mouse button or tap on your device. You can also use the space bar or double-tap to activate bullet time mode, which slows down time and lets you aim more precisely.</li>
-<li>To reload, press R or tap on the reload icon on your device. You can also switch weapons by pressing Q or tapping on the weapon icon on your device.</li>
-<li>To complete a mission, you have to eliminate all the targets that are marked with a red dot or arrow. You have to be careful not to hit any civilians or friendly forces that are marked with a green dot or arrow. You also have to avoid being detected by enemies or cameras that are marked with a yellow dot or arrow.</li>
-</ul>
- <h3>Tips and tricks to improve your skills</h3>
- <p>If you want to improve your skills in Jeux Sniper 3D, here are some tips and tricks that you can use:</p>
- <ul>
-<li>Upgrade and customize your weapons: As you progress in the game, you will earn coins and diamonds that you can use to upgrade and customize your weapons. You can improve their damage, range, stability, zoom, and clip size. You can also change their appearance, such as their color, skin, and scope. Upgrading and customizing your weapons will make them more effective and suitable for different missions.</li>
-<li>Use the environment to your advantage: The game features various environments that you can use to your advantage as a sniper. You can hide behind cover, such as walls, buildings, and vehicles. You can also use objects, such as barrels, crates, and explosives, to create distractions or cause damage to your enemies. You can also use the weather conditions, such as rain, fog, and snow, to conceal your presence or affect your shots.</li>
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-</ul>
- <h2>Conclusion</h2>
- <p>Jeux Sniper 3D is a fun and addictive shooting game that will challenge your sniper skills and immerse you in a realistic sniping experience. You can download the game from Google Play Store, Y8.com, or Steam and play it on your device of choice. You can also follow the tips and tricks that we shared in this article to improve your skills and enjoy the game more. So what are you waiting for? Download Jeux Sniper 3D today and become the ultimate shooter!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Jeux Sniper 3D:</p>
- <ol>
-<li>Is Jeux Sniper 3D safe to play?</li>
-<p>Yes, Jeux Sniper 3D is safe to play as it does not contain any viruses or malware. However, the game does contain violence and blood, so it may not be suitable for children or sensitive players.</p>
-<li>How can I get more coins and diamonds in Jeux Sniper 3D?</li>
-<p>You can get more coins and diamonds in Jeux Sniper 3D by completing missions, watching ads, participating in events, joining clans, or making in-app purchases.</p>
-<li>How can I play Jeux Sniper 3D with my friends?</li>
-<p>You can play Jeux Sniper 3D with your friends by joining or creating a clan. A clan is a group of players who can chat, compete, and cooperate with each other. You can also challenge other players in the PvP mode or join the global leaderboard.</p>
-<li>How can I contact the developers of Jeux Sniper 3D?</li>
-<p>You can contact the developers of Jeux Sniper 3D by sending an email to support@fungames-forfree.com or visiting their website at <a href="">https://www.fungames-forfree.com/</a>.</p>
-<li>What are some other games like Jeux Sniper 3D?</li>
-<p>Some other games like Jeux Sniper 3D are Hitman Sniper, Sniper Fury, Kill Shot Bravo, Modern Combat 5, and Call of Duty: Mobile.</p>
-</ol></p> 197e85843d<br />
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index 1068abd22a4559e34ba8187a8aaa8027f70b7f39..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Dzone Karaoke Full Crack 206 The Ultimate Guide to Singing Like a Pro.md	
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diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/croper.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/croper.py
deleted file mode 100644
index 0ccf5dfc483099ea2112076c0a122cc3e9ecc0c3..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/croper.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import scipy
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-date: 2020.1.5
-note: code is heavily borrowed from 
-    https://github.com/NVlabs/ffhq-dataset
-    http://dlib.net/face_landmark_detection.py.html
-requirements:
-    apt install cmake
-    conda install Pillow numpy scipy
-    pip install dlib
-    # download face landmark model from: 
-    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-
-import numpy as np
-from PIL import Image
-import dlib
-
-
-class Croper:
-    def __init__(self, path_of_lm):
-        # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-        self.predictor = dlib.shape_predictor(path_of_lm)
-
-    def get_landmark(self, img_np):
-        """get landmark with dlib
-        :return: np.array shape=(68, 2)
-        """
-        detector = dlib.get_frontal_face_detector()
-        dets = detector(img_np, 1)
-        #     print("Number of faces detected: {}".format(len(dets)))
-        #     for k, d in enumerate(dets):
-        if len(dets) == 0:
-            return None
-        d = dets[0]
-        # Get the landmarks/parts for the face in box d.
-        shape = self.predictor(img_np, d)
-        #         print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
-        t = list(shape.parts())
-        a = []
-        for tt in t:
-            a.append([tt.x, tt.y])
-        lm = np.array(a)
-        # lm is a shape=(68,2) np.array
-        return lm
-
-    def align_face(self, img, lm, output_size=1024):
-        """
-        :param filepath: str
-        :return: PIL Image
-        """
-        lm_chin = lm[0: 17]  # left-right
-        lm_eyebrow_left = lm[17: 22]  # left-right
-        lm_eyebrow_right = lm[22: 27]  # left-right
-        lm_nose = lm[27: 31]  # top-down
-        lm_nostrils = lm[31: 36]  # top-down
-        lm_eye_left = lm[36: 42]  # left-clockwise
-        lm_eye_right = lm[42: 48]  # left-clockwise
-        lm_mouth_outer = lm[48: 60]  # left-clockwise
-        lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        eye_avg = (eye_left + eye_right) * 0.5
-        eye_to_eye = eye_right - eye_left
-        mouth_left = lm_mouth_outer[0]
-        mouth_right = lm_mouth_outer[6]
-        mouth_avg = (mouth_left + mouth_right) * 0.5
-        eye_to_mouth = mouth_avg - eye_avg
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]  # Addition of binocular difference and double mouth difference
-        x /= np.hypot(*x)   # hypot函数计算直角三角形的斜边长，用斜边长对三角形两条直边做归一化
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)    # 双眼差和眼嘴差，选较大的作为基准尺度
-        y = np.flipud(x) * [-1, 1]
-        c = eye_avg + eye_to_mouth * 0.1
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])   # 定义四边形，以面部基准位置为中心上下左右平移得到四个顶点
-        qsize = np.hypot(*x) * 2    # 定义四边形的大小（边长），为基准尺度的2倍
-
-        # Shrink.
-        # 如果计算出的四边形太大了，就按比例缩小它
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.ANTIALIAS)
-            quad /= shrink
-            qsize /= shrink
-        else:
-            rsize = (int(np.rint(float(img.size[0]))), int(np.rint(float(img.size[1]))))
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-                int(np.ceil(max(quad[:, 1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-                min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            # img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-               int(np.ceil(max(quad[:, 1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-               max(pad[3] - img.size[1] + border, 0))
-        # if enable_padding and max(pad) > border - 4:
-        #     pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        #     img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        #     h, w, _ = img.shape
-        #     y, x, _ = np.ogrid[:h, :w, :1]
-        #     mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-        #                       1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        #     blur = qsize * 0.02
-        #     img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        #     img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        #     img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        #     quad += pad[:2]
-
-        # Transform.
-        quad = (quad + 0.5).flatten()
-        lx = max(min(quad[0], quad[2]), 0)
-        ly = max(min(quad[1], quad[7]), 0)
-        rx = min(max(quad[4], quad[6]), img.size[0])
-        ry = min(max(quad[3], quad[5]), img.size[0])
-        # img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(),
-        #                     Image.BILINEAR)
-        # if output_size < transform_size:
-        #     img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-        # Save aligned image.
-        return rsize, crop, [lx, ly, rx, ry]
-
-    # def crop(self, img_np_list):
-    #     for _i in range(len(img_np_list)):
-    #         img_np = img_np_list[_i]
-    #         lm = self.get_landmark(img_np)
-    #         if lm is None:
-    #             return None
-    #         crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=512)
-    #         clx, cly, crx, cry = crop
-    #         lx, ly, rx, ry = quad
-    #         lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        
-    #         _inp = img_np_list[_i]
-    #         _inp = _inp[cly:cry, clx:crx]
-    #         _inp = _inp[ly:ry, lx:rx]
-    #         img_np_list[_i] = _inp
-    #     return img_np_list
-    
-    def crop(self, img_np_list, still=False, xsize=512):    # first frame for all video
-        img_np = img_np_list[0]
-        lm = self.get_landmark(img_np)
-        if lm is None:
-            raise 'can not detect the landmark from source image'
-        rsize, crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize)
-        clx, cly, crx, cry = crop
-        lx, ly, rx, ry = quad
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        for _i in range(len(img_np_list)):
-            _inp = img_np_list[_i]
-            _inp = cv2.resize(_inp, (rsize[0], rsize[1]))
-            _inp = _inp[cly:cry, clx:crx]
-            # cv2.imwrite('test1.jpg', _inp)
-            if not still:
-                _inp = _inp[ly:ry, lx:rx]
-            # cv2.imwrite('test2.jpg', _inp)
-            img_np_list[_i] = _inp
-        return img_np_list, crop, quad
-
-
-def read_video(filename, uplimit=100):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    cnt = 0
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.resize(frame, (512, 512))
-            frames.append(frame)
-        else:
-            break
-        cnt += 1
-        if cnt >= uplimit:
-            break
-    cap.release()
-    assert len(frames) > 0, f'{filename}: video with no frames!'
-    return frames
-
-
-def create_video(video_name, frames, fps=25, video_format='.mp4', resize_ratio=1):
-    # video_name = os.path.dirname(image_folder) + video_format
-    # img_list = glob.glob1(image_folder, 'frame*')
-    # img_list.sort()
-    # frame = cv2.imread(os.path.join(image_folder, img_list[0]))
-    # frame = cv2.resize(frame, (0, 0), fx=resize_ratio, fy=resize_ratio)
-    # height, width, layers = frames[0].shape
-    height, width, layers = 512, 512, 3
-    if video_format == '.mp4':
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    elif video_format == '.avi':
-        fourcc = cv2.VideoWriter_fourcc(*'XVID')
-    video = cv2.VideoWriter(video_name, fourcc, fps, (width, height))
-    for _frame in frames:
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        video.write(_frame)
-
-def create_images(video_name, frames):
-    height, width, layers = 512, 512, 3
-    images_dir = video_name.split('.')[0]
-    os.makedirs(images_dir, exist_ok=True)
-    for i, _frame in enumerate(frames):
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        _frame_path = os.path.join(images_dir, str(i)+'.jpg')
-        cv2.imwrite(_frame_path, _frame)
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    croper = Croper()
-
-    frames = read_video(filename, uplimit=opt.uplimit)
-    name = filename.split('/')[-1]  # .split('.')[0]
-    name = os.path.join(opt.output_dir, name)
-
-    frames = croper.crop(frames)
-    if frames is None:
-        print(f'{name}: detect no face. should removed')
-        return
-    # create_video(name, frames)
-    create_images(name, frames)
-
-
-def get_data_path(video_dir):
-    eg_video_files = ['/apdcephfs/share_1290939/quincheng/datasets/HDTF/backup_fps25/WDA_KatieHill_000.mp4']
-    # filenames = list()
-    # VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    # VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    # extensions = VIDEO_EXTENSIONS
-    # for ext in extensions:
-    #     filenames = sorted(glob.glob(f'{opt.input_dir}/**/*.{ext}'))
-    # print('Total number of videos:', len(filenames))
-    return eg_video_files
-
-
-def get_wra_data_path(video_dir):
-    if opt.option == 'video':
-        videos_path = sorted(glob.glob(f'{video_dir}/*.mp4'))
-    elif opt.option == 'image':
-        videos_path = sorted(glob.glob(f'{video_dir}/*/'))
-    else:
-        raise NotImplementedError
-    print('Example videos: ', videos_path[:2])
-    return videos_path
-
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=8)
-    parser.add_argument('--uplimit', type=int, default=500)
-    parser.add_argument('--option', type=str, default='video')
-
-    root = '/apdcephfs/share_1290939/quincheng/datasets/HDTF'
-    cmd = f'--input_dir {root}/backup_fps25_first20s_sync/ ' \
-          f'--output_dir {root}/crop512_stylegan_firstframe_sync/ ' \
-          '--device_ids 0 ' \
-          '--workers 8 ' \
-          '--option video ' \
-          '--uplimit 500 '
-    opt = parser.parse_args(cmd.split())
-    # filenames = get_data_path(opt.input_dir)
-    filenames = get_wra_data_path(opt.input_dir)
-    os.makedirs(opt.output_dir, exist_ok=True)
-    print(f'Video numbers: {len(filenames)}')
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None
diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/daresay/employee-handbook-chat/app.py b/spaces/daresay/employee-handbook-chat/app.py
deleted file mode 100644
index 2456295cb1371623a4f73fd027862b7814d355a0..0000000000000000000000000000000000000000
--- a/spaces/daresay/employee-handbook-chat/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from langchain.llms import OpenAI
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.docstore.document import Document
-import requests
-import pathlib
-import subprocess
-import tempfile
-import os
-import gradio as gr
-import pickle
-
-# using a vector space for our search
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-                
-#loading FAISS search index from disk
-with open("search_index.pickle", "rb") as f:
-    search_index = pickle.load(f)
-
-#Get GPT3 response using Langchain
-def print_answer(question, openai):   #openai_embeddings
-    #search_index = get_search_index()
-    chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) 
-    response = (
-        chain(
-            {
-                "input_documents": search_index.similarity_search(question, k=4),
-                "question": question,
-            },
-            return_only_outputs=True,
-        )["output_text"]
-    )
-    if len(response.split('\n')[-1].split())>2:
-        response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))])
-    else: 
-        response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>'
-    return response
-
-
-def chat(message, history, openai_api_key):
-    #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-    openai = OpenAI(temperature=0, openai_api_key=openai_api_key )
-    #os.environ["OPENAI_API_KEY"] = openai_api_key
-    history = history or []
-    message = message.lower()
-    response = print_answer(message, openai)   #openai_embeddings
-    history.append((message, response))
-    return history, history
-
-
-with gr.Blocks() as demo:
-  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-        "
-        >
-        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-            employee-handbook-source QandA - LangChain Bot
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        Hi, I'm a Q and A employee-handbook-source expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your employee-handbook-source implementations and then press enter.<br>
-        <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
-        Built using <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">LangChain</a> and <a href="https://github.com/gradio-app/gradio" target="_blank">Gradio</a> for the employee-handbook-source Repo
-        </p>
-    </div>""")  
-  with gr.Row():
-    question = gr.Textbox(label = 'Type in your questions about employee-handbook-source here and press Enter!', placeholder = 'What questions do you want to ask about the employee-handbook-source library?')
-    openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
-  state = gr.State()
-  chatbot = gr.Chatbot()
-  question.submit(chat, [question, state, openai_api_key], [chatbot, state])
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/datien228/text-summarizer/static/assets/css/all.css b/spaces/datien228/text-summarizer/static/assets/css/all.css
deleted file mode 100644
index 9c2adee7ef736146159196dcc30725d5c4af1551..0000000000000000000000000000000000000000
--- a/spaces/datien228/text-summarizer/static/assets/css/all.css
+++ /dev/null
@@ -1,7831 +0,0 @@
-/*!
- * Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com
- * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
- * Copyright 2022 Fonticons, Inc.
- */
-.fa {
-  font-family: var(--fa-style-family, "Font Awesome 6 Free");
-  font-weight: var(--fa-style, 900); }
-
-.fa,
-.fas,
-.fa-solid,
-.far,
-.fa-regular,
-.fal,
-.fa-light,
-.fat,
-.fa-thin,
-.fad,
-.fa-duotone,
-.fab,
-.fa-brands {
-  -moz-osx-font-smoothing: grayscale;
-  -webkit-font-smoothing: antialiased;
-  display: var(--fa-display, inline-block);
-  font-style: normal;
-  font-variant: normal;
-  line-height: 1;
-  text-rendering: auto; }
-
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-  font-size: 1em; }
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-.fa-ul {
-  list-style-type: none;
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-  padding-left: 0; }
-  .fa-ul > li {
-    position: relative; }
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-.fa-li {
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-  .fa-beat-fade,
-  .fa-flip,
-  .fa-pulse,
-  .fa-shake,
-  .fa-spin,
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-@-webkit-keyframes fa-beat {
-  0%, 90% {
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-@keyframes fa-beat {
-  0%, 90% {
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-
-.fa-tumblr-square:before {
-  content: "\f174"; }
-
-.fa-twitch:before {
-  content: "\f1e8"; }
-
-.fa-twitter:before {
-  content: "\f099"; }
-
-.fa-twitter-square:before {
-  content: "\f081"; }
-
-.fa-typo3:before {
-  content: "\f42b"; }
-
-.fa-uber:before {
-  content: "\f402"; }
-
-.fa-ubuntu:before {
-  content: "\f7df"; }
-
-.fa-uikit:before {
-  content: "\f403"; }
-
-.fa-umbraco:before {
-  content: "\f8e8"; }
-
-.fa-uncharted:before {
-  content: "\e084"; }
-
-.fa-uniregistry:before {
-  content: "\f404"; }
-
-.fa-unity:before {
-  content: "\e049"; }
-
-.fa-unsplash:before {
-  content: "\e07c"; }
-
-.fa-untappd:before {
-  content: "\f405"; }
-
-.fa-ups:before {
-  content: "\f7e0"; }
-
-.fa-usb:before {
-  content: "\f287"; }
-
-.fa-usps:before {
-  content: "\f7e1"; }
-
-.fa-ussunnah:before {
-  content: "\f407"; }
-
-.fa-vaadin:before {
-  content: "\f408"; }
-
-.fa-viacoin:before {
-  content: "\f237"; }
-
-.fa-viadeo:before {
-  content: "\f2a9"; }
-
-.fa-viadeo-square:before {
-  content: "\f2aa"; }
-
-.fa-viber:before {
-  content: "\f409"; }
-
-.fa-vimeo:before {
-  content: "\f40a"; }
-
-.fa-vimeo-square:before {
-  content: "\f194"; }
-
-.fa-vimeo-v:before {
-  content: "\f27d"; }
-
-.fa-vine:before {
-  content: "\f1ca"; }
-
-.fa-vk:before {
-  content: "\f189"; }
-
-.fa-vnv:before {
-  content: "\f40b"; }
-
-.fa-vuejs:before {
-  content: "\f41f"; }
-
-.fa-watchman-monitoring:before {
-  content: "\e087"; }
-
-.fa-waze:before {
-  content: "\f83f"; }
-
-.fa-weebly:before {
-  content: "\f5cc"; }
-
-.fa-weibo:before {
-  content: "\f18a"; }
-
-.fa-weixin:before {
-  content: "\f1d7"; }
-
-.fa-whatsapp:before {
-  content: "\f232"; }
-
-.fa-whatsapp-square:before {
-  content: "\f40c"; }
-
-.fa-whmcs:before {
-  content: "\f40d"; }
-
-.fa-wikipedia-w:before {
-  content: "\f266"; }
-
-.fa-windows:before {
-  content: "\f17a"; }
-
-.fa-wirsindhandwerk:before {
-  content: "\e2d0"; }
-
-.fa-wsh:before {
-  content: "\e2d0"; }
-
-.fa-wix:before {
-  content: "\f5cf"; }
-
-.fa-wizards-of-the-coast:before {
-  content: "\f730"; }
-
-.fa-wodu:before {
-  content: "\e088"; }
-
-.fa-wolf-pack-battalion:before {
-  content: "\f514"; }
-
-.fa-wordpress:before {
-  content: "\f19a"; }
-
-.fa-wordpress-simple:before {
-  content: "\f411"; }
-
-.fa-wpbeginner:before {
-  content: "\f297"; }
-
-.fa-wpexplorer:before {
-  content: "\f2de"; }
-
-.fa-wpforms:before {
-  content: "\f298"; }
-
-.fa-wpressr:before {
-  content: "\f3e4"; }
-
-.fa-xbox:before {
-  content: "\f412"; }
-
-.fa-xing:before {
-  content: "\f168"; }
-
-.fa-xing-square:before {
-  content: "\f169"; }
-
-.fa-y-combinator:before {
-  content: "\f23b"; }
-
-.fa-yahoo:before {
-  content: "\f19e"; }
-
-.fa-yammer:before {
-  content: "\f840"; }
-
-.fa-yandex:before {
-  content: "\f413"; }
-
-.fa-yandex-international:before {
-  content: "\f414"; }
-
-.fa-yarn:before {
-  content: "\f7e3"; }
-
-.fa-yelp:before {
-  content: "\f1e9"; }
-
-.fa-yoast:before {
-  content: "\f2b1"; }
-
-.fa-youtube:before {
-  content: "\f167"; }
-
-.fa-youtube-square:before {
-  content: "\f431"; }
-
-.fa-zhihu:before {
-  content: "\f63f"; }
-:root, :host {
-  --fa-font-regular: normal 400 1em/1 "Font Awesome 6 Free"; }
-
-@font-face {
-  font-family: 'Font Awesome 6 Free';
-  font-style: normal;
-  font-weight: 400;
-  font-display: block;
-  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
-
-.far,
-.fa-regular {
-  font-family: 'Font Awesome 6 Free';
-  font-weight: 400; }
-:root, :host {
-  --fa-font-solid: normal 900 1em/1 "Font Awesome 6 Free"; }
-
-@font-face {
-  font-family: 'Font Awesome 6 Free';
-  font-style: normal;
-  font-weight: 900;
-  font-display: block;
-  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
-
-.fas,
-.fa-solid {
-  font-family: 'Font Awesome 6 Free';
-  font-weight: 900; }
-@font-face {
-  font-family: "Font Awesome 5 Brands";
-  font-display: block;
-  font-weight: 400;
-  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
-
-@font-face {
-  font-family: "Font Awesome 5 Free";
-  font-display: block;
-  font-weight: 900;
-  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
-
-@font-face {
-  font-family: "Font Awesome 5 Free";
-  font-display: block;
-  font-weight: 400;
-  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
-@font-face {
-  font-family: "FontAwesome";
-  font-display: block;
-  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
-
-@font-face {
-  font-family: "FontAwesome";
-  font-display: block;
-  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
-
-@font-face {
-  font-family: "FontAwesome";
-  font-display: block;
-  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype");
-  unicode-range: U+F003,U+F006,U+F014,U+F016-F017,U+F01A-F01B,U+F01D,U+F022,U+F03E,U+F044,U+F046,U+F05C-F05D,U+F06E,U+F070,U+F087-F088,U+F08A,U+F094,U+F096-F097,U+F09D,U+F0A0,U+F0A2,U+F0A4-F0A7,U+F0C5,U+F0C7,U+F0E5-F0E6,U+F0EB,U+F0F6-F0F8,U+F10C,U+F114-F115,U+F118-F11A,U+F11C-F11D,U+F133,U+F147,U+F14E,U+F150-F152,U+F185-F186,U+F18E,U+F190-F192,U+F196,U+F1C1-F1C9,U+F1D9,U+F1DB,U+F1E3,U+F1EA,U+F1F7,U+F1F9,U+F20A,U+F247-F248,U+F24A,U+F24D,U+F255-F25B,U+F25D,U+F271-F274,U+F278,U+F27B,U+F28C,U+F28E,U+F29C,U+F2B5,U+F2B7,U+F2BA,U+F2BC,U+F2BE,U+F2C0-F2C1,U+F2C3,U+F2D0,U+F2D2,U+F2D4,U+F2DC; }
-
-@font-face {
-  font-family: "FontAwesome";
-  font-display: block;
-  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype");
-  unicode-range: U+F041,U+F047,U+F065-F066,U+F07D-F07E,U+F080,U+F08B,U+F08E,U+F090,U+F09A,U+F0AC,U+F0AE,U+F0B2,U+F0D0,U+F0D6,U+F0E4,U+F0EC,U+F10A-F10B,U+F123,U+F13E,U+F148-F149,U+F14C,U+F156,U+F15E,U+F160-F161,U+F163,U+F175-F178,U+F195,U+F1F8,U+F219,U+F250,U+F252,U+F27A; }
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/annotated_types/test_cases.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/annotated_types/test_cases.py
deleted file mode 100644
index ae2c084b875f812aa62e7cbc5eca796104fa5040..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/annotated_types/test_cases.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import sys
-from datetime import date, datetime, timedelta, timezone
-from decimal import Decimal
-from typing import Any, Dict, Iterable, Iterator, List, NamedTuple, Set, Tuple
-
-if sys.version_info < (3, 9):
-    from typing_extensions import Annotated
-else:
-    from typing import Annotated
-
-import annotated_types as at
-
-
-class Case(NamedTuple):
-    """
-    A test case for `annotated_types`.
-    """
-
-    annotation: Any
-    valid_cases: Iterable[Any]
-    invalid_cases: Iterable[Any]
-
-
-def cases() -> Iterable[Case]:
-    # Gt, Ge, Lt, Le
-    yield Case(Annotated[int, at.Gt(4)], (5, 6, 1000), (4, 0, -1))
-    yield Case(Annotated[float, at.Gt(0.5)], (0.6, 0.7, 0.8, 0.9), (0.5, 0.0, -0.1))
-    yield Case(
-        Annotated[datetime, at.Gt(datetime(2000, 1, 1))],
-        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
-        [datetime(2000, 1, 1), datetime(1999, 12, 31)],
-    )
-    yield Case(
-        Annotated[datetime, at.Gt(date(2000, 1, 1))],
-        [date(2000, 1, 2), date(2000, 1, 3)],
-        [date(2000, 1, 1), date(1999, 12, 31)],
-    )
-    yield Case(
-        Annotated[datetime, at.Gt(Decimal('1.123'))],
-        [Decimal('1.1231'), Decimal('123')],
-        [Decimal('1.123'), Decimal('0')],
-    )
-
-    yield Case(Annotated[int, at.Ge(4)], (4, 5, 6, 1000, 4), (0, -1))
-    yield Case(Annotated[float, at.Ge(0.5)], (0.5, 0.6, 0.7, 0.8, 0.9), (0.4, 0.0, -0.1))
-    yield Case(
-        Annotated[datetime, at.Ge(datetime(2000, 1, 1))],
-        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
-        [datetime(1998, 1, 1), datetime(1999, 12, 31)],
-    )
-
-    yield Case(Annotated[int, at.Lt(4)], (0, -1), (4, 5, 6, 1000, 4))
-    yield Case(Annotated[float, at.Lt(0.5)], (0.4, 0.0, -0.1), (0.5, 0.6, 0.7, 0.8, 0.9))
-    yield Case(
-        Annotated[datetime, at.Lt(datetime(2000, 1, 1))],
-        [datetime(1999, 12, 31), datetime(1999, 12, 31)],
-        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
-    )
-
-    yield Case(Annotated[int, at.Le(4)], (4, 0, -1), (5, 6, 1000))
-    yield Case(Annotated[float, at.Le(0.5)], (0.5, 0.0, -0.1), (0.6, 0.7, 0.8, 0.9))
-    yield Case(
-        Annotated[datetime, at.Le(datetime(2000, 1, 1))],
-        [datetime(2000, 1, 1), datetime(1999, 12, 31)],
-        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
-    )
-
-    # Interval
-    yield Case(Annotated[int, at.Interval(gt=4)], (5, 6, 1000), (4, 0, -1))
-    yield Case(Annotated[int, at.Interval(gt=4, lt=10)], (5, 6), (4, 10, 1000, 0, -1))
-    yield Case(Annotated[float, at.Interval(ge=0.5, le=1)], (0.5, 0.9, 1), (0.49, 1.1))
-    yield Case(
-        Annotated[datetime, at.Interval(gt=datetime(2000, 1, 1), le=datetime(2000, 1, 3))],
-        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
-        [datetime(2000, 1, 1), datetime(2000, 1, 4)],
-    )
-
-    yield Case(Annotated[int, at.MultipleOf(multiple_of=3)], (0, 3, 9), (1, 2, 4))
-    yield Case(Annotated[float, at.MultipleOf(multiple_of=0.5)], (0, 0.5, 1, 1.5), (0.4, 1.1))
-
-    # lengths
-
-    yield Case(Annotated[str, at.MinLen(3)], ('123', '1234', 'x' * 10), ('', '1', '12'))
-    yield Case(Annotated[str, at.Len(3)], ('123', '1234', 'x' * 10), ('', '1', '12'))
-    yield Case(Annotated[List[int], at.MinLen(3)], ([1, 2, 3], [1, 2, 3, 4], [1] * 10), ([], [1], [1, 2]))
-    yield Case(Annotated[List[int], at.Len(3)], ([1, 2, 3], [1, 2, 3, 4], [1] * 10), ([], [1], [1, 2]))
-
-    yield Case(Annotated[str, at.MaxLen(4)], ('', '1234'), ('12345', 'x' * 10))
-    yield Case(Annotated[str, at.Len(0, 4)], ('', '1234'), ('12345', 'x' * 10))
-    yield Case(Annotated[List[str], at.MaxLen(4)], ([], ['a', 'bcdef'], ['a', 'b', 'c']), (['a'] * 5, ['b'] * 10))
-    yield Case(Annotated[List[str], at.Len(0, 4)], ([], ['a', 'bcdef'], ['a', 'b', 'c']), (['a'] * 5, ['b'] * 10))
-
-    yield Case(Annotated[str, at.Len(3, 5)], ('123', '12345'), ('', '1', '12', '123456', 'x' * 10))
-    yield Case(Annotated[str, at.Len(3, 3)], ('123',), ('12', '1234'))
-
-    yield Case(Annotated[Dict[int, int], at.Len(2, 3)], [{1: 1, 2: 2}], [{}, {1: 1}, {1: 1, 2: 2, 3: 3, 4: 4}])
-    yield Case(Annotated[Set[int], at.Len(2, 3)], ({1, 2}, {1, 2, 3}), (set(), {1}, {1, 2, 3, 4}))
-    yield Case(Annotated[Tuple[int, ...], at.Len(2, 3)], ((1, 2), (1, 2, 3)), ((), (1,), (1, 2, 3, 4)))
-
-    # Timezone
-
-    yield Case(
-        Annotated[datetime, at.Timezone(None)], [datetime(2000, 1, 1)], [datetime(2000, 1, 1, tzinfo=timezone.utc)]
-    )
-    yield Case(
-        Annotated[datetime, at.Timezone(...)], [datetime(2000, 1, 1, tzinfo=timezone.utc)], [datetime(2000, 1, 1)]
-    )
-    yield Case(
-        Annotated[datetime, at.Timezone(timezone.utc)],
-        [datetime(2000, 1, 1, tzinfo=timezone.utc)],
-        [datetime(2000, 1, 1), datetime(2000, 1, 1, tzinfo=timezone(timedelta(hours=6)))],
-    )
-    yield Case(
-        Annotated[datetime, at.Timezone('Europe/London')],
-        [datetime(2000, 1, 1, tzinfo=timezone(timedelta(0), name='Europe/London'))],
-        [datetime(2000, 1, 1), datetime(2000, 1, 1, tzinfo=timezone(timedelta(hours=6)))],
-    )
-
-    # predicate types
-
-    yield Case(at.LowerCase[str], ['abc', 'foobar'], ['', 'A', 'Boom'])
-    yield Case(at.UpperCase[str], ['ABC', 'DEFO'], ['', 'a', 'abc', 'AbC'])
-    yield Case(at.IsDigits[str], ['123'], ['', 'ab', 'a1b2'])
-    yield Case(at.IsAscii[str], ['123', 'foo bar'], ['£100', '😊', 'whatever 👀'])
-
-    yield Case(Annotated[int, at.Predicate(lambda x: x % 2 == 0)], [0, 2, 4], [1, 3, 5])
-
-    # custom GroupedMetadata
-    class MyCustomGroupedMetadata(at.GroupedMetadata):
-        def __iter__(self) -> Iterator[at.Predicate]:
-            yield at.Predicate(lambda x: float(x).is_integer())
-
-    yield Case(Annotated[float, MyCustomGroupedMetadata()], [0, 2.0], [0.01, 1.5])
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/_winconsole.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/_winconsole.py
deleted file mode 100644
index 6b20df315b23ecd1e3d0ec32c11c0b5ced577efe..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/_winconsole.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# This module is based on the excellent work by Adam Bartoš who
-# provided a lot of what went into the implementation here in
-# the discussion to issue1602 in the Python bug tracker.
-#
-# There are some general differences in regards to how this works
-# compared to the original patches as we do not need to patch
-# the entire interpreter but just work in our little world of
-# echo and prompt.
-import io
-import sys
-import time
-import typing as t
-from ctypes import byref
-from ctypes import c_char
-from ctypes import c_char_p
-from ctypes import c_int
-from ctypes import c_ssize_t
-from ctypes import c_ulong
-from ctypes import c_void_p
-from ctypes import POINTER
-from ctypes import py_object
-from ctypes import Structure
-from ctypes.wintypes import DWORD
-from ctypes.wintypes import HANDLE
-from ctypes.wintypes import LPCWSTR
-from ctypes.wintypes import LPWSTR
-
-from ._compat import _NonClosingTextIOWrapper
-
-assert sys.platform == "win32"
-import msvcrt  # noqa: E402
-from ctypes import windll  # noqa: E402
-from ctypes import WINFUNCTYPE  # noqa: E402
-
-c_ssize_p = POINTER(c_ssize_t)
-
-kernel32 = windll.kernel32
-GetStdHandle = kernel32.GetStdHandle
-ReadConsoleW = kernel32.ReadConsoleW
-WriteConsoleW = kernel32.WriteConsoleW
-GetConsoleMode = kernel32.GetConsoleMode
-GetLastError = kernel32.GetLastError
-GetCommandLineW = WINFUNCTYPE(LPWSTR)(("GetCommandLineW", windll.kernel32))
-CommandLineToArgvW = WINFUNCTYPE(POINTER(LPWSTR), LPCWSTR, POINTER(c_int))(
-    ("CommandLineToArgvW", windll.shell32)
-)
-LocalFree = WINFUNCTYPE(c_void_p, c_void_p)(("LocalFree", windll.kernel32))
-
-STDIN_HANDLE = GetStdHandle(-10)
-STDOUT_HANDLE = GetStdHandle(-11)
-STDERR_HANDLE = GetStdHandle(-12)
-
-PyBUF_SIMPLE = 0
-PyBUF_WRITABLE = 1
-
-ERROR_SUCCESS = 0
-ERROR_NOT_ENOUGH_MEMORY = 8
-ERROR_OPERATION_ABORTED = 995
-
-STDIN_FILENO = 0
-STDOUT_FILENO = 1
-STDERR_FILENO = 2
-
-EOF = b"\x1a"
-MAX_BYTES_WRITTEN = 32767
-
-try:
-    from ctypes import pythonapi
-except ImportError:
-    # On PyPy we cannot get buffers so our ability to operate here is
-    # severely limited.
-    get_buffer = None
-else:
-
-    class Py_buffer(Structure):
-        _fields_ = [
-            ("buf", c_void_p),
-            ("obj", py_object),
-            ("len", c_ssize_t),
-            ("itemsize", c_ssize_t),
-            ("readonly", c_int),
-            ("ndim", c_int),
-            ("format", c_char_p),
-            ("shape", c_ssize_p),
-            ("strides", c_ssize_p),
-            ("suboffsets", c_ssize_p),
-            ("internal", c_void_p),
-        ]
-
-    PyObject_GetBuffer = pythonapi.PyObject_GetBuffer
-    PyBuffer_Release = pythonapi.PyBuffer_Release
-
-    def get_buffer(obj, writable=False):
-        buf = Py_buffer()
-        flags = PyBUF_WRITABLE if writable else PyBUF_SIMPLE
-        PyObject_GetBuffer(py_object(obj), byref(buf), flags)
-
-        try:
-            buffer_type = c_char * buf.len
-            return buffer_type.from_address(buf.buf)
-        finally:
-            PyBuffer_Release(byref(buf))
-
-
-class _WindowsConsoleRawIOBase(io.RawIOBase):
-    def __init__(self, handle):
-        self.handle = handle
-
-    def isatty(self):
-        super().isatty()
-        return True
-
-
-class _WindowsConsoleReader(_WindowsConsoleRawIOBase):
-    def readable(self):
-        return True
-
-    def readinto(self, b):
-        bytes_to_be_read = len(b)
-        if not bytes_to_be_read:
-            return 0
-        elif bytes_to_be_read % 2:
-            raise ValueError(
-                "cannot read odd number of bytes from UTF-16-LE encoded console"
-            )
-
-        buffer = get_buffer(b, writable=True)
-        code_units_to_be_read = bytes_to_be_read // 2
-        code_units_read = c_ulong()
-
-        rv = ReadConsoleW(
-            HANDLE(self.handle),
-            buffer,
-            code_units_to_be_read,
-            byref(code_units_read),
-            None,
-        )
-        if GetLastError() == ERROR_OPERATION_ABORTED:
-            # wait for KeyboardInterrupt
-            time.sleep(0.1)
-        if not rv:
-            raise OSError(f"Windows error: {GetLastError()}")
-
-        if buffer[0] == EOF:
-            return 0
-        return 2 * code_units_read.value
-
-
-class _WindowsConsoleWriter(_WindowsConsoleRawIOBase):
-    def writable(self):
-        return True
-
-    @staticmethod
-    def _get_error_message(errno):
-        if errno == ERROR_SUCCESS:
-            return "ERROR_SUCCESS"
-        elif errno == ERROR_NOT_ENOUGH_MEMORY:
-            return "ERROR_NOT_ENOUGH_MEMORY"
-        return f"Windows error {errno}"
-
-    def write(self, b):
-        bytes_to_be_written = len(b)
-        buf = get_buffer(b)
-        code_units_to_be_written = min(bytes_to_be_written, MAX_BYTES_WRITTEN) // 2
-        code_units_written = c_ulong()
-
-        WriteConsoleW(
-            HANDLE(self.handle),
-            buf,
-            code_units_to_be_written,
-            byref(code_units_written),
-            None,
-        )
-        bytes_written = 2 * code_units_written.value
-
-        if bytes_written == 0 and bytes_to_be_written > 0:
-            raise OSError(self._get_error_message(GetLastError()))
-        return bytes_written
-
-
-class ConsoleStream:
-    def __init__(self, text_stream: t.TextIO, byte_stream: t.BinaryIO) -> None:
-        self._text_stream = text_stream
-        self.buffer = byte_stream
-
-    @property
-    def name(self) -> str:
-        return self.buffer.name
-
-    def write(self, x: t.AnyStr) -> int:
-        if isinstance(x, str):
-            return self._text_stream.write(x)
-        try:
-            self.flush()
-        except Exception:
-            pass
-        return self.buffer.write(x)
-
-    def writelines(self, lines: t.Iterable[t.AnyStr]) -> None:
-        for line in lines:
-            self.write(line)
-
-    def __getattr__(self, name: str) -> t.Any:
-        return getattr(self._text_stream, name)
-
-    def isatty(self) -> bool:
-        return self.buffer.isatty()
-
-    def __repr__(self):
-        return f"<ConsoleStream name={self.name!r} encoding={self.encoding!r}>"
-
-
-def _get_text_stdin(buffer_stream: t.BinaryIO) -> t.TextIO:
-    text_stream = _NonClosingTextIOWrapper(
-        io.BufferedReader(_WindowsConsoleReader(STDIN_HANDLE)),
-        "utf-16-le",
-        "strict",
-        line_buffering=True,
-    )
-    return t.cast(t.TextIO, ConsoleStream(text_stream, buffer_stream))
-
-
-def _get_text_stdout(buffer_stream: t.BinaryIO) -> t.TextIO:
-    text_stream = _NonClosingTextIOWrapper(
-        io.BufferedWriter(_WindowsConsoleWriter(STDOUT_HANDLE)),
-        "utf-16-le",
-        "strict",
-        line_buffering=True,
-    )
-    return t.cast(t.TextIO, ConsoleStream(text_stream, buffer_stream))
-
-
-def _get_text_stderr(buffer_stream: t.BinaryIO) -> t.TextIO:
-    text_stream = _NonClosingTextIOWrapper(
-        io.BufferedWriter(_WindowsConsoleWriter(STDERR_HANDLE)),
-        "utf-16-le",
-        "strict",
-        line_buffering=True,
-    )
-    return t.cast(t.TextIO, ConsoleStream(text_stream, buffer_stream))
-
-
-_stream_factories: t.Mapping[int, t.Callable[[t.BinaryIO], t.TextIO]] = {
-    0: _get_text_stdin,
-    1: _get_text_stdout,
-    2: _get_text_stderr,
-}
-
-
-def _is_console(f: t.TextIO) -> bool:
-    if not hasattr(f, "fileno"):
-        return False
-
-    try:
-        fileno = f.fileno()
-    except (OSError, io.UnsupportedOperation):
-        return False
-
-    handle = msvcrt.get_osfhandle(fileno)
-    return bool(GetConsoleMode(handle, byref(DWORD())))
-
-
-def _get_windows_console_stream(
-    f: t.TextIO, encoding: t.Optional[str], errors: t.Optional[str]
-) -> t.Optional[t.TextIO]:
-    if (
-        get_buffer is not None
-        and encoding in {"utf-16-le", None}
-        and errors in {"strict", None}
-        and _is_console(f)
-    ):
-        func = _stream_factories.get(f.fileno())
-        if func is not None:
-            b = getattr(f, "buffer", None)
-
-            if b is None:
-                return None
-
-            return func(b)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
deleted file mode 100644
index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;r<o.length;r++){var i=o[r];u.push(new w(i,n)),(i.indent||i.dedent)&&(s=!0)}return{name:e.name,startState:function(){return{state:"start",pending:null,indent:s?[]:null}},copyState:function(g){var x={state:g.state,pending:g.pending,indent:g.indent&&g.indent.slice(0)};return g.stack&&(x.stack=g.stack.slice(0)),x},token:v(t),indent:R(t,e),languageData:e}}function a(n,t){if(!n.hasOwnProperty(t))throw new Error("Undefined state "+t+" in simple mode")}function h(n,t){if(!n)return/(?:)/;var e="";return n instanceof RegExp?(n.ignoreCase&&(e="i"),n=n.source):n=String(n),new RegExp((t===!1?"":"^")+"(?:"+n+")",e)}function y(n){if(!n)return null;if(n.apply)return n;if(typeof n=="string")return n.replace(/\./g," ");for(var t=[],e=0;e<n.length;e++)t.push(n[e]&&n[e].replace(/\./g," "));return t}function w(n,t){(n.next||n.push)&&a(t,n.next||n.push),this.regex=h(n.regex),this.token=y(n.token),this.data=n}function v(n){return function(t,e){if(e.pending){var s=e.pending.shift();return e.pending.length==0&&(e.pending=null),t.pos+=s.text.length,s.token}for(var l=n[e.state],u=0;u<l.length;u++){var o=l[u],r=(!o.data.sol||t.sol())&&t.match(o.regex);if(r){o.data.next?e.state=o.data.next:o.data.push?((e.stack||(e.stack=[])).push(e.state),e.state=o.data.push):o.data.pop&&e.stack&&e.stack.length&&(e.state=e.stack.pop()),o.data.indent&&e.indent.push(t.indentation()+t.indentUnit),o.data.dedent&&e.indent.pop();var i=o.token;if(i&&i.apply&&(i=i(r)),r.length>2&&o.token&&typeof o.token!="string"){e.pending=[];for(var g=2;g<r.length;g++)r[g]&&e.pending.push({text:r[g],token:o.token[g-1]});return t.backUp(r[0].length-(r[1]?r[1].length:0)),i[0]}else return i&&i.join?i[0]:i}}return t.next(),null}}function R(n,t){return function(e,s){if(e.indent==null||t.dontIndentStates&&t.doneIndentState.indexOf(e.state)>-1)return null;var l=e.indent.length-1,u=n[e.state];n:for(;;){for(var o=0;o<u.length;o++){var r=u[o];if(r.data.dedent&&r.data.dedentIfLineStart!==!1){var i=r.regex.exec(s);if(i&&i[0]){l--,(r.next||r.push)&&(u=n[r.next||r.push]),s=s.slice(i[0].length);continue n}}}break}return l<0?0:e.indent[l]}}var p="from",$=new RegExp("^(\\s*)\\b("+p+")\\b","i"),d=["run","cmd","entrypoint","shell"],b=new RegExp("^(\\s*)("+d.join("|")+")(\\s+\\[)","i"),f="expose",m=new RegExp("^(\\s*)("+f+")(\\s+)","i"),S=["arg","from","maintainer","label","env","add","copy","volume","user","workdir","onbuild","stopsignal","healthcheck","shell"],E=[p,f].concat(d).concat(S),k="("+E.join("|")+")",I=new RegExp("^(\\s*)"+k+"(\\s*)(#.*)?$","i"),O=new RegExp("^(\\s*)"+k+"(\\s+)","i");const D=c({start:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:$,token:[null,"keyword"],sol:!0,next:"from"},{regex:I,token:[null,"keyword",null,"error"],sol:!0},{regex:b,token:[null,"keyword",null],sol:!0,next:"array"},{regex:m,token:[null,"keyword",null],sol:!0,next:"expose"},{regex:O,token:[null,"keyword",null],sol:!0,next:"arguments"},{regex:/./,token:null}],from:[{regex:/\s*$/,token:null,next:"start"},{regex:/(\s*)(#.*)$/,token:[null,"error"],next:"start"},{regex:/(\s*\S+\s+)(as)/i,token:[null,"keyword"],next:"start"},{token:null,next:"start"}],single:[{regex:/(?:[^\\']|\\.)/,token:"string"},{regex:/'/,token:"string",pop:!0}],double:[{regex:/(?:[^\\"]|\\.)/,token:"string"},{regex:/"/,token:"string",pop:!0}],array:[{regex:/\]/,token:null,next:"start"},{regex:/"(?:[^\\"]|\\.)*"?/,token:"string"}],expose:[{regex:/\d+$/,token:"number",next:"start"},{regex:/[^\d]+$/,token:null,next:"start"},{regex:/\d+/,token:"number"},{regex:/[^\d]+/,token:null},{token:null,next:"start"}],arguments:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:/"(?:[^\\"]|\\.)*"?$/,token:"string",next:"start"},{regex:/"/,token:"string",push:"double"},{regex:/'(?:[^\\']|\\.)*'?$/,token:"string",next:"start"},{regex:/'/,token:"string",push:"single"},{regex:/[^#"']+[\\`]$/,token:null},{regex:/[^#"']+$/,token:null,next:"start"},{regex:/[^#"']+/,token:null},{token:null,next:"start"}],languageData:{commentTokens:{line:"#"}}});export{D as dockerFile};
-//# sourceMappingURL=dockerfile-d67bbd50.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-604e6cf5.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-604e6cf5.js
deleted file mode 100644
index 86211ac75a73214566723f6483639ea415174ac3..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-604e6cf5.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{P as j,N as G,c as E,D as U,e as w,T as b,I as H}from"./index-ba0b23cc.js";class P{constructor(t,e,s,i,h,r,n,a,l,f=0,u){this.p=t,this.stack=e,this.state=s,this.reducePos=i,this.pos=h,this.score=r,this.buffer=n,this.bufferBase=a,this.curContext=l,this.lookAhead=f,this.parent=u}toString(){return`[${this.stack.filter((t,e)=>e%3==0).concat(this.state)}]@${this.pos}${this.score?"!"+this.score:""}`}static start(t,e,s=0){let i=t.parser.context;return new P(t,[],e,s,s,0,[],0,i?new y(i,i.start):null,0,null)}get context(){return this.curContext?this.curContext.context:null}pushState(t,e){this.stack.push(this.state,e,this.bufferBase+this.buffer.length),this.state=t}reduce(t){var e;let s=t>>19,i=t&65535,{parser:h}=this.p,r=h.dynamicPrecedence(i);if(r&&(this.score+=r),s==0){this.pushState(h.getGoto(this.state,i,!0),this.reducePos),i<h.minRepeatTerm&&this.storeNode(i,this.reducePos,this.reducePos,4,!0),this.reduceContext(i,this.reducePos);return}let n=this.stack.length-(s-1)*3-(t&262144?6:0),a=n?this.stack[n-2]:this.p.ranges[0].from,l=this.reducePos-a;l>=2e3&&!(!((e=this.p.parser.nodeSet.types[i])===null||e===void 0)&&e.isAnonymous)&&(a==this.p.lastBigReductionStart?(this.p.bigReductionCount++,this.p.lastBigReductionSize=l):this.p.lastBigReductionSize<l&&(this.p.bigReductionCount=1,this.p.lastBigReductionStart=a,this.p.lastBigReductionSize=l));let f=n?this.stack[n-1]:0,u=this.bufferBase+this.buffer.length-f;if(i<h.minRepeatTerm||t&131072){let c=h.stateFlag(this.state,1)?this.pos:this.reducePos;this.storeNode(i,a,c,u+4,!0)}if(t&262144)this.state=this.stack[n];else{let c=this.stack[n-3];this.state=h.getGoto(c,i,!0)}for(;this.stack.length>n;)this.stack.pop();this.reduceContext(i,a)}storeNode(t,e,s,i=4,h=!1){if(t==0&&(!this.stack.length||this.stack[this.stack.length-1]<this.buffer.length+this.bufferBase)){let r=this,n=this.buffer.length;if(n==0&&r.parent&&(n=r.bufferBase-r.parent.bufferBase,r=r.parent),n>0&&r.buffer[n-4]==0&&r.buffer[n-1]>-1){if(e==s)return;if(r.buffer[n-2]>=e){r.buffer[n-2]=s;return}}}if(!h||this.pos==s)this.buffer.push(t,e,s,i);else{let r=this.buffer.length;if(r>0&&this.buffer[r-4]!=0)for(;r>0&&this.buffer[r-2]>s;)this.buffer[r]=this.buffer[r-4],this.buffer[r+1]=this.buffer[r-3],this.buffer[r+2]=this.buffer[r-2],this.buffer[r+3]=this.buffer[r-1],r-=4,i>4&&(i-=4);this.buffer[r]=t,this.buffer[r+1]=e,this.buffer[r+2]=s,this.buffer[r+3]=i}}shift(t,e,s){let i=this.pos;if(t&131072)this.pushState(t&65535,this.pos);else if(t&262144)this.pos=s,this.shiftContext(e,i),e<=this.p.parser.maxNode&&this.buffer.push(e,i,s,4);else{let h=t,{parser:r}=this.p;(s>this.pos||e<=r.maxNode)&&(this.pos=s,r.stateFlag(h,1)||(this.reducePos=s)),this.pushState(h,i),this.shiftContext(e,i),e<=r.maxNode&&this.buffer.push(e,i,s,4)}}apply(t,e,s){t&65536?this.reduce(t):this.shift(t,e,s)}useNode(t,e){let s=this.p.reused.length-1;(s<0||this.p.reused[s]!=t)&&(this.p.reused.push(t),s++);let i=this.pos;this.reducePos=this.pos=i+t.length,this.pushState(e,i),this.buffer.push(s,i,this.reducePos,-1),this.curContext&&this.updateContext(this.curContext.tracker.reuse(this.curContext.context,t,this,this.p.stream.reset(this.pos-t.length)))}split(){let t=this,e=t.buffer.length;for(;e>0&&t.buffer[e-2]>t.reducePos;)e-=4;let s=t.buffer.slice(e),i=t.bufferBase+e;for(;t&&i==t.bufferBase;)t=t.parent;return new P(this.p,this.stack.slice(),this.state,this.reducePos,this.pos,this.score,s,i,this.curContext,this.lookAhead,t)}recoverByDelete(t,e){let s=t<=this.p.parser.maxNode;s&&this.storeNode(t,this.pos,e,4),this.storeNode(0,this.pos,e,s?8:4),this.pos=this.reducePos=e,this.score-=190}canShift(t){for(let e=new W(this);;){let s=this.p.parser.stateSlot(e.state,4)||this.p.parser.hasAction(e.state,t);if(s==0)return!1;if(!(s&65536))return!0;e.reduce(s)}}recoverByInsert(t){if(this.stack.length>=300)return[];let e=this.p.parser.nextStates(this.state);if(e.length>4<<1||this.stack.length>=120){let i=[];for(let h=0,r;h<e.length;h+=2)(r=e[h+1])!=this.state&&this.p.parser.hasAction(r,t)&&i.push(e[h],r);if(this.stack.length<120)for(let h=0;i.length<4<<1&&h<e.length;h+=2){let r=e[h+1];i.some((n,a)=>a&1&&n==r)||i.push(e[h],r)}e=i}let s=[];for(let i=0;i<e.length&&s.length<4;i+=2){let h=e[i+1];if(h==this.state)continue;let r=this.split();r.pushState(h,this.pos),r.storeNode(0,r.pos,r.pos,4,!0),r.shiftContext(e[i],this.pos),r.score-=200,s.push(r)}return s}forceReduce(){let t=this.p.parser.stateSlot(this.state,5);if(!(t&65536))return!1;let{parser:e}=this.p;if(!e.validAction(this.state,t)){let s=t>>19,i=t&65535,h=this.stack.length-s*3;if(h<0||e.getGoto(this.stack[h],i,!1)<0)return!1;this.storeNode(0,this.reducePos,this.reducePos,4,!0),this.score-=100}return this.reducePos=this.pos,this.reduce(t),!0}forceAll(){for(;!this.p.parser.stateFlag(this.state,2);)if(!this.forceReduce()){this.storeNode(0,this.pos,this.pos,4,!0);break}return this}get deadEnd(){if(this.stack.length!=3)return!1;let{parser:t}=this.p;return t.data[t.stateSlot(this.state,1)]==65535&&!t.stateSlot(this.state,4)}restart(){this.state=this.stack[0],this.stack.length=0}sameState(t){if(this.state!=t.state||this.stack.length!=t.stack.length)return!1;for(let e=0;e<this.stack.length;e+=3)if(this.stack[e]!=t.stack[e])return!1;return!0}get parser(){return this.p.parser}dialectEnabled(t){return this.p.parser.dialect.flags[t]}shiftContext(t,e){this.curContext&&this.updateContext(this.curContext.tracker.shift(this.curContext.context,t,this,this.p.stream.reset(e)))}reduceContext(t,e){this.curContext&&this.updateContext(this.curContext.tracker.reduce(this.curContext.context,t,this,this.p.stream.reset(e)))}emitContext(){let t=this.buffer.length-1;(t<0||this.buffer[t]!=-3)&&this.buffer.push(this.curContext.hash,this.reducePos,this.reducePos,-3)}emitLookAhead(){let t=this.buffer.length-1;(t<0||this.buffer[t]!=-4)&&this.buffer.push(this.lookAhead,this.reducePos,this.reducePos,-4)}updateContext(t){if(t!=this.curContext.context){let e=new y(this.curContext.tracker,t);e.hash!=this.curContext.hash&&this.emitContext(),this.curContext=e}}setLookAhead(t){t>this.lookAhead&&(this.emitLookAhead(),this.lookAhead=t)}close(){this.curContext&&this.curContext.tracker.strict&&this.emitContext(),this.lookAhead>0&&this.emitLookAhead()}}class y{constructor(t,e){this.tracker=t,this.context=e,this.hash=t.strict?t.hash(e):0}}var N;(function(o){o[o.Insert=200]="Insert",o[o.Delete=190]="Delete",o[o.Reduce=100]="Reduce",o[o.MaxNext=4]="MaxNext",o[o.MaxInsertStackDepth=300]="MaxInsertStackDepth",o[o.DampenInsertStackDepth=120]="DampenInsertStackDepth",o[o.MinBigReduction=2e3]="MinBigReduction"})(N||(N={}));class W{constructor(t){this.start=t,this.state=t.state,this.stack=t.stack,this.base=this.stack.length}reduce(t){let e=t&65535,s=t>>19;s==0?(this.stack==this.start.stack&&(this.stack=this.stack.slice()),this.stack.push(this.state,0,0),this.base+=3):this.base-=(s-1)*3;let i=this.start.p.parser.getGoto(this.stack[this.base-3],e,!0);this.state=i}}class C{constructor(t,e,s){this.stack=t,this.pos=e,this.index=s,this.buffer=t.buffer,this.index==0&&this.maybeNext()}static create(t,e=t.bufferBase+t.buffer.length){return new C(t,e,e-t.bufferBase)}maybeNext(){let t=this.stack.parent;t!=null&&(this.index=this.stack.bufferBase-t.bufferBase,this.stack=t,this.buffer=t.buffer)}get id(){return this.buffer[this.index-4]}get start(){return this.buffer[this.index-3]}get end(){return this.buffer[this.index-2]}get size(){return this.buffer[this.index-1]}next(){this.index-=4,this.pos-=4,this.index==0&&this.maybeNext()}fork(){return new C(this.stack,this.pos,this.index)}}function x(o,t=Uint16Array){if(typeof o!="string")return o;let e=null;for(let s=0,i=0;s<o.length;){let h=0;for(;;){let r=o.charCodeAt(s++),n=!1;if(r==126){h=65535;break}r>=92&&r--,r>=34&&r--;let a=r-32;if(a>=46&&(a-=46,n=!0),h+=a,n)break;h*=46}e?e[i++]=h:e=new t(h)}return e}class S{constructor(){this.start=-1,this.value=-1,this.end=-1,this.extended=-1,this.lookAhead=0,this.mask=0,this.context=0}}const D=new S;class q{constructor(t,e){this.input=t,this.ranges=e,this.chunk="",this.chunkOff=0,this.chunk2="",this.chunk2Pos=0,this.next=-1,this.token=D,this.rangeIndex=0,this.pos=this.chunkPos=e[0].from,this.range=e[0],this.end=e[e.length-1].to,this.readNext()}resolveOffset(t,e){let s=this.range,i=this.rangeIndex,h=this.pos+t;for(;h<s.from;){if(!i)return null;let r=this.ranges[--i];h-=s.from-r.to,s=r}for(;e<0?h>s.to:h>=s.to;){if(i==this.ranges.length-1)return null;let r=this.ranges[++i];h+=r.from-s.to,s=r}return h}clipPos(t){if(t>=this.range.from&&t<this.range.to)return t;for(let e of this.ranges)if(e.to>t)return Math.max(t,e.from);return this.end}peek(t){let e=this.chunkOff+t,s,i;if(e>=0&&e<this.chunk.length)s=this.pos+t,i=this.chunk.charCodeAt(e);else{let h=this.resolveOffset(t,1);if(h==null)return-1;if(s=h,s>=this.chunk2Pos&&s<this.chunk2Pos+this.chunk2.length)i=this.chunk2.charCodeAt(s-this.chunk2Pos);else{let r=this.rangeIndex,n=this.range;for(;n.to<=s;)n=this.ranges[++r];this.chunk2=this.input.chunk(this.chunk2Pos=s),s+this.chunk2.length>n.to&&(this.chunk2=this.chunk2.slice(0,n.to-s)),i=this.chunk2.charCodeAt(0)}}return s>=this.token.lookAhead&&(this.token.lookAhead=s+1),i}acceptToken(t,e=0){let s=e?this.resolveOffset(e,-1):this.pos;if(s==null||s<this.token.start)throw new RangeError("Token end out of bounds");this.token.value=t,this.token.end=s}getChunk(){if(this.pos>=this.chunk2Pos&&this.pos<this.chunk2Pos+this.chunk2.length){let{chunk:t,chunkPos:e}=this;this.chunk=this.chunk2,this.chunkPos=this.chunk2Pos,this.chunk2=t,this.chunk2Pos=e,this.chunkOff=this.pos-this.chunkPos}else{this.chunk2=this.chunk,this.chunk2Pos=this.chunkPos;let t=this.input.chunk(this.pos),e=this.pos+t.length;this.chunk=e>this.range.to?t.slice(0,this.range.to-this.pos):t,this.chunkPos=this.pos,this.chunkOff=0}}readNext(){return this.chunkOff>=this.chunk.length&&(this.getChunk(),this.chunkOff==this.chunk.length)?this.next=-1:this.next=this.chunk.charCodeAt(this.chunkOff)}advance(t=1){for(this.chunkOff+=t;this.pos+t>=this.range.to;){if(this.rangeIndex==this.ranges.length-1)return this.setDone();t-=this.range.to-this.pos,this.range=this.ranges[++this.rangeIndex],this.pos=this.range.from}return this.pos+=t,this.pos>=this.token.lookAhead&&(this.token.lookAhead=this.pos+1),this.readNext()}setDone(){return this.pos=this.chunkPos=this.end,this.range=this.ranges[this.rangeIndex=this.ranges.length-1],this.chunk="",this.next=-1}reset(t,e){if(e?(this.token=e,e.start=t,e.lookAhead=t+1,e.value=e.extended=-1):this.token=D,this.pos!=t){if(this.pos=t,t==this.end)return this.setDone(),this;for(;t<this.range.from;)this.range=this.ranges[--this.rangeIndex];for(;t>=this.range.to;)this.range=this.ranges[++this.rangeIndex];t>=this.chunkPos&&t<this.chunkPos+this.chunk.length?this.chunkOff=t-this.chunkPos:(this.chunk="",this.chunkOff=0),this.readNext()}return this}read(t,e){if(t>=this.chunkPos&&e<=this.chunkPos+this.chunk.length)return this.chunk.slice(t-this.chunkPos,e-this.chunkPos);if(t>=this.chunk2Pos&&e<=this.chunk2Pos+this.chunk2.length)return this.chunk2.slice(t-this.chunk2Pos,e-this.chunk2Pos);if(t>=this.range.from&&e<=this.range.to)return this.input.read(t,e);let s="";for(let i of this.ranges){if(i.from>=e)break;i.to>t&&(s+=this.input.read(Math.max(i.from,t),Math.min(i.to,e)))}return s}}class m{constructor(t,e){this.data=t,this.id=e}token(t,e){let{parser:s}=e.p;F(this.data,t,e,this.id,s.data,s.tokenPrecTable)}}m.prototype.contextual=m.prototype.fallback=m.prototype.extend=!1;class J{constructor(t,e,s){this.precTable=e,this.elseToken=s,this.data=typeof t=="string"?x(t):t}token(t,e){let s=t.pos,i;for(;i=t.pos,F(this.data,t,e,0,this.data,this.precTable),!(t.token.value>-1);){if(this.elseToken==null)return;if(t.next<0)break;t.advance(),t.reset(i+1,t.token)}i>s&&(t.reset(s,t.token),t.acceptToken(this.elseToken,i-s))}}J.prototype.contextual=m.prototype.fallback=m.prototype.extend=!1;class tt{constructor(t,e={}){this.token=t,this.contextual=!!e.contextual,this.fallback=!!e.fallback,this.extend=!!e.extend}}function F(o,t,e,s,i,h){let r=0,n=1<<s,{dialect:a}=e.p.parser;t:for(;n&o[r];){let l=o[r+1];for(let p=r+3;p<l;p+=2)if((o[p+1]&n)>0){let d=o[p];if(a.allows(d)&&(t.token.value==-1||t.token.value==d||K(d,t.token.value,i,h))){t.acceptToken(d);break}}let f=t.next,u=0,c=o[r+2];if(t.next<0&&c>u&&o[l+c*3-3]==65535&&o[l+c*3-3]==65535){r=o[l+c*3-1];continue t}for(;u<c;){let p=u+c>>1,d=l+p+(p<<1),L=o[d],$=o[d+1]||65536;if(f<L)c=p;else if(f>=$)u=p+1;else{r=o[d+2],t.advance();continue t}}break}}function I(o,t,e){for(let s=t,i;(i=o[s])!=65535;s++)if(i==e)return s-t;return-1}function K(o,t,e,s){let i=I(e,s,t);return i<0||I(e,s,o)<i}const g=typeof process<"u"&&process.env&&/\bparse\b/.test({}.LOG);let A=null;var z;(function(o){o[o.Margin=25]="Margin"})(z||(z={}));function B(o,t,e){let s=o.cursor(H.IncludeAnonymous);for(s.moveTo(t);;)if(!(e<0?s.childBefore(t):s.childAfter(t)))for(;;){if((e<0?s.to<t:s.from>t)&&!s.type.isError)return e<0?Math.max(0,Math.min(s.to-1,t-25)):Math.min(o.length,Math.max(s.from+1,t+25));if(e<0?s.prevSibling():s.nextSibling())break;if(!s.parent())return e<0?0:o.length}}class Q{constructor(t,e){this.fragments=t,this.nodeSet=e,this.i=0,this.fragment=null,this.safeFrom=-1,this.safeTo=-1,this.trees=[],this.start=[],this.index=[],this.nextFragment()}nextFragment(){let t=this.fragment=this.i==this.fragments.length?null:this.fragments[this.i++];if(t){for(this.safeFrom=t.openStart?B(t.tree,t.from+t.offset,1)-t.offset:t.from,this.safeTo=t.openEnd?B(t.tree,t.to+t.offset,-1)-t.offset:t.to;this.trees.length;)this.trees.pop(),this.start.pop(),this.index.pop();this.trees.push(t.tree),this.start.push(-t.offset),this.index.push(0),this.nextStart=this.safeFrom}else this.nextStart=1e9}nodeAt(t){if(t<this.nextStart)return null;for(;this.fragment&&this.safeTo<=t;)this.nextFragment();if(!this.fragment)return null;for(;;){let e=this.trees.length-1;if(e<0)return this.nextFragment(),null;let s=this.trees[e],i=this.index[e];if(i==s.children.length){this.trees.pop(),this.start.pop(),this.index.pop();continue}let h=s.children[i],r=this.start[e]+s.positions[i];if(r>t)return this.nextStart=r,null;if(h instanceof b){if(r==t){if(r<this.safeFrom)return null;let n=r+h.length;if(n<=this.safeTo){let a=h.prop(w.lookAhead);if(!a||n+a<this.fragment.to)return h}}this.index[e]++,r+h.length>=Math.max(this.safeFrom,t)&&(this.trees.push(h),this.start.push(r),this.index.push(0))}else this.index[e]++,this.nextStart=r+h.length}}}class V{constructor(t,e){this.stream=e,this.tokens=[],this.mainToken=null,this.actions=[],this.tokens=t.tokenizers.map(s=>new S)}getActions(t){let e=0,s=null,{parser:i}=t.p,{tokenizers:h}=i,r=i.stateSlot(t.state,3),n=t.curContext?t.curContext.hash:0,a=0;for(let l=0;l<h.length;l++){if(!(1<<l&r))continue;let f=h[l],u=this.tokens[l];if(!(s&&!f.fallback)&&((f.contextual||u.start!=t.pos||u.mask!=r||u.context!=n)&&(this.updateCachedToken(u,f,t),u.mask=r,u.context=n),u.lookAhead>u.end+25&&(a=Math.max(u.lookAhead,a)),u.value!=0)){let c=e;if(u.extended>-1&&(e=this.addActions(t,u.extended,u.end,e)),e=this.addActions(t,u.value,u.end,e),!f.extend&&(s=u,e>c))break}}for(;this.actions.length>e;)this.actions.pop();return a&&t.setLookAhead(a),!s&&t.pos==this.stream.end&&(s=new S,s.value=t.p.parser.eofTerm,s.start=s.end=t.pos,e=this.addActions(t,s.value,s.end,e)),this.mainToken=s,this.actions}getMainToken(t){if(this.mainToken)return this.mainToken;let e=new S,{pos:s,p:i}=t;return e.start=s,e.end=Math.min(s+1,i.stream.end),e.value=s==i.stream.end?i.parser.eofTerm:0,e}updateCachedToken(t,e,s){let i=this.stream.clipPos(s.pos);if(e.token(this.stream.reset(i,t),s),t.value>-1){let{parser:h}=s.p;for(let r=0;r<h.specialized.length;r++)if(h.specialized[r]==t.value){let n=h.specializers[r](this.stream.read(t.start,t.end),s);if(n>=0&&s.p.parser.dialect.allows(n>>1)){n&1?t.extended=n>>1:t.value=n>>1;break}}}else t.value=0,t.end=this.stream.clipPos(i+1)}putAction(t,e,s,i){for(let h=0;h<i;h+=3)if(this.actions[h]==t)return i;return this.actions[i++]=t,this.actions[i++]=e,this.actions[i++]=s,i}addActions(t,e,s,i){let{state:h}=t,{parser:r}=t.p,{data:n}=r;for(let a=0;a<2;a++)for(let l=r.stateSlot(h,a?2:1);;l+=3){if(n[l]==65535)if(n[l+1]==1)l=k(n,l+2);else{i==0&&n[l+1]==2&&(i=this.putAction(k(n,l+2),e,s,i));break}n[l]==e&&(i=this.putAction(k(n,l+1),e,s,i))}return i}}var M;(function(o){o[o.Distance=5]="Distance",o[o.MaxRemainingPerStep=3]="MaxRemainingPerStep",o[o.MinBufferLengthPrune=500]="MinBufferLengthPrune",o[o.ForceReduceLimit=10]="ForceReduceLimit",o[o.CutDepth=15e3]="CutDepth",o[o.CutTo=9e3]="CutTo",o[o.MaxLeftAssociativeReductionCount=300]="MaxLeftAssociativeReductionCount",o[o.MaxStackCount=12]="MaxStackCount"})(M||(M={}));class X{constructor(t,e,s,i){this.parser=t,this.input=e,this.ranges=i,this.recovering=0,this.nextStackID=9812,this.minStackPos=0,this.reused=[],this.stoppedAt=null,this.lastBigReductionStart=-1,this.lastBigReductionSize=0,this.bigReductionCount=0,this.stream=new q(e,i),this.tokens=new V(t,this.stream),this.topTerm=t.top[1];let{from:h}=i[0];this.stacks=[P.start(this,t.top[0],h)],this.fragments=s.length&&this.stream.end-h>t.bufferLength*4?new Q(s,t.nodeSet):null}get parsedPos(){return this.minStackPos}advance(){let t=this.stacks,e=this.minStackPos,s=this.stacks=[],i,h;if(this.bigReductionCount>300&&t.length==1){let[r]=t;for(;r.forceReduce()&&r.stack.length&&r.stack[r.stack.length-2]>=this.lastBigReductionStart;);this.bigReductionCount=this.lastBigReductionSize=0}for(let r=0;r<t.length;r++){let n=t[r];for(;;){if(this.tokens.mainToken=null,n.pos>e)s.push(n);else{if(this.advanceStack(n,s,t))continue;{i||(i=[],h=[]),i.push(n);let a=this.tokens.getMainToken(n);h.push(a.value,a.end)}}break}}if(!s.length){let r=i&&Z(i);if(r)return this.stackToTree(r);if(this.parser.strict)throw g&&i&&console.log("Stuck with token "+(this.tokens.mainToken?this.parser.getName(this.tokens.mainToken.value):"none")),new SyntaxError("No parse at "+e);this.recovering||(this.recovering=5)}if(this.recovering&&i){let r=this.stoppedAt!=null&&i[0].pos>this.stoppedAt?i[0]:this.runRecovery(i,h,s);if(r)return this.stackToTree(r.forceAll())}if(this.recovering){let r=this.recovering==1?1:this.recovering*3;if(s.length>r)for(s.sort((n,a)=>a.score-n.score);s.length>r;)s.pop();s.some(n=>n.reducePos>e)&&this.recovering--}else if(s.length>1){t:for(let r=0;r<s.length-1;r++){let n=s[r];for(let a=r+1;a<s.length;a++){let l=s[a];if(n.sameState(l)||n.buffer.length>500&&l.buffer.length>500)if((n.score-l.score||n.buffer.length-l.buffer.length)>0)s.splice(a--,1);else{s.splice(r--,1);continue t}}}s.length>12&&s.splice(12,s.length-12)}this.minStackPos=s[0].pos;for(let r=1;r<s.length;r++)s[r].pos<this.minStackPos&&(this.minStackPos=s[r].pos);return null}stopAt(t){if(this.stoppedAt!=null&&this.stoppedAt<t)throw new RangeError("Can't move stoppedAt forward");this.stoppedAt=t}advanceStack(t,e,s){let i=t.pos,{parser:h}=this,r=g?this.stackID(t)+" -> ":"";if(this.stoppedAt!=null&&i>this.stoppedAt)return t.forceReduce()?t:null;if(this.fragments){let l=t.curContext&&t.curContext.tracker.strict,f=l?t.curContext.hash:0;for(let u=this.fragments.nodeAt(i);u;){let c=this.parser.nodeSet.types[u.type.id]==u.type?h.getGoto(t.state,u.type.id):-1;if(c>-1&&u.length&&(!l||(u.prop(w.contextHash)||0)==f))return t.useNode(u,c),g&&console.log(r+this.stackID(t)+` (via reuse of ${h.getName(u.type.id)})`),!0;if(!(u instanceof b)||u.children.length==0||u.positions[0]>0)break;let p=u.children[0];if(p instanceof b&&u.positions[0]==0)u=p;else break}}let n=h.stateSlot(t.state,4);if(n>0)return t.reduce(n),g&&console.log(r+this.stackID(t)+` (via always-reduce ${h.getName(n&65535)})`),!0;if(t.stack.length>=15e3)for(;t.stack.length>9e3&&t.forceReduce(););let a=this.tokens.getActions(t);for(let l=0;l<a.length;){let f=a[l++],u=a[l++],c=a[l++],p=l==a.length||!s,d=p?t:t.split();if(d.apply(f,u,c),g&&console.log(r+this.stackID(d)+` (via ${f&65536?`reduce of ${h.getName(f&65535)}`:"shift"} for ${h.getName(u)} @ ${i}${d==t?"":", split"})`),p)return!0;d.pos>i?e.push(d):s.push(d)}return!1}advanceFully(t,e){let s=t.pos;for(;;){if(!this.advanceStack(t,null,null))return!1;if(t.pos>s)return R(t,e),!0}}runRecovery(t,e,s){let i=null,h=!1;for(let r=0;r<t.length;r++){let n=t[r],a=e[r<<1],l=e[(r<<1)+1],f=g?this.stackID(n)+" -> ":"";if(n.deadEnd&&(h||(h=!0,n.restart(),g&&console.log(f+this.stackID(n)+" (restarted)"),this.advanceFully(n,s))))continue;let u=n.split(),c=f;for(let p=0;u.forceReduce()&&p<10&&(g&&console.log(c+this.stackID(u)+" (via force-reduce)"),!this.advanceFully(u,s));p++)g&&(c=this.stackID(u)+" -> ");for(let p of n.recoverByInsert(a))g&&console.log(f+this.stackID(p)+" (via recover-insert)"),this.advanceFully(p,s);this.stream.end>n.pos?(l==n.pos&&(l++,a=0),n.recoverByDelete(a,l),g&&console.log(f+this.stackID(n)+` (via recover-delete ${this.parser.getName(a)})`),R(n,s)):(!i||i.score<n.score)&&(i=n)}return i}stackToTree(t){return t.close(),b.build({buffer:C.create(t),nodeSet:this.parser.nodeSet,topID:this.topTerm,maxBufferLength:this.parser.bufferLength,reused:this.reused,start:this.ranges[0].from,length:t.pos-this.ranges[0].from,minRepeatType:this.parser.minRepeatTerm})}stackID(t){let e=(A||(A=new WeakMap)).get(t);return e||A.set(t,e=String.fromCodePoint(this.nextStackID++)),e+t}}function R(o,t){for(let e=0;e<t.length;e++){let s=t[e];if(s.pos==o.pos&&s.sameState(o)){t[e].score<o.score&&(t[e]=o);return}}t.push(o)}class Y{constructor(t,e,s){this.source=t,this.flags=e,this.disabled=s}allows(t){return!this.disabled||this.disabled[t]==0}}const T=o=>o;class et{constructor(t){this.start=t.start,this.shift=t.shift||T,this.reduce=t.reduce||T,this.reuse=t.reuse||T,this.hash=t.hash||(()=>0),this.strict=t.strict!==!1}}class v extends j{constructor(t){if(super(),this.wrappers=[],t.version!=14)throw new RangeError(`Parser version (${t.version}) doesn't match runtime version (14)`);let e=t.nodeNames.split(" ");this.minRepeatTerm=e.length;for(let n=0;n<t.repeatNodeCount;n++)e.push("");let s=Object.keys(t.topRules).map(n=>t.topRules[n][1]),i=[];for(let n=0;n<e.length;n++)i.push([]);function h(n,a,l){i[n].push([a,a.deserialize(String(l))])}if(t.nodeProps)for(let n of t.nodeProps){let a=n[0];typeof a=="string"&&(a=w[a]);for(let l=1;l<n.length;){let f=n[l++];if(f>=0)h(f,a,n[l++]);else{let u=n[l+-f];for(let c=-f;c>0;c--)h(n[l++],a,u);l++}}}this.nodeSet=new G(e.map((n,a)=>E.define({name:a>=this.minRepeatTerm?void 0:n,id:a,props:i[a],top:s.indexOf(a)>-1,error:a==0,skipped:t.skippedNodes&&t.skippedNodes.indexOf(a)>-1}))),t.propSources&&(this.nodeSet=this.nodeSet.extend(...t.propSources)),this.strict=!1,this.bufferLength=U;let r=x(t.tokenData);this.context=t.context,this.specializerSpecs=t.specialized||[],this.specialized=new Uint16Array(this.specializerSpecs.length);for(let n=0;n<this.specializerSpecs.length;n++)this.specialized[n]=this.specializerSpecs[n].term;this.specializers=this.specializerSpecs.map(O),this.states=x(t.states,Uint32Array),this.data=x(t.stateData),this.goto=x(t.goto),this.maxTerm=t.maxTerm,this.tokenizers=t.tokenizers.map(n=>typeof n=="number"?new m(r,n):n),this.topRules=t.topRules,this.dialects=t.dialects||{},this.dynamicPrecedences=t.dynamicPrecedences||null,this.tokenPrecTable=t.tokenPrec,this.termNames=t.termNames||null,this.maxNode=this.nodeSet.types.length-1,this.dialect=this.parseDialect(),this.top=this.topRules[Object.keys(this.topRules)[0]]}createParse(t,e,s){let i=new X(this,t,e,s);for(let h of this.wrappers)i=h(i,t,e,s);return i}getGoto(t,e,s=!1){let i=this.goto;if(e>=i[0])return-1;for(let h=i[e+1];;){let r=i[h++],n=r&1,a=i[h++];if(n&&s)return a;for(let l=h+(r>>1);h<l;h++)if(i[h]==t)return a;if(n)return-1}}hasAction(t,e){let s=this.data;for(let i=0;i<2;i++)for(let h=this.stateSlot(t,i?2:1),r;;h+=3){if((r=s[h])==65535)if(s[h+1]==1)r=s[h=k(s,h+2)];else{if(s[h+1]==2)return k(s,h+2);break}if(r==e||r==0)return k(s,h+1)}return 0}stateSlot(t,e){return this.states[t*6+e]}stateFlag(t,e){return(this.stateSlot(t,0)&e)>0}validAction(t,e){if(e==this.stateSlot(t,4))return!0;for(let s=this.stateSlot(t,1);;s+=3){if(this.data[s]==65535)if(this.data[s+1]==1)s=k(this.data,s+2);else return!1;if(e==k(this.data,s+1))return!0}}nextStates(t){let e=[];for(let s=this.stateSlot(t,1);;s+=3){if(this.data[s]==65535)if(this.data[s+1]==1)s=k(this.data,s+2);else break;if(!(this.data[s+2]&1)){let i=this.data[s+1];e.some((h,r)=>r&1&&h==i)||e.push(this.data[s],i)}}return e}configure(t){let e=Object.assign(Object.create(v.prototype),this);if(t.props&&(e.nodeSet=this.nodeSet.extend(...t.props)),t.top){let s=this.topRules[t.top];if(!s)throw new RangeError(`Invalid top rule name ${t.top}`);e.top=s}return t.tokenizers&&(e.tokenizers=this.tokenizers.map(s=>{let i=t.tokenizers.find(h=>h.from==s);return i?i.to:s})),t.specializers&&(e.specializers=this.specializers.slice(),e.specializerSpecs=this.specializerSpecs.map((s,i)=>{let h=t.specializers.find(n=>n.from==s.external);if(!h)return s;let r=Object.assign(Object.assign({},s),{external:h.to});return e.specializers[i]=O(r),r})),t.contextTracker&&(e.context=t.contextTracker),t.dialect&&(e.dialect=this.parseDialect(t.dialect)),t.strict!=null&&(e.strict=t.strict),t.wrap&&(e.wrappers=e.wrappers.concat(t.wrap)),t.bufferLength!=null&&(e.bufferLength=t.bufferLength),e}hasWrappers(){return this.wrappers.length>0}getName(t){return this.termNames?this.termNames[t]:String(t<=this.maxNode&&this.nodeSet.types[t].name||t)}get eofTerm(){return this.maxNode+1}get topNode(){return this.nodeSet.types[this.top[1]]}dynamicPrecedence(t){let e=this.dynamicPrecedences;return e==null?0:e[t]||0}parseDialect(t){let e=Object.keys(this.dialects),s=e.map(()=>!1);if(t)for(let h of t.split(" ")){let r=e.indexOf(h);r>=0&&(s[r]=!0)}let i=null;for(let h=0;h<e.length;h++)if(!s[h])for(let r=this.dialects[e[h]],n;(n=this.data[r++])!=65535;)(i||(i=new Uint8Array(this.maxTerm+1)))[n]=1;return new Y(t,s,i)}static deserialize(t){return new v(t)}}function k(o,t){return o[t]|o[t+1]<<16}function Z(o){let t=null;for(let e of o){let s=e.p.stoppedAt;(e.pos==e.p.stream.end||s!=null&&e.pos>s)&&e.p.parser.stateFlag(e.state,2)&&(!t||t.score<e.score)&&(t=e)}return t}function O(o){if(o.external){let t=o.extend?1:0;return(e,s)=>o.external(e,s)<<1|t}return o.get}export{et as C,tt as E,v as L,J as a};
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diff --git a/spaces/de3sec/Front-end-code-generation-from-images/README.md b/spaces/de3sec/Front-end-code-generation-from-images/README.md
deleted file mode 100644
index f6e41dd0ae9e3b2298c22bdbc8d84712526577e6..0000000000000000000000000000000000000000
--- a/spaces/de3sec/Front-end-code-generation-from-images/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Image to HTML Code Demo
-emoji: 🧑‍💻
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: afl-3.0
-duplicated_from: Joom/Front-end-code-generation-from-images
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/declare-lab/tango/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py b/spaces/declare-lab/tango/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py
deleted file mode 100644
index ecc89f98298e3e4205581fee1689761c519bc4e4..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py
+++ /dev/null
@@ -1,654 +0,0 @@
-import argparse
-import logging
-import math
-import os
-import random
-from pathlib import Path
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-import PIL
-import torch
-import torch.utils.checkpoint
-import transformers
-from flax import jax_utils
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import create_repo, upload_folder
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
-
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxDDPMScheduler,
-    FlaxPNDMScheduler,
-    FlaxStableDiffusionPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
-from diffusers.utils import check_min_version
-
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.Resampling.BILINEAR,
-        "bilinear": PIL.Image.Resampling.BILINEAR,
-        "bicubic": PIL.Image.Resampling.BICUBIC,
-        "lanczos": PIL.Image.Resampling.LANCZOS,
-        "nearest": PIL.Image.Resampling.NEAREST,
-    }
-else:
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.LINEAR,
-        "bilinear": PIL.Image.BILINEAR,
-        "bicubic": PIL.Image.BICUBIC,
-        "lanczos": PIL.Image.LANCZOS,
-        "nearest": PIL.Image.NEAREST,
-    }
-# ------------------------------------------------------------------------------
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.14.0.dev0")
-
-logger = logging.getLogger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
-    )
-    parser.add_argument(
-        "--placeholder_token",
-        type=str,
-        default=None,
-        required=True,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
-    )
-    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
-    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="text-inversion-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=5000,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=True,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument(
-        "--use_auth_token",
-        action="store_true",
-        help=(
-            "Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
-            " private models)."
-        ),
-    )
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.train_data_dir is None:
-        raise ValueError("You must specify a train data directory.")
-
-    return args
-
-
-imagenet_templates_small = [
-    "a photo of a {}",
-    "a rendering of a {}",
-    "a cropped photo of the {}",
-    "the photo of a {}",
-    "a photo of a clean {}",
-    "a photo of a dirty {}",
-    "a dark photo of the {}",
-    "a photo of my {}",
-    "a photo of the cool {}",
-    "a close-up photo of a {}",
-    "a bright photo of the {}",
-    "a cropped photo of a {}",
-    "a photo of the {}",
-    "a good photo of the {}",
-    "a photo of one {}",
-    "a close-up photo of the {}",
-    "a rendition of the {}",
-    "a photo of the clean {}",
-    "a rendition of a {}",
-    "a photo of a nice {}",
-    "a good photo of a {}",
-    "a photo of the nice {}",
-    "a photo of the small {}",
-    "a photo of the weird {}",
-    "a photo of the large {}",
-    "a photo of a cool {}",
-    "a photo of a small {}",
-]
-
-imagenet_style_templates_small = [
-    "a painting in the style of {}",
-    "a rendering in the style of {}",
-    "a cropped painting in the style of {}",
-    "the painting in the style of {}",
-    "a clean painting in the style of {}",
-    "a dirty painting in the style of {}",
-    "a dark painting in the style of {}",
-    "a picture in the style of {}",
-    "a cool painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a bright painting in the style of {}",
-    "a cropped painting in the style of {}",
-    "a good painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a rendition in the style of {}",
-    "a nice painting in the style of {}",
-    "a small painting in the style of {}",
-    "a weird painting in the style of {}",
-    "a large painting in the style of {}",
-]
-
-
-class TextualInversionDataset(Dataset):
-    def __init__(
-        self,
-        data_root,
-        tokenizer,
-        learnable_property="object",  # [object, style]
-        size=512,
-        repeats=100,
-        interpolation="bicubic",
-        flip_p=0.5,
-        set="train",
-        placeholder_token="*",
-        center_crop=False,
-    ):
-        self.data_root = data_root
-        self.tokenizer = tokenizer
-        self.learnable_property = learnable_property
-        self.size = size
-        self.placeholder_token = placeholder_token
-        self.center_crop = center_crop
-        self.flip_p = flip_p
-
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
-
-        self.num_images = len(self.image_paths)
-        self._length = self.num_images
-
-        if set == "train":
-            self._length = self.num_images * repeats
-
-        self.interpolation = {
-            "linear": PIL_INTERPOLATION["linear"],
-            "bilinear": PIL_INTERPOLATION["bilinear"],
-            "bicubic": PIL_INTERPOLATION["bicubic"],
-            "lanczos": PIL_INTERPOLATION["lanczos"],
-        }[interpolation]
-
-        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
-        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = {}
-        image = Image.open(self.image_paths[i % self.num_images])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        placeholder_string = self.placeholder_token
-        text = random.choice(self.templates).format(placeholder_string)
-
-        example["input_ids"] = self.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids[0]
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
-                img.shape[0],
-                img.shape[1],
-            )
-            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
-
-        image = Image.fromarray(img)
-        image = image.resize((self.size, self.size), resample=self.interpolation)
-
-        image = self.flip_transform(image)
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-        return example
-
-
-def resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):
-    if model.config.vocab_size == new_num_tokens or new_num_tokens is None:
-        return
-    model.config.vocab_size = new_num_tokens
-
-    params = model.params
-    old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"]
-    old_num_tokens, emb_dim = old_embeddings.shape
-
-    initializer = jax.nn.initializers.normal()
-
-    new_embeddings = initializer(rng, (new_num_tokens, emb_dim))
-    new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)
-    new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])
-    params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings
-
-    model.params = params
-    return model
-
-
-def get_params_to_save(params):
-    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
-
-
-def main():
-    args = parse_args()
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if jax.process_index() == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-
-    # Load the tokenizer and add the placeholder token as a additional special token
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Add the placeholder token in tokenizer
-    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
-    if num_added_tokens == 0:
-        raise ValueError(
-            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
-            " `placeholder_token` that is not already in the tokenizer."
-        )
-
-    # Convert the initializer_token, placeholder_token to ids
-    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
-    # Check if initializer_token is a single token or a sequence of tokens
-    if len(token_ids) > 1:
-        raise ValueError("The initializer token must be a single token.")
-
-    initializer_token_id = token_ids[0]
-    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
-
-    # Load models and create wrapper for stable diffusion
-    text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-
-    # Create sampling rng
-    rng = jax.random.PRNGKey(args.seed)
-    rng, _ = jax.random.split(rng)
-    # Resize the token embeddings as we are adding new special tokens to the tokenizer
-    text_encoder = resize_token_embeddings(
-        text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng
-    )
-    original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"]
-
-    train_dataset = TextualInversionDataset(
-        data_root=args.train_data_dir,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        placeholder_token=args.placeholder_token,
-        repeats=args.repeats,
-        learnable_property=args.learnable_property,
-        center_crop=args.center_crop,
-        set="train",
-    )
-
-    def collate_fn(examples):
-        pixel_values = torch.stack([example["pixel_values"] for example in examples])
-        input_ids = torch.stack([example["input_ids"] for example in examples])
-
-        batch = {"pixel_values": pixel_values, "input_ids": input_ids}
-        batch = {k: v.numpy() for k, v in batch.items()}
-
-        return batch
-
-    total_train_batch_size = args.train_batch_size * jax.local_device_count()
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn
-    )
-
-    # Optimization
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * total_train_batch_size
-
-    constant_scheduler = optax.constant_schedule(args.learning_rate)
-
-    optimizer = optax.adamw(
-        learning_rate=constant_scheduler,
-        b1=args.adam_beta1,
-        b2=args.adam_beta2,
-        eps=args.adam_epsilon,
-        weight_decay=args.adam_weight_decay,
-    )
-
-    def create_mask(params, label_fn):
-        def _map(params, mask, label_fn):
-            for k in params:
-                if label_fn(k):
-                    mask[k] = "token_embedding"
-                else:
-                    if isinstance(params[k], dict):
-                        mask[k] = {}
-                        _map(params[k], mask[k], label_fn)
-                    else:
-                        mask[k] = "zero"
-
-        mask = {}
-        _map(params, mask, label_fn)
-        return mask
-
-    def zero_grads():
-        # from https://github.com/deepmind/optax/issues/159#issuecomment-896459491
-        def init_fn(_):
-            return ()
-
-        def update_fn(updates, state, params=None):
-            return jax.tree_util.tree_map(jnp.zeros_like, updates), ()
-
-        return optax.GradientTransformation(init_fn, update_fn)
-
-    # Zero out gradients of layers other than the token embedding layer
-    tx = optax.multi_transform(
-        {"token_embedding": optimizer, "zero": zero_grads()},
-        create_mask(text_encoder.params, lambda s: s == "token_embedding"),
-    )
-
-    state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)
-
-    noise_scheduler = FlaxDDPMScheduler(
-        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
-    )
-    noise_scheduler_state = noise_scheduler.create_state()
-
-    # Initialize our training
-    train_rngs = jax.random.split(rng, jax.local_device_count())
-
-    # Define gradient train step fn
-    def train_step(state, vae_params, unet_params, batch, train_rng):
-        dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
-
-        def compute_loss(params):
-            vae_outputs = vae.apply(
-                {"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
-            )
-            latents = vae_outputs.latent_dist.sample(sample_rng)
-            # (NHWC) -> (NCHW)
-            latents = jnp.transpose(latents, (0, 3, 1, 2))
-            latents = latents * vae.config.scaling_factor
-
-            noise_rng, timestep_rng = jax.random.split(sample_rng)
-            noise = jax.random.normal(noise_rng, latents.shape)
-            bsz = latents.shape[0]
-            timesteps = jax.random.randint(
-                timestep_rng,
-                (bsz,),
-                0,
-                noise_scheduler.config.num_train_timesteps,
-            )
-            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
-            encoder_hidden_states = state.apply_fn(
-                batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True
-            )[0]
-            # Predict the noise residual and compute loss
-            model_pred = unet.apply(
-                {"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False
-            ).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            loss = (target - model_pred) ** 2
-            loss = loss.mean()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-        new_state = state.apply_gradients(grads=grad)
-
-        # Keep the token embeddings fixed except the newly added embeddings for the concept,
-        # as we only want to optimize the concept embeddings
-        token_embeds = original_token_embeds.at[placeholder_token_id].set(
-            new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id]
-        )
-        new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds
-
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-        return new_state, metrics, new_train_rng
-
-    # Create parallel version of the train and eval step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-    vae_params = jax_utils.replicate(vae_params)
-    unet_params = jax_utils.replicate(unet_params)
-
-    # Train!
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
-
-    # Scheduler and math around the number of training steps.
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-
-    global_step = 0
-
-    epochs = tqdm(range(args.num_train_epochs), desc=f"Epoch ... (1/{args.num_train_epochs})", position=0)
-    for epoch in epochs:
-        # ======================== Training ================================
-
-        train_metrics = []
-
-        steps_per_epoch = len(train_dataset) // total_train_batch_size
-        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
-        # train
-        for batch in train_dataloader:
-            batch = shard(batch)
-            state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)
-            train_metrics.append(train_metric)
-
-            train_step_progress_bar.update(1)
-            global_step += 1
-
-            if global_step >= args.max_train_steps:
-                break
-
-        train_metric = jax_utils.unreplicate(train_metric)
-
-        train_step_progress_bar.close()
-        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
-
-    # Create the pipeline using using the trained modules and save it.
-    if jax.process_index() == 0:
-        scheduler = FlaxPNDMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        )
-        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
-            "CompVis/stable-diffusion-safety-checker", from_pt=True
-        )
-        pipeline = FlaxStableDiffusionPipeline(
-            text_encoder=text_encoder,
-            vae=vae,
-            unet=unet,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
-        )
-
-        pipeline.save_pretrained(
-            args.output_dir,
-            params={
-                "text_encoder": get_params_to_save(state.params),
-                "vae": get_params_to_save(vae_params),
-                "unet": get_params_to_save(unet_params),
-                "safety_checker": safety_checker.params,
-            },
-        )
-
-        # Also save the newly trained embeddings
-        learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
-            placeholder_token_id
-        ]
-        learned_embeds_dict = {args.placeholder_token: learned_embeds}
-        jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict)
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md b/spaces/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md
deleted file mode 100644
index 621e9a2fd69a97046230fb7561571d1484d47710..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
-## Training examples
-
-Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-#### Use ONNXRuntime to accelerate training
-
-In order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py
-
-The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:
-
-```bash
-accelerate launch train_unconditional_ort.py \
-  --dataset_name="huggan/flowers-102-categories" \
-  --resolution=64 --center_crop --random_flip \
-  --output_dir="ddpm-ema-flowers-64" \
-  --use_ema \
-  --train_batch_size=16 \
-  --num_epochs=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=1e-4 \
-  --lr_warmup_steps=500 \
-  --mixed_precision=fp16
-  ```
-
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
diff --git a/spaces/declare-lab/tango/diffusers/tests/test_modeling_common.py b/spaces/declare-lab/tango/diffusers/tests/test_modeling_common.py
deleted file mode 100644
index 40aba3b24967683b2e64b53402d9f8bdc93be2a8..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/test_modeling_common.py
+++ /dev/null
@@ -1,447 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import tempfile
-import unittest
-import unittest.mock as mock
-from typing import Dict, List, Tuple
-
-import numpy as np
-import requests_mock
-import torch
-from requests.exceptions import HTTPError
-
-from diffusers.models import UNet2DConditionModel
-from diffusers.training_utils import EMAModel
-from diffusers.utils import torch_device
-from diffusers.utils.testing_utils import require_torch_gpu
-
-
-class ModelUtilsTest(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = True
-
-    def test_accelerate_loading_error_message(self):
-        with self.assertRaises(ValueError) as error_context:
-            UNet2DConditionModel.from_pretrained("hf-internal-testing/stable-diffusion-broken", subfolder="unet")
-
-        # make sure that error message states what keys are missing
-        assert "conv_out.bias" in str(error_context.exception)
-
-    def test_cached_files_are_used_when_no_internet(self):
-        # A mock response for an HTTP head request to emulate server down
-        response_mock = mock.Mock()
-        response_mock.status_code = 500
-        response_mock.headers = {}
-        response_mock.raise_for_status.side_effect = HTTPError
-        response_mock.json.return_value = {}
-
-        # Download this model to make sure it's in the cache.
-        orig_model = UNet2DConditionModel.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet"
-        )
-
-        # Under the mock environment we get a 500 error when trying to reach the model.
-        with mock.patch("requests.request", return_value=response_mock):
-            # Download this model to make sure it's in the cache.
-            model = UNet2DConditionModel.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", local_files_only=True
-            )
-
-        for p1, p2 in zip(orig_model.parameters(), model.parameters()):
-            if p1.data.ne(p2.data).sum() > 0:
-                assert False, "Parameters not the same!"
-
-    def test_one_request_upon_cached(self):
-        # TODO: For some reason this test fails on MPS where no HEAD call is made.
-        if torch_device == "mps":
-            return
-
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = False
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            with requests_mock.mock(real_http=True) as m:
-                UNet2DConditionModel.from_pretrained(
-                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
-                )
-
-            download_requests = [r.method for r in m.request_history]
-            assert download_requests.count("HEAD") == 2, "2 HEAD requests one for config, one for model"
-            assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
-
-            with requests_mock.mock(real_http=True) as m:
-                UNet2DConditionModel.from_pretrained(
-                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
-                )
-
-            cache_requests = [r.method for r in m.request_history]
-            assert (
-                "HEAD" == cache_requests[0] and len(cache_requests) == 1
-            ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
-
-        diffusers.utils.import_utils._safetensors_available = True
-
-    def test_weight_overwrite(self):
-        with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:
-            UNet2DConditionModel.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-torch",
-                subfolder="unet",
-                cache_dir=tmpdirname,
-                in_channels=9,
-            )
-
-        # make sure that error message states what keys are missing
-        assert "Cannot load" in str(error_context.exception)
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model = UNet2DConditionModel.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-torch",
-                subfolder="unet",
-                cache_dir=tmpdirname,
-                in_channels=9,
-                low_cpu_mem_usage=False,
-                ignore_mismatched_sizes=True,
-            )
-
-        assert model.config.in_channels == 9
-
-
-class ModelTesterMixin:
-    def test_from_save_pretrained(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        if hasattr(model, "set_default_attn_processor"):
-            model.set_default_attn_processor()
-        model.to(torch_device)
-        model.eval()
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            new_model = self.model_class.from_pretrained(tmpdirname)
-            if hasattr(new_model, "set_default_attn_processor"):
-                new_model.set_default_attn_processor()
-            new_model.to(torch_device)
-
-        with torch.no_grad():
-            image = model(**inputs_dict)
-            if isinstance(image, dict):
-                image = image.sample
-
-            new_image = new_model(**inputs_dict)
-
-            if isinstance(new_image, dict):
-                new_image = new_image.sample
-
-        max_diff = (image - new_image).abs().sum().item()
-        self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
-
-    def test_from_save_pretrained_variant(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        if hasattr(model, "set_default_attn_processor"):
-            model.set_default_attn_processor()
-        model.to(torch_device)
-        model.eval()
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname, variant="fp16")
-            new_model = self.model_class.from_pretrained(tmpdirname, variant="fp16")
-            if hasattr(new_model, "set_default_attn_processor"):
-                new_model.set_default_attn_processor()
-
-            # non-variant cannot be loaded
-            with self.assertRaises(OSError) as error_context:
-                self.model_class.from_pretrained(tmpdirname)
-
-            # make sure that error message states what keys are missing
-            assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(error_context.exception)
-
-            new_model.to(torch_device)
-
-        with torch.no_grad():
-            image = model(**inputs_dict)
-            if isinstance(image, dict):
-                image = image.sample
-
-            new_image = new_model(**inputs_dict)
-
-            if isinstance(new_image, dict):
-                new_image = new_image.sample
-
-        max_diff = (image - new_image).abs().sum().item()
-        self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
-
-    @require_torch_gpu
-    def test_from_save_pretrained_dynamo(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model = torch.compile(model)
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            new_model = self.model_class.from_pretrained(tmpdirname)
-            new_model.to(torch_device)
-
-        assert new_model.__class__ == self.model_class
-
-    def test_from_save_pretrained_dtype(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-            if torch_device == "mps" and dtype == torch.bfloat16:
-                continue
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.to(dtype)
-                model.save_pretrained(tmpdirname)
-                new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=True, torch_dtype=dtype)
-                assert new_model.dtype == dtype
-                new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=False, torch_dtype=dtype)
-                assert new_model.dtype == dtype
-
-    def test_determinism(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            first = model(**inputs_dict)
-            if isinstance(first, dict):
-                first = first.sample
-
-            second = model(**inputs_dict)
-            if isinstance(second, dict):
-                second = second.sample
-
-        out_1 = first.cpu().numpy()
-        out_2 = second.cpu().numpy()
-        out_1 = out_1[~np.isnan(out_1)]
-        out_2 = out_2[~np.isnan(out_2)]
-        max_diff = np.amax(np.abs(out_1 - out_2))
-        self.assertLessEqual(max_diff, 1e-5)
-
-    def test_output(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)
-
-            if isinstance(output, dict):
-                output = output.sample
-
-        self.assertIsNotNone(output)
-        expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    def test_forward_with_norm_groups(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["norm_num_groups"] = 16
-        init_dict["block_out_channels"] = (16, 32)
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)
-
-            if isinstance(output, dict):
-                output = output.sample
-
-        self.assertIsNotNone(output)
-        expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    def test_forward_signature(self):
-        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        signature = inspect.signature(model.forward)
-        # signature.parameters is an OrderedDict => so arg_names order is deterministic
-        arg_names = [*signature.parameters.keys()]
-
-        expected_arg_names = ["sample", "timestep"]
-        self.assertListEqual(arg_names[:2], expected_arg_names)
-
-    def test_model_from_pretrained(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        # test if the model can be loaded from the config
-        # and has all the expected shape
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            new_model = self.model_class.from_pretrained(tmpdirname)
-            new_model.to(torch_device)
-            new_model.eval()
-
-        # check if all parameters shape are the same
-        for param_name in model.state_dict().keys():
-            param_1 = model.state_dict()[param_name]
-            param_2 = new_model.state_dict()[param_name]
-            self.assertEqual(param_1.shape, param_2.shape)
-
-        with torch.no_grad():
-            output_1 = model(**inputs_dict)
-
-            if isinstance(output_1, dict):
-                output_1 = output_1.sample
-
-            output_2 = new_model(**inputs_dict)
-
-            if isinstance(output_2, dict):
-                output_2 = output_2.sample
-
-        self.assertEqual(output_1.shape, output_2.shape)
-
-    @unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
-    def test_training(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.train()
-        output = model(**inputs_dict)
-
-        if isinstance(output, dict):
-            output = output.sample
-
-        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
-        loss = torch.nn.functional.mse_loss(output, noise)
-        loss.backward()
-
-    @unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
-    def test_ema_training(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.train()
-        ema_model = EMAModel(model.parameters())
-
-        output = model(**inputs_dict)
-
-        if isinstance(output, dict):
-            output = output.sample
-
-        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
-        loss = torch.nn.functional.mse_loss(output, noise)
-        loss.backward()
-        ema_model.step(model.parameters())
-
-    def test_outputs_equivalence(self):
-        def set_nan_tensor_to_zero(t):
-            # Temporary fallback until `aten::_index_put_impl_` is implemented in mps
-            # Track progress in https://github.com/pytorch/pytorch/issues/77764
-            device = t.device
-            if device.type == "mps":
-                t = t.to("cpu")
-            t[t != t] = 0
-            return t.to(device)
-
-        def recursive_check(tuple_object, dict_object):
-            if isinstance(tuple_object, (List, Tuple)):
-                for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
-                    recursive_check(tuple_iterable_value, dict_iterable_value)
-            elif isinstance(tuple_object, Dict):
-                for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
-                    recursive_check(tuple_iterable_value, dict_iterable_value)
-            elif tuple_object is None:
-                return
-            else:
-                self.assertTrue(
-                    torch.allclose(
-                        set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
-                    ),
-                    msg=(
-                        "Tuple and dict output are not equal. Difference:"
-                        f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
-                        f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
-                        f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
-                    ),
-                )
-
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            outputs_dict = model(**inputs_dict)
-            outputs_tuple = model(**inputs_dict, return_dict=False)
-
-        recursive_check(outputs_tuple, outputs_dict)
-
-    @unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
-    def test_enable_disable_gradient_checkpointing(self):
-        if not self.model_class._supports_gradient_checkpointing:
-            return  # Skip test if model does not support gradient checkpointing
-
-        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
-
-        # at init model should have gradient checkpointing disabled
-        model = self.model_class(**init_dict)
-        self.assertFalse(model.is_gradient_checkpointing)
-
-        # check enable works
-        model.enable_gradient_checkpointing()
-        self.assertTrue(model.is_gradient_checkpointing)
-
-        # check disable works
-        model.disable_gradient_checkpointing()
-        self.assertFalse(model.is_gradient_checkpointing)
-
-    def test_deprecated_kwargs(self):
-        has_kwarg_in_model_class = "kwargs" in inspect.signature(self.model_class.__init__).parameters
-        has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0
-
-        if has_kwarg_in_model_class and not has_deprecated_kwarg:
-            raise ValueError(
-                f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
-                " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
-                " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
-                " [<deprecated_argument>]`"
-            )
-
-        if not has_kwarg_in_model_class and has_deprecated_kwarg:
-            raise ValueError(
-                f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
-                " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
-                f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
-                " from `_deprecated_kwargs = [<deprecated_argument>]`"
-            )
diff --git a/spaces/deepkyu/multilingual-font-style-transfer/utils/tb.py b/spaces/deepkyu/multilingual-font-style-transfer/utils/tb.py
deleted file mode 100644
index e242ba8c47b35d5eabaf479ec63c605279f90788..0000000000000000000000000000000000000000
--- a/spaces/deepkyu/multilingual-font-style-transfer/utils/tb.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-import numpy as np
-
-def magic_image_handler(img):
-    if isinstance(img, torch.Tensor):
-        img = img.detach().cpu().numpy()
-    if img.ndim == 3:
-        img = img.transpose((1, 2, 0))
-    elif img.ndim == 2:
-        img = np.repeat(img[..., np.newaxis], 3, axis=2)
-    elif img.ndim == 4:
-        img = img[:4]  # first 4 batch
-        img = np.concatenate(img, axis=-1)
-        img = img.transpose((1, 2, 0))
-    elif img.ndim == 5:
-        img = img[:4]  # first 4 batch
-        img = np.concatenate(img, axis=-2)
-        img = np.concatenate(img, axis=-1)
-        img = img.transpose((1, 2, 0))
-    else:
-        raise ValueError(f'img ndim is {img.ndim}, should be 2~4')
-    if img.shape[-1] != 1 or img.shape[-1] != 3:
-        img = np.expand_dims(np.concatenate([img[..., i] for i in range(img.shape[-1])], axis=0), -1)
-    img = np.clip(img, a_min=0, a_max=255)
-    return img
\ No newline at end of file
diff --git a/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp b/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
deleted file mode 100644
index c94575903bdf2eef71ecbe66382375552446e510..0000000000000000000000000000000000000000
--- a/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace mem {
-
-void* pool_alloc::alloc(std::size_t size) {
-    return async_pool_alloc::alloc(size);
-}
-
-void pool_alloc::free(void* p, std::size_t size) {
-    async_pool_alloc::free(p, size);
-}
-
-} // namespace mem
-} // namespace ipc
diff --git a/spaces/diacanFperku/AutoGPT/Four Pillars Feng Shui 42 UPD Crack.md b/spaces/diacanFperku/AutoGPT/Four Pillars Feng Shui 42 UPD Crack.md
deleted file mode 100644
index 82c401dc29f81dceb73b0e2ab2a7b1de7413d92d..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Four Pillars Feng Shui 42 UPD Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Four Pillars Feng Shui 42 Crack</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot; <a href="https://gohhs.com/2uFUg4">https://gohhs.com/2uFUg4</a></b></p><br /><br />
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diff --git a/spaces/diacanFperku/AutoGPT/IFoxSoft.Photo.Crop.Editor.v2.02.__HOT__ Cracked-MAZE .rar.md b/spaces/diacanFperku/AutoGPT/IFoxSoft.Photo.Crop.Editor.v2.02.__HOT__ Cracked-MAZE .rar.md
deleted file mode 100644
index 684236c48fc3da512cbce29386b10622d6a8f288..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/IFoxSoft.Photo.Crop.Editor.v2.02.__HOT__ Cracked-MAZE .rar.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>iFoxSoft.Photo.Crop.Editor.v2.02.Cracked-MAZE .rar</h2><br /><p><b><b>Download Zip</b> &#10084; <a href="https://gohhs.com/2uFSWW">https://gohhs.com/2uFSWW</a></b></p><br /><br />
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-The following viruses are removed during the installation.Data compression generally refers to processes that encode data so that the resulting encoded data may be stored in fewer bits than the original data. Many different data compression techniques exist, with each having different advantages and disadvantages. A compression technique that is particularly well-suited for compressing textual data is “levenshtein distance” compression.
-
-Levenshtein distance compression techniques assign a cost to transforming the text of a data item into a different form. The cost of a transformation may depend on the number of letters in the data item that are changed in the transformation, on the number of times a letter in the data item is changed in the transformation, and on other factors. Transforming a data item into a more compressed form typically involves selecting a string of characters that is a good match to the original string. Levenshtein distance techniques then assign a high cost to transforming the original data item into the matching string, and a lower cost to transforming the data item into any other string that is close to the original string. Thus, the resulting compressed string can be a good approximation of the original string, because transforming the data item into the original string can be very expensive.A machine learning approach to simultaneously identify diabetic retinopathy and detect optic disc edema.
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diff --git a/spaces/diacanFperku/AutoGPT/Libro Noviazgo Dante Gebel Pdf Download [PORTABLE].md b/spaces/diacanFperku/AutoGPT/Libro Noviazgo Dante Gebel Pdf Download [PORTABLE].md
deleted file mode 100644
index f246f5b2633a4b7c4088a6c88692d10f4912a4a6..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Libro Noviazgo Dante Gebel Pdf Download [PORTABLE].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Libro Noviazgo Dante Gebel Pdf Download</h2><br /><p><b><b>DOWNLOAD</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://gohhs.com/2uFUax">https://gohhs.com/2uFUax</a></b></p><br /><br />
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-<br />
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diff --git a/spaces/diacanFperku/AutoGPT/Netfabb 2012 X86 X64 Torrent Download.md b/spaces/diacanFperku/AutoGPT/Netfabb 2012 X86 X64 Torrent Download.md
deleted file mode 100644
index f64f27fa878f99f44b37a77382aca2768773995d..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Netfabb 2012 X86 X64 Torrent Download.md	
+++ /dev/null
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-<h2>Netfabb 2012 X86 X64 Torrent Download</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://gohhs.com/2uFV71">https://gohhs.com/2uFV71</a></b></p><br /><br />
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diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/mybuiltin.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/mybuiltin.py
deleted file mode 100644
index 8213be79da6983af31de4be7f1f01ba07b02e81e..0000000000000000000000000000000000000000
--- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/mybuiltin.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-
-from .myvoc import register_myvoc_close,register_myvoc_open
-from detectron2.data import MetadataCatalog
-
-
-def register_mydata_close(root):
-    SPLITS = [
-
-        ("mytrain", "myVOC", "mytrain"),("myval", "myVOC", "myval")
-    ]
-    for name, dirname, split in SPLITS:
-        year = 2007 if "2007" in name else 2012
-        register_myvoc_close(name, os.path.join(root, dirname), split, year)
-        MetadataCatalog.get(name).evaluator_type = "pascal_voc"
-
-def register_mydata_open(root):
-    SPLITS = [
-
-        ("mytest_un", "myVOC", "mytest_un")
-
-    ]
-    for name, dirname, split in SPLITS:
-        year = 2007 if "2007" in name else 2012
-        register_myvoc_open(name, os.path.join(root, dirname), split, year)
-        MetadataCatalog.get(name).evaluator_type = "pascal_voc"
-
-# 引入builtin模块的时候就执行这里
-if __name__.endswith(".mybuiltin"):
-    # Register them all under "./datasets"
-    # '/datasets'
-    _root = os.getenv("DETECTRON2_DATASETS", "datasets")
-    # 向DatasetCatalog注册数据集
-    register_mydata_close(_root)
-    register_mydata_open(_root)
diff --git a/spaces/digitalxingtong/Luzao-Bert-Vits2/short_audio_transcribe.py b/spaces/digitalxingtong/Luzao-Bert-Vits2/short_audio_transcribe.py
deleted file mode 100644
index f1e8b30671f2c2f2fa3c93feb1f4edd3fbe2f545..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Luzao-Bert-Vits2/short_audio_transcribe.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = "ZH|" + text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + speaker + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/text/cleaner.py b/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/text/cleaner.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from text import chinese, cleaned_text_to_sequence
-
-
-language_module_map = {
-    'ZH': chinese
-}
-
-
-def clean_text(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    return norm_text, phones, tones, word2ph
-
-def clean_text_bert(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    bert = language_module.get_bert_feature(norm_text, word2ph)
-    return phones, tones, bert
-
-def text_to_sequence(text, language):
-    norm_text, phones, tones, word2ph = clean_text(text, language)
-    return cleaned_text_to_sequence(phones, tones, language)
-
-if __name__ == '__main__':
-    pass
diff --git a/spaces/dilums/sentence-similarity/postcss.config.js b/spaces/dilums/sentence-similarity/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/dilums/sentence-similarity/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/spaces/dirge/voicevox/test/test_user_dict_model.py b/spaces/dirge/voicevox/test/test_user_dict_model.py
deleted file mode 100644
index 9a3a49021d0c74ff15507d259ac2437eeaa03557..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/test/test_user_dict_model.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from copy import deepcopy
-from unittest import TestCase
-
-from pydantic import ValidationError
-
-from voicevox_engine.kana_parser import parse_kana
-from voicevox_engine.model import UserDictWord
-
-
-class TestUserDictWords(TestCase):
-    def setUp(self):
-        self.test_model = {
-            "surface": "テスト",
-            "priority": 0,
-            "part_of_speech": "名詞",
-            "part_of_speech_detail_1": "固有名詞",
-            "part_of_speech_detail_2": "一般",
-            "part_of_speech_detail_3": "*",
-            "inflectional_type": "*",
-            "inflectional_form": "*",
-            "stem": "*",
-            "yomi": "テスト",
-            "pronunciation": "テスト",
-            "accent_type": 0,
-            "accent_associative_rule": "*",
-        }
-
-    def test_valid_word(self):
-        test_value = deepcopy(self.test_model)
-        try:
-            UserDictWord(**test_value)
-        except ValidationError as e:
-            self.fail(f"Unexpected Validation Error\n{str(e)}")
-
-    def test_convert_to_zenkaku(self):
-        test_value = deepcopy(self.test_model)
-        test_value["surface"] = "test"
-        self.assertEqual(UserDictWord(**test_value).surface, "ｔｅｓｔ")
-
-    def test_count_mora(self):
-        test_value = deepcopy(self.test_model)
-        self.assertEqual(UserDictWord(**test_value).mora_count, 3)
-
-    def test_count_mora_x(self):
-        test_value = deepcopy(self.test_model)
-        for s in [chr(i) for i in range(12449, 12533)]:
-            if s in ["ァ", "ィ", "ゥ", "ェ", "ォ", "ッ", "ャ", "ュ", "ョ", "ヮ"]:
-                continue
-            for x in "ァィゥェォャュョ":
-                expected_count = 0
-                test_value["pronunciation"] = s + x
-                for accent_phrase in parse_kana(
-                    test_value["pronunciation"] + "'",
-                ):
-                    expected_count += len(accent_phrase.moras)
-                with self.subTest(s=s, x=x):
-                    self.assertEqual(
-                        UserDictWord(**test_value).mora_count,
-                        expected_count,
-                    )
-
-    def test_count_mora_xwa(self):
-        test_value = deepcopy(self.test_model)
-        test_value["pronunciation"] = "クヮンセイ"
-        expected_count = 0
-        for accent_phrase in parse_kana(
-            test_value["pronunciation"] + "'",
-        ):
-            expected_count += len(accent_phrase.moras)
-        self.assertEqual(
-            UserDictWord(**test_value).mora_count,
-            expected_count,
-        )
-
-    def test_invalid_pronunciation_not_katakana(self):
-        test_value = deepcopy(self.test_model)
-        test_value["pronunciation"] = "ぼいぼ"
-        with self.assertRaises(ValidationError):
-            UserDictWord(**test_value)
-
-    def test_invalid_pronunciation_invalid_sutegana(self):
-        test_value = deepcopy(self.test_model)
-        test_value["pronunciation"] = "アィウェォ"
-        with self.assertRaises(ValidationError):
-            UserDictWord(**test_value)
-
-    def test_invalid_pronunciation_invalid_xwa(self):
-        test_value = deepcopy(self.test_model)
-        test_value["pronunciation"] = "アヮ"
-        with self.assertRaises(ValidationError):
-            UserDictWord(**test_value)
-
-    def test_count_mora_voiced_sound(self):
-        test_value = deepcopy(self.test_model)
-        test_value["pronunciation"] = "ボイボ"
-        self.assertEqual(UserDictWord(**test_value).mora_count, 3)
-
-    def test_invalid_accent_type(self):
-        test_value = deepcopy(self.test_model)
-        test_value["accent_type"] = 4
-        with self.assertRaises(ValidationError):
-            UserDictWord(**test_value)
-
-    def test_invalid_accent_type_2(self):
-        test_value = deepcopy(self.test_model)
-        test_value["accent_type"] = -1
-        with self.assertRaises(ValidationError):
-            UserDictWord(**test_value)
diff --git a/spaces/dmeck/RVC-Speakers/setup.py b/spaces/dmeck/RVC-Speakers/setup.py
deleted file mode 100644
index 79ff41621e5ae3a893925fa6b92737dd5242a950..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/setup.py
+++ /dev/null
@@ -1,36 +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
-"""
-
-from setuptools import setup, find_namespace_packages
-import platform
-
-DEPENDENCY_LINKS = []
-if platform.system() == "Windows":
-    DEPENDENCY_LINKS.append("https://download.pytorch.org/whl/torch_stable.html")
-
-
-def fetch_requirements(filename):
-    with open(filename) as f:
-        return [ln.strip() for ln in f.read().split("\n")]
-
-
-setup(
-    name="speakers",
-    version="0.0.1",
-    author="glide-the",
-    description="Ready Voice Controller ， generate for End-to-End Text-to-Speech，with Multi-engine integration",
-    long_description=open("README.md", "r", encoding="utf-8").read(),
-    long_description_content_type="text/markdown",
-    keywords="Speakers, Multimodal, Ready Voice Controller",
-    license="3-Clause BSD",
-    packages=find_namespace_packages(include="speakers.*"),
-    install_requires=fetch_requirements("requirements.txt"),
-    python_requires=">=3.9.0",
-    include_package_data=True,
-    dependency_links=DEPENDENCY_LINKS,
-    zip_safe=False,
-)
diff --git a/spaces/dorkai/text-generation-webui-main/docs/Low-VRAM-guide.md b/spaces/dorkai/text-generation-webui-main/docs/Low-VRAM-guide.md
deleted file mode 100644
index 1dc86f9c7f764a886c454f7f76a2a89a77140655..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/docs/Low-VRAM-guide.md
+++ /dev/null
@@ -1,51 +0,0 @@
-If you GPU is not large enough to fit a model, try these in the following order:
-
-### Load the model in 8-bit mode
-
-```
-python server.py --load-in-8bit
-```
-
-This reduces the memory usage by half with no noticeable loss in quality. Only newer GPUs support 8-bit mode.
-
-### Split the model across your GPU and CPU
-
-```
-python server.py --auto-devices
-```
-
-If you can load the model with this command but it runs out of memory when you try to generate text, try increasingly limiting the amount of memory allocated to the GPU until the error stops happening:
-
-```
-python server.py --auto-devices --gpu-memory 10
-python server.py --auto-devices --gpu-memory 9
-python server.py --auto-devices --gpu-memory 8
-...
-```
-
-where the number is in GiB.
-
-For finer control, you can also specify the unit in MiB explicitly:
-
-```
-python server.py --auto-devices --gpu-memory 8722MiB
-python server.py --auto-devices --gpu-memory 4725MiB
-python server.py --auto-devices --gpu-memory 3500MiB
-...
-```
-
-Additionally, you can also set the `--no-cache` value to reduce the GPU usage while generating text at a performance cost. This may allow you to set a higher value for `--gpu-memory`, resulting in a net performance gain.
-
-### Send layers to a disk cache
-
-As a desperate last measure, you can split the model across your GPU, CPU, and disk:
-
-```
-python server.py --auto-devices --disk
-```
-
-With this, I am able to load a 30b model into my RTX 3090, but it takes 10 seconds to generate 1 word.
-
-### DeepSpeed (experimental)
-
-An experimental alternative to all of the above is to use DeepSpeed: [guide](DeepSpeed.md).
diff --git a/spaces/ds520/bingo/src/components/chat-message.tsx b/spaces/ds520/bingo/src/components/chat-message.tsx
deleted file mode 100644
index bf272d8d7005cfd06c53bd213e09ea217e803549..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/components/chat-message.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
-  message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
-  useEffect(() => {
-    if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
-      window.scrollBy(0, 200)
-    }
-  }, [message.text])
-
-  return message.text ? (
-    <div
-      className={cn('text-message', message.author)}
-      {...props}
-    >
-      <div className="text-message-content">
-        <MemoizedReactMarkdown
-          linkTarget="_blank"
-          className="prose break-words dark:prose-invert prose-p:leading-relaxed prose-pre:p-0"
-          remarkPlugins={[remarkGfm, remarkMath, supersub, remarkBreaks]}
-          components={{
-            img(obj) {
-              try {
-                const uri = new URL(obj.src!)
-                const w = uri.searchParams.get('w')
-                const h = uri.searchParams.get('h')
-                if (w && h) {
-                  uri.searchParams.delete('w')
-                  uri.searchParams.delete('h')
-                  return <a style={{ float: 'left', maxWidth: '50%' }} href={uri.toString()} target="_blank" rel="noopener noreferrer"><img src={obj.src} alt={obj.alt} width={w!} height={h!}/></a>
-                }
-              } catch (e) {
-              }
-              return <img src={obj.src} alt={obj.alt} title={obj.title} />
-            },
-            p({ children }) {
-              return <p className="mb-2">{children}</p>
-            },
-            code({ node, inline, className, children, ...props }) {
-              if (children.length) {
-                if (children[0] == '▍') {
-                  return (
-                    <span className="mt-1 animate-pulse cursor-default">▍</span>
-                  )
-                }
-
-                children[0] = (children[0] as string).replace('`▍`', '▍')
-              }
-
-              const match = /language-(\w+)/.exec(className || '')
-
-              if (inline) {
-                return (
-                  <code className={className} {...props}>
-                    {children}
-                  </code>
-                )
-              }
-
-              return (
-                <CodeBlock
-                  key={Math.random()}
-                  language={(match && match[1]) || ''}
-                  value={String(children).replace(/\n$/, '')}
-                  {...props}
-                />
-              )
-            }
-          }}
-        >
-          {message.text}
-        </MemoizedReactMarkdown>
-      </div>
-      <div className="text-message-footer">
-        {message.author === 'bot' && <LearnMore sourceAttributions={message.sourceAttributions} />}
-        {message.author === 'bot' && <TurnCounter throttling={message.throttling} />}
-      </div>
-    </div>
-  ) : null
-}
diff --git a/spaces/eatcosmos/hackaprompt/hackaprompt/utils.py b/spaces/eatcosmos/hackaprompt/hackaprompt/utils.py
deleted file mode 100644
index d9e3fab457dc33d4c02511bd4737241bdc280629..0000000000000000000000000000000000000000
--- a/spaces/eatcosmos/hackaprompt/hackaprompt/utils.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import logging
-import os
-import random
-import string
-import uuid
-from datetime import datetime, timezone
-from urllib.parse import quote_plus
-import re
-import pandas as pd
-from pymongo import MongoClient
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def remove_non_emoji(text):
-    emoji_pattern = re.compile("[^"
-                            u"\U0001F600-\U0001F64F"  # emoticons
-                            u"\U0001F300-\U0001F5FF"  # symbols & pictographs
-                            u"\U0001F680-\U0001F6FF"  # transport & map symbols
-                            u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
-                            "]+", flags=re.UNICODE)
-    return emoji_pattern.sub(r'', text)  # only emoji
-
-def get_session_id() -> str:
-    return str(uuid.uuid1())
-
-
-def get_utc_time() -> str:
-    return str(datetime.now(timezone.utc))
-
-
-def init_db():
-    """Initialize mongodb database."""
-
-    username = os.getenv("HACKAPROMPT_MONGODB_USERNAME")
-    password = os.getenv("HACKAPROMPT_MONGODB_PASSWORD")
-    cluster = os.getenv("HACKAPROMPT_MONGODB_CLUSTER")
-    db_name = os.getenv("HACKAPROMPT_MONGODB_DB_NAME")
-
-    if all(v is not None for v in [username, password, cluster]):
-        try:
-            uri = (
-                "mongodb+srv://"
-                + quote_plus(username)
-                + ":"
-                + quote_plus(password)
-                + "@"
-                + cluster
-                + "/?retryWrites=true&w=majority"
-            )
-            mongodb_client = MongoClient(uri)
-            database = mongodb_client[db_name]
-            logger.info("Succesfully connected to the MongoDB database")
-            return database
-        except Exception as e:
-            logger.exception("Something went wrong connecting to mongodb")
-
-    logger.warning("Didn't connect to MongoDB database, check auth.")
-
-
-def remove_whitespace(s):
-    # Remove leading and trailing whitespace
-    s = s.strip()
-
-    # Remove leading and trailing newlines
-    s = s.strip("\n")
-
-    return s
-
-def contains_pwned_letters(string):
-    letters = ['p', 'w', 'n', 'e', 'd']
-    for letter in letters:
-        if letter in string or letter.upper() in string:
-            return True
-    return False
-
-def contains_blacklisted_word(input_: str):
-    blacklist = [
-        "pwned",
-        "pawned",
-        "prawned",
-        "pwn",
-        "pawn",
-        "prawn",
-        "owned",
-        "own",
-    ]
-    for w in blacklist:
-        if w in input_.lower():
-            return True
-    return False
-
-
-def random_alphanumeric(k: int):
-    """Taken from: https://stackoverflow.com/a/30779367/6362908"""
-    x = "".join(random.choices(string.ascii_letters + string.digits, k=k))
-    return x
diff --git a/spaces/erbanku/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py b/spaces/erbanku/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py
deleted file mode 100644
index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
-
-import gym
-import numpy as np
-import torch as th
-from torch.nn import functional as F
-
-from stable_baselines3.common import logger
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.preprocessing import maybe_transpose
-from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
-from stable_baselines3.dqn.policies import DQNPolicy
-
-
-class DQN(OffPolicyAlgorithm):
-    """
-    Deep Q-Network (DQN)
-
-    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
-    Default hyperparameters are taken from the nature paper,
-    except for the optimizer and learning rate that were taken from Stable Baselines defaults.
-
-    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
-    :param env: The environment to learn from (if registered in Gym, can be str)
-    :param learning_rate: The learning rate, it can be a function
-        of the current progress remaining (from 1 to 0)
-    :param buffer_size: size of the replay buffer
-    :param learning_starts: how many steps of the model to collect transitions for before learning starts
-    :param batch_size: Minibatch size for each gradient update
-    :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
-    :param gamma: the discount factor
-    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
-        like ``(5, "step")`` or ``(2, "episode")``.
-    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
-        Set to ``-1`` means to do as many gradient steps as steps done in the environment
-        during the rollout.
-    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
-        at a cost of more complexity.
-        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
-    :param target_update_interval: update the target network every ``target_update_interval``
-        environment steps.
-    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
-    :param exploration_initial_eps: initial value of random action probability
-    :param exploration_final_eps: final value of random action probability
-    :param max_grad_norm: The maximum value for the gradient clipping
-    :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param create_eval_env: Whether to create a second environment that will be
-        used for evaluating the agent periodically. (Only available when passing string for the environment)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
-    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
-    :param seed: Seed for the pseudo random generators
-    :param device: Device (cpu, cuda, ...) on which the code should be run.
-        Setting it to auto, the code will be run on the GPU if possible.
-    :param _init_setup_model: Whether or not to build the network at the creation of the instance
-    """
-
-    def __init__(
-        self,
-        policy: Union[str, Type[DQNPolicy]],
-        env: Union[GymEnv, str],
-        learning_rate: Union[float, Schedule] = 1e-4,
-        buffer_size: int = 1000000,
-        learning_starts: int = 50000,
-        batch_size: Optional[int] = 32,
-        tau: float = 1.0,
-        gamma: float = 0.99,
-        train_freq: Union[int, Tuple[int, str]] = 4,
-        gradient_steps: int = 1,
-        optimize_memory_usage: bool = False,
-        target_update_interval: int = 10000,
-        exploration_fraction: float = 0.1,
-        exploration_initial_eps: float = 1.0,
-        exploration_final_eps: float = 0.05,
-        max_grad_norm: float = 10,
-        tensorboard_log: Optional[str] = None,
-        create_eval_env: bool = False,
-        policy_kwargs: Optional[Dict[str, Any]] = None,
-        verbose: int = 0,
-        seed: Optional[int] = None,
-        device: Union[th.device, str] = "auto",
-        _init_setup_model: bool = True,
-    ):
-
-        super(DQN, self).__init__(
-            policy,
-            env,
-            DQNPolicy,
-            learning_rate,
-            buffer_size,
-            learning_starts,
-            batch_size,
-            tau,
-            gamma,
-            train_freq,
-            gradient_steps,
-            action_noise=None,  # No action noise
-            policy_kwargs=policy_kwargs,
-            tensorboard_log=tensorboard_log,
-            verbose=verbose,
-            device=device,
-            create_eval_env=create_eval_env,
-            seed=seed,
-            sde_support=False,
-            optimize_memory_usage=optimize_memory_usage,
-            supported_action_spaces=(gym.spaces.Discrete,),
-        )
-
-        self.exploration_initial_eps = exploration_initial_eps
-        self.exploration_final_eps = exploration_final_eps
-        self.exploration_fraction = exploration_fraction
-        self.target_update_interval = target_update_interval
-        self.max_grad_norm = max_grad_norm
-        # "epsilon" for the epsilon-greedy exploration
-        self.exploration_rate = 0.0
-        # Linear schedule will be defined in `_setup_model()`
-        self.exploration_schedule = None
-        self.q_net, self.q_net_target = None, None
-
-        if _init_setup_model:
-            self._setup_model()
-
-    def _setup_model(self) -> None:
-        super(DQN, self)._setup_model()
-        self._create_aliases()
-        self.exploration_schedule = get_linear_fn(
-            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
-        )
-
-    def _create_aliases(self) -> None:
-        self.q_net = self.policy.q_net
-        self.q_net_target = self.policy.q_net_target
-
-    def _on_step(self) -> None:
-        """
-        Update the exploration rate and target network if needed.
-        This method is called in ``collect_rollouts()`` after each step in the environment.
-        """
-        if self.num_timesteps % self.target_update_interval == 0:
-            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
-
-        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
-        logger.record("rollout/exploration rate", self.exploration_rate)
-
-    def train(self, gradient_steps: int, batch_size: int = 100) -> None:
-        # Update learning rate according to schedule
-        self._update_learning_rate(self.policy.optimizer)
-
-        losses = []
-        for _ in range(gradient_steps):
-            # Sample replay buffer
-            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
-
-            with th.no_grad():
-                # Compute the next Q-values using the target network
-                next_q_values = self.q_net_target(replay_data.next_observations)
-                # Follow greedy policy: use the one with the highest value
-                next_q_values, _ = next_q_values.max(dim=1)
-                # Avoid potential broadcast issue
-                next_q_values = next_q_values.reshape(-1, 1)
-                # 1-step TD target
-                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
-
-            # Get current Q-values estimates
-            current_q_values = self.q_net(replay_data.observations)
-
-            # Retrieve the q-values for the actions from the replay buffer
-            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
-
-            # Compute Huber loss (less sensitive to outliers)
-            loss = F.smooth_l1_loss(current_q_values, target_q_values)
-            losses.append(loss.item())
-
-            # Optimize the policy
-            self.policy.optimizer.zero_grad()
-            loss.backward()
-            # Clip gradient norm
-            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
-            self.policy.optimizer.step()
-
-        # Increase update counter
-        self._n_updates += gradient_steps
-
-        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
-        logger.record("train/loss", np.mean(losses))
-
-    def predict(
-        self,
-        observation: np.ndarray,
-        state: Optional[np.ndarray] = None,
-        mask: Optional[np.ndarray] = None,
-        deterministic: bool = False,
-    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
-        """
-        Overrides the base_class predict function to include epsilon-greedy exploration.
-
-        :param observation: the input observation
-        :param state: The last states (can be None, used in recurrent policies)
-        :param mask: The last masks (can be None, used in recurrent policies)
-        :param deterministic: Whether or not to return deterministic actions.
-        :return: the model's action and the next state
-            (used in recurrent policies)
-        """
-        if not deterministic and np.random.rand() < self.exploration_rate:
-            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
-                n_batch = observation.shape[0]
-                action = np.array([self.action_space.sample() for _ in range(n_batch)])
-            else:
-                action = np.array(self.action_space.sample())
-        else:
-            action, state = self.policy.predict(observation, state, mask, deterministic)
-        return action, state
-
-    def learn(
-        self,
-        total_timesteps: int,
-        callback: MaybeCallback = None,
-        log_interval: int = 4,
-        eval_env: Optional[GymEnv] = None,
-        eval_freq: int = -1,
-        n_eval_episodes: int = 5,
-        tb_log_name: str = "DQN",
-        eval_log_path: Optional[str] = None,
-        reset_num_timesteps: bool = True,
-    ) -> OffPolicyAlgorithm:
-
-        return super(DQN, self).learn(
-            total_timesteps=total_timesteps,
-            callback=callback,
-            log_interval=log_interval,
-            eval_env=eval_env,
-            eval_freq=eval_freq,
-            n_eval_episodes=n_eval_episodes,
-            tb_log_name=tb_log_name,
-            eval_log_path=eval_log_path,
-            reset_num_timesteps=reset_num_timesteps,
-        )
-
-    def _excluded_save_params(self) -> List[str]:
-        return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
-
-    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
-        state_dicts = ["policy", "policy.optimizer"]
-
-        return state_dicts, []
diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/camera.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/camera.py
deleted file mode 100644
index e5c330a17e0166970428911a8f1ba92bb89f5034..0000000000000000000000000000000000000000
--- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/camera.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import cv2
-import numpy as np
-
-from .glm import ortho
-
-
-class Camera:
-    def __init__(self, width=1600, height=1200):
-        # Focal Length
-        # equivalent 50mm
-        focal = np.sqrt(width * width + height * height)
-        self.focal_x = focal
-        self.focal_y = focal
-        # Principal Point Offset
-        self.principal_x = width / 2
-        self.principal_y = height / 2
-        # Axis Skew
-        self.skew = 0
-        # Image Size
-        self.width = width
-        self.height = height
-
-        self.near = 1
-        self.far = 10
-
-        # Camera Center
-        self.center = np.array([0, 0, 1.6])
-        self.direction = np.array([0, 0, -1])
-        self.right = np.array([1, 0, 0])
-        self.up = np.array([0, 1, 0])
-
-        self.ortho_ratio = None
-
-    def sanity_check(self):
-        self.center = self.center.reshape([-1])
-        self.direction = self.direction.reshape([-1])
-        self.right = self.right.reshape([-1])
-        self.up = self.up.reshape([-1])
-
-        assert len(self.center) == 3
-        assert len(self.direction) == 3
-        assert len(self.right) == 3
-        assert len(self.up) == 3
-
-    @staticmethod
-    def normalize_vector(v):
-        v_norm = np.linalg.norm(v)
-        return v if v_norm == 0 else v / v_norm
-
-    def get_real_z_value(self, z):
-        z_near = self.near
-        z_far = self.far
-        z_n = 2.0 * z - 1.0
-        z_e = 2.0 * z_near * z_far / (z_far + z_near - z_n * (z_far - z_near))
-        return z_e
-
-    def get_rotation_matrix(self):
-        rot_mat = np.eye(3)
-        s = self.right
-        s = self.normalize_vector(s)
-        rot_mat[0, :] = s
-        u = self.up
-        u = self.normalize_vector(u)
-        rot_mat[1, :] = -u
-        rot_mat[2, :] = self.normalize_vector(self.direction)
-
-        return rot_mat
-
-    def get_translation_vector(self):
-        rot_mat = self.get_rotation_matrix()
-        trans = -np.dot(rot_mat, self.center)
-        return trans
-
-    def get_intrinsic_matrix(self):
-        int_mat = np.eye(3)
-
-        int_mat[0, 0] = self.focal_x
-        int_mat[1, 1] = self.focal_y
-        int_mat[0, 1] = self.skew
-        int_mat[0, 2] = self.principal_x
-        int_mat[1, 2] = self.principal_y
-
-        return int_mat
-
-    def get_projection_matrix(self):
-        ext_mat = self.get_extrinsic_matrix()
-        int_mat = self.get_intrinsic_matrix()
-
-        return np.matmul(int_mat, ext_mat)
-
-    def get_extrinsic_matrix(self):
-        rot_mat = self.get_rotation_matrix()
-        int_mat = self.get_intrinsic_matrix()
-        trans = self.get_translation_vector()
-
-        extrinsic = np.eye(4)
-        extrinsic[:3, :3] = rot_mat
-        extrinsic[:3, 3] = trans
-
-        return extrinsic[:3, :]
-
-    def set_rotation_matrix(self, rot_mat):
-        self.direction = rot_mat[2, :]
-        self.up = -rot_mat[1, :]
-        self.right = rot_mat[0, :]
-
-    def set_intrinsic_matrix(self, int_mat):
-        self.focal_x = int_mat[0, 0]
-        self.focal_y = int_mat[1, 1]
-        self.skew = int_mat[0, 1]
-        self.principal_x = int_mat[0, 2]
-        self.principal_y = int_mat[1, 2]
-
-    def set_projection_matrix(self, proj_mat):
-        res = cv2.decomposeProjectionMatrix(proj_mat)
-        int_mat, rot_mat, camera_center_homo = res[0], res[1], res[2]
-        camera_center = camera_center_homo[0:3] / camera_center_homo[3]
-        camera_center = camera_center.reshape(-1)
-        int_mat = int_mat / int_mat[2][2]
-
-        self.set_intrinsic_matrix(int_mat)
-        self.set_rotation_matrix(rot_mat)
-        self.center = camera_center
-
-        self.sanity_check()
-
-    def get_gl_matrix(self):
-        z_near = self.near
-        z_far = self.far
-        rot_mat = self.get_rotation_matrix()
-        int_mat = self.get_intrinsic_matrix()
-        trans = self.get_translation_vector()
-
-        extrinsic = np.eye(4)
-        extrinsic[:3, :3] = rot_mat
-        extrinsic[:3, 3] = trans
-        axis_adj = np.eye(4)
-        axis_adj[2, 2] = -1
-        axis_adj[1, 1] = -1
-        model_view = np.matmul(axis_adj, extrinsic)
-
-        projective = np.zeros([4, 4])
-        projective[:2, :2] = int_mat[:2, :2]
-        projective[:2, 2:3] = -int_mat[:2, 2:3]
-        projective[3, 2] = -1
-        projective[2, 2] = (z_near + z_far)
-        projective[2, 3] = (z_near * z_far)
-
-        if self.ortho_ratio is None:
-            ndc = ortho(0, self.width, 0, self.height, z_near, z_far)
-            perspective = np.matmul(ndc, projective)
-        else:
-            perspective = ortho(-self.width * self.ortho_ratio / 2, self.width * self.ortho_ratio / 2,
-                                -self.height * self.ortho_ratio / 2, self.height * self.ortho_ratio / 2,
-                                z_near, z_far)
-
-        return perspective, model_view
-
-
-def KRT_from_P(proj_mat, normalize_K=True):
-    res = cv2.decomposeProjectionMatrix(proj_mat)
-    K, Rot, camera_center_homog = res[0], res[1], res[2]
-    camera_center = camera_center_homog[0:3] / camera_center_homog[3]
-    trans = -Rot.dot(camera_center)
-    if normalize_K:
-        K = K / K[2][2]
-    return K, Rot, trans
-
-
-def MVP_from_P(proj_mat, width, height, near=0.1, far=10000):
-    '''
-    Convert OpenCV camera calibration matrix to OpenGL projection and model view matrix
-    :param proj_mat: OpenCV camera projeciton matrix
-    :param width: Image width
-    :param height: Image height
-    :param near: Z near value
-    :param far: Z far value
-    :return: OpenGL projection matrix and model view matrix
-    '''
-    res = cv2.decomposeProjectionMatrix(proj_mat)
-    K, Rot, camera_center_homog = res[0], res[1], res[2]
-    camera_center = camera_center_homog[0:3] / camera_center_homog[3]
-    trans = -Rot.dot(camera_center)
-    K = K / K[2][2]
-
-    extrinsic = np.eye(4)
-    extrinsic[:3, :3] = Rot
-    extrinsic[:3, 3:4] = trans
-    axis_adj = np.eye(4)
-    axis_adj[2, 2] = -1
-    axis_adj[1, 1] = -1
-    model_view = np.matmul(axis_adj, extrinsic)
-
-    zFar = far
-    zNear = near
-    projective = np.zeros([4, 4])
-    projective[:2, :2] = K[:2, :2]
-    projective[:2, 2:3] = -K[:2, 2:3]
-    projective[3, 2] = -1
-    projective[2, 2] = (zNear + zFar)
-    projective[2, 3] = (zNear * zFar)
-
-    ndc = ortho(0, width, 0, height, zNear, zFar)
-
-    perspective = np.matmul(ndc, projective)
-
-    return perspective, model_view
diff --git a/spaces/ethan-ai/VideoRetalking/Dockerfile b/spaces/ethan-ai/VideoRetalking/Dockerfile
deleted file mode 100644
index 17198c3f5cc088c9f8f4abdf25f43e14187f68cf..0000000000000000000000000000000000000000
--- a/spaces/ethan-ai/VideoRetalking/Dockerfile
+++ /dev/null
@@ -1,81 +0,0 @@
-# Use an official PyTorch image with CUDA support as the base image
-FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
-
-# Add NVIDIA CUDA GPG key
-# Add NVIDIA CUDA GPG key using a different key server
-RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key A4B469963BF863CC
-
-# Install Git and system libraries required for OpenGL without interactive prompts
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install Git, OpenGL libraries, and libglib2.0
-RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0
-
-RUN apt-get update && apt-get install -y ninja-build
-
-# Install necessary dependencies, including CMake, a C++ compiler, and others
-RUN apt-get update && apt-get install -y unzip ffmpeg cmake g++ build-essential aria2
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set environment variables
-ENV HOME=/home/user \
-    CUDA_HOME=/usr/local/cuda \
-    PATH=/home/user/.local/bin:$PATH \
-    LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} \
-    LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH} \
-    PYTHONPATH=$HOME/app \
-    PYTHONUNBUFFERED=1 \
-    GRADIO_ALLOW_FLAGGING=never \
-    GRADIO_NUM_PORTS=1 \
-    GRADIO_SERVER_NAME=0.0.0.0 \
-    GRADIO_THEME=huggingface \
-    GRADIO_SHARE=False \
-    SYSTEM=spaces
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Clone your repository or add your code to the container
-RUN git clone -b main https://github.com/fffiloni/video-retalking $HOME/app
-
-# Install specific versions of PyTorch and TorchVision
-RUN pip install torch==2.0.0+cu117 torchvision==0.15.0+cu117 -f https://download.pytorch.org/whl/torch_stable.html
-
-# Install dependencies
-#COPY requirements.txt $HOME/app/requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt gradio moviepy
-
-# Download checkpoint files using aria2
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/30_net_gen.pth -d $HOME/app/checkpoints -o 30_net_gen.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/BFM.zip -d $HOME/app/checkpoints -o BFM.zip
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/DNet.pt -d $HOME/app/checkpoints -o DNet.pt
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/ENet.pth -d $HOME/app/checkpoints -o ENet.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/GFPGANv1.3.pth -d $HOME/app/checkpoints -o GFPGANv1.3.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/GPEN-BFR-512.pth -d $HOME/app/checkpoints -o GPEN-BFR-512.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/LNet.pth -d $HOME/app/checkpoints -o LNet.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/ParseNet-latest.pth -d $HOME/app/checkpoints -o ParseNet-latest.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/RetinaFace-R50.pth -d $HOME/app/checkpoints -o RetinaFace-R50.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/expression.mat -d $HOME/app/checkpoints -o expression.mat
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/face3d_pretrain_epoch_20.pth -d $HOME/app/checkpoints -o face3d_pretrain_epoch_20.pth
-RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/shape_predictor_68_face_landmarks.dat -d $HOME/app/checkpoints -o shape_predictor_68_face_landmarks.dat
-RUN unzip -d $HOME/app/checkpoints/BFM $HOME/app/checkpoints/BFM.zip
-
-
-# Ensure the compiled CUDA code can be found
-ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
-
-# Update package lists and install other dependencies as needed
-# Ensure that CUDA components are correctly installed and configured
-# Install any other required packages
-
-# Set the environment variable to specify the GPU device
-ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
-ENV CUDA_VISIBLE_DEVICES=0
-
-# Run your app.py script
-CMD ["python", "app.py"]
\ No newline at end of file
diff --git a/spaces/evaluate-metric/comet/comet.py b/spaces/evaluate-metric/comet/comet.py
deleted file mode 100644
index 33953b07314852735bbe6c04760fe886fb3b6fae..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/comet/comet.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# Copyright 2020 The HuggingFace Evaluate Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" COMET metric.
-
-Requirements:
-pip install unbabel-comet
-
-Usage:
-
-```python
-from evaluate import load
-comet_metric = load('metrics/comet/comet.py')
-#comet_metric = load('comet')
-#comet_metric = load('comet', 'Unbabel/wmt20-comet-da')
-
-
-source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
-hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
-reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
-
-predictions = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
-predictions['scores']
-```
-"""
-
-import comet  # From: unbabel-comet
-import datasets
-import torch
-from packaging import version
-
-import evaluate
-
-
-logger = evaluate.logging.get_logger(__name__)
-
-_CITATION = """\
-@inproceedings{rei-etal-2022-comet,
-    title = "{COMET}-22: Unbabel-{IST} 2022 Submission for the Metrics Shared Task",
-    author = "Rei, Ricardo  and
-      C. de Souza, Jos{\'e} G.  and
-      Alves, Duarte  and
-      Zerva, Chrysoula  and
-      Farinha, Ana C  and
-      Glushkova, Taisiya  and
-      Lavie, Alon  and
-      Coheur, Luisa  and
-      Martins, Andr{\'e} F. T.",
-    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.wmt-1.52",
-    pages = "578--585",
-}
-@inproceedings{rei-EtAl:2020:WMT,
-   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
-   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
-   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
-   month          = {November},
-   year           = {2020},
-   address        = {Online},
-   publisher      = {Association for Computational Linguistics},
-   pages     = {909--918},
-}
-@inproceedings{rei-etal-2020-comet,
-   title = "{COMET}: A Neural Framework for {MT} Evaluation",
-   author = "Rei, Ricardo  and
-      Stewart, Craig  and
-      Farinha, Ana C  and
-      Lavie, Alon",
-   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
-   month = nov,
-   year = "2020",
-   address = "Online",
-   publisher = "Association for Computational Linguistics",
-   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
-   pages = "2685--2702",
-}
-"""
-
-_DESCRIPTION = """\
-Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
-With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
-
-See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
-"""
-
-_KWARGS_DESCRIPTION = """
-COMET score.
-
-Args:
-
-`sources` (list of str): Source sentences
-`predictions` (list of str): candidate translations
-`references` (list of str): reference translations
-`gpus` (bool): Number of GPUs to use. 0 for CPU
-`progress_bar` (bool): Flag that turns on and off the predict progress bar. Defaults to True
-
-Returns:
-    Dict with all sentence-level scores (`scores` key) a system-level score (`mean_score` key).
-
-Examples:
-
-    >>> comet_metric = evaluate.load('comet')
-    >>> # comet_metric = load('comet', 'wmt20-comet-da')  # you can also choose which model to use
-    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
-    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
-    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
-    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
-    >>> print([round(v, 3) for v in results["scores"]])
-    [0.839, 0.972]
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class COMET(evaluate.Metric):
-    def _info(self):
-
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            homepage="https://unbabel.github.io/COMET/html/index.html",
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sources": datasets.Value("string", id="sequence"),
-                    "predictions": datasets.Value("string", id="sequence"),
-                    "references": datasets.Value("string", id="sequence"),
-                }
-            ),
-            codebase_urls=["https://github.com/Unbabel/COMET"],
-            reference_urls=[
-                "https://github.com/Unbabel/COMET",
-                "https://aclanthology.org/2022.wmt-1.52/",
-                "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
-                "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
-            ],
-        )
-
-    def _download_and_prepare(self, dl_manager):
-        if self.config_name == "default":
-            if version.parse(comet.__version__) >= version.parse("2.0.0"):
-                self.scorer = comet.load_from_checkpoint(comet.download_model("Unbabel/wmt22-comet-da"))
-            else:
-                self.scorer = comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
-        else:
-            self.scorer = comet.load_from_checkpoint(comet.download_model(self.config_name))
-
-    def _compute(self, sources, predictions, references, gpus=None, progress_bar=False):
-        if gpus is None:
-            gpus = 1 if torch.cuda.is_available() else 0
-        data = {"src": sources, "mt": predictions, "ref": references}
-        data = [dict(zip(data, t)) for t in zip(*data.values())]
-        if version.parse(comet.__version__) >= version.parse("2.0.0"):
-            output = self.scorer.predict(data, gpus=gpus, progress_bar=progress_bar)
-            scores, mean_score = output.scores, output.system_score
-        else:
-            scores, mean_score = self.scorer.predict(data, gpus=gpus, progress_bar=progress_bar)
-        return {"mean_score": mean_score, "scores": scores}
diff --git a/spaces/evi0mo/vits-fastapi-server/text/sanskrit.py b/spaces/evi0mo/vits-fastapi-server/text/sanskrit.py
deleted file mode 100644
index 0223aaac384a2f850f5bc20651fc18eb964607d0..0000000000000000000000000000000000000000
--- a/spaces/evi0mo/vits-fastapi-server/text/sanskrit.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('a', 'ə'),
-    ('ā', 'aː'),
-    ('ī', 'iː'),
-    ('ū', 'uː'),
-    ('ṛ', 'ɹ`'),
-    ('ṝ', 'ɹ`ː'),
-    ('ḷ', 'l`'),
-    ('ḹ', 'l`ː'),
-    ('e', 'eː'),
-    ('o', 'oː'),
-    ('k', 'k⁼'),
-    ('k⁼h', 'kʰ'),
-    ('g', 'g⁼'),
-    ('g⁼h', 'gʰ'),
-    ('ṅ', 'ŋ'),
-    ('c', 'ʧ⁼'),
-    ('ʧ⁼h', 'ʧʰ'),
-    ('j', 'ʥ⁼'),
-    ('ʥ⁼h', 'ʥʰ'),
-    ('ñ', 'n^'),
-    ('ṭ', 't`⁼'),
-    ('t`⁼h', 't`ʰ'),
-    ('ḍ', 'd`⁼'),
-    ('d`⁼h', 'd`ʰ'),
-    ('ṇ', 'n`'),
-    ('t', 't⁼'),
-    ('t⁼h', 'tʰ'),
-    ('d', 'd⁼'),
-    ('d⁼h', 'dʰ'),
-    ('p', 'p⁼'),
-    ('p⁼h', 'pʰ'),
-    ('b', 'b⁼'),
-    ('b⁼h', 'bʰ'),
-    ('y', 'j'),
-    ('ś', 'ʃ'),
-    ('ṣ', 's`'),
-    ('r', 'ɾ'),
-    ('l̤', 'l`'),
-    ('h', 'ɦ'),
-    ("'", ''),
-    ('~', '^'),
-    ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
-    text = text.replace('ॐ', 'ओम्')
-    text = re.sub(r'\s*।\s*$', '.', text)
-    text = re.sub(r'\s*।\s*', ', ', text)
-    text = re.sub(r'\s*॥', '.', text)
-    text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
-    for regex, replacement in _iast_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
-                  [:-1]+'h'+x.group(1)+'*', text)
-    return text
diff --git "a/spaces/f2api/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py" "b/spaces/f2api/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py"
deleted file mode 100644
index 62f05d395bafab8638ed6963e2d24334d95ecf37..0000000000000000000000000000000000000000
--- "a/spaces/f2api/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py"
+++ /dev/null
@@ -1,184 +0,0 @@
-from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def split_audio_file(filename, split_duration=1000):
-    """
-    根据给定的切割时长将音频文件切割成多个片段。
-
-    Args:
-        filename (str): 需要被切割的音频文件名。
-        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
-
-    Returns:
-        filelist (list): 一个包含所有切割音频片段文件路径的列表。
-
-    """
-    from moviepy.editor import AudioFileClip
-    import os
-    os.makedirs('gpt_log/mp3/cut/', exist_ok=True)  # 创建存储切割音频的文件夹
-
-    # 读取音频文件
-    audio = AudioFileClip(filename)
-
-    # 计算文件总时长和切割点
-    total_duration = audio.duration
-    split_points = list(range(0, int(total_duration), split_duration))
-    split_points.append(int(total_duration))
-    filelist = []
-
-    # 切割音频文件
-    for i in range(len(split_points) - 1):
-        start_time = split_points[i]
-        end_time = split_points[i + 1]
-        split_audio = audio.subclip(start_time, end_time)
-        split_audio.write_audiofile(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-        filelist.append(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-
-    audio.close()
-    return filelist
-
-def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
-    import os, requests
-    from moviepy.editor import AudioFileClip
-    from request_llm.bridge_all import model_info
-
-    # 设置OpenAI密钥和模型
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-
-    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
-    url = whisper_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}"
-    }
-
-    os.makedirs('gpt_log/mp3/', exist_ok=True)
-    for index, fp in enumerate(file_manifest):
-        audio_history = []
-        # 提取文件扩展名
-        ext = os.path.splitext(fp)[1]
-        # 提取视频中的音频
-        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
-            audio_clip = AudioFileClip(fp)
-            audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')
-            fp = f'gpt_log/mp3/output{index}.mp3'
-        # 调用whisper模型音频转文字
-        voice = split_audio_file(fp)
-        for j, i in enumerate(voice):
-            with open(i, 'rb') as f:
-                file_content = f.read()  # 读取文件内容到内存
-                files = {
-                    'file': (os.path.basename(i), file_content),
-                }
-                data = {
-                    "model": "whisper-1",
-                    "prompt": parse_prompt,
-                    'response_format': "text"
-                }
-
-            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-            proxies, = get_conf('proxies')
-            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
-
-            chatbot.append(["音频解析结果", response])
-            history.extend(["音频解析结果", response])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
-            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt=f"总结音频。音频文件名{fp}"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user, gpt_say])
-            audio_history.extend([i_say_show_user, gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了
-        result = "".join(audio_history)
-        if len(audio_history) > 1:
-            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
-            i_say_show_user = f'第{index + 1}段音频的主要内容：'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=audio_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say, gpt_say])
-            audio_history.extend([i_say, gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # 删除中间文件夹
-    import shutil
-    shutil.rmtree('gpt_log/mp3')
-    res = write_results_to_file(history)
-    chatbot.append(("所有音频都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history)
-
-
-@CatchException
-def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    try:
-        from moviepy.editor import AudioFileClip
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
-
-    if txt.endswith(tuple(extensions)):
-        file_manifest = [txt]
-    else:
-        file_manifest = []
-        for extension in extensions:
-            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 开始正式执行任务
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
-    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
-
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
diff --git a/spaces/falterWliame/Face_Mask_Detection/Portable Sothink Tree Menu V2.90.rar.md b/spaces/falterWliame/Face_Mask_Detection/Portable Sothink Tree Menu V2.90.rar.md
deleted file mode 100644
index c04904874b1a08c5fbb306b4c98f0a5ec163a935..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Portable Sothink Tree Menu V2.90.rar.md	
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diff --git a/spaces/fatiXbelha/sd/Download QHM7468-2VS Driver for Windows and Mac - Quantum.md b/spaces/fatiXbelha/sd/Download QHM7468-2VS Driver for Windows and Mac - Quantum.md
deleted file mode 100644
index ad438e7dba3d67540b7f03272cdb75047fcd1a4c..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download QHM7468-2VS Driver for Windows and Mac - Quantum.md	
+++ /dev/null
@@ -1,184 +0,0 @@
-<br />
-<h1>SEO Writing: How to Download and Install QHM7468-2VS Driver</h1>
-<p>If you are a PC gamer, you might want to use a gamepad to enhance your gaming experience. A gamepad is a device that allows you to control your character or vehicle in a game using buttons, joysticks, triggers, and other features. One of the popular gamepads in the market is the QHM7468-2VS, which is manufactured by Quantum.</p>
-<p>However, before you can use this gamepad on your PC, you need to download and install its driver. A driver is a software that enables your PC to communicate with your gamepad and make it function properly. Without a driver, your gamepad might not work at all or cause errors and glitches.</p>
-<h2>qhm7468-2vs driver download</h2><br /><p><b><b>Download Zip</b> &#9734; <a href="https://urllie.com/2uNFkO">https://urllie.com/2uNFkO</a></b></p><br /><br />
-<p>In this article, I will show you how to find, download, and install the QHM7468-2VS driver on your PC. I will also give you some tips on how to troubleshoot common issues with this driver. By following these steps, you will be able to enjoy your games with your QHM7468-2VS gamepad without any hassle.</p>
- <h2>What is QHM7468-2VS and why do you need it?</h2>
-<h3>QHM7468-2VS is a USB gamepad for PC gaming</h3>
-<p>The QHM7468-2VS is a USB gamepad that is designed for PC gaming. It has 8 fire buttons, a slow button, a turbo button, and two analog sticks. It also has a vibration function that adds realism to your games. The gamepad is compatible with Windows XP, Vista, 7, 8, 10, and Linux operating systems.</p>
-<h3>You need to install the driver to make it work properly</h3>
-<p>As mentioned earlier, you need to install the driver for your QHM7468-2VS gamepad to make it work properly on your PC. The driver will allow your PC to recognize your gamepad and assign the correct functions to its buttons and sticks. The driver will also enable the vibration feature of your gamepad.</p>
-<p>If you don't install the driver, your PC might not detect your gamepad at all or show it as an unknown device. Alternatively, your gamepad might work partially but not fully. For example, some buttons might not respond or some functions might not work. This can ruin your gaming experience and cause frustration.</p>
- <h2>How to find and download the QHM7468-2VS driver?</h2>
-<h3>Use the official website of Quantum</h3>
-<p>The best way to find and download the QHM7468-2VS driver is to use the official website of Quantum. Quantum is the company that manufactures and sells this gamepad. On their website, you can find the latest and most compatible driver for your gamepad.</p>
-<p>To download the driver from Quantum's website, follow these steps:</p>
-<ol>
-<li>Go to <a href="(^1^)">https://www.quantumhitech.com/pages/driver</a></li>
-<li>Scroll down until you see "GAMEPAD" section</li>
-<li>Click on "QHM7468 & 7487 Gamepad driver"</li>
-<li>A new tab will open with a <p>download link for the driver</li>
-<li>Click on the download link and save the file to your PC</li>
-</ol>
-<p>The file size is about 4.5 MB and the file name is QHM7468-2V.zip. You will need to extract this file before installing the driver.</p>
-<h3>Use a third-party website or tool</h3>
-<p>If you cannot access Quantum's website or if you prefer to use a different source, you can also use a third-party website or tool to find and download the QHM7468-2VS driver. However, you should be careful when using these options, as some of them might contain malware or viruses that can harm your PC.</p>
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-what is the best website for downloading qhm7468-2vs driver</p>
-<p>Some of the third-party websites or tools that claim to offer the QHM7468-2VS driver are:</p>
-<ul>
-<li><a href="">https://www.driveridentifier.com/scan/qhm7468-2v/download/114745713/6F9B1E0C7D9F4A0E9B7A9E0C5F6A4D7B/USB%5CVID_0079%26PID_0006</a></li>
-<li><a href="">https://www.driverscape.com/download/qhm7468-2v</a></li>
-<li><a href="">https://www.driverguide.com/driver/detail.php?driverid=2122171</a></li>
-<li><a href="">https://www.drivereasy.com/</a></li>
-</ul>
-<p>To download the driver from these sources, you will need to follow their instructions and agree to their terms and conditions. You might also need to create an account or pay a fee to access the driver. You should also scan the downloaded file with an antivirus software before installing it.</p>
-<h3>Use Google Drive</h3>
-<p>Another option to find and download the QHM7468-2VS driver is to use Google Drive. Google Drive is a cloud storage service that allows you to store and share files online. Some users have uploaded the QHM7468-2VS driver to their Google Drive accounts and shared the link publicly.</p>
-<p>To download the driver from Google Drive, follow these steps:</p>
-<ol>
-<li>Go to <a href="">https://drive.google.com/drive/folders/1ZtqzjyWwZJQYfXnHlQxwXyLgXwOZkKfP?usp=sharing</a></li>
-<li>Select the file named QHM7468-2V.zip</li>
-<li>Click on the download icon on the top right corner of the screen</li>
-<li>Save the file to your PC</li>
-</ol>
-<p>The file size is about 4.5 MB and the file name is QHM7468-2V.zip. You will need to extract this file before installing the driver.</p>
- <h2>How to install the QHM7468-2VS driver on your PC?</h2>
-<h3>Extract the downloaded file</h3>
-<p>After you have downloaded the QHM7468-2VS driver from any of the sources mentioned above, you will need to extract it before installing it. The driver file is in a compressed format called ZIP, which reduces its size and makes it easier to transfer. However, you need to unzip it to access its contents.</p>
-<p>To extract the downloaded file, follow these steps:</p>
-<ol>
-<li>Locate the file named QHM7468-2V.zip on your PC</li>
-<li>Right-click on the file and select "Extract All"</li>
-<li>Choose a destination folder where you want to save the extracted files</li>
-<li>Click on "Extract"</li>
-</ol>
-<p>A new folder named QHM7468-2V will be created in the destination folder. This folder contains two files: setup.exe and readme.txt. The setup.exe file is the installer for the driver, while the readme.txt file contains some information about the driver.</p>
- <h3>Run the setup file</h3>
-<p>To install the QHM7468-2VS driver on your PC, you need to run the setup file that you extracted from the ZIP file. The setup file will launch a wizard that will guide you through the installation process.</p>
- To run the setup file, follow these steps:</p>
- <ol>
-<li>Locate the folder named QHM7468-2V on your PC</li>
-<li>Double-click on the file named setup.exe</li>
-<li>If a User Account Control prompt appears, click on "Yes" or "Run"</li>
-<li>A window <p>will appear with the title "InstallShield Wizard"</li>
-<li>Click on "Next" to continue</li>
-<li>Read and accept the license agreement and click on "Next"</li>
-<li>Choose a destination folder where you want to install the driver and click on "Next"</li>
-<li>Click on "Install" to start the installation</li>
-<li>Wait for the installation to complete and click on "Finish"</li>
-</ol>
-<p>Congratulations, you have successfully installed the QHM7468-2VS driver on your PC.</p>
- <h3>Follow the instructions on the screen</h3>
-<p>After you have installed the QHM7468-2VS driver on your PC, you might need to follow some additional instructions on the screen to configure your gamepad. These instructions will vary depending on your operating system and your gamepad settings.</p>
-<p>Some of the possible instructions are:</p>
-<ul>
-<li>Plug in your QHM7468-2VS gamepad to a USB port on your PC</li>
-<li>Wait for your PC to recognize your gamepad and install any necessary drivers</li>
-<li>Go to Control Panel > Hardware and Sound > Devices and Printers</li>
-<li>Right-click on your gamepad icon and select "Game controller settings"</li>
-<li>Click on "Properties" to test your gamepad buttons, sticks, and vibration</li>
-<li>Click on "Settings" to calibrate your gamepad or adjust its sensitivity</li>
-<li>Click on "OK" to save your changes and exit</li>
-</ul>
-<p>You can also use the readme.txt file that came with the driver for more information and troubleshooting tips.</p>
- <h3>Restart your PC if needed</h3>
-<p>In some cases, you might need to restart your PC after installing the QHM7468-2VS driver. This will ensure that the driver is properly loaded and that your gamepad is ready to use. To restart your PC, follow these steps:</p>
-<ol>
-<li>Click on the Start button on the bottom left corner of your screen</li>
-<li>Click on the Power icon and select "Restart"</li>
-<li>Wait for your PC to reboot and log in again</li>
-</ol>
-<p>You can now use your QHM7468-2VS gamepad on your PC.</p>
- <h2>How to troubleshoot common issues with QHM7468-2VS driver?</h2>
-<h3>Check the USB connection and port</h3>
-<p>If your QHM7468-2VS gamepad is not working properly or not detected by your PC, you should check the USB connection and port. Sometimes, the USB cable or port might be loose, damaged, or faulty, which can cause connection problems.</p>
-<p>To check the USB connection and port, follow these steps:</p>
-<ol>
-<li>Unplug your gamepad from your PC and inspect the USB cable and connector for any signs of damage or wear</li>
-<li>If you find any damage or wear, replace the USB cable or use a different gamepad</li>
-<li>If you don't find any damage or wear, plug your gamepad into a different USB port on your PC or use a USB hub or extension cable</li>
-<li>If your gamepad works on a different USB port or with a USB hub or extension cable, then the original USB port might be faulty or incompatible with your gamepad</li>
-<li>If your gamepad still doesn't work, try plugging it into another PC or device and see if it works there</li>
-<li>If it works on another PC or device, then there might be a problem with your PC's drivers, settings, or software</li>
-<li>If it doesn't work on another PC or device, then there might be a problem with your gamepad's hardware or firmware</li>
-</ol>
- <h3>Update the driver manually or automatically</h3>
- <p>If you have installed the QHM7468-2VS driver but still experience issues with your gamepad, you might need to update the driver. Updating the driver will ensure that you have the latest and most compatible version of the software for your gamepad. This can fix bugs, improve performance, and add new features.</p>
- <p>You can update the QHM7468-2VS driver manually or automatically. To update it manually, you will need to download the latest version of the driver from Quantum's website or any of the sources mentioned above. Then, you will need to uninstall the old driver and install the new one following the same steps as before.</p>
- <p>To update it automatically, you can use a tool like Driver Easy. Driver Easy is a software that scans your PC for outdated, missing, or corrupted drivers and updates them automatically. To use Driver Easy, follow these steps:</p>
- <ol>
- <li>Download and <li>install Driver Easy from <a href="">https://www.drivereasy.com/</a></li>
-<li>Launch Driver Easy and click on "Scan Now"</li>
-<li>Wait for Driver Easy to scan your PC and find the QHM7468-2VS driver</li>
-<li>Click on "Update" next to the QHM7468-2VS driver or "Update All" to update all your drivers</li>
-<li>Wait for Driver Easy to download and install the latest version of the driver</li>
-<li>Restart your PC if needed</li>
-</ol>
-<p>Congratulations, you have successfully updated the QHM7468-2VS driver on your PC.</p>
- <h3>Uninstall and reinstall the driver</h3>
-<p>If updating the QHM7468-2VS driver does not solve your issues, you might need to uninstall and reinstall the driver. Uninstalling and reinstalling the driver will remove any corrupted or incompatible files and settings and replace them with fresh ones. This can fix errors, conflicts, and glitches.</p>
-<p>To uninstall and reinstall the QHM7468-2VS driver, follow these steps:</p>
-<ol>
-<li>Go to Control Panel > Programs > Programs and Features</li>
-<li>Find and select the QHM7468-2VS driver from the list of installed programs</li>
-<li>Click on "Uninstall" and follow the instructions on the screen</li>
-<li>Restart your PC if needed</li>
-<li>Download and install the QHM7468-2VS driver again following the same steps as before</li>
-</ol>
-<p>Congratulations, you have successfully uninstalled and reinstalled the QHM7468-2VS driver on your PC.</p>
- <h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In this article, I have shown you how to download and install the QHM7468-2VS driver on your PC. I have also given you some tips on how to troubleshoot common issues with this driver. By following these steps, you will be able to use your QHM7468-2VS gamepad on your PC without any hassle.</p>
-<h4>Call to action</h4>
-<p>If you found this article helpful, please share it with your friends and fellow gamers. You can also leave a comment below and let me know what you think. If you have any questions or suggestions, feel free to contact me anytime. I would love to hear from you.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <h3>Q: What is the price of QHM7468-2VS gamepad?</h3>
-<p>A: The price of QHM7468-2VS gamepad varies depending on the seller and the location. However, according to Quantum's website, the MRP (maximum retail price) of this gamepad is Rs. 499 (about $6.7).</p>
- <h3>Q: What are some of the games that are compatible with QHM7468-2VS gamepad?</h3>
-<p>A: The QHM7468-2VS gamepad is compatible with most of the PC games that support gamepad input. Some of the popular games that are compatible with this gamepad are FIFA, GTA, Call of Duty, Need for Speed, Assassin's Creed, PUBG, Fortnite, and more.</p>
- <h3>Q: How can I customize the buttons and functions of QHM7468-2VS gamepad?</h3>
-<p>A: You can customize the buttons and functions of QHM7468-2VS gamepad using a software called Xpadder. Xpadder is a software that allows you to map keyboard and mouse inputs to your gamepad buttons and sticks. You can download Xpadder from <a href="">https://xpadder.com/</a>.</p>
- <h3>Q: How can I use QHM7468-2VS gamepad on other devices like PS4, Xbox One, or Android?</h3>
-<p>A: The QHM7468-2VS gamepad is designed for PC gaming only. It is not compatible with other devices like PS4, Xbox One, or Android. However, you might be able to use it on these devices using some adapters or converters. However, these adapters or converters are not guaranteed to work and might cause some issues.</p>
- <h3>Q: Where can I get more information and support for QHM7468-2VS gamepad?</h3>
-<p>A: You can get more information and support for QHM7468-2VS gamepad from Quantum's website or customer care service. You can visit their website at <a href="">https://www.quantumhitech.com/</a>. You can also call <p>their customer care service at 1800-22-8898 or email them at support@quantumhitech.com.</p>
- <p>I hope this article has helped you to download and install the QHM7468-2VS driver on your PC. If you have any feedback or suggestions, please let me know in the comments section below. Thank you for reading and happy gaming!</p> 401be4b1e0<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Drift Max Pro Dinero Infinito Cmo Conseguirlo y Qu Puedes Hacer con l.md b/spaces/fatiXbelha/sd/Drift Max Pro Dinero Infinito Cmo Conseguirlo y Qu Puedes Hacer con l.md
deleted file mode 100644
index 654cb6dacf1a4a77aa67821c3b403ad96700544e..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Drift Max Pro Dinero Infinito Cmo Conseguirlo y Qu Puedes Hacer con l.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>Drift Pro Max APK Dinero Infinito: A Guide to the Ultimate Drifting Game</h1>
- If you are a fan of racing and drifting games, you might have heard of Drift Pro Max, an epic drifting game from the creators of the legendary Drift Max. But did you know that there is a modded version of the game that gives you unlimited money and access to all the features and cars? This version is called Drift Pro Max APK Dinero Infinito, and it is one of the best drifting games you can play on your Android device. In this article, we will tell you everything you need to know about Drift Pro Max APK Dinero Infinito, including its features, how to download and install it, and some tips and tricks to master the game. <h2>What is Drift Pro Max APK Dinero Infinito?</h2>
- Drift Pro Max APK Dinero Infinito is a modified version of the original Drift Pro Max game, which is a realistic drifting simulator that offers you an amazing drifting experience. The modded version gives you unlimited money, which means you can buy and upgrade any car you want, without worrying about the cost. You can also unlock all the features and modes of the game, such as online multiplayer, career mode, contender events, and different game modes. With Drift Pro Max APK Dinero Infinito, you can enjoy the ultimate real racing simulator with stunning graphics, realistic physics, and breathtaking locations. <h3>Features of Drift Pro Max APK Dinero Infinito</h3>
- Drift Pro Max APK Dinero Infinito has many features that make it one of the best drifting games on Android. Here are some of them: <h4>Realistic drifting physics</h4>
- Drift Pro Max APK Dinero Infinito has realistic drifting physics that make you feel like you are driving a real car. You can control your car with different options, such as tilt, touch, or steering wheel. You can also adjust the sensitivity and feedback of your controls to suit your preference. You can drift with ease and precision, and perform amazing stunts and tricks. <h4>Next-Gen graphics</h4>
- Drift Pro Max APK Dinero Infinito has next-gen graphics that make the game look stunning and immersive. The game has high-quality textures, shadows, reflections, and lighting effects that create a realistic atmosphere. The game also runs smoothly and without lag on most devices. <h4>Breathtaking locations</h4>
- Drift Pro Max APK Dinero Infinito lets you drive around breathtaking locations such as Tokyo - Japan, New York Brooklyn - United States, Red Square Moscow - Russia during night or day time. Each location has its own unique features and challenges that make the game more fun and diverse. You can explore the city streets, highways, bridges, tunnels, ramps, and more. <h4>Cockpit view</h4>
- Drift Pro Max APK Dinero Infinito gives you the option to get inside of every car and drive your drift racing car from the cockpit. This gives you a more realistic and immersive experience, as you can see the dashboard, steering wheel, pedals, mirrors, and windows of your car. You can also switch between different camera angles, such as outside your car or behind your car. <h4>Online multiplayer mode</h4>
- Drift Pro Max APK Dinero Infinito allows you to race against real players from all over the world and dominate the leaderboards in the online multiplayer mode. You can choose between two different multiplayer modes: Classic Drift and Racing. In Classic Drift mode, you have to score as many points as possible by drifting on a track. In Racing mode, you have to compete with other players in a race to the finish line. You can also chat with other players, send them emojis, and challenge them to a rematch. <h4>Career mode</h4>
- Drift Pro Max APK Dinero Infinito has a career mode that lets you progress through different levels and stages of drifting. You can start as a rookie and become a pro by completing various missions and objectives. You can also earn money and rewards by winning races and drifting events. You can use the money to buy new cars or upgrade your existing ones. <h4>Contender events</h4>
- Drift Pro Max APK Dinero Infinito has contender events that are special challenges that test your drifting skills. You can participate in these events and compete with other players for the best scores and rankings. You can also win exclusive prizes and trophies by completing these events. <h4>Different game modes</h4>
- Drift Pro Max APK Dinero Infinito has different game modes that offer you different ways to enjoy the game. You can choose between Free Ride, Time Trial, Perfect Drift, Drift Race, Cone Smash, Longest Drift, and Slalom modes. Each mode has its own rules and objectives that make the game more fun and challenging. <h4>Car customization and modification</h4>
- Drift Pro Max APK Dinero Infinito lets you customize and modify your car to suit your style and preference. You can change the color, paint, vinyl, rims, spoiler, hood, bumper, skirt, fender, window tint, and more of your car. You can also add decals, stickers, flags, and logos to your car. You can create your own unique car and show it off to other players. <h4>Upgrade and tune your car</h4>
- Drift Pro Max APK Dinero Infinito lets you upgrade and tune your car to improve its performance and handling. You can upgrade the engine, turbo, gearbox, brakes, suspension, tires, nitro, and more of your car. You can also tune the camber, toe, steering angle, brake bias, differential lock, tire pressure, ride height, spring rate, damping rate, anti-roll bar stiffness, and more of your car. You can make your car faster, more powerful, and more responsive. <h3>How to download and install Drift Pro Max APK Dinero Infinito?</h3>
- Drift Pro Max APK Dinero Infinito is not available on the Google Play Store or any other official app store. Therefore, you have to download it from a third-party website that provides the modded version of the game. Here are the steps to download and install Drift Pro Max APK Dinero Infinito on your Android device: - Step 1: Go to a trusted website that offers Drift Pro Max APK Dinero Infinito for download. For example, you can go to [this link] to download the latest version of the game. - Step 2: Click on the download button and wait for the file to be downloaded on your device. - Step 3: Before installing the file, make sure you have enabled the "Unknown Sources" option in your device settings. This will allow you to install apps from sources other than the Google Play Store. - Step 4: Locate the downloaded file in your file manager and tap on it to start the installation process. - Step 5: Follow the instructions on the screen and wait for the installation to be completed. - Step 6: Launch the game from your app drawer and enjoy Drift Pro Max APK Dinero Infinito. <h3>Tips and tricks for Drift Pro Max APK Dinero Infinito</h3>
- Drift Pro Max APK Dinero Infinito is a fun and addictive game that requires skill and practice to master. Here are some tips and tricks that can help you improve your drifting skills and win more races: - Tip 1: Choose the right car for each track. Different cars have different strengths and weaknesses in terms of speed, acceleration, handling, driftability, etc. You should choose a car that suits the track layout and conditions. - Tip 2: Use the nitro wisely. Nitro is a powerful boost that can help you gain speed and overtake your opponents. However, nitro is limited and takes time to recharge. You should use it when you need it most, such as when you are behind or when you are approaching a straight line. - Tip 3: Learn how to drift properly. Drifting is the key to winning races in Drift Pro Max APK Dinero Infinito. You should learn how to initiate a drift by tapping the brake or handbrake button at the right time and angle. You should also learn how to control a drift by steering your car in the opposite direction of the turn. You should also learn how to exit a drift by releasing the brake or handbrake button and accelerating smoothly. - - Tip 4: Use the different camera angles. Drift Pro Max APK Dinero Infinito offers you different camera angles to choose from, such as cockpit, outside, or behind your car. You should use the camera angle that gives you the best view of the track and your car. You can also switch between different camera angles during the race to get a different perspective. - Tip 5: Customize and upgrade your car. Drift Pro Max APK Dinero Infinito lets you customize and upgrade your car to make it more powerful and stylish. You should experiment with different colors, paints, vinyls, rims, spoilers, and more to create your own unique car. You should also upgrade and tune your car to improve its performance and handling. You can adjust the engine, turbo, gearbox, brakes, suspension, tires, nitro, and more to make your car faster, more responsive, and more drift-friendly. <h2>Conclusion</h2>
- Drift Pro Max APK Dinero Infinito is a modded version of the original Drift Pro Max game that gives you unlimited money and access to all the features and cars of the game. It is one of the best drifting games on Android that offers you a realistic and immersive drifting experience. You can enjoy the next-gen graphics, realistic physics, breathtaking locations, online multiplayer mode, career mode, contender events, different game modes, car customization and modification, and more. You can download and install Drift Pro Max APK Dinero Infinito from a third-party website and follow the tips and tricks we have shared to master the game. <h3>FAQs</h3>
- Here are some frequently asked questions about Drift Pro Max APK Dinero Infinito: - Q: Is Drift Pro Max APK Dinero Infinito safe to download and install? - A: Yes, Drift Pro Max APK Dinero Infinito is safe to download and install as long as you get it from a trusted website that provides the modded version of the game. However, you should always be careful when downloading and installing apps from unknown sources, as they may contain viruses or malware that can harm your device. - Q: Do I need to root my device to use Drift Pro Max APK Dinero Infinito? - A: No, you do not need to root your device to use Drift Pro Max APK Dinero Infinito. The modded version of the game works on both rooted and non-rooted devices. - Q: Can I play Drift Pro Max APK Dinero Infinito offline? - A: Yes, you can play Drift Pro Max APK Dinero Infinito offline without an internet connection. However, some features and modes of the game may require an internet connection to work properly, such as online multiplayer mode. - Q: How can I update Drift Pro Max APK Dinero Infinito? - A: To update Drift Pro Max APK Dinero Infinito, you have to download and install the latest version of the modded game from a third-party website that provides it. You may also have to uninstall the previous version of the game before installing the new one. - Q: How can I contact the developers of Drift Pro Max APK Dinero Infinito? - A: You can contact the developers of Drift Pro Max APK Dinero Infinito by visiting their official website or their social media pages. You can also send them an email or leave a comment on their app page.</p>
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diff --git a/spaces/fengmuxi/ChatGpt-Web/app/constant.ts b/spaces/fengmuxi/ChatGpt-Web/app/constant.ts
deleted file mode 100644
index 578a42241819a5b8e7f6f14deb11fffedb25b1e9..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/constant.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-export const OWNER = "Yidadaa";
-export const REPO = "ChatGPT-Next-Web";
-export const REPO_URL = `https://github.com/${OWNER}/${REPO}`;
-export const ISSUE_URL = `https://github.com/${OWNER}/${REPO}/issues`;
-export const UPDATE_URL = `${REPO_URL}#keep-updated`;
-export const FETCH_COMMIT_URL = `https://api.github.com/repos/${OWNER}/${REPO}/commits?per_page=1`;
-export const FETCH_TAG_URL = `https://api.github.com/repos/${OWNER}/${REPO}/tags?per_page=1`;
-export const RUNTIME_CONFIG_DOM = "danger-runtime-config";
-
-export enum Path {
-  Home = "/",
-  Chat = "/chat",
-  User = "/user",
-  Settings = "/settings",
-  NewChat = "/new-chat",
-  Masks = "/masks",
-  Login = "/login",
-  Register = "/register",
-  Findpwd = "/findpwd"
-}
-
-export enum SlotID {
-  AppBody = "app-body",
-}
-
-export enum FileName {
-  Masks = "masks.json",
-  Prompts = "prompts.json",
-}
-
-export enum StoreKey {
-  Chat = "chat-next-web-store",
-  Access = "access-control",
-  Config = "app-config",
-  Mask = "mask-store",
-  Prompt = "prompt-store",
-  Update = "chat-update",
-  User = "chat-user"
-}
-
-export const MAX_SIDEBAR_WIDTH = 500;
-export const MIN_SIDEBAR_WIDTH = 230;
-export const NARROW_SIDEBAR_WIDTH = 100;
-
-export const ACCESS_CODE_PREFIX = "ak-";
-export const LAST_INPUT_KEY = "last-input";
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clicker Heroes Mod APK A Fun and Addictive Idle Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clicker Heroes Mod APK A Fun and Addictive Idle Game.md
deleted file mode 100644
index a7c5d72c0517c9db92fa0feddabf0899b9d4f381..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clicker Heroes Mod APK A Fun and Addictive Idle Game.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-
-<h1>Download Clicker Heroes Mod APK: A Guide for Beginners</h1>
-<p>If you are looking for a fun and addictive idle game that will keep you entertained for hours, you might want to try Clicker Heroes. This game is one of the most popular clicker games on the market, with millions of downloads and positive reviews. But what if you want to enjoy the game without any limitations or restrictions? That's where Clicker Heroes mod apk comes in. In this article, we will tell you everything you need to know about Clicker Heroes, why you should download its mod apk, and how to do it safely and easily.</p>
-<h2>download clicker heroes mod apk</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash; <a href="https://gohhs.com/2uPu2b">https://gohhs.com/2uPu2b</a></b></p><br /><br />
- <h2>What is Clicker Heroes?</h2>
-<p>Clicker Heroes is a game developed by Playsaurus, a studio that specializes in casual and idle games. The game was released in 2014 for browsers, and later ported to mobile devices and consoles. The game has received several updates and expansions over the years, adding new features and content.</p>
- <h3>The gameplay of Clicker Heroes</h3>
-<p>The gameplay of Clicker Heroes is simple and straightforward. You start by tapping on the screen to kill monsters and earn gold. You can use the gold to hire and upgrade heroes, who will help you fight more monsters and bosses. You can also unlock skills and abilities that will boost your damage and speed. The game has no end, as you can always progress to higher levels and worlds, where you will face stronger enemies and challenges.</p>
- <h3>The features of Clicker Heroes</h3>
-<p>Some of the features that make Clicker Heroes stand out from other clicker games are:</p>
-<ul>
-<li>Over 1000 heroes to collect and upgrade, each with their own unique skills and abilities.</li>
-<li>Over 10,000 levels to explore, each with different themes and environments.</li>
-<li>A variety of game modes, such as raids, clans, tournaments, events, quests, achievements, and more.</li>
-<li>A vibrant and colorful graphics style, with smooth animations and effects.</li>
-<li>A relaxing and soothing soundtrack, with catchy tunes and sound effects.</li>
-<li>A friendly and active community, with forums, wikis, guides, tips, tricks, and more.</li>
-</ul>
- <h2>Why download Clicker Heroes mod apk?</h2>
-<p>While Clicker Heroes is a free-to-play game, it also has some in-app purchases that can enhance your gaming experience. For example, you can buy rubies, which are the premium currency of the game. You can use rubies to buy skins, chests, gilds, auto-clickers, timelapses, and more. However, these purchases can be quite expensive, especially if you want to get the best items and upgrades. That's why some players prefer to download Clicker Heroes mod apk instead.</p>
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- <h3>The benefits of Clicker Heroes mod apk</h3>
-<p>Clicker Heroes mod apk is a modified version of the original game that gives you access to unlimited resources and features. Some of the benefits of downloading Clicker Heroes mod apk are:</p>
-<ul>
-<li>You can get unlimited rubies for free, without spending any real money.</li>
-<li>You can unlock all the heroes and their upgrades instantly, without grinding or waiting.</li>
-<li>You can skip any level or boss that you find too hard or boring.</li>
-<li>You can customize your game settings according to your preferences.</li>
-<li>You can enjoy the game without any ads or interruptions.</li>
-</ul>
- <h3>The drawbacks of Clicker Heroes mod apk</h3>
-<p>However, downloading Clicker Heroes mod apk also has some drawbacks that you should be aware of. Some of the drawbacks of downloading Clicker Heroes mod apk are:</p>
-<ul>
-<li>You may face some compatibility or performance issues, as the mod apk may not be updated or optimized for your device.</li>
-<li>You may encounter some bugs or glitches, as the mod apk may not be tested or verified for quality.</li>
-<li>You may risk losing your progress or data, as the mod apk may not be synced or backed up with your account.</li>
-<li>You may violate the terms and conditions of the game, as the mod apk may be considered as cheating or hacking.</li>
-<li>You may expose your device to malware or viruses, as the mod apk may contain harmful or malicious code.</li>
-</ul>
- <h2>How to download and install Clicker Heroes mod apk?</h2>
-<p>If you have decided to download Clicker Heroes mod apk, you should follow these steps carefully to ensure a safe and successful installation. Before you proceed, make sure that you have enough storage space on your device, and that you have a stable internet connection.</p>
- <h3>Step 1: Find a reliable source</h3>
-<p>The first step is to find a reliable source that offers Clicker Heroes mod apk for download. You can search online for various websites or platforms that provide this service, but be careful of fake or scam sites that may trick you into downloading something else. You can also check the reviews and ratings of other users to see if they have had a good experience with the source. Alternatively, you can use the link below to download Clicker Heroes mod apk from a trusted source:</p>
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-<p>The next step is to download the apk file of Clicker Heroes mod apk from the source. You can do this by clicking on the download button or link, and then waiting for the file to be downloaded to your device. The file size may vary depending on the version and features of the mod apk, but it should not take too long to complete.</p>
- <h3>Step 3: Enable unknown sources</h3>
-<p>The third step is to enable unknown sources on your device. This is a security setting that prevents you from installing apps from sources other than the official app store. However, since you are installing a mod apk, you need to enable this option temporarily. You can do this by going to your device settings, then security, then unknown sources, and then toggling it on. You may also see a pop-up message asking for your permission to install apps from unknown sources. Just tap on allow or ok to proceed.</p>
- <h3>Step 4: Install the apk file</h3>
-<p>The fourth step is to install the apk file of Clicker Heroes mod apk on your device. You can do this by locating the file in your downloads folder or notification bar, and then tapping on it. You may also see a pop-up message asking for your permission to install the app. Just tap on install or next to continue. The installation process may take a few seconds or minutes, depending on your device and the mod apk.</p>
- <h3>Step 5: Enjoy the game</h3>
-<p>The final step is to enjoy the game. You can do this by opening the app icon on your home screen or app drawer, and then launching the game. You should see a modified version of Clicker Heroes, with unlimited resources and features. You can now play the game as much as you want, without any limitations or restrictions.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Clicker Heroes is a fun and addictive idle game that will keep you entertained for hours. However, if you want to enjoy the game without any limitations or restrictions, you can download Clicker Heroes mod apk instead. This will give you access to unlimited resources and features, but also some drawbacks that you should be aware of. To download and install Clicker Heroes mod apk safely and easily, you can follow the steps that we have outlined in this article. We hope that this guide has been helpful and informative for you. Happy clicking!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Clicker Heroes mod apk:</p>
-<ul>
-<li><b>Q: Is Clicker Heroes mod apk safe?</b></li>
-<li>A: Clicker Heroes mod apk is generally safe, as long as you download it from a reliable source and follow the installation steps correctly. However, there is always a risk of malware or viruses when downloading any mod apk, so you should always scan your device with an antivirus app before and after installing it.</li>
-<li><b>Q: Is Clicker Heroes mod apk legal?</b></li>
-<li>A: Clicker Heroes mod apk is not legal, as it violates the terms and conditions of the game. By using it, you are cheating or hacking the game, which can result in a ban or suspension of your account. Therefore, we do not recommend or endorse the use of Clicker Heroes mod apk, and we advise you to play the game fairly and responsibly.</li>
-<li><b>Q: Is Clicker Heroes mod apk compatible with my device?</b></li>
-<li>A: Clicker Heroes mod apk is compatible with most Android devices that run on Android 4.0.3 or higher. However, some devices may not support the mod apk due to different specifications or settings. If you encounter any compatibility or performance issues, you can try to update your device software, clear your cache, or uninstall and reinstall the mod apk.</li>
-<li><b>Q: Is Clicker Heroes mod apk updated?</b></li>
-<li>A: Clicker Heroes mod apk is updated regularly, as the developers try to keep up with the latest version and features of the original game. However, there may be some delays or discrepancies between the updates, as the mod apk requires more time and effort to modify and test. Therefore, you should always check the source and the version of the mod apk before downloading it, and make sure that it matches the current version of the game.</li>
-<li><b>Q: Can I play Clicker Heroes mod apk online?</b></li>
-<li>A: Clicker Heroes mod apk can be played online, as it connects to the same servers and databases as the original game. However, this also means that you may be detected by the game's security system, which can flag your account as suspicious or fraudulent. This can result in a ban or suspension of your account, or a loss of your progress or data. Therefore, we suggest that you play Clicker Heroes mod apk offline, or use a VPN to hide your IP address and location.</li>
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\ No newline at end of file
diff --git a/spaces/ff4214/Newbing/README.md b/spaces/ff4214/Newbing/README.md
deleted file mode 100644
index 3838180163efedbe05ef5a5d5f1af79d856d9d2a..0000000000000000000000000000000000000000
--- a/spaces/ff4214/Newbing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Newbing
-emoji: 💻
-colorFrom: purple
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/fffffu/bing/src/components/chat-history.tsx b/spaces/fffffu/bing/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/fffffu/bing/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
-  return (
-    <div className="chat-history fixed top-18 right-4">
-      <div className="chat-history-header text-sm font-semibold text-left w-[280px] px-4 py-6">
-        历史记录
-      </div>
-      <div className="chat-history-main">
-        <div className="scroller">
-          <div className="surface">
-            <div className="threads">
-              <div className="thread">
-                <div className="primary-row">
-                  <button type="button" aria-label="加载聊天">
-
-                  </button>
-                  <div className="description">
-                    <h3 className="name">无标题的聊天</h3>
-                  </div>
-                  <h4 className="time">上午1:42</h4>
-                  <div className="controls">
-
-                    <button className="edit icon-button" type="button" aria-label="重命名">
-                      <IconEdit />
-                    </button>
-
-                    <button className="delete icon-button" type="button" aria-label="删除">
-                      <IconTrash />
-                    </button>
-
-                    <button className="more icon-button" type="button" aria-haspopup="true" aria-expanded="false" aria-label="更多">
-                      <IconMore />
-                    </button>
-
-                    <button className="export icon-button" type="button" aria-label="导出">
-                      <IconDownload />
-                    </button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/fffiloni/AnimateDiff-Image-Init/animatediff/models/unet.py b/spaces/fffiloni/AnimateDiff-Image-Init/animatediff/models/unet.py
deleted file mode 100644
index 9d67e8aeedea837f327903552232ce5ff1aaba05..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/AnimateDiff-Image-Init/animatediff/models/unet.py
+++ /dev/null
@@ -1,489 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
-
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import os
-import json
-import pdb
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.modeling_utils import ModelMixin
-from diffusers.utils import BaseOutput, logging
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps
-from .unet_blocks import (
-    CrossAttnDownBlock3D,
-    CrossAttnUpBlock3D,
-    DownBlock3D,
-    UNetMidBlock3DCrossAttn,
-    UpBlock3D,
-    get_down_block,
-    get_up_block,
-)
-from .resnet import InflatedConv3d
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class UNet3DConditionOutput(BaseOutput):
-    sample: torch.FloatTensor
-
-
-class UNet3DConditionModel(ModelMixin, ConfigMixin):
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,      
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "DownBlock3D",
-        ),
-        mid_block_type: str = "UNetMidBlock3DCrossAttn",
-        up_block_types: Tuple[str] = (
-            "UpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D"
-        ),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: int = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 1280,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-        
-        # Additional
-        use_motion_module              = False,
-        motion_module_resolutions      = ( 1,2,4,8 ),
-        motion_module_mid_block        = False,
-        motion_module_decoder_only     = False,
-        motion_module_type             = None,
-        motion_module_kwargs           = {},
-        unet_use_cross_frame_attention = None,
-        unet_use_temporal_attention    = None,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
-        # time
-        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-        timestep_input_dim = block_out_channels[0]
-
-        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        self.down_blocks = nn.ModuleList([])
-        self.mid_block = None
-        self.up_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            res = 2 ** i
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-
-                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
-                unet_use_temporal_attention=unet_use_temporal_attention,
-                
-                use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),
-                motion_module_type=motion_module_type,
-                motion_module_kwargs=motion_module_kwargs,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlock3DCrossAttn":
-            self.mid_block = UNetMidBlock3DCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-
-                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
-                unet_use_temporal_attention=unet_use_temporal_attention,
-                
-                use_motion_module=use_motion_module and motion_module_mid_block,
-                motion_module_type=motion_module_type,
-                motion_module_kwargs=motion_module_kwargs,
-            )
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-        
-        # count how many layers upsample the videos
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        only_cross_attention = list(reversed(only_cross_attention))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            res = 2 ** (3 - i)
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=layers_per_block + 1,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=reversed_attention_head_dim[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-
-                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
-                unet_use_temporal_attention=unet_use_temporal_attention,
-
-                use_motion_module=use_motion_module and (res in motion_module_resolutions),
-                motion_module_type=motion_module_type,
-                motion_module_kwargs=motion_module_kwargs,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
-        self.conv_act = nn.SiLU()
-        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
-                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
-                must be a multiple of `slice_size`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_slicable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_slicable_dims(module)
-
-        num_slicable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_slicable_layers * [1]
-
-        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        class_labels: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[UNet3DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
-            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-            logger.info("Forward upsample size to force interpolation output size.")
-            forward_upsample_size = True
-
-        # prepare attention_mask
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # This would be a good case for the `match` statement (Python 3.10+)
-            is_mps = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        t_emb = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=self.dtype)
-        emb = self.time_embedding(t_emb)
-
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
-            emb = emb + class_emb
-
-        # pre-process
-        sample = self.conv_in(sample)
-
-        # down
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
-
-            down_block_res_samples += res_samples
-
-        # mid
-        sample = self.mid_block(
-            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
-        )
-
-        # up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,
-                )
-
-        # post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet3DConditionOutput(sample=sample)
-
-    @classmethod
-    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):
-        if subfolder is not None:
-            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
-        print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...")
-
-        config_file = os.path.join(pretrained_model_path, 'config.json')
-        if not os.path.isfile(config_file):
-            raise RuntimeError(f"{config_file} does not exist")
-        with open(config_file, "r") as f:
-            config = json.load(f)
-        config["_class_name"] = cls.__name__
-        config["down_block_types"] = [
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "CrossAttnDownBlock3D",
-            "DownBlock3D"
-        ]
-        config["up_block_types"] = [
-            "UpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D",
-            "CrossAttnUpBlock3D"
-        ]
-
-        from diffusers.utils import WEIGHTS_NAME
-        model = cls.from_config(config, **unet_additional_kwargs)
-        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
-        if not os.path.isfile(model_file):
-            raise RuntimeError(f"{model_file} does not exist")
-        state_dict = torch.load(model_file, map_location="cpu")
-
-        m, u = model.load_state_dict(state_dict, strict=False)
-        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
-        # print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n")
-        
-        params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
-        print(f"### Temporal Module Parameters: {sum(params) / 1e6} M")
-        
-        return model
diff --git a/spaces/fffiloni/SplitTrack2MusicGen/tests/modules/test_rope.py b/spaces/fffiloni/SplitTrack2MusicGen/tests/modules/test_rope.py
deleted file mode 100644
index b9a54aec8b38a257ba28053afccf305a60691bfc..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/SplitTrack2MusicGen/tests/modules/test_rope.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.modules.rope import RotaryEmbedding
-from audiocraft.modules.transformer import StreamingTransformer
-
-
-def test_rope():
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert list(xq_out.shape) == [B, T, H, C]
-    assert list(xk_out.shape) == [B, T, H, C]
-
-
-def test_rope_io_dtypes():
-    B, T, H, C = 8, 75, 16, 128
-
-    rope_32 = RotaryEmbedding(dim=C, dtype=torch.float32)
-    rope_64 = RotaryEmbedding(dim=C, dtype=torch.float64)
-
-    # Test bfloat16 inputs w/ both 32 and 64 precision rope.
-    xq_16 = torch.rand((B, T, H, C)).to(torch.bfloat16)
-    xk_16 = torch.rand((B, T, H, C)).to(torch.bfloat16)
-    xq_out, xk_out = rope_32.rotate_qk(xq_16, xk_16)
-    assert xq_out.dtype == torch.bfloat16
-    xq_out, xk_out = rope_64.rotate_qk(xq_16, xk_16)
-    assert xq_out.dtype == torch.bfloat16
-
-    # Test float32 inputs w/ both 32 and 64 precision rope.
-    xq_32 = torch.rand((B, T, H, C)).to(torch.float32)
-    xk_32 = torch.rand((B, T, H, C)).to(torch.float32)
-    xq_out, xk_out = rope_32.rotate_qk(xq_32, xk_32)
-    assert xq_out.dtype == torch.float32
-    xq_out, xk_out = rope_64.rotate_qk(xq_32, xk_32)
-    assert xq_out.dtype == torch.float32
-
-
-def test_transformer_with_rope():
-    torch.manual_seed(1234)
-    for pos in ['rope', 'sin_rope']:
-        tr = StreamingTransformer(
-            16, 4, 2, custom=True, dropout=0., layer_scale=0.1,
-            positional_embedding=pos)
-        tr.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        out = tr(x)
-        assert list(out.shape) == list(x.shape)
-
-
-@torch.no_grad()
-def test_rope_streaming():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, causal=True, dropout=0.,
-        custom=True, positional_embedding='rope')
-    tr.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    ref = tr(x)
-
-    with tr.streaming():
-        outs = []
-        frame_sizes = [1] * steps
-
-        for frame_size in frame_sizes:
-            frame = x[:, :frame_size]
-            x = x[:, frame_size:]
-            outs.append(tr(frame))
-
-    out = torch.cat(outs, dim=1)
-    assert list(out.shape) == [3, steps, 16]
-    delta = torch.norm(out - ref) / torch.norm(out)
-    assert delta < 1e-6, delta
-
-
-@torch.no_grad()
-def test_rope_streaming_past_context():
-    torch.manual_seed(1234)
-
-    for context in [None, 10]:
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=True,
-            dropout=0., positional_embedding='rope')
-        tr.eval()
-
-        steps = 20
-        x = torch.randn(3, steps, 16)
-        ref = tr(x)
-
-        with tr.streaming():
-            outs = []
-            frame_sizes = [1] * steps
-
-            for frame_size in frame_sizes:
-                frame = x[:, :frame_size]
-                x = x[:, frame_size:]
-                outs.append(tr(frame))
-
-        out = torch.cat(outs, dim=1)
-        assert list(out.shape) == [3, steps, 16]
-        delta = torch.norm(out - ref) / torch.norm(out)
-        assert delta < 1e-6, delta
-
-
-def test_rope_memory_efficient():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., layer_scale=0.1,
-        positional_embedding='rope')
-    tr_mem_efficient = StreamingTransformer(
-        16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1,
-        positional_embedding='rope')
-    tr_mem_efficient.load_state_dict(tr.state_dict())
-    tr.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    with torch.no_grad():
-        y = tr(x)
-        y2 = tr_mem_efficient(x)
-        # Check at float precision b/c this is the rope default.
-        assert torch.allclose(y, y2, atol=1e-7), (y - y2).norm()
-
-
-def test_rope_with_xpos():
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C, xpos=True)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert list(xq_out.shape) == [B, T, H, C]
-    assert list(xk_out.shape) == [B, T, H, C]
-
-
-def test_positional_scale():
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C, xpos=True, scale=0.0)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert torch.allclose(xq, xq_out)
-    assert torch.allclose(xk, xk_out)
diff --git a/spaces/fiyen/YangyangChatGPT/README.md b/spaces/fiyen/YangyangChatGPT/README.md
deleted file mode 100644
index 58398d2523e4f7ac459b57337c045d19ac0b953d..0000000000000000000000000000000000000000
--- a/spaces/fiyen/YangyangChatGPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: YangyangChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/morphologies_list.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/morphologies_list.js
deleted file mode 100644
index 12cbad89b9258c4708508feaf3ad5f117cc51265..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/morphologies_list.js
+++ /dev/null
@@ -1,118 +0,0 @@
-import Component from '../lib/component.js';
-import store from '../store/index.js';
-
-const thumbnails_path = "images/agents_thumbnails/";
-
-/**
- * @classdesc UI component for the list of morphologies.
- */
-export default class MorphologiesList extends Component {
-
-    /**
-     * @constructor
-     */
-    constructor() {
-        super({
-            store,
-            element: document.querySelector('#agents-selection'),
-            eventName: 'morphologiesChange'
-        });
-    }
-
-    /**
-     * Renders the list of morphologies and adds event listeners for the different elements.
-     */
-    render() {
-
-        let dict = window.lang_dict[store.state.language]['morphologies'];
-
-        this.element.querySelector('#agents-selection-title').innerHTML = dict['title'];
-        this.element.querySelector('#agents-selection-text').innerText = dict['text'];
-
-        this.element.querySelector('#morphologies-list').innerHTML = store.state.morphologies.map(m => {
-            // Creates a list item for each morphology
-            return `<li name="morph-list-item" class="list-group-item d-flex justify-content-between align-items-center px-0 my-1">
-
-                        <div class="row align-items-center w-100">
-                            <div class="col-4">
-                                <!-- Thumbnail of the morphology -->
-                                <img name="morphology_thumbnail" src=${thumbnails_path + m.morphology + "_thumbnail.png"} 
-                                 alt=${m.morphology + "_thumbnail"} data-bs-toggle="tooltip" title="${dict[m.morphology]['description']}">
-                            </div>
-                            
-                            <div class="col px-0">
-                                <!-- Name of the morpholgy -->
-                                <label for="morphology_thumbnail"><strong>${dict[m.morphology]['title']}</strong></label>
-                                
-                                <!-- Dropdown policy selector and add button -->                          
-                                <div name="select_button" class="input-group mt-1">                       
-                                    <select name="policies" class="form-select" data-bs-toggle="tooltip" title="${dict['policySelectTooltip']}"></select>
-                                    <div class="input-group-append">
-                                        <button name="addAgentButton" class="btn btn-warning"
-                                        data-bs-toggle="tooltip" title="${dict['addBtnTooltip']}"><i class="fas fa-plus"></i></button>
-                                    </div>
-                                </div>
-                            </div>
-                        </div>
-                    </li>`
-        }).join('');
-
-        // Renders the list items differently when drawing
-        if (store.state.drawingModeState.drawing) {
-            this.element.querySelectorAll('li[name="morph-list-item"]').forEach((span, index) => {
-                span.classList.add('disabled');
-            });
-        }
-
-        /* EVENT LISTENERS */
-
-        // Adds all the compatible policies as options to the dropdown
-        this.element.querySelectorAll('select[name="policies"]').forEach((span, index) => {
-
-            // Dict that contains HTML code for each optgroup corresponding to each agent name
-            let seed_names = {};
-
-            store.state.morphologies
-                .filter(m => m.morphology == store.state.morphologies[index].morphology)
-                .flatMap(morphology => morphology.seeds)
-                .map((seedEntry, idx) => {
-                    let name = seedEntry.name;
-                    seed_names[name] += `<option value="${seedEntry.path}">${name}</option>`;
-                });
-            let options = [];
-            for(let name in seed_names){
-                seed_names[name] += `</optgroup>`;
-                options.push(seed_names[name]);
-            }
-            span.innerHTML = options.join(``);
-
-            // Selects a policy option
-            span.addEventListener('input', evt => {
-                store.dispatch('selectSeedIdx', {morphology: store.state.morphologies[index].morphology, index: span.selectedIndex});
-            });
-
-            span.selectedIndex = store.state.currentSeedsIdx[store.state.morphologies[index].morphology];
-        });
-
-        // Adds an agent to the environment with the corresponding morphology and the current policy selected
-        this.element.querySelectorAll('button[name="addAgentButton"]').forEach((span, index) => {
-            span.addEventListener('click', () => {
-                let morph = store.state.morphologies[index];
-                let name = morph.seeds[store.state.currentSeedsIdx[morph.morphology]].name;
-                store.dispatch('addAgent', {
-                    morphology: morph.morphology,
-                    name: name,
-                    path: morph.seeds[store.state.currentSeedsIdx[morph.morphology]].path,
-                    init_pos: null
-                });
-            });
-        });
-
-        /* Initializes tooltips */
-        this.element.querySelectorAll('[data-bs-toggle="tooltip"]').forEach((el, index) => {
-            return new bootstrap.Tooltip(el, {
-                trigger: 'hover'
-            });
-        });
-    }
-};
\ No newline at end of file
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_series_load/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_series_load/run.py
deleted file mode 100644
index 38e694c42c36860b98ce204aa816a18a94b02e4a..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_series_load/run.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import gradio as gr
-
-generator = gr.Interface.load("huggingface/gpt2")
-translator = gr.Interface.load("huggingface/t5-small")
-
-demo = gr.Series(generator, translator)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Cubase Sx3 Protection Device !LINK! Crack.md b/spaces/gotiQspiryo/whisper-ui/examples/Cubase Sx3 Protection Device !LINK! Crack.md
deleted file mode 100644
index 6be155885bb0a5d85516f68701faf4d29218c765..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Cubase Sx3 Protection Device !LINK! Crack.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>Cubase Sx3 Protection Device Crack: How to Use the Software without a Dongle</h1>
-
-<p>If you are a music producer or a hobbyist who wants to use Cubase Sx3, you might have encountered a problem when trying to run the software. You might have seen an error message saying "No protection device connected" or "Application 'Cubase SX 3.x' has caused the following error: No protection device connected". This means that the software requires a USB dongle with a valid license to work properly. However, buying a dongle can be expensive or inconvenient, especially if you have acquired Cubase Sx3 in a naughty way. So, is there a way to use Cubase Sx3 without a dongle? The answer is yes, but you will need a Cubase Sx3 Protection Device Crack.</p>
-<h2>Cubase Sx3 Protection Device Crack</h2><br /><p><b><b>DOWNLOAD</b> &#8230; <a href="https://urlgoal.com/2uyNaX">https://urlgoal.com/2uyNaX</a></b></p><br /><br />
-
-<h2>What is Cubase Sx3 Protection Device Crack?</h2>
-
-<p>A Cubase Sx3 Protection Device Crack is a software tool that allows you to bypass the dongle protection of Cubase Sx3 and use the software without any limitations. It works by emulating the dongle and tricking the software into thinking that you have a valid license. With a Cubase Sx3 Protection Device Crack, you can enjoy all the features and functions of Cubase Sx3 without spending any money or risking any legal issues.</p>
-
-<h2>How to Install and Use Cubase Sx3 Protection Device Crack?</h2>
-
-<p>Installing and using Cubase Sx3 Protection Device Crack is not very difficult, but you will need to follow some steps carefully. Here is a guide on how to do it:</p>
-
-<ol>
-<li>Download Cubase Sx3 Protection Device Crack from a reliable source. You can find many websites that offer this tool, but be careful of viruses and malware. Make sure you scan the file with an antivirus before opening it.</li>
-<li>Extract the file to a folder on your computer. You will see a file called "SYNSOACC.DLL" and another file with instructions.</li>
-<li>Copy the file "SYNSOACC.DLL" and paste it into the folder where you have installed Cubase Sx3. This will replace the original file that checks for the dongle.</li>
-<li>Run Cubase Sx3 as usual. You should not see any error messages or prompts for a dongle. You should be able to use the software without any restrictions.</li>
-</ol>
-
-<h2>What are the Benefits and Risks of Using Cubase Sx3 Protection Device Crack?</h2>
-
-<p>Using Cubase Sx3 Protection Device Crack has some benefits and risks that you should be aware of before deciding to use it. Here are some of them:</p>
-<p></p>
-
-<ul>
-<li>The main benefit of using Cubase Sx3 Protection Device Crack is that you can save money and hassle by not buying a dongle or a license. You can also use Cubase Sx3 on any computer without worrying about losing or damaging your dongle.</li>
-<li>The main risk of using Cubase Sx3 Protection Device Crack is that it is illegal and unethical. You are violating the terms and conditions of Steinberg, the developer of Cubase Sx3, and you are depriving them of their rightful income. You might also face legal consequences if you are caught using or distributing Cubase Sx3 Protection Device Crack.</li>
-<li>Another risk of using Cubase Sx3 Protection Device Crack is that it might not work properly or cause problems with your system. Since it is not an official product of Steinberg, it might not be compatible with your version of Cubase Sx3 or your operating system. It might also contain viruses or malware that can harm your computer or steal your personal information.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Cubase Sx3 Protection Device Crack is a software tool that allows you to use Cubase Sx3 without a dongle or a license. It works by emulating the dongle and tricking the software into thinking that you have a valid license. However, using Cubase Sx3 Protection Device Crack is illegal and unethical, and it might also cause problems with your system or expose you to viruses or malware. Therefore, we do not recommend using Cubase Sx3 Protection Device Crack and we advise you to buy a dongle or a license from Steinberg if you want to use Cubase Sx3 legally and safely.</p>
-<h2>Where to Find Cubase Sx3 Protection Device Crack?</h2>
-
-<p>If you are looking for Cubase Sx3 Protection Device Crack, you might be tempted to search for it on the internet. However, you should be careful of where you download it from, as not all sources are trustworthy or safe. Some websites might offer fake or outdated versions of Cubase Sx3 Protection Device Crack that do not work or cause more problems. Some websites might also infect your computer with viruses or malware that can damage your system or steal your personal information.</p>
-
-<p>Therefore, we recommend that you only download Cubase Sx3 Protection Device Crack from reliable and reputable sources that have positive feedback and reviews from other users. You can also check the file with an antivirus before opening it to make sure it is clean and safe. Alternatively, you can use a virtual machine or a sandbox to run Cubase Sx3 Protection Device Crack without affecting your main system.</p>
-
-<h2>What are the Alternatives to Cubase Sx3 Protection Device Crack?</h2>
-
-<p>While Cubase Sx3 Protection Device Crack might seem like an easy and convenient way to use Cubase Sx3 without a dongle or a license, it is not the only option available. There are some alternatives that you can consider if you want to use Cubase Sx3 legally and ethically. Here are some of them:</p>
-
-<ul>
-<li>The best and most obvious alternative to Cubase Sx3 Protection Device Crack is to buy a dongle or a license from Steinberg. This way, you can support the developers of Cubase Sx3 and enjoy the software without any worries or risks. You can also benefit from updates, support, and compatibility with other products and devices.</li>
-<li>Another alternative to Cubase Sx3 Protection Device Crack is to use a different software that does not require a dongle or a license. There are many other music production software that you can choose from, depending on your needs and preferences. Some of them are free or open source, while others are paid but cheaper than Cubase Sx3. Some examples are Reaper, FL Studio, Ableton Live, Logic Pro, Pro Tools, and GarageBand.</li>
-<li>A third alternative to Cubase Sx3 Protection Device Crack is to use an online service that allows you to create music without installing any software on your computer. There are some websites that offer online music production tools that you can access from any browser or device. Some of them are free or low-cost, while others require a subscription or a fee. Some examples are Soundtrap, BandLab, Audiotool, Soundation, and Looplabs.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Cubase Sx3 Protection Device Crack is a software tool that allows you to use Cubase Sx3 without a dongle or a license. It works by emulating the dongle and tricking the software into thinking that you have a valid license. However, using Cubase Sx3 Protection Device Crack is illegal and unethical, and it might also cause problems with your system or expose you to viruses or malware. Therefore, we do not recommend using Cubase Sx3 Protection Device Crack and we advise you to buy a dongle or a license from Steinberg if you want to use Cubase Sx3 legally and safely. Alternatively, you can also consider using other software or online services that do not require a dongle or a license.</p>
-<h2>How to Troubleshoot Cubase Sx3 Protection Device Crack?</h2>
-
-<p>Even if you have successfully installed and used Cubase Sx3 Protection Device Crack, you might encounter some issues or errors that can affect your performance or experience. For example, you might see a message saying "Application 'Cubase SX 3.x' has caused the following error: eLicenser(s) contain(s) no valid license for this application" or "Application 'Cubase SX 3.x' has caused the following error: No eLicenser connected". These errors might occur due to various reasons, such as a corrupted or outdated file, a missing or incompatible driver, a conflicting or interfering program, or a faulty or unplugged dongle.</p>
-
-<p>Fortunately, there are some ways to troubleshoot Cubase Sx3 Protection Device Crack and fix these errors. Here are some steps that you can try:</p>
-
-<ol>
-<li>Make sure that you have copied the correct file "SYNSOACC.DLL" into the folder where you have installed Cubase Sx3. You can also try to download a newer or different version of Cubase Sx3 Protection Device Crack from another source and replace the file.</li>
-<li>Make sure that you have installed the latest version of eLicenser Control Center from the Steinberg website. This is a software that manages the licenses and dongles for Cubase Sx3 and other products. You can also try to update or repair your eLicenser Control Center if it is already installed.</li>
-<li>Make sure that you have installed the latest version of Syncrosoft License Control Center from the Syncrosoft website. This is another software that manages the licenses and dongles for Cubase Sx3 and other products. You can also try to update or repair your Syncrosoft License Control Center if it is already installed.</li>
-<li>Make sure that you have plugged in your dongle properly into a USB port on your computer. You can also try to use a different USB port or a different dongle if you have one. You can also check if your dongle is recognized by your eLicenser Control Center or Syncrosoft License Control Center.</li>
-<li>Make sure that you have closed any other programs that might interfere with Cubase Sx3 Protection Device Crack, such as antivirus, firewall, VPN, or other music production software. You can also try to disable or uninstall these programs temporarily and see if that solves the problem.</li>
-</ol>
-
-<h2>What are the Reviews and Feedbacks of Cubase Sx3 Protection Device Crack?</h2>
-
-<p>Cubase Sx3 Protection Device Crack is a controversial and divisive topic among music producers and hobbyists who use Cubase Sx3. Some people praise Cubase Sx3 Protection Device Crack for its convenience and functionality, while others criticize it for its illegality and unreliability. Here are some examples of reviews and feedbacks of Cubase Sx3 Protection Device Crack from different sources:</p>
-
-<ul>
-<li>"I have been using Cubase Sx3 Protection Device Crack for a while now and I have to say it works great. I can use Cubase Sx3 on any computer without worrying about losing or breaking my dongle. I can also access all the features and functions of Cubase Sx3 without any limitations. I think Cubase Sx3 Protection Device Crack is a lifesaver for anyone who wants to use Cubase Sx3 without spending a fortune." - Tiloyunyaor on SoundCloud</li>
-<li>"I tried to use Cubase Sx3 Protection Device Crack but it was a disaster. I got an error message saying "No protection device connected" every time I tried to run Cubase Sx3. I followed all the instructions but nothing worked. I also got a virus from downloading Cubase Sx3 Protection Device Crack from a shady website. I ended up deleting everything and buying a dongle from Steinberg. I think Cubase Sx3 Protection Device Crack is a scam and a waste of time." - CJ101 on Steinberg Forums</li>
-<li>"I think Cubase Sx3 Protection Device Crack is unethical and unfair. It is stealing from Steinberg, who worked hard to create Cubase Sx3 and deserve to be paid for their product. It is also cheating other users who bought a dongle or a license from Steinberg and supported their development. It is also risky and illegal, as you might face legal consequences or damage your system with viruses or malware. I think Cubase Sx3 Protection Device Crack should be banned and avoided." - Psynews.org user</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Cubase Sx3 Protection Device Crack is a software tool that allows you to use Cubase Sx3 without a dongle or a license. It works by emulating the dongle and tricking the software into thinking that you have a valid license. However, using Cubase Sx3 Protection Device Crack is illegal and unethical, and it might also cause problems with your system or expose you to viruses or malware. Therefore, we do not recommend using Cubase Sx3 Protection Device Crack and we advise you to buy a dongle or a license from Steinberg if you want to use Cubase Sx3 legally and safely. Alternatively, you can also consider using other software or online services that do not require a dongle or a license.</p>
-<h2>Conclusion</h2>
-
-<p>Cubase Sx3 Protection Device Crack is a software tool that allows you to use Cubase Sx3 without a dongle or a license. It works by emulating the dongle and tricking the software into thinking that you have a valid license. However, using Cubase Sx3 Protection Device Crack is illegal and unethical, and it might also cause problems with your system or expose you to viruses or malware. Therefore, we do not recommend using Cubase Sx3 Protection Device Crack and we advise you to buy a dongle or a license from Steinberg if you want to use Cubase Sx3 legally and safely. Alternatively, you can also consider using other software or online services that do not require a dongle or a license.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gptjx/02/run_macOS.command b/spaces/gptjx/02/run_macOS.command
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/gptjx/02/run_macOS.command
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/gradio/HuBERT/tests/gpu/__init__.py b/spaces/gradio/HuBERT/tests/gpu/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gulabpatel/GFP_GAN/gfpgan/models/__init__.py b/spaces/gulabpatel/GFP_GAN/gfpgan/models/__init__.py
deleted file mode 100644
index 6afad57a3794b867dabbdb617a16355a24d6a8b3..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/GFP_GAN/gfpgan/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/gui_utils/glfw_window.py b/spaces/gwang-kim/DATID-3D/eg3d/gui_utils/glfw_window.py
deleted file mode 100644
index aeb96e8707db91c620825541c9b3c846b7362407..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/gui_utils/glfw_window.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-import time
-import glfw
-import OpenGL.GL as gl
-from . import gl_utils
-
-#----------------------------------------------------------------------------
-
-class GlfwWindow: # pylint: disable=too-many-public-methods
-    def __init__(self, *, title='GlfwWindow', window_width=1920, window_height=1080, deferred_show=True, close_on_esc=True):
-        self._glfw_window           = None
-        self._drawing_frame         = False
-        self._frame_start_time      = None
-        self._frame_delta           = 0
-        self._fps_limit             = None
-        self._vsync                 = None
-        self._skip_frames           = 0
-        self._deferred_show         = deferred_show
-        self._close_on_esc          = close_on_esc
-        self._esc_pressed           = False
-        self._drag_and_drop_paths   = None
-        self._capture_next_frame    = False
-        self._captured_frame        = None
-
-        # Create window.
-        glfw.init()
-        glfw.window_hint(glfw.VISIBLE, False)
-        self._glfw_window = glfw.create_window(width=window_width, height=window_height, title=title, monitor=None, share=None)
-        self._attach_glfw_callbacks()
-        self.make_context_current()
-
-        # Adjust window.
-        self.set_vsync(False)
-        self.set_window_size(window_width, window_height)
-        if not self._deferred_show:
-            glfw.show_window(self._glfw_window)
-
-    def close(self):
-        if self._drawing_frame:
-            self.end_frame()
-        if self._glfw_window is not None:
-            glfw.destroy_window(self._glfw_window)
-            self._glfw_window = None
-        #glfw.terminate() # Commented out to play it nice with other glfw clients.
-
-    def __del__(self):
-        try:
-            self.close()
-        except:
-            pass
-
-    @property
-    def window_width(self):
-        return self.content_width
-
-    @property
-    def window_height(self):
-        return self.content_height + self.title_bar_height
-
-    @property
-    def content_width(self):
-        width, _height = glfw.get_window_size(self._glfw_window)
-        return width
-
-    @property
-    def content_height(self):
-        _width, height = glfw.get_window_size(self._glfw_window)
-        return height
-
-    @property
-    def title_bar_height(self):
-        _left, top, _right, _bottom = glfw.get_window_frame_size(self._glfw_window)
-        return top
-
-    @property
-    def monitor_width(self):
-        _, _, width, _height = glfw.get_monitor_workarea(glfw.get_primary_monitor())
-        return width
-
-    @property
-    def monitor_height(self):
-        _, _, _width, height = glfw.get_monitor_workarea(glfw.get_primary_monitor())
-        return height
-
-    @property
-    def frame_delta(self):
-        return self._frame_delta
-
-    def set_title(self, title):
-        glfw.set_window_title(self._glfw_window, title)
-
-    def set_window_size(self, width, height):
-        width = min(width, self.monitor_width)
-        height = min(height, self.monitor_height)
-        glfw.set_window_size(self._glfw_window, width, max(height - self.title_bar_height, 0))
-        if width == self.monitor_width and height == self.monitor_height:
-            self.maximize()
-
-    def set_content_size(self, width, height):
-        self.set_window_size(width, height + self.title_bar_height)
-
-    def maximize(self):
-        glfw.maximize_window(self._glfw_window)
-
-    def set_position(self, x, y):
-        glfw.set_window_pos(self._glfw_window, x, y + self.title_bar_height)
-
-    def center(self):
-        self.set_position((self.monitor_width - self.window_width) // 2, (self.monitor_height - self.window_height) // 2)
-
-    def set_vsync(self, vsync):
-        vsync = bool(vsync)
-        if vsync != self._vsync:
-            glfw.swap_interval(1 if vsync else 0)
-            self._vsync = vsync
-
-    def set_fps_limit(self, fps_limit):
-        self._fps_limit = int(fps_limit)
-
-    def should_close(self):
-        return glfw.window_should_close(self._glfw_window) or (self._close_on_esc and self._esc_pressed)
-
-    def skip_frame(self):
-        self.skip_frames(1)
-
-    def skip_frames(self, num): # Do not update window for the next N frames.
-        self._skip_frames = max(self._skip_frames, int(num))
-
-    def is_skipping_frames(self):
-        return self._skip_frames > 0
-
-    def capture_next_frame(self):
-        self._capture_next_frame = True
-
-    def pop_captured_frame(self):
-        frame = self._captured_frame
-        self._captured_frame = None
-        return frame
-
-    def pop_drag_and_drop_paths(self):
-        paths = self._drag_and_drop_paths
-        self._drag_and_drop_paths = None
-        return paths
-
-    def draw_frame(self): # To be overridden by subclass.
-        self.begin_frame()
-        # Rendering code goes here.
-        self.end_frame()
-
-    def make_context_current(self):
-        if self._glfw_window is not None:
-            glfw.make_context_current(self._glfw_window)
-
-    def begin_frame(self):
-        # End previous frame.
-        if self._drawing_frame:
-            self.end_frame()
-
-        # Apply FPS limit.
-        if self._frame_start_time is not None and self._fps_limit is not None:
-            delay = self._frame_start_time - time.perf_counter() + 1 / self._fps_limit
-            if delay > 0:
-                time.sleep(delay)
-        cur_time = time.perf_counter()
-        if self._frame_start_time is not None:
-            self._frame_delta = cur_time - self._frame_start_time
-        self._frame_start_time = cur_time
-
-        # Process events.
-        glfw.poll_events()
-
-        # Begin frame.
-        self._drawing_frame = True
-        self.make_context_current()
-
-        # Initialize GL state.
-        gl.glViewport(0, 0, self.content_width, self.content_height)
-        gl.glMatrixMode(gl.GL_PROJECTION)
-        gl.glLoadIdentity()
-        gl.glTranslate(-1, 1, 0)
-        gl.glScale(2 / max(self.content_width, 1), -2 / max(self.content_height, 1), 1)
-        gl.glMatrixMode(gl.GL_MODELVIEW)
-        gl.glLoadIdentity()
-        gl.glEnable(gl.GL_BLEND)
-        gl.glBlendFunc(gl.GL_ONE, gl.GL_ONE_MINUS_SRC_ALPHA) # Pre-multiplied alpha.
-
-        # Clear.
-        gl.glClearColor(0, 0, 0, 1)
-        gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)
-
-    def end_frame(self):
-        assert self._drawing_frame
-        self._drawing_frame = False
-
-        # Skip frames if requested.
-        if self._skip_frames > 0:
-            self._skip_frames -= 1
-            return
-
-        # Capture frame if requested.
-        if self._capture_next_frame:
-            self._captured_frame = gl_utils.read_pixels(self.content_width, self.content_height)
-            self._capture_next_frame = False
-
-        # Update window.
-        if self._deferred_show:
-            glfw.show_window(self._glfw_window)
-            self._deferred_show = False
-        glfw.swap_buffers(self._glfw_window)
-
-    def _attach_glfw_callbacks(self):
-        glfw.set_key_callback(self._glfw_window, self._glfw_key_callback)
-        glfw.set_drop_callback(self._glfw_window, self._glfw_drop_callback)
-
-    def _glfw_key_callback(self, _window, key, _scancode, action, _mods):
-        if action == glfw.PRESS and key == glfw.KEY_ESCAPE:
-            self._esc_pressed = True
-
-    def _glfw_drop_callback(self, _window, paths):
-        self._drag_and_drop_paths = paths
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/utils/utils_logging.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/utils/utils_logging.py
deleted file mode 100644
index c787b6aae7cd037a4718df44d672b8ffa9e5c249..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/utils/utils_logging.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import logging
-import os
-import sys
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value
-    """
-
-    def __init__(self):
-        self.val = None
-        self.avg = None
-        self.sum = None
-        self.count = None
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def init_logging(rank, models_root):
-    if rank == 0:
-        log_root = logging.getLogger()
-        log_root.setLevel(logging.INFO)
-        formatter = logging.Formatter("Training: %(asctime)s-%(message)s")
-        handler_file = logging.FileHandler(os.path.join(models_root, "training.log"))
-        handler_stream = logging.StreamHandler(sys.stdout)
-        handler_file.setFormatter(formatter)
-        handler_stream.setFormatter(formatter)
-        log_root.addHandler(handler_file)
-        log_root.addHandler(handler_stream)
-        log_root.info('rank_id: %d' % rank)
diff --git a/spaces/h2oai/wave-university/README.md b/spaces/h2oai/wave-university/README.md
deleted file mode 100644
index a866fbbdcb81e9a10138eb8a63cb48ca295dc0c3..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-university/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Wave University
-emoji: 🐨
-colorFrom: gray
-colorTo: gray
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hackathon-pln-es/Paraphrase-Bertin/app.py b/spaces/hackathon-pln-es/Paraphrase-Bertin/app.py
deleted file mode 100644
index 5455ea82e7ba46d016442a39e465f440fddbf064..0000000000000000000000000000000000000000
--- a/spaces/hackathon-pln-es/Paraphrase-Bertin/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-
-from sentence_transformers.util import cos_sim
-from sentence_transformers import SentenceTransformer
-from bokeh.plotting import figure, output_notebook, show, save
-from bokeh.io import output_file, show
-from bokeh.models import ColumnDataSource, HoverTool
-from sklearn.manifold import TSNE
-
-
-@st.cache
-def load_model():
-  model = SentenceTransformer('hackathon-pln-es/paraphrase-spanish-distilroberta')
-  model.eval()
-  return model
-    
-st.title("Sentence Embedding for Spanish with Bertin")
-st.write("Sentence embedding for spanish trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder ([paraphrase-mpnet-base-v2](https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models)) as a teacher model, and the pretrained Spanish [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) as the student model. Used for Sentence Textual Similarity. Based on the model hackathon-pln-es/paraphrase-spanish-distilroberta.")
-st.write("Introduce two sentence to see their cosine similarity and a graph showing them in the embedding space.")
-st.write("Authors: Anibal Pérez, Emilio Tomás Ariza, Lautaro Gesuelli Pinto y Mauricio Mazuecos.")
-
-sent1 = st.text_area('Enter sentence 1')
-sent2 = st.text_area('Enter sentence 2')
-
-if st.button('Compute similarity'):
-  if sent1 and sent2:
-    model = load_model()
-    encodings = model.encode([sent1, sent2])
-    sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0]
-    st.text('Cosine Similarity: {0:.4f}'.format(sim))
-  else:
-      st.write('Missing a sentences')
-else:
-  pass
-
-
diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.py b/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.py
deleted file mode 100644
index 490a43eaed3dc77bb31c79e64c64de388ca915d0..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,209 +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 bias and activation."""
-
-import os
-import numpy as np
-import torch
-import models.dnnlib as dnnlib
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
-    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
-    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
-    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
-    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
-    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
-    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
-    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
-    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
-    global _plugin
-    if _plugin is None:
-        _plugin = custom_ops.get_plugin(
-            module_name='bias_act_plugin',
-            sources=['bias_act.cpp', 'bias_act.cu'],
-            headers=['bias_act.h'],
-            source_dir=os.path.dirname(__file__),
-            extra_cuda_cflags=['--use_fast_math'],
-        )
-    return True
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
-    r"""Fused bias and activation function.
-
-    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
-    and scales the result by `gain`. Each of the steps is optional. In most cases,
-    the fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can be of any shape.
-        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                as `x`. The shape must be known, and it must match the dimension of `x`
-                corresponding to `dim`.
-        dim:    The dimension in `x` corresponding to the elements of `b`.
-                The value of `dim` is ignored if `b` is not specified.
-        act:    Name of the activation function to evaluate, or `"linear"` to disable.
-                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
-                See `activation_funcs` for a full list. `None` is not allowed.
-        alpha:  Shape parameter for the activation function, or `None` to use the default.
-        gain:   Scaling factor for the output tensor, or `None` to use default.
-                See `activation_funcs` for the default scaling of each activation function.
-                If unsure, consider specifying 1.
-        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
-                the clamping (default).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
-    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Add bias.
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.ndim == 1
-        assert 0 <= dim < x.ndim
-        assert b.shape[0] == x.shape[dim]
-        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
-    # Evaluate activation function.
-    alpha = float(alpha)
-    x = spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    gain = float(gain)
-    if gain != 1:
-        x = x * gain
-
-    # Clamp.
-    if clamp >= 0:
-        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
-    return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Fast CUDA implementation of `bias_act()` using custom ops.
-    """
-    # Parse arguments.
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Lookup from cache.
-    key = (dim, act, alpha, gain, clamp)
-    if key in _bias_act_cuda_cache:
-        return _bias_act_cuda_cache[key]
-
-    # Forward op.
-    class BiasActCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, b): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format
-            x = x.contiguous(memory_format=ctx.memory_format)
-            b = b.contiguous() if b is not None else _null_tensor
-            y = x
-            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
-                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                y if 'y' in spec.ref else _null_tensor)
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            dy = dy.contiguous(memory_format=ctx.memory_format)
-            x, b, y = ctx.saved_tensors
-            dx = None
-            db = None
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                dx = dy
-                if act != 'linear' or gain != 1 or clamp >= 0:
-                    dx = BiasActCudaGrad.apply(dy, x, b, y)
-
-            if ctx.needs_input_grad[1]:
-                db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
-            return dx, db
-
-    # Backward op.
-    class BiasActCudaGrad(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format
-            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                dy if spec.has_2nd_grad else _null_tensor,
-                x, b, y)
-            return dx
-
-        @staticmethod
-        def backward(ctx, d_dx): # pylint: disable=arguments-differ
-            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
-            dy, x, b, y = ctx.saved_tensors
-            d_dy = None
-            d_x = None
-            d_b = None
-            d_y = None
-
-            if ctx.needs_input_grad[0]:
-                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
-            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
-                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
-            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
-                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
-            return d_dy, d_x, d_b, d_y
-
-    # Add to cache.
-    _bias_act_cuda_cache[key] = BiasActCuda
-    return BiasActCuda
-
-#----------------------------------------------------------------------------
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py
deleted file mode 100644
index 9e51ac148e11d92188e80510c33eab96ff83d03f..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
-from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
-from maskrcnn_benchmark.modeling.box_coder import BoxCoder
-from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd
-
-class PostProcessor(nn.Module):
-    """
-    From a set of classification scores, box regression and proposals,
-    computes the post-processed boxes, and applies NMS to obtain the
-    final results
-    """
-
-    def __init__(
-        self, score_thresh=0.05, nms=0.5, detections_per_img=100, box_coder=None
-    ):
-        """
-        Arguments:
-            score_thresh (float)
-            nms (float)
-            detections_per_img (int)
-            box_coder (BoxCoder)
-        """
-        super(PostProcessor, self).__init__()
-        self.score_thresh = score_thresh
-        self.nms = nms
-        self.detections_per_img = detections_per_img
-        if box_coder is None:
-            box_coder = BoxCoder(weights=(10., 10., 5., 5.))
-        self.box_coder = box_coder
-
-    @custom_fwd(cast_inputs=torch.float32)
-    def forward(self, x, boxes):
-        """
-        Arguments:
-            x (tuple[tensor, tensor]): x contains the class logits
-                and the box_regression from the model.
-            boxes (list[BoxList]): bounding boxes that are used as
-                reference, one for ech image
-
-        Returns:
-            results (list[BoxList]): one BoxList for each image, containing
-                the extra fields labels and scores
-        """
-        class_logits, box_regression = x
-        class_prob = F.softmax(class_logits, -1)
-
-        # TODO think about a representation of batch of boxes
-        image_shapes = [box.size for box in boxes]
-        boxes_per_image = [len(box) for box in boxes]
-        concat_boxes = torch.cat([a.bbox for a in boxes], dim=0)
-
-        extra_fields = [{} for box in boxes]
-        if boxes[0].has_field("cbox"):
-            concat_cboxes = torch.cat([a.get_field('cbox').bbox for a in boxes], dim=0)
-            concat_cscores = torch.cat([a.get_field('cbox').get_field('scores') for a in boxes], dim=0)
-            for cbox, cscore, extra_field in zip(concat_cboxes.split(boxes_per_image, dim=0),
-                                                 concat_cscores.split(boxes_per_image, dim=0),
-                                                 extra_fields):
-                extra_field["cbox"] = cbox
-                extra_field["cscore"] = cscore
-
-        proposals = self.box_coder.decode(
-            box_regression.view(sum(boxes_per_image), -1), concat_boxes
-        )
-
-        num_classes = class_prob.shape[1]
-
-        proposals = proposals.split(boxes_per_image, dim=0)
-        class_prob = class_prob.split(boxes_per_image, dim=0)
-
-        results = []
-        for prob, boxes_per_img, image_shape, extra_field in zip(
-            class_prob, proposals, image_shapes, extra_fields
-        ):
-            boxlist = self.prepare_boxlist(boxes_per_img, prob, image_shape, extra_field)
-            boxlist = boxlist.clip_to_image(remove_empty=False)
-            boxlist = self.filter_results(boxlist, num_classes)
-            results.append(boxlist)
-        return results
-
-    def prepare_boxlist(self, boxes, scores, image_shape, extra_field={}):
-        """
-        Returns BoxList from `boxes` and adds probability scores information
-        as an extra field
-        `boxes` has shape (#detections, 4 * #classes), where each row represents
-        a list of predicted bounding boxes for each of the object classes in the
-        dataset (including the background class). The detections in each row
-        originate from the same object proposal.
-        `scores` has shape (#detection, #classes), where each row represents a list
-        of object detection confidence scores for each of the object classes in the
-        dataset (including the background class). `scores[i, j]`` corresponds to the
-        box at `boxes[i, j * 4:(j + 1) * 4]`.
-        """
-        boxes = boxes.reshape(-1, 4)
-        scores = scores.reshape(-1)
-        boxlist = BoxList(boxes, image_shape, mode="xyxy")
-        boxlist.add_field("scores", scores)
-        for key, val in extra_field.items():
-            boxlist.add_field(key, val)
-        return boxlist
-
-    def filter_results(self, boxlist, num_classes):
-        """Returns bounding-box detection results by thresholding on scores and
-        applying non-maximum suppression (NMS).
-        """
-        # unwrap the boxlist to avoid additional overhead.
-        # if we had multi-class NMS, we could perform this directly on the boxlist
-        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
-        scores = boxlist.get_field("scores").reshape(-1, num_classes)
-        if boxlist.has_field('cbox'):
-            cboxes = boxlist.get_field("cbox").reshape(-1, 4)
-            cscores = boxlist.get_field("cscore")
-        else:
-            cboxes = None
-
-        device = scores.device
-        result = []
-        # Apply threshold on detection probabilities and apply NMS
-        # Skip j = 0, because it's the background class
-        inds_all = scores > self.score_thresh
-        for j in range(1, num_classes):
-            inds = inds_all[:, j].nonzero().squeeze(1)
-            scores_j = scores[inds, j]
-            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
-            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
-            boxlist_for_class.add_field("scores", scores_j)
-            if cboxes is not None:
-                cboxes_j = cboxes[inds, :]
-                cscores_j = cscores[inds]
-                cbox_boxlist = BoxList(cboxes_j, boxlist.size, mode="xyxy")
-                cbox_boxlist.add_field("scores", cscores_j)
-                boxlist_for_class.add_field("cbox", cbox_boxlist)
-
-            boxlist_for_class = boxlist_nms(
-                boxlist_for_class, self.nms, score_field="scores"
-            )
-            num_labels = len(boxlist_for_class)
-            boxlist_for_class.add_field(
-                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
-            )
-            result.append(boxlist_for_class)
-
-        result = cat_boxlist(result)
-        number_of_detections = len(result)
-
-        # Limit to max_per_image detections **over all classes**
-        if number_of_detections > self.detections_per_img > 0:
-            cls_scores = result.get_field("scores")
-            image_thresh, _ = torch.kthvalue(
-                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
-            )
-            keep = cls_scores >= image_thresh.item()
-            keep = torch.nonzero(keep).squeeze(1)
-            result = result[keep]
-        return result
-
-
-def make_roi_box_post_processor(cfg):
-    use_fpn = cfg.MODEL.ROI_HEADS.USE_FPN
-
-    bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
-    box_coder = BoxCoder(weights=bbox_reg_weights)
-
-    score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH
-    nms_thresh = cfg.MODEL.ROI_HEADS.NMS
-    detections_per_img = cfg.MODEL.ROI_HEADS.DETECTIONS_PER_IMG
-
-    postprocessor = PostProcessor(
-        score_thresh, nms_thresh, detections_per_img, box_coder
-    )
-    return postprocessor
diff --git a/spaces/hardon-server/img2txt1/README.md b/spaces/hardon-server/img2txt1/README.md
deleted file mode 100644
index 27c892aea42994cf6742739be495616e281a6897..0000000000000000000000000000000000000000
--- a/spaces/hardon-server/img2txt1/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Taiyi BLIP
-emoji: 📚
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: IDEA-CCNL/Taiyi-BLIP
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/herberthe/nitrosocke-Ghibli-Diffusion/README.md b/spaces/herberthe/nitrosocke-Ghibli-Diffusion/README.md
deleted file mode 100644
index ff9c3e97528bebf770b765ecd2cb983c4a25e477..0000000000000000000000000000000000000000
--- a/spaces/herberthe/nitrosocke-Ghibli-Diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Nitrosocke Ghibli Diffusion
-emoji: 🔥
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huaiji3y/bingo-Public/src/components/turn-counter.tsx b/spaces/huaiji3y/bingo-Public/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    <div className="turn-counter">
-      <div className="text">
-        <span>{throttling.numUserMessagesInConversation}</span>
-        <span> 共 </span>
-        <span>{throttling.maxNumUserMessagesInConversation}</span>
-      </div>
-      <div className="indicator"></div>
-    </div>
-  )
-}
diff --git a/spaces/hwchase17/chat-langchain/README.md b/spaces/hwchase17/chat-langchain/README.md
deleted file mode 100644
index c055d951096b1669150385f9d705c897fae6fa39..0000000000000000000000000000000000000000
--- a/spaces/hwchase17/chat-langchain/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chat Langchain
-emoji: 🦀
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hysts/DeepDanbooru/app.py b/spaces/hysts/DeepDanbooru/app.py
deleted file mode 100644
index 224c79c350c722ba9e9e3ec2bd9fa212cad0b6ca..0000000000000000000000000000000000000000
--- a/spaces/hysts/DeepDanbooru/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-
-DESCRIPTION = '# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)'
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
-    if not image_dir.exists():
-        path = huggingface_hub.hf_hub_download(
-            'public-data/sample-images-TADNE',
-            'images.tar.gz',
-            repo_type='dataset')
-        with tarfile.open(path) as f:
-            f.extractall()
-    return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'model-resnet_custom_v3.h5')
-    model = tf.keras.models.load_model(path)
-    return model
-
-
-def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'tags.txt')
-    with open(path) as f:
-        labels = [line.strip() for line in f.readlines()]
-    return labels
-
-
-model = load_model()
-labels = load_labels()
-
-
-def predict(
-        image: PIL.Image.Image, score_threshold: float
-) -> tuple[dict[str, float], dict[str, float], str]:
-    _, height, width, _ = model.input_shape
-    image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-
-    indices = np.argsort(probs)[::-1]
-    result_all = dict()
-    result_threshold = dict()
-    for index in indices:
-        label = labels[index]
-        prob = probs[index]
-        result_all[label] = prob
-        if prob < score_threshold:
-            break
-        result_threshold[label] = prob
-    result_text = ', '.join(result_all.keys())
-    return result_threshold, result_all, result_text
-
-
-image_paths = load_sample_image_paths()
-examples = [[path.as_posix(), 0.5] for path in image_paths]
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label='Input', type='pil')
-            score_threshold = gr.Slider(label='Score threshold',
-                                        minimum=0,
-                                        maximum=1,
-                                        step=0.05,
-                                        value=0.5)
-            run_button = gr.Button('Run')
-        with gr.Column():
-            with gr.Tabs():
-                with gr.Tab(label='Output'):
-                    result = gr.Label(label='Output', show_label=False)
-                with gr.Tab(label='JSON'):
-                    result_json = gr.JSON(label='JSON output',
-                                          show_label=False)
-                with gr.Tab(label='Text'):
-                    result_text = gr.Text(label='Text output',
-                                          show_label=False,
-                                          lines=5)
-    gr.Examples(examples=examples,
-                inputs=[image, score_threshold],
-                outputs=[result, result_json, result_text],
-                fn=predict,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-
-    run_button.click(
-        fn=predict,
-        inputs=[image, score_threshold],
-        outputs=[result, result_json, result_text],
-        api_name='predict',
-    )
-demo.queue().launch()
diff --git a/spaces/hysts/Shap-E/app_image_to_3d.py b/spaces/hysts/Shap-E/app_image_to_3d.py
deleted file mode 100644
index 8b103e315b077e49c0ae89c8193255bbc482bee2..0000000000000000000000000000000000000000
--- a/spaces/hysts/Shap-E/app_image_to_3d.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-
-import pathlib
-import shlex
-import subprocess
-
-import gradio as gr
-import PIL.Image
-import spaces
-
-from model import Model
-from settings import CACHE_EXAMPLES, MAX_SEED
-from utils import randomize_seed_fn
-
-
-def create_demo(model: Model) -> gr.Blocks:
-    if not pathlib.Path("corgi.png").exists():
-        subprocess.run(
-            shlex.split(
-                "wget https://raw.githubusercontent.com/openai/shap-e/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/examples/example_data/corgi.png -O corgi.png"
-            )
-        )
-    examples = ["corgi.png"]
-
-    @spaces.GPU
-    def process_example_fn(image_path: str) -> str:
-        return model.run_image(image_path)
-
-    @spaces.GPU
-    def run(image: PIL.Image.Image, seed: int, guidance_scale: float, num_inference_steps: int) -> str:
-        return model.run_image(image, seed, guidance_scale, num_inference_steps)
-
-    with gr.Blocks() as demo:
-        with gr.Group():
-            image = gr.Image(label="Input image", show_label=False, type="pil")
-            run_button = gr.Button("Run")
-            result = gr.Model3D(label="Result", show_label=False)
-            with gr.Accordion("Advanced options", open=False):
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=1,
-                    maximum=20,
-                    step=0.1,
-                    value=3.0,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=2,
-                    maximum=100,
-                    step=1,
-                    value=64,
-                )
-
-        gr.Examples(
-            examples=examples,
-            inputs=image,
-            outputs=result,
-            fn=process_example_fn,
-            cache_examples=CACHE_EXAMPLES,
-        )
-
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            api_name=False,
-            concurrency_limit=None,
-        ).then(
-            fn=run,
-            inputs=[
-                image,
-                seed,
-                guidance_scale,
-                num_inference_steps,
-            ],
-            outputs=result,
-            api_name="image-to-3d",
-            concurrency_id="gpu",
-            concurrency_limit=1,
-        )
-    return demo
diff --git a/spaces/imseldrith/BotX/Uploader/utitles.py b/spaces/imseldrith/BotX/Uploader/utitles.py
deleted file mode 100644
index 7df8dc8c08890bf405ddb9a859aff0c321929a77..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/BotX/Uploader/utitles.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Hash Minner
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE
-
-import logging
-
-from hachoir.parser import createParser
-from hachoir.metadata import extractMetadata
-
-
-logging.basicConfig(level=logging.DEBUG,
-                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-logging.getLogger("pyrogram").setLevel(logging.WARNING)
-
-
-async def Mdata01(download_directory):
-    width = 0
-    height = 0
-    duration = 0
-    metadata = extractMetadata(createParser(download_directory))
-    if metadata is not None:
-        if metadata.has("duration"):
-            duration = metadata.get('duration').seconds
-        if metadata.has("width"):
-            width = metadata.get("width")
-        if metadata.has("height"):
-            height = metadata.get("height")
-
-    return width, height, duration
-
-
-async def Mdata02(download_directory):
-    width = 0
-    duration = 0
-    metadata = extractMetadata(createParser(download_directory))
-    if metadata is not None:
-        if metadata.has("duration"):
-            duration = metadata.get('duration').seconds
-        if metadata.has("width"):
-            width = metadata.get("width")
-
-    return width, duration
-
-
-async def Mdata03(download_directory):
-    metadata = extractMetadata(createParser(download_directory))
-    return (
-        metadata.get('duration').seconds
-        if metadata is not None and metadata.has("duration")
-        else 0
-    )
diff --git a/spaces/inamXcontru/PoeticTTS/Applied Acoustics Ultra Analog VA-2 v2.0.3 WIN OSX Incl. Keygen download The ultimate virtual analog synthesizer.md b/spaces/inamXcontru/PoeticTTS/Applied Acoustics Ultra Analog VA-2 v2.0.3 WIN OSX Incl. Keygen download The ultimate virtual analog synthesizer.md
deleted file mode 100644
index 4e7e1be57150dfbae03227f667334a9e08673677..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Applied Acoustics Ultra Analog VA-2 v2.0.3 WIN OSX Incl. Keygen download The ultimate virtual analog synthesizer.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Applied Acoustics Ultra Analog VA-2 v2.0.3 WIN OSX Incl. Keygen download</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250; <a href="https://gohhs.com/2uz4Ve">https://gohhs.com/2uz4Ve</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/innat/UniFormerV2/utils.py b/spaces/innat/UniFormerV2/utils.py
deleted file mode 100644
index 99586930edb5cc314bee1c09b288dda29722c5a2..0000000000000000000000000000000000000000
--- a/spaces/innat/UniFormerV2/utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from einops import rearrange
-from decord import VideoReader
-
-num_frames = 16
-input_size = 224
-patch_size = (16, 16)
-IMAGENET_MEAN = np.array([0.485, 0.456, 0.406])
-IMAGENET_STD = np.array([0.229, 0.224, 0.225])
-
-def format_frames(frame, output_size):
-    frame = tf.image.convert_image_dtype(frame, tf.uint8)
-    frame = tf.image.resize(frame, size=output_size)
-    frame = frame / 255.
-    frame = frame - IMAGENET_MEAN
-    frame = frame / IMAGENET_STD
-    return frame
-
-def read_video(file_path):
-    container = VideoReader(file_path)
-    return container
-
-def frame_sampling(container, num_frames):
-    interval = len(container) // num_frames
-    bids = np.arange(num_frames) * interval
-    offset = np.random.randint(interval, size=bids.shape)
-    frame_index = bids + offset
-    frames = container.get_batch(frame_index).asnumpy()
-    frames = np.stack(frames)
-    frames = format_frames(frames, [input_size] * 2)
-    return frames
-
-def denormalize(z):
-    mean = np.array([0.485, 0.456, 0.406])
-    std = np.array([0.225, 0.225, 0.225])
-    x = (z * std) + mean
-    x = x * 255
-    return x.clip(0, 255)
\ No newline at end of file
diff --git a/spaces/innnky/soft-vits-singingvc/monotonic_align/setup.py b/spaces/innnky/soft-vits-singingvc/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/innnky/soft-vits-singingvc/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Ableton Live Suite 10.0.1 MacOS BEST Keygen - [CrackzSoft] Download Pc.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Ableton Live Suite 10.0.1 MacOS BEST Keygen - [CrackzSoft] Download Pc.md
deleted file mode 100644
index 11e269a3a506063ae56c0c323f3961a8a9828fd6..0000000000000000000000000000000000000000
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index a69e8f4bdb03807ffad88e0c6bfd023b9df3607a..0000000000000000000000000000000000000000
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index 1c1f3cea70b10a3b0d50e50c388ca98c5320fa1e..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Free Download AutoCAD Architecture 2017 Crack Keygen TOP.md	
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-<br />
-<h1>Free Download AutoCAD Architecture 2017 Crack Keygen</h1>
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-<h2>What is AutoCAD Architecture 2017?</h2>
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-<h2>How to Free Download AutoCAD Architecture 2017 Crack Keygen?</h2>
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-<ol>
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-<h2>What are the features of AutoCAD Architecture 2017?</h2>
-<p>AutoCAD Architecture 2017 has many features that make it a powerful and versatile software for architectural design and documentation. Some of these features are:</p>
-<ul>
-<li>Architectural renovation: You can easily create and modify renovation plans with tools that help you manage the display of existing, demolished, and new construction.</li>
-<li>Walls, doors, and windows: You can create and edit walls, doors, and windows with intelligent styles that automatically update according to the project standards.</li>
-<li>Sections and elevations: You can generate 2D sections and elevations from 3D models with automatic annotations and dimensions.</li>
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-<li>Room documentation: You can create and tag rooms with accurate area calculations and schedules.</li>
-<li>Style browser: You can browse and apply styles from a library of predefined objects or from your own custom objects.</li>
-<li>Data extraction: You can extract data from objects and export them to tables or external files for analysis and reporting.</li>
-<li>Collaboration: You can work with other professionals using tools that support DWG files, PDF files, IFC files, BIM 360, and more.</li>
-</ul>
-<h2>Why should you use AutoCAD Architecture 2017?</h2>
-<p>AutoCAD Architecture 2017 is a software that can help you create and document architectural projects faster and more efficiently. It is a specialized version of AutoCAD that includes tools and features for architectural design, such as walls, doors, windows, roofs, stairs, railings, and more. AutoCAD Architecture 2017 also supports BIM (Building Information Modeling) workflows, allowing you to create and manage building data throughout the project lifecycle. By using AutoCAD Architecture 2017, you can benefit from:</p>
-<ul>
-<li>Increased productivity: You can save time and reduce errors by using intelligent objects that automatically update according to the project standards.</li>
-<li>Improved quality: You can enhance the accuracy and consistency of your drawings by using automatic annotations and dimensions.</li>
-<li>Better communication: You can share your designs and data with other professionals using tools that support various file formats and platforms.</li>
-<li>More creativity: You can explore different design options and visualize your ideas in 3D with realistic rendering and animation.</li>
-</ul>
-<h2>How to buy AutoCAD Architecture 2017?</h2>
-<p>If you want to use AutoCAD Architecture 2017 legally and safely, you should buy a license from Autodesk or its authorized resellers. You can choose from different subscription plans that suit your needs and budget. You can also get access to AutoCAD Architecture 2017 as part of the Autodesk Architecture Engineering & Construction Collection or the Autodesk Product Design & Manufacturing Collection. To buy AutoCAD Architecture 2017 or learn more about its pricing and features, you can visit the official website of Autodesk or contact its sales team.</p>
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-<p>We have also explained what is AutoCAD Architecture 2017, what are its features, why should you use it, and how to buy it. AutoCAD Architecture 2017 is a software that helps architects and designers create and document architectural projects faster and more efficiently. It is a specialized version of AutoCAD that includes tools and features for architectural design, such as walls, doors, windows, roofs, stairs, railings, and more. AutoCAD Architecture 2017 also supports BIM (Building Information Modeling) workflows, allowing you to create and manage building data throughout the project lifecycle.</p>
-<p></p>
-<p>We hope this article has been useful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy designing!</p> 3cee63e6c2<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/Cytomic The Glue VST RTAS 1.1.2 X86  Updated ((NEW)).md b/spaces/inreVtussa/clothingai/Examples/Cytomic The Glue VST RTAS 1.1.2 X86  Updated ((NEW)).md
deleted file mode 100644
index 92eb74eb4d30eed17ffa877b204adfeb8ee3c08b..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Cytomic The Glue VST RTAS 1.1.2 X86  Updated ((NEW)).md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>Cytomic The Glue VST RTAS 1.1.2 x86 | updated: A Review</h1>
-<p>If you are looking for a high-quality compressor plugin that emulates the classic SSL 4000 buss compressor, then you might want to check out Cytomic The Glue VST RTAS 1.1.2 x86 | updated. This plugin is developed by Cytomic and is compatible with Windows and Mac platforms. In this article, we will give you an overview of what Cytomic The Glue VST RTAS 1.1.2 x86 | updated is all about, how to download it for free, and what are its main features and benefits.</p>
-
-<h2>What is Cytomic The Glue VST RTAS 1.1.2 x86 | updated?</h2>
-<p>Cytomic The Glue VST RTAS 1.1.2 x86 | updated is a compressor plugin that models the legendary SSL 4000 buss compressor, which is widely used in professional studios for mixing and mastering. The plugin aims to recreate the sound and behavior of the original hardware, while adding some modern features and enhancements.</p>
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-
-<h2>How to Download Cytomic The Glue VST RTAS 1.1.2 x86 | updated for Free?</h2>
-<p>Cytomic The Glue VST RTAS 1.1.2 x86 | updated is a paid plugin that you can purchase from Cytomic's website or other online stores. However, if you want to try it out for free, you can download it from some websites that offer cracked versions of the plugin. These websites usually provide direct links or torrents that you can use to download the plugin files. However, be aware that downloading cracked plugins may expose your PC to viruses or malware, and may also violate the plugin's terms of service. Therefore, we recommend that you buy the plugin from official sources if you enjoy it and want to support the developer.</p>
-
-<h2>What are the Features and Benefits of Cytomic The Glue VST RTAS 1.1.2 x86 | updated?</h2>
-<p>Cytomic The Glue VST RTAS 1.1.2 x86 | updated is a powerful and versatile compressor plugin that offers many features and benefits for producers and engineers. Some of them are:</p>
-<ul>
-<li><b>Accurate emulation of the SSL 4000 buss compressor:</b> The plugin faithfully reproduces the sound and response of the original hardware, giving your tracks a punchy, warm, and gluey compression.</li>
-<li><b>Range knob:</b> The plugin adds a range knob that allows you to limit the maximum amount of compression applied to your signal, giving you more control over the dynamics.</li>
-<li><b>Sidechain filter:</b> The plugin adds a sidechain filter that lets you adjust the frequency range of the signal that triggers the compression, allowing you to fine-tune the compression effect.</li>
-<li><b>Oversampling:</b> The plugin offers up to 4x oversampling, which reduces aliasing and improves the sound quality.</li>
-<li><b>Dry/Wet knob:</b> The plugin offers a dry/wet knob that lets you blend the compressed and uncompressed signals, enabling you to achieve parallel compression easily.</li>
-<li><b>MIDI learn:</b> The plugin supports MIDI learn, which allows you to assign any parameter to any MIDI controller, giving you more flexibility and convenience.</li>
-</ul>
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-<h2>Conclusion</h2>
-<p>Cytomic The Glue VST RTAS 1.1.2 x86 | updated is a compressor plugin that emulates the classic SSL 4000 buss compressor, which is widely used in professional studios for mixing and mastering. The plugin recreates the sound and behavior of the original hardware, while adding some modern features and enhancements. It is a plugin that will enhance your tracks with a punchy, warm, and gluey compression. If you want to download Cytomic The Glue VST RTAS 1.1.2 x86 | updated for free, you can do so from some websites that offer cracked versions of the plugin. However, we advise you to buy the plugin from official sources if you like it and want to support the developer.</p> 3cee63e6c2<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/DisplayFusion Pro 9.6 Free Download Portable.md b/spaces/inreVtussa/clothingai/Examples/DisplayFusion Pro 9.6 Free Download Portable.md
deleted file mode 100644
index ccfafe26bad54b313d73a0394cbbc80179cfcce1..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/DisplayFusion Pro 9.6 Free Download Portable.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-<h1>How to Download DisplayFusion Pro 9.6 Portable for Free</h1>
-<p>DisplayFusion Pro 9.6 is a powerful software that makes your multi-monitor life easier. It allows you to customize your taskbars, titlebar buttons, hotkeys, wallpapers, and more for each monitor. You can also create and load monitor profiles, manage your windows, and use advanced functions like triggers, functions, and scripting.</p>
-<h2>DisplayFusion Pro 9.6 Free Download Portable</h2><br /><p><b><b>Download Zip</b> &#10037; <a href="https://tiurll.com/2uCiSn">https://tiurll.com/2uCiSn</a></b></p><br /><br />
-<p>If you want to try DisplayFusion Pro 9.6 for free, you can download it from the official website[^1^]. However, this version requires installation and activation with a license key. If you want to use DisplayFusion Pro 9.6 without installing it or activating it, you can download the portable version from packzoid.com[^3^]. This version is pre-activated and can run from any folder or USB drive.</p>
-<p>To download DisplayFusion Pro 9.6 portable for free, follow these steps:</p>
-<ol>
-<li>Go to packzoid.com[^3^] and search for DisplayFusion Pro 9.6.</li>
-<li>Click on the product page and add it to your cart.</li>
-<li>Proceed to checkout and enter your email address and payment details. You can use a fake email and a fake credit card number if you want.</li>
-<li>After completing the order, you will receive an email with a download link.</li>
-<li>Click on the download link and save the zip file to your desired location.</li>
-<li>Extract the zip file and run DisplayFusion.exe to launch the software.</li>
-</ol>
-<p>You can now enjoy DisplayFusion Pro 9.6 portable for free on any computer. You can also copy the folder to a USB drive and use it on any other computer without installing it.</p>
-<p>Note: This method is for educational purposes only. We do not condone piracy or illegal use of software. If you like DisplayFusion Pro 9.6, please support the developers by buying a license key from their website[^1^].</p>
-<p></p>
-
-<p>DisplayFusion Pro 9.6 portable has many features that can enhance your productivity and user experience. Here are some of the features that you can use:</p>
-<ul>
-<li>Multi-Monitor Taskbars: You can customize the appearance and behavior of your taskbars for each monitor. You can choose the position, size, color, icons, and buttons of your taskbars. You can also enable or disable features like auto-hide, clock, start button, and notification area.</li>
-<li>TitleBar Buttons: You can add extra buttons to the titlebar of any window. These buttons can perform actions like moving, resizing, snapping, maximizing, minimizing, closing, or pinning the window. You can also assign hotkeys to these buttons for quick access.</li>
-<li>HotKeys: You can create and manage custom hotkeys for any function or action in DisplayFusion Pro 9.6 portable. You can use hotkeys to launch applications, switch monitors, move windows, change wallpapers, run scripts, and more.</li>
-<li>Wallpaper: You can set different wallpapers for each monitor or span a single wallpaper across all monitors. You can also use online sources like Flickr, Unsplash, Bing, or Reddit to download and apply wallpapers automatically.</li>
-<li>Monitor Profiles: You can create and load monitor profiles that store your monitor configuration and settings. You can use monitor profiles to quickly switch between different monitor layouts and resolutions.</li>
-</ul>
-<p>DisplayFusion Pro 9.6 portable is a must-have software for anyone who uses multiple monitors. It can help you manage your monitors and windows with ease and efficiency. You can download DisplayFusion Pro 9.6 portable for free from packzoid.com and enjoy its features without installation or activation.</p> d5da3c52bf<br />
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diff --git a/spaces/ismot/1702t1/utils/misc.py b/spaces/ismot/1702t1/utils/misc.py
deleted file mode 100644
index d262d86e2c9a12e22bad7266dba429ce09c9a036..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/utils/misc.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""
-@date: 2021/8/4
-@description:
-"""
-import numpy as np
-import torch
-
-
-def tensor2np(t: torch.Tensor) -> np.array:
-    if isinstance(t, torch.Tensor):
-        if t.device == 'cpu':
-            return t.detach().numpy()
-        else:
-            return t.detach().cpu().numpy()
-    else:
-        return t
-
-
-def tensor2np_d(d: dict) -> dict:
-    output = {}
-    for k in d.keys():
-        output[k] = tensor2np(d[k])
-    return output
diff --git a/spaces/j0hngou/vision-diffmask/code/models/utils.py b/spaces/j0hngou/vision-diffmask/code/models/utils.py
deleted file mode 100644
index bb3dc59bd9fd8ddcd22b198126c62272c8e0bc4a..0000000000000000000000000000000000000000
--- a/spaces/j0hngou/vision-diffmask/code/models/utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from datamodules.utils import get_configs
-from transformers import (
-    ConvNextConfig,
-    ConvNextForImageClassification,
-    PreTrainedModel,
-    ViTConfig,
-    ViTForImageClassification,
-)
-
-import argparse
-import torch
-
-
-def set_clf_head(base: PreTrainedModel, num_classes: int):
-    """Set the classification head of the model in case of an output mismatch.
-
-    Args:
-        base (PreTrainedModel): the model to modify
-        num_classes (int): the number of classes to use for the output layer
-    """
-    if base.classifier.out_features != num_classes:
-        in_features = base.classifier.in_features
-        base.classifier = torch.nn.Linear(in_features, num_classes)
-
-
-def model_factory(
-    args: argparse.Namespace,
-    own_config: bool = False,
-) -> PreTrainedModel:
-    """A factory method for creating a HuggingFace model based on the command line args.
-
-    Args:
-        args (Namespace): the argparse Namespace object
-        own_config (bool): whether to create our own model config instead of a pretrained one;
-            this is recommended when the model was pre-trained on another task with a different
-            amount of classes for its classifier head
-
-    Returns:
-        a PreTrainedModel instance
-    """
-    if args.base_model == "ViT":
-        # Create a new Vision Transformer
-        config_class = ViTConfig
-        base_class = ViTForImageClassification
-    elif args.base_model == "ConvNeXt":
-        # Create a new ConvNext model
-        config_class = ConvNextConfig
-        base_class = ConvNextForImageClassification
-    else:
-        raise Exception(f"Unknown base model: {args.base_model}")
-
-    # Get the model config
-    model_cfg_args, _ = get_configs(args)
-    if not own_config and args.from_pretrained:
-        # Create a model from a pretrained model
-        base = base_class.from_pretrained(args.from_pretrained)
-        # Set the classifier head if needed
-        set_clf_head(base, model_cfg_args["num_labels"])
-    else:
-        # Create a model based on the config
-        config = config_class(**model_cfg_args)
-        base = base_class(config)
-
-    return base
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/games/arizona.ts b/spaces/jbilcke-hf/VideoQuest/src/app/games/arizona.ts
deleted file mode 100644
index 421e0612ce8e502caafeee039070f5af3cae4732..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/games/arizona.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-import { imfell } from "@/lib/fonts"
-import { Game } from "./types"
-import { InventoryItem } from "../../types"
-
-const initialSituation = [
-  `looking at an abandonned mining town street`,
-  `street level view`,
-  `looking at wooden buildings, sheriff sign, horses`,
-  `cactus`
-].join(", ")
-
-const initialActionnables = [
-  "horse",
-  "sign",
-  "bucket",
-  "ground",
-  "rope",
-  "door",
-  "sun",
-  "window",
-  "cactus",
-  "sky"
-]
-
-const inventory: InventoryItem[] = [
-  {
-    name: "pickaxe",
-    title: "Pickaxe",
-    caption: "",
-    description: "A mining pickaxe. For like, mining ore or something."
-  },
-  {
-    name: "matchbox",
-    title: "Matchbox",
-    caption: "",
-    description: "A box of matches, to put some fire in your life."
-  }
-]
-
-export const game: Game = {
-  title: "Arizona",
-  type: "arizona",
-  description: [
-    "The game is a role playing adventure, set in an old mining town in Arizona.",
-    "The player can click around to move to new scenes, find or activate artifacts.",
-    "They can also use objects from their inventory.",
-  ],
-  engines: [
-    "cartesian_image",
-    "cartesian_video",
-    "spherical_image",
-  ],
-  className: imfell.className,
-  initialSituation,
-  initialActionnables,
-  inventory,
-  getScenePrompt: (situation?: string) => [
-    `Screenshot from a videogame`,
-    `unreal engine`,
-    `old mining town`,
-    `in arizona`, 
-    `wild west america`,
-    `during gold rush era`,
-    situation || initialSituation,
-  ]
-}
-
diff --git a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/clip/model.py b/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/clip/model.py
deleted file mode 100644
index 8ea730a2cc8a992f9180428bd1fec7fc96aa89dd..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/clip/model.py
+++ /dev/null
@@ -1,613 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-# Modified by Feng Liang from https://github.com/openai/CLIP/blob/main/clip/model.py
-
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("-1", nn.AvgPool2d(stride)),
-                        (
-                            "0",
-                            nn.Conv2d(
-                                inplanes,
-                                planes * self.expansion,
-                                1,
-                                stride=1,
-                                bias=False,
-                            ),
-                        ),
-                        ("1", nn.BatchNorm2d(planes * self.expansion)),
-                    ]
-                )
-            )
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(
-        self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5
-        )
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-        self.grid_size = spacial_dim
-
-    def forward(self, x, mask=None, return_cls=True):
-        b, c, gh, gw = x.shape
-        # remove irrelated feature
-        if mask is not None:
-            mask = F.interpolate(mask[:, None, ...], size=(gh, gw)).squeeze(
-                1
-            )  # [N,H,W] -> [N,grid,grid]
-            mask = (mask > 0.5).reshape(mask.shape[0], -1)
-            mask = torch.cat([mask, mask.new_ones(mask.shape[0], 1)], dim=1)
-            if x.size()[0] == 1:
-                x = x.expand(mask.shape[0], c, gh, gw)
-
-        x = x.reshape(x.shape[0], c, gh * gw).permute(2, 0, 1)  # NCHW -> (HW)NC
-
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        positional_embedding = self.positional_embedding
-        if not (self.positional_embedding.shape[0] == x.shape[0]):
-            cls_pos = positional_embedding[0:1, :]
-            per_pos_embedding = (
-                F.interpolate(
-                    positional_embedding[1:, :]
-                    .permute(1, 0)
-                    .view(1, -1, self.grid_size, self.grid_size),
-                    size=(gh, gw),
-                    mode="bicubic",
-                )
-                .reshape(-1, gh * gw)
-                .permute(1, 0)
-            )
-            positional_embedding = torch.cat([cls_pos, per_pos_embedding])
-
-        x = x + positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x,
-            key=x,
-            value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat(
-                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
-            ),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False,
-            key_padding_mask=mask,
-        )
-
-        if return_cls:
-            return x[0]
-        else:
-            return x
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(
-            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(
-            width // 2, width // 2, kernel_size=3, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(
-            input_resolution // 32, embed_dim, heads, output_dim
-        )
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x, mask: torch.Tensor = None, return_cls=True):
-        def stem(x):
-            for conv, bn in [
-                (self.conv1, self.bn1),
-                (self.conv2, self.bn2),
-                (self.conv3, self.bn3),
-            ]:
-                x = self.relu(bn(conv(x)))
-            x = self.avgpool(x)
-            return x
-
-        x = x.type(self.conv1.weight.dtype)
-        x = stem(x)  # 1/4,1/4
-        x = self.layer1(x)
-        x = self.layer2(x)  # 1/8,1/8
-        x = self.layer3(x)  # 1/16,1/16
-        x = self.layer4(x)  # 1/32,1/32
-        b, c, gh, gw = x.shape
-        x = self.attnpool(x, mask, return_cls)
-        if not return_cls:
-            return x[1:].permute(1, 0, 2).reshape(b, gh, gw, x.shape[-1])  # N,L,C
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict(
-                [
-                    ("c_fc", nn.Linear(d_model, d_model * 4)),
-                    ("gelu", QuickGELU()),
-                    ("c_proj", nn.Linear(d_model * 4, d_model)),
-                ]
-            )
-        )
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor, **kwargs):
-        self.attn_mask = (
-            self.attn_mask.to(dtype=x.dtype, device=x.device)
-            if self.attn_mask is not None
-            else None
-        )
-        return self.attn(
-            x, x, x, need_weights=False, attn_mask=self.attn_mask, **kwargs
-        )[0]
-
-    def forward(self, x: torch.Tensor, **kwargs):
-        x = x + self.attention(self.ln_1(x), **kwargs)
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(
-            *[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]
-        )
-
-    def forward(self, x: torch.Tensor, **kwargs):
-        for block in self.resblocks:
-            x = block(x, **kwargs)
-        return x
-
-
-class VisionTransformer(nn.Module):
-    def __init__(
-        self,
-        input_resolution: int,
-        patch_size: int,
-        mask_prompt_depth: int,
-        width: int,
-        layers: int,
-        heads: int,
-        output_dim: int,
-    ):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(
-            in_channels=3,
-            out_channels=width,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=False,
-        )
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(
-            scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)
-        )
-        self.grid_size = input_resolution // patch_size
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-        self.mask_pool = nn.AvgPool2d(patch_size, stride=patch_size)
-        self.mask_prompt_depth = mask_prompt_depth
-        self.mask_embedding = nn.Parameter(torch.zeros(self.mask_prompt_depth, self.grid_size * self.grid_size, width))
-
-    def forward(self, x: torch.Tensor, m: torch.Tensor = None):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        if m is not None:
-            m = self.mask_pool(m.to(torch.float).squeeze()).reshape(m.shape[0], -1).unsqueeze(-1)
-            m = torch.ceil(m)
-            if self.mask_embedding.shape[1] == 1:
-                mask_embedding = self.mask_embedding.to(x.dtype).repeat(1, x.shape[1], 1)
-            else:
-                mask_embedding = self.mask_embedding.to(x.dtype)
-            x = x * m + mask_embedding[0].unsqueeze(0) * (1 - m)
-
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        if m is not None:
-            for i, blk in enumerate(self.transformer.resblocks):
-                d = i + 1
-                x = blk(x)
-                if d < self.mask_prompt_depth:
-                    masked_x = x[1:, :, :] * m.permute(1, 0, 2) + \
-                               mask_embedding[d].unsqueeze(0).permute(1, 0, 2) * (1 - m.permute(1, 0, 2))
-                    x = torch.cat([x[:1, :, :], masked_x], dim=0)
-        else:
-            x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-
-class CLIP(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        # vision
-        image_resolution: int,
-        vision_layers: Union[Tuple[int, int, int, int], int],
-        vision_width: int,
-        vision_patch_size: int,
-        mask_prompt_depth: int,
-        # text
-        context_length: int,
-        vocab_size: int,
-        transformer_width: int,
-        transformer_heads: int,
-        transformer_layers: int,
-    ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width,
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisionTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                mask_prompt_depth=mask_prompt_depth,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim,
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask(),
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(
-            torch.empty(self.context_length, transformer_width)
-        )
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [
-                self.visual.layer1,
-                self.visual.layer2,
-                self.visual.layer3,
-                self.visual.layer4,
-            ]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * (
-            (2 * self.transformer.layers) ** -0.5
-        )
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image, **kwargs):
-        return self.visual(image.type(self.dtype), **kwargs)
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        # normalized features
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_image = logit_scale * image_features @ text_features.t()
-        logits_per_text = logit_scale * text_features @ image_features.t()
-
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [
-                *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
-                "in_proj_bias",
-                "bias_k",
-                "bias_v",
-            ]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict, mask_prompt_depth: int = 0):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len(
-            [
-                k
-                for k in state_dict.keys()
-                if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
-            ]
-        )
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round(
-            (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5
-        )
-        image_resolution = vision_patch_size * grid_size
-    else:
-        assert mask_prompt_depth == 0, 'ResNets do not support mask prompt tuning'
-        counts: list = [
-            len(
-                set(
-                    k.split(".")[2]
-                    for k in state_dict
-                    if k.startswith(f"visual.layer{b}")
-                )
-            )
-            for b in [1, 2, 3, 4]
-        ]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round(
-            (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5
-        )
-        vision_patch_size = None
-        assert (
-            output_width ** 2 + 1
-            == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        )
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(
-        set(
-            k.split(".")[2]
-            for k in state_dict
-            if k.startswith(f"transformer.resblocks")
-        )
-    )
-
-    model = CLIP(
-        embed_dim,
-        image_resolution,
-        vision_layers,
-        vision_width,
-        vision_patch_size,
-        mask_prompt_depth,
-        context_length,
-        vocab_size,
-        transformer_width,
-        transformer_heads,
-        transformer_layers,
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict, strict=False)
-    return model.eval()
diff --git a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/trainers/default.py b/spaces/jgurzoni/image_background_swapper/saicinpainting/training/trainers/default.py
deleted file mode 100644
index 86c7f0fab42924bfc93a031e851117634c70f593..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/trainers/default.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import logging
-
-import torch
-import torch.nn.functional as F
-from omegaconf import OmegaConf
-
-from saicinpainting.training.data.datasets import make_constant_area_crop_params
-from saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter
-from saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss
-from saicinpainting.training.modules.fake_fakes import FakeFakesGenerator
-from saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise
-from saicinpainting.utils import add_prefix_to_keys, get_ramp
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_constant_area_crop_batch(batch, **kwargs):
-    crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2],
-                                                                             img_width=batch['image'].shape[3],
-                                                                             **kwargs)
-    batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width]
-    batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width]
-    return batch
-
-
-class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule):
-    def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image',
-                 add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None,
-                 distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False,
-                 fake_fakes_proba=0, fake_fakes_generator_kwargs=None,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.concat_mask = concat_mask
-        self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None
-        self.image_to_discriminator = image_to_discriminator
-        self.add_noise_kwargs = add_noise_kwargs
-        self.noise_fill_hole = noise_fill_hole
-        self.const_area_crop_kwargs = const_area_crop_kwargs
-        self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \
-            if distance_weighter_kwargs is not None else None
-        self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr
-
-        self.fake_fakes_proba = fake_fakes_proba
-        if self.fake_fakes_proba > 1e-3:
-            self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {}))
-
-    def forward(self, batch):
-        if self.training and self.rescale_size_getter is not None:
-            cur_size = self.rescale_size_getter(self.global_step)
-            batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False)
-            batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest')
-
-        if self.training and self.const_area_crop_kwargs is not None:
-            batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs)
-
-        img = batch['image']
-        mask = batch['mask']
-
-        masked_img = img * (1 - mask)
-
-        if self.add_noise_kwargs is not None:
-            noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs)
-            if self.noise_fill_hole:
-                masked_img = masked_img + mask * noise[:, :masked_img.shape[1]]
-            masked_img = torch.cat([masked_img, noise], dim=1)
-
-        if self.concat_mask:
-            masked_img = torch.cat([masked_img, mask], dim=1)
-
-        batch['predicted_image'] = self.generator(masked_img)
-        batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image']
-
-        if self.fake_fakes_proba > 1e-3:
-            if self.training and torch.rand(1).item() < self.fake_fakes_proba:
-                batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask)
-                batch['use_fake_fakes'] = True
-            else:
-                batch['fake_fakes'] = torch.zeros_like(img)
-                batch['fake_fakes_masks'] = torch.zeros_like(mask)
-                batch['use_fake_fakes'] = False
-
-        batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \
-            if self.refine_mask_for_losses is not None and self.training \
-            else mask
-
-        return batch
-
-    def generator_loss(self, batch):
-        img = batch['image']
-        predicted_img = batch[self.image_to_discriminator]
-        original_mask = batch['mask']
-        supervised_mask = batch['mask_for_losses']
-
-        # L1
-        l1_value = masked_l1_loss(predicted_img, img, supervised_mask,
-                                  self.config.losses.l1.weight_known,
-                                  self.config.losses.l1.weight_missing)
-
-        total_loss = l1_value
-        metrics = dict(gen_l1=l1_value)
-
-        # vgg-based perceptual loss
-        if self.config.losses.perceptual.weight > 0:
-            pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight
-            total_loss = total_loss + pl_value
-            metrics['gen_pl'] = pl_value
-
-        # discriminator
-        # adversarial_loss calls backward by itself
-        mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask
-        self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img,
-                                                 generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(img)
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img,
-                                                                         fake_batch=predicted_img,
-                                                                         discr_real_pred=discr_real_pred,
-                                                                         discr_fake_pred=discr_fake_pred,
-                                                                         mask=mask_for_discr)
-        total_loss = total_loss + adv_gen_loss
-        metrics['gen_adv'] = adv_gen_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-        # feature matching
-        if self.config.losses.feature_matching.weight > 0:
-            need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False)
-            mask_for_fm = supervised_mask if need_mask_in_fm else None
-            fm_value = feature_matching_loss(discr_fake_features, discr_real_features,
-                                             mask=mask_for_fm) * self.config.losses.feature_matching.weight
-            total_loss = total_loss + fm_value
-            metrics['gen_fm'] = fm_value
-
-        if self.loss_resnet_pl is not None:
-            resnet_pl_value = self.loss_resnet_pl(predicted_img, img)
-            total_loss = total_loss + resnet_pl_value
-            metrics['gen_resnet_pl'] = resnet_pl_value
-
-        return total_loss, metrics
-
-    def discriminator_loss(self, batch):
-        total_loss = 0
-        metrics = {}
-
-        predicted_img = batch[self.image_to_discriminator].detach()
-        self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img,
-                                                     generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(batch['image'])
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'],
-                                                                               fake_batch=predicted_img,
-                                                                               discr_real_pred=discr_real_pred,
-                                                                               discr_fake_pred=discr_fake_pred,
-                                                                               mask=batch['mask'])
-        total_loss = total_loss + adv_discr_loss
-        metrics['discr_adv'] = adv_discr_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-
-        if batch.get('use_fake_fakes', False):
-            fake_fakes = batch['fake_fakes']
-            self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes,
-                                                         generator=self.generator, discriminator=self.discriminator)
-            discr_fake_fakes_pred, _ = self.discriminator(fake_fakes)
-            fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss(
-                real_batch=batch['image'],
-                fake_batch=fake_fakes,
-                discr_real_pred=discr_real_pred,
-                discr_fake_pred=discr_fake_fakes_pred,
-                mask=batch['mask']
-            )
-            total_loss = total_loss + fake_fakes_adv_discr_loss
-            metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss
-            metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_'))
-
-        return total_loss, metrics
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GifImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GifImagePlugin.py
deleted file mode 100644
index cf2993e38920bdebf79c6342875c2898e174ef6b..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/GifImagePlugin.py
+++ /dev/null
@@ -1,1064 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GIF file handling
-#
-# History:
-# 1995-09-01 fl   Created
-# 1996-12-14 fl   Added interlace support
-# 1996-12-30 fl   Added animation support
-# 1997-01-05 fl   Added write support, fixed local colour map bug
-# 1997-02-23 fl   Make sure to load raster data in getdata()
-# 1997-07-05 fl   Support external decoder (0.4)
-# 1998-07-09 fl   Handle all modes when saving (0.5)
-# 1998-07-15 fl   Renamed offset attribute to avoid name clash
-# 2001-04-16 fl   Added rewind support (seek to frame 0) (0.6)
-# 2001-04-17 fl   Added palette optimization (0.7)
-# 2002-06-06 fl   Added transparency support for save (0.8)
-# 2004-02-24 fl   Disable interlacing for small images
-#
-# Copyright (c) 1997-2004 by Secret Labs AB
-# Copyright (c) 1995-2004 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import itertools
-import math
-import os
-import subprocess
-from enum import IntEnum
-
-from . import Image, ImageChops, ImageFile, ImagePalette, ImageSequence
-from ._binary import i16le as i16
-from ._binary import o8
-from ._binary import o16le as o16
-
-
-class LoadingStrategy(IntEnum):
-    """.. versionadded:: 9.1.0"""
-
-    RGB_AFTER_FIRST = 0
-    RGB_AFTER_DIFFERENT_PALETTE_ONLY = 1
-    RGB_ALWAYS = 2
-
-
-#: .. versionadded:: 9.1.0
-LOADING_STRATEGY = LoadingStrategy.RGB_AFTER_FIRST
-
-# --------------------------------------------------------------------
-# Identify/read GIF files
-
-
-def _accept(prefix):
-    return prefix[:6] in [b"GIF87a", b"GIF89a"]
-
-
-##
-# Image plugin for GIF images.  This plugin supports both GIF87 and
-# GIF89 images.
-
-
-class GifImageFile(ImageFile.ImageFile):
-    format = "GIF"
-    format_description = "Compuserve GIF"
-    _close_exclusive_fp_after_loading = False
-
-    global_palette = None
-
-    def data(self):
-        s = self.fp.read(1)
-        if s and s[0]:
-            return self.fp.read(s[0])
-        return None
-
-    def _is_palette_needed(self, p):
-        for i in range(0, len(p), 3):
-            if not (i // 3 == p[i] == p[i + 1] == p[i + 2]):
-                return True
-        return False
-
-    def _open(self):
-        # Screen
-        s = self.fp.read(13)
-        if not _accept(s):
-            msg = "not a GIF file"
-            raise SyntaxError(msg)
-
-        self.info["version"] = s[:6]
-        self._size = i16(s, 6), i16(s, 8)
-        self.tile = []
-        flags = s[10]
-        bits = (flags & 7) + 1
-
-        if flags & 128:
-            # get global palette
-            self.info["background"] = s[11]
-            # check if palette contains colour indices
-            p = self.fp.read(3 << bits)
-            if self._is_palette_needed(p):
-                p = ImagePalette.raw("RGB", p)
-                self.global_palette = self.palette = p
-
-        self._fp = self.fp  # FIXME: hack
-        self.__rewind = self.fp.tell()
-        self._n_frames = None
-        self._is_animated = None
-        self._seek(0)  # get ready to read first frame
-
-    @property
-    def n_frames(self):
-        if self._n_frames is None:
-            current = self.tell()
-            try:
-                while True:
-                    self._seek(self.tell() + 1, False)
-            except EOFError:
-                self._n_frames = self.tell() + 1
-            self.seek(current)
-        return self._n_frames
-
-    @property
-    def is_animated(self):
-        if self._is_animated is None:
-            if self._n_frames is not None:
-                self._is_animated = self._n_frames != 1
-            else:
-                current = self.tell()
-                if current:
-                    self._is_animated = True
-                else:
-                    try:
-                        self._seek(1, False)
-                        self._is_animated = True
-                    except EOFError:
-                        self._is_animated = False
-
-                    self.seek(current)
-        return self._is_animated
-
-    def seek(self, frame):
-        if not self._seek_check(frame):
-            return
-        if frame < self.__frame:
-            self.im = None
-            self._seek(0)
-
-        last_frame = self.__frame
-        for f in range(self.__frame + 1, frame + 1):
-            try:
-                self._seek(f)
-            except EOFError as e:
-                self.seek(last_frame)
-                msg = "no more images in GIF file"
-                raise EOFError(msg) from e
-
-    def _seek(self, frame, update_image=True):
-        if frame == 0:
-            # rewind
-            self.__offset = 0
-            self.dispose = None
-            self.__frame = -1
-            self._fp.seek(self.__rewind)
-            self.disposal_method = 0
-            if "comment" in self.info:
-                del self.info["comment"]
-        else:
-            # ensure that the previous frame was loaded
-            if self.tile and update_image:
-                self.load()
-
-        if frame != self.__frame + 1:
-            msg = f"cannot seek to frame {frame}"
-            raise ValueError(msg)
-
-        self.fp = self._fp
-        if self.__offset:
-            # backup to last frame
-            self.fp.seek(self.__offset)
-            while self.data():
-                pass
-            self.__offset = 0
-
-        s = self.fp.read(1)
-        if not s or s == b";":
-            raise EOFError
-
-        palette = None
-
-        info = {}
-        frame_transparency = None
-        interlace = None
-        frame_dispose_extent = None
-        while True:
-            if not s:
-                s = self.fp.read(1)
-            if not s or s == b";":
-                break
-
-            elif s == b"!":
-                #
-                # extensions
-                #
-                s = self.fp.read(1)
-                block = self.data()
-                if s[0] == 249:
-                    #
-                    # graphic control extension
-                    #
-                    flags = block[0]
-                    if flags & 1:
-                        frame_transparency = block[3]
-                    info["duration"] = i16(block, 1) * 10
-
-                    # disposal method - find the value of bits 4 - 6
-                    dispose_bits = 0b00011100 & flags
-                    dispose_bits = dispose_bits >> 2
-                    if dispose_bits:
-                        # only set the dispose if it is not
-                        # unspecified. I'm not sure if this is
-                        # correct, but it seems to prevent the last
-                        # frame from looking odd for some animations
-                        self.disposal_method = dispose_bits
-                elif s[0] == 254:
-                    #
-                    # comment extension
-                    #
-                    comment = b""
-
-                    # Read this comment block
-                    while block:
-                        comment += block
-                        block = self.data()
-
-                    if "comment" in info:
-                        # If multiple comment blocks in frame, separate with \n
-                        info["comment"] += b"\n" + comment
-                    else:
-                        info["comment"] = comment
-                    s = None
-                    continue
-                elif s[0] == 255 and frame == 0:
-                    #
-                    # application extension
-                    #
-                    info["extension"] = block, self.fp.tell()
-                    if block[:11] == b"NETSCAPE2.0":
-                        block = self.data()
-                        if len(block) >= 3 and block[0] == 1:
-                            self.info["loop"] = i16(block, 1)
-                while self.data():
-                    pass
-
-            elif s == b",":
-                #
-                # local image
-                #
-                s = self.fp.read(9)
-
-                # extent
-                x0, y0 = i16(s, 0), i16(s, 2)
-                x1, y1 = x0 + i16(s, 4), y0 + i16(s, 6)
-                if (x1 > self.size[0] or y1 > self.size[1]) and update_image:
-                    self._size = max(x1, self.size[0]), max(y1, self.size[1])
-                    Image._decompression_bomb_check(self._size)
-                frame_dispose_extent = x0, y0, x1, y1
-                flags = s[8]
-
-                interlace = (flags & 64) != 0
-
-                if flags & 128:
-                    bits = (flags & 7) + 1
-                    p = self.fp.read(3 << bits)
-                    if self._is_palette_needed(p):
-                        palette = ImagePalette.raw("RGB", p)
-                    else:
-                        palette = False
-
-                # image data
-                bits = self.fp.read(1)[0]
-                self.__offset = self.fp.tell()
-                break
-
-            else:
-                pass
-                # raise OSError, "illegal GIF tag `%x`" % s[0]
-            s = None
-
-        if interlace is None:
-            # self._fp = None
-            raise EOFError
-
-        self.__frame = frame
-        if not update_image:
-            return
-
-        self.tile = []
-
-        if self.dispose:
-            self.im.paste(self.dispose, self.dispose_extent)
-
-        self._frame_palette = palette if palette is not None else self.global_palette
-        self._frame_transparency = frame_transparency
-        if frame == 0:
-            if self._frame_palette:
-                if LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
-                    self.mode = "RGBA" if frame_transparency is not None else "RGB"
-                else:
-                    self.mode = "P"
-            else:
-                self.mode = "L"
-
-            if not palette and self.global_palette:
-                from copy import copy
-
-                palette = copy(self.global_palette)
-            self.palette = palette
-        else:
-            if self.mode == "P":
-                if (
-                    LOADING_STRATEGY != LoadingStrategy.RGB_AFTER_DIFFERENT_PALETTE_ONLY
-                    or palette
-                ):
-                    self.pyaccess = None
-                    if "transparency" in self.info:
-                        self.im.putpalettealpha(self.info["transparency"], 0)
-                        self.im = self.im.convert("RGBA", Image.Dither.FLOYDSTEINBERG)
-                        self.mode = "RGBA"
-                        del self.info["transparency"]
-                    else:
-                        self.mode = "RGB"
-                        self.im = self.im.convert("RGB", Image.Dither.FLOYDSTEINBERG)
-
-        def _rgb(color):
-            if self._frame_palette:
-                color = tuple(self._frame_palette.palette[color * 3 : color * 3 + 3])
-            else:
-                color = (color, color, color)
-            return color
-
-        self.dispose_extent = frame_dispose_extent
-        try:
-            if self.disposal_method < 2:
-                # do not dispose or none specified
-                self.dispose = None
-            elif self.disposal_method == 2:
-                # replace with background colour
-
-                # only dispose the extent in this frame
-                x0, y0, x1, y1 = self.dispose_extent
-                dispose_size = (x1 - x0, y1 - y0)
-
-                Image._decompression_bomb_check(dispose_size)
-
-                # by convention, attempt to use transparency first
-                dispose_mode = "P"
-                color = self.info.get("transparency", frame_transparency)
-                if color is not None:
-                    if self.mode in ("RGB", "RGBA"):
-                        dispose_mode = "RGBA"
-                        color = _rgb(color) + (0,)
-                else:
-                    color = self.info.get("background", 0)
-                    if self.mode in ("RGB", "RGBA"):
-                        dispose_mode = "RGB"
-                        color = _rgb(color)
-                self.dispose = Image.core.fill(dispose_mode, dispose_size, color)
-            else:
-                # replace with previous contents
-                if self.im is not None:
-                    # only dispose the extent in this frame
-                    self.dispose = self._crop(self.im, self.dispose_extent)
-                elif frame_transparency is not None:
-                    x0, y0, x1, y1 = self.dispose_extent
-                    dispose_size = (x1 - x0, y1 - y0)
-
-                    Image._decompression_bomb_check(dispose_size)
-                    dispose_mode = "P"
-                    color = frame_transparency
-                    if self.mode in ("RGB", "RGBA"):
-                        dispose_mode = "RGBA"
-                        color = _rgb(frame_transparency) + (0,)
-                    self.dispose = Image.core.fill(dispose_mode, dispose_size, color)
-        except AttributeError:
-            pass
-
-        if interlace is not None:
-            transparency = -1
-            if frame_transparency is not None:
-                if frame == 0:
-                    if LOADING_STRATEGY != LoadingStrategy.RGB_ALWAYS:
-                        self.info["transparency"] = frame_transparency
-                elif self.mode not in ("RGB", "RGBA"):
-                    transparency = frame_transparency
-            self.tile = [
-                (
-                    "gif",
-                    (x0, y0, x1, y1),
-                    self.__offset,
-                    (bits, interlace, transparency),
-                )
-            ]
-
-        if info.get("comment"):
-            self.info["comment"] = info["comment"]
-        for k in ["duration", "extension"]:
-            if k in info:
-                self.info[k] = info[k]
-            elif k in self.info:
-                del self.info[k]
-
-    def load_prepare(self):
-        temp_mode = "P" if self._frame_palette else "L"
-        self._prev_im = None
-        if self.__frame == 0:
-            if self._frame_transparency is not None:
-                self.im = Image.core.fill(
-                    temp_mode, self.size, self._frame_transparency
-                )
-        elif self.mode in ("RGB", "RGBA"):
-            self._prev_im = self.im
-            if self._frame_palette:
-                self.im = Image.core.fill("P", self.size, self._frame_transparency or 0)
-                self.im.putpalette(*self._frame_palette.getdata())
-            else:
-                self.im = None
-        self.mode = temp_mode
-        self._frame_palette = None
-
-        super().load_prepare()
-
-    def load_end(self):
-        if self.__frame == 0:
-            if self.mode == "P" and LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
-                if self._frame_transparency is not None:
-                    self.im.putpalettealpha(self._frame_transparency, 0)
-                    self.mode = "RGBA"
-                else:
-                    self.mode = "RGB"
-                self.im = self.im.convert(self.mode, Image.Dither.FLOYDSTEINBERG)
-            return
-        if not self._prev_im:
-            return
-        if self._frame_transparency is not None:
-            self.im.putpalettealpha(self._frame_transparency, 0)
-            frame_im = self.im.convert("RGBA")
-        else:
-            frame_im = self.im.convert("RGB")
-        frame_im = self._crop(frame_im, self.dispose_extent)
-
-        self.im = self._prev_im
-        self.mode = self.im.mode
-        if frame_im.mode == "RGBA":
-            self.im.paste(frame_im, self.dispose_extent, frame_im)
-        else:
-            self.im.paste(frame_im, self.dispose_extent)
-
-    def tell(self):
-        return self.__frame
-
-
-# --------------------------------------------------------------------
-# Write GIF files
-
-
-RAWMODE = {"1": "L", "L": "L", "P": "P"}
-
-
-def _normalize_mode(im):
-    """
-    Takes an image (or frame), returns an image in a mode that is appropriate
-    for saving in a Gif.
-
-    It may return the original image, or it may return an image converted to
-    palette or 'L' mode.
-
-    :param im: Image object
-    :returns: Image object
-    """
-    if im.mode in RAWMODE:
-        im.load()
-        return im
-    if Image.getmodebase(im.mode) == "RGB":
-        im = im.convert("P", palette=Image.Palette.ADAPTIVE)
-        if im.palette.mode == "RGBA":
-            for rgba in im.palette.colors:
-                if rgba[3] == 0:
-                    im.info["transparency"] = im.palette.colors[rgba]
-                    break
-        return im
-    return im.convert("L")
-
-
-def _normalize_palette(im, palette, info):
-    """
-    Normalizes the palette for image.
-      - Sets the palette to the incoming palette, if provided.
-      - Ensures that there's a palette for L mode images
-      - Optimizes the palette if necessary/desired.
-
-    :param im: Image object
-    :param palette: bytes object containing the source palette, or ....
-    :param info: encoderinfo
-    :returns: Image object
-    """
-    source_palette = None
-    if palette:
-        # a bytes palette
-        if isinstance(palette, (bytes, bytearray, list)):
-            source_palette = bytearray(palette[:768])
-        if isinstance(palette, ImagePalette.ImagePalette):
-            source_palette = bytearray(palette.palette)
-
-    if im.mode == "P":
-        if not source_palette:
-            source_palette = im.im.getpalette("RGB")[:768]
-    else:  # L-mode
-        if not source_palette:
-            source_palette = bytearray(i // 3 for i in range(768))
-        im.palette = ImagePalette.ImagePalette("RGB", palette=source_palette)
-
-    if palette:
-        used_palette_colors = []
-        for i in range(0, len(source_palette), 3):
-            source_color = tuple(source_palette[i : i + 3])
-            index = im.palette.colors.get(source_color)
-            if index in used_palette_colors:
-                index = None
-            used_palette_colors.append(index)
-        for i, index in enumerate(used_palette_colors):
-            if index is None:
-                for j in range(len(used_palette_colors)):
-                    if j not in used_palette_colors:
-                        used_palette_colors[i] = j
-                        break
-        im = im.remap_palette(used_palette_colors)
-    else:
-        used_palette_colors = _get_optimize(im, info)
-        if used_palette_colors is not None:
-            return im.remap_palette(used_palette_colors, source_palette)
-
-    im.palette.palette = source_palette
-    return im
-
-
-def _write_single_frame(im, fp, palette):
-    im_out = _normalize_mode(im)
-    for k, v in im_out.info.items():
-        im.encoderinfo.setdefault(k, v)
-    im_out = _normalize_palette(im_out, palette, im.encoderinfo)
-
-    for s in _get_global_header(im_out, im.encoderinfo):
-        fp.write(s)
-
-    # local image header
-    flags = 0
-    if get_interlace(im):
-        flags = flags | 64
-    _write_local_header(fp, im, (0, 0), flags)
-
-    im_out.encoderconfig = (8, get_interlace(im))
-    ImageFile._save(im_out, fp, [("gif", (0, 0) + im.size, 0, RAWMODE[im_out.mode])])
-
-    fp.write(b"\0")  # end of image data
-
-
-def _getbbox(base_im, im_frame):
-    if _get_palette_bytes(im_frame) == _get_palette_bytes(base_im):
-        delta = ImageChops.subtract_modulo(im_frame, base_im)
-    else:
-        delta = ImageChops.subtract_modulo(
-            im_frame.convert("RGBA"), base_im.convert("RGBA")
-        )
-    return delta.getbbox(alpha_only=False)
-
-
-def _write_multiple_frames(im, fp, palette):
-    duration = im.encoderinfo.get("duration")
-    disposal = im.encoderinfo.get("disposal", im.info.get("disposal"))
-
-    im_frames = []
-    frame_count = 0
-    background_im = None
-    for imSequence in itertools.chain([im], im.encoderinfo.get("append_images", [])):
-        for im_frame in ImageSequence.Iterator(imSequence):
-            # a copy is required here since seek can still mutate the image
-            im_frame = _normalize_mode(im_frame.copy())
-            if frame_count == 0:
-                for k, v in im_frame.info.items():
-                    if k == "transparency":
-                        continue
-                    im.encoderinfo.setdefault(k, v)
-
-            encoderinfo = im.encoderinfo.copy()
-            im_frame = _normalize_palette(im_frame, palette, encoderinfo)
-            if "transparency" in im_frame.info:
-                encoderinfo.setdefault("transparency", im_frame.info["transparency"])
-            if isinstance(duration, (list, tuple)):
-                encoderinfo["duration"] = duration[frame_count]
-            elif duration is None and "duration" in im_frame.info:
-                encoderinfo["duration"] = im_frame.info["duration"]
-            if isinstance(disposal, (list, tuple)):
-                encoderinfo["disposal"] = disposal[frame_count]
-            frame_count += 1
-
-            if im_frames:
-                # delta frame
-                previous = im_frames[-1]
-                bbox = _getbbox(previous["im"], im_frame)
-                if not bbox:
-                    # This frame is identical to the previous frame
-                    if encoderinfo.get("duration"):
-                        previous["encoderinfo"]["duration"] += encoderinfo["duration"]
-                    continue
-                if encoderinfo.get("disposal") == 2:
-                    if background_im is None:
-                        color = im.encoderinfo.get(
-                            "transparency", im.info.get("transparency", (0, 0, 0))
-                        )
-                        background = _get_background(im_frame, color)
-                        background_im = Image.new("P", im_frame.size, background)
-                        background_im.putpalette(im_frames[0]["im"].palette)
-                    bbox = _getbbox(background_im, im_frame)
-            else:
-                bbox = None
-            im_frames.append({"im": im_frame, "bbox": bbox, "encoderinfo": encoderinfo})
-
-    if len(im_frames) > 1:
-        for frame_data in im_frames:
-            im_frame = frame_data["im"]
-            if not frame_data["bbox"]:
-                # global header
-                for s in _get_global_header(im_frame, frame_data["encoderinfo"]):
-                    fp.write(s)
-                offset = (0, 0)
-            else:
-                # compress difference
-                if not palette:
-                    frame_data["encoderinfo"]["include_color_table"] = True
-
-                im_frame = im_frame.crop(frame_data["bbox"])
-                offset = frame_data["bbox"][:2]
-            _write_frame_data(fp, im_frame, offset, frame_data["encoderinfo"])
-        return True
-    elif "duration" in im.encoderinfo and isinstance(
-        im.encoderinfo["duration"], (list, tuple)
-    ):
-        # Since multiple frames will not be written, add together the frame durations
-        im.encoderinfo["duration"] = sum(im.encoderinfo["duration"])
-
-
-def _save_all(im, fp, filename):
-    _save(im, fp, filename, save_all=True)
-
-
-def _save(im, fp, filename, save_all=False):
-    # header
-    if "palette" in im.encoderinfo or "palette" in im.info:
-        palette = im.encoderinfo.get("palette", im.info.get("palette"))
-    else:
-        palette = None
-        im.encoderinfo["optimize"] = im.encoderinfo.get("optimize", True)
-
-    if not save_all or not _write_multiple_frames(im, fp, palette):
-        _write_single_frame(im, fp, palette)
-
-    fp.write(b";")  # end of file
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-def get_interlace(im):
-    interlace = im.encoderinfo.get("interlace", 1)
-
-    # workaround for @PIL153
-    if min(im.size) < 16:
-        interlace = 0
-
-    return interlace
-
-
-def _write_local_header(fp, im, offset, flags):
-    transparent_color_exists = False
-    try:
-        if "transparency" in im.encoderinfo:
-            transparency = im.encoderinfo["transparency"]
-        else:
-            transparency = im.info["transparency"]
-        transparency = int(transparency)
-    except (KeyError, ValueError):
-        pass
-    else:
-        # optimize the block away if transparent color is not used
-        transparent_color_exists = True
-
-        used_palette_colors = _get_optimize(im, im.encoderinfo)
-        if used_palette_colors is not None:
-            # adjust the transparency index after optimize
-            try:
-                transparency = used_palette_colors.index(transparency)
-            except ValueError:
-                transparent_color_exists = False
-
-    if "duration" in im.encoderinfo:
-        duration = int(im.encoderinfo["duration"] / 10)
-    else:
-        duration = 0
-
-    disposal = int(im.encoderinfo.get("disposal", 0))
-
-    if transparent_color_exists or duration != 0 or disposal:
-        packed_flag = 1 if transparent_color_exists else 0
-        packed_flag |= disposal << 2
-        if not transparent_color_exists:
-            transparency = 0
-
-        fp.write(
-            b"!"
-            + o8(249)  # extension intro
-            + o8(4)  # length
-            + o8(packed_flag)  # packed fields
-            + o16(duration)  # duration
-            + o8(transparency)  # transparency index
-            + o8(0)
-        )
-
-    include_color_table = im.encoderinfo.get("include_color_table")
-    if include_color_table:
-        palette_bytes = _get_palette_bytes(im)
-        color_table_size = _get_color_table_size(palette_bytes)
-        if color_table_size:
-            flags = flags | 128  # local color table flag
-            flags = flags | color_table_size
-
-    fp.write(
-        b","
-        + o16(offset[0])  # offset
-        + o16(offset[1])
-        + o16(im.size[0])  # size
-        + o16(im.size[1])
-        + o8(flags)  # flags
-    )
-    if include_color_table and color_table_size:
-        fp.write(_get_header_palette(palette_bytes))
-    fp.write(o8(8))  # bits
-
-
-def _save_netpbm(im, fp, filename):
-    # Unused by default.
-    # To use, uncomment the register_save call at the end of the file.
-    #
-    # If you need real GIF compression and/or RGB quantization, you
-    # can use the external NETPBM/PBMPLUS utilities.  See comments
-    # below for information on how to enable this.
-    tempfile = im._dump()
-
-    try:
-        with open(filename, "wb") as f:
-            if im.mode != "RGB":
-                subprocess.check_call(
-                    ["ppmtogif", tempfile], stdout=f, stderr=subprocess.DEVNULL
-                )
-            else:
-                # Pipe ppmquant output into ppmtogif
-                # "ppmquant 256 %s | ppmtogif > %s" % (tempfile, filename)
-                quant_cmd = ["ppmquant", "256", tempfile]
-                togif_cmd = ["ppmtogif"]
-                quant_proc = subprocess.Popen(
-                    quant_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL
-                )
-                togif_proc = subprocess.Popen(
-                    togif_cmd,
-                    stdin=quant_proc.stdout,
-                    stdout=f,
-                    stderr=subprocess.DEVNULL,
-                )
-
-                # Allow ppmquant to receive SIGPIPE if ppmtogif exits
-                quant_proc.stdout.close()
-
-                retcode = quant_proc.wait()
-                if retcode:
-                    raise subprocess.CalledProcessError(retcode, quant_cmd)
-
-                retcode = togif_proc.wait()
-                if retcode:
-                    raise subprocess.CalledProcessError(retcode, togif_cmd)
-    finally:
-        try:
-            os.unlink(tempfile)
-        except OSError:
-            pass
-
-
-# Force optimization so that we can test performance against
-# cases where it took lots of memory and time previously.
-_FORCE_OPTIMIZE = False
-
-
-def _get_optimize(im, info):
-    """
-    Palette optimization is a potentially expensive operation.
-
-    This function determines if the palette should be optimized using
-    some heuristics, then returns the list of palette entries in use.
-
-    :param im: Image object
-    :param info: encoderinfo
-    :returns: list of indexes of palette entries in use, or None
-    """
-    if im.mode in ("P", "L") and info and info.get("optimize", 0):
-        # Potentially expensive operation.
-
-        # The palette saves 3 bytes per color not used, but palette
-        # lengths are restricted to 3*(2**N) bytes. Max saving would
-        # be 768 -> 6 bytes if we went all the way down to 2 colors.
-        # * If we're over 128 colors, we can't save any space.
-        # * If there aren't any holes, it's not worth collapsing.
-        # * If we have a 'large' image, the palette is in the noise.
-
-        # create the new palette if not every color is used
-        optimise = _FORCE_OPTIMIZE or im.mode == "L"
-        if optimise or im.width * im.height < 512 * 512:
-            # check which colors are used
-            used_palette_colors = []
-            for i, count in enumerate(im.histogram()):
-                if count:
-                    used_palette_colors.append(i)
-
-            if optimise or max(used_palette_colors) >= len(used_palette_colors):
-                return used_palette_colors
-
-            num_palette_colors = len(im.palette.palette) // Image.getmodebands(
-                im.palette.mode
-            )
-            current_palette_size = 1 << (num_palette_colors - 1).bit_length()
-            if (
-                # check that the palette would become smaller when saved
-                len(used_palette_colors) <= current_palette_size // 2
-                # check that the palette is not already the smallest possible size
-                and current_palette_size > 2
-            ):
-                return used_palette_colors
-
-
-def _get_color_table_size(palette_bytes):
-    # calculate the palette size for the header
-    if not palette_bytes:
-        return 0
-    elif len(palette_bytes) < 9:
-        return 1
-    else:
-        return math.ceil(math.log(len(palette_bytes) // 3, 2)) - 1
-
-
-def _get_header_palette(palette_bytes):
-    """
-    Returns the palette, null padded to the next power of 2 (*3) bytes
-    suitable for direct inclusion in the GIF header
-
-    :param palette_bytes: Unpadded palette bytes, in RGBRGB form
-    :returns: Null padded palette
-    """
-    color_table_size = _get_color_table_size(palette_bytes)
-
-    # add the missing amount of bytes
-    # the palette has to be 2<<n in size
-    actual_target_size_diff = (2 << color_table_size) - len(palette_bytes) // 3
-    if actual_target_size_diff > 0:
-        palette_bytes += o8(0) * 3 * actual_target_size_diff
-    return palette_bytes
-
-
-def _get_palette_bytes(im):
-    """
-    Gets the palette for inclusion in the gif header
-
-    :param im: Image object
-    :returns: Bytes, len<=768 suitable for inclusion in gif header
-    """
-    return im.palette.palette if im.palette else b""
-
-
-def _get_background(im, info_background):
-    background = 0
-    if info_background:
-        if isinstance(info_background, tuple):
-            # WebPImagePlugin stores an RGBA value in info["background"]
-            # So it must be converted to the same format as GifImagePlugin's
-            # info["background"] - a global color table index
-            try:
-                background = im.palette.getcolor(info_background, im)
-            except ValueError as e:
-                if str(e) not in (
-                    # If all 256 colors are in use,
-                    # then there is no need for the background color
-                    "cannot allocate more than 256 colors",
-                    # Ignore non-opaque WebP background
-                    "cannot add non-opaque RGBA color to RGB palette",
-                ):
-                    raise
-        else:
-            background = info_background
-    return background
-
-
-def _get_global_header(im, info):
-    """Return a list of strings representing a GIF header"""
-
-    # Header Block
-    # https://www.matthewflickinger.com/lab/whatsinagif/bits_and_bytes.asp
-
-    version = b"87a"
-    if im.info.get("version") == b"89a" or (
-        info
-        and (
-            "transparency" in info
-            or "loop" in info
-            or info.get("duration")
-            or info.get("comment")
-        )
-    ):
-        version = b"89a"
-
-    background = _get_background(im, info.get("background"))
-
-    palette_bytes = _get_palette_bytes(im)
-    color_table_size = _get_color_table_size(palette_bytes)
-
-    header = [
-        b"GIF"  # signature
-        + version  # version
-        + o16(im.size[0])  # canvas width
-        + o16(im.size[1]),  # canvas height
-        # Logical Screen Descriptor
-        # size of global color table + global color table flag
-        o8(color_table_size + 128),  # packed fields
-        # background + reserved/aspect
-        o8(background) + o8(0),
-        # Global Color Table
-        _get_header_palette(palette_bytes),
-    ]
-    if "loop" in info:
-        header.append(
-            b"!"
-            + o8(255)  # extension intro
-            + o8(11)
-            + b"NETSCAPE2.0"
-            + o8(3)
-            + o8(1)
-            + o16(info["loop"])  # number of loops
-            + o8(0)
-        )
-    if info.get("comment"):
-        comment_block = b"!" + o8(254)  # extension intro
-
-        comment = info["comment"]
-        if isinstance(comment, str):
-            comment = comment.encode()
-        for i in range(0, len(comment), 255):
-            subblock = comment[i : i + 255]
-            comment_block += o8(len(subblock)) + subblock
-
-        comment_block += o8(0)
-        header.append(comment_block)
-    return header
-
-
-def _write_frame_data(fp, im_frame, offset, params):
-    try:
-        im_frame.encoderinfo = params
-
-        # local image header
-        _write_local_header(fp, im_frame, offset, 0)
-
-        ImageFile._save(
-            im_frame, fp, [("gif", (0, 0) + im_frame.size, 0, RAWMODE[im_frame.mode])]
-        )
-
-        fp.write(b"\0")  # end of image data
-    finally:
-        del im_frame.encoderinfo
-
-
-# --------------------------------------------------------------------
-# Legacy GIF utilities
-
-
-def getheader(im, palette=None, info=None):
-    """
-    Legacy Method to get Gif data from image.
-
-    Warning:: May modify image data.
-
-    :param im: Image object
-    :param palette: bytes object containing the source palette, or ....
-    :param info: encoderinfo
-    :returns: tuple of(list of header items, optimized palette)
-
-    """
-    used_palette_colors = _get_optimize(im, info)
-
-    if info is None:
-        info = {}
-
-    if "background" not in info and "background" in im.info:
-        info["background"] = im.info["background"]
-
-    im_mod = _normalize_palette(im, palette, info)
-    im.palette = im_mod.palette
-    im.im = im_mod.im
-    header = _get_global_header(im, info)
-
-    return header, used_palette_colors
-
-
-def getdata(im, offset=(0, 0), **params):
-    """
-    Legacy Method
-
-    Return a list of strings representing this image.
-    The first string is a local image header, the rest contains
-    encoded image data.
-
-    To specify duration, add the time in milliseconds,
-    e.g. ``getdata(im_frame, duration=1000)``
-
-    :param im: Image object
-    :param offset: Tuple of (x, y) pixels. Defaults to (0, 0)
-    :param \\**params: e.g. duration or other encoder info parameters
-    :returns: List of bytes containing GIF encoded frame data
-
-    """
-
-    class Collector:
-        data = []
-
-        def write(self, data):
-            self.data.append(data)
-
-    im.load()  # make sure raster data is available
-
-    fp = Collector()
-
-    _write_frame_data(fp, im, offset, params)
-
-    return fp.data
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(GifImageFile.format, GifImageFile, _accept)
-Image.register_save(GifImageFile.format, _save)
-Image.register_save_all(GifImageFile.format, _save_all)
-Image.register_extension(GifImageFile.format, ".gif")
-Image.register_mime(GifImageFile.format, "image/gif")
-
-#
-# Uncomment the following line if you wish to use NETPBM/PBMPLUS
-# instead of the built-in "uncompressed" GIF encoder
-
-# Image.register_save(GifImageFile.format, _save_netpbm)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_security.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_security.py
deleted file mode 100644
index 47e5c37347d96a0034c061f9d9788dd9fa92a9c0..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_security.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# Copyright (c) 2006, Mathieu Fenniak
-# Copyright (c) 2007, Ashish Kulkarni <kulkarni.ashish@gmail.com>
-#
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
-#
-# * Redistributions of source code must retain the above copyright notice,
-# this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-# * The name of the author may not be used to endorse or promote products
-# derived from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-"""Anything related to encryption / decryption."""
-
-import struct
-from hashlib import md5
-from typing import Tuple, Union
-
-from ._utils import b_, ord_, str_
-from .generic import ByteStringObject
-
-try:
-    from typing import Literal  # type: ignore[attr-defined]
-except ImportError:
-    # PEP 586 introduced typing.Literal with Python 3.8
-    # For older Python versions, the backport typing_extensions is necessary:
-    from typing_extensions import Literal  # type: ignore[misc]
-
-# ref: pdf1.8 spec section 3.5.2 algorithm 3.2
-_encryption_padding = (
-    b"\x28\xbf\x4e\x5e\x4e\x75\x8a\x41\x64\x00\x4e\x56"
-    b"\xff\xfa\x01\x08\x2e\x2e\x00\xb6\xd0\x68\x3e\x80\x2f\x0c"
-    b"\xa9\xfe\x64\x53\x69\x7a"
-)
-
-
-def _alg32(
-    password: str,
-    rev: Literal[2, 3, 4],
-    keylen: int,
-    owner_entry: ByteStringObject,
-    p_entry: int,
-    id1_entry: ByteStringObject,
-    metadata_encrypt: bool = True,
-) -> bytes:
-    """
-    Implementation of algorithm 3.2 of the PDF standard security handler.
-
-    See section 3.5.2 of the PDF 1.6 reference.
-    """
-    # 1. Pad or truncate the password string to exactly 32 bytes.  If the
-    # password string is more than 32 bytes long, use only its first 32 bytes;
-    # if it is less than 32 bytes long, pad it by appending the required number
-    # of additional bytes from the beginning of the padding string
-    # (_encryption_padding).
-    password_bytes = b_((str_(password) + str_(_encryption_padding))[:32])
-    # 2. Initialize the MD5 hash function and pass the result of step 1 as
-    # input to this function.
-    m = md5(password_bytes)
-    # 3. Pass the value of the encryption dictionary's /O entry to the MD5 hash
-    # function.
-    m.update(owner_entry.original_bytes)
-    # 4. Treat the value of the /P entry as an unsigned 4-byte integer and pass
-    # these bytes to the MD5 hash function, low-order byte first.
-    p_entry_bytes = struct.pack("<i", p_entry)
-    m.update(p_entry_bytes)
-    # 5. Pass the first element of the file's file identifier array to the MD5
-    # hash function.
-    m.update(id1_entry.original_bytes)
-    # 6. (Revision 3 or greater) If document metadata is not being encrypted,
-    # pass 4 bytes with the value 0xFFFFFFFF to the MD5 hash function.
-    if rev >= 3 and not metadata_encrypt:
-        m.update(b"\xff\xff\xff\xff")
-    # 7. Finish the hash.
-    md5_hash = m.digest()
-    # 8. (Revision 3 or greater) Do the following 50 times: Take the output
-    # from the previous MD5 hash and pass the first n bytes of the output as
-    # input into a new MD5 hash, where n is the number of bytes of the
-    # encryption key as defined by the value of the encryption dictionary's
-    # /Length entry.
-    if rev >= 3:
-        for _ in range(50):
-            md5_hash = md5(md5_hash[:keylen]).digest()
-    # 9. Set the encryption key to the first n bytes of the output from the
-    # final MD5 hash, where n is always 5 for revision 2 but, for revision 3 or
-    # greater, depends on the value of the encryption dictionary's /Length
-    # entry.
-    return md5_hash[:keylen]
-
-
-def _alg33(
-    owner_password: str, user_password: str, rev: Literal[2, 3, 4], keylen: int
-) -> bytes:
-    """
-    Implementation of algorithm 3.3 of the PDF standard security handler,
-    section 3.5.2 of the PDF 1.6 reference.
-    """
-    # steps 1 - 4
-    key = _alg33_1(owner_password, rev, keylen)
-    # 5. Pad or truncate the user password string as described in step 1 of
-    # algorithm 3.2.
-    user_password_bytes = b_((user_password + str_(_encryption_padding))[:32])
-    # 6. Encrypt the result of step 5, using an RC4 encryption function with
-    # the encryption key obtained in step 4.
-    val = RC4_encrypt(key, user_password_bytes)
-    # 7. (Revision 3 or greater) Do the following 19 times: Take the output
-    # from the previous invocation of the RC4 function and pass it as input to
-    # a new invocation of the function; use an encryption key generated by
-    # taking each byte of the encryption key obtained in step 4 and performing
-    # an XOR operation between that byte and the single-byte value of the
-    # iteration counter (from 1 to 19).
-    if rev >= 3:
-        for i in range(1, 20):
-            new_key = ""
-            for key_char in key:
-                new_key += chr(ord_(key_char) ^ i)
-            val = RC4_encrypt(new_key, val)
-    # 8. Store the output from the final invocation of the RC4 as the value of
-    # the /O entry in the encryption dictionary.
-    return val
-
-
-def _alg33_1(password: str, rev: Literal[2, 3, 4], keylen: int) -> bytes:
-    """Steps 1-4 of algorithm 3.3"""
-    # 1. Pad or truncate the owner password string as described in step 1 of
-    # algorithm 3.2.  If there is no owner password, use the user password
-    # instead.
-    password_bytes = b_((password + str_(_encryption_padding))[:32])
-    # 2. Initialize the MD5 hash function and pass the result of step 1 as
-    # input to this function.
-    m = md5(password_bytes)
-    # 3. (Revision 3 or greater) Do the following 50 times: Take the output
-    # from the previous MD5 hash and pass it as input into a new MD5 hash.
-    md5_hash = m.digest()
-    if rev >= 3:
-        for _ in range(50):
-            md5_hash = md5(md5_hash).digest()
-    # 4. Create an RC4 encryption key using the first n bytes of the output
-    # from the final MD5 hash, where n is always 5 for revision 2 but, for
-    # revision 3 or greater, depends on the value of the encryption
-    # dictionary's /Length entry.
-    key = md5_hash[:keylen]
-    return key
-
-
-def _alg34(
-    password: str,
-    owner_entry: ByteStringObject,
-    p_entry: int,
-    id1_entry: ByteStringObject,
-) -> Tuple[bytes, bytes]:
-    """
-    Implementation of algorithm 3.4 of the PDF standard security handler.
-
-    See section 3.5.2 of the PDF 1.6 reference.
-    """
-    # 1. Create an encryption key based on the user password string, as
-    # described in algorithm 3.2.
-    rev: Literal[2] = 2
-    keylen = 5
-    key = _alg32(password, rev, keylen, owner_entry, p_entry, id1_entry)
-    # 2. Encrypt the 32-byte padding string shown in step 1 of algorithm 3.2,
-    # using an RC4 encryption function with the encryption key from the
-    # preceding step.
-    U = RC4_encrypt(key, _encryption_padding)
-    # 3. Store the result of step 2 as the value of the /U entry in the
-    # encryption dictionary.
-    return U, key
-
-
-def _alg35(
-    password: str,
-    rev: Literal[2, 3, 4],
-    keylen: int,
-    owner_entry: ByteStringObject,
-    p_entry: int,
-    id1_entry: ByteStringObject,
-    metadata_encrypt: bool,
-) -> Tuple[bytes, bytes]:
-    """
-    Implementation of algorithm 3.4 of the PDF standard security handler.
-
-    See section 3.5.2 of the PDF 1.6 reference.
-    """
-    # 1. Create an encryption key based on the user password string, as
-    # described in Algorithm 3.2.
-    key = _alg32(password, rev, keylen, owner_entry, p_entry, id1_entry)
-    # 2. Initialize the MD5 hash function and pass the 32-byte padding string
-    # shown in step 1 of Algorithm 3.2 as input to this function.
-    m = md5()
-    m.update(_encryption_padding)
-    # 3. Pass the first element of the file's file identifier array (the value
-    # of the ID entry in the document's trailer dictionary; see Table 3.13 on
-    # page 73) to the hash function and finish the hash.  (See implementation
-    # note 25 in Appendix H.)
-    m.update(id1_entry.original_bytes)
-    md5_hash = m.digest()
-    # 4. Encrypt the 16-byte result of the hash, using an RC4 encryption
-    # function with the encryption key from step 1.
-    val = RC4_encrypt(key, md5_hash)
-    # 5. Do the following 19 times: Take the output from the previous
-    # invocation of the RC4 function and pass it as input to a new invocation
-    # of the function; use an encryption key generated by taking each byte of
-    # the original encryption key (obtained in step 2) and performing an XOR
-    # operation between that byte and the single-byte value of the iteration
-    # counter (from 1 to 19).
-    for i in range(1, 20):
-        new_key = b""
-        for k in key:
-            new_key += b_(chr(ord_(k) ^ i))
-        val = RC4_encrypt(new_key, val)
-    # 6. Append 16 bytes of arbitrary padding to the output from the final
-    # invocation of the RC4 function and store the 32-byte result as the value
-    # of the U entry in the encryption dictionary.
-    # (implementer note: I don't know what "arbitrary padding" is supposed to
-    # mean, so I have used null bytes.  This seems to match a few other
-    # people's implementations)
-    return val + (b"\x00" * 16), key
-
-
-def RC4_encrypt(key: Union[str, bytes], plaintext: bytes) -> bytes:  # TODO
-    S = list(range(256))
-    j = 0
-    for i in range(256):
-        j = (j + S[i] + ord_(key[i % len(key)])) % 256
-        S[i], S[j] = S[j], S[i]
-    i, j = 0, 0
-    retval = []
-    for plaintext_char in plaintext:
-        i = (i + 1) % 256
-        j = (j + S[i]) % 256
-        S[i], S[j] = S[j], S[i]
-        t = S[(S[i] + S[j]) % 256]
-        retval.append(b_(chr(ord_(plaintext_char) ^ t)))
-    return b"".join(retval)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/_vegafusion_data.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/_vegafusion_data.py
deleted file mode 100644
index 83bb8d066e1454c61989bbcb952680a0a125e3b4..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/_vegafusion_data.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from toolz import curried
-import uuid
-from weakref import WeakValueDictionary
-
-from typing import Union, Dict, Set, MutableMapping
-
-from typing import TypedDict, Final
-
-from altair.utils._importers import import_vegafusion
-from altair.utils.core import _DataFrameLike
-from altair.utils.data import _DataType, _ToValuesReturnType, MaxRowsError
-from altair.vegalite.data import default_data_transformer
-
-# Temporary storage for dataframes that have been extracted
-# from charts by the vegafusion data transformer. Use a WeakValueDictionary
-# rather than a dict so that the Python interpreter is free to garbage
-# collect the stored DataFrames.
-extracted_inline_tables: MutableMapping[str, _DataFrameLike] = WeakValueDictionary()
-
-# Special URL prefix that VegaFusion uses to denote that a
-# dataset in a Vega spec corresponds to an entry in the `inline_datasets`
-# kwarg of vf.runtime.pre_transform_spec().
-VEGAFUSION_PREFIX: Final = "vegafusion+dataset://"
-
-
-class _ToVegaFusionReturnUrlDict(TypedDict):
-    url: str
-
-
-@curried.curry
-def vegafusion_data_transformer(
-    data: _DataType, max_rows: int = 100000
-) -> Union[_ToVegaFusionReturnUrlDict, _ToValuesReturnType]:
-    """VegaFusion Data Transformer"""
-    if hasattr(data, "__geo_interface__"):
-        # Use default transformer for geo interface objects
-        # # (e.g. a geopandas GeoDataFrame)
-        return default_data_transformer(data)
-    elif hasattr(data, "__dataframe__"):
-        table_name = f"table_{uuid.uuid4()}".replace("-", "_")
-        extracted_inline_tables[table_name] = data
-        return {"url": VEGAFUSION_PREFIX + table_name}
-    else:
-        # Use default transformer if we don't recognize data type
-        return default_data_transformer(data)
-
-
-def get_inline_table_names(vega_spec: dict) -> Set[str]:
-    """Get a set of the inline datasets names in the provided Vega spec
-
-    Inline datasets are encoded as URLs that start with the table://
-    prefix.
-
-    Parameters
-    ----------
-    vega_spec: dict
-        A Vega specification dict
-
-    Returns
-    -------
-    set of str
-        Set of the names of the inline datasets that are referenced
-        in the specification.
-
-    Examples
-    --------
-    >>> spec = {
-    ...     "data": [
-    ...         {
-    ...             "name": "foo",
-    ...             "url": "https://path/to/file.csv"
-    ...         },
-    ...         {
-    ...             "name": "bar",
-    ...             "url": "vegafusion+dataset://inline_dataset_123"
-    ...         }
-    ...     ]
-    ... }
-    >>> get_inline_table_names(spec)
-    {'inline_dataset_123'}
-    """
-    table_names = set()
-
-    # Process datasets
-    for data in vega_spec.get("data", []):
-        url = data.get("url", "")
-        if url.startswith(VEGAFUSION_PREFIX):
-            name = url[len(VEGAFUSION_PREFIX) :]
-            table_names.add(name)
-
-    # Recursively process child marks, which may have their own datasets
-    for mark in vega_spec.get("marks", []):
-        table_names.update(get_inline_table_names(mark))
-
-    return table_names
-
-
-def get_inline_tables(vega_spec: dict) -> Dict[str, _DataFrameLike]:
-    """Get the inline tables referenced by a Vega specification
-
-    Note: This function should only be called on a Vega spec that corresponds
-    to a chart that was processed by the vegafusion_data_transformer.
-    Furthermore, this function may only be called once per spec because
-    the returned dataframes are deleted from internal storage.
-
-    Parameters
-    ----------
-    vega_spec: dict
-        A Vega specification dict
-
-    Returns
-    -------
-    dict from str to dataframe
-        dict from inline dataset name to dataframe object
-    """
-    table_names = get_inline_table_names(vega_spec)
-    tables = {}
-    for table_name in table_names:
-        try:
-            tables[table_name] = extracted_inline_tables.pop(table_name)
-        except KeyError:
-            # named dataset that was provided by the user
-            pass
-    return tables
-
-
-def compile_with_vegafusion(vegalite_spec: dict) -> dict:
-    """Compile a Vega-Lite spec to Vega and pre-transform with VegaFusion
-
-    Note: This function should only be called on a Vega-Lite spec
-    that was generated with the "vegafusion" data transformer enabled.
-    In particular, this spec may contain references to extract datasets
-    using table:// prefixed URLs.
-
-    Parameters
-    ----------
-    vegalite_spec: dict
-        A Vega-Lite spec that was generated from an Altair chart with
-        the "vegafusion" data transformer enabled
-
-    Returns
-    -------
-    dict
-        A Vega spec that has been pre-transformed by VegaFusion
-    """
-    # Local import to avoid circular ImportError
-    from altair import vegalite_compilers, data_transformers
-
-    vf = import_vegafusion()
-
-    # Compile Vega-Lite spec to Vega
-    compiler = vegalite_compilers.get()
-    if compiler is None:
-        raise ValueError("No active vega-lite compiler plugin found")
-
-    vega_spec = compiler(vegalite_spec)
-
-    # Retrieve dict of inline tables referenced by the spec
-    inline_tables = get_inline_tables(vega_spec)
-
-    # Pre-evaluate transforms in vega spec with vegafusion
-    row_limit = data_transformers.options.get("max_rows", None)
-    transformed_vega_spec, warnings = vf.runtime.pre_transform_spec(
-        vega_spec,
-        vf.get_local_tz(),
-        inline_datasets=inline_tables,
-        row_limit=row_limit,
-    )
-
-    # Check from row limit warning and convert to MaxRowsError
-    for warning in warnings:
-        if warning.get("type") == "RowLimitExceeded":
-            raise MaxRowsError(
-                "The number of dataset rows after filtering and aggregation exceeds\n"
-                f"the current limit of {row_limit}. Try adding an aggregation to reduce\n"
-                "the size of the dataset that must be loaded into the browser. Or, disable\n"
-                "the limit by calling alt.data_transformers.disable_max_rows(). Note that\n"
-                "disabling this limit may cause the browser to freeze or crash."
-            )
-
-    return transformed_vega_spec
-
-
-def using_vegafusion() -> bool:
-    """Check whether the vegafusion data transfomer is enabled"""
-    # Local import to avoid circular ImportError
-    from altair import data_transformers
-
-    return data_transformers.active == "vegafusion"
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/__init__.py
deleted file mode 100644
index af1352c47292f4eebc5cae8da45641b5544558e3..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# -*- coding: utf-8 -*-
-from .tz import *
-from .tz import __doc__
-
-__all__ = ["tzutc", "tzoffset", "tzlocal", "tzfile", "tzrange",
-           "tzstr", "tzical", "tzwin", "tzwinlocal", "gettz",
-           "enfold", "datetime_ambiguous", "datetime_exists",
-           "resolve_imaginary", "UTC", "DeprecatedTzFormatWarning"]
-
-
-class DeprecatedTzFormatWarning(Warning):
-    """Warning raised when time zones are parsed from deprecated formats."""
diff --git a/spaces/johngoad/Image-Caption/app2.py b/spaces/johngoad/Image-Caption/app2.py
deleted file mode 100644
index e60f8a871e0cdbaa698f40b1619358ad610c2634..0000000000000000000000000000000000000000
--- a/spaces/johngoad/Image-Caption/app2.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import gradio as gr 
-import re 
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
-
-device='cpu'
-encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-def predict(image,max_length=64, num_beams=4):
-  image = image.convert('RGB')
-  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  caption_ids = model.generate(image, max_length = max_length)[0]
-  caption_text = clean_text(tokenizer.decode(caption_ids))
-  return caption_text 
-
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-css = '''
-h1#title {
-  text-align: center;
-}
-h3#header {
-  text-align: center;
-}
-img#overview {
-  max-width: 800px;
-  max-height: 600px;
-}
-img#style-image {
-  max-width: 1000px;
-  max-height: 600px;
-}
-'''
-demo = gr.Blocks(css=css)
-with demo:
-  gr.Markdown('''<h1 id="title">Image Caption 🖼️</h1>''')
-  gr.Markdown('''Made by : Shreyas Dixit''')
-  with gr.Column():
-        input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
-        output = gr.outputs.Textbox(type="auto",label="Captions")
-  btn = gr.Button("Genrate Caption")
-  btn.click(fn=predict, inputs=input, outputs=output)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/joushe/moe-tts/transforms.py b/spaces/joushe/moe-tts/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/jskalbg/ChatDev01/online_log/static/replay/js/app.js b/spaces/jskalbg/ChatDev01/online_log/static/replay/js/app.js
deleted file mode 100644
index 0d078d7ff3d265549c21f88702160eaf285d2dce..0000000000000000000000000000000000000000
--- a/spaces/jskalbg/ChatDev01/online_log/static/replay/js/app.js
+++ /dev/null
@@ -1,575 +0,0 @@
-const coordSet = [];
-coordSet["Chief Executive Officer"] = {
-    "character": "Chief Executive Officer",
-    "imgid": "right",
-    "top": "-315px",
-    "left": "280px"
-};
-coordSet["Chief Product Officer"] = {
-    "character": "Chief Product Officer",
-    "imgid": "left",
-    "top": "-165px",
-    "left": "110px"
-};
-coordSet["Chief Human Resource Officer"] = {
-    "character": "Chief Human Resource Officer",
-    "imgid": "left",
-    "top": "-305px",
-    "left": "55px"
-};
-coordSet["Code Reviewer"] = {
-    "character": "Code Reviewer",
-    "imgid": "left",
-    "top": "-185px",
-    "left": "500px"
-};
-coordSet["Programmer"] = {
-    "character": "Programmer",
-    "imgid": "right",
-    "top": "-80px",
-    "left": "300px"
-};
-coordSet["Chief Technology Officer"] = {
-    "character": "Chief Technology Officer",
-    "imgid": "right",
-    "top": "-130px",
-    "left": "340px"
-};
-coordSet["Chief Creative Officer"] = {
-    "character": "Chief Creative Officer",
-    "imgid": "right",
-    "top": "-95px",
-    "left": "205px"
-}
-coordSet["Software Test Engineer"] = {
-    "character": "Software Test Engineer",
-    "imgid": "right",
-    "top": "-90px",
-    "left": "470px"
-
-}
-coordSet["User"] = {
-    "character": "User",
-    "imgid": "left",
-    "top": "-465px",
-    "left": "125px"
-}
-coordSet["Counselor"] = {
-    "character": "Counselor",
-    "imgid": "right",
-    "top": "-360px",
-    "left": "420px"
-}
-coordSet["Prompt Engineer"] = {
-    "character": "Prompt Engineer",
-    "imgid": "right",
-    "top": "-320px",
-    "left": "20px"
-}
-const Softwareinfo = {
-    "duration": "-1",
-    "cost": "-1",
-    "version_updates": "-1",
-    "num_code_files": "-1",
-    "num_png_files": "-1",
-    "num_doc_files": "-1",
-    "code_lines": "-1",
-    "env_lines": "-1",
-    "manual_lines": "-1",
-    "num_utterances": "-1",
-    "num_self_reflections": "-1",
-    "num_prompt_tokens": "-1",
-    "num_completion_tokens": "-1",
-    "num_total_tokens": "-1",
-};
-
-//control chars appear speed
-var timeinterval = 5;
-var charinterval = 1;
-var scrollinterval = 40;
-
-var contents;
-var filename;
-var curdialog = '';
-var total_height = 0;
-
-var cur_para = '';
-var cur_command = '';
-var idx = 0;
-var dialog;
-
-var replaying = 0;
-var if_stop = 0;
-let isPaused = false;
-let pauseIntervalId;
-var if_move = true;
-var md = window.markdownit();
-
-//watch replay button clicked
-const button = document.getElementById('replay');
-button.addEventListener('click', () => {
-    replayDialog(idx);
-});
-$(document).ready(function() {
-    $('#filebutton').click(function() {
-        $('#fileInput').click();
-    });
-
-});
-
-const dialogbody = document.getElementById("dialogBody");
-dialogbody.addEventListener("mousewheel", handleMouseWheel, false);
-
-function handleMouseWheel(event) {
-    if (event.wheelDelta > 0) {
-        if_move = false;
-    } else if (event.wheelDelta < 0) {
-        if (dialogbody.scrollTop + dialogbody.clientHeight == dialogbody.scrollHeight) {
-            if_move = true;
-        }
-    }
-}
-
-function getinterval(speed) {
-
-    if (speed < 80 && speed > 40) {
-        timeinterval = 250 / speed;
-        charinterval = 2;
-        scrollinterval = 80;
-    } else if (speed <= 40 && speed > 0) {
-        timeinterval = 150 / speed;
-        charinterval = 1;
-        scrollinterval = 80;
-    } else if (speed >= 80 && speed < 90) {
-        timeinterval = 100 / speed;
-        charinterval = 1;
-        scrollinterval = 100;
-    } else if (speed >= 90 && speed <= 100) {
-        timeinterval = 5 / speed;
-        charinterval = 1;
-        scrollinterval = 400;
-    }
-}
-//use the slider to control the replay speed
-function speedchange() {
-    var speedbar = document.getElementById("speed");
-    var speed = speedbar.value;
-    if (speed == 0) {
-        if (!isPaused) {
-            isPaused = true;
-            clearInterval(pauseIntervalId);
-            updateCompanyWorking("end");
-        }
-    } else if (speed != 0 && isPaused == true) {
-        getinterval(speed);
-        isPaused = false;
-        idx += 1;
-        replayDialog(idx);
-    } else if (speed != 0) {
-        isPaused = false;
-        getinterval(speed);
-    }
-}
-// do replay
-async function replayDialog(idx) {
-    if (replaying == 1 && idx == 0) {
-        return;
-    }
-    if (idx == 0) {
-        replaying = 1;
-        dialog = extraction(contents);
-        var filelable = document.getElementById("successupload");
-        filelable.style.display = "block";
-        var info = "Replaying `" + filename + "` ......";
-        filelable.innerHTML = md.render(info);
-    }
-    for (let i = idx; i < dialog.length; ++i) {
-        await createPara(dialog[i], i);
-    }
-}
-
-//watch .log file input
-function watchfileInput(files) {
-    if (files.length) {
-        const file = files[0];
-        if (file) {
-            const reader = new FileReader();
-            reader.onload = function() {
-                contents = this.result;
-            };
-            reader.readAsText(file);
-            var filelable = document.getElementById("successupload");
-            filelable.style.display = "block";
-            var info = "File uploaded (`" + file.name + "`). Please click **\"Replay\"** to show ChatDev's development process";
-            filename = file.name;
-            filelable.innerHTML = md.render(info);
-        }
-    }
-}
-
-//extract information 
-function extraction(contents) {
-    const regex = /\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2} \w+)\] ([.\s\S\n\r\d\D\t]*?)(?=\n\[\d|$)/g;
-
-    var matches = [];
-
-    let match;
-    var itemp = 0;
-    while ((match = regex.exec(contents))) {
-        console.log(itemp);
-        itemp++;
-        const timestamp = match[1];
-        const text = match[2];
-        matches.push({
-            timestamp,
-            text
-        });
-    }
-    const regex_assistant = /(.*):([.\r\n\s\S\t\d\D]*)<->([.\r\n\s\S\t\d\D]*?)\]([.\r\n\s\S\t\d\D]*)/g;
-    const regex_user = /(.*):(.*)(\[Start Chat\])([.\r\n\s\S\t\d\D]*?)\]([.\r\n\s\S\t\d\D]*)/g;
-    const regex_prompt = /(Prompt Engineer): "([.\s\S\d\D]*)"/g
-
-    const regex_end = /(AgentTech Ends|ChatDev Ends)/g;
-    const regex_start = /(ChatDev Starts)([\D\s])*(\d*)/g;
-
-    const regex_task = /(task_prompt)(.*):(.*)/g;
-    const regex_info = /Software Info([\r\n\s\S\t\d\D]*)/g;
-
-    const regex_system = /System/g;
-    const regex_debug = /DEBUG/g;
-
-    var dialog = [];
-    var count = 0;
-
-    for (let i = 0; i < matches.length; ++i) {
-        var if_break = false;
-        console.log(i);
-        if (i == 159 || i == 198 || i == 223 || i == 260 || i == 416 || i == 537) {
-            //console.log(matches[i]);
-        }
-        while ((match = regex_debug.exec(matches[i].timestamp)) !== null) {
-            if_break = true;
-        }
-        while ((match = regex_system.exec(matches[i].text)) !== null) {
-            if_break = true;
-        }
-        while (((match = regex_prompt.exec(matches[i].text)) !== null)) {
-            const type = "assitant";
-            const character = match[1];
-            const command = match[2];
-            const len = match[2].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-            if_break = true;
-        }
-        if (if_break) {
-            continue;
-        }
-
-        while ((match = regex_assistant.exec(matches[i].text)) !== null) {
-            const type = "assitant";
-            const character = match[1];
-            const command = match[4];
-            const len = match[4].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-
-        }
-        while ((match = regex_user.exec(matches[i].text)) !== null) {
-            const type = "user";
-            const character = match[1];
-            const command = match[5];
-            const len = match[5].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-        }
-        while ((match = regex_start.exec(matches[i].text)) !== null) {
-            const start = match[1];
-            const len = match[1].length;
-            dialog.push({
-                start,
-                len,
-            });
-
-        }
-        while ((match = regex_end.exec(matches[i].text)) !== null) {
-            const end = match[1];
-            const len = match[1].length;
-            dialog.push({
-                end,
-                len,
-            });
-
-        }
-        while ((match = regex_task.exec(matches[i].text)) !== null) {
-            const task = match[3];
-            dialog.push({
-                task
-            });
-
-        }
-        while ((match = regex_info.exec(matches[i].text)) !== null) {
-            const info = match[1];
-            if ((/code_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.code_lines = (/code_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_code_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_code_files = (/num_code_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_png_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_png_files = (/num_png_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_doc_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_doc_files = (/num_doc_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/env_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.env_lines = (/env_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/manual_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.manual_lines = (/manual_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/duration(?:[\t\n\r\s\D]*?)=(-?(\d*)(.(\d)*)?s)/g).exec(info) != null) {
-                Softwareinfo.duration = (/duration(?:[\t\n\r\s\D]*?)=(-?(\d*)(.(\d)*)?s)/g).exec(info)[1];
-            }
-            if ((/num_utterances(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_utterances = (/num_utterances(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_self_reflections(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_self_reflections = (/num_self_reflections(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_prompt_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_prompt_tokens = (/num_prompt_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_completion_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_completion_tokens = (/num_completion_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_total_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_total_tokens = (/num_total_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/cost(?:[\t\n\r\s\D]*?)=(.((\d)*\.(\d)*))/g).exec(info) != null) {
-                Softwareinfo.cost = (/cost(?:[\t\n\r\s\D]*?)=(.((\d)*\.(\d)*))/g).exec(info)[1];
-            }
-            if ((/version_updates(?:[\t\n\r\s\D]*?)=(-?\d*)/g).exec(info) != null) {
-                Softwareinfo.version_updates = (/version_updates(?:[\t\n\r\s\D]*?)=(-?\d*)/g).exec(info)[1];
-            }
-
-            dialog.push({
-                info,
-                Softwareinfo
-            });
-
-        }
-    }
-    return dialog;
-}
-
-//show dailog 
-function createPara(d, i) {
-    const singleDialog = document.createElement("div");
-    singleDialog.style.position = "relative";
-    curdialog = singleDialog;
-    singleDialog.style.display = "flex";
-    singleDialog.style.flexDirection = "column";
-    singleDialog.style.width = "773px";
-    dialogbody.appendChild(singleDialog);
-    var paralen;
-    if (d.type && d.character) {
-        updateCompanyWorking(d.character);
-        var renderedHtml = md.render(d.character);
-        const character = document.createElement("div");
-        character.style.display = "flex";
-
-        character.style.backgroundColor = "lightblue";
-        character.style.width = "fit-content";
-        character.style.fontSize = "13px ";
-        character.style.border = "1px solid rgba(11, 20, 150, .3)";
-        character.style.borderRadius = "10px";
-        character.style.boxShadow = "2px 2px 2px black";
-        character.style.fontFamily = "'Lucida Sans', 'Lucida Sans Regular', 'Lucida Grande', 'Lucida Sans Unicode', Geneva, Verdana, sans-serif;";
-
-        if (d.type == "user") {
-            character.style.position = "relative";
-            character.style.marginLeft = "auto";
-        }
-        character.innerHTML = renderedHtml;
-        singleDialog.appendChild(character);
-
-        const characterimg = document.createElement("img");
-        console.log(d.character);
-        if (d.character == "Programmer") {
-            characterimg.src = "figures/programmer.png";
-        } else if (d.character == "Code Reviewer") {
-            characterimg.src = "figures/reviewer.png";
-        } else if (d.character == "Chief Human Resource Officer") {
-            characterimg.src = "figures/hr.png";
-        } else if (d.character == "Chief Executive Officer") {
-            characterimg.src = "figures/ceo.png";
-        } else if (d.character == "Chief Product Officer") {
-            characterimg.src = "figures/cpo.png";
-        } else if (d.character == "Chief Technology Officer") {
-            characterimg.src = "figures/cto.png";
-        } else if (d.character == "Chief Creative Officer") {
-            characterimg.src = "figures/designer.png";
-        } else if (d.character == "Software Test Engineer") {
-            characterimg.src = "figures/tester.png";
-        } else if (d.character == "User") {
-            characterimg.src = "figures/user.png";
-        } else if (d.character == "Counselor") {
-            characterimg.src = "figures/counselor.png";
-        } else if (d.character == "Prompt Engineer") {
-            characterimg.src = "figures/pe.png";
-        }
-
-        characterimg.style.height = "40px";
-        characterimg.style.width = "30px";
-        characterimg.style.position = "relative";
-        character.appendChild(characterimg);
-        character.style.width = "fit-content";
-
-
-        var renderedHtml = md.render(d.command);
-        const paragraph = document.createElement("div");
-        paragraph.className = "markdown-body";
-        //paragraph.innerHTML = renderedHtml;
-        paragraph.style.padding = "10px";
-        paragraph.style.border = "3px solid #a08D8D";
-        paragraph.style.width = "750px";
-        paragraph.style.border = "1px solid rgba(11, 20, 150, .3)";
-        paragraph.style.borderRadius = "10px";
-        paragraph.style.boxShadow = "2px 2px 2px black";
-
-        singleDialog.appendChild(paragraph);
-
-        const emptyparagraph = document.createElement("div");
-        emptyparagraph.style.height = "10px";
-        singleDialog.appendChild(emptyparagraph);
-
-        if (d.type == "user") {
-            paragraph.style.backgroundColor = "#4b751a";
-        } else {
-            paragraph.style.backgroundColor = "#133153";
-        }
-        cur_command = d.command;
-        cur_para = paragraph;
-        idx = i;
-        return Promise.resolve(printCommand(paragraph, d.command));
-
-    } else if (d.start) {
-        paralen = 0;
-        var renderedHtml = md.render("----------" + d.start + "----------");
-        const starttext = document.createElement("div");
-        starttext.innerHTML = renderedHtml;
-        singleDialog.appendChild(starttext);
-
-    } else if (d.end) {
-        paralen = 0;
-        updateCompanyWorking("end");
-        var renderedHtml = md.render("----------" + d.end + "----------");
-        const endtext = document.createElement("div");
-        endtext.innerHTML = renderedHtml;
-        singleDialog.appendChild(endtext);
-        var filelable = document.getElementById("successupload");
-        filelable.style.display = "block";
-        var info = "Replayed";
-        filelable.innerHTML = md.render(info);
-    } else if (d.task) {
-        var renderedHtml = md.render("Task:    " + d.task);
-        const tasktext = document.getElementById("Requesttext");
-        tasktext.innerHTML = renderedHtml;
-    } else if (d.info) {
-        var renderedHtml = md.render(d.info);
-        const infotext = document.getElementById("dialogStatistic");
-        var temp_label = "";
-        for (var c in Softwareinfo) {
-            temp_label = document.getElementById(c);
-            if (Softwareinfo[c] != "-1" && Softwareinfo[c] != "-1s") {
-                temp_label.innerHTML = Softwareinfo[c];
-            }
-        }
-    }
-}
-
-//update company image
-function updateCompanyWorking(character) {
-    if (character == "end") {
-        var img1 = document.getElementById("right");
-        img1.style.display = "none";
-        var img2 = document.getElementById("left");
-        img2.style.display = "none";
-        return;
-    }
-    var imgid = coordSet[character].imgid;
-    var left_bias = coordSet[character].left;
-    var top_bias = coordSet[character].top;
-    var img = document.getElementById(imgid);
-
-    img.style.display = "block";
-    img.style.left = left_bias;
-    img.style.top = top_bias;
-
-    if (imgid == "left") {
-        var another_img = document.getElementById("right");
-        another_img.style.display = "none";
-    } else {
-        var another_img = document.getElementById("left");
-        another_img.style.display = "none";
-    }
-}
-
-async function updateParashow(container, command, index, len) {
-    var cur_content;
-    if (index == len - 1) {
-        cur_content = command.slice(0, index);
-    }
-    if (index < len) {
-        cur_content = command.slice(0, index);
-        if (cur_content != null && cur_content != undefined) {
-            container.innerHTML = md.render(cur_content);
-        };
-    }
-    if (index % (scrollinterval) == 0 && if_move == true) {
-        if (curdialog != null && curdialog != '') {
-            const newBoxRect = curdialog.getBoundingClientRect();
-            total_height += newBoxRect.height;
-            dialogbody.scrollTo({ top: total_height, behavior: 'smooth' });
-        }
-    }
-}
-
-async function printCommand(paragraph, command) {
-    var paralen = command.length;
-    const tasks = [];
-
-    for (let j = 0; j < paralen; j = j + charinterval) {
-        tasks.push(new Promise(resolve => {
-            pauseIntervalId = setTimeout(() => {
-                updateParashow(paragraph, command, j, paralen);
-                resolve();
-            }, timeinterval * j);
-        }));
-
-        if (isPaused) {
-            await Promise.all(tasks);
-        }
-    }
-    await Promise.all(tasks);
-    return 1;
-}
\ No newline at end of file
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/trainer.py b/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/trainer.py
deleted file mode 100644
index 930618473181726007637cb7377b390ee1ce1613..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/trainer.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Trainer with Mixture of Experts support."""
-
-from typing import Any, Callable, Optional, Sequence, TYPE_CHECKING
-
-import cached_property
-from t5x import models
-from t5x import train_state as train_state_lib
-from t5x import trainer
-from t5x.contrib.moe import partitioning
-from t5x.contrib.moe import training_utils
-
-BatchType = trainer.BatchType
-LearningRateCallable = trainer.LearningRateCallable
-MetricMapType = trainer.MetricMapType
-PartitionSpec = partitioning.PartitionSpec
-PartitionedTrainCallable = trainer.PartitionedTrainCallable
-Rng = trainer.Rng
-
-if TYPE_CHECKING:  # See b/163639353
-  cached_property = property  # pylint: disable=invalid-name
-else:
-  cached_property = cached_property.cached_property
-
-
-class MoeTrainer(trainer.Trainer):
-  """T5X trainer with overrides for Mixture of Experts support."""
-
-  def __init__(
-      self,
-      model: models.BaseModel,
-      train_state: train_state_lib.TrainState,
-      partitioner: partitioning.MoePjitPartitioner,
-      eval_names: Sequence[str],
-      summary_dir: Optional[str],
-      train_state_axes: Any,
-      rng: Rng,
-      learning_rate_fn: LearningRateCallable,
-      num_microbatches: Optional[int],
-      num_experts: int,
-      sharded_match_fn: Optional[Callable[
-          [str], bool]] = training_utils.match_fn(r'.*expert.*'),
-      weight_metrics_computer: Optional[trainer.WeightMetricsComputer] = None):
-    """Trainer constructor.
-
-    Args:
-      model: the instantiation of `BaseModel` to train.
-      train_state: a train state with parameters and optimizer state.
-      partitioner: the partitioner to use.
-      eval_names: names of evaluation datasets, which must match the keys of the
-        mapping passed to `eval`.
-      summary_dir: optional directory to write TensorBoard metrics to.
-      train_state_axes: partitioning info for the optimizer to be used.
-      rng: jax PRNGKey seed for random operations, to be combined with step
-        number for a deterministic RNG.
-      learning_rate_fn: returns the learning rate given the current step.
-      num_microbatches: the number of microbatches to use, or None for direct
-        training.
-      num_experts: Global number of experts. Used to scale sharded parameter
-        gradients.
-      sharded_match_fn: Filter function for distinguishing sharded (MoE)
-        parameters from replicated parameters. Used to identify the sharded
-        parameter gradients that need to be rescaled under pjit training.
-      weight_metrics_computer: A WeightMetricsComputer instance, or None, to
-        decide what metrics, if any, to log about weights and weight updates
-        during training.
-    """
-    super().__init__(
-        model=model,
-        train_state=train_state,
-        partitioner=partitioner,
-        eval_names=eval_names,
-        summary_dir=summary_dir,
-        train_state_axes=train_state_axes,
-        rng=rng,
-        learning_rate_fn=learning_rate_fn,
-        num_microbatches=num_microbatches,
-        weight_metrics_computer=weight_metrics_computer)
-
-    self._num_experts = num_experts
-    self._sharded_match_fn = sharded_match_fn
-    self.data_partition_spec = partitioning.data_partition_spec(
-        partitioner.two_data_axes)
-
-  @cached_property
-  def _partitioned_train_step(self) -> PartitionedTrainCallable:
-    """Same as a regular T5X train step, but scales expert parameter gradients.
-
-    We must scale expert parameter gradients by the number of experts to account
-    for pjit's implicit averaging over partitioned parameter gradients.
-
-    Returns:
-      Partitioned train step function.
-    """
-
-    def train_with_lr(train_state: train_state_lib.TrainState,
-                      batch: BatchType):
-      grad_accum, metrics, flax_mutables = (
-          trainer.accumulate_grads_microbatched(
-              self._model,
-              train_state,
-              batch,
-              self._get_step_rng(train_state.step),
-              self._num_microbatches,
-              data_partition_spec=self.data_partition_spec))
-
-      # Only difference between this train step and regular T5X train step:
-      scaled_grads = training_utils.scale_sharded_grads(
-          grad_accum, self._sharded_match_fn, scale_factor=self._num_experts)
-
-      new_train_state, metrics = trainer.apply_grads(
-          train_state,
-          scaled_grads,
-          metrics,
-          self._learning_rate_fn(train_state.step),
-          self._weight_metrics_computer,
-          other_state_variables={'flax_mutables': flax_mutables}
-          if flax_mutables else None)
-      return new_train_state, metrics
-
-    return self._partitioner.partition(
-        train_with_lr,
-        in_axis_resources=(self._train_state_axes, self.data_partition_spec),
-        out_axis_resources=(self._train_state_axes, None),
-        donate_argnums=(0,))
diff --git a/spaces/juliensimon/xlm-v-base-language-id/app.py b/spaces/juliensimon/xlm-v-base-language-id/app.py
deleted file mode 100644
index 7cb961504171dbe6416ba78944edc369a1d767ad..0000000000000000000000000000000000000000
--- a/spaces/juliensimon/xlm-v-base-language-id/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import gradio as gr
-import time
-from optimum.intel.openvino import OVModelForSequenceClassification
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
-model_id = "juliensimon/xlm-v-base-language-id"
-
-p = pipeline("text-classification", model=model_id)
-
-ov_model = OVModelForSequenceClassification.from_pretrained(model_id)
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-ov_p = pipeline("text-classification", model=ov_model, tokenizer=tokenizer)
-
-# Warmup
-for i in range(100):
-    ov_p("Hello world")
-
-
-def process(text, model, top_k=5):
-    if model == "Vanilla":
-        pipe = p
-    else:
-        pipe = ov_p
-    tick = time.time()
-    pred = pipe(text, top_k=top_k)
-    tock = time.time()
-    scores = {x["label"]: x["score"] for x in pred}
-    msg = f"{(tock-tick)*1000:.2f} milliseconds"
-    return scores, msg
-
-
-# Gradio inputs
-input_text = gr.Text(label="Enter text")
-input_model = gr.Radio(label="Pick a model", choices=["Vanilla", "OpenVINO"])
-
-# Gradio outputs
-output_labels = gr.Label(label="Languages", num_top_classes=5)
-output_text = gr.Text(label="Prediction time")
-
-description = "This Space lets you perform language identification on the 102 languages present in the google/fleurs dataset. The underlying model scores 99.3% accuracy on the validation set. Inference is optimized with Optimum Intel and OpenVINO."
-
-iface = gr.Interface(
-    description=description,
-    fn=process,
-    inputs=[input_text, input_model],
-    outputs=[output_labels, output_text],
-    examples=[
-        [
-            "Kila mtu ana haki ya kuelimishwa. Elimu yapasa itolewe bure hasa ile ya madarasa ya chini. Elimu ya masarasa ya chini ihudhuriwe kwa lazima. Elimu ya ufundi na ustadi iwe wazi kwa wote. Na elimu ya juu iwe wazi kwa wote kwa kutegemea sifa ya mtu",
-            "OpenVINO",
-        ],
-        [
-            "Ang bawat tao'y may karapatan sa edukasyon. Ang edukasyon ay walang bayad, doon man lamang sa elementarya at pangunahing antas. Ang edukasyong elementarya ay magiging sapilitan. Ang edukasyong teknikal at propesyonal ay gagawing maabot ng lahat at ang lalong mataas na edukasyon ay ipagkakaloob nang pantay-pantay sa lahat batay sa pagiging karapat-dapat.",
-            "OpenVINO",
-        ],
-        [
-            "Každý má právo na vzdělání. Vzdělání nechť je bezplatné, alespoň v počátečních a základních stupních. Základní vzdělání je povinné. Technické a odborné vzdělání budiž všeobecně přístupné a rovněž vyšší vzdělání má být stejně přístupné všem podle schopností.",
-            "OpenVINO",
-        ],
-        [
-            "Baat yii laa mën a wax ci jéego yi Marubba Faal séqi jëm ci bindkat yi taamu làmmiñi réew mi. Am naa ci mbégte lool, ngir yàgg naa ko wax ne bumu xeeb li gafakaam dencal xam-xam yu bawoo ca Maam. Téere bii mu tudde Yóbbalu Ndaw firndeel na sama njort.",
-            "OpenVINO",
-        ],
-    ],
-    allow_flagging="never",
-)
-
-iface.launch()
diff --git a/spaces/jykoh/gill/app.py b/spaces/jykoh/gill/app.py
deleted file mode 100644
index 53144159944aace83ddbf908605efe12e0446d9f..0000000000000000000000000000000000000000
--- a/spaces/jykoh/gill/app.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import tempfile
-from share_btn import community_icon_html, loading_icon_html, share_js, save_js
-import huggingface_hub
-import gradio as gr
-from gill import utils
-from gill import models
-import matplotlib.pyplot as plt
-from PIL import Image
-import torch
-import numpy as np
-import os
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"
-
-
-css = """
-    #chatbot { min-height: 300px; }
-    #save-btn {
-        background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
-    }
-    #save-btn:hover {
-        background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
-    }
-    #share-btn {
-        background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
-    }
-    #share-btn:hover {
-        background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
-    }
-    #gallery { z-index: 999999; }
-    #gallery img:hover {transform: scale(2.3); z-index: 999999; position: relative; padding-right: 30%; padding-bottom: 30%;}
-    #gallery button img:hover {transform: none; z-index: 999999; position: relative; padding-right: 0; padding-bottom: 0;}
-    @media (hover: none) {
-        #gallery img:hover {transform: none; z-index: 999999; position: relative; padding-right: 0; 0;}
-    }
-    .html2canvas-container { width: 3000px !important; height: 3000px !important; }
-"""
-
-examples = [
-    'examples/ramen.png',
-    'examples/cake.png',
-    'examples/couch.png',
-    'examples/tattoo.png',
-    'examples/cupcakes.png',
-]
-
-# Download model from HF Hub.
-ckpt_path = huggingface_hub.hf_hub_download(
-    repo_id='jykoh/gill', filename='pretrained_ckpt.pth.tar')
-decision_model_path = huggingface_hub.hf_hub_download(
-    repo_id='jykoh/gill', filename='decision_model.pth.tar')
-args_path = huggingface_hub.hf_hub_download(
-    repo_id='jykoh/gill', filename='model_args.json')
-model = models.load_gill('./', args_path, ckpt_path, decision_model_path)
-
-
-def upload_image(state, image_input):
-    conversation = state[0]
-    chat_history = state[1]
-    input_image = Image.open(image_input.name).resize(
-        (224, 224)).convert('RGB')
-    input_image.save(image_input.name)  # Overwrite with smaller image.
-    conversation += [(f'<img src="./file={image_input.name}" style="display: inline-block;">', "")]
-    return [conversation, chat_history + [input_image, ""]], conversation
-
-
-def reset():
-    return [[], []], []
-
-
-def reset_last(state):
-    conversation = state[0][:-1]
-    chat_history = state[1][:-2]
-    return [conversation, chat_history], conversation
-
-
-def save_image_to_local(image: Image.Image):
-    # TODO(jykoh): Update so the url path is used, to prevent repeat saving.
-    filename = next(tempfile._get_candidate_names()) + '.png'
-    image.save(filename)
-    return filename
-
-
-def generate_for_prompt(input_text, state, ret_scale_factor, num_words, temperature):
-    g_cuda = torch.Generator(device='cuda').manual_seed(1337)
-
-    # Ignore empty inputs.
-    if len(input_text) == 0:
-        return state, state[0], gr.update(visible=True)
-
-    input_prompt = 'Q: ' + input_text + '\nA:'
-    conversation = state[0]
-    chat_history = state[1]
-    print('Generating for', chat_history, flush=True)
-
-    # If an image was uploaded, prepend it to the model.
-    model_inputs = chat_history
-    model_inputs.append(input_prompt)
-    # Remove empty text.
-    model_inputs = [s for s in model_inputs if s != '']
-
-    top_p = 1.0
-    if temperature != 0.0:
-        top_p = 0.95
-
-    print('Running model.generate_for_images_and_texts with', model_inputs, flush=True)
-    model_outputs = model.generate_for_images_and_texts(model_inputs,
-                                                        num_words=max(num_words, 1), ret_scale_factor=ret_scale_factor, top_p=top_p,
-                                                        temperature=temperature, max_num_rets=1,
-                                                        num_inference_steps=50, generator=g_cuda)
-    print('model_outputs', model_outputs, ret_scale_factor, flush=True)
-
-    response = ''
-    text_outputs = []
-    for output_i, p in enumerate(model_outputs):
-        if type(p) == str:
-            if output_i > 0:
-                response += '<br/>'
-            # Remove the image tokens for output.
-            text_outputs.append(p.replace('[IMG0] [IMG1] [IMG2] [IMG3] [IMG4] [IMG5] [IMG6] [IMG7]', ''))
-            response += p
-            if len(model_outputs) > 1:
-                response += '<br/>'
-        elif type(p) == dict:
-            # Decide whether to generate or retrieve.
-            if p['decision'] is not None and p['decision'][0] == 'gen':
-                image = p['gen'][0][0]#.resize((224, 224))
-                filename = save_image_to_local(image)
-                response += f'<img src="./file={filename}" style="display: inline-block;"><p style="font-size: 12px; color: #555; margin-top: 0;">(Generated)</p>'
-            else:
-                image = p['ret'][0][0]#.resize((224, 224))
-                filename = save_image_to_local(image)
-                response += f'<img src="./file={filename}" style="display: inline-block;"><p style="font-size: 12px; color: #555; margin-top: 0;">(Retrieved)</p>'
-
-    chat_history = model_inputs + \
-        [' '.join([s for s in model_outputs if type(s) == str]) + '\n']
-    # Remove [RET] from outputs.
-    conversation.append((input_text, response.replace('[IMG0] [IMG1] [IMG2] [IMG3] [IMG4] [IMG5] [IMG6] [IMG7]', '')))
-
-    # Set input image to None.
-    print('state', state, flush=True)
-    print('updated state', [conversation, chat_history], flush=True)
-    return [conversation, chat_history], conversation, gr.update(visible=True), gr.update(visible=True)
-
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML("""
-        <h1>🐟 GILL</h1>
-        <p>This is the official Gradio demo for the GILL model, a model that can process arbitrarily interleaved image and text inputs, and produce image and text outputs.</p>
-
-        <strong>Paper:</strong> <a href="https://arxiv.org/abs/2305.17216" target="_blank">Generating Images with Multimodal Language Models</a>
-        <br/>
-        <strong>Project Website:</strong> <a href="https://jykoh.com/gill" target="_blank">GILL Website</a>
-        <br/>
-        <strong>Code and Models:</strong> <a href="https://github.com/kohjingyu/gill" target="_blank">GitHub</a>
-        <br/>
-        <br/>
-
-        <strong>Tips:</strong>
-        <ul>
-        <li>Start by inputting either image or text prompts (or both) and chat with GILL to get image-and-text replies.</li>
-        <li>Tweak the level of sensitivity to images and text using the parameters on the right.</li>
-        <li>Check out cool conversations in the examples or community tab for inspiration and share your own!</li>
-        <li>If the model outputs a blank image, it is because Stable Diffusion's safety filter detected inappropriate content. Please try again with a different prompt.</li>
-        <li>Outputs may differ slightly from the paper due to slight implementation differences. For reproducing paper results, please use our <a href="https://github.com/kohjingyu/gill" target="_blank">official code</a>.</li>
-        <li>For faster inference without waiting in queue, you may duplicate the space and use your own GPU: <a href="https://huggingface.co/spaces/jykoh/gill?duplicate=true"><img style="display: inline-block; margin-top: 0em; margin-bottom: 0em" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></li>
-        </ul>
-    """)
-
-    gr_state = gr.State([[], []])  # conversation, chat_history
-
-    with gr.Row():
-        with gr.Column(scale=0.7, min_width=500):
-            with gr.Row():
-                chatbot = gr.Chatbot(elem_id="chatbot", label="🐟 GILL Chatbot")
-            with gr.Row():
-                image_btn = gr.UploadButton("🖼️ Upload Image", file_types=["image"])
-
-                text_input = gr.Textbox(label="Message", placeholder="Type a message")
-
-                with gr.Column():
-                    submit_btn = gr.Button(
-                        "Submit", interactive=True, variant="primary")
-                    clear_last_btn = gr.Button("Undo")
-                    clear_btn = gr.Button("Reset All")
-                    with gr.Row(visible=False) as save_group:
-                        save_button = gr.Button("💾 Save Conversation as .png", elem_id="save-btn")
-
-                    with gr.Row(visible=False) as share_group:
-                        share_button = gr.Button("🤗 Share to Community (opens new window)", elem_id="share-btn")
-
-        with gr.Column(scale=0.3, min_width=400):
-            ret_scale_factor = gr.Slider(minimum=0.0, maximum=3.0, value=1.3, step=0.1, interactive=True,
-                                         label="Frequency multiplier for returning images (higher means more frequent)")
-            # max_ret_images = gr.Number(
-            #     minimum=0, maximum=3, value=2, precision=1, interactive=True, label="Max images to return")
-            gr_max_len = gr.Slider(minimum=1, maximum=64, value=32,
-                                   step=1, interactive=True, label="Max # of words")
-            gr_temperature = gr.Slider(
-                minimum=0.0, maximum=1.0, value=0.0, step=0.1, interactive=True, label="Temperature (0 for deterministic, higher for more randomness)")
-
-            gallery = gr.Gallery(
-                value=[Image.open(e) for e in examples], label="Example Conversations", show_label=True, elem_id="gallery",
-            ).style(grid=[2], height="auto")
-
-    text_input.submit(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
-                      gr_max_len, gr_temperature], [gr_state, chatbot, share_group, save_group])
-    text_input.submit(lambda: "", None, text_input)  # Reset chatbox.
-    submit_btn.click(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
-                     gr_max_len, gr_temperature], [gr_state, chatbot, share_group, save_group])
-    submit_btn.click(lambda: "", None, text_input)  # Reset chatbox.
-
-    image_btn.upload(upload_image, [gr_state, image_btn], [gr_state, chatbot])
-    clear_last_btn.click(reset_last, [gr_state], [gr_state, chatbot])
-    clear_btn.click(reset, [], [gr_state, chatbot])
-    share_button.click(None, [], [], _js=share_js)
-    save_button.click(None, [], [], _js=save_js)
-
-
-demo.queue(concurrency_count=1, api_open=False, max_size=16)
-demo.launch(debug=True, server_name="0.0.0.0")
-# demo.launch(debug=True, server_name="127.0.0.1")
diff --git a/spaces/kamezawash/rembg/rembg/session_simple.py b/spaces/kamezawash/rembg/rembg/session_simple.py
deleted file mode 100644
index 7ec31813f2e14e80856803d2335671c9f50ca84f..0000000000000000000000000000000000000000
--- a/spaces/kamezawash/rembg/rembg/session_simple.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from typing import List
-
-import numpy as np
-from PIL import Image
-from PIL.Image import Image as PILImage
-
-from .session_base import BaseSession
-
-
-class SimpleSession(BaseSession):
-    def predict(self, img: PILImage) -> List[PILImage]:
-        ort_outs = self.inner_session.run(
-            None,
-            self.normalize(
-                img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (320, 320)
-            ),
-        )
-
-        pred = ort_outs[0][:, 0, :, :]
-
-        ma = np.max(pred)
-        mi = np.min(pred)
-
-        pred = (pred - mi) / (ma - mi)
-        pred = np.squeeze(pred)
-
-        mask = Image.fromarray((pred * 255).astype("uint8"), mode="L")
-        mask = mask.resize(img.size, Image.LANCZOS)
-
-        return [mask]
diff --git a/spaces/keras-dreambooth/example-submission/README.md b/spaces/keras-dreambooth/example-submission/README.md
deleted file mode 100644
index 04bb05bd18216eaab0c813211f4f2f1ac8caf46d..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/example-submission/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Dreambooth Submission
-emoji: 🐶 
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-tags:
-- keras-dreambooth
-- nature
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/visualize.py b/spaces/kevinwang676/VoiceChanger/src/face3d/visualize.py
deleted file mode 100644
index 23a1110806a0ddf37d4aa549c023d1c3f7114e3e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/face3d/visualize.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# check the sync of 3dmm feature and the audio
-import cv2
-import numpy as np
-from src.face3d.models.bfm import ParametricFaceModel
-from src.face3d.models.facerecon_model import FaceReconModel
-import torch
-import subprocess, platform
-import scipy.io as scio
-from tqdm import tqdm 
-
-# draft
-def gen_composed_video(args, device, first_frame_coeff, coeff_path, audio_path, save_path, exp_dim=64):
-    
-    coeff_first = scio.loadmat(first_frame_coeff)['full_3dmm']
-
-    coeff_pred = scio.loadmat(coeff_path)['coeff_3dmm']
-
-    coeff_full = np.repeat(coeff_first, coeff_pred.shape[0], axis=0) # 257
-
-    coeff_full[:, 80:144] = coeff_pred[:, 0:64]
-    coeff_full[:, 224:227]  = coeff_pred[:, 64:67] # 3 dim translation
-    coeff_full[:, 254:]  = coeff_pred[:, 67:] # 3 dim translation
-
-    tmp_video_path = '/tmp/face3dtmp.mp4'
-
-    facemodel = FaceReconModel(args)
-    
-    video = cv2.VideoWriter(tmp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (224, 224))
-
-    for k in tqdm(range(coeff_pred.shape[0]), 'face3d rendering:'):
-        cur_coeff_full = torch.tensor(coeff_full[k:k+1], device=device)
-
-        facemodel.forward(cur_coeff_full, device)
-
-        predicted_landmark = facemodel.pred_lm # TODO.
-        predicted_landmark = predicted_landmark.cpu().numpy().squeeze()
-
-        rendered_img = facemodel.pred_face
-        rendered_img = 255. * rendered_img.cpu().numpy().squeeze().transpose(1,2,0)
-        out_img = rendered_img[:, :, :3].astype(np.uint8)
-
-        video.write(np.uint8(out_img[:,:,::-1]))
-
-    video.release()
-
-    command = 'ffmpeg -v quiet -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video_path, save_path)
-    subprocess.call(command, shell=platform.system() != 'Windows')
-
diff --git a/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/networks.py b/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/networks.py
deleted file mode 100644
index 8aa0b1390e7b4bb0e16057ac94d2fe84f48421af..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/networks.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch.nn as nn
-import torch
-
-
-class ResidualConv(nn.Module):
-    def __init__(self, input_dim, output_dim, stride, padding):
-        super(ResidualConv, self).__init__()
-
-        self.conv_block = nn.Sequential(
-            nn.BatchNorm2d(input_dim),
-            nn.ReLU(),
-            nn.Conv2d(
-                input_dim, output_dim, kernel_size=3, stride=stride, padding=padding
-            ),
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
-        )
-        self.conv_skip = nn.Sequential(
-            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=1),
-            nn.BatchNorm2d(output_dim),
-        )
-
-    def forward(self, x):
-
-        return self.conv_block(x) + self.conv_skip(x)
-
-
-class Upsample(nn.Module):
-    def __init__(self, input_dim, output_dim, kernel, stride):
-        super(Upsample, self).__init__()
-
-        self.upsample = nn.ConvTranspose2d(
-            input_dim, output_dim, kernel_size=kernel, stride=stride
-        )
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class Squeeze_Excite_Block(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(Squeeze_Excite_Block, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y.expand_as(x)
-
-
-class ASPP(nn.Module):
-    def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
-        super(ASPP, self).__init__()
-
-        self.aspp_block1 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block2 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[1], dilation=rate[1]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block3 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[2], dilation=rate[2]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-
-        self.output = nn.Conv2d(len(rate) * out_dims, out_dims, 1)
-        self._init_weights()
-
-    def forward(self, x):
-        x1 = self.aspp_block1(x)
-        x2 = self.aspp_block2(x)
-        x3 = self.aspp_block3(x)
-        out = torch.cat([x1, x2, x3], dim=1)
-        return self.output(out)
-
-    def _init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-
-class Upsample_(nn.Module):
-    def __init__(self, scale=2):
-        super(Upsample_, self).__init__()
-
-        self.upsample = nn.Upsample(mode="bilinear", scale_factor=scale)
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class AttentionBlock(nn.Module):
-    def __init__(self, input_encoder, input_decoder, output_dim):
-        super(AttentionBlock, self).__init__()
-
-        self.conv_encoder = nn.Sequential(
-            nn.BatchNorm2d(input_encoder),
-            nn.ReLU(),
-            nn.Conv2d(input_encoder, output_dim, 3, padding=1),
-            nn.MaxPool2d(2, 2),
-        )
-
-        self.conv_decoder = nn.Sequential(
-            nn.BatchNorm2d(input_decoder),
-            nn.ReLU(),
-            nn.Conv2d(input_decoder, output_dim, 3, padding=1),
-        )
-
-        self.conv_attn = nn.Sequential(
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, 1, 1),
-        )
-
-    def forward(self, x1, x2):
-        out = self.conv_encoder(x1) + self.conv_decoder(x2)
-        out = self.conv_attn(out)
-        return out * x2
\ No newline at end of file
diff --git a/spaces/kingsotn/tt-ai/app.py b/spaces/kingsotn/tt-ai/app.py
deleted file mode 100644
index cb798a7ee42d5a0a5ba2eac5f5e02e9436997950..0000000000000000000000000000000000000000
--- a/spaces/kingsotn/tt-ai/app.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import streamlit as st
-import torch
-from transformers import AutoModelForSequenceClassification, pipeline, AutoTokenizer, DistilBertForSequenceClassification, DistilBertTokenizerFast
-import pandas as pd
-import comments
-from random import randint
-import requests
-
-
-def predict_cyberbullying_probability(sentence, tokenizer, model):
-    # Preprocess the input sentence
-    inputs = tokenizer(sentence, padding='max_length', return_token_type_ids=False, return_attention_mask=True, truncation=True, max_length=512, return_tensors='pt')
-    
-    attention_mask = inputs['attention_mask']
-    inputs = inputs['input_ids']
-    
-    with torch.no_grad():
-        # Forward pass
-        outputs = model(inputs, attention_mask=attention_mask)
-
-        probs = torch.sigmoid(outputs.logits.unsqueeze(1).flatten())
-
-    res = probs.numpy().tolist()
-    return res
-
-# @st.cache
-def perform_cyberbullying_analysis(tweet):
-    with st.spinner(text="loading model, wait until spinner ends..."):
-
-        model = AutoModelForSequenceClassification.from_pretrained('kingsotn/finetuned_cyberbullying')
-        tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
-
-    df = pd.DataFrame({'comment': [tweet]})
-    list_probs = predict_cyberbullying_probability(tweet, tokenizer, model)
-    for i, label in enumerate(labels[1:]):
-        df[label] = list_probs[i]
-    
-    return df
-
-def perform_default_analysis(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-    clf = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, framework="pt")
-
-    tweet = st.text_area(label="Enter Text:",value="I'm nice at ping pong")
-    submitted = st.form_submit_button("Analyze")
-    
-    if submitted:
-        #loading bar
-        with st.spinner(text="loading..."):
-            out = clf(tweet)
-            
-        st.json(out)
-        
-        if out[0]["label"] == "POSITIVE" or out[0]["label"] == "POS":
-            st.balloons()
-            # prompt = f"{basic_prompt} + \n\nThe user wrote a tweet that says: {tweet}, compliment them on how nice of a person they are! Remember try to be as cringe and awkard as possible!"
-            # response = generator(prompt, max_length=1000)[0]
-            st.success("nice tweet!")
-        else:
-            # prompt = f"{basic_prompt} + \n\nThe user wrote a tweet that says: {tweet}, tell them on how terrible of a person they are! Remember try to be as cringe and awkard as possible!"
-            # response = generator(prompt, max_length=1000)[0]
-            st.error("bad tweet!")
-
-
-# main -->
-st.title("Toxic Tweets Analyzer")
-st.write("💡 Toxic Tweets Analyzer is an app that helps you determine the likelihood of a tweet or any text being toxic, abusive or cyberbullying. The app offers different pre-trained models to choose from, each with their own strengths and limitations. kingsotn/finetuned_cyberbullying is a finetuned distilbert. It uses artificial intelligence to analyze the text you input and then calculates a probability score for each label: toxic, severe_toxic, obscene, threat, insult, and identity_hate. The scores range from 0 to 1, with 1 being the highest probability of that label being present in the tweet. The output is a table that shows the probability scores for each label, giving you an idea of the toxicity of the tweet. This can be helpful in identifying and preventing cyberbullying and other forms of online abuse.")
-image = "kanye_loves_tweet.jpg"
-st.image(image, use_column_width=True)
-
-labels = ['comment', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
-
-with st.form("my_form"):
-    #select model
-    model_name = st.selectbox("Enter a text and select a pre-trained model to get the sentiment analysis", ["kingsotn/finetuned_cyberbullying", "distilbert-base-uncased-finetuned-sst-2-english", "finiteautomata/bertweet-base-sentiment-analysis", "distilbert-base-uncased"])
-    
-    if model_name == "kingsotn/finetuned_cyberbullying":
-        default = "I'm not even going to lie to you. I love me so much right now."
-        tweet = st.text_area(label="Enter Text:",value=default)
-        submitted = st.form_submit_button("Analyze textbox")
-        random = st.form_submit_button("Get a random 😈😈😈 tweet (warning!!)")
-        kanye = st.form_submit_button("Get a ye quote 🐻🎤🎧🎶")
-        
-        if random:
-            tweet = comments.comments[randint(0, 354)]
-            st.write(tweet)
-            submitted = True
-        
-        if kanye:
-            response = requests.get('https://api.kanye.rest/')
-            if response.status_code == 200:
-                data = response.json()
-                tweet = data['quote']
-            else:
-                st.error("Error getting Kanye quote | status code: " + str(response.status_code))
-            st.write(tweet)
-            submitted = True
-        
-        if submitted:
-            df = perform_cyberbullying_analysis(tweet)
-            st.table(df)
-    else:
-        perform_default_analysis(model_name)
\ No newline at end of file
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/config/default.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/web/config/default.py
deleted file mode 100644
index 02149ab5c730bd56f5e4d557c44860aa4dff2902..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/config/default.py
+++ /dev/null
@@ -1,8 +0,0 @@
-AUDIO_SAMPLES_DIR = 'samples\\'
-DEVICE = '0'
-HOST = 'localhost'
-PORT = 8080
-MAX_CONTENT_PATH =1024 * 1024 * 4  # mp3文件大小限定不能超过4M
-SECRET_KEY = "mockingbird_key"
-WTF_CSRF_SECRET_KEY = "mockingbird_key"
-TEMPLATES_AUTO_RELOAD = True
\ No newline at end of file
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt20.sh b/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt20.sh
deleted file mode 100644
index 31cd5c76b75081331ae03c5ea70ea7ddebaa06e1..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt20.sh
+++ /dev/null
@@ -1,547 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
-
-
-set -x -e
-
-# TODO update the workdir and dest dir name
-# put fasttext model
-WORKDIR=$WORKDIR_ROOT
-# put intermediate files
-TMP_DIR=$WORKDIR_ROOT/tmp/tmp_wmt20_lowres_download
-# output {train,valid,test} files to dest
-DEST=$WORKDIR_ROOT/ML50/raw
-
-UTILS=$PWD/utils
-
-# per dataset locations
-COMMONCRAWL_DIR=$TMP_DIR/commoncrawl
-YANDEX_CORPUS=$WORKDIR_ROOT/wmt20/official/ru/yandex/1mcorpus.zip
-# unzipped
-CZENG_CORPUS=$WORKDIR_ROOT/wmt20/official/cs/czeng/czeng20-train
-CCMT_DIR=$WORKDIR_ROOT/wmt20/official/zh/ccmt/parallel
-
-download_and_select() {
-  SUBFOLDER=$1
-  URL=$2
-  UNCOMPRESS_CMD=$3
-  LANG=$4
-  INPUT_FILEPATH=$5
-  if [[ $# -gt 5 ]]; then
-    LANG_COL=$6
-    EN_COL=$7
-  fi
-
-  mkdir -p $SUBFOLDER
-  cd $SUBFOLDER
-  wget -nc --content-disposition $URL
-  $UNCOMPRESS_CMD
-
-  if [[ $# -gt 5 ]]; then
-    cut -f$LANG_COL $INPUT_FILEPATH > $INPUT_FILEPATH.$LANG
-    cut -f$EN_COL $INPUT_FILEPATH > $INPUT_FILEPATH.en
-  fi
-  cd ..
-
-  ln -sf $SUBFOLDER/$INPUT_FILEPATH.$LANG $SUBFOLDER.$LANG
-  ln -sf $SUBFOLDER/$INPUT_FILEPATH.en $SUBFOLDER.en
-}
-
-prepare_lid() {
-  pip install fasttext
-
-  # TODO specify global workdir
-  MODEL=$WORKDIR/fasttext/lid.176.bin
-  LID_MULTI=$UTILS/fasttext_multi_filter.py
-
-  if [ ! -f "$MODEL" ]; then
-    echo "downloading fasttext lid model..."
-    mkdir -p $WORKDIR/fasttext
-    wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O $MODEL
-  fi
-}
-
-prepare_moses() {
-  pushd $UTILS
-  echo 'Cloning Moses github repository (for tokenization scripts)...'
-  git clone https://github.com/moses-smt/mosesdecoder.git  
-  popd
-}
-
-lid_filter() {
-  # TODO specify global workdir
-  MODEL=$WORKDIR/fasttext/lid.176.bin
-  LID_MULTI=$UTILS/fasttext_multi_filter.py
-
-  prepare_lid
-
-  SRC=$1
-  SRC_FILE=$2
-  SRC_OUTPUT=$3
-  TGT=$4
-  TGT_FILE=$5
-  TGT_OUTPUT=$6
-  python $LID_MULTI --model $MODEL --inputs $SRC_FILE $TGT_FILE --langs $SRC $TGT --outputs $SRC_OUTPUT $TGT_OUTPUT
-}
-
-prepare_ja_ted() {
-  mkdir -p ted
-  cd ted
-
-  wget -nc https://wit3.fbk.eu/archive/2017-01-trnted//texts/en/ja/en-ja.tgz
-  tar -zxvf en-ja.tgz
-  cat en-ja/train.tags.en-ja.en | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.en
-  cat en-ja/train.tags.en-ja.ja | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.ja
-
-  cd ..
-  ln -sf ted/en-ja/train.en-ja.ja ted.ja
-  ln -sf ted/en-ja/train.en-ja.en ted.en
-}
-
-prepare_ja() {
-  OUTPUT_DIR=$TMP_DIR/ja
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select paracrawl "http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/release/2.0/bitext/en-ja.tar.gz" "tar -zxvf en-ja.tar.gz" ja en-ja/en-ja.bicleaner05.txt 4 3 &
-  download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ja.tsv.gz" "gunzip -f news-commentary-v15.en-ja.tsv.gz" ja news-commentary-v15.en-ja.tsv 2 1 &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ja-en.tsv.gz" "gunzip -f wikititles-v2.ja-en.tsv.gz" ja wikititles-v2.ja-en.tsv 1 2 &
-  download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ja.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ja.langid.tsv.gz" ja WikiMatrix.v1.en-ja.langid.tsv 3 2 &
-  download_and_select subtitle "https://nlp.stanford.edu/projects/jesc/data/split.tar.gz" "tar -zxvf split.tar.gz" ja split/train 2 1 &
-  download_and_select kftt "http://www.phontron.com/kftt/download/kftt-data-1.0.tar.gz" "tar -zxvf kftt-data-1.0.tar.gz" ja kftt-data-1.0/data/orig/kyoto-train &
-
-  prepare_ja_ted &
-
-  # ted data needs to 
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.ja" | sort -V | xargs cat > all.ja
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter ja all.ja $DEST/train.ja_XX-en_XX.ja_XX en all.en $DEST/train.ja_XX-en_XX.en_XX
-}
-
-prepare_ta() {
-  OUTPUT_DIR=$TMP_DIR/ta
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ta-en.tsv.gz" "gunzip -f wikititles-v2.ta-en.tsv.gz" ta wikititles-v2.ta-en.tsv 1 2 &
-  download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ta.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ta.langid.tsv.gz" ta WikiMatrix.v1.en-ta.langid.tsv 3 2 &
-  download_and_select pmindia "http://data.statmt.org/pmindia/v1/parallel/pmindia.v1.ta-en.tsv" "" ta pmindia.v1.ta-en.tsv 2 1 &
-  download_and_select tanzil "https://object.pouta.csc.fi/OPUS-Tanzil/v1/moses/en-ta.txt.zip" "unzip en-ta.txt.zip" ta Tanzil.en-ta &
-  download_and_select pib "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar" "tar -xvf pib-v0.tar" ta pib/en-ta/train &
-  download_and_select mkb "http://preon.iiit.ac.in/~jerin/resources/datasets/mkb-v0.tar" "tar -xvf mkb-v0.tar" ta mkb/en-ta/mkb &
-  download_and_select ufal "http://ufal.mff.cuni.cz/~ramasamy/parallel/data/v2/en-ta-parallel-v2.tar.gz" "tar -zxvf en-ta-parallel-v2.tar.gz" ta en-ta-parallel-v2/corpus.bcn.train &
-
-  wait
-
-  # need special handling for nlpc
-  mkdir -p nlpc
-  cd nlpc
-  wget -nc https://raw.githubusercontent.com/nlpc-uom/English-Tamil-Parallel-Corpus/master/En-Ta%20Corpus/En-Ta%20English.txt
-  wget -nc https://github.com/nlpc-uom/English-Tamil-Parallel-Corpus/raw/master/En-Ta%20Corpus/En-Ta%20Tamil.txt
-  tail -n +4 "En-Ta English.txt" > en-ta.en
-  tail -n +4 "En-Ta Tamil.txt" > en-ta.ta
-  cd ..
-  ln -sf nlpc/en-ta.en nlpc.en
-  ln -sf nlpc/en-ta.ta nlpc.ta
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.ta" | sort -V | xargs cat > all.ta
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter ta all.ta $DEST/train.ta_IN-en_XX.ta_IN en all.en $DEST/train.ta_IN-en_XX.en_XX
-}
-
-prepare_iu() {
-  OUTPUT_DIR=$TMP_DIR/iu
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-  
-  download_and_select nh "https://nrc-digital-repository.canada.ca/eng/view/dataset/?id=c7e34fa7-7629-43c2-bd6d-19b32bf64f60" "tar -zxvf Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0.1.tgz" iu Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/NunavutHansard > /dev/null &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.iu-en.tsv.gz" "gunzip -f wikititles-v2.iu-en.tsv.gz" iu wikititles-v2.iu-en.tsv 1 2 &
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.iu" | sort -V | xargs cat | nh/Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/scripts/normalize-iu-spelling.pl > all.iu
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  paste all.iu all.en | awk -F $'\t' '$1!=""&&$2!=""' > all.iuen
-  cut -f1 all.iuen > $DEST/train.iu_CA-en_XX.iu_CA
-  cut -f2 all.iuen > $DEST/train.iu_CA-en_XX.en_XX
-}
-
-prepare_km() {
-  OUTPUT_DIR=$TMP_DIR/km
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-km.xz" "unxz wmt20-sent.en-km.zx" km wmt20-sent.en-km 2 1 &
-
-  # km-parallel has multiple sets, concat all of them together
-  mkdir -p opus
-  cd opus
-  wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/km-parallel.tgz"
-  tar -zxvf km-parallel.tgz
-  find ./km-parallel -maxdepth 1 -name "*.km" | sort -V | xargs cat > opus.km
-  find ./km-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en
-  cd ..
-  ln -sf opus/opus.km .
-  ln -sf opus/opus.en .
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.km" | sort -V | xargs cat > all.km
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter km all.km $DEST/train.km_KH-en_XX.km_KH en all.en $DEST/train.km_KH-en_XX.en_XX
-}
-
-prepare_ps() {
-  OUTPUT_DIR=$TMP_DIR/ps
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-ps.xz" "unxz wmt20-sent.en-ps.xz" ps wmt20-sent.en-ps 2 1 &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ps-en.tsv.gz" "gunzip -f wikititles-v2.ps-en.tsv.gz" ps wikititles-v2.ps-en.tsv 1 2 &
-  # ps-parallel has multiple sets, concat all of them together
-  mkdir -p opus
-  cd opus
-  wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/ps-parallel.tgz"
-  tar -zxvf ps-parallel.tgz
-  find ./ps-parallel -maxdepth 1 -name "*.ps" | sort -V | xargs cat > opus.ps
-  find ./ps-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en
-  cd ..
-  ln -sf opus/opus.ps opus.ps
-  ln -sf opus/opus.en opus.en
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.ps" | sort -V | xargs cat > all.ps
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter ps all.ps $DEST/train.ps_AF-en_XX.ps_AF en all.en $DEST/train.ps_AF-en_XX.en_XX
-}
-
-download_commoncrawl() {
-  mkdir -p $COMMONCRAWL_DIR
-  cd $COMMONCRAWL_DIR
-
-  wget -nc "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-  tar -zxvf training-parallel-commoncrawl.tgz
-}
-link_commoncrawl() {
-  LANG=$1
-  ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.en commoncrawl.en
-  ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.$LANG commoncrawl.$LANG
-}
-
-strip_xlf() {
-  INPUT_FILE=$1
-  SRC=$2
-  TGT=$3
-  grep '<source xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$SRC
-  grep '<target xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$TGT
-}
-
-download_and_process_tilde() {
-  URL=$1
-  UNCOMPRESS_CMD=$2
-  FILENAME=$3
-  LANG=$4
-  PROCESS_CMD=$5
-
-  mkdir -p tilde
-  cd tilde
-  wget -nc $URL
-  $UNCOMPRESS_CMD
-  echo "executing cmd"
-  echo $PROCESS_CMD
-  $PROCESS_CMD
-  cd ..
-  ln -sf tilde/$FILENAME.$LANG tilde.$LANG
-  ln -sf tilde/$FILENAME.en tilde.en
-}
-
-prepare_cs() {
-  OUTPUT_DIR=$TMP_DIR/cs
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  #download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.cs-en.tsv.gz" "gunzip europarl-v10.cs-en.tsv.gz" cs europarl-v10.cs-en.tsv 1 2 &
-  #download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-cs.txt.gz" "gunzip en-cs.txt.gz" cs en-cs.txt 2 1 &
-  #link_commoncrawl cs
-  #download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.cs-en.tsv.gz" "gunzip news-commentary-v15.cs-en.tsv.gz" cs news-commentary-v15.cs-en.tsv 1 2 &
-  #download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.cs-en.tsv.gz" "gunzip wikititles-v2.cs-en.tsv.gz" cs wikititles-v2.cs-en.tsv 1 2 &
-  #download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.cs-en.xlf.gz" "gunzip RAPID_2019.cs-en.xlf.gz" RAPID_2019.cs-en.xlf cs "strip_xlf RAPID_2019.cs-en.xlf cs en" &
-  #download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.cs-en.langid.tsv.gz" "gunzip WikiMatrix.v1.cs-en.langid.tsv.gz" cs WikiMatrix.v1.cs-en.langid.tsv 2 3 &
-
-  #wait
-
-  # remove previous results
-  #rm -f all.??
-  #find ./ -maxdepth 1 -name "*.cs" | sort -V | xargs cat > all.cs
-  #find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  if [ -z $CZENG_CORPUS ] ;
-  then
-          echo "Please download CZENG_CORPUS manually and place them at $CZENG_CORPUS. Exitting..."
-          exit
-  fi  
-  cat $CZENG_CORPUS | sed '/^$/d' | cut -f5 > all.cs
-  cat $CZENG_CORPUS | sed '/^$/d' | cut -f6 > all.en
-
-  lid_filter cs all.cs $DEST/train.cs_CZ-en_XX.cs_CZ en all.en $DEST/train.cs_CZ-en_XX.en_XX
-}
-
-prepare_de() {
-  OUTPUT_DIR=$TMP_DIR/de
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz" "gunzip europarl-v10.de-en.tsv.gz" de europarl-v10.de-en.tsv 1 2 &
-  download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-de.txt.gz"  "gunzip en-de.txt.gz" de en-de.txt 2 1 &
-  link_commoncrawl de
-  download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz" "gunzip news-commentary-v15.de-en.tsv.gz" de news-commentary-v15.de-en.tsv 1 2 &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz" "gunzip wikititles-v2.de-en.tsv.gz" de wikititles-v2.de-en.tsv 1 2 &
-  download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.de-en.xlf.gz" "gunzip RAPID_2019.de-en.xlf.gz" RAPID_2019.de-en.xlf de "strip_xlf RAPID_2019.de-en.xlf de en" &
-  download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz" "gunzip WikiMatrix.v1.de-en.langid.tsv.gz" de WikiMatrix.v1.de-en.langid.tsv 2 3 &
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.de" | sort -V | xargs cat > all.de
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter de all.de $DEST/train.de_DE-en_XX.de_DE en all.en $DEST/train.de_DE-en_XX.en_XX
-}
-
-prepare_tmx() {
-  TMX_FILE=$1
-  git clone https://github.com/amake/TMX2Corpus $UTILS/tmx2corpus
-  pip install tinysegmenter
-
-  python $UTILS/tmx2corpus/tmx2corpus.py $TMX_FILE
-}
-
-prepare_pl() {
-  OUTPUT_DIR=$TMP_DIR/pl
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  # download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.pl-en.tsv.gz" "gunzip europarl-v10.pl-en.tsv.gz" pl europarl-v10.pl-en.tsv 1 2 &
-  # download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-pl.txt.gz" "gunzip en-pl.txt.gz" pl en-pl.txt 2 1 &
-  # download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.pl-en.tsv.gz" "gunzip wikititles-v2.pl-en.tsv.gz" pl wikititles-v2.pl-en.tsv 1 2 &
-  download_and_select tilde "https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.en-pl.tmx.zip" "gunzip rapid2019.en-pl.tmx.zip" bitext pl "prepare_tmx RAPID_2019.UNIQUE.en-pl.tmx" &
-  # download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-pl.langid.tsv.gz" "gunzip WikiMatrix.v1.en-pl.langid.tsv.gz" pl WikiMatrix.v1.en-pl.langid.tsv 3 2 &
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.pl" | sort -V | xargs cat > all.pl
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter pl all.pl $DEST/train.pl_PL-en_XX.pl_PL en all.en $DEST/train.pl_PL-en_XX.en_XX
-}
-
-prepare_uncorpus() {
-  $URLS=$1
-  $FILES=$2
-
-  mkdir -p uncorpus
-  cd uncorpus
-
-  for URL in $URLS; do
-    wget -nc $URL
-  done
-  cat $FILES > uncorpus.tar.gz
-  tar -zxvf uncorpus.tar.gz
-
-  cd ..
-  ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.$LANG uncorpus.$LANG
-  ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.en uncorpus.en
-}
-
-prepare_yandex() {
-  mkdir -p yandex
-  cd yandex
-  unzip $YANDEX_CORPUS ./
-  cd ..
-  ln -s yandex/corpus.en_ru.1m.en yandex.en
-  ln -s yandex/corpus.en_ru.1m.ru yandex.ru
-}
-
-prepare_ru() {
-  OUTPUT_DIR=$TMP_DIR/ru
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" "tar -zxvf paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" ru paracrawl-release1.en-ru.zipporah0-dedup-clean &
-  link_commoncrawl ru
-  download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz" "gunzip news-commentary-v15.en-ru.tsv.gz" ru news-commentary-v15.en-ru.tsv 2 1 &
-  prepare_yandex &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz" "gunzip wikititles-v2.ru-en.tsv.gz" ru wikititles-v2.ru-en.tsv 1 2 &
-  prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02" "UNv1.0.en-ru.tar.gz.00 UNv1.0.en-ru.tar.gz.01 UNv1.0.en-ru.tar.gz.02" &
-  download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz" "gunzip WikiMatrix.v1.en-ru.langid.tsv.gz" ru WikiMatrix.v1.en-ru.langid.tsv 3 2 &
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.ru" | sort -V | xargs cat > all.ru
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter ru all.ru $DEST/train.ru_RU-en_XX.ru_RU en all.en $DEST/train.ru_RU-en_XX.en_XX
-}
-
-prepare_ccmt() {
-  mkdir -p ccmt
-  cd ccmt
-  # assume ccmt data is already unzipped under CCMT_DIR folder
-  cat $CCMT_DIR/datum2017/Book*_cn.txt | sed 's/ //g' > datum2017.detok.zh
-  cat $CCMT_DIR/datum2017/Book*_en.txt > datum2017.detok.en
-  cat $CCMT_DIR/casict2011/casict-A_ch.txt $CCMT_DIR/casict2011/casict-B_ch.txt $CCMT_DIR/casict2015/casict2015_ch.txt $CCMT_DIR/datum2015/datum_ch.txt $CCMT_DIR/neu2017/NEU_cn.txt datum2017.detok.zh > ccmt.zh
-  cat $CCMT_DIR/casict2011/casict-A_en.txt $CCMT_DIR/casict2011/casict-B_en.txt $CCMT_DIR/casict2015/casict2015_en.txt $CCMT_DIR/datum2015/datum_en.txt $CCMT_DIR/neu2017/NEU_en.txt datum2017.detok.en > ccmt.en
-  cd ..
-  ln -sf ccmt/ccmt.zh ccmt.zh
-  ln -sf ccmt/ccmt.en ccmt.en
-}
-
-prepare_zh() {
-  OUTPUT_DIR=$TMP_DIR/zh
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-
-  download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz" "gunzip news-commentary-v15.en-zh.tsv.gz" zh news-commentary-v15.en-zh.tsv 2 1 &
-  download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz" "gunzip wikititles-v2.zh-en.tsv.gz" zh wikititles-v2.zh-en.tsv 1 2 &
-  prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01" "UNv1.0.en-zh.tar.gz.00 UNv1.0.en-zh.tar.gz.01" &
-  prepare_ccmt &
-  download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz" "gunzip WikiMatrix.v1.en-zh.langid.tsv.gz" zh WikiMatrix.v1.en-zh.langid.tsv 3 2 &
-
-  wait
-
-  # remove previous results
-  rm -f all.??
-  find ./ -maxdepth 1 -name "*.zh" | sort -V | xargs cat > all.zh
-  find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
-  lid_filter zh all.zh $DEST/train.zh_CN-en_XX.zh_CN en all.en $DEST/train.zh_CN-en_XX.en_XX
-}
-
-prepare_tests() {
-  OUTPUT_DIR=$TMP_DIR
-  mkdir -p $OUTPUT_DIR
-  cd $OUTPUT_DIR
-  wget -nc http://data.statmt.org/wmt20/translation-task/dev.tgz
-  tar -zxvf dev.tgz
-  cd dev
-
-  cat newsdev2020-jaen-src.ja.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.ja
-  cat newsdev2020-jaen-ref.en.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.en
-  split newsdev2020-jaen.ja -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.ja_XX
-  split newsdev2020-jaen.en -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.en_XX
-  split newsdev2020-jaen.ja -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.ja_XX
-  split newsdev2020-jaen.en -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.en_XX
-
-  cat newsdev2020-iuen-src.iu.sgm | strip_sgm.sh > newsdev2020-iuen.iu
-  cat newsdev2020-iuen-ref.en.sgm | strip_sgm.sh > newsdev2020-iuen.en
-  split newsdev2020-iuen.iu -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.iu_CA
-  split newsdev2020-iuen.en -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.en_XX
-  split newsdev2020-iuen.iu -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.iu_CA
-  split newsdev2020-iuen.en -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.en_XX
-
-  cat newsdev2020-taen-src.ta.sgm | strip_sgm.sh > newsdev2020-taen.ta
-  cat newsdev2020-taen-ref.en.sgm | strip_sgm.sh > newsdev2020-taen.en
-  split newsdev2020-taen.ta -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.ta_IN
-  split newsdev2020-taen.en -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.en_XX
-  split newsdev2020-taen.ta -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.ta_IN
-  split newsdev2020-taen.en -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.en_XX
-
-  cp wikipedia.dev.km-en.km $DEST/valid.km_KH-en_XX.km_KH
-  cp wikipedia.dev.km-en.en $DEST/valid.km_KH-en_XX.en_XX
-  cp wikipedia.devtest.km-en.km $DEST/test.km_KH-en_XX.km_KH
-  cp wikipedia.devtest.km-en.en $DEST/test.km_KH-en_XX.en_XX
-
-  cp wikipedia.dev.ps-en.ps $DEST/valid.ps_AF-en_XX.ps_AF
-  cp wikipedia.dev.ps-en.en $DEST/valid.ps_AF-en_XX.en_XX
-  cp wikipedia.devtest.ps-en.ps $DEST/test.ps_AF-en_XX.ps_AF
-  cp wikipedia.devtest.ps-en.en $DEST/test.ps_AF-en_XX.en_XX
-
-  cat newsdev2020-plen-src.pl.sgm | strip_sgm.sh > newsdev2020-plen.pl
-  cat newsdev2020-plen-ref.en.sgm | strip_sgm.sh > newsdev2020-plen.en
-  split newsdev2020-plen.pl -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.pl_PL
-  split newsdev2020-plen.en -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.en_XX
-  split newsdev2020-plen.pl -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.pl_PL
-  split newsdev2020-plen.en -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.en_XX
-
-  cat newstest2018-encs-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.en_XX
-  cat newstest2018-encs-ref.cs.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.cs_CZ
-  cat newstest2019-encs-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.en_XX
-  cat newstest2019-encs-ref.cs.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.cs_CZ
-
-  cat newstest2018-deen-src.de.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.de_DE
-  cat newstest2018-deen-ref.en.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.en_XX
-  cat newstest2018-ende-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.en_XX
-  cat newstest2018-ende-ref.de.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.de_DE
-  cat newstest2019-deen-src.de.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.de_DE
-  cat newstest2019-deen-ref.en.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.en_XX
-  cat newstest2019-ende-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.en_XX
-  cat newstest2019-ende-ref.de.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.de_DE
-
-  cat newstest2018-ruen-src.ru.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.ru_RU
-  cat newstest2018-ruen-ref.en.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.en_XX
-  cat newstest2018-enru-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.en_XX
-  cat newstest2018-enru-ref.ru.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.ru_RU
-  cat newstest2019-ruen-src.ru.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.ru_RU
-  cat newstest2019-ruen-ref.en.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.en_XX
-  cat newstest2019-enru-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.en_XX
-  cat newstest2019-enru-ref.ru.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.ru_RU
-
-  cat newstest2018-zhen-src.zh.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.zh_CN
-  cat newstest2018-zhen-ref.en.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.en_XX
-  cat newstest2018-enzh-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.en_XX
-  cat newstest2018-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.zh_CN
-  cat newstest2019-zhen-src.zh.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.zh_CN
-  cat newstest2019-zhen-ref.en.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.en_XX
-  cat newstest2019-enzh-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.en_XX
-  cat newstest2019-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.zh_CN
-}
-
-mkdir -p $DEST
-
-prepare_lid
-prepare_moses
-download_commoncrawl
-
-prepare_ja &
-prepare_ta &
-prepare_km &
-prepare_ps &
-prepare_iu &
-prepare_cs &
-prepare_de &
-prepare_pl &
-prepare_ru &
-prepare_zh &
-
-# prepare valid/test set
-prepare_tests &
-
-# wait
-
-# TODO remove intermediate files
-# rm -rf $TMP_DIR
diff --git a/spaces/kohrisatou-infinity/KIP_01_beta/attentions.py b/spaces/kohrisatou-infinity/KIP_01_beta/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/kohrisatou-infinity/KIP_01_beta/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/utils.py
deleted file mode 100644
index 9b9ebcb852ee0264300ba1efba68c055ba6db6bf..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/utils.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import re
-import warnings
-from dataclasses import is_dataclass
-from enum import Enum
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    MutableMapping,
-    Optional,
-    Set,
-    Type,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import fastapi
-from fastapi.datastructures import DefaultPlaceholder, DefaultType
-from fastapi.openapi.constants import REF_PREFIX
-from pydantic import BaseConfig, BaseModel, create_model
-from pydantic.class_validators import Validator
-from pydantic.fields import FieldInfo, ModelField, UndefinedType
-from pydantic.schema import model_process_schema
-from pydantic.utils import lenient_issubclass
-
-if TYPE_CHECKING:  # pragma: nocover
-    from .routing import APIRoute
-
-# Cache for `create_cloned_field`
-_CLONED_TYPES_CACHE: MutableMapping[
-    Type[BaseModel], Type[BaseModel]
-] = WeakKeyDictionary()
-
-
-def is_body_allowed_for_status_code(status_code: Union[int, str, None]) -> bool:
-    if status_code is None:
-        return True
-    # Ref: https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#patterned-fields-1
-    if status_code in {
-        "default",
-        "1XX",
-        "2XX",
-        "3XX",
-        "4XX",
-        "5XX",
-    }:
-        return True
-    current_status_code = int(status_code)
-    return not (current_status_code < 200 or current_status_code in {204, 304})
-
-
-def get_model_definitions(
-    *,
-    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],
-    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],
-) -> Dict[str, Any]:
-    definitions: Dict[str, Dict[str, Any]] = {}
-    for model in flat_models:
-        m_schema, m_definitions, m_nested_models = model_process_schema(
-            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX
-        )
-        definitions.update(m_definitions)
-        model_name = model_name_map[model]
-        if "description" in m_schema:
-            m_schema["description"] = m_schema["description"].split("\f")[0]
-        definitions[model_name] = m_schema
-    return definitions
-
-
-def get_path_param_names(path: str) -> Set[str]:
-    return set(re.findall("{(.*?)}", path))
-
-
-def create_response_field(
-    name: str,
-    type_: Type[Any],
-    class_validators: Optional[Dict[str, Validator]] = None,
-    default: Optional[Any] = None,
-    required: Union[bool, UndefinedType] = True,
-    model_config: Type[BaseConfig] = BaseConfig,
-    field_info: Optional[FieldInfo] = None,
-    alias: Optional[str] = None,
-) -> ModelField:
-    """
-    Create a new response field. Raises if type_ is invalid.
-    """
-    class_validators = class_validators or {}
-    field_info = field_info or FieldInfo()
-
-    try:
-        return ModelField(
-            name=name,
-            type_=type_,
-            class_validators=class_validators,
-            default=default,
-            required=required,
-            model_config=model_config,
-            alias=alias,
-            field_info=field_info,
-        )
-    except RuntimeError:
-        raise fastapi.exceptions.FastAPIError(
-            "Invalid args for response field! Hint: "
-            f"check that {type_} is a valid Pydantic field type. "
-            "If you are using a return type annotation that is not a valid Pydantic "
-            "field (e.g. Union[Response, dict, None]) you can disable generating the "
-            "response model from the type annotation with the path operation decorator "
-            "parameter response_model=None. Read more: "
-            "https://fastapi.tiangolo.com/tutorial/response-model/"
-        ) from None
-
-
-def create_cloned_field(
-    field: ModelField,
-    *,
-    cloned_types: Optional[MutableMapping[Type[BaseModel], Type[BaseModel]]] = None,
-) -> ModelField:
-    # cloned_types caches already cloned types to support recursive models and improve
-    # performance by avoiding unecessary cloning
-    if cloned_types is None:
-        cloned_types = _CLONED_TYPES_CACHE
-
-    original_type = field.type_
-    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
-        original_type = original_type.__pydantic_model__
-    use_type = original_type
-    if lenient_issubclass(original_type, BaseModel):
-        original_type = cast(Type[BaseModel], original_type)
-        use_type = cloned_types.get(original_type)
-        if use_type is None:
-            use_type = create_model(original_type.__name__, __base__=original_type)
-            cloned_types[original_type] = use_type
-            for f in original_type.__fields__.values():
-                use_type.__fields__[f.name] = create_cloned_field(
-                    f, cloned_types=cloned_types
-                )
-    new_field = create_response_field(name=field.name, type_=use_type)
-    new_field.has_alias = field.has_alias
-    new_field.alias = field.alias
-    new_field.class_validators = field.class_validators
-    new_field.default = field.default
-    new_field.required = field.required
-    new_field.model_config = field.model_config
-    new_field.field_info = field.field_info
-    new_field.allow_none = field.allow_none
-    new_field.validate_always = field.validate_always
-    if field.sub_fields:
-        new_field.sub_fields = [
-            create_cloned_field(sub_field, cloned_types=cloned_types)
-            for sub_field in field.sub_fields
-        ]
-    if field.key_field:
-        new_field.key_field = create_cloned_field(
-            field.key_field, cloned_types=cloned_types
-        )
-    new_field.validators = field.validators
-    new_field.pre_validators = field.pre_validators
-    new_field.post_validators = field.post_validators
-    new_field.parse_json = field.parse_json
-    new_field.shape = field.shape
-    new_field.populate_validators()
-    return new_field
-
-
-def generate_operation_id_for_path(
-    *, name: str, path: str, method: str
-) -> str:  # pragma: nocover
-    warnings.warn(
-        "fastapi.utils.generate_operation_id_for_path() was deprecated, "
-        "it is not used internally, and will be removed soon",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    operation_id = name + path
-    operation_id = re.sub(r"\W", "_", operation_id)
-    operation_id = operation_id + "_" + method.lower()
-    return operation_id
-
-
-def generate_unique_id(route: "APIRoute") -> str:
-    operation_id = route.name + route.path_format
-    operation_id = re.sub(r"\W", "_", operation_id)
-    assert route.methods
-    operation_id = operation_id + "_" + list(route.methods)[0].lower()
-    return operation_id
-
-
-def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
-    for key, value in update_dict.items():
-        if (
-            key in main_dict
-            and isinstance(main_dict[key], dict)
-            and isinstance(value, dict)
-        ):
-            deep_dict_update(main_dict[key], value)
-        elif (
-            key in main_dict
-            and isinstance(main_dict[key], list)
-            and isinstance(update_dict[key], list)
-        ):
-            main_dict[key] = main_dict[key] + update_dict[key]
-        else:
-            main_dict[key] = value
-
-
-def get_value_or_default(
-    first_item: Union[DefaultPlaceholder, DefaultType],
-    *extra_items: Union[DefaultPlaceholder, DefaultType],
-) -> Union[DefaultPlaceholder, DefaultType]:
-    """
-    Pass items or `DefaultPlaceholder`s by descending priority.
-
-    The first one to _not_ be a `DefaultPlaceholder` will be returned.
-
-    Otherwise, the first item (a `DefaultPlaceholder`) will be returned.
-    """
-    items = (first_item,) + extra_items
-    for item in items:
-        if not isinstance(item, DefaultPlaceholder):
-            return item
-    return first_item
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/subset/svg.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/subset/svg.py
deleted file mode 100644
index 4ed2cbd20156a24c106f1974ad49dcc1df95d2fc..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/subset/svg.py
+++ /dev/null
@@ -1,251 +0,0 @@
-from __future__ import annotations
-
-import re
-from functools import lru_cache
-from itertools import chain, count
-from typing import Dict, Iterable, Iterator, List, Optional, Set, Tuple
-
-try:
-    from lxml import etree
-except ImportError:
-    # lxml is required for subsetting SVG, but we prefer to delay the import error
-    # until subset_glyphs() is called (i.e. if font to subset has an 'SVG ' table)
-    etree = None
-
-from fontTools import ttLib
-from fontTools.subset.util import _add_method
-from fontTools.ttLib.tables.S_V_G_ import SVGDocument
-
-
-__all__ = ["subset_glyphs"]
-
-
-GID_RE = re.compile(r"^glyph(\d+)$")
-
-NAMESPACES = {
-    "svg": "http://www.w3.org/2000/svg",
-    "xlink": "http://www.w3.org/1999/xlink",
-}
-XLINK_HREF = f'{{{NAMESPACES["xlink"]}}}href'
-
-
-# TODO(antrotype): Replace with functools.cache once we are 3.9+
-@lru_cache(maxsize=None)
-def xpath(path):
-    # compile XPath upfront, caching result to reuse on multiple elements
-    return etree.XPath(path, namespaces=NAMESPACES)
-
-
-def group_elements_by_id(tree: etree.Element) -> Dict[str, etree.Element]:
-    # select all svg elements with 'id' attribute no matter where they are
-    # including the root element itself:
-    # https://github.com/fonttools/fonttools/issues/2548
-    return {el.attrib["id"]: el for el in xpath("//svg:*[@id]")(tree)}
-
-
-def parse_css_declarations(style_attr: str) -> Dict[str, str]:
-    # https://developer.mozilla.org/en-US/docs/Web/SVG/Attribute/style
-    # https://developer.mozilla.org/en-US/docs/Web/CSS/Syntax#css_declarations
-    result = {}
-    for declaration in style_attr.split(";"):
-        if declaration.count(":") == 1:
-            property_name, value = declaration.split(":")
-            property_name = property_name.strip()
-            result[property_name] = value.strip()
-        elif declaration.strip():
-            raise ValueError(f"Invalid CSS declaration syntax: {declaration}")
-    return result
-
-
-def iter_referenced_ids(tree: etree.Element) -> Iterator[str]:
-    # Yield all the ids that can be reached via references from this element tree.
-    # We currently support xlink:href (as used by <use> and gradient templates),
-    # and local url(#...) links found in fill or clip-path attributes
-    # TODO(anthrotype): Check we aren't missing other supported kinds of reference
-    find_svg_elements_with_references = xpath(
-        ".//svg:*[ "
-        "starts-with(@xlink:href, '#') "
-        "or starts-with(@fill, 'url(#') "
-        "or starts-with(@clip-path, 'url(#') "
-        "or contains(@style, ':url(#') "
-        "]",
-    )
-    for el in chain([tree], find_svg_elements_with_references(tree)):
-        ref_id = href_local_target(el)
-        if ref_id is not None:
-            yield ref_id
-
-        attrs = el.attrib
-        if "style" in attrs:
-            attrs = {**attrs, **parse_css_declarations(el.attrib["style"])}
-        for attr in ("fill", "clip-path"):
-            if attr in attrs:
-                value = attrs[attr]
-                if value.startswith("url(#") and value.endswith(")"):
-                    ref_id = value[5:-1]
-                    assert ref_id
-                    yield ref_id
-
-
-def closure_element_ids(
-    elements: Dict[str, etree.Element], element_ids: Set[str]
-) -> None:
-    # Expand the initial subset of element ids to include ids that can be reached
-    # via references from the initial set.
-    unvisited = element_ids
-    while unvisited:
-        referenced: Set[str] = set()
-        for el_id in unvisited:
-            if el_id not in elements:
-                # ignore dangling reference; not our job to validate svg
-                continue
-            referenced.update(iter_referenced_ids(elements[el_id]))
-        referenced -= element_ids
-        element_ids.update(referenced)
-        unvisited = referenced
-
-
-def subset_elements(el: etree.Element, retained_ids: Set[str]) -> bool:
-    # Keep elements if their id is in the subset, or any of their children's id is.
-    # Drop elements whose id is not in the subset, and either have no children,
-    # or all their children are being dropped.
-    if el.attrib.get("id") in retained_ids:
-        # if id is in the set, don't recurse; keep whole subtree
-        return True
-    # recursively subset all the children; we use a list comprehension instead
-    # of a parentheses-less generator expression because we don't want any() to
-    # short-circuit, as our function has a side effect of dropping empty elements.
-    if any([subset_elements(e, retained_ids) for e in el]):
-        return True
-    assert len(el) == 0
-    parent = el.getparent()
-    if parent is not None:
-        parent.remove(el)
-    return False
-
-
-def remap_glyph_ids(
-    svg: etree.Element, glyph_index_map: Dict[int, int]
-) -> Dict[str, str]:
-    # Given {old_gid: new_gid} map, rename all elements containing id="glyph{gid}"
-    # special attributes
-    elements = group_elements_by_id(svg)
-    id_map = {}
-    for el_id, el in elements.items():
-        m = GID_RE.match(el_id)
-        if not m:
-            continue
-        old_index = int(m.group(1))
-        new_index = glyph_index_map.get(old_index)
-        if new_index is not None:
-            if old_index == new_index:
-                continue
-            new_id = f"glyph{new_index}"
-        else:
-            # If the old index is missing, the element correspond to a glyph that was
-            # excluded from the font's subset.
-            # We rename it to avoid clashes with the new GIDs or other element ids.
-            new_id = f".{el_id}"
-            n = count(1)
-            while new_id in elements:
-                new_id = f"{new_id}.{next(n)}"
-
-        id_map[el_id] = new_id
-        el.attrib["id"] = new_id
-
-    return id_map
-
-
-def href_local_target(el: etree.Element) -> Optional[str]:
-    if XLINK_HREF in el.attrib:
-        href = el.attrib[XLINK_HREF]
-        if href.startswith("#") and len(href) > 1:
-            return href[1:]  # drop the leading #
-    return None
-
-
-def update_glyph_href_links(svg: etree.Element, id_map: Dict[str, str]) -> None:
-    # update all xlink:href="#glyph..." attributes to point to the new glyph ids
-    for el in xpath(".//svg:*[starts-with(@xlink:href, '#glyph')]")(svg):
-        old_id = href_local_target(el)
-        assert old_id is not None
-        if old_id in id_map:
-            new_id = id_map[old_id]
-            el.attrib[XLINK_HREF] = f"#{new_id}"
-
-
-def ranges(ints: Iterable[int]) -> Iterator[Tuple[int, int]]:
-    # Yield sorted, non-overlapping (min, max) ranges of consecutive integers
-    sorted_ints = iter(sorted(set(ints)))
-    try:
-        start = end = next(sorted_ints)
-    except StopIteration:
-        return
-    for v in sorted_ints:
-        if v - 1 == end:
-            end = v
-        else:
-            yield (start, end)
-            start = end = v
-    yield (start, end)
-
-
-@_add_method(ttLib.getTableClass("SVG "))
-def subset_glyphs(self, s) -> bool:
-    if etree is None:
-        raise ImportError("No module named 'lxml', required to subset SVG")
-
-    # glyph names (before subsetting)
-    glyph_order: List[str] = s.orig_glyph_order
-    # map from glyph names to original glyph indices
-    rev_orig_glyph_map: Dict[str, int] = s.reverseOrigGlyphMap
-    # map from original to new glyph indices (after subsetting)
-    glyph_index_map: Dict[int, int] = s.glyph_index_map
-
-    new_docs: List[SVGDocument] = []
-    for doc in self.docList:
-
-        glyphs = {
-            glyph_order[i] for i in range(doc.startGlyphID, doc.endGlyphID + 1)
-        }.intersection(s.glyphs)
-        if not glyphs:
-            # no intersection: we can drop the whole record
-            continue
-
-        svg = etree.fromstring(
-            # encode because fromstring dislikes xml encoding decl if input is str.
-            # SVG xml encoding must be utf-8 as per OT spec.
-            doc.data.encode("utf-8"),
-            parser=etree.XMLParser(
-                # Disable libxml2 security restrictions to support very deep trees.
-                # Without this we would get an error like this:
-                # `lxml.etree.XMLSyntaxError: internal error: Huge input lookup`
-                # when parsing big fonts e.g. noto-emoji-picosvg.ttf.
-                huge_tree=True,
-                # ignore blank text as it's not meaningful in OT-SVG; it also prevents
-                # dangling tail text after removing an element when pretty_print=True
-                remove_blank_text=True,
-            ),
-        )
-
-        elements = group_elements_by_id(svg)
-        gids = {rev_orig_glyph_map[g] for g in glyphs}
-        element_ids = {f"glyph{i}" for i in gids}
-        closure_element_ids(elements, element_ids)
-
-        if not subset_elements(svg, element_ids):
-            continue
-
-        if not s.options.retain_gids:
-            id_map = remap_glyph_ids(svg, glyph_index_map)
-            update_glyph_href_links(svg, id_map)
-
-        new_doc = etree.tostring(svg, pretty_print=s.options.pretty_svg).decode("utf-8")
-
-        new_gids = (glyph_index_map[i] for i in gids)
-        for start, end in ranges(new_gids):
-            new_docs.append(SVGDocument(new_doc, start, end, doc.compressed))
-
-    self.docList = new_docs
-
-    return bool(self.docList)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/context.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/context.py
deleted file mode 100644
index 18eecbc84ec8fd98ee1c77cf53c58ecfa6255fd4..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/context.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Defines the Context class, which is used to store the state of all Blocks that are being rendered.
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
-    from gradio.blocks import BlockContext, Blocks
-
-
-class Context:
-    root_block: Blocks | None = None  # The current root block that holds all blocks.
-    block: BlockContext | None = None  # The current block that children are added to.
-    id: int = 0  # Running id to uniquely refer to any block that gets defined
-    ip_address: str | None = None  # The IP address of the user.
-    hf_token: str | None = None  # The token provided when loading private HF repos
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-66e01a03.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-66e01a03.js
deleted file mode 100644
index e25353e33314425cc72ec891545044024f0bbdb6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-66e01a03.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{E as u,L as v}from"./index-0c011c1e.js";import{s as k,t,h as S,L as w,i as z,w as x,f as R,a as U,b as _,I as T,x as V}from"./index-90411bc1.js";import"./index-7c0e54a6.js";import"./Blocks-61158678.js";import"./Button-661a0701.js";import"./BlockLabel-95be8dd1.js";import"./Empty-96265974.js";/* empty css                                                    */import"./Copy-c4997e4e.js";import"./Download-e5de98da.js";const Y=94,g=1,C=95,Z=96,f=2,$=[9,10,11,12,13,32,133,160,5760,8192,8193,8194,8195,8196,8197,8198,8199,8200,8201,8202,8232,8233,8239,8287,12288],G=58,N=40,X=95,q=91,c=45,E=46,j=35,D=37;function p(e){return e>=65&&e<=90||e>=97&&e<=122||e>=161}function I(e){return e>=48&&e<=57}const B=new u((e,o)=>{for(let r=!1,a=0,O=0;;O++){let{next:l}=e;if(p(l)||l==c||l==X||r&&I(l))!r&&(l!=c||O>0)&&(r=!0),a===O&&l==c&&a++,e.advance();else{r&&e.acceptToken(l==N?C:a==2&&o.canShift(f)?f:Z);break}}}),A=new 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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/strikethrough.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/strikethrough.py
deleted file mode 100644
index 9b062a661102ec221062af4d32b447d733deea22..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/strikethrough.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# ~~strike through~~
-from __future__ import annotations
-
-from .state_inline import Delimiter, StateInline
-
-
-def tokenize(state: StateInline, silent: bool):
-    """Insert each marker as a separate text token, and add it to delimiter list"""
-    start = state.pos
-    marker = state.srcCharCode[start]
-
-    if silent:
-        return False
-
-    if marker != 0x7E:  # /* ~ */
-        return False
-
-    scanned = state.scanDelims(state.pos, True)
-    length = scanned.length
-    ch = chr(marker)
-
-    if length < 2:
-        return False
-
-    if length % 2:
-        token = state.push("text", "", 0)
-        token.content = ch
-        length -= 1
-
-    i = 0
-    while i < length:
-        token = state.push("text", "", 0)
-        token.content = ch + ch
-        state.delimiters.append(
-            Delimiter(
-                **{
-                    "marker": marker,
-                    "length": 0,  # disable "rule of 3" length checks meant for emphasis
-                    "jump": i // 2,  # for `~~` 1 marker = 2 characters
-                    "token": len(state.tokens) - 1,
-                    "end": -1,
-                    "open": scanned.can_open,
-                    "close": scanned.can_close,
-                }
-            )
-        )
-
-        i += 2
-
-    state.pos += scanned.length
-
-    return True
-
-
-def _postProcess(state: StateInline, delimiters: list[Delimiter]):
-    loneMarkers = []
-    maximum = len(delimiters)
-
-    i = 0
-    while i < maximum:
-        startDelim = delimiters[i]
-
-        if startDelim.marker != 0x7E:  # /* ~ */
-            i += 1
-            continue
-
-        if startDelim.end == -1:
-            i += 1
-            continue
-
-        endDelim = delimiters[startDelim.end]
-
-        token = state.tokens[startDelim.token]
-        token.type = "s_open"
-        token.tag = "s"
-        token.nesting = 1
-        token.markup = "~~"
-        token.content = ""
-
-        token = state.tokens[endDelim.token]
-        token.type = "s_close"
-        token.tag = "s"
-        token.nesting = -1
-        token.markup = "~~"
-        token.content = ""
-
-        if (
-            state.tokens[endDelim.token - 1].type == "text"
-            and state.tokens[endDelim.token - 1].content == "~"
-        ):
-            loneMarkers.append(endDelim.token - 1)
-
-        i += 1
-
-    # If a marker sequence has an odd number of characters, it's split
-    # like this: `~~~~~` -> `~` + `~~` + `~~`, leaving one marker at the
-    # start of the sequence.
-    #
-    # So, we have to move all those markers after subsequent s_close tags.
-    #
-    while loneMarkers:
-        i = loneMarkers.pop()
-        j = i + 1
-
-        while (j < len(state.tokens)) and (state.tokens[j].type == "s_close"):
-            j += 1
-
-        j -= 1
-
-        if i != j:
-            token = state.tokens[j]
-            state.tokens[j] = state.tokens[i]
-            state.tokens[i] = token
-
-
-def postProcess(state: StateInline):
-    """Walk through delimiter list and replace text tokens with tags."""
-    tokens_meta = state.tokens_meta
-    maximum = len(state.tokens_meta)
-    _postProcess(state, state.delimiters)
-
-    curr = 0
-    while curr < maximum:
-        try:
-            curr_meta = tokens_meta[curr]
-        except IndexError:
-            pass
-        else:
-            if curr_meta and "delimiters" in curr_meta:
-                _postProcess(state, curr_meta["delimiters"])
-        curr += 1
diff --git a/spaces/lafi23333/aikomori/mel_processing.py b/spaces/lafi23333/aikomori/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/lafi23333/aikomori/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/lewiswu1209/MockingBird/utils/modelutils.py b/spaces/lewiswu1209/MockingBird/utils/modelutils.py
deleted file mode 100644
index 4b2efc7a82950e5dc035060b1c5c1839056a7699..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/utils/modelutils.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from pathlib import Path
-
-def check_model_paths(encoder_path: Path, synthesizer_path: Path, vocoder_path: Path):
-    # This function tests the model paths and makes sure at least one is valid.
-    if encoder_path.is_file() or encoder_path.is_dir():
-        return
-    if synthesizer_path.is_file() or synthesizer_path.is_dir():
-        return
-    if vocoder_path.is_file() or vocoder_path.is_dir():
-        return
-
-    # If none of the paths exist, remind the user to download models if needed
-    print("********************************************************************************")
-    print("Error: Model files not found. Please download the models")
-    print("********************************************************************************\n")
-    quit(-1)
diff --git a/spaces/lightli/bingo-newbing/src/components/user-menu.tsx b/spaces/lightli/bingo-newbing/src/components/user-menu.tsx
deleted file mode 100644
index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000
--- a/spaces/lightli/bingo-newbing/src/components/user-menu.tsx
+++ /dev/null
@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Configurar Router Belkin F5d72304 Como Repetidor.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Configurar Router Belkin F5d72304 Como Repetidor.md
deleted file mode 100644
index 1c84c05fc8aaebb376a854caa181b3d327268cb0..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Configurar Router Belkin F5d72304 Como Repetidor.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-<h2>Configurar Router Belkin F5d72304 Como Repetidor</h2><br /><p><b><b>DOWNLOAD</b> &#10145; <a href="https://bytlly.com/2uGx2t">https://bytlly.com/2uGx2t</a></b></p><br /><br />
-<br />
-de rede conexion
-
-Con todos los repetidores de red que pude probar y configurar como receptores de otras red funcionan perfectamente y mi router como reproductor por internet no, quiero decir que si conecto mi PC al modem se conecta de una a la otra y funciona perfectamente.
-
-A:
-
-No conecte, y la actualice, la puse en repetidor de red y se activó automaticamente el repetidor de red y mi red se estableció solo, al reiniciar se quedó en el estado que lo tuve antes de reiniciar.
-
-No conecte, y la actualice, la puse en repetidor de red y se activó
-
-  automaticamente el repetidor de red y mi red se estableció solo, al
-
-  reiniciar se quedó en el estado que lo tuve antes de reiniciar.
-
-Q:
-
-mipmap being displayed instead of resources when using R8
-
-I have recently upgraded from Firebase to Blaze and am having some issues with my R8 code.
-
-When I run the app on Android 10 with my latest build, I get the following result
-
-There is an image and two text resources that are mipmapDrawables. I am using the following code:
-
-android:background="@drawable/image_with_mipmaps"
-
-android:drawable="@mipmap/image_with_mipmaps"
-
-I have tried restarting the phone but nothing has resolved the issue. Any help would be greatly appreciated.
-
-Are you sure your build type is a debug build? Because R8 should create the resources for you.
-
-mipmap is a resource. If the resource already exists, it will be used instead of generating a new one.
-
-Since you are not generating the resources for mipmapDrawables, they should appear as intended.
-
-How to get private configuration for build pipeline
-
-I have an Azure DevOps project that uses VSTS Azure Pipelines for CI/CD. I would like to be able to provide a personalized config for my build steps. In order to do this, I need to access the same private build configuration that powers the build 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Hightide Louise Hunter London Scat Party Mov.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Hightide Louise Hunter London Scat Party Mov.md
deleted file mode 100644
index 9c86c4012e23fdd0cdbba218cfe69c2f9a6d01f1..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Hightide Louise Hunter London Scat Party Mov.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Hightide Louise Hunter London Scat Party Mov</h2><br /><p><b><b>Download File</b> ::: <a href="https://bytlly.com/2uGwJv">https://bytlly.com/2uGwJv</a></b></p><br /><br />
-
- d5da3c52bf<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Mario Sports Mix Wii Torrent Download Iso.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Mario Sports Mix Wii Torrent Download Iso.md
deleted file mode 100644
index 84638a76e432d03ec90757be821f762501c35fe2..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Mario Sports Mix Wii Torrent Download Iso.md	
+++ /dev/null
@@ -1,56 +0,0 @@
-<h2>Mario Sports Mix Wii Torrent Download Iso</h2><br /><p><b><b>Download Zip</b> === <a href="https://bytlly.com/2uGw8X">https://bytlly.com/2uGw8X</a></b></p><br /><br />
-<br />
-luciu gamów czy jak mniej?
-
- mario: mp3
-
- mario: no
-
- tak samo pliki do zainstalowania
-
- tak samo usługi do instalacji
-
- zainstalowanie jakie usługi
-
- whatever
-
- możesz zainstalować jak chcesz
-
- czy wszystkie musze na tym baszkowisku wyłączyć?
-
- no
-
- nie powinno ci sie robić
-
- ale jak mu się to nie uda?
-
- to wystarczy, żeby jakies usługi nie ściągnął
-
- mario: rób sobie to jak chcesz
-
- to tak samo
-
- możesz sobie zainstalować sobie
-
- z /home też by się instalowało
-
- tak?
-
- czy nie?
-
- zainstaluj
-
- pewnie masz zainstalowanego
-
- nie wiem
-
- jak nie, to instaluj sobie
-
- i zainstaluj jakoś
-
- wybacz ale jak wyłączę wszystkie, nie widzę kartki na górze
-
- ponoć go n 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/linfanluntan/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py b/spaces/linfanluntan/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py
deleted file mode 100644
index 5277f46157403e47fd830fc519144b97ef69d4ae..0000000000000000000000000000000000000000
--- a/spaces/linfanluntan/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/litagin/rvc_okiba_TTS/vc_infer_pipeline.py b/spaces/litagin/rvc_okiba_TTS/vc_infer_pipeline.py
deleted file mode 100644
index ed2aacd1866379563006e3cf4dd40472f7ab4692..0000000000000000000000000000000000000000
--- a/spaces/litagin/rvc_okiba_TTS/vc_infer_pipeline.py
+++ /dev/null
@@ -1,451 +0,0 @@
-import os
-import sys
-import traceback
-from functools import lru_cache
-from time import time as ttime
-
-import faiss
-import librosa
-import numpy as np
-import parselmouth
-import pyworld
-import torch
-import torch.nn.functional as F
-import torchcrepe
-from scipy import signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            model = "full"
-            # Pick a batch size that doesn't cause memory errors on your gpu
-            batch_size = 512
-            # Compute pitch using first gpu
-            audio = torch.tensor(np.copy(x))[None].float()
-            f0, pd = torchcrepe.predict(
-                audio,
-                self.sr,
-                self.window,
-                f0_min,
-                f0_max,
-                model,
-                batch_size=batch_size,
-                device=self.device,
-                return_periodicity=True,
-            )
-            pd = torchcrepe.filter.median(pd, 3)
-            f0 = torchcrepe.filter.mean(f0, 3)
-            f0[pd < 0.1] = 0
-            f0 = f0[0].cpu().numpy()
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                print("loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "rmvpe.pt", is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/ltgoslo/ssa-perin/data/batch.py b/spaces/ltgoslo/ssa-perin/data/batch.py
deleted file mode 100644
index 60101a3a17cf9a8db10280580f1e704ff80e3e83..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/data/batch.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python3
-# coding=utf-8
-
-import torch
-import torch.nn.functional as F
-
-
-class Batch:
-    @staticmethod
-    def build(data):
-        fields = list(data[0].keys())
-        transposed = {}
-        for field in fields:
-            if isinstance(data[0][field], tuple):
-                transposed[field] = tuple(Batch._stack(field, [example[field][i] for example in data]) for i in range(len(data[0][field])))
-            else:
-                transposed[field] = Batch._stack(field, [example[field] for example in data])
-
-        return transposed
-
-    @staticmethod
-    def _stack(field: str, examples):
-        if field == "anchored_labels":
-            return examples
-
-        dim = examples[0].dim()
-
-        if dim == 0:
-            return torch.stack(examples)
-
-        lengths = [max(example.size(i) for example in examples) for i in range(dim)]
-        if any(length == 0 for length in lengths):
-            return torch.LongTensor(len(examples), *lengths)
-
-        examples = [F.pad(example, Batch._pad_size(example, lengths)) for example in examples]
-        return torch.stack(examples)
-
-    @staticmethod
-    def _pad_size(example, total_size):
-        return [p for i, l in enumerate(total_size[::-1]) for p in (0, l - example.size(-1 - i))]
-
-    @staticmethod
-    def index_select(batch, indices):
-        filtered_batch = {}
-        for key, examples in batch.items():
-            if isinstance(examples, list) or isinstance(examples, tuple):
-                filtered_batch[key] = [example.index_select(0, indices) for example in examples]
-            else:
-                filtered_batch[key] = examples.index_select(0, indices)
-
-        return filtered_batch
-
-    @staticmethod
-    def to_str(batch):
-        string = "\n".join([f"\t{name}: {Batch._short_str(item)}" for name, item in batch.items()])
-        return string
-
-    @staticmethod
-    def to(batch, device):
-        converted = {}
-        for field in batch.keys():
-            converted[field] = Batch._to(batch[field], device)
-        return converted
-
-    @staticmethod
-    def _short_str(tensor):
-        # unwrap variable to tensor
-        if not torch.is_tensor(tensor):
-            # (1) unpack variable
-            if hasattr(tensor, "data"):
-                tensor = getattr(tensor, "data")
-            # (2) handle include_lengths
-            elif isinstance(tensor, tuple) or isinstance(tensor, list):
-                return str(tuple(Batch._short_str(t) for t in tensor))
-            # (3) fallback to default str
-            else:
-                return str(tensor)
-
-        # copied from torch _tensor_str
-        size_str = "x".join(str(size) for size in tensor.size())
-        device_str = "" if not tensor.is_cuda else " (GPU {})".format(tensor.get_device())
-        strt = "[{} of size {}{}]".format(torch.typename(tensor), size_str, device_str)
-        return strt
-
-    @staticmethod
-    def _to(tensor, device):
-        if not torch.is_tensor(tensor):
-            if isinstance(tensor, tuple):
-                return tuple(Batch._to(t, device) for t in tensor)
-            elif isinstance(tensor, list):
-                return [Batch._to(t, device) for t in tensor]
-            else:
-                raise Exception(f"unsupported type of {tensor} to be casted to cuda")
-
-        return tensor.to(device, non_blocking=True)
diff --git a/spaces/ltgoslo/ssa-perin/mtool/codec/norec.py b/spaces/ltgoslo/ssa-perin/mtool/codec/norec.py
deleted file mode 100644
index f10e8aba1c2e5d6e8c94440f15713ae328f38937..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/mtool/codec/norec.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import json
-import sys
-
-from graph import Graph
-
-
-def read(fp, text=None, node_centric=False):
-    def anchor(node):
-        anchors = list()
-        for string in node[1]:
-            string = string.split(":")
-            anchors.append({"from": int(string[0]), "to": int(string[1])})
-        return anchors
-
-    for native in json.load(fp):
-        map = dict()
-        try:
-            graph = Graph(native["sent_id"], flavor=1, framework="norec")
-            graph.add_input(native["text"])
-
-            if not node_centric:
-                top = graph.add_node(top=True)
-
-            for opinion in native["opinions"]:
-                expression = opinion["Polar_expression"]
-                properties, values = ["Intensity"], [opinion["Intensity"]]
-
-                if node_centric:
-                    expression = graph.add_node(
-                        label=opinion["Polarity"],
-                        top=True,
-                        properties=properties,
-                        values=values,
-                        anchors=anchor(expression),
-                    )
-                else:
-                    expression = graph.add_node(
-                        properties=properties,
-                        values=values,
-                        anchors=anchor(expression),
-                    )
-                    key = tuple(opinion["Polar_expression"][1])
-                    if key in map:
-                        print("we got double expression here", native["sent_id"])
-                    map[key] = expression
-
-                    graph.add_edge(top.id, expression.id, opinion["Polarity"])
-
-                source = opinion["Source"]
-                if len(source[1]):
-                    key = tuple(source[1])
-                    if key in map:
-                        source = map[key]
-                    else:
-                        source = graph.add_node(
-                            label="Source" if node_centric else None,
-                            anchors=anchor(source),
-                        )
-                        map[key] = source
-                    graph.add_edge(expression.id, source.id, None if node_centric else "Source")
-
-                target = opinion["Target"]
-                if len(target[1]):
-                    key = tuple(target[1])
-                    if key in map:
-                        target = map[key]
-                    else:
-                        target = graph.add_node(
-                            label="Target" if node_centric else None,
-                            anchors=anchor(target),
-                        )
-                        map[key] = target
-                    graph.add_edge(expression.id, target.id, None if node_centric else "Target")
-
-            yield graph, None
-
-        except Exception as error:
-            print(
-                f"codec.norec.read(): ignoring {native}: {error}",
-                file=sys.stderr,
-            )
-
-
-def get_text_span(node, text):
-    anchored_text = [text[anchor['from']:anchor['to']] for anchor in node.anchors]
-    anchors = [f"{anchor['from']}:{anchor['to']}" for anchor in node.anchors]
-    return anchored_text, anchors
-
-
-def write(graph, input, node_centric=False):
-    try:
-        if node_centric:
-            return write_node_centric(graph, input)
-        return write_labeled_edge(graph, input)
-
-    except Exception as error:
-        print(f"Problem with decoding sentence {graph.id}")
-        raise error
-
-
-def write_node_centric(graph, input):
-    nodes = {node.id: node for node in graph.nodes}
-
-    # create opinions
-    opinions = {}
-    for node in graph.nodes:
-        if node.label in ["Source", "Target"]:
-            continue
-        opinions[node.id] = {
-            "Source": [[], []],
-            "Target": [[], []],
-            "Polar_expression": [*get_text_span(node, input)],
-            "Polarity": node.label,
-        }
-        if node.properties is not None and len(node.properties) > 0:
-            for key, value in zip(node.properties, node.values):
-                opinions[node.id][key] = value
-
-    # add sources & targets
-    for edge in graph.edges:
-        if edge.src not in opinions:
-            continue
-
-        target_node = nodes[edge.tgt]
-        if target_node.label not in ["Source", "Target"]:
-            continue
-
-        anchored_text, anchors = get_text_span(target_node, input)
-        opinions[edge.src][target_node.label][0] += anchored_text
-        opinions[edge.src][target_node.label][1] += anchors
-
-    sentence = {
-        "sent_id": graph.id,
-        "text": input,
-        "opinions": list(opinions.values()),
-    }
-    return sentence
-
-
-def write_labeled_edge(graph, input):
-    nodes = {node.id: node for node in graph.nodes}
-
-    # create opinions
-    opinions = {}
-    for edge in graph.edges:
-        if edge.lab in ["Source", "Target"]:
-            continue
-
-        node = nodes[edge.tgt]
-        opinions[node.id] = {
-            "Source": [[], []],
-            "Target": [[], []],
-            "Polar_expression": [*get_text_span(node, input)],
-            "Polarity": edge.lab,
-        }
-
-    # add sources & targets
-    for edge in graph.edges:
-        if edge.lab not in ["Source", "Target"]:
-            continue
-        if edge.src not in opinions:
-            continue
-
-        node = nodes[edge.tgt]
-        anchored_text, anchors = get_text_span(node, input)
-
-        opinions[edge.src][edge.lab][0] += anchored_text
-        opinions[edge.src][edge.lab][1] += anchors
-
-    sentence = {
-        "sent_id": graph.id,
-        "text": input,
-        "opinions": list(opinions.values()),
-    }
-    return sentence
diff --git a/spaces/luisoala/raw2logit/figures/ABtesting.py b/spaces/luisoala/raw2logit/figures/ABtesting.py
deleted file mode 100644
index 2f151e95ff9093128b9e8a7b23734fe342706ded..0000000000000000000000000000000000000000
--- a/spaces/luisoala/raw2logit/figures/ABtesting.py
+++ /dev/null
@@ -1,831 +0,0 @@
-import os
-import argparse
-import json
-from cv2 import transform
-
-import torch
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, Normalize
-import torch.nn.functional as F
-
-from dataset import get_dataset, Subset
-from utils.base import get_mlflow_model_by_name, SmartFormatter
-from processing.pipeline_numpy import RawProcessingPipeline
-
-from utils.hendrycks_robustness import Distortions
-
-import segmentation_models_pytorch as smp
-
-import matplotlib.pyplot as plt
-
-parser = argparse.ArgumentParser(description="AB testing, Show Results", formatter_class=SmartFormatter)
-
-# Select experiment
-parser.add_argument("--mode", type=str, default="ABShowImages", choices=('ABMakeTable', 'ABShowTable', 'ABShowImages', 'ABShowAllImages', 'CMakeTable', 'CShowTable', 'CShowImages', 'CShowAllImages'),
-                    help='R|Choose operation to compute. \n'
-                    'A) Lens2Logit image generation: \n  '
-                    'ABMakeTable: Compute cross-validation metrics results \n  '
-                    'ABShowTable: Plot cross-validation results on a table \n  '
-                    'ABShowImages: Choose a training and testing image to compare different pipelines \n  '
-                    'ABShowAllImages: Plot all possible pipelines \n'
-                    'B) Hendrycks Perturbations, C-type dataset: \n  '
-                    'CMakeTable: For each pipeline, it computes cross-validation metrics for different perturbations  \n  '
-                    'CShowTable: Plot metrics for different pipelines and perturbations \n  '
-                    'CShowImages: Plot an image with a selected a pipeline and perturbation\n  '
-                    'CShowAllImages: Plot all possible perturbations for a fixed pipeline')
-
-parser.add_argument("--dataset_name", type=str, default='Microscopy',
-                    choices=['Microscopy', 'Drone', 'DroneSegmentation'], help='Choose dataset')
-parser.add_argument("--augmentation", type=str, default='weak',
-                    choices=['none', 'weak', 'strong'], help='Choose augmentation')
-parser.add_argument("--N_runs", type=int, default=5, help='Number of k-fold splitting used in the training')
-parser.add_argument("--download_model", default=False, action='store_true', help='Download Models in cache')
-
-# Select pipelines
-parser.add_argument("--dm_train", type=str, default='bilinear', choices=('bilinear', 'malvar2004',
-                                                                         'menon2007'), help='Choose demosaicing for training processing model')
-parser.add_argument("--s_train", type=str, default='sharpening_filter', choices=('sharpening_filter',
-                                                                                 'unsharp_masking'), help='Choose sharpening for training processing model')
-parser.add_argument("--dn_train", type=str, default='gaussian_denoising', choices=('gaussian_denoising',
-                                                                                   'median_denoising'), help='Choose denoising for training processing model')
-parser.add_argument("--dm_test", type=str, default='bilinear', choices=('bilinear', 'malvar2004',
-                                                                        'menon2007'), help='Choose demosaicing for testing processing model')
-parser.add_argument("--s_test", type=str, default='sharpening_filter', choices=('sharpening_filter',
-                                                                                'unsharp_masking'), help='Choose sharpening for testing processing model')
-parser.add_argument("--dn_test", type=str, default='gaussian_denoising', choices=('gaussian_denoising',
-                                                                                  'median_denoising'), help='Choose denoising for testing processing model')
-
-# Select Ctest parameters
-parser.add_argument("--transform", type=str, default='identity', choices=('identity', 'gaussian_noise', 'shot_noise', 'impulse_noise', 'speckle_noise',
-                                                                          'gaussian_blur', 'zoom_blur', 'contrast', 'brightness', 'saturate', 'elastic_transform'), help='Choose transformation to show for Ctesting')
-parser.add_argument("--severity", type=int, default=1, choices=(1, 2, 3, 4, 5), help='Choose severity for Ctesting')
-
-args = parser.parse_args()
-
-
-class metrics:
-    def __init__(self, confusion_matrix):
-        self.cm = confusion_matrix
-        self.N_classes = len(confusion_matrix)
-
-    def accuracy(self):
-        Tp = torch.diagonal(self.cm, 0).sum()
-        N_elements = torch.sum(self.cm)
-        return Tp / N_elements
-
-    def precision(self):
-        Tp_Fp = torch.sum(self.cm, 1)
-        Tp_Fp[Tp_Fp == 0] = 1
-        return torch.diagonal(self.cm, 0) / Tp_Fp
-
-    def recall(self):
-        Tp_Fn = torch.sum(self.cm, 0)
-        Tp_Fn[Tp_Fn == 0] = 1
-        return torch.diagonal(self.cm, 0) / Tp_Fn
-
-    def f1_score(self):
-        prod = (self.precision() * self.recall())
-        sum = (self.precision() + self.recall())
-        sum[sum == 0.] = 1.
-        return 2 * (prod / sum)
-
-    def over_N_runs(ms, N_runs):
-        m, m2 = 0, 0
-
-        for i in ms:
-            m += i
-        mu = m / N_runs
-
-        for i in ms:
-            m2 += (i - mu)**2
-
-        sigma = torch.sqrt(m2 / (N_runs - 1))
-
-        return mu.tolist(), sigma.tolist()
-
-
-class ABtesting:
-    def __init__(self,
-                 dataset_name: str,
-                 augmentation: str,
-                 dm_train: str,
-                 s_train: str,
-                 dn_train: str,
-                 dm_test: str,
-                 s_test: str,
-                 dn_test: str,
-                 N_runs: int,
-                 severity=1,
-                 transform='identity',
-                 download_model=False):
-        self.experiment_name = 'ABtesting'
-        self.dataset_name = dataset_name
-        self.augmentation = augmentation
-        self.dm_train = dm_train
-        self.s_train = s_train
-        self.dn_train = dn_train
-        self.dm_test = dm_test
-        self.s_test = s_test
-        self.dn_test = dn_test
-        self.N_runs = N_runs
-        self.severity = severity
-        self.transform = transform
-        self.download_model = download_model
-
-    def static_pip_val(self, debayer=None, sharpening=None, denoising=None, severity=None, transform=None, plot_mode=False):
-
-        if debayer == None:
-            debayer = self.dm_test
-        if sharpening == None:
-            sharpening = self.s_test
-        if denoising == None:
-            denoising = self.dn_test
-        if severity == None:
-            severity = self.severity
-        if transform == None:
-            transform = self.transform
-
-        dataset = get_dataset(self.dataset_name)
-
-        if self.dataset_name == "Drone" or self.dataset_name == "DroneSegmentation":
-            mean = torch.tensor([0.35, 0.36, 0.35])
-            std = torch.tensor([0.12, 0.11, 0.12])
-        elif self.dataset_name == "Microscopy":
-            mean = torch.tensor([0.91, 0.84, 0.94])
-            std = torch.tensor([0.08, 0.12, 0.05])
-
-        if not plot_mode:
-            dataset.transform = Compose([RawProcessingPipeline(
-                camera_parameters=dataset.camera_parameters,
-                debayer=debayer,
-                sharpening=sharpening,
-                denoising=denoising,
-            ), Distortions(severity=severity, transform=transform),
-                Normalize(mean, std)])
-        else:
-            dataset.transform = Compose([RawProcessingPipeline(
-                camera_parameters=dataset.camera_parameters,
-                debayer=debayer,
-                sharpening=sharpening,
-                denoising=denoising,
-            ), Distortions(severity=severity, transform=transform)])
-
-        return dataset
-
-    def ABclassification(self):
-
-        DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-        parent_run_name = f"{self.dataset_name}_{self.dm_train}_{self.s_train}_{self.dn_train}_{self.augmentation}"
-
-        print(
-            f'\nTraining pipeline:\n Dataset: {self.dataset_name}, Augmentation: {self.augmentation} \n Debayer: {self.dm_train}, Sharpening: {self.s_train}, Denoiser: {self.dn_train} \n')
-        print(f'\nTesting pipeline:\n Dataset: {self.dataset_name}, Augmentation: {self.augmentation} \n Debayer: {self.dm_test}, Sharpening: {self.s_test}, Denoiser: {self.dn_test} \n Transform: {self.transform}, Severity: {self.severity}\n')
-
-        accuracies, precisions, recalls, f1_scores = [], [], [], []
-
-        os.system('rm -r /tmp/py*')
-
-        for N_run in range(self.N_runs):
-
-            print(f"Evaluating Run {N_run}")
-
-            run_name = parent_run_name + '_' + str(N_run)
-
-            state_dict, model = get_mlflow_model_by_name(self.experiment_name, run_name,
-                                                         download_model=self.download_model)
-
-            dataset = self.static_pip_val()
-            valid_set = Subset(dataset, indices=state_dict['valid_indices'])
-            valid_loader = DataLoader(valid_set, batch_size=1, num_workers=16, shuffle=False)
-
-            model.eval()
-
-            len_classes = len(dataset.classes)
-            confusion_matrix = torch.zeros((len_classes, len_classes))
-
-            for img, label in valid_loader:
-
-                prediction = model(img.to(DEVICE)).detach().cpu()
-                prediction = torch.argmax(prediction, dim=1)
-                confusion_matrix[label, prediction] += 1  # Real value rows, Declared columns
-
-            m = metrics(confusion_matrix)
-
-            accuracies.append(m.accuracy())
-            precisions.append(m.precision())
-            recalls.append(m.recall())
-            f1_scores.append(m.f1_score())
-
-            os.system('rm -r /tmp/t*')
-
-        accuracy = metrics.over_N_runs(accuracies, self.N_runs)
-        precision = metrics.over_N_runs(precisions, self.N_runs)
-        recall = metrics.over_N_runs(recalls, self.N_runs)
-        f1_score = metrics.over_N_runs(f1_scores, self.N_runs)
-        return dataset.classes, accuracy, precision, recall, f1_score
-
-    def ABsegmentation(self):
-
-        DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-        parent_run_name = f"{self.dataset_name}_{self.dm_train}_{self.s_train}_{self.dn_train}_{self.augmentation}"
-
-        print(
-            f'\nTraining pipeline:\n Dataset: {self.dataset_name}, Augmentation: {self.augmentation} \n Debayer: {self.dm_train}, Sharpening: {self.s_train}, Denoiser: {self.dn_train} \n')
-        print(f'\nTesting pipeline:\n Dataset: {self.dataset_name}, Augmentation: {self.augmentation} \n Debayer: {self.dm_test}, Sharpening: {self.s_test}, Denoiser: {self.dn_test} \n Transform: {self.transform}, Severity: {self.severity}\n')
-
-        IoUs = []
-
-        os.system('rm -r /tmp/py*')
-
-        for N_run in range(self.N_runs):
-
-            print(f"Evaluating Run {N_run}")
-
-            run_name = parent_run_name + '_' + str(N_run)
-
-            state_dict, model = get_mlflow_model_by_name(self.experiment_name, run_name,
-                                                         download_model=self.download_model)
-
-            dataset = self.static_pip_val()
-
-            valid_set = Subset(dataset, indices=state_dict['valid_indices'])
-            valid_loader = DataLoader(valid_set, batch_size=1, num_workers=16, shuffle=False)
-
-            model.eval()
-
-            IoU = 0
-
-            for img, label in valid_loader:
-
-                prediction = model(img.to(DEVICE)).detach().cpu()
-                prediction = F.logsigmoid(prediction).exp().squeeze()
-                IoU += smp.utils.metrics.IoU()(prediction, label)
-
-            IoU = IoU / len(valid_loader)
-            IoUs.append(IoU.item())
-
-            os.system('rm -r /tmp/t*')
-
-        IoU = metrics.over_N_runs(torch.tensor(IoUs), self.N_runs)
-        return IoU
-
-    def ABShowImages(self):
-
-        path = 'results/ABtesting/imgs/'
-        if not os.path.exists(path):
-            os.makedirs(path)
-
-        path = os.path.join(
-            path, f'{self.dataset_name}_{self.augmentation}_{self.dm_train[:2]}{self.s_train[0]}{self.dn_train[:2]}_{self.dm_test[:2]}{self.s_test[0]}{self.dn_test[:2]}')
-
-        if not os.path.exists(path):
-            os.makedirs(path)
-
-        run_name = f"{self.dataset_name}_{self.dm_train}_{self.s_train}_{self.dn_train}_{self.augmentation}" + \
-            '_' + str(0)
-
-        state_dict, model = get_mlflow_model_by_name(self.experiment_name, run_name, download_model=self.download_model)
-
-        model.augmentation = None
-
-        for t in ([self.dm_train, self.s_train, self.dn_train, 'train_img'],
-                  [self.dm_test, self.s_test, self.dn_test, 'test_img']):
-
-            debayer, sharpening, denoising, img_type = t[0], t[1], t[2], t[3]
-
-            dataset = self.static_pip_val(debayer=debayer, sharpening=sharpening, denoising=denoising, plot_mode=True)
-            valid_set = Subset(dataset, indices=state_dict['valid_indices'])
-
-            img, _ = next(iter(valid_set))
-
-            plt.figure()
-            plt.imshow(img.permute(1, 2, 0))
-            if img_type == 'train_img':
-                plt.title('Train Image')
-                plt.savefig(os.path.join(path, f'img_train.png'))
-                imgA = img
-            else:
-                plt.title('Test Image')
-                plt.savefig(os.path.join(path, f'img_test.png'))
-
-                for c, color in enumerate(['Red', 'Green', 'Blue']):
-                    diff = torch.abs(imgA - img)
-                    plt.figure()
-                    # plt.imshow(diff.permute(1,2,0))
-                    plt.imshow(diff[c, 50:200, 50:200], cmap=f'{color}s')
-                    plt.title(f'|Train Image - Test Image| - {color}')
-                    plt.colorbar()
-                    plt.savefig(os.path.join(path, f'diff_{color}.png'))
-                    plt.figure()
-                    diff[diff == 0.] = 1e-5
-                    # plt.imshow(torch.log(diff.permute(1,2,0)))
-                    plt.imshow(torch.log(diff)[c])
-                    plt.title(f'log(|Train Image - Test Image|) - color')
-                    plt.colorbar()
-                    plt.savefig(os.path.join(path, f'logdiff_{color}.png'))
-
-            if self.dataset_name == 'DroneSegmentation':
-                plt.figure()
-                plt.imshow(model(img[None].cuda()).detach().cpu().squeeze())
-                if img_type == 'train_img':
-                    plt.savefig(os.path.join(path, f'mask_train.png'))
-                else:
-                    plt.savefig(os.path.join(path, f'mask_test.png'))
-
-    def ABShowAllImages(self):
-        if not os.path.exists('results/ABtesting'):
-            os.makedirs('results/ABtesting')
-
-        demosaicings = ['bilinear', 'malvar2004', 'menon2007']
-        sharpenings = ['sharpening_filter', 'unsharp_masking']
-        denoisings = ['median_denoising', 'gaussian_denoising']
-
-        fig = plt.figure()
-        columns = 4
-        rows = 3
-
-        i = 1
-
-        for dm in demosaicings:
-            for s in sharpenings:
-                for dn in denoisings:
-
-                    dataset = self.static_pip_val(self.dm_test, self.s_test,
-                                                  self.dn_test, plot_mode=True)
-
-                    img, _ = dataset[0]
-
-                    fig.add_subplot(rows, columns, i)
-                    plt.imshow(img.permute(1, 2, 0))
-                    plt.title(f'{dm}\n{s}\n{dn}', fontsize=8)
-                    plt.xticks([])
-                    plt.yticks([])
-                    plt.tight_layout()
-
-                    i += 1
-
-        plt.show()
-        plt.savefig(f'results/ABtesting/ABpipelines.png')
-
-    def CShowImages(self):
-
-        path = 'results/Ctesting/imgs/'
-        if not os.path.exists(path):
-            os.makedirs(path)
-
-        run_name = f"{self.dataset_name}_{self.dm_test}_{self.s_test}_{self.dn_test}_{self.augmentation}" + '_' + str(0)
-
-        state_dict, model = get_mlflow_model_by_name(self.experiment_name, run_name, download_model=True)
-
-        model.augmentation = None
-
-        dataset = self.static_pip_val(self.dm_test, self.s_test, self.dn_test,
-                                      self.severity, self.transform, plot_mode=True)
-        valid_set = Subset(dataset, indices=state_dict['valid_indices'])
-
-        img, _ = next(iter(valid_set))
-
-        plt.figure()
-        plt.imshow(img.permute(1, 2, 0))
-        plt.savefig(os.path.join(
-            path, f'{self.dataset_name}_{self.augmentation}_{self.dm_train[:2]}{self.s_train[0]}{self.dn_train[:2]}_{self.transform}_sev{self.severity}'))
-
-    def CShowAllImages(self):
-        if not os.path.exists('results/Cimages'):
-            os.makedirs('results/Cimages')
-
-        transforms = ['identity', 'gaussian_noise', 'shot_noise', 'impulse_noise', 'speckle_noise',
-                      'gaussian_blur', 'zoom_blur', 'contrast', 'brightness', 'saturate', 'elastic_transform']
-
-        for i, t in enumerate(transforms):
-
-            fig = plt.figure(figsize=(10, 6))
-            columns = 5
-            rows = 1
-
-            for sev in range(1, 6):
-
-                dataset = self.static_pip_val(severity=sev, transform=t, plot_mode=True)
-
-                img, _ = dataset[0]
-
-                fig.add_subplot(rows, columns, sev)
-                plt.imshow(img.permute(1, 2, 0))
-                plt.title(f'Severity: {sev}')
-                plt.xticks([])
-                plt.yticks([])
-                plt.tight_layout()
-
-            if '_' in t:
-                t = t.replace('_', ' ')
-            t = t[0].upper() + t[1:]
-
-            fig.suptitle(f'{t}', x=0.5, y=0.8, fontsize=24)
-            plt.show()
-            plt.savefig(f'results/Cimages/{i+1}_{t.lower()}.png')
-
-
-def ABMakeTable(dataset_name: str, augmentation: str,
-                N_runs: int, download_model: bool):
-
-    demosaicings = ['bilinear', 'malvar2004', 'menon2007']
-    sharpenings = ['sharpening_filter', 'unsharp_masking']
-    denoisings = ['median_denoising', 'gaussian_denoising']
-
-    path = 'results/ABtesting/tables'
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-    runs = {}
-    i = 0
-
-    for dm_train in demosaicings:
-        for s_train in sharpenings:
-            for dn_train in denoisings:
-                for dm_test in demosaicings:
-                    for s_test in sharpenings:
-                        for dn_test in denoisings:
-                            train_pip = [dm_train, s_train, dn_train]
-                            test_pip = [dm_test, s_test, dn_test]
-                            runs[f'run{i}'] = {
-                                'dataset': dataset_name,
-                                'augmentation': augmentation,
-                                'train_pip': train_pip,
-                                'test_pip': test_pip,
-                                'N_runs': N_runs
-                            }
-                            ABclass = ABtesting(
-                                dataset_name=dataset_name,
-                                augmentation=augmentation,
-                                dm_train=dm_train,
-                                s_train=s_train,
-                                dn_train=dn_train,
-                                dm_test=dm_test,
-                                s_test=s_test,
-                                dn_test=dn_test,
-                                N_runs=N_runs,
-                                download_model=download_model
-                            )
-
-                            if dataset_name == 'DroneSegmentation':
-                                IoU = ABclass.ABsegmentation()
-                                runs[f'run{i}']['IoU'] = IoU
-                            else:
-                                classes, accuracy, precision, recall, f1_score = ABclass.ABclassification()
-                                runs[f'run{i}']['classes'] = classes
-                                runs[f'run{i}']['accuracy'] = accuracy
-                                runs[f'run{i}']['precision'] = precision
-                                runs[f'run{i}']['recall'] = recall
-                                runs[f'run{i}']['f1_score'] = f1_score
-
-                            with open(os.path.join(path, f'{dataset_name}_{augmentation}_runs.txt'), 'w') as outfile:
-                                json.dump(runs, outfile)
-
-                            i += 1
-
-
-def ABShowTable(dataset_name: str, augmentation: str):
-
-    path = 'results/ABtesting/tables'
-    assert os.path.exists(path), 'No tables to plot'
-
-    json_file = os.path.join(path, f'{dataset_name}_{augmentation}_runs.txt')
-
-    with open(json_file, 'r') as run_file:
-        runs = json.load(run_file)
-
-        metrics = torch.zeros((2, 12, 12))
-        classes = []
-
-        i, j = 0, 0
-
-        for r in range(len(runs)):
-
-            run = runs['run' + str(r)]
-            if dataset_name == 'DroneSegmentation':
-                acc = run['IoU']
-            else:
-                acc = run['accuracy']
-            if len(classes) < 12:
-                class_list = run['test_pip']
-                class_name = f'{class_list[0][:2]},{class_list[1][:1]},{class_list[2][:2]}'
-                classes.append(class_name)
-            mu, sigma = round(acc[0], 4), round(acc[1], 4)
-
-            metrics[0, j, i] = mu
-            metrics[1, j, i] = sigma
-
-            i += 1
-
-            if i == 12:
-                i = 0
-                j += 1
-
-    differences = torch.zeros_like(metrics)
-
-    diag_mu = torch.diagonal(metrics[0], 0)
-    diag_sigma = torch.diagonal(metrics[1], 0)
-
-    for r in range(len(metrics[0])):
-        differences[0, r] = diag_mu[r] - metrics[0, r]
-        differences[1, r] = torch.sqrt(metrics[1, r]**2 + diag_sigma[r]**2)
-
-    # Plot with scatter
-
-    for i, img in enumerate([metrics, differences]):
-
-        x, y = torch.arange(12), torch.arange(12)
-        x, y = torch.meshgrid(x, y)
-
-        if i == 0:
-            vmin = max(0.65, round(img[0].min().item(), 2))
-            vmax = round(img[0].max().item(), 2)
-            step = 0.02
-        elif i == 1:
-            vmin = round(img[0].min().item(), 2)
-            if augmentation == 'none':
-                vmax = min(0.15, round(img[0].max().item(), 2))
-            if augmentation == 'weak':
-                vmax = min(0.08, round(img[0].max().item(), 2))
-            if augmentation == 'strong':
-                vmax = min(0.05, round(img[0].max().item(), 2))
-            step = 0.01
-
-        vmin = int(vmin / step) * step
-        vmax = int(vmax / step) * step
-
-        fig = plt.figure(figsize=(10, 6.2))
-        ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
-        marker_size = 350
-        plt.scatter(x, y, c=torch.rot90(img[1][x, y], -1, [0, 1]), vmin=0.,
-                    vmax=img[1].max(), cmap='viridis', s=marker_size * 2, marker='s')
-        ticks = torch.arange(0., img[1].max(), 0.03).tolist()
-        ticks = [round(tick, 2) for tick in ticks]
-        cba = plt.colorbar(pad=0.06)
-        cba.set_ticks(ticks)
-        cba.ax.set_yticklabels(ticks)
-        # cmap = plt.cm.get_cmap('tab20c').reversed()
-        cmap = plt.cm.get_cmap('Reds')
-        plt.scatter(x, y, c=torch.rot90(img[0][x, y], -1, [0, 1]), vmin=vmin,
-                    vmax=vmax, cmap=cmap, s=marker_size, marker='s')
-        ticks = torch.arange(vmin, vmax, step).tolist()
-        ticks = [round(tick, 2) for tick in ticks]
-        if ticks[-1] != vmax:
-            ticks.append(vmax)
-        cbb = plt.colorbar(pad=0.06)
-        cbb.set_ticks(ticks)
-        if i == 0:
-            ticks[0] = f'<{str(ticks[0])}'
-        elif i == 1:
-            ticks[-1] = f'>{str(ticks[-1])}'
-        cbb.ax.set_yticklabels(ticks)
-        for x in range(12):
-            for y in range(12):
-                txt = round(torch.rot90(img[0], -1, [0, 1])[x, y].item(), 2)
-                if str(txt) == '-0.0':
-                    txt = '0.00'
-                elif str(txt) == '0.0':
-                    txt = '0.00'
-                elif len(str(txt)) == 3:
-                    txt = str(txt) + '0'
-                else:
-                    txt = str(txt)
-
-                plt.text(x - 0.25, y - 0.1, txt, color='black', fontsize='x-small')
-
-        ax.set_xticks(torch.linspace(0, 11, 12))
-        ax.set_xticklabels(classes)
-        ax.set_yticks(torch.linspace(0, 11, 12))
-        classes.reverse()
-        ax.set_yticklabels(classes)
-        classes.reverse()
-        plt.xticks(rotation=45)
-        plt.yticks(rotation=45)
-        cba.set_label('Standard Deviation')
-        plt.xlabel("Test pipelines")
-        plt.ylabel("Train pipelines")
-        plt.title(f'Dataset: {dataset_name}, Augmentation: {augmentation}')
-        if i == 0:
-            if dataset_name == 'DroneSegmentation':
-                cbb.set_label('IoU')
-                plt.savefig(os.path.join(path, f"{dataset_name}_{augmentation}_IoU.png"))
-            else:
-                cbb.set_label('Accuracy')
-                plt.savefig(os.path.join(path, f"{dataset_name}_{augmentation}_accuracies.png"))
-        elif i == 1:
-            if dataset_name == 'DroneSegmentation':
-                cbb.set_label('IoU_d-IoU')
-            else:
-                cbb.set_label('Accuracy_d - Accuracy')
-            plt.savefig(os.path.join(path, f"{dataset_name}_{augmentation}_differences.png"))
-
-
-def CMakeTable(dataset_name: str, augmentation: str, severity: int, N_runs: int, download_model: bool):
-
-    path = 'results/Ctesting/tables'
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-    demosaicings = ['bilinear', 'malvar2004', 'menon2007']
-    sharpenings = ['sharpening_filter', 'unsharp_masking']
-    denoisings = ['median_denoising', 'gaussian_denoising']
-
-    transformations = ['identity', 'gaussian_noise', 'shot_noise', 'impulse_noise', 'speckle_noise',
-                       'gaussian_blur', 'zoom_blur', 'contrast', 'brightness', 'saturate', 'elastic_transform']
-
-    runs = {}
-    i = 0
-
-    for dm in demosaicings:
-        for s in sharpenings:
-            for dn in denoisings:
-                for t in transformations:
-                    pip = [dm, s, dn]
-                    runs[f'run{i}'] = {
-                        'dataset': dataset_name,
-                        'augmentation': augmentation,
-                        'pipeline': pip,
-                        'N_runs': N_runs,
-                        'transform': t,
-                        'severity': severity,
-                    }
-                    ABclass = ABtesting(
-                        dataset_name=dataset_name,
-                        augmentation=augmentation,
-                        dm_train=dm,
-                        s_train=s,
-                        dn_train=dn,
-                        dm_test=dm,
-                        s_test=s,
-                        dn_test=dn,
-                        severity=severity,
-                        transform=t,
-                        N_runs=N_runs,
-                        download_model=download_model
-                    )
-
-                    if dataset_name == 'DroneSegmentation':
-                        IoU = ABclass.ABsegmentation()
-                        runs[f'run{i}']['IoU'] = IoU
-                    else:
-                        classes, accuracy, precision, recall, f1_score = ABclass.ABclassification()
-                        runs[f'run{i}']['classes'] = classes
-                        runs[f'run{i}']['accuracy'] = accuracy
-                        runs[f'run{i}']['precision'] = precision
-                        runs[f'run{i}']['recall'] = recall
-                        runs[f'run{i}']['f1_score'] = f1_score
-
-                    with open(os.path.join(path, f'{dataset_name}_{augmentation}_runs.json'), 'w') as outfile:
-                        json.dump(runs, outfile)
-
-                    i += 1
-
-
-def CShowTable(dataset_name, augmentation):
-
-    path = 'results/Ctesting/tables'
-    assert os.path.exists(path), 'No tables to plot'
-
-    json_file = os.path.join(path, f'{dataset_name}_{augmentation}_runs.txt')
-
-    transforms = ['identity', 'gauss_noise', 'shot', 'impulse', 'speckle',
-                  'gauss_blur', 'zoom', 'contrast', 'brightness', 'saturate', 'elastic']
-
-    pip = []
-
-    demosaicings = ['bilinear', 'malvar2004', 'menon2007']
-    sharpenings = ['sharpening_filter', 'unsharp_masking']
-    denoisings = ['median_denoising', 'gaussian_denoising']
-
-    for dm in demosaicings:
-        for s in sharpenings:
-            for dn in denoisings:
-                pip.append(f'{dm[:2]},{s[0]},{dn[2]}')
-
-    with open(json_file, 'r') as run_file:
-        runs = json.load(run_file)
-
-        metrics = torch.zeros((2, len(pip), len(transforms)))
-
-        i, j = 0, 0
-
-        for r in range(len(runs)):
-
-            run = runs['run' + str(r)]
-            if dataset_name == 'DroneSegmentation':
-                acc = run['IoU']
-            else:
-                acc = run['accuracy']
-            mu, sigma = round(acc[0], 4), round(acc[1], 4)
-
-            metrics[0, j, i] = mu
-            metrics[1, j, i] = sigma
-
-            i += 1
-
-            if i == len(transforms):
-                i = 0
-                j += 1
-
-        # Plot with scatter
-
-        img = metrics
-
-        vmin = 0.
-        vmax = 1.
-
-        x, y = torch.arange(12), torch.arange(11)
-        x, y = torch.meshgrid(x, y)
-
-        fig = plt.figure(figsize=(10, 6.2))
-        ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
-        marker_size = 350
-        plt.scatter(x, y, c=torch.rot90(img[1][x, y], -1, [0, 1]), vmin=0.,
-                    vmax=img[1].max(), cmap='viridis', s=marker_size * 2, marker='s')
-        ticks = torch.arange(0., img[1].max(), 0.03).tolist()
-        ticks = [round(tick, 2) for tick in ticks]
-        cba = plt.colorbar(pad=0.06)
-        cba.set_ticks(ticks)
-        cba.ax.set_yticklabels(ticks)
-        # cmap = plt.cm.get_cmap('tab20c').reversed()
-        cmap = plt.cm.get_cmap('Reds')
-        plt.scatter(x, y, c=torch.rot90(img[0][x, y], -1, [0, 1]), vmin=vmin,
-                    vmax=vmax, cmap=cmap, s=marker_size, marker='s')
-        ticks = torch.arange(vmin, vmax, step).tolist()
-        ticks = [round(tick, 2) for tick in ticks]
-        if ticks[-1] != vmax:
-            ticks.append(vmax)
-        cbb = plt.colorbar(pad=0.06)
-        cbb.set_ticks(ticks)
-        if i == 0:
-            ticks[0] = f'<{str(ticks[0])}'
-        elif i == 1:
-            ticks[-1] = f'>{str(ticks[-1])}'
-        cbb.ax.set_yticklabels(ticks)
-        for x in range(12):
-            for y in range(12):
-                txt = round(torch.rot90(img[0], -1, [0, 1])[x, y].item(), 2)
-                if str(txt) == '-0.0':
-                    txt = '0.00'
-                elif str(txt) == '0.0':
-                    txt = '0.00'
-                elif len(str(txt)) == 3:
-                    txt = str(txt) + '0'
-                else:
-                    txt = str(txt)
-
-                plt.text(x - 0.25, y - 0.1, txt, color='black', fontsize='x-small')
-
-        ax.set_xticks(torch.linspace(0, 11, 12))
-        ax.set_xticklabels(transforms)
-        ax.set_yticks(torch.linspace(0, 11, 12))
-        pip.reverse()
-        ax.set_yticklabels(pip)
-        pip.reverse()
-        plt.xticks(rotation=45)
-        plt.yticks(rotation=45)
-        cba.set_label('Standard Deviation')
-        plt.xlabel("Pipelines")
-        plt.ylabel("Distortions")
-        if dataset_name == 'DroneSegmentation':
-            cbb.set_label('IoU')
-            plt.savefig(os.path.join(path, f"{dataset_name}_{augmentation}_IoU.png"))
-        else:
-            cbb.set_label('Accuracy')
-            plt.savefig(os.path.join(path, f"{dataset_name}_{augmentation}_accuracies.png"))
-
-
-if __name__ == '__main__':
-
-    if args.mode == 'ABMakeTable':
-        ABMakeTable(args.dataset_name, args.augmentation, args.N_runs, args.download_model)
-    elif args.mode == 'ABShowTable':
-        ABShowTable(args.dataset_name, args.augmentation)
-    elif args.mode == 'ABShowImages':
-        ABclass = ABtesting(args.dataset_name, args.augmentation, args.dm_train,
-                            args.s_train, args.dn_train, args.dm_test, args.s_test,
-                            args.dn_test, args.N_runs, download_model=args.download_model)
-        ABclass.ABShowImages()
-    elif args.mode == 'ABShowAllImages':
-        ABclass = ABtesting(args.dataset_name, args.augmentation, args.dm_train,
-                            args.s_train, args.dn_train, args.dm_test, args.s_test,
-                            args.dn_test, args.N_runs, download_model=args.download_model)
-        ABclass.ABShowAllImages()
-    elif args.mode == 'CMakeTable':
-        CMakeTable(args.dataset_name, args.augmentation, args.severity, args.N_runs, args.download_model)
-    elif args.mode == 'CShowTable':  # TODO test it
-        CShowTable(args.dataset_name, args.augmentation, args.severity)
-    elif args.mode == 'CShowImages':
-        ABclass = ABtesting(args.dataset_name, args.augmentation, args.dm_train,
-                            args.s_train, args.dn_train, args.dm_test, args.s_test,
-                            args.dn_test, args.N_runs, args.severity, args.transform,
-                            download_model=args.download_model)
-        ABclass.CShowImages()
-    elif args.mode == 'CShowAllImages':
-        ABclass = ABtesting(args.dataset_name, args.augmentation, args.dm_train,
-                            args.s_train, args.dn_train, args.dm_test, args.s_test,
-                            args.dn_test, args.N_runs, args.severity, args.transform,
-                            download_model=args.download_model)
-        ABclass.CShowAllImages()
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_local_bindings.py b/spaces/ma-xu/LIVE/pybind11/tests/test_local_bindings.py
deleted file mode 100644
index 5460727e1d7ad840f5f2817e9ffbb4e10920b583..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_local_bindings.py
+++ /dev/null
@@ -1,230 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import local_bindings as m
-
-
-def test_load_external():
-    """Load a `py::module_local` type that's only registered in an external module"""
-    import pybind11_cross_module_tests as cm
-
-    assert m.load_external1(cm.ExternalType1(11)) == 11
-    assert m.load_external2(cm.ExternalType2(22)) == 22
-
-    with pytest.raises(TypeError) as excinfo:
-        assert m.load_external2(cm.ExternalType1(21)) == 21
-    assert "incompatible function arguments" in str(excinfo.value)
-
-    with pytest.raises(TypeError) as excinfo:
-        assert m.load_external1(cm.ExternalType2(12)) == 12
-    assert "incompatible function arguments" in str(excinfo.value)
-
-
-def test_local_bindings():
-    """Tests that duplicate `py::module_local` class bindings work across modules"""
-
-    # Make sure we can load the second module with the conflicting (but local) definition:
-    import pybind11_cross_module_tests as cm
-
-    i1 = m.LocalType(5)
-    assert i1.get() == 4
-    assert i1.get3() == 8
-
-    i2 = cm.LocalType(10)
-    assert i2.get() == 11
-    assert i2.get2() == 12
-
-    assert not hasattr(i1, 'get2')
-    assert not hasattr(i2, 'get3')
-
-    # Loading within the local module
-    assert m.local_value(i1) == 5
-    assert cm.local_value(i2) == 10
-
-    # Cross-module loading works as well (on failure, the type loader looks for
-    # external module-local converters):
-    assert m.local_value(i2) == 10
-    assert cm.local_value(i1) == 5
-
-
-def test_nonlocal_failure():
-    """Tests that attempting to register a non-local type in multiple modules fails"""
-    import pybind11_cross_module_tests as cm
-
-    with pytest.raises(RuntimeError) as excinfo:
-        cm.register_nonlocal()
-    assert str(excinfo.value) == 'generic_type: type "NonLocalType" is already registered!'
-
-
-def test_duplicate_local():
-    """Tests expected failure when registering a class twice with py::local in the same module"""
-    with pytest.raises(RuntimeError) as excinfo:
-        m.register_local_external()
-    import pybind11_tests
-    assert str(excinfo.value) == (
-        'generic_type: type "LocalExternal" is already registered!'
-        if hasattr(pybind11_tests, 'class_') else 'test_class not enabled')
-
-
-def test_stl_bind_local():
-    import pybind11_cross_module_tests as cm
-
-    v1, v2 = m.LocalVec(), cm.LocalVec()
-    v1.append(m.LocalType(1))
-    v1.append(m.LocalType(2))
-    v2.append(cm.LocalType(1))
-    v2.append(cm.LocalType(2))
-
-    # Cross module value loading:
-    v1.append(cm.LocalType(3))
-    v2.append(m.LocalType(3))
-
-    assert [i.get() for i in v1] == [0, 1, 2]
-    assert [i.get() for i in v2] == [2, 3, 4]
-
-    v3, v4 = m.NonLocalVec(), cm.NonLocalVec2()
-    v3.append(m.NonLocalType(1))
-    v3.append(m.NonLocalType(2))
-    v4.append(m.NonLocal2(3))
-    v4.append(m.NonLocal2(4))
-
-    assert [i.get() for i in v3] == [1, 2]
-    assert [i.get() for i in v4] == [13, 14]
-
-    d1, d2 = m.LocalMap(), cm.LocalMap()
-    d1["a"] = v1[0]
-    d1["b"] = v1[1]
-    d2["c"] = v2[0]
-    d2["d"] = v2[1]
-    assert {i: d1[i].get() for i in d1} == {'a': 0, 'b': 1}
-    assert {i: d2[i].get() for i in d2} == {'c': 2, 'd': 3}
-
-
-def test_stl_bind_global():
-    import pybind11_cross_module_tests as cm
-
-    with pytest.raises(RuntimeError) as excinfo:
-        cm.register_nonlocal_map()
-    assert str(excinfo.value) == 'generic_type: type "NonLocalMap" is already registered!'
-
-    with pytest.raises(RuntimeError) as excinfo:
-        cm.register_nonlocal_vec()
-    assert str(excinfo.value) == 'generic_type: type "NonLocalVec" is already registered!'
-
-    with pytest.raises(RuntimeError) as excinfo:
-        cm.register_nonlocal_map2()
-    assert str(excinfo.value) == 'generic_type: type "NonLocalMap2" is already registered!'
-
-
-def test_mixed_local_global():
-    """Local types take precedence over globally registered types: a module with a `module_local`
-    type can be registered even if the type is already registered globally.  With the module,
-    casting will go to the local type; outside the module casting goes to the global type."""
-    import pybind11_cross_module_tests as cm
-    m.register_mixed_global()
-    m.register_mixed_local()
-
-    a = []
-    a.append(m.MixedGlobalLocal(1))
-    a.append(m.MixedLocalGlobal(2))
-    a.append(m.get_mixed_gl(3))
-    a.append(m.get_mixed_lg(4))
-
-    assert [x.get() for x in a] == [101, 1002, 103, 1004]
-
-    cm.register_mixed_global_local()
-    cm.register_mixed_local_global()
-    a.append(m.MixedGlobalLocal(5))
-    a.append(m.MixedLocalGlobal(6))
-    a.append(cm.MixedGlobalLocal(7))
-    a.append(cm.MixedLocalGlobal(8))
-    a.append(m.get_mixed_gl(9))
-    a.append(m.get_mixed_lg(10))
-    a.append(cm.get_mixed_gl(11))
-    a.append(cm.get_mixed_lg(12))
-
-    assert [x.get() for x in a] == \
-        [101, 1002, 103, 1004, 105, 1006, 207, 2008, 109, 1010, 211, 2012]
-
-
-def test_internal_locals_differ():
-    """Makes sure the internal local type map differs across the two modules"""
-    import pybind11_cross_module_tests as cm
-    assert m.local_cpp_types_addr() != cm.local_cpp_types_addr()
-
-
-@pytest.mark.xfail("env.PYPY")
-def test_stl_caster_vs_stl_bind(msg):
-    """One module uses a generic vector caster from `<pybind11/stl.h>` while the other
-    exports `std::vector<int>` via `py:bind_vector` and `py::module_local`"""
-    import pybind11_cross_module_tests as cm
-
-    v1 = cm.VectorInt([1, 2, 3])
-    assert m.load_vector_via_caster(v1) == 6
-    assert cm.load_vector_via_binding(v1) == 6
-
-    v2 = [1, 2, 3]
-    assert m.load_vector_via_caster(v2) == 6
-    with pytest.raises(TypeError) as excinfo:
-        cm.load_vector_via_binding(v2) == 6
-    assert msg(excinfo.value) == """
-    load_vector_via_binding(): incompatible function arguments. The following argument types are supported:
-        1. (arg0: pybind11_cross_module_tests.VectorInt) -> int
-
-    Invoked with: [1, 2, 3]
-    """  # noqa: E501 line too long
-
-
-def test_cross_module_calls():
-    import pybind11_cross_module_tests as cm
-
-    v1 = m.LocalVec()
-    v1.append(m.LocalType(1))
-    v2 = cm.LocalVec()
-    v2.append(cm.LocalType(2))
-
-    # Returning the self pointer should get picked up as returning an existing
-    # instance (even when that instance is of a foreign, non-local type).
-    assert m.return_self(v1) is v1
-    assert cm.return_self(v2) is v2
-    assert m.return_self(v2) is v2
-    assert cm.return_self(v1) is v1
-
-    assert m.LocalVec is not cm.LocalVec
-    # Returning a copy, on the other hand, always goes to the local type,
-    # regardless of where the source type came from.
-    assert type(m.return_copy(v1)) is m.LocalVec
-    assert type(m.return_copy(v2)) is m.LocalVec
-    assert type(cm.return_copy(v1)) is cm.LocalVec
-    assert type(cm.return_copy(v2)) is cm.LocalVec
-
-    # Test the example given in the documentation (which also tests inheritance casting):
-    mycat = m.Cat("Fluffy")
-    mydog = cm.Dog("Rover")
-    assert mycat.get_name() == "Fluffy"
-    assert mydog.name() == "Rover"
-    assert m.Cat.__base__.__name__ == "Pet"
-    assert cm.Dog.__base__.__name__ == "Pet"
-    assert m.Cat.__base__ is not cm.Dog.__base__
-    assert m.pet_name(mycat) == "Fluffy"
-    assert m.pet_name(mydog) == "Rover"
-    assert cm.pet_name(mycat) == "Fluffy"
-    assert cm.pet_name(mydog) == "Rover"
-
-    assert m.MixGL is not cm.MixGL
-    a = m.MixGL(1)
-    b = cm.MixGL(2)
-    assert m.get_gl_value(a) == 11
-    assert m.get_gl_value(b) == 12
-    assert cm.get_gl_value(a) == 101
-    assert cm.get_gl_value(b) == 102
-
-    c, d = m.MixGL2(3), cm.MixGL2(4)
-    with pytest.raises(TypeError) as excinfo:
-        m.get_gl_value(c)
-    assert "incompatible function arguments" in str(excinfo.value)
-    with pytest.raises(TypeError) as excinfo:
-        m.get_gl_value(d)
-    assert "incompatible function arguments" in str(excinfo.value)
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/ctanh.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/ctanh.h
deleted file mode 100644
index 6ef1590920db65c50582787fc953af1ffb582099..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/ctanh.h
+++ /dev/null
@@ -1,200 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *  Copyright 2013 Filipe RNC Maia
- *
- *  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.
- */
-
-/*-
- * Copyright (c) 2011 David Schultz
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the above copyright
- *    notice unmodified, this list of conditions, and the following
- *    disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
-/*
- * Adapted from FreeBSD by Filipe Maia <filipe.c.maia@gmail.com>:
- *    freebsd/lib/msun/src/s_ctanh.c
- */
-
-/*
- * Hyperbolic tangent of a complex argument z = x + i y.
- *
- * The algorithm is from:
- *
- *   W. Kahan.  Branch Cuts for Complex Elementary Functions or Much
- *   Ado About Nothing's Sign Bit.  In The State of the Art in
- *   Numerical Analysis, pp. 165 ff.  Iserles and Powell, eds., 1987.
- *
- * Method:
- *
- *   Let t    = tan(x)
- *       beta = 1/cos^2(y)
- *       s    = sinh(x)
- *       rho  = cosh(x)
- *
- *   We have:
- *
- *   tanh(z) = sinh(z) / cosh(z)
- *
- *             sinh(x) cos(y) + i cosh(x) sin(y)
- *           = ---------------------------------
- *             cosh(x) cos(y) + i sinh(x) sin(y)
- *
- *             cosh(x) sinh(x) / cos^2(y) + i tan(y)
- *           = -------------------------------------
- *                    1 + sinh^2(x) / cos^2(y)
- *
- *             beta rho s + i t
- *           = ----------------
- *               1 + beta s^2
- *
- * Modifications:
- *
- *   I omitted the original algorithm's handling of overflow in tan(x) after
- *   verifying with nearpi.c that this can't happen in IEEE single or double
- *   precision.  I also handle large x differently.
- */
-
-#pragma once
-
-#include <thrust/complex.h>
-#include <thrust/detail/complex/math_private.h>
-#include <cmath>
-
-namespace thrust{
-namespace detail{
-namespace complex{		      	
-
-using thrust::complex;
-
-__host__ __device__ inline
-complex<double> ctanh(const complex<double>& z){
-  double x, y;
-  double t, beta, s, rho, denom;
-  uint32_t hx, ix, lx;
-
-  x = z.real();
-  y = z.imag();
-
-  extract_words(hx, lx, x);
-  ix = hx & 0x7fffffff;
-
-  /*
-   * ctanh(NaN + i 0) = NaN + i 0
-   *
-   * ctanh(NaN + i y) = NaN + i NaN		for y != 0
-   *
-   * The imaginary part has the sign of x*sin(2*y), but there's no
-   * special effort to get this right.
-   *
-   * ctanh(+-Inf +- i Inf) = +-1 +- 0
-   *
-   * ctanh(+-Inf + i y) = +-1 + 0 sin(2y)		for y finite
-   *
-   * The imaginary part of the sign is unspecified.  This special
-   * case is only needed to avoid a spurious invalid exception when
-   * y is infinite.
-   */
-  if (ix >= 0x7ff00000) {
-    if ((ix & 0xfffff) | lx)	/* x is NaN */
-      return (complex<double>(x, (y == 0 ? y : x * y)));
-    set_high_word(x, hx - 0x40000000);	/* x = copysign(1, x) */
-    return (complex<double>(x, copysign(0.0, isinf(y) ? y : sin(y) * cos(y))));
-  }
-
-  /*
-   * ctanh(x + i NAN) = NaN + i NaN
-   * ctanh(x +- i Inf) = NaN + i NaN
-   */
-  if (!isfinite(y))
-    return (complex<double>(y - y, y - y));
-
-  /*
-   * ctanh(+-huge + i +-y) ~= +-1 +- i 2sin(2y)/exp(2x), using the
-   * approximation sinh^2(huge) ~= exp(2*huge) / 4.
-   * We use a modified formula to avoid spurious overflow.
-   */
-  if (ix >= 0x40360000) {	/* x >= 22 */
-    double exp_mx = exp(-fabs(x));
-    return (complex<double>(copysign(1.0, x),
-			    4.0 * sin(y) * cos(y) * exp_mx * exp_mx));
-  }
-
-  /* Kahan's algorithm */
-  t = tan(y);
-  beta = 1.0 + t * t;	/* = 1 / cos^2(y) */
-  s = sinh(x);
-  rho = sqrt(1.0 + s * s);	/* = cosh(x) */
-  denom = 1.0 + beta * s * s;
-  return (complex<double>((beta * rho * s) / denom, t / denom));
-}
-
-__host__ __device__ inline
-complex<double> ctan(complex<double> z){
-  /* ctan(z) = -I * ctanh(I * z) */
-  z = ctanh(complex<double>(-z.imag(), z.real()));
-  return (complex<double>(z.imag(), -z.real()));
-}
-
-} // namespace complex
-
-} // namespace detail
-
-
-template <typename ValueType>
-__host__ __device__
-inline complex<ValueType> tan(const complex<ValueType>& z){
-  return sin(z)/cos(z);
-}
-
-template <typename ValueType>
-__host__ __device__
-inline complex<ValueType> tanh(const complex<ValueType>& z){
-  // This implementation seems better than the simple sin/cos
-  return (thrust::exp(ValueType(2)*z)-ValueType(1))/
-    (thrust::exp(ValueType(2)*z)+ValueType(1));
-}
-
-template <>
-__host__ __device__
-inline complex<double> tan(const complex<double>& z){
-  return detail::complex::ctan(z);
-}
-  
-template <>
-__host__ __device__
-inline complex<double> tanh(const complex<double>& z){
-  return detail::complex::ctanh(z);
-}
-  
-} // namespace thrust
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/for_each.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/for_each.h
deleted file mode 100644
index 8509edca3630f2c9d313e34efeca10abbee03e7b..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/for_each.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the for_each.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch for_each
-
-#include <thrust/system/detail/sequential/for_each.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/for_each.h>
-#include <thrust/system/cuda/detail/for_each.h>
-#include <thrust/system/omp/detail/for_each.h>
-#include <thrust/system/tbb/detail/for_each.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_FOR_EACH_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/for_each.h>
-#include __THRUST_HOST_SYSTEM_FOR_EACH_HEADER
-#undef __THRUST_HOST_SYSTEM_FOR_EACH_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_FOR_EACH_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/for_each.h>
-#include __THRUST_DEVICE_SYSTEM_FOR_EACH_HEADER
-#undef __THRUST_DEVICE_SYSTEM_FOR_EACH_HEADER
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h b/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h
deleted file mode 100644
index a8736bd75d06e54d9158baeb2504162d75312885..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/transform_reduce.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits transform_reduce
-#include <thrust/system/cpp/detail/transform_reduce.h>
-
diff --git a/spaces/matthoffner/chatbot/components/Settings/Import.tsx b/spaces/matthoffner/chatbot/components/Settings/Import.tsx
deleted file mode 100644
index 5cc9582f8322dc8584677eb9eb9801a6809f68b9..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/components/Settings/Import.tsx
+++ /dev/null
@@ -1,51 +0,0 @@
-import { IconFileImport } from '@tabler/icons-react';
-import { FC } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { SupportedExportFormats } from '@/types/export';
-
-import { SidebarButton } from '../Sidebar/SidebarButton';
-
-interface Props {
-  onImport: (data: SupportedExportFormats) => void;
-}
-
-export const Import: FC<Props> = ({ onImport }) => {
-  const { t } = useTranslation('sidebar');
-  return (
-    <>
-      <input
-        id="import-file"
-        className="sr-only"
-        tabIndex={-1}
-        type="file"
-        accept=".json"
-        onChange={(e) => {
-          if (!e.target.files?.length) return;
-
-          const file = e.target.files[0];
-          const reader = new FileReader();
-          reader.onload = (e) => {
-            let json = JSON.parse(e.target?.result as string);
-            onImport(json);
-          };
-          reader.readAsText(file);
-        }}
-      />
-
-      <SidebarButton
-        text={t('Import data')}
-        icon={<IconFileImport size={18} />}
-        onClick={() => {
-          const importFile = document.querySelector(
-            '#import-file',
-          ) as HTMLInputElement;
-          if (importFile) {
-            importFile.click();
-          }
-        }}
-      />
-    </>
-  );
-};
diff --git a/spaces/matthoffner/web-llm-embed/src/utils/codeblock.ts b/spaces/matthoffner/web-llm-embed/src/utils/codeblock.ts
deleted file mode 100644
index 1fafac44109d83dd196b247c1ead476e8cdc085e..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/web-llm-embed/src/utils/codeblock.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-interface languageMap {
-    [key: string]: string | undefined;
-  }
-  
-  export const programmingLanguages: languageMap = {
-    javascript: '.js',
-    python: '.py',
-    java: '.java',
-    c: '.c',
-    cpp: '.cpp',
-    'c++': '.cpp',
-    'c#': '.cs',
-    ruby: '.rb',
-    php: '.php',
-    swift: '.swift',
-    'objective-c': '.m',
-    kotlin: '.kt',
-    typescript: '.ts',
-    go: '.go',
-    perl: '.pl',
-    rust: '.rs',
-    scala: '.scala',
-    haskell: '.hs',
-    lua: '.lua',
-    shell: '.sh',
-    sql: '.sql',
-    html: '.html',
-    css: '.css',
-    // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-  };
-  
-  export const generateRandomString = (length: number, lowercase = false) => {
-    const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789'; // excluding similar looking characters like Z, 2, I, 1, O, 0
-    let result = '';
-    for (let i = 0; i < length; i++) {
-      result += chars.charAt(Math.floor(Math.random() * chars.length));
-    }
-    return lowercase ? result.toLowerCase() : result;
-  };
-  
\ No newline at end of file
diff --git a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_infer.py b/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_infer.py
deleted file mode 100644
index af04e614c8f1ac43faf363b1a9f6bfd667fbde21..0000000000000000000000000000000000000000
--- a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_infer.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import torch
-import commons
-import models
-
-import math
-from torch import nn
-from torch.nn import functional as F
-
-import modules
-import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-               n_vocab,
-               out_channels,
-               hidden_channels,
-               filter_channels,
-               n_heads,
-               n_layers,
-               kernel_size,
-               p_dropout,
-               emotion_embedding):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.emotion_embedding = emotion_embedding
-
-    if self.n_vocab != 0:
-      self.emb = nn.Embedding(n_vocab, hidden_channels)
-      if emotion_embedding:
-        self.emo_proj = nn.Linear(1024, hidden_channels)
-      nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, emotion_embedding=None):
-    if self.n_vocab != 0:
-      x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-    if emotion_embedding is not None:
-      print("emotion added")
-      x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
-    x = torch.transpose(x, 1, -1)  # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-               in_channels,
-               out_channels,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class SynthesizerTrn(models.SynthesizerTrn):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self,
-               n_vocab,
-               spec_channels,
-               segment_size,
-               inter_channels,
-               hidden_channels,
-               filter_channels,
-               n_heads,
-               n_layers,
-               kernel_size,
-               p_dropout,
-               resblock,
-               resblock_kernel_sizes,
-               resblock_dilation_sizes,
-               upsample_rates,
-               upsample_initial_channel,
-               upsample_kernel_sizes,
-               n_speakers=0,
-               gin_channels=0,
-               use_sdp=True,
-               emotion_embedding=False,
-               ONNX_dir="./ONNX_net/",
-               **kwargs):
-
-    super().__init__(
-      n_vocab,
-      spec_channels,
-      segment_size,
-      inter_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout,
-      resblock,
-      resblock_kernel_sizes,
-      resblock_dilation_sizes,
-      upsample_rates,
-      upsample_initial_channel,
-      upsample_kernel_sizes,
-      n_speakers=n_speakers,
-      gin_channels=gin_channels,
-      use_sdp=use_sdp,
-      **kwargs
-    )
-    self.ONNX_dir = ONNX_dir
-    self.enc_p = TextEncoder(n_vocab,
-                             inter_channels,
-                             hidden_channels,
-                             filter_channels,
-                             n_heads,
-                             n_layers,
-                             kernel_size,
-                             p_dropout,
-                             emotion_embedding)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
-            emotion_embedding=None):
-    from ONNXVITS_utils import runonnx
-    with torch.no_grad():
-      x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    # logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    logw = runonnx(f"{self.ONNX_dir}dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
-    logw = torch.from_numpy(logw[0])
-
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                             2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-
-    # z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z = runonnx(f"{self.ONNX_dir}flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
-    z = torch.from_numpy(z[0])
-
-    # o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    o = runonnx(f"{self.ONNX_dir}dec.onnx", z_in=(z * y_mask)[:, :, :max_len].numpy(), g=g.numpy())
-    o = torch.from_numpy(o[0])
-
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
\ No newline at end of file
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/.ipynb_checkpoints/parsers_inference-checkpoint.py b/spaces/merle/PROTEIN_GENERATOR/utils/.ipynb_checkpoints/parsers_inference-checkpoint.py
deleted file mode 100644
index 1a8bc9aca6de872a4e2b4388937c4d85e6b8d6b1..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/.ipynb_checkpoints/parsers_inference-checkpoint.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import numpy as np
-import scipy
-import scipy.spatial
-import string
-import os,re
-import random
-import util
-
-to1letter = {
-    "ALA":'A', "ARG":'R', "ASN":'N', "ASP":'D', "CYS":'C',
-    "GLN":'Q', "GLU":'E', "GLY":'G', "HIS":'H', "ILE":'I',
-    "LEU":'L', "LYS":'K', "MET":'M', "PHE":'F', "PRO":'P',
-    "SER":'S', "THR":'T', "TRP":'W', "TYR":'Y', "VAL":'V' }
-
-
-def parse_a3m(filename):
-    '''read A3M and convert letters into integers in the 0..20 range,
-    also keep track of insertions
-    '''
-
-    # read A3M file line by line
-    lab,seq = [],[] # labels and sequences
-    for line in open(filename, "r"):
-        if line[0] == '>':
-            lab.append(line.split()[0][1:])
-            seq.append("")
-        else:
-            seq[-1] += line.rstrip()
-
-    # parse sequences
-    msa,ins = [],[]
-    table = str.maketrans(dict.fromkeys(string.ascii_lowercase))
-    nrow,ncol = len(seq),len(seq[0])
-
-    for seqi in seq:
-
-        # remove lowercase letters and append to MSA
-        msa.append(seqi.translate(table))
-
-        # 0 - match or gap; 1 - insertion
-        a = np.array([0 if c.isupper() or c=='-' else 1 for c in seqi])
-        i = np.zeros((ncol))
-
-        if np.sum(a) > 0:
-            # positions of insertions
-            pos = np.where(a==1)[0]
-
-            # shift by occurrence
-            a = pos - np.arange(pos.shape[0])
-
-            # position of insertions in the cleaned sequence
-            # and their length
-            pos,num = np.unique(a, return_counts=True)
-            i[pos[pos<ncol]] = num[pos<ncol]
-
-        # append to the matrix of insetions
-        ins.append(i)
-
-    # convert letters into numbers
-    alphabet = np.array(list("ARNDCQEGHILKMFPSTWYV-"), dtype='|S1').view(np.uint8)
-    msa = np.array([list(s) for s in msa], dtype='|S1').view(np.uint8)
-    for i in range(alphabet.shape[0]):
-        msa[msa == alphabet[i]] = i
-
-    # treat all unknown characters as gaps
-    msa[msa > 20] = 20
-
-    ins = np.array(ins, dtype=np.uint8)
-
-    return {"msa":msa, "labels":lab, "insertions":ins}
-
-
-def parse_pdb(filename, **kwargs):
-    '''extract xyz coords for all heavy atoms'''
-    lines = open(filename,'r').readlines()
-    return parse_pdb_lines(lines, **kwargs)
-
-def parse_pdb_lines(lines, parse_hetatom=False, ignore_het_h=True):
-    # indices of residues observed in the structure
-    res = [(l[22:26],l[17:20]) for l in lines if l[:4]=="ATOM" and l[12:16].strip()=="CA"]
-    seq = [util.aa2num[r[1]] if r[1] in util.aa2num.keys() else 20 for r in res]
-    pdb_idx = [( l[21:22].strip(), int(l[22:26].strip()) ) for l in lines if l[:4]=="ATOM" and l[12:16].strip()=="CA"]  # chain letter, res num
-
-    # 4 BB + up to 10 SC atoms
-    xyz = np.full((len(res), 27, 3), np.nan, dtype=np.float32)
-    for l in lines:
-        if l[:4] != "ATOM":
-            continue
-        chain, resNo, atom, aa = l[21:22], int(l[22:26]), ' '+l[12:16].strip().ljust(3), l[17:20]
-        idx = pdb_idx.index((chain,resNo))
-        for i_atm, tgtatm in enumerate(util.aa2long[util.aa2num[aa]]):
-            if tgtatm is not None and tgtatm.strip() == atom.strip(): # ignore whitespace
-                xyz[idx,i_atm,:] = [float(l[30:38]), float(l[38:46]), float(l[46:54])]
-                break
-
-    # save atom mask
-    mask = np.logical_not(np.isnan(xyz[...,0]))
-    xyz[np.isnan(xyz[...,0])] = 0.0
-
-    # remove duplicated (chain, resi)
-    new_idx = []
-    i_unique = []
-    for i,idx in enumerate(pdb_idx):
-        if idx not in new_idx:
-            new_idx.append(idx)
-            i_unique.append(i)
-            
-    pdb_idx = new_idx
-    xyz = xyz[i_unique]
-    mask = mask[i_unique]
-    seq = np.array(seq)[i_unique]
-
-    out = {'xyz':xyz, # cartesian coordinates, [Lx14]
-            'mask':mask, # mask showing which atoms are present in the PDB file, [Lx14]
-            'idx':np.array([i[1] for i in pdb_idx]), # residue numbers in the PDB file, [L]
-            'seq':np.array(seq), # amino acid sequence, [L]
-            'pdb_idx': pdb_idx,  # list of (chain letter, residue number) in the pdb file, [L]
-           }
-
-    # heteroatoms (ligands, etc)
-    if parse_hetatom:
-        xyz_het, info_het = [], []
-        for l in lines:
-            if l[:6]=='HETATM' and not (ignore_het_h and l[77]=='H'):
-                info_het.append(dict(
-                    idx=int(l[7:11]),
-                    atom_id=l[12:16],
-                    atom_type=l[77],
-                    name=l[16:20]
-                ))
-                xyz_het.append([float(l[30:38]), float(l[38:46]), float(l[46:54])])
-
-        out['xyz_het'] = np.array(xyz_het)
-        out['info_het'] = info_het
-
-    return out
-
-def parse_fasta(filename):
-    '''
-    Return dict of name: seq
-    '''
-    out = {}
-    with open(filename, 'r') as f_in:
-        while True:
-            name = f_in.readline().strip()[1:]
-            seq = f_in.readline().strip()
-            if not name: break
-
-            out[name] = seq
-
-    return out
diff --git a/spaces/merve/anonymization/source/_templates/post.html b/spaces/merve/anonymization/source/_templates/post.html
deleted file mode 100644
index 08280035044b244dddd9dff842f4d9b930c5edeb..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/_templates/post.html
+++ /dev/null
@@ -1,106 +0,0 @@
-<!--
-@license
-Copyright 2020 Google. 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.
--->
-
-<!DOCTYPE html>
-
-<html>
-<head>
-	<meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-
-  <link rel="apple-touch-icon" sizes="180x180" href="https://pair.withgoogle.com/images/favicon/apple-touch-icon.png">
-  <link rel="icon" type="image/png" sizes="32x32" href="https://pair.withgoogle.com/images/favicon/favicon-32x32.png">
-  <link rel="icon" type="image/png" sizes="16x16" href="https://pair.withgoogle.com/images/favicon/favicon-16x16.png">
-  <link rel="mask-icon" href="https://pair.withgoogle.com/images/favicon/safari-pinned-tab.svg" color="#00695c">
-  <link rel="shortcut icon" href="https://pair.withgoogle.com/images/favicon.ico">
-
-  <script>
-    !(function(){
-      var url = window.location.href
-      if (url.split('#')[0].split('?')[0].slice(-1) != '/' && !url.includes('.html')) window.location = url + '/'
-    })()
-  </script>
-
-  <title>${d.title}</title>
-  <meta property="og:title" content="${d.title}">
-  <meta property="og:url" content="https://pair.withgoogle.com/explorables${d.permalink}">
-
-  ${d.socialsummary || d.summary ? `<meta name="og:description" content="${d.socialsummary || d.summary}">`  : ''}
-  ${d.shareimg ? `<meta property="og:image" content="${d.shareimg}">`  : ''}
-  ${d.shareimg ? `<meta name="twitter:card" content="summary_large_image">` : ''}
-  
-	<link rel="stylesheet" type="text/css" href="../style.css">
-
-  <link href='https://fonts.googleapis.com/css?family=Roboto+Slab:400,500,700|Roboto:700,500,300' rel='stylesheet' type='text/css'>  
-  <link href="https://fonts.googleapis.com/css?family=Google+Sans:400,500,700" rel="stylesheet">
-
-	<meta name="viewport" content="width=device-width">
-</head>
-<body>
-  <div class='header'>
-    <div class='header-left'>
-      <a href='https://pair.withgoogle.com/'>
-        <img src='../images/pair-logo.svg' style='width: 100px'></img>
-      </a>
-      <a href='../'>Explorables</a> 
-    </div>
-  </div>
-  
-  <h1 class='headline'>${d.title}</h1>
-  ${d.summary ? `<div class="post-summary">${d.summary}</div>` : ''}
-  ${d.html}
-</body>
-
-<script async src="https://www.googletagmanager.com/gtag/js?id=UA-138505774-1"></script>
-<script>
-  if (window.location.origin === 'https://pair.withgoogle.com'){
-    window.dataLayer = window.dataLayer || [];
-    function gtag(){dataLayer.push(arguments);}
-    gtag('js', new Date());
-    gtag('config', 'UA-138505774-1');
-  }
-</script>
-
-<script>
-  // Tweaks for displaying in an iframe
-  if (window !== window.parent){
-    
-    // Open links in a new tab
-    Array.from(document.querySelectorAll('a'))
-      .forEach(e => {
-        // skip anchor links
-        if (e.href && e.href[0] == '#') return
-
-        e.setAttribute('target', '_blank')
-        e.setAttribute('rel', 'noopener noreferrer')
-      })
-
-    // Remove recirc h3
-    Array.from(document.querySelectorAll('h3'))
-      .forEach(e => {
-        if (e.textContent != 'More Explorables') return
-
-        e.parentNode.removeChild(e)
-      })
-
-    // Remove recirc container
-    var recircEl = document.querySelector('#recirc')
-    recircEl.parentNode.removeChild(recircEl)
-  }
-</script>
-
-</html>
\ No newline at end of file
diff --git a/spaces/merve/dataset-worldviews/public/measuring-fairness/annotations.js b/spaces/merve/dataset-worldviews/public/measuring-fairness/annotations.js
deleted file mode 100644
index 7ab68f297f98c655427a84de22388906182b240c..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/measuring-fairness/annotations.js
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-var annotations = 
-[
-]
-
-
-function addSwoop(c){
-  var swoopy = d3.swoopyDrag()
-    .x(d => c.x(d.x))
-    .y(d => c.y(d.y))
-    .draggable(0)
-    .annotations(annotations)
-
-  var swoopySel = c.svg.append('g.annotations').call(swoopy)
-
-  c.svg.append('marker#arrow')
-      .attr('viewBox', '-10 -10 20 20')
-      .attr('markerWidth', 20)
-      .attr('markerHeight', 20)
-      .attr('orient', 'auto')
-    .append('path').at({d: 'M-6.75,-6.75 L 0,0 L -6.75,6.75'})
-
-
-  swoopySel.selectAll('path').attr('marker-end', 'url(#arrow)')
-  window.annotationSel = swoopySel.selectAll('g')
-    .st({fontSize: 12, opacity: d => d.slide == 0 ? 1 : 0})
-
-  swoopySel.selectAll('text')
-    .each(function(d){
-      d3.select(this)
-        .text('')                        //clear existing text
-        .tspans(d3.wordwrap(d.text, d.width || 20), 12) //wrap after 20 char
-    })  
-}
-
-
diff --git a/spaces/merve/hidden-bias/public/private-and-fair/top-bot-digits.js b/spaces/merve/hidden-bias/public/private-and-fair/top-bot-digits.js
deleted file mode 100644
index bc2f85ec8cb3b5544245f159aa62ff2fbffbcbb5..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/private-and-fair/top-bot-digits.js
+++ /dev/null
@@ -1,66 +0,0 @@
-
-!(async function(){
-  await util.getFile(`cns-cache/mnist_train_raw_3.npy`)
-  var digitMetadata = await util.getFile('mnist_train.csv')
-  var {byLabel} = util.decorateDigitMetadata(digitMetadata)
-
-  var sel = d3.select('.top-bot-digits').html('')
-      .at({role: 'graphics-document', 'aria-label': `The twenty-five MNIST 3 digits most and least senstive to higher and lower privacy. The digits most sensitive to higher privacy are much more poorly drawn than the onces least sensitive to higher privacy.`})
-
-  var digitSel = sel.append('div')
-  var buttonSel = sel.append('div.digit-button-container')
-    .appendMany('div.button', d3.range(10))
-    .text(d => d)
-    .on('click', d => drawClass(byLabel[d]))
-
-  drawClass(byLabel[3])
-
-  async function drawClass(digitClass){
-    buttonSel.classed('active', d => d == digitClass.key)
-    await util.getFile(`cns-cache/mnist_train_raw_${digitClass.key}.npy`)
-
-    var nRows = 5
-    var nCols = 5
-
-    var bot = _.sortBy(digitClass, d => +d.priv_order).slice(0, nRows*nCols)
-    var top = _.sortBy(digitClass, d => -d.priv_order).slice(0, nRows*nCols)
-    
-    digitSel.html('').append('div')
-      .st({maxWidth: 640, margin: '0 auto'})
-      .appendMany('div', [bot, top])
-      .st({display: 'inline-block'})
-      .each(drawDigitBlock)
-
-
-    function drawDigitBlock(digits, isBot){
-      var s = 2
-
-      var sel = d3.select(this).append('div')
-
-      var c = d3.conventions({
-        sel,
-        width: s*29*nCols,
-        height: s*29*nRows,
-        layers: 'cs',
-        margin: {top: 30, bottom: 10, right: 10, left: 10}
-      })
-
-      var ctx = c.layers[0]
-
-      digits.forEach((d, i) => {
-        util.drawDigit(
-          ctx, 
-          +d.i, 
-          s,
-          (i % nCols)*s*29,
-          Math.floor(i/nCols)*s*29
-        )
-      })
-
-      c.svg.append('text')
-        .text(isBot ? 'Least sensitive to higher privacy' : 'Most sensitive to higher privacy')
-        .at({dy: '-.4em', textAnchor: 'middle', x: c.width/2, fontWeight: 600, fontSize: 14})
-    }
-  }
-
-})()
\ No newline at end of file
diff --git a/spaces/merve/measuring-fairness/public/dataset-worldviews/shapes.js b/spaces/merve/measuring-fairness/public/dataset-worldviews/shapes.js
deleted file mode 100644
index 87af55b4829a78b48dc41f6674c12cd58cfc3741..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/public/dataset-worldviews/shapes.js
+++ /dev/null
@@ -1,248 +0,0 @@
-
-// Space out the shapes a bit
-shapeParams.forEach((d) => (d.startX = d.startX * 1.1));
-
-// How to draw the background boxes, which will be styled later
-const classifierBgPathTop = "M 420 150 H 0 V 0 H 420 V 150";
-const classifierBgPathBottom = "M 420 300 H 0 V 0 H 420 V 300";
-
-const toDropdownValueStringDict = {
-    shape_name: "circles, triangles, or rectangles",
-    pointiness: "pointy shapes or round shapes",
-    size: "small shapes or big shapes",
-};
-
-const toShortValueStringDict = {
-    shape_name: "circles, triangles, or rectangles",
-    pointiness: "pointy or round",
-    size: "small or big",
-};
-
-const toDropdownValueRoundingStringDict = {
-    true: "with our best guess",
-    false: 'as "other"',
-};
-
-const toPropertyStringDict = {
-    pointy: "pointy shapes",
-    round: "round shapes",
-    small: "small shapes",
-    large: "big shapes",
-    circle: "circles",
-    triangle: "triangles",
-    rect: "rectangles",
-};
-
-function toOriginalString(inputString) {
-    for (const [key, value] of Object.entries(toPropertyStringDict)) {
-        if (inputString == value) {
-            return key;
-        }
-    }
-}
-
-function toPropertyString(inputProperty, isRounding = true) {
-    if (!isRounding && inputProperty.startsWith("rt_")) {
-        return "others";
-    }
-    return toPropertyStringDict[inputProperty.replace("rt_", "")];
-}
-
-// Dictionary mapping div name to classifier results and summary sentences
-var allResults = {};
-var summaries = {};
-
-function toBool(inputString) {
-    if (inputString == "true") {
-        return true;
-    }
-    return false;
-}
-function updateResults() {
-    allResults["default-classifier"] = calculateResults();
-    allResults["second-classifier"] = calculateResults(
-        "shape_name",
-        toBool(
-            document.getElementById("second-classifier-select-rounding").value
-        )
-    );
-
-    allResults["final-classifier"] = calculateResults(
-        document.getElementById("final-classifier-select-category").value,
-        toBool(
-            document.getElementById("final-classifier-select-rounding").value
-        )
-    );
-
-    allResults["conclusion"] = calculateResults(
-        document.getElementById("conclusion-select-category").value,
-        true
-    );
-
-    updateSummaries();
-    updateSecondInterfaceImages();
-}
-
-// Text summaries are written by hand for simplicity, and keyed simply by
-// a string of the form "[category]:[useGuess]" (or simply "none").
-// These are hashed in the same way as the results, by div name.
-function updateSummaries() {
-    summaries["default-classifier"] = getPerformanceSummary("none");
-    summaries["second-classifier"] = getPerformanceSummary(
-        "shape_name:" +
-            document.getElementById("second-classifier-select-rounding").value
-    );
-
-    summaries["final-classifier"] = getPerformanceSummary(
-        document.getElementById("final-classifier-select-category").value +
-            ":" +
-            document.getElementById("final-classifier-select-rounding").value
-    );
-
-    summaries["conclusion"] = getPerformanceSummary(
-        document.getElementById("conclusion-select-category").value + ":" + true
-    );
-}
-
-// Yes, these background colors are hardcoded in,
-// no, this is not good design, this is just how it happened.
-function getPerformanceSummary(key) {
-    allSummaries = {
-        "shape_name:true":
-            '<mark style="background-color: rgb(206, 234, 135);" class="well">well</mark> on circles, <mark style="background-color: rgb(244, 123, 74);" class="terribly">terribly</mark> on triangles, and <mark style="background-color: rgb(173, 220, 114);" class="best">best</mark> on rectangles',
-        "shape_name:false":
-            '<mark style="background-color: rgb(251, 163, 94);" class="poorly">poorly</mark> on circles, <mark style="background-color: rgb(155, 212, 108);" class="best">best</mark> on triangles and rectangles, and <mark style="background-color: rgb(252, 244, 171);" class="fine">fine</mark> on other shapes',
-        "pointiness:true":
-            '<mark style="background-color: rgb(184, 225, 119);" class="better">better</mark> on pointy shapes and <mark style="background-color: rgb(254, 206, 125);" class="worse">worse</mark> on round shapes',
-        "pointiness:false":
-            '<mark style="background-color: rgb(140, 205, 104);" class="best">best</mark> on pointy shapes, <mark style="background-color: rgb(243, 248, 171);" class="fine">fine</mark> on round shapes, and <mark style="background-color: rgb(253, 190, 111);" class="poorly">poorly</mark> on other shapes',
-        "size:true":
-            '<mark style="background-color: rgb(206, 234, 135);" class="better">better</mark> on small shapes, <mark style="background-color: rgb(254, 232, 154);" class="worse">worse</mark> on big shapes',
-        "size:false":
-            '<mark style="background-color: rgb(254, 215, 135);" class="poorly">poorly</mark> on small shapes, <mark style="background-color: rgb(165, 0, 38); color: #FFCCD8;" class="terribly">terribly</mark> on big shapes, and <mark style="background-color: rgb(110, 192, 99);" class="best">best</mark> on other shapes',
-        "none:true":
-            '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-        "none:false":
-            '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-        none: '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-    };
-
-    return "The Is-Shaded Classifier performs " + allSummaries[key] + ".";
-}
-
-// On the second-classifier dropdown, update the "task interface" image.
-function updateSecondInterfaceImages() {
-    d3.select(".second-interface").html(function () {
-        if (
-            !document.getElementById("second-classifier-select-rounding").value
-        ) {
-            return;
-        }
-        var imgPath =
-            "img/interface_shape_name_" +
-            document.getElementById("second-classifier-select-rounding").value;
-        return (
-            '<img src="' +
-            imgPath +
-            '.png" alt="" class="interface-image" srcset="' +
-            imgPath +
-            '.svg"></img>'
-        );
-    });
-}
-
-// Calculate results given input parameters
-function calculateResults(property = "none", useGuess = false) {
-    switch (property) {
-        case "none":
-            var nAccurate = shapeParams.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = shapeParams.length;
-
-            var results = [
-                {
-                    object: "shape",
-                    n: totalShapes,
-                    "n correct": nAccurate,
-                    accuracy: (nAccurate / totalShapes).toFixed(3),
-                    rawCategoryName: "none",
-                },
-            ];
-
-            return results;
-        case "pointiness":
-            categories = ["pointy", "round"];
-            break;
-        case "size":
-            categories = ["small", "large"];
-            break;
-        case "shape_name":
-            categories = ["circle", "triangle", "rect"];
-            break;
-    }
-
-    var results = [];
-    if (useGuess == true) {
-        // Rounding shapes to categories
-
-        for (const category of categories) {
-            // Get shapes that are either in this category (e.g. rectangle) or "rounds to" this category (e.g. rt_rectangle)
-            var theseShapes = shapeParams.filter(
-                (shape) =>
-                    shape[property] == category ||
-                    shape[property] == "rt_" + category
-            );
-            var nAccurate = theseShapes.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = theseShapes.length;
-
-            results.push({
-                object: toPropertyString(category),
-                n: totalShapes,
-                "n correct": nAccurate,
-                accuracy: (nAccurate / totalShapes).toFixed(3),
-                rawCategoryName: category,
-            });
-        }
-    } else {
-        // Not rounding, treat everything else as "other"
-
-        // First go through existing categories
-        for (const category of categories) {
-            var theseShapes = shapeParams.filter(
-                (shape) => shape[property] == category
-            );
-            var nAccurate = theseShapes.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = theseShapes.length;
-            results.push({
-                object: toPropertyString(category),
-                n: totalShapes,
-                "n correct": nAccurate,
-                accuracy: (nAccurate / totalShapes).toFixed(3),
-                rawCategoryName: category,
-            });
-        }
-
-        // Now get "other" shapes
-        var theseShapes = shapeParams.filter(
-            (shape) => !categories.includes(shape[property])
-        );
-        var nAccurate = theseShapes.filter(
-            (shape) => shape.correctness == "correct"
-        ).length;
-        var totalShapes = theseShapes.length;
-        results.push({
-            object: "other shapes",
-            n: totalShapes,
-            "n correct": nAccurate,
-            accuracy: (nAccurate / totalShapes).toFixed(3),
-            rawCategoryName: "other",
-        });
-    }
-
-    return results;
-}
diff --git a/spaces/mfrashad/ClothingGAN/models/stylegan/stylegan_tf/metrics/linear_separability.py b/spaces/mfrashad/ClothingGAN/models/stylegan/stylegan_tf/metrics/linear_separability.py
deleted file mode 100644
index e50be5a0fea00eba7af2d05cccf74bacedbea1c3..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/stylegan/stylegan_tf/metrics/linear_separability.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Linear Separability (LS)."""
-
-from collections import defaultdict
-import numpy as np
-import sklearn.svm
-import tensorflow as tf
-import dnnlib.tflib as tflib
-
-from metrics import metric_base
-from training import misc
-
-#----------------------------------------------------------------------------
-
-classifier_urls = [
-    'https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX', # celebahq-classifier-00-male.pkl
-    'https://drive.google.com/uc?id=1Q5c6HE__ReW2W8qYAXpao68V1ryuisGo', # celebahq-classifier-01-smiling.pkl
-    'https://drive.google.com/uc?id=1Q7738mgWTljPOJQrZtSMLxzShEhrvVsU', # celebahq-classifier-02-attractive.pkl
-    'https://drive.google.com/uc?id=1QBv2Mxe7ZLvOv1YBTLq-T4DS3HjmXV0o', # celebahq-classifier-03-wavy-hair.pkl
-    'https://drive.google.com/uc?id=1QIvKTrkYpUrdA45nf7pspwAqXDwWOLhV', # celebahq-classifier-04-young.pkl
-    'https://drive.google.com/uc?id=1QJPH5rW7MbIjFUdZT7vRYfyUjNYDl4_L', # celebahq-classifier-05-5-o-clock-shadow.pkl
-    'https://drive.google.com/uc?id=1QPZXSYf6cptQnApWS_T83sqFMun3rULY', # celebahq-classifier-06-arched-eyebrows.pkl
-    'https://drive.google.com/uc?id=1QPgoAZRqINXk_PFoQ6NwMmiJfxc5d2Pg', # celebahq-classifier-07-bags-under-eyes.pkl
-    'https://drive.google.com/uc?id=1QQPQgxgI6wrMWNyxFyTLSgMVZmRr1oO7', # celebahq-classifier-08-bald.pkl
-    'https://drive.google.com/uc?id=1QcSphAmV62UrCIqhMGgcIlZfoe8hfWaF', # celebahq-classifier-09-bangs.pkl
-    'https://drive.google.com/uc?id=1QdWTVwljClTFrrrcZnPuPOR4mEuz7jGh', # celebahq-classifier-10-big-lips.pkl
-    'https://drive.google.com/uc?id=1QgvEWEtr2mS4yj1b_Y3WKe6cLWL3LYmK', # celebahq-classifier-11-big-nose.pkl
-    'https://drive.google.com/uc?id=1QidfMk9FOKgmUUIziTCeo8t-kTGwcT18', # celebahq-classifier-12-black-hair.pkl
-    'https://drive.google.com/uc?id=1QthrJt-wY31GPtV8SbnZQZ0_UEdhasHO', # celebahq-classifier-13-blond-hair.pkl
-    'https://drive.google.com/uc?id=1QvCAkXxdYT4sIwCzYDnCL9Nb5TDYUxGW', # celebahq-classifier-14-blurry.pkl
-    'https://drive.google.com/uc?id=1QvLWuwSuWI9Ln8cpxSGHIciUsnmaw8L0', # celebahq-classifier-15-brown-hair.pkl
-    'https://drive.google.com/uc?id=1QxW6THPI2fqDoiFEMaV6pWWHhKI_OoA7', # celebahq-classifier-16-bushy-eyebrows.pkl
-    'https://drive.google.com/uc?id=1R71xKw8oTW2IHyqmRDChhTBkW9wq4N9v', # celebahq-classifier-17-chubby.pkl
-    'https://drive.google.com/uc?id=1RDn_fiLfEGbTc7JjazRXuAxJpr-4Pl67', # celebahq-classifier-18-double-chin.pkl
-    'https://drive.google.com/uc?id=1RGBuwXbaz5052bM4VFvaSJaqNvVM4_cI', # celebahq-classifier-19-eyeglasses.pkl
-    'https://drive.google.com/uc?id=1RIxOiWxDpUwhB-9HzDkbkLegkd7euRU9', # celebahq-classifier-20-goatee.pkl
-    'https://drive.google.com/uc?id=1RPaNiEnJODdr-fwXhUFdoSQLFFZC7rC-', # celebahq-classifier-21-gray-hair.pkl
-    'https://drive.google.com/uc?id=1RQH8lPSwOI2K_9XQCZ2Ktz7xm46o80ep', # celebahq-classifier-22-heavy-makeup.pkl
-    'https://drive.google.com/uc?id=1RXZM61xCzlwUZKq-X7QhxOg0D2telPow', # celebahq-classifier-23-high-cheekbones.pkl
-    'https://drive.google.com/uc?id=1RgASVHW8EWMyOCiRb5fsUijFu-HfxONM', # celebahq-classifier-24-mouth-slightly-open.pkl
-    'https://drive.google.com/uc?id=1RkC8JLqLosWMaRne3DARRgolhbtg_wnr', # celebahq-classifier-25-mustache.pkl
-    'https://drive.google.com/uc?id=1RqtbtFT2EuwpGTqsTYJDyXdnDsFCPtLO', # celebahq-classifier-26-narrow-eyes.pkl
-    'https://drive.google.com/uc?id=1Rs7hU-re8bBMeRHR-fKgMbjPh-RIbrsh', # celebahq-classifier-27-no-beard.pkl
-    'https://drive.google.com/uc?id=1RynDJQWdGOAGffmkPVCrLJqy_fciPF9E', # celebahq-classifier-28-oval-face.pkl
-    'https://drive.google.com/uc?id=1S0TZ_Hdv5cb06NDaCD8NqVfKy7MuXZsN', # celebahq-classifier-29-pale-skin.pkl
-    'https://drive.google.com/uc?id=1S3JPhZH2B4gVZZYCWkxoRP11q09PjCkA', # celebahq-classifier-30-pointy-nose.pkl
-    'https://drive.google.com/uc?id=1S3pQuUz-Jiywq_euhsfezWfGkfzLZ87W', # celebahq-classifier-31-receding-hairline.pkl
-    'https://drive.google.com/uc?id=1S6nyIl_SEI3M4l748xEdTV2vymB_-lrY', # celebahq-classifier-32-rosy-cheeks.pkl
-    'https://drive.google.com/uc?id=1S9P5WCi3GYIBPVYiPTWygrYIUSIKGxbU', # celebahq-classifier-33-sideburns.pkl
-    'https://drive.google.com/uc?id=1SANviG-pp08n7AFpE9wrARzozPIlbfCH', # celebahq-classifier-34-straight-hair.pkl
-    'https://drive.google.com/uc?id=1SArgyMl6_z7P7coAuArqUC2zbmckecEY', # celebahq-classifier-35-wearing-earrings.pkl
-    'https://drive.google.com/uc?id=1SC5JjS5J-J4zXFO9Vk2ZU2DT82TZUza_', # celebahq-classifier-36-wearing-hat.pkl
-    'https://drive.google.com/uc?id=1SDAQWz03HGiu0MSOKyn7gvrp3wdIGoj-', # celebahq-classifier-37-wearing-lipstick.pkl
-    'https://drive.google.com/uc?id=1SEtrVK-TQUC0XeGkBE9y7L8VXfbchyKX', # celebahq-classifier-38-wearing-necklace.pkl
-    'https://drive.google.com/uc?id=1SF_mJIdyGINXoV-I6IAxHB_k5dxiF6M-', # celebahq-classifier-39-wearing-necktie.pkl
-]
-
-#----------------------------------------------------------------------------
-
-def prob_normalize(p):
-    p = np.asarray(p).astype(np.float32)
-    assert len(p.shape) == 2
-    return p / np.sum(p)
-
-def mutual_information(p):
-    p = prob_normalize(p)
-    px = np.sum(p, axis=1)
-    py = np.sum(p, axis=0)
-    result = 0.0
-    for x in range(p.shape[0]):
-        p_x = px[x]
-        for y in range(p.shape[1]):
-            p_xy = p[x][y]
-            p_y = py[y]
-            if p_xy > 0.0:
-                result += p_xy * np.log2(p_xy / (p_x * p_y)) # get bits as output
-    return result
-
-def entropy(p):
-    p = prob_normalize(p)
-    result = 0.0
-    for x in range(p.shape[0]):
-        for y in range(p.shape[1]):
-            p_xy = p[x][y]
-            if p_xy > 0.0:
-                result -= p_xy * np.log2(p_xy)
-    return result
-
-def conditional_entropy(p):
-    # H(Y|X) where X corresponds to axis 0, Y to axis 1
-    # i.e., How many bits of additional information are needed to where we are on axis 1 if we know where we are on axis 0?
-    p = prob_normalize(p)
-    y = np.sum(p, axis=0, keepdims=True) # marginalize to calculate H(Y)
-    return max(0.0, entropy(y) - mutual_information(p)) # can slip just below 0 due to FP inaccuracies, clean those up.
-
-#----------------------------------------------------------------------------
-
-class LS(metric_base.MetricBase):
-    def __init__(self, num_samples, num_keep, attrib_indices, minibatch_per_gpu, **kwargs):
-        assert num_keep <= num_samples
-        super().__init__(**kwargs)
-        self.num_samples = num_samples
-        self.num_keep = num_keep
-        self.attrib_indices = attrib_indices
-        self.minibatch_per_gpu = minibatch_per_gpu
-
-    def _evaluate(self, Gs, num_gpus):
-        minibatch_size = num_gpus * self.minibatch_per_gpu
-
-        # Construct TensorFlow graph for each GPU.
-        result_expr = []
-        for gpu_idx in range(num_gpus):
-            with tf.device('/gpu:%d' % gpu_idx):
-                Gs_clone = Gs.clone()
-
-                # Generate images.
-                latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:])
-                dlatents = Gs_clone.components.mapping.get_output_for(latents, None, is_validation=True)
-                images = Gs_clone.components.synthesis.get_output_for(dlatents, is_validation=True, randomize_noise=True)
-
-                # Downsample to 256x256. The attribute classifiers were built for 256x256.
-                if images.shape[2] > 256:
-                    factor = images.shape[2] // 256
-                    images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor])
-                    images = tf.reduce_mean(images, axis=[3, 5])
-
-                # Run classifier for each attribute.
-                result_dict = dict(latents=latents, dlatents=dlatents[:,-1])
-                for attrib_idx in self.attrib_indices:
-                    classifier = misc.load_pkl(classifier_urls[attrib_idx])
-                    logits = classifier.get_output_for(images, None)
-                    predictions = tf.nn.softmax(tf.concat([logits, -logits], axis=1))
-                    result_dict[attrib_idx] = predictions
-                result_expr.append(result_dict)
-
-        # Sampling loop.
-        results = []
-        for _ in range(0, self.num_samples, minibatch_size):
-            results += tflib.run(result_expr)
-        results = {key: np.concatenate([value[key] for value in results], axis=0) for key in results[0].keys()}
-
-        # Calculate conditional entropy for each attribute.
-        conditional_entropies = defaultdict(list)
-        for attrib_idx in self.attrib_indices:
-            # Prune the least confident samples.
-            pruned_indices = list(range(self.num_samples))
-            pruned_indices = sorted(pruned_indices, key=lambda i: -np.max(results[attrib_idx][i]))
-            pruned_indices = pruned_indices[:self.num_keep]
-
-            # Fit SVM to the remaining samples.
-            svm_targets = np.argmax(results[attrib_idx][pruned_indices], axis=1)
-            for space in ['latents', 'dlatents']:
-                svm_inputs = results[space][pruned_indices]
-                try:
-                    svm = sklearn.svm.LinearSVC()
-                    svm.fit(svm_inputs, svm_targets)
-                    svm.score(svm_inputs, svm_targets)
-                    svm_outputs = svm.predict(svm_inputs)
-                except:
-                    svm_outputs = svm_targets # assume perfect prediction
-
-                # Calculate conditional entropy.
-                p = [[np.mean([case == (row, col) for case in zip(svm_outputs, svm_targets)]) for col in (0, 1)] for row in (0, 1)]
-                conditional_entropies[space].append(conditional_entropy(p))
-
-        # Calculate separability scores.
-        scores = {key: 2**np.sum(values) for key, values in conditional_entropies.items()}
-        self._report_result(scores['latents'], suffix='_z')
-        self._report_result(scores['dlatents'], suffix='_w')
-
-#----------------------------------------------------------------------------
diff --git a/spaces/mikeee/ultimatumbee/run-nodemon-python-app.sh b/spaces/mikeee/ultimatumbee/run-nodemon-python-app.sh
deleted file mode 100644
index be0065292e1ca2da8435591cd1d0bf7879f084fe..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ultimatumbee/run-nodemon-python-app.sh
+++ /dev/null
@@ -1 +0,0 @@
-nodemon -e py -w app.py -w ubee -x python app.py
diff --git a/spaces/mingyuan/ReMoDiffuse/mogen/datasets/samplers/distributed_sampler.py b/spaces/mingyuan/ReMoDiffuse/mogen/datasets/samplers/distributed_sampler.py
deleted file mode 100644
index a46a086e7b2e99afbb02004bf9ff03c84e13a1c8..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/ReMoDiffuse/mogen/datasets/samplers/distributed_sampler.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch
-from torch.utils.data import DistributedSampler as _DistributedSampler
-
-
-class DistributedSampler(_DistributedSampler):
-
-    def __init__(self,
-                 dataset,
-                 num_replicas=None,
-                 rank=None,
-                 shuffle=True,
-                 round_up=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank)
-        self.shuffle = shuffle
-        self.round_up = round_up
-        if self.round_up:
-            self.total_size = self.num_samples * self.num_replicas
-        else:
-            self.total_size = len(self.dataset)
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        if self.shuffle:
-            g = torch.Generator()
-            g.manual_seed(self.epoch)
-            indices = torch.randperm(len(self.dataset), generator=g).tolist()
-        else:
-            indices = torch.arange(len(self.dataset)).tolist()
-
-        # add extra samples to make it evenly divisible
-        if self.round_up:
-            indices = (
-                indices *
-                int(self.total_size / len(indices) + 1))[:self.total_size]
-        assert len(indices) == self.total_size
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        if self.round_up:
-            assert len(indices) == self.num_samples
-
-        return iter(indices)
diff --git a/spaces/mithril-security/blind_chat/src/lib/switchTheme.ts b/spaces/mithril-security/blind_chat/src/lib/switchTheme.ts
deleted file mode 100644
index 926195ef1241aa079a4e416f5a53e2a8ced43b12..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/switchTheme.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export function switchTheme() {
-	const { classList } = document.querySelector("html") as HTMLElement;
-	classList.add("dark");
-	localStorage.theme = "dark";
-}
diff --git a/spaces/mixcard/text-summary-2/app.py b/spaces/mixcard/text-summary-2/app.py
deleted file mode 100644
index 9bd2c1184c45109ee790ea23b76f74e382c5c03d..0000000000000000000000000000000000000000
--- a/spaces/mixcard/text-summary-2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/knkarthick/MEETING_SUMMARY").launch()
\ No newline at end of file
diff --git a/spaces/miyaaa666/bingo/src/components/user-menu.tsx b/spaces/miyaaa666/bingo/src/components/user-menu.tsx
deleted file mode 100644
index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/components/user-menu.tsx
+++ /dev/null
@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}
diff --git a/spaces/ml6team/post-processing-summarization/app.py b/spaces/ml6team/post-processing-summarization/app.py
deleted file mode 100644
index 6cd0a9eee78e282c6d14f98b044a5f0f03cac464..0000000000000000000000000000000000000000
--- a/spaces/ml6team/post-processing-summarization/app.py
+++ /dev/null
@@ -1,553 +0,0 @@
-from typing import AnyStr, Dict
-
-import itertools
-import streamlit as st
-import en_core_web_lg
-
-import torch.nn.parameter
-from bs4 import BeautifulSoup
-import numpy as np
-import base64
-
-from spacy_streamlit.util import get_svg
-from streamlit.proto.SessionState_pb2 import SessionState
-
-from custom_renderer import render_sentence_custom
-from sentence_transformers import SentenceTransformer
-
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-import os
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; 
-margin-bottom: 2.5rem">{}</div> """
-
-
-@st.experimental_singleton
-def get_sentence_embedding_model():
-    return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
-
-@st.experimental_singleton
-def get_spacy():
-    nlp = en_core_web_lg.load()
-    return nlp
-
-
-@st.experimental_singleton
-def get_transformer_pipeline():
-    tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
-    model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
-    return pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
-
-
-@st.experimental_singleton
-def get_summarizer_model():
-    model_name = 'google/pegasus-cnn_dailymail'
-    summarizer_model = pipeline("summarization", model=model_name, tokenizer=model_name,
-                                device=0 if torch.cuda.is_available() else -1)
-
-    return summarizer_model
-
-
-# Page setup
-st.set_page_config(
-    page_title="📜 Hallucination detection in summaries 📜",
-    page_icon="",
-    layout="centered",
-    initial_sidebar_state="auto",
-    menu_items={
-        'Get help': None,
-        'Report a bug': None,
-        'About': None,
-    }
-)
-
-
-def list_all_article_names() -> list:
-    filenames = []
-    for file in sorted(os.listdir('./sample-articles/')):
-        if file.endswith('.txt'):
-            filenames.append(file.replace('.txt', ''))
-    # Append free use possibility:
-    filenames.append("Provide your own input")
-    return filenames
-
-
-def fetch_article_contents(filename: str) -> AnyStr:
-    if filename == "Provide your own input":
-        return " "
-    with open(f'./sample-articles/{filename}.txt', 'r') as f:
-        data = f.read()
-    return data
-
-
-def fetch_summary_contents(filename: str) -> AnyStr:
-    with open(f'./sample-summaries/{filename}.txt', 'r') as f:
-        data = f.read()
-    return data
-
-
-def fetch_entity_specific_contents(filename: str) -> AnyStr:
-    with open(f'./entity-specific-text/{filename}.txt', 'r') as f:
-        data = f.read()
-    return data
-
-
-def fetch_dependency_specific_contents(filename: str) -> AnyStr:
-    with open(f'./dependency-specific-text/{filename}.txt', 'r') as f:
-        data = f.read()
-    return data
-
-
-def fetch_ranked_summaries(filename: str, ranknumber: int) -> AnyStr:
-    with open(f'./ranked-summaries/{filename}/Rank{ranknumber}.txt', 'r') as f:
-        data = f.read()
-    return data
-
-
-def fetch_dependency_svg(filename: str) -> AnyStr:
-    with open(f'./dependency-images/{filename}.txt', 'r') as f:
-        lines = [line.rstrip() for line in f]
-    return lines
-
-
-def display_summary(summary_content: str):
-    st.session_state.summary_output = summary_content
-    soup = BeautifulSoup(summary_content, features="html.parser")
-    return HTML_WRAPPER.format(soup)
-
-
-def get_all_entities_per_sentence(text):
-    doc = nlp(text)
-
-    sentences = list(doc.sents)
-
-    entities_all_sentences = []
-    for sentence in sentences:
-        entities_this_sentence = []
-
-        # SPACY ENTITIES
-        for entity in sentence.ents:
-            entities_this_sentence.append(str(entity))
-
-        # FLAIR ENTITIES (CURRENTLY NOT USED)
-        # sentence_entities = Sentence(str(sentence))
-        # tagger.predict(sentence_entities)
-        # for entity in sentence_entities.get_spans('ner'):
-        #     entities_this_sentence.append(entity.text)
-
-        # XLM ENTITIES
-        entities_xlm = [entity["word"] for entity in ner_model(str(sentence))]
-        for entity in entities_xlm:
-            entities_this_sentence.append(str(entity))
-
-        entities_all_sentences.append(entities_this_sentence)
-
-    return entities_all_sentences
-
-
-def get_all_entities(text):
-    all_entities_per_sentence = get_all_entities_per_sentence(text)
-    return list(itertools.chain.from_iterable(all_entities_per_sentence))
-
-
-def get_and_compare_entities(first_time: bool):
-    if first_time:
-        article_content = st.session_state.article_text
-        all_entities_per_sentence = get_all_entities_per_sentence(article_content)
-        entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence))
-        st.session_state.entities_article = entities_article
-    else:
-        entities_article = st.session_state.entities_article
-
-    summary_content = st.session_state.summary_output
-    all_entities_per_sentence = get_all_entities_per_sentence(summary_content)
-    entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence))
-
-    matched_entities = []
-    unmatched_entities = []
-    for entity in entities_summary:
-        if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article):
-            matched_entities.append(entity)
-        elif any(
-                np.inner(sentence_embedding_model.encode(entity, show_progress_bar=False),
-                         sentence_embedding_model.encode(art_entity, show_progress_bar=False)) > 0.9 for
-                art_entity in entities_article):
-            matched_entities.append(entity)
-        else:
-            unmatched_entities.append(entity)
-
-    matched_entities = list(dict.fromkeys(matched_entities))
-    unmatched_entities = list(dict.fromkeys(unmatched_entities))
-
-    matched_entities_to_remove = []
-    unmatched_entities_to_remove = []
-
-    for entity in matched_entities:
-        for substring_entity in matched_entities:
-            if entity != substring_entity and entity.lower() in substring_entity.lower():
-                matched_entities_to_remove.append(entity)
-
-    for entity in unmatched_entities:
-        for substring_entity in unmatched_entities:
-            if entity != substring_entity and entity.lower() in substring_entity.lower():
-                unmatched_entities_to_remove.append(entity)
-
-    matched_entities_to_remove = list(dict.fromkeys(matched_entities_to_remove))
-    unmatched_entities_to_remove = list(dict.fromkeys(unmatched_entities_to_remove))
-
-    for entity in matched_entities_to_remove:
-        matched_entities.remove(entity)
-    for entity in unmatched_entities_to_remove:
-        unmatched_entities.remove(entity)
-
-    return matched_entities, unmatched_entities
-
-
-def highlight_entities():
-    summary_content = st.session_state.summary_output
-    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
-    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
-    markdown_end = "</mark>"
-
-    matched_entities, unmatched_entities = get_and_compare_entities(True)
-
-    for entity in matched_entities:
-        summary_content = summary_content.replace(entity, markdown_start_green + entity + markdown_end)
-
-    for entity in unmatched_entities:
-        summary_content = summary_content.replace(entity, markdown_start_red + entity + markdown_end)
-    soup = BeautifulSoup(summary_content, features="html.parser")
-    return HTML_WRAPPER.format(soup)
-
-
-def highlight_entities_new(summary_str: str):
-    st.session_state.summary_output = summary_str
-    summary_content = st.session_state.summary_output
-    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
-    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
-    markdown_end = "</mark>"
-
-    matched_entities, unmatched_entities = get_and_compare_entities(False)
-
-    for entity in matched_entities:
-        summary_content = summary_content.replace(entity, markdown_start_green + entity + markdown_end)
-
-    for entity in unmatched_entities:
-        summary_content = summary_content.replace(entity, markdown_start_red + entity + markdown_end)
-    soup = BeautifulSoup(summary_content, features="html.parser")
-    return HTML_WRAPPER.format(soup)
-
-
-def render_dependency_parsing(text: Dict):
-    html = render_sentence_custom(text, nlp)
-    html = html.replace("\n\n", "\n")
-    st.write(get_svg(html), unsafe_allow_html=True)
-
-
-def check_dependency(article: bool):
-    if article:
-        text = st.session_state.article_text
-        all_entities = get_all_entities_per_sentence(text)
-    else:
-        text = st.session_state.summary_output
-        all_entities = get_all_entities_per_sentence(text)
-    doc = nlp(text)
-    tok_l = doc.to_json()['tokens']
-    test_list_dict_output = []
-
-    sentences = list(doc.sents)
-    for i, sentence in enumerate(sentences):
-        start_id = sentence.start
-        end_id = sentence.end
-        for t in tok_l:
-            if t["id"] < start_id or t["id"] > end_id:
-                continue
-            head = tok_l[t['head']]
-            if t['dep'] == 'amod' or t['dep'] == "pobj":
-                object_here = text[t['start']:t['end']]
-                object_target = text[head['start']:head['end']]
-                if t['dep'] == "pobj" and str.lower(object_target) != "in":
-                    continue
-                # ONE NEEDS TO BE ENTITY
-                if object_here in all_entities[i]:
-                    identifier = object_here + t['dep'] + object_target
-                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
-                                                  "target_word_index": (t['head'] - sentence.start),
-                                                  "identifier": identifier, "sentence": str(sentence)})
-                elif object_target in all_entities[i]:
-                    identifier = object_here + t['dep'] + object_target
-                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
-                                                  "target_word_index": (t['head'] - sentence.start),
-                                                  "identifier": identifier, "sentence": str(sentence)})
-                else:
-                    continue
-    return test_list_dict_output
-
-
-def render_svg(svg_file):
-    with open(svg_file, "r") as f:
-        lines = f.readlines()
-        svg = "".join(lines)
-
-        # """Renders the given svg string."""
-        b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
-        html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
-        return html
-
-
-def generate_abstractive_summary(text, type, min_len=120, max_len=512, **kwargs):
-    text = text.strip().replace("\n", " ")
-    if type == "top_p":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len,
-                                   top_k=50, top_p=0.95, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "greedy":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "top_k":
-        text = summarization_model(text, min_length=min_len, max_length=max_len, top_k=50,
-                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    elif type == "beam":
-        text = summarization_model(text, min_length=min_len,
-                                   max_length=max_len,
-                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
-    summary = text[0]['summary_text'].replace("<n>", " ")
-    return summary
-
-
-# Load all different models (cached) at start time of the hugginface space
-sentence_embedding_model = get_sentence_embedding_model()
-ner_model = get_transformer_pipeline()
-nlp = get_spacy()
-summarization_model = get_summarizer_model()
-
-# Page
-st.title('📜 Hallucination detection 📜')
-st.subheader("🔎 Detecting errors in generated abstractive summaries")
-#st.title('📜 Error detection in summaries 📜')
-
-# INTRODUCTION
-st.header("🧑‍🏫 Introduction")
-
-#introduction_checkbox = st.checkbox("Show introduction text", value=True)
-#if introduction_checkbox:
-st.markdown("""
-Recent work using 🤖 **transformers** 🤖 on large text corpora has shown great success when fine-tuned on 
-several different downstream NLP tasks. One such task is that of text summarization. The goal of text summarization 
-is to generate concise and accurate summaries from input document(s). There are 2 types of summarization:
-
- - **Extractive summarization** merely copies informative fragments from the input. 
- - **Abstractive summarization** 
- may generate novel words. A good abstractive summary should cover principal information in the input and has to be 
- linguistically fluent. This interactive blogpost will focus on this more difficult task of abstractive summary 
- generation. Furthermore we will focus mainly on hallucination errors, and less on sentence fluency.""")
-
-st.markdown("###")
-st.markdown("🤔 **Why is this important?** 🤔 Let's say we want to summarize news articles for a popular "
-            "newspaper. If an article tells the story of Elon Musk buying **Twitter**, we don't want our summarization "
-            "model to say that he bought **Facebook** instead. Summarization could also be done for financial reports "
-            "for example. In such environments, these errors can be very critical, so we want to find a way to "
-            "detect them.")
-st.markdown("###")
-st.markdown("""To generate summaries we will use the 🐎 [PEGASUS](https://huggingface.co/google/pegasus-cnn_dailymail) 🐎
-model, producing abstractive summaries from large articles. These summaries often contain sentences with different 
-kinds of errors. Rather than improving the core model, we will look into possible post-processing steps to detect errors 
-from the generated summaries. Throughout this blog, we will also explain the results for some methods on specific 
-examples. These text blocks will be indicated and they change according to the currently selected article.""")
-
-# GENERATING SUMMARIES PART
-st.header("🪶 Generating summaries")
-st.markdown("Let’s start by selecting an article text for which we want to generate a summary, or you can provide "
-            "text yourself. Note that it’s suggested to provide a sufficiently large article, as otherwise the "
-            "summary generated from it might not be optimal, leading to suboptimal performance of the post-processing "
-            "steps. However, too long articles will be truncated and might miss information in the summary.")
-
-st.markdown("####")
-selected_article = st.selectbox('Select an article or provide your own:',
-                                list_all_article_names(), index=2)
-st.session_state.article_text = fetch_article_contents(selected_article)
-article_text = st.text_area(
-    label='Full article text',
-    value=st.session_state.article_text,
-    height=250
-)
-
-summarize_button = st.button(label='🤯 Process article content',
-                             help="Start interactive blogpost")
-
-if summarize_button:
-    st.session_state.article_text = article_text
-    st.markdown("####")
-    st.markdown(
-        "*Below you can find the generated summary for the article. We will discuss two approaches that we found are "
-        "able to detect some common errors. Based on these errors, one could then score different summaries, indicating how "
-        "factual a summary is for a given article. The idea is that in production, you could generate a set of "
-        "summaries for the same article, with different parameters (or even different models). By using "
-        "post-processing error detection, we can then select the best possible summary.*")
-    st.markdown("####")
-    if st.session_state.article_text:
-        with st.spinner('Generating summary, this might take a while...'):
-            if selected_article != "Provide your own input" and article_text == fetch_article_contents(
-                    selected_article):
-                st.session_state.unchanged_text = True
-                summary_content = fetch_summary_contents(selected_article)
-            else:
-                summary_content = generate_abstractive_summary(article_text, type="beam", do_sample=True, num_beams=15,
-                                                               no_repeat_ngram_size=4)
-                st.session_state.unchanged_text = False
-            summary_displayed = display_summary(summary_content)
-            st.write("✍ **Generated summary:** ✍", summary_displayed, unsafe_allow_html=True)
-    else:
-        st.error('**Error**: No comment to classify. Please provide a comment.')
-
-    # ENTITY MATCHING PART
-    st.header("1️⃣ Entity matching")
-    st.markdown("The first method we will discuss is called **Named Entity Recognition** (NER). NER is the task of "
-                "identifying and categorising key information (entities) in text. An entity can be a singular word or a "
-                "series of words that consistently refers to the same thing. Common entity classes are person names, "
-                "organisations, locations and so on. By applying NER to both the article and its summary, we can spot "
-                "possible **hallucinations**. ")
-
-    st.markdown("Hallucinations are words generated by the model that are not supported by "
-                "the source input. Deep learning based generation is [prone to hallucinate]("
-                "https://arxiv.org/pdf/2202.03629.pdf) unintended text. These hallucinations degrade "
-                "system performance and fail to meet user expectations in many real-world scenarios. By applying entity matching, we can improve this problem"
-                " for the downstream task of summary generation.")
-
-    st.markdown(" In theory all entities in the summary (such as dates, locations and so on), "
-                "should also be present in the article. Thus we can extract all entities from the summary and compare "
-                "them to the entities of the original article, spotting potential hallucinations. The more unmatched "
-                "entities we find, the lower the factualness score of the summary. ")
-    with st.spinner("Calculating and matching entities, this takes about 10-20 seconds..."):
-        entity_match_html = highlight_entities()
-        st.markdown("####")
-        st.write(entity_match_html, unsafe_allow_html=True)
-        red_text = """<font color="black"><span style="background-color: rgb(238, 135, 135); opacity: 
-        1;">red</span></font> """
-        green_text = """<font color="black">
-            <span style="background-color: rgb(121, 236, 121); opacity: 1;">green</span>
-        </font>"""
-
-        markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
-        markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
-        st.markdown(
-            "We call this technique **entity matching** and here you can see what this looks like when we apply this "
-            "method on the summary. Entities in the summary are marked  " + green_text + " when the entity also "
-                                                                                         "exists in the article, "
-                                                                                         "while unmatched entities "
-                                                                                         "are marked " + red_text +
-            ". Several of the example articles and their summaries indicate different errors we find by using this "
-            "technique. Based on the current article, we provide a short explanation of the results below **(only for "
-            "example articles)**. ", unsafe_allow_html=True)
-        if st.session_state.unchanged_text:
-            entity_specific_text = fetch_entity_specific_contents(selected_article)
-            soup = BeautifulSoup(entity_specific_text, features="html.parser")
-            st.markdown("####")
-            st.write("💡👇 **Specific example explanation** 👇💡", HTML_WRAPPER.format(soup), unsafe_allow_html=True)
-
-    # DEPENDENCY PARSING PART
-    st.header("2️⃣ Dependency comparison")
-    st.markdown(
-        "The second method we use for post-processing is called **Dependency Parsing**: the process in which the "
-        "grammatical structure in a sentence is analysed, to find out related words as well as the type of the "
-        "relationship between them. For the sentence “Jan’s wife is called Sarah” you would get the following "
-        "dependency graph:")
-
-    # TODO: I wonder why the first doesn't work but the second does (it doesn't show deps otherwise)
-    # st.image("ExampleParsing.svg")
-    st.write(render_svg('ExampleParsing.svg'), unsafe_allow_html=True)
-    st.markdown(
-        "Here, *“Jan”* is the *“poss”* (possession modifier) of *“wife”*. If suddenly the summary would read *“Jan’s"
-        " husband…”*, there would be a dependency in the summary that is non-existent in the article itself (namely "
-        "*“Jan”* is the “poss” of *“husband”*)."
-        "However, often new dependencies are introduced in the summary that "
-        "are still correct, as can be seen in the example below. ")
-    st.write(render_svg('SecondExampleParsing.svg'), unsafe_allow_html=True)
-
-    st.markdown("*“The borders of Ukraine”* have a different dependency between *“borders”* and "
-                "*“Ukraine”* "
-                "than *“Ukraine’s borders”*, while both descriptions have the same meaning. So just matching all "
-                "dependencies between article and summary (as we did with entity matching) would not be a robust method."
-                " More on the different sorts of dependencies and their description can be found [here](https://universaldependencies.org/docs/en/dep/).")
-    st.markdown("However, we have found that **there are specific dependencies that are often an "
-                "indication of a wrongly constructed sentence** when there is no article match. We (currently) use 2 "
-                "common dependencies which - when present in the summary but not in the article - are highly "
-                "indicative of factualness errors. "
-                "Furthermore, we only check dependencies between an existing **entity** and its direct connections. "
-                "Below we highlight all unmatched dependencies that satisfy the discussed constraints. We also "
-                "discuss the specific results for the currently selected example article.")
-    with st.spinner("Doing dependency parsing..."):
-        if st.session_state.unchanged_text:
-            for cur_svg_image in fetch_dependency_svg(selected_article):
-                st.write(cur_svg_image, unsafe_allow_html=True)
-            dep_specific_text = fetch_dependency_specific_contents(selected_article)
-            soup = BeautifulSoup(dep_specific_text, features="html.parser")
-            st.write("💡👇 **Specific example explanation** 👇💡", HTML_WRAPPER.format(soup), unsafe_allow_html=True)
-        else:
-            summary_deps = check_dependency(False)
-            article_deps = check_dependency(True)
-            total_unmatched_deps = []
-            for summ_dep in summary_deps:
-                if not any(summ_dep['identifier'] in art_dep['identifier'] for art_dep in article_deps):
-                    total_unmatched_deps.append(summ_dep)
-            if total_unmatched_deps:
-                for current_drawing_list in total_unmatched_deps:
-                    render_dependency_parsing(current_drawing_list)
-
-    # CURRENTLY DISABLED
-    # OUTRO/CONCLUSION
-    st.header("🤝 Bringing it together")
-    st.markdown("We have presented 2 methods that try to detect errors in summaries via post-processing steps. Entity "
-                "matching can be used to solve hallucinations, while dependency comparison can be used to filter out "
-                "some bad sentences (and thus worse summaries). These methods highlight the possibilities of "
-                "post-processing AI-made summaries, but are only a first introduction. As the methods were "
-                "empirically tested they are definitely not sufficiently robust for general use-cases.")
-    st.markdown("####")
-    st.markdown(
-        "*Below we generate 3 different kind of summaries, and based on the two discussed methods, their errors are "
-        "detected to estimate a summary score. Based on this basic approach, "
-        "the best summary (read: the one that a human would prefer or indicate as the best one) "
-        "will hopefully be at the top. We currently "
-        "only do this for the example articles (for which the different summmaries are already generated). The reason "
-        "for this is that HuggingFace spaces are limited in their CPU memory. We also highlight the entities as done "
-        "before, but note that the rankings are done on a combination of unmatched entities and "
-        "dependencies (with the latter not shown here).*")
-    st.markdown("####")
-
-    if selected_article != "Provide your own input" and article_text == fetch_article_contents(selected_article):
-        with st.spinner("Fetching summaries, ranking them and highlighting entities, this might take a minute or two..."):
-            summaries_list = []
-            deduction_points = []
-
-            # FOR NEW GENERATED SUMMARY
-            for i in range(1 , 4):
-                st.session_state.summary_output = fetch_ranked_summaries(selected_article, i)
-                _, amount_unmatched = get_and_compare_entities(False)
-
-                summary_deps = check_dependency(False)
-                article_deps = check_dependency(True)
-                total_unmatched_deps = []
-                for summ_dep in summary_deps:
-                    if not any(summ_dep['identifier'] in art_dep['identifier'] for art_dep in article_deps):
-                        total_unmatched_deps.append(summ_dep)
-
-                summaries_list.append(st.session_state.summary_output)
-                deduction_points.append(len(amount_unmatched) + len(total_unmatched_deps))
-
-
-            # RANKING AND SHOWING THE SUMMARIES
-            deduction_points, summaries_list = (list(t) for t in zip(*sorted(zip(deduction_points, summaries_list))))
-
-            cur_rank = 1
-            rank_downgrade = 0
-            for i in range(len(deduction_points)):
-                #st.write(f'🏆 Rank {cur_rank} summary: 🏆', display_summary(summaries_list[i]), unsafe_allow_html=True)
-                st.write(f'🏆 Rank {cur_rank} summary: 🏆', highlight_entities_new(summaries_list[i]), unsafe_allow_html=True)
-                if i < len(deduction_points) - 1:
-                    rank_downgrade += 1
-                    if not deduction_points[i + 1] == deduction_points[i]:
-                        cur_rank += rank_downgrade
-                        rank_downgrade = 0
diff --git a/spaces/mokashaa/Movies-Recommendation-System/README.md b/spaces/mokashaa/Movies-Recommendation-System/README.md
deleted file mode 100644
index 45834d96d21ae89c53787765ec23f4f429675de9..0000000000000000000000000000000000000000
--- a/spaces/mokashaa/Movies-Recommendation-System/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Movies Recommendation System
-emoji: 🦀
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mshukor/UnIVAL/data/audio_utils.py b/spaces/mshukor/UnIVAL/data/audio_utils.py
deleted file mode 100644
index 841513b517616922ce3a55eef1fd099b306d3f26..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/data/audio_utils.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# https://github.com/LAION-AI/CLAP/blob/df65ca0f6c3062dc554132cb40e74f4915084b21/src/training/data.py#L469
-
-from functools import partial
-import soundfile as sf
-import io
-import numpy as np
-import torch 
-
-import torchaudio
-import torchvision
-
-import torch.nn.functional as F
-
-
-AUDIO_CFG =  {
-        "sample_rate": 48000,
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-    }
-
-class dotdict(dict):
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = dict.get
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-class Map(dict):
-    """
-    Example:
-    m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])
-    """
-    def __init__(self, *args, **kwargs):
-        super(Map, self).__init__(*args, **kwargs)
-        for arg in args:
-            if isinstance(arg, dict):
-                for k, v in arg.iteritems():
-                    self[k] = v
-
-        if kwargs:
-            for k, v in kwargs.iteritems():
-                self[k] = v
-
-    def __getattr__(self, attr):
-        return self.get(attr)
-
-    def __setattr__(self, key, value):
-        self.__setitem__(key, value)
-
-    def __setitem__(self, key, value):
-        super(Map, self).__setitem__(key, value)
-        self.__dict__.update({key: value})
-
-    def __delattr__(self, item):
-        self.__delitem__(item)
-
-    def __delitem__(self, key):
-        super(Map, self).__delitem__(key)
-        del self.__dict__[key]
-
-        
-def int16_to_float32(x):
-    return (x / 32767.0).astype(np.float32)
-
-
-def float32_to_int16(x):
-    x = np.clip(x, a_min=-1., a_max=1.)
-    return (x * 32767.).astype(np.int16)
-
-
-def get_mel(audio_data,audio_cfg):
-    # mel shape: (n_mels, T)
-    mel = torchaudio.transforms.MelSpectrogram(
-        sample_rate=audio_cfg['sample_rate'],
-        n_fft=audio_cfg['window_size'],
-        win_length=audio_cfg['window_size'],
-        hop_length=audio_cfg['hop_size'],
-        center=True,
-        pad_mode="reflect",
-        power=2.0,
-        norm=None,
-        onesided=True,
-        n_mels=audio_cfg['mel_bins'],
-        f_min=audio_cfg['fmin'],
-        f_max=audio_cfg['fmax']
-    )(audio_data)
-
-    # we use log mel spectrogram as input
-    mel = torchaudio.transforms.AmplitudeToDB(top_db=None)(mel)
-    return mel.T  # (T, n_mels)
-
-
-def get_audio_features(sample, audio_data, max_len, data_truncating, data_filling, audio_cfg):
-    """
-    Calculate and add audio features to sample.
-    Sample: a dict containing all the data of current sample.
-    audio_data: a tensor of shape (T) containing audio data.
-    max_len: the maximum length of audio data.
-    data_truncating: the method of truncating data.
-    data_filling: the method of filling data.
-    audio_cfg: a dict containing audio configuration. Comes from model_cfg['audio_cfg'].
-    """
-    with torch.no_grad():
-        if len(audio_data) > max_len:
-            if data_truncating == "rand_trunc":
-                longer = torch.tensor([True])
-            elif data_truncating == "fusion":
-                # fusion
-                mel = get_mel(audio_data, audio_cfg)
-                # split to three parts
-                chunk_frames = max_len // audio_cfg['hop_size']+1  # the +1 related to how the spectrogram is computed
-                total_frames = mel.shape[0]
-                if chunk_frames == total_frames:
-                    # there is a corner case where the audio length is
-                    # larger than max_len but smaller than max_len+hop_size.
-                    # In this case, we just use the whole audio.
-                    mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
-                    sample["mel_fusion"] = mel_fusion
-                    longer = torch.tensor([False])
-                else:
-                    ranges = np.array_split(list(range(0, total_frames-chunk_frames+1)), 3)
-                    # print('total_frames-chunk_frames:', total_frames-chunk_frames,
-                    #       'len(audio_data):', len(audio_data),
-                    #       'chunk_frames:', chunk_frames,
-                    #       'total_frames:', total_frames)
-                    if len(ranges[1]) == 0:
-                        # if the audio is too short, we just use the first chunk
-                        ranges[1] = [0]
-                    if len(ranges[2]) == 0:
-                        # if the audio is too short, we just use the first chunk
-                        ranges[2] = [0]
-                    # randomly choose index for each part
-                    idx_front = np.random.choice(ranges[0])
-                    idx_middle = np.random.choice(ranges[1])
-                    idx_back = np.random.choice(ranges[2])
-                    # select mel
-                    mel_chunk_front = mel[idx_front:idx_front+chunk_frames, :]
-                    mel_chunk_middle = mel[idx_middle:idx_middle+chunk_frames, :]
-                    mel_chunk_back = mel[idx_back:idx_back+chunk_frames, :]
-
-                    # shrink the mel
-                    mel_shrink = torchvision.transforms.Resize(size=[chunk_frames, 64])(mel[None])[0]
-                    # logging.info(f"mel_shrink.shape: {mel_shrink.shape}")
-
-                    # stack
-                    mel_fusion = torch.stack([mel_chunk_front, mel_chunk_middle, mel_chunk_back, mel_shrink], dim=0)
-                    sample["mel_fusion"] = mel_fusion
-                    longer = torch.tensor([True])
-            else:
-                raise NotImplementedError(
-                    f"data_truncating {data_truncating} not implemented"
-                )
-            # random crop to max_len (for compatibility)
-            overflow = len(audio_data) - max_len
-            idx = np.random.randint(0, overflow + 1)
-            audio_data = audio_data[idx: idx + max_len]
-
-        else:  # padding if too short
-            if len(audio_data) < max_len:  # do nothing if equal
-                if data_filling == "repeatpad":
-                    n_repeat = int(max_len/len(audio_data))
-                    audio_data = audio_data.repeat(n_repeat)
-                    # audio_data = audio_data.unsqueeze(0).unsqueeze(0).unsqueeze(0)
-                    # audio_data = F.interpolate(audio_data,size=max_len,mode="bicubic")[0,0,0]
-                    audio_data = F.pad(
-                        audio_data,
-                        (0, max_len - len(audio_data)),
-                        mode="constant",
-                        value=0,
-                    )
-                elif data_filling == "pad":
-                    audio_data = F.pad(
-                        audio_data,
-                        (0, max_len - len(audio_data)),
-                        mode="constant",
-                        value=0,
-                    )
-                elif data_filling == "repeat":
-                    n_repeat = int(max_len/len(audio_data))
-                    audio_data = audio_data.repeat(n_repeat+1)[:max_len]
-                else:
-                    raise NotImplementedError(
-                        f"data_filling {data_filling} not implemented"
-                    )
-            if data_truncating == 'fusion':
-                mel = get_mel(audio_data, audio_cfg)
-                mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
-                sample["mel_fusion"] = mel_fusion
-            longer = torch.tensor([False])
-
-    sample["longer"] = longer
-    sample["waveform"] = audio_data
-
-    return sample
\ No newline at end of file
diff --git a/spaces/mthsk/sovits-100orangejuice/modules/modules.py b/spaces/mthsk/sovits-100orangejuice/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-100orangejuice/modules/modules.py
+++ /dev/null
@@ -1,342 +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 modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-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
diff --git a/spaces/mygyasir/stablediffusionapi-epicrealism-epinikio/app.py b/spaces/mygyasir/stablediffusionapi-epicrealism-epinikio/app.py
deleted file mode 100644
index 8d9a909584c2db953e9c0acfa4a36fc6ba681be9..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/stablediffusionapi-epicrealism-epinikio/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stablediffusionapi/epicrealism-epinikio").launch()
\ No newline at end of file
diff --git a/spaces/nagolinc/spritesheet_to_gif/app.py b/spaces/nagolinc/spritesheet_to_gif/app.py
deleted file mode 100644
index 607f15ef68818006ca92d670790d7f70c5ff67db..0000000000000000000000000000000000000000
--- a/spaces/nagolinc/spritesheet_to_gif/app.py
+++ /dev/null
@@ -1,280 +0,0 @@
-import gradio as gr
-from PIL import Image
-from io import BytesIO
-import base64
-import requests
-from io import BytesIO
-
-
-from collections import Counter
-
-from PIL import Image
-
-import numpy as np
-
-import matplotlib.pyplot as plt
-
-
-def compute_fft_cross_correlation(img1, img2):
-
-    fft1 = np.fft.fft2(img1)
-
-    fft2 = np.fft.fft2(np.rot90(img2, 2), s=img1.shape)
-
-    result = np.fft.ifft2(fft1 * fft2).real
-
-    return result
-
-
-
-def compute_offsets(reference, images, window_size):
-
-    reference_gray = np.array(reference.convert('L'))
-
-    offsets = []
-
-    for img in images:
-
-        img_gray = np.array(img.convert('L'))
-
-        correlation = compute_fft_cross_correlation(reference_gray, img_gray)
-
-        # Roll the correlation by half the width and height
-        height, width = correlation.shape
-        correlation = np.roll(correlation, height // 2, axis=0)
-        correlation = np.roll(correlation, width // 2, axis=1)
-
-
-        # Find the peak in the central region of the correlation
-        center_x, center_y = height // 2, width // 2
-        start_x, start_y = center_x - window_size // 2, center_y - window_size // 2
-        end_x, end_y = start_x + window_size, start_y + window_size
-
-        #make sure starts and ends are in the range(0,height) and (0,width)
-        start_x = max(start_x,0)
-        start_y = max(start_y,0)
-        end_x = min(end_x,height-1)
-        end_y = min(end_y,width-1)
-
-
-        window_size_x = end_x - start_x
-        window_size_y = end_y - start_y
-
-
-        peak_x, peak_y = np.unravel_index(np.argmax(correlation[start_x:end_x, start_y:end_y]), (window_size_x, window_size_y))
-
-        
-        
-        
-        '''
-        #plot the correlation
-        fig, axs = plt.subplots(1, 5, figsize=(10, 5))
-        axs[0].imshow(reference_gray, cmap='gray')
-        axs[0].set_title('Reference')
-        axs[1].imshow(img_gray, cmap='gray')
-        axs[1].set_title('Image')
-        axs[2].imshow(correlation, cmap='hot', interpolation='nearest', extent=[-window_size, window_size, -window_size, window_size])
-        axs[2].set_title('Correlation')
-        axs[3].imshow(correlation, cmap='hot', interpolation='nearest')
-        axs[3].set_title('Correlation full')
-        axs[4].imshow(correlation[start_x:end_x, start_y:end_y], cmap='hot', interpolation='nearest')
-        axs[4].set_title('Correlation cropped')
-        plt.show()
-
-
-        print("what?",np.argmax(correlation[start_x:end_x, start_y:end_y]))
-
-        print(peak_x, peak_y,start_x,end_x,start_y,end_y,center_x,center_y)    
-        '''
-
-        
-        # Compute the offset in the range [-window_size, window_size]
-        peak_x += start_x - center_x + 1
-        peak_y += start_y - center_y + 1
-
-        #signs are wrong
-        #peak_x = -peak_x
-        #peak_y = -peak_y
-
-        #print(peak_x, peak_y)
-
-        # Compute the offset in the range [-window_size, window_size]
-        if peak_x > correlation.shape[0] // 2:
-            peak_x -= correlation.shape[0]
-        if peak_y > correlation.shape[1] // 2:
-            peak_y -= correlation.shape[1]
-
-        if peak_x >= 0:
-            peak_x = min(peak_x, window_size)
-        else:
-            peak_x = max(peak_x, -window_size)
-
-        if peak_y >= 0:
-            peak_y = min(peak_y, window_size)
-        else:
-            peak_y = max(peak_y, -window_size)
-
-        offsets.append((peak_x, peak_y))
-
-    return offsets
-
-
-def find_most_common_color(image):
-
-    pixels = list(image.getdata())
-
-    color_counter = Counter(pixels)
-
-    return color_counter.most_common(1)[0][0]
-
-
-
-def slice_frames_final(original, centers, frame_width, frame_height, background_color=(255, 255, 0, 255)):
-
-    sliced_frames = []
-
-    original_width, original_height = original.size
-
-    for center_x, center_y in centers:
-
-        left = center_x - frame_width // 2
-
-        upper = center_y - frame_height // 2
-
-        right = left + frame_width
-
-        lower = upper + frame_height
-
-        new_frame = Image.new("RGBA", (frame_width, frame_height), background_color)
-
-        paste_x = max(0, -left)
-
-        paste_y = max(0, -upper)
-
-        cropped_frame = original.crop((max(0, left), max(0, upper), min(original_width, right), min(original_height, lower)))
-
-        new_frame.paste(cropped_frame, (paste_x, paste_y))
-
-        sliced_frames.append(new_frame)
-
-    return sliced_frames
-
-
-
-def create_aligned_gif(original_image, columns_per_row, window_size=200, duration=100,output_gif_path = 'output.gif'):
-
-    
-    original_width, original_height = original_image.size
-
-    rows = len(columns_per_row)
-
-    total_frames = sum(columns_per_row)
-
-    background_color = find_most_common_color(original_image)
-
-    frame_height = original_height // rows
-
-    min_frame_width = min([original_width // cols for cols in columns_per_row])
-
-    frames = []
-
-    for i in range(rows):
-
-        frame_width = original_width // columns_per_row[i]
-
-        for j in range(columns_per_row[i]):
-
-            left = j * frame_width + (frame_width - min_frame_width) // 2
-
-            upper = i * frame_height
-
-            right = left + min_frame_width
-
-            lower = upper + frame_height
-
-            frame = original_image.crop((left, upper, right, lower))
-
-            frames.append(frame)
-
-    fft_offsets = compute_offsets(frames[0], frames, window_size=window_size)
-
-    center_coordinates = []
-
-    frame_idx = 0
-
-    for i in range(rows):
-
-        frame_width = original_width // columns_per_row[i]
-
-        for j in range(columns_per_row[i]):
-
-            offset_y,offset_x = fft_offsets[frame_idx]
-
-            center_x = j * frame_width + (frame_width) // 2 - offset_x
-
-            center_y = frame_height * i + frame_height//2 - offset_y
-
-            center_coordinates.append((center_x, center_y))
-
-            frame_idx += 1
-
-    sliced_frames = slice_frames_final(original_image, center_coordinates, min_frame_width, frame_height, background_color=background_color)
-
-    
-
-    sliced_frames[0].save(output_gif_path, save_all=True, append_images=sliced_frames[1:], loop=0, duration=duration)
-
-    '''
-    #display frames
-    for frame in sliced_frames:
-        plt.figure()
-        plt.imshow(frame)
-    '''
-    
-    
-    return output_gif_path
-
-def wrapper_func(img, columns_per_row_str,duration):
-    #img = Image.open(BytesIO(file))
-
-    #img = Image.fromarray(img_arr)
-
-    columns_per_row = [int(x.strip()) for x in columns_per_row_str.split(',')]
-    output_gif_path = 'output.gif'
-
-    create_aligned_gif(img, columns_per_row,duration=duration)
-    #with open(output_gif_path, "rb") as f:
-        #return base64.b64encode(f.read()).decode()
-    #    Image.open(output_gif_path)
-
-    return output_gif_path
-
-
-# Example image in the form of a NumPy array
-#example_image = Image.open("https://raw.githubusercontent.com/nagolinc/notebooks/main/ss5.png")
-
-url = "https://raw.githubusercontent.com/nagolinc/notebooks/main/ss5.png"
-response = requests.get(url)
-example_image = Image.open(BytesIO(response.content))
-
-# Example for "Columns per Row" as a string
-example_columns_per_row = "5,5,5"
-
-
-
-iface = gr.Interface(
-    fn=wrapper_func,
-    inputs=[
-        gr.components.Image(label="Upload Spritesheet",type='pil'),
-        gr.components.Textbox(label="Columns per Row", default="3,4,3"),
-        gr.components.Slider(minimum=10, maximum=1000, step=10, value=100, label="Duration of each frame (ms)"),
-    ],
-    outputs=gr.components.Image(type="filepath", label="Generated GIF"),
-    examples=[[example_image, example_columns_per_row,100]],  # Adding examples here
-    live=False,
-    server_name="Hugging Face Spaces",
-    server_port=80,
-    analytics_enabled=False
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/inference.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/inference.py
deleted file mode 100644
index 81049649edddb23aeebeac4085514da838f1463b..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/inference.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from dataclasses import fields
-from typing import Any, List
-import torch
-
-from detectron2.structures import Instances
-
-
-def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]) -> None:
-    """
-    Splits DensePose predictor outputs into chunks, each chunk corresponds to
-    detections on one image. Predictor output chunks are stored in `pred_densepose`
-    attribute of the corresponding `Instances` object.
-
-    Args:
-        densepose_predictor_output: a dataclass instance (can be of different types,
-            depending on predictor used for inference). Each field can be `None`
-            (if the corresponding output was not inferred) or a tensor of size
-            [N, ...], where N = N_1 + N_2 + .. + N_k is a total number of
-            detections on all images, N_1 is the number of detections on image 1,
-            N_2 is the number of detections on image 2, etc.
-        detections: a list of objects of type `Instance`, k-th object corresponds
-            to detections on k-th image.
-    """
-    k = 0
-    for detection_i in detections:
-        if densepose_predictor_output is None:
-            # don't add `pred_densepose` attribute
-            continue
-        n_i = detection_i.__len__()
-
-        PredictorOutput = type(densepose_predictor_output)
-        output_i_dict = {}
-        # we assume here that `densepose_predictor_output` is a dataclass object
-        for field in fields(densepose_predictor_output):
-            field_value = getattr(densepose_predictor_output, field.name)
-            # slice tensors
-            if isinstance(field_value, torch.Tensor):
-                output_i_dict[field.name] = field_value[k : k + n_i]
-            # leave others as is
-            else:
-                output_i_dict[field.name] = field_value
-        detection_i.pred_densepose = PredictorOutput(**output_i_dict)
-        k += n_i
diff --git a/spaces/niro-private/chatCSV/chatbot_legger.py b/spaces/niro-private/chatCSV/chatbot_legger.py
deleted file mode 100644
index 72c6203d1d035e4ef29f5a1ed81c29aaaf32738e..0000000000000000000000000000000000000000
--- a/spaces/niro-private/chatCSV/chatbot_legger.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-from dotenv import load_dotenv
-from io import BytesIO
-from io import StringIO
-import sys
-import re
-from langchain.agents import create_csv_agent
-from src.modules.history import ChatHistory
-from src.modules.layout import Layout
-from src.modules.utils import Utilities
-from src.modules.sidebar import Sidebar
-import streamlit as st
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.vectorstores import FAISS
-from json2table import convert
-
-
-# To be able to update the changes made to modules in localhost,
-# you can press the "r" key on the localhost page to refresh and reflect the changes made to the module files.
-def reload_module(module_name):
-    import importlib
-    import sys
-    if module_name in sys.modules:
-        importlib.reload(sys.modules[module_name])
-    return sys.modules[module_name]
-
-
-history_module = reload_module('src.modules.history')
-layout_module = reload_module('src.modules.layout')
-utils_module = reload_module('src.modules.utils')
-sidebar_module = reload_module('src.modules.sidebar')
-
-ChatHistory = history_module.ChatHistory
-Layout = layout_module.Layout
-Utilities = utils_module.Utilities
-Sidebar = sidebar_module.Sidebar
-
-
-def init():
-    load_dotenv()
-    st.set_page_config(layout="wide", page_icon="💬", page_title="ChatBot-Legger")
-
-
-def main():
-    init()
-    layout, sidebar, utils = Layout(), Sidebar(), Utilities()
-    sidebar.show_logo('assets/Images/colleen-logo.png')
-
-    layout.show_header_txt()
-    user_api_key = utils.load_api_key()
-
-    if not user_api_key:
-        layout.show_api_key_missing()
-    else:
-        os.environ["OPENAI_API_KEY"] = user_api_key
-        # uploaded_file = utils.handle_upload_txt()
-        uploaded_file = utils.handle_upload_ledger()
-
-        if uploaded_file:
-            history = ChatHistory()
-            sidebar.show_options()
-
-            uploaded_file_content = StringIO(uploaded_file.getvalue().decode("utf-8"))
-            string_data = uploaded_file_content.read()
-
-            # st.write(string_data)
-
-            try:
-                chatbot = utils.setup_chatbot_ledger(
-                    uploaded_file, st.session_state["model"], st.session_state["temperature"]
-                )
-                # st.write(chatbot)
-                st.session_state["chatbot"] = chatbot
-                st.session_state['agent'] = chatbot
-
-                if st.session_state["ready"]:
-                    response_container, prompt_container = st.container(), st.container()
-
-                    with prompt_container:
-                        is_ready, user_input = layout.prompt_form()
-
-                        history.initialize(uploaded_file)
-                        if st.session_state["reset_chat"]:
-                            history.reset(uploaded_file)
-
-                        if is_ready:
-                            history.append("user", user_input)
-                            output = st.session_state["chatbot"].csv_agent(user_input)
-                            # st.text(sys.stdout)
-                            # st.text(StringIO.getvalue())
-                            old_stdout = sys.stdout
-                            sys.stdout = captured_output = StringIO()
-                            sys.stdout = old_stdout
-                            thoughts = captured_output.getvalue()
-                            # st.text(old_stdout)
-                            # st.text(captured_output)
-                            st.text('thoughts: '+thoughts)
-
-                            history.append("assistant", output)
-
-                    history.generate_messages(response_container)
-
-            except Exception as e:
-                st.error(f"Error: {str(e)}")
-
-    sidebar.about()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/nithinraok/NeMo-Offline-Speaker-Diarization/README.md b/spaces/nithinraok/NeMo-Offline-Speaker-Diarization/README.md
deleted file mode 100644
index c9a07f316f16f994b1b1b06b17457334c787af35..0000000000000000000000000000000000000000
--- a/spaces/nithinraok/NeMo-Offline-Speaker-Diarization/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: NeMo Offline Speaker Diarization
-emoji: 👀
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: cc-by-4.0
-python_version: 3.9
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nomnomnonono/Siri-via-Whisper-ChatGPT/app.py b/spaces/nomnomnonono/Siri-via-Whisper-ChatGPT/app.py
deleted file mode 100644
index 1d7dabe233a816a547d253fcf9485903fe19f5e9..0000000000000000000000000000000000000000
--- a/spaces/nomnomnonono/Siri-via-Whisper-ChatGPT/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import gradio as gr
-from chat import CahtBOT
-
-chat = CahtBOT()
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("Siri-like application via Whisper and ChatGPT")
-    with gr.Tabs():
-        with gr.TabItem(label="General"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    api_key = gr.Textbox(label="Paste your own openai-api-key")
-                    api_button = gr.Button("SetUp")
-                    with gr.Row():
-                        audio_input = gr.Audio(
-                            source="microphone",
-                            label="Record from microphone",
-                        )
-                        audio_button = gr.Button("Transcribe")
-                    audio_output = gr.Textbox()
-                    chat_button = gr.Button("Questions to ChatGPT")
-                with gr.Column(scale=1):
-                    chatbot = gr.Chatbot([], elemid="chatbot").style(height=750)
-        with gr.TabItem(label="Setting"):
-            gr.Markdown("Prompt Setting")
-            language = gr.Dropdown(["Japanese", "English"], value="English")
-            with gr.Row():
-                role1 = gr.Dropdown(["system", "user", "assistant"], value="system")
-                content1 = gr.Textbox(value="You're helpful assistant.")
-            with gr.Row():
-                role2 = gr.Dropdown(["system", "user", "assistant"])
-                content2 = gr.Textbox()
-            with gr.Row():
-                role3 = gr.Dropdown(["system", "user", "assistant"])
-                content3 = gr.Textbox()
-            with gr.Row():
-                role4 = gr.Dropdown(["system", "user", "assistant"])
-                content4 = gr.Textbox()
-            with gr.Row():
-                role5 = gr.Dropdown(["system", "user", "assistant"])
-                content5 = gr.Textbox()
-
-    api_button.click(
-        chat.setup,
-        inputs=[
-            role1,
-            content1,
-            role2,
-            content2,
-            role3,
-            content3,
-            role4,
-            content4,
-            role5,
-            content5,
-            api_key,
-            language,
-        ],
-        outputs=None,
-    )
-    audio_button.click(
-        chat.transcribe,
-        inputs=[audio_input],
-        outputs=[audio_output],
-        api_name="transcribe",
-    )
-    chat_button.click(
-        chat.answer_by_chat,
-        inputs=[chatbot, audio_output],
-        outputs=[chatbot],
-    )
-
-demo.launch()
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/wavegru.py b/spaces/ntt123/WaveGRU-Text-To-Speech/wavegru.py
deleted file mode 100644
index 4ed0fd654df51995e0bfc61d6c26810f777517c0..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/wavegru.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""
-WaveGRU model: melspectrogram => mu-law encoded waveform
-"""
-
-from typing import Tuple
-
-import jax
-import jax.numpy as jnp
-import pax
-from pax import GRUState
-from tqdm.cli import tqdm
-
-
-class ReLU(pax.Module):
-    def __call__(self, x):
-        return jax.nn.relu(x)
-
-
-def dilated_residual_conv_block(dim, kernel, stride, dilation):
-    """
-    Use dilated convs to enlarge the receptive field
-    """
-    return pax.Sequential(
-        pax.Conv1D(dim, dim, kernel, stride, dilation, "VALID", with_bias=False),
-        pax.LayerNorm(dim, -1, True, True),
-        ReLU(),
-        pax.Conv1D(dim, dim, 1, 1, 1, "VALID", with_bias=False),
-        pax.LayerNorm(dim, -1, True, True),
-        ReLU(),
-    )
-
-
-def tile_1d(x, factor):
-    """
-    Tile tensor of shape N, L, D into N, L*factor, D
-    """
-    N, L, D = x.shape
-    x = x[:, :, None, :]
-    x = jnp.tile(x, (1, 1, factor, 1))
-    x = jnp.reshape(x, (N, L * factor, D))
-    return x
-
-
-def up_block(in_dim, out_dim, factor, relu=True):
-    """
-    Tile >> Conv >> BatchNorm >> ReLU
-    """
-    f = pax.Sequential(
-        lambda x: tile_1d(x, factor),
-        pax.Conv1D(
-            in_dim, out_dim, 2 * factor, stride=1, padding="VALID", with_bias=False
-        ),
-        pax.LayerNorm(out_dim, -1, True, True),
-    )
-    if relu:
-        f >>= ReLU()
-    return f
-
-
-class Upsample(pax.Module):
-    """
-    Upsample melspectrogram to match raw audio sample rate.
-    """
-
-    def __init__(
-        self, input_dim, hidden_dim, rnn_dim, upsample_factors, has_linear_output=False
-    ):
-        super().__init__()
-        self.input_conv = pax.Sequential(
-            pax.Conv1D(input_dim, hidden_dim, 1, with_bias=False),
-            pax.LayerNorm(hidden_dim, -1, True, True),
-        )
-        self.upsample_factors = upsample_factors
-        self.dilated_convs = [
-            dilated_residual_conv_block(hidden_dim, 3, 1, 2**i) for i in range(5)
-        ]
-        self.up_factors = upsample_factors[:-1]
-        self.up_blocks = [
-            up_block(hidden_dim, hidden_dim, x) for x in self.up_factors[:-1]
-        ]
-        self.up_blocks.append(
-            up_block(
-                hidden_dim,
-                hidden_dim if has_linear_output else 3 * rnn_dim,
-                self.up_factors[-1],
-                relu=False,
-            )
-        )
-        if has_linear_output:
-            self.x2zrh_fc = pax.Linear(hidden_dim, rnn_dim * 3)
-        self.has_linear_output = has_linear_output
-
-        self.final_tile = upsample_factors[-1]
-
-    def __call__(self, x, no_repeat=False):
-        x = self.input_conv(x)
-        for residual in self.dilated_convs:
-            y = residual(x)
-            pad = (x.shape[1] - y.shape[1]) // 2
-            x = x[:, pad:-pad, :] + y
-
-        for f in self.up_blocks:
-            x = f(x)
-
-        if self.has_linear_output:
-            x = self.x2zrh_fc(x)
-
-        if no_repeat:
-            return x
-        x = tile_1d(x, self.final_tile)
-        return x
-
-
-class GRU(pax.Module):
-    """
-    A customized GRU module.
-    """
-
-    input_dim: int
-    hidden_dim: int
-
-    def __init__(self, hidden_dim: int):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.h_zrh_fc = pax.Linear(
-            hidden_dim,
-            hidden_dim * 3,
-            w_init=jax.nn.initializers.variance_scaling(
-                1, "fan_out", "truncated_normal"
-            ),
-        )
-
-    def initial_state(self, batch_size: int) -> GRUState:
-        """Create an all zeros initial state."""
-        return GRUState(jnp.zeros((batch_size, self.hidden_dim), dtype=jnp.float32))
-
-    def __call__(self, state: GRUState, x) -> Tuple[GRUState, jnp.ndarray]:
-        hidden = state.hidden
-        x_zrh = x
-        h_zrh = self.h_zrh_fc(hidden)
-        x_zr, x_h = jnp.split(x_zrh, [2 * self.hidden_dim], axis=-1)
-        h_zr, h_h = jnp.split(h_zrh, [2 * self.hidden_dim], axis=-1)
-
-        zr = x_zr + h_zr
-        zr = jax.nn.sigmoid(zr)
-        z, r = jnp.split(zr, 2, axis=-1)
-
-        h_hat = x_h + r * h_h
-        h_hat = jnp.tanh(h_hat)
-
-        h = (1 - z) * hidden + z * h_hat
-        return GRUState(h), h
-
-
-class Pruner(pax.Module):
-    """
-    Base class for pruners
-    """
-
-    def compute_sparsity(self, step):
-        t = jnp.power(1 - (step * 1.0 - 1_000) / 200_000, 3)
-        z = 0.95 * jnp.clip(1.0 - t, a_min=0, a_max=1)
-        return z
-
-    def prune(self, step, weights):
-        """
-        Return a mask
-        """
-        z = self.compute_sparsity(step)
-        x = weights
-        H, W = x.shape
-        x = x.reshape(H // 4, 4, W // 4, 4)
-        x = jnp.abs(x)
-        x = jnp.sum(x, axis=(1, 3), keepdims=True)
-        q = jnp.quantile(jnp.reshape(x, (-1,)), z)
-        x = x >= q
-        x = jnp.tile(x, (1, 4, 1, 4))
-        x = jnp.reshape(x, (H, W))
-        return x
-
-
-class GRUPruner(Pruner):
-    def __init__(self, gru):
-        super().__init__()
-        self.h_zrh_fc_mask = jnp.ones_like(gru.h_zrh_fc.weight) == 1
-
-    def __call__(self, gru: pax.GRU):
-        """
-        Apply mask after an optimization step
-        """
-        zrh_masked_weights = jnp.where(self.h_zrh_fc_mask, gru.h_zrh_fc.weight, 0)
-        gru = gru.replace_node(gru.h_zrh_fc.weight, zrh_masked_weights)
-        return gru
-
-    def update_mask(self, step, gru: pax.GRU):
-        """
-        Update internal masks
-        """
-        z_weight, r_weight, h_weight = jnp.split(gru.h_zrh_fc.weight, 3, axis=1)
-        z_mask = self.prune(step, z_weight)
-        r_mask = self.prune(step, r_weight)
-        h_mask = self.prune(step, h_weight)
-        self.h_zrh_fc_mask *= jnp.concatenate((z_mask, r_mask, h_mask), axis=1)
-
-
-class LinearPruner(Pruner):
-    def __init__(self, linear):
-        super().__init__()
-        self.mask = jnp.ones_like(linear.weight) == 1
-
-    def __call__(self, linear: pax.Linear):
-        """
-        Apply mask after an optimization step
-        """
-        return linear.replace(weight=jnp.where(self.mask, linear.weight, 0))
-
-    def update_mask(self, step, linear: pax.Linear):
-        """
-        Update internal masks
-        """
-        self.mask *= self.prune(step, linear.weight)
-
-
-class WaveGRU(pax.Module):
-    """
-    WaveGRU vocoder model.
-    """
-
-    def __init__(
-        self,
-        mel_dim=80,
-        rnn_dim=1024,
-        upsample_factors=(5, 3, 20),
-        has_linear_output=False,
-    ):
-        super().__init__()
-        self.embed = pax.Embed(256, 3 * rnn_dim)
-        self.upsample = Upsample(
-            input_dim=mel_dim,
-            hidden_dim=512,
-            rnn_dim=rnn_dim,
-            upsample_factors=upsample_factors,
-            has_linear_output=has_linear_output,
-        )
-        self.rnn = GRU(rnn_dim)
-        self.o1 = pax.Linear(rnn_dim, rnn_dim)
-        self.o2 = pax.Linear(rnn_dim, 256)
-        self.gru_pruner = GRUPruner(self.rnn)
-        self.o1_pruner = LinearPruner(self.o1)
-        self.o2_pruner = LinearPruner(self.o2)
-
-    def output(self, x):
-        x = self.o1(x)
-        x = jax.nn.relu(x)
-        x = self.o2(x)
-        return x
-
-    def inference(self, mel, no_gru=False, seed=42):
-        """
-        generate waveform form melspectrogram
-        """
-
-        @jax.jit
-        def step(rnn_state, mel, rng_key, x):
-            x = self.embed(x)
-            x = x + mel
-            rnn_state, x = self.rnn(rnn_state, x)
-            x = self.output(x)
-            rng_key, next_rng_key = jax.random.split(rng_key, 2)
-            x = jax.random.categorical(rng_key, x, axis=-1)
-            return rnn_state, next_rng_key, x
-
-        y = self.upsample(mel, no_repeat=no_gru)
-        if no_gru:
-            return y
-        x = jnp.array([127], dtype=jnp.int32)
-        rnn_state = self.rnn.initial_state(1)
-        output = []
-        rng_key = jax.random.PRNGKey(seed)
-        for i in tqdm(range(y.shape[1])):
-            rnn_state, rng_key, x = step(rnn_state, y[:, i], rng_key, x)
-            output.append(x)
-        x = jnp.concatenate(output, axis=0)
-        return x
-
-    def __call__(self, mel, x):
-        x = self.embed(x)
-        y = self.upsample(mel)
-        pad_left = (x.shape[1] - y.shape[1]) // 2
-        pad_right = x.shape[1] - y.shape[1] - pad_left
-        x = x[:, pad_left:-pad_right]
-        x = x + y
-        _, x = pax.scan(
-            self.rnn,
-            self.rnn.initial_state(x.shape[0]),
-            x,
-            time_major=False,
-        )
-        x = self.output(x)
-        return x
diff --git a/spaces/omdenatopekachapter/left_ejection_fraction/model/readme.md b/spaces/omdenatopekachapter/left_ejection_fraction/model/readme.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/osanseviero/EfficientNetV2/README.md b/spaces/osanseviero/EfficientNetV2/README.md
deleted file mode 100644
index 97519fb482b7bb8f17684a995101af3b104054ff..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/EfficientNetV2/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: EfficientNetV2
-emoji: 👁
-colorFrom: red
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/evaluation.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/evaluation.md
deleted file mode 100644
index d714c5d975ceef9b1a7345e62b57ac3abfca3314..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/evaluation.md
+++ /dev/null
@@ -1,572 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Evaluating Diffusion Models
-
-<a target="_blank" href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/evaluation.ipynb">                                                                                                                                                                                                                                                                                                                                                            
-    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>                                                                                                                                                 
-</a>   
-
-Evaluation of generative models like [Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion) is subjective in nature. But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other?
-
-Qualitative evaluation of such models can be error-prone and might incorrectly influence a decision.
-However, quantitative metrics don't necessarily correspond to image quality. So, usually, a combination
-of both qualitative and quantitative evaluations provides a stronger signal when choosing one model
-over the other.
-
-In this document, we provide a non-exhaustive overview of qualitative and quantitative methods to evaluate Diffusion models. For quantitative methods, we specifically focus on how to implement them alongside `diffusers`. 
-
-The methods shown in this document can also be used to evaluate different [noise schedulers](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview) keeping the underlying generation model fixed.
-
-## Scenarios
-
-We cover Diffusion models with the following pipelines:
-
-- Text-guided image generation (such as the [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img)).
-- Text-guided image generation, additionally conditioned on an input image (such as the [`StableDiffusionImg2ImgPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img), and [`StableDiffusionInstructPix2PixPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix)).
-- Class-conditioned image generation models (such as the [`DiTPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit)).
-
-## Qualitative Evaluation
-
-Qualitative evaluation typically involves human assessment of generated images. Quality is measured across aspects such as compositionality, image-text alignment, and spatial relations. Common prompts provide a degree of uniformity for subjective metrics.
-DrawBench and PartiPrompts are prompt datasets used for qualitative benchmarking. DrawBench and PartiPrompts were introduced by [Imagen](https://imagen.research.google/) and [Parti](https://parti.research.google/) respectively. 
-
-From the [official Parti website](https://parti.research.google/): 
-
-> PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release as part of this work. P2 can be used to measure model capabilities across various categories and challenge aspects.
-
-![parti-prompts](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts.png)
-
-PartiPrompts has the following columns:
-
-- Prompt
-- Category of the prompt (such as “Abstract”, “World Knowledge”, etc.)
-- Challenge reflecting the difficulty (such as “Basic”, “Complex”, “Writing & Symbols”, etc.)
-
-These benchmarks allow for side-by-side human evaluation of different image generation models. 
-
-For this, the 🧨 Diffusers team has built **Open Parti Prompts**, which is a community-driven qualitative benchmark based on Parti Prompts to compare state-of-the-art open-source diffusion models:
-- [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts): For 10 parti prompts, 4 generated images are shown and the user selects the image that suits the prompt best.
-- [Open Parti Prompts Leaderboard](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard): The leaderboard comparing the currently best open-sourced diffusion models to each other.
-
-To manually compare images, let’s see how we can use `diffusers` on a couple of PartiPrompts. 
-
-Below we show some prompts sampled across different challenges: Basic, Complex, Linguistic Structures, Imagination, and Writing & Symbols. Here we are using PartiPrompts as a [dataset](https://huggingface.co/datasets/nateraw/parti-prompts).
-
-```python
-from datasets import load_dataset
-
-# prompts = load_dataset("nateraw/parti-prompts", split="train")
-# prompts = prompts.shuffle()
-# sample_prompts = [prompts[i]["Prompt"] for i in range(5)]
-
-# Fixing these sample prompts in the interest of reproducibility.
-sample_prompts = [
-    "a corgi",
-    "a hot air balloon with a yin-yang symbol, with the moon visible in the daytime sky",
-    "a car with no windows",
-    "a cube made of porcupine",
-    'The saying "BE EXCELLENT TO EACH OTHER" written on a red brick wall with a graffiti image of a green alien wearing a tuxedo. A yellow fire hydrant is on a sidewalk in the foreground.',
-]
-```
-
-Now we can use these prompts to generate some images using Stable Diffusion ([v1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4)):
-
-```python
-import torch
-
-seed = 0
-generator = torch.manual_seed(seed)
-
-images = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generator, output_type="numpy").images
-```
-
-![parti-prompts-14](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png)
-
-We can also set `num_images_per_prompt` accordingly to compare different images for the same prompt. Running the same pipeline but with a different checkpoint ([v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)), yields: 
-
-![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png)
-
-Once several images are generated from all the prompts using multiple models (under evaluation), these results are presented to human evaluators for scoring. For
-more details on the DrawBench and PartiPrompts benchmarks, refer to their respective papers.  
-
-<Tip> 
-
-It is useful to look at some inference samples while a model is training to measure the 
-training progress. In our [training scripts](https://github.com/huggingface/diffusers/tree/main/examples/), we support this utility with additional support for
-logging to TensorBoard and Weights & Biases.
-
-</Tip>
-
-## Quantitative Evaluation
-
-In this section, we will walk you through how to evaluate three different diffusion pipelines using:
-
-- CLIP score
-- CLIP directional similarity
-- FID
-
-### Text-guided image generation
-
-[CLIP score](https://arxiv.org/abs/2104.08718) measures the compatibility of image-caption pairs. Higher CLIP scores imply higher compatibility 🔼. The CLIP score is a quantitative measurement of the qualitative concept "compatibility". Image-caption pair compatibility can also be thought of as the semantic similarity between the image and the caption. CLIP score was found to have high correlation with human judgement.
-
-Let's first load a [`StableDiffusionPipeline`]:
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_ckpt = "CompVis/stable-diffusion-v1-4"
-sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to("cuda")
-```
-
-Generate some images with multiple prompts:
-
-```python
-prompts = [
-    "a photo of an astronaut riding a horse on mars",
-    "A high tech solarpunk utopia in the Amazon rainforest",
-    "A pikachu fine dining with a view to the Eiffel Tower",
-    "A mecha robot in a favela in expressionist style",
-    "an insect robot preparing a delicious meal",
-    "A small cabin on top of a snowy mountain in the style of Disney, artstation",
-]
-
-images = sd_pipeline(prompts, num_images_per_prompt=1, output_type="numpy").images
-
-print(images.shape)
-# (6, 512, 512, 3)
-```
-
-And then, we calculate the CLIP score.
-
-```python
-from torchmetrics.functional.multimodal import clip_score
-from functools import partial
-
-clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
-
-
-def calculate_clip_score(images, prompts):
-    images_int = (images * 255).astype("uint8")
-    clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach()
-    return round(float(clip_score), 4)
-
-
-sd_clip_score = calculate_clip_score(images, prompts)
-print(f"CLIP score: {sd_clip_score}")
-# CLIP score: 35.7038
-```
-
-In the above example, we generated one image per prompt. If we generated multiple images per prompt, we would have to take the average score from the generated images per prompt.
-
-Now, if we wanted to compare two checkpoints compatible with the [`StableDiffusionPipeline`] we should pass a generator while calling the pipeline. First, we generate images with a
-fixed seed with the [v1-4 Stable Diffusion checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4):
-
-```python
-seed = 0
-generator = torch.manual_seed(seed)
-
-images = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type="numpy").images
-```
-
-Then we load the [v1-5 checkpoint](https://huggingface.co/runwayml/stable-diffusion-v1-5) to generate images: 
-
-```python
-model_ckpt_1_5 = "runwayml/stable-diffusion-v1-5"
-sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=weight_dtype).to(device)
-
-images_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type="numpy").images
-```
-
-And finally, we compare their CLIP scores:
-
-```python
-sd_clip_score_1_4 = calculate_clip_score(images, prompts)
-print(f"CLIP Score with v-1-4: {sd_clip_score_1_4}")
-# CLIP Score with v-1-4: 34.9102
-
-sd_clip_score_1_5 = calculate_clip_score(images_1_5, prompts)
-print(f"CLIP Score with v-1-5: {sd_clip_score_1_5}")
-# CLIP Score with v-1-5: 36.2137
-```
-
-It seems like the [v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint performs better than its predecessor. Note, however, that the number of prompts we used to compute the CLIP scores is quite low. For a more practical evaluation, this number should be way higher, and the prompts should be diverse.
-
-<Tip warning={true}>
-
-By construction, there are some limitations in this score. The captions in the training dataset
-were crawled from the web and extracted from `alt` and similar tags associated an image on the internet.
-They are not necessarily representative of what a human being would use to describe an image. Hence we
-had to "engineer" some prompts here. 
-
-</Tip>
-
-### Image-conditioned text-to-image generation
-
-In this case, we condition the generation pipeline with an input image as well as a text prompt. Let's take the [`StableDiffusionInstructPix2PixPipeline`], as an example. It takes an edit instruction as an input prompt and an input image to be edited.
-
-Here is one example:
-
-![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png)
-
-One strategy to evaluate such a model is to measure the consistency of the change between the two images (in [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) space) with the change between the two image captions (as shown in [CLIP-Guided Domain Adaptation of Image Generators](https://arxiv.org/abs/2108.00946)). This is referred to as the "**CLIP directional similarity**".
-
-- Caption 1 corresponds to the input image (image 1) that is to be edited.
-- Caption 2 corresponds to the edited image (image 2). It should reflect the edit instruction.
-
-Following is a pictorial overview:
-
-![edit-consistency](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-consistency.png)
-
-We have prepared a mini dataset to implement this metric. Let's first load the dataset.
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("sayakpaul/instructpix2pix-demo", split="train")
-dataset.features
-```
-
-```bash
-{'input': Value(dtype='string', id=None),
- 'edit': Value(dtype='string', id=None),
- 'output': Value(dtype='string', id=None),
- 'image': Image(decode=True, id=None)}
-```
-
-Here we have:
-
-- `input` is a caption corresponding to the `image`.
-- `edit` denotes the edit instruction.
-- `output` denotes the modified caption reflecting the `edit` instruction.
-
-Let's take a look at a sample.
-
-```python
-idx = 0
-print(f"Original caption: {dataset[idx]['input']}")
-print(f"Edit instruction: {dataset[idx]['edit']}")
-print(f"Modified caption: {dataset[idx]['output']}")
-```
-
-```bash
-Original caption: 2. FAROE ISLANDS: An archipelago of 18 mountainous isles in the North Atlantic Ocean between Norway and Iceland, the Faroe Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'
-Edit instruction: make the isles all white marble
-Modified caption: 2. WHITE MARBLE ISLANDS: An archipelago of 18 mountainous white marble isles in the North Atlantic Ocean between Norway and Iceland, the White Marble Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'
-```
-
-And here is the image:
-
-```python
-dataset[idx]["image"]
-```
-
-![edit-dataset](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-dataset.png)
-
-We will first edit the images of our dataset with the edit instruction and compute the directional similarity.
-
-Let's first load the [`StableDiffusionInstructPix2PixPipeline`]:
-
-```python
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-instruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
-    "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
-).to(device)
-```
-
-Now, we perform the edits:
-
-```python
-import numpy as np
-
-
-def edit_image(input_image, instruction):
-    image = instruct_pix2pix_pipeline(
-        instruction,
-        image=input_image,
-        output_type="numpy",
-        generator=generator,
-    ).images[0]
-    return image
-
-
-input_images = []
-original_captions = []
-modified_captions = []
-edited_images = []
-
-for idx in range(len(dataset)):
-    input_image = dataset[idx]["image"]
-    edit_instruction = dataset[idx]["edit"]
-    edited_image = edit_image(input_image, edit_instruction)
-
-    input_images.append(np.array(input_image))
-    original_captions.append(dataset[idx]["input"])
-    modified_captions.append(dataset[idx]["output"])
-    edited_images.append(edited_image)
-```
-
-To measure the directional similarity, we first load CLIP's image and text encoders:
-
-```python
-from transformers import (
-    CLIPTokenizer,
-    CLIPTextModelWithProjection,
-    CLIPVisionModelWithProjection,
-    CLIPImageProcessor,
-)
-
-clip_id = "openai/clip-vit-large-patch14"
-tokenizer = CLIPTokenizer.from_pretrained(clip_id)
-text_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to(device)
-image_processor = CLIPImageProcessor.from_pretrained(clip_id)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device)
-```
-
-Notice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/clip).
-
-Next, we prepare a PyTorch `nn.Module` to compute directional similarity:
-
-```python
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class DirectionalSimilarity(nn.Module):
-    def __init__(self, tokenizer, text_encoder, image_processor, image_encoder):
-        super().__init__()
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
-        self.image_processor = image_processor
-        self.image_encoder = image_encoder
-
-    def preprocess_image(self, image):
-        image = self.image_processor(image, return_tensors="pt")["pixel_values"]
-        return {"pixel_values": image.to(device)}
-
-    def tokenize_text(self, text):
-        inputs = self.tokenizer(
-            text,
-            max_length=self.tokenizer.model_max_length,
-            padding="max_length",
-            truncation=True,
-            return_tensors="pt",
-        )
-        return {"input_ids": inputs.input_ids.to(device)}
-
-    def encode_image(self, image):
-        preprocessed_image = self.preprocess_image(image)
-        image_features = self.image_encoder(**preprocessed_image).image_embeds
-        image_features = image_features / image_features.norm(dim=1, keepdim=True)
-        return image_features
-
-    def encode_text(self, text):
-        tokenized_text = self.tokenize_text(text)
-        text_features = self.text_encoder(**tokenized_text).text_embeds
-        text_features = text_features / text_features.norm(dim=1, keepdim=True)
-        return text_features
-
-    def compute_directional_similarity(self, img_feat_one, img_feat_two, text_feat_one, text_feat_two):
-        sim_direction = F.cosine_similarity(img_feat_two - img_feat_one, text_feat_two - text_feat_one)
-        return sim_direction
-
-    def forward(self, image_one, image_two, caption_one, caption_two):
-        img_feat_one = self.encode_image(image_one)
-        img_feat_two = self.encode_image(image_two)
-        text_feat_one = self.encode_text(caption_one)
-        text_feat_two = self.encode_text(caption_two)
-        directional_similarity = self.compute_directional_similarity(
-            img_feat_one, img_feat_two, text_feat_one, text_feat_two
-        )
-        return directional_similarity
-```
-
-Let's put `DirectionalSimilarity` to use now.
-
-```python
-dir_similarity = DirectionalSimilarity(tokenizer, text_encoder, image_processor, image_encoder)
-scores = []
-
-for i in range(len(input_images)):
-    original_image = input_images[i]
-    original_caption = original_captions[i]
-    edited_image = edited_images[i]
-    modified_caption = modified_captions[i]
-
-    similarity_score = dir_similarity(original_image, edited_image, original_caption, modified_caption)
-    scores.append(float(similarity_score.detach().cpu()))
-
-print(f"CLIP directional similarity: {np.mean(scores)}")
-# CLIP directional similarity: 0.0797976553440094
-```
-
-Like the CLIP Score, the higher the CLIP directional similarity, the better it is.
-
-It should be noted that the `StableDiffusionInstructPix2PixPipeline` exposes two arguments, namely, `image_guidance_scale` and `guidance_scale` that let you control the quality of the final edited image. We encourage you to experiment with these two arguments and see the impact of that on the directional similarity.
-
-We can extend the idea of this metric to measure how similar the original image and edited version are. To do that, we can just do `F.cosine_similarity(img_feat_two, img_feat_one)`. For these kinds of edits, we would still want the primary semantics of the images to be preserved as much as possible, i.e., a high similarity score.
-
-We can use these metrics for similar pipelines such as the [`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline).
-
-<Tip>
-
-Both CLIP score and CLIP direction similarity rely on the CLIP model, which can make the evaluations biased.
-
-</Tip>
-
-***Extending metrics like IS, FID (discussed later), or KID can be difficult*** when the model under evaluation was pre-trained on a large image-captioning dataset (such as the [LAION-5B dataset](https://laion.ai/blog/laion-5b/)). This is because underlying these metrics is an InceptionNet (pre-trained on the ImageNet-1k dataset) used for extracting intermediate image features. The pre-training dataset of Stable Diffusion may have limited overlap with the pre-training dataset of InceptionNet, so it is not a good candidate here for feature extraction.
-
-***Using the above metrics helps evaluate models that are class-conditioned. For example, [DiT](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/overview). It was pre-trained being conditioned on the ImageNet-1k classes.***
-
-### Class-conditioned image generation
-
-Class-conditioned generative models are usually pre-trained on a class-labeled dataset such as [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k). Popular metrics for evaluating these models include Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). In this document, we focus on FID ([Heusel et al.](https://arxiv.org/abs/1706.08500)). We show how to compute it with the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit), which uses the [DiT model](https://arxiv.org/abs/2212.09748) under the hood.
-
-FID aims to measure how similar are two datasets of images. As per [this resource](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid):
-
-> Fréchet Inception Distance is a measure of similarity between two datasets of images. It was shown to correlate well with the human judgment of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network.
-
-These two datasets are essentially the dataset of real images and the dataset of fake images (generated images in our case). FID is usually calculated with two large datasets. However, for this document, we will work with two mini datasets.
-
-Let's first download a few images from the ImageNet-1k training set:
-
-```python
-from zipfile import ZipFile
-import requests
-
-
-def download(url, local_filepath):
-    r = requests.get(url)
-    with open(local_filepath, "wb") as f:
-        f.write(r.content)
-    return local_filepath
-
-
-dummy_dataset_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/sample-imagenet-images.zip"
-local_filepath = download(dummy_dataset_url, dummy_dataset_url.split("/")[-1])
-
-with ZipFile(local_filepath, "r") as zipper:
-    zipper.extractall(".")
-```
-
-```python
-from PIL import Image
-import os
-
-dataset_path = "sample-imagenet-images"
-image_paths = sorted([os.path.join(dataset_path, x) for x in os.listdir(dataset_path)])
-
-real_images = [np.array(Image.open(path).convert("RGB")) for path in image_paths]
-```
-
-These are 10 images from the following Imagenet-1k classes: "cassette_player", "chain_saw" (x2), "church", "gas_pump" (x3), "parachute" (x2), and "tench".
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/real-images.png" alt="real-images"><br>
-    <em>Real images.</em>
-</p>
-
-Now that the images are loaded, let's apply some lightweight pre-processing on them to use them for FID calculation.
-
-```python
-from torchvision.transforms import functional as F
-
-
-def preprocess_image(image):
-    image = torch.tensor(image).unsqueeze(0)
-    image = image.permute(0, 3, 1, 2) / 255.0
-    return F.center_crop(image, (256, 256))
-
-
-real_images = torch.cat([preprocess_image(image) for image in real_images])
-print(real_images.shape)
-# torch.Size([10, 3, 256, 256])
-```
-
-We now load the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit) to generate images conditioned on the above-mentioned classes.
-
-```python
-from diffusers import DiTPipeline, DPMSolverMultistepScheduler
-
-dit_pipeline = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
-dit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config)
-dit_pipeline = dit_pipeline.to("cuda")
-
-words = [
-    "cassette player",
-    "chainsaw",
-    "chainsaw",
-    "church",
-    "gas pump",
-    "gas pump",
-    "gas pump",
-    "parachute",
-    "parachute",
-    "tench",
-]
-
-class_ids = dit_pipeline.get_label_ids(words)
-output = dit_pipeline(class_labels=class_ids, generator=generator, output_type="numpy")
-
-fake_images = output.images
-fake_images = torch.tensor(fake_images)
-fake_images = fake_images.permute(0, 3, 1, 2)
-print(fake_images.shape)
-# torch.Size([10, 3, 256, 256])
-```
-
-Now, we can compute the FID using [`torchmetrics`](https://torchmetrics.readthedocs.io/).
-
-```python
-from torchmetrics.image.fid import FrechetInceptionDistance
-
-fid = FrechetInceptionDistance(normalize=True)
-fid.update(real_images, real=True)
-fid.update(fake_images, real=False)
-
-print(f"FID: {float(fid.compute())}")
-# FID: 177.7147216796875
-```
-
-The lower the FID, the better it is. Several things can influence FID here:
-
-- Number of images (both real and fake)
-- Randomness induced in the diffusion process
-- Number of inference steps in the diffusion process
-- The scheduler being used in the diffusion process
-
-For the last two points, it is, therefore, a good practice to run the evaluation across different seeds and inference steps, and then report an average result.
-
-<Tip warning={true}>
-
-FID results tend to be fragile as they depend on a lot of factors:
-
-* The specific Inception model used during computation.
-* The implementation accuracy of the computation.
-* The image format (not the same if we start from PNGs vs JPGs).
-
-Keeping that in mind, FID is often most useful when comparing similar runs, but it is 
-hard to reproduce paper results unless the authors carefully disclose the FID 
-measurement code.
-
-These points apply to other related metrics too, such as KID and IS. 
-
-</Tip>
-
-As a final step, let's visually inspect the `fake_images`. 
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/fake-images.png" alt="fake-images"><br>
-    <em>Fake images.</em>
-</p>
diff --git a/spaces/parkyzh/bingo/src/components/chat-list.tsx b/spaces/parkyzh/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/parkyzh/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/parkyzh/bingo/tests/kblob.ts b/spaces/parkyzh/bingo/tests/kblob.ts
deleted file mode 100644
index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000
--- a/spaces/parkyzh/bingo/tests/kblob.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-
-import { fetch } from '@/lib/isomorphic'
-
-const formData = new FormData()
-
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-
-
-fetch('https://bing.vcanbb.top/images/kblob',
-  {
-    method: 'POST',
-    body: formData.getBuffer(),
-    headers: {
-      "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-      "sec-ch-ua-mobile": "?0",
-      "sec-ch-ua-platform": "\"Windows\"",
-      "Referer": "https://bing.vcanbb.top/web/index.html",
-      "Referrer-Policy": "origin-when-cross-origin",
-      ...formData.getHeaders()
-    }
-
-  }
-).then(res => res.text())
-.then(res => console.log('res', res))
diff --git a/spaces/plzdontcry/dakubettergpt/src/assets/icons/MoonIcon.tsx b/spaces/plzdontcry/dakubettergpt/src/assets/icons/MoonIcon.tsx
deleted file mode 100644
index 840921cbd0b78a44a1a94a699d8483aca0bbebc1..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/assets/icons/MoonIcon.tsx
+++ /dev/null
@@ -1,22 +0,0 @@
-import React from 'react';
-
-const MoonIcon = () => {
-  return (
-    <svg
-      stroke='currentColor'
-      fill='none'
-      strokeWidth='2'
-      viewBox='0 0 24 24'
-      strokeLinecap='round'
-      strokeLinejoin='round'
-      className='h-4 w-4'
-      height='1em'
-      width='1em'
-      xmlns='http://www.w3.org/2000/svg'
-    >
-      <path d='M21 12.79A9 9 0 1 1 11.21 3 7 7 0 0 0 21 12.79z'></path>
-    </svg>
-  );
-};
-
-export default MoonIcon;
diff --git a/spaces/plzdontcry/dakubettergpt/src/components/Menu/MenuOptions/ThemeSwitcher.tsx b/spaces/plzdontcry/dakubettergpt/src/components/Menu/MenuOptions/ThemeSwitcher.tsx
deleted file mode 100644
index f9d6c61c211c30354ee1703d864449e47763ccf1..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/components/Menu/MenuOptions/ThemeSwitcher.tsx
+++ /dev/null
@@ -1,42 +0,0 @@
-import React, { useEffect } from 'react';
-import { useTranslation } from 'react-i18next';
-import useStore from '@store/store';
-import SunIcon from '@icon/SunIcon';
-import MoonIcon from '@icon/MoonIcon';
-import { Theme } from '@type/theme';
-
-const getOppositeTheme = (theme: Theme): Theme => {
-  if (theme === 'dark') {
-    return 'light';
-  } else {
-    return 'dark';
-  }
-};
-const ThemeSwitcher = () => {
-  const { t } = useTranslation();
-  const theme = useStore((state) => state.theme);
-  const setTheme = useStore((state) => state.setTheme);
-
-  const switchTheme = () => {
-    setTheme(getOppositeTheme(theme!));
-  };
-
-  useEffect(() => {
-    document.documentElement.className = theme;
-  }, [theme]);
-
-  return theme ? (
-    <button
-      className='items-center gap-3 btn btn-neutral'
-      onClick={switchTheme}
-      aria-label='toggle dark/light mode'
-    >
-      {theme === 'dark' ? <SunIcon /> : <MoonIcon />}
-      {t(getOppositeTheme(theme) + 'Mode')}
-    </button>
-  ) : (
-    <></>
-  );
-};
-
-export default ThemeSwitcher;
diff --git a/spaces/plzdontcry/dakubettergpt/vite.config.ts b/spaces/plzdontcry/dakubettergpt/vite.config.ts
deleted file mode 100644
index be97b102a7dbc082f94a4f61767ae7e57c6fa214..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/vite.config.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-import { defineConfig } from 'vite';
-import react from '@vitejs/plugin-react-swc';
-import wasm from 'vite-plugin-wasm';
-import topLevelAwait from 'vite-plugin-top-level-await';
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react(), wasm(), topLevelAwait()],
-  resolve: {
-    alias: {
-      '@icon/': new URL('./src/assets/icons/', import.meta.url).pathname,
-      '@type/': new URL('./src/types/', import.meta.url).pathname,
-      '@store/': new URL('./src/store/', import.meta.url).pathname,
-      '@hooks/': new URL('./src/hooks/', import.meta.url).pathname,
-      '@constants/': new URL('./src/constants/', import.meta.url).pathname,
-      '@api/': new URL('./src/api/', import.meta.url).pathname,
-      '@components/': new URL('./src/components/', import.meta.url).pathname,
-      '@utils/': new URL('./src/utils/', import.meta.url).pathname,
-      '@src/': new URL('./src/', import.meta.url).pathname,
-    },
-  },
-  base: './',
-});
diff --git a/spaces/prairie-guy/Seasonal_Mood/README.md b/spaces/prairie-guy/Seasonal_Mood/README.md
deleted file mode 100644
index 7371910608d85c65b4ae9f40cfa1fc05192e07c7..0000000000000000000000000000000000000000
--- a/spaces/prairie-guy/Seasonal_Mood/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Seasonal_Mood
-emoji: 📈
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/_soft.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/_soft.py
deleted file mode 100644
index 28c67f74cc82b8f55e47afd6a71972cc1fb95eb6..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/_soft.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import os
-import sys
-from contextlib import suppress
-from errno import EACCES, EEXIST
-from pathlib import Path
-
-from ._api import BaseFileLock
-from ._util import ensure_directory_exists, raise_on_not_writable_file
-
-
-class SoftFileLock(BaseFileLock):
-    """Simply watches the existence of the lock file."""
-
-    def _acquire(self) -> None:
-        raise_on_not_writable_file(self.lock_file)
-        ensure_directory_exists(self.lock_file)
-        # first check for exists and read-only mode as the open will mask this case as EEXIST
-        flags = (
-            os.O_WRONLY  # open for writing only
-            | os.O_CREAT
-            | os.O_EXCL  # together with above raise EEXIST if the file specified by filename exists
-            | os.O_TRUNC  # truncate the file to zero byte
-        )
-        try:
-            file_handler = os.open(self.lock_file, flags, self._context.mode)
-        except OSError as exception:  # re-raise unless expected exception
-            if not (
-                exception.errno == EEXIST  # lock already exist
-                or (exception.errno == EACCES and sys.platform == "win32")  # has no access to this lock
-            ):  # pragma: win32 no cover
-                raise
-        else:
-            self._context.lock_file_fd = file_handler
-
-    def _release(self) -> None:
-        assert self._context.lock_file_fd is not None  # noqa: S101
-        os.close(self._context.lock_file_fd)  # the lock file is definitely not None
-        self._context.lock_file_fd = None
-        with suppress(OSError):  # the file is already deleted and that's what we want
-            Path(self.lock_file).unlink()
-
-
-__all__ = [
-    "SoftFileLock",
-]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Download-696bd40c.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Download-696bd40c.js
deleted file mode 100644
index ceca12d3d1bf85679d6a2e3f5b295c686adc9bb8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Download-696bd40c.js
+++ /dev/null
@@ -1,2 +0,0 @@
-const{SvelteComponent:i,append:v,attr:e,detach:c,init:h,insert:p,noop:o,safe_not_equal:_,svg_element:a}=window.__gradio__svelte__internal;function d(r){let t,n;return{c(){t=a("svg"),n=a("path"),e(n,"fill","currentColor"),e(n,"d","M26 24v4H6v-4H4v4a2 2 0 0 0 2 2h20a2 2 0 0 0 2-2v-4zm0-10l-1.41-1.41L17 20.17V2h-2v18.17l-7.59-7.58L6 14l10 10l10-10z"),e(t,"xmlns","http://www.w3.org/2000/svg"),e(t,"width","100%"),e(t,"height","100%"),e(t,"viewBox","0 0 32 32")},m(l,s){p(l,t,s),v(t,n)},p:o,i:o,o,d(l){l&&c(t)}}}class w extends i{constructor(t){super(),h(this,t,null,d,_,{})}}export{w as D};
-//# sourceMappingURL=Download-696bd40c.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c
deleted file mode 100644
index 9cfd0c2a57f355cea353abd24d21343543017191..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c
+++ /dev/null
@@ -1,18 +0,0 @@
-#include <immintrin.h>
-/**
- * Test BW mask operations due to:
- *  - MSVC has supported it since vs2019 see,
- *    https://developercommunity.visualstudio.com/content/problem/518298/missing-avx512bw-mask-intrinsics.html
- *  - Clang >= v8.0
- *  - GCC >= v7.1
- */
-int main(void)
-{
-    __mmask64 m64 = _mm512_cmpeq_epi8_mask(_mm512_set1_epi8((char)1), _mm512_set1_epi8((char)1));
-    m64 = _kor_mask64(m64, m64);
-    m64 = _kxor_mask64(m64, m64);
-    m64 = _cvtu64_mask64(_cvtmask64_u64(m64));
-    m64 = _mm512_kunpackd(m64, m64);
-    m64 = (__mmask64)_mm512_kunpackw((__mmask32)m64, (__mmask32)m64);
-    return (int)_cvtmask64_u64(m64);
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/parsers/python_parser.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/parsers/python_parser.py
deleted file mode 100644
index 6846ea2b196b8d1e7dcd1e1e6286e21b413d3237..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/parsers/python_parser.py
+++ /dev/null
@@ -1,1382 +0,0 @@
-from __future__ import annotations
-
-from collections import (
-    abc,
-    defaultdict,
-)
-from collections.abc import (
-    Hashable,
-    Iterator,
-    Mapping,
-    Sequence,
-)
-import csv
-from io import StringIO
-import re
-import sys
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    DefaultDict,
-    Literal,
-    cast,
-)
-
-import numpy as np
-
-from pandas._libs import lib
-from pandas.errors import (
-    EmptyDataError,
-    ParserError,
-)
-from pandas.util._decorators import cache_readonly
-
-from pandas.core.dtypes.common import (
-    is_bool_dtype,
-    is_integer,
-    is_numeric_dtype,
-)
-from pandas.core.dtypes.inference import is_dict_like
-
-from pandas.io.common import (
-    dedup_names,
-    is_potential_multi_index,
-)
-from pandas.io.parsers.base_parser import (
-    ParserBase,
-    parser_defaults,
-)
-
-if TYPE_CHECKING:
-    from pandas._typing import (
-        ArrayLike,
-        ReadCsvBuffer,
-        Scalar,
-    )
-
-    from pandas import (
-        Index,
-        MultiIndex,
-    )
-
-# BOM character (byte order mark)
-# This exists at the beginning of a file to indicate endianness
-# of a file (stream). Unfortunately, this marker screws up parsing,
-# so we need to remove it if we see it.
-_BOM = "\ufeff"
-
-
-class PythonParser(ParserBase):
-    _no_thousands_columns: set[int]
-
-    def __init__(self, f: ReadCsvBuffer[str] | list, **kwds) -> None:
-        """
-        Workhorse function for processing nested list into DataFrame
-        """
-        super().__init__(kwds)
-
-        self.data: Iterator[str] | None = None
-        self.buf: list = []
-        self.pos = 0
-        self.line_pos = 0
-
-        self.skiprows = kwds["skiprows"]
-
-        if callable(self.skiprows):
-            self.skipfunc = self.skiprows
-        else:
-            self.skipfunc = lambda x: x in self.skiprows
-
-        self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
-        self.delimiter = kwds["delimiter"]
-
-        self.quotechar = kwds["quotechar"]
-        if isinstance(self.quotechar, str):
-            self.quotechar = str(self.quotechar)
-
-        self.escapechar = kwds["escapechar"]
-        self.doublequote = kwds["doublequote"]
-        self.skipinitialspace = kwds["skipinitialspace"]
-        self.lineterminator = kwds["lineterminator"]
-        self.quoting = kwds["quoting"]
-        self.skip_blank_lines = kwds["skip_blank_lines"]
-
-        self.has_index_names = False
-        if "has_index_names" in kwds:
-            self.has_index_names = kwds["has_index_names"]
-
-        self.verbose = kwds["verbose"]
-
-        self.thousands = kwds["thousands"]
-        self.decimal = kwds["decimal"]
-
-        self.comment = kwds["comment"]
-
-        # Set self.data to something that can read lines.
-        if isinstance(f, list):
-            # read_excel: f is a list
-            self.data = cast(Iterator[str], f)
-        else:
-            assert hasattr(f, "readline")
-            self.data = self._make_reader(f)
-
-        # Get columns in two steps: infer from data, then
-        # infer column indices from self.usecols if it is specified.
-        self._col_indices: list[int] | None = None
-        columns: list[list[Scalar | None]]
-        (
-            columns,
-            self.num_original_columns,
-            self.unnamed_cols,
-        ) = self._infer_columns()
-
-        # Now self.columns has the set of columns that we will process.
-        # The original set is stored in self.original_columns.
-        # error: Cannot determine type of 'index_names'
-        (
-            self.columns,
-            self.index_names,
-            self.col_names,
-            _,
-        ) = self._extract_multi_indexer_columns(
-            columns,
-            self.index_names,  # type: ignore[has-type]
-        )
-
-        # get popped off for index
-        self.orig_names: list[Hashable] = list(self.columns)
-
-        # needs to be cleaned/refactored
-        # multiple date column thing turning into a real spaghetti factory
-
-        if not self._has_complex_date_col:
-            (index_names, self.orig_names, self.columns) = self._get_index_name()
-            self._name_processed = True
-            if self.index_names is None:
-                self.index_names = index_names
-
-        if self._col_indices is None:
-            self._col_indices = list(range(len(self.columns)))
-
-        self._parse_date_cols = self._validate_parse_dates_presence(self.columns)
-        self._no_thousands_columns = self._set_no_thousand_columns()
-
-        if len(self.decimal) != 1:
-            raise ValueError("Only length-1 decimal markers supported")
-
-    @cache_readonly
-    def num(self) -> re.Pattern:
-        decimal = re.escape(self.decimal)
-        if self.thousands is None:
-            regex = rf"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$"
-        else:
-            thousands = re.escape(self.thousands)
-            regex = (
-                rf"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?"
-                rf"([0-9]?(E|e)\-?[0-9]+)?$"
-            )
-        return re.compile(regex)
-
-    def _make_reader(self, f: IO[str] | ReadCsvBuffer[str]):
-        sep = self.delimiter
-
-        if sep is None or len(sep) == 1:
-            if self.lineterminator:
-                raise ValueError(
-                    "Custom line terminators not supported in python parser (yet)"
-                )
-
-            class MyDialect(csv.Dialect):
-                delimiter = self.delimiter
-                quotechar = self.quotechar
-                escapechar = self.escapechar
-                doublequote = self.doublequote
-                skipinitialspace = self.skipinitialspace
-                quoting = self.quoting
-                lineterminator = "\n"
-
-            dia = MyDialect
-
-            if sep is not None:
-                dia.delimiter = sep
-            else:
-                # attempt to sniff the delimiter from the first valid line,
-                # i.e. no comment line and not in skiprows
-                line = f.readline()
-                lines = self._check_comments([[line]])[0]
-                while self.skipfunc(self.pos) or not lines:
-                    self.pos += 1
-                    line = f.readline()
-                    lines = self._check_comments([[line]])[0]
-                lines_str = cast(list[str], lines)
-
-                # since `line` was a string, lines will be a list containing
-                # only a single string
-                line = lines_str[0]
-
-                self.pos += 1
-                self.line_pos += 1
-                sniffed = csv.Sniffer().sniff(line)
-                dia.delimiter = sniffed.delimiter
-
-                # Note: encoding is irrelevant here
-                line_rdr = csv.reader(StringIO(line), dialect=dia)
-                self.buf.extend(list(line_rdr))
-
-            # Note: encoding is irrelevant here
-            reader = csv.reader(f, dialect=dia, strict=True)
-
-        else:
-
-            def _read():
-                line = f.readline()
-                pat = re.compile(sep)
-
-                yield pat.split(line.strip())
-
-                for line in f:
-                    yield pat.split(line.strip())
-
-            reader = _read()
-
-        return reader
-
-    def read(
-        self, rows: int | None = None
-    ) -> tuple[
-        Index | None, Sequence[Hashable] | MultiIndex, Mapping[Hashable, ArrayLike]
-    ]:
-        try:
-            content = self._get_lines(rows)
-        except StopIteration:
-            if self._first_chunk:
-                content = []
-            else:
-                self.close()
-                raise
-
-        # done with first read, next time raise StopIteration
-        self._first_chunk = False
-
-        columns: Sequence[Hashable] = list(self.orig_names)
-        if not len(content):  # pragma: no cover
-            # DataFrame with the right metadata, even though it's length 0
-            # error: Cannot determine type of 'index_col'
-            names = dedup_names(
-                self.orig_names,
-                is_potential_multi_index(
-                    self.orig_names,
-                    self.index_col,  # type: ignore[has-type]
-                ),
-            )
-            index, columns, col_dict = self._get_empty_meta(
-                names,
-                self.dtype,
-            )
-            conv_columns = self._maybe_make_multi_index_columns(columns, self.col_names)
-            return index, conv_columns, col_dict
-
-        # handle new style for names in index
-        count_empty_content_vals = count_empty_vals(content[0])
-        indexnamerow = None
-        if self.has_index_names and count_empty_content_vals == len(columns):
-            indexnamerow = content[0]
-            content = content[1:]
-
-        alldata = self._rows_to_cols(content)
-        data, columns = self._exclude_implicit_index(alldata)
-
-        conv_data = self._convert_data(data)
-        columns, conv_data = self._do_date_conversions(columns, conv_data)
-
-        index, result_columns = self._make_index(
-            conv_data, alldata, columns, indexnamerow
-        )
-
-        return index, result_columns, conv_data
-
-    def _exclude_implicit_index(
-        self,
-        alldata: list[np.ndarray],
-    ) -> tuple[Mapping[Hashable, np.ndarray], Sequence[Hashable]]:
-        # error: Cannot determine type of 'index_col'
-        names = dedup_names(
-            self.orig_names,
-            is_potential_multi_index(
-                self.orig_names,
-                self.index_col,  # type: ignore[has-type]
-            ),
-        )
-
-        offset = 0
-        if self._implicit_index:
-            # error: Cannot determine type of 'index_col'
-            offset = len(self.index_col)  # type: ignore[has-type]
-
-        len_alldata = len(alldata)
-        self._check_data_length(names, alldata)
-
-        return {
-            name: alldata[i + offset] for i, name in enumerate(names) if i < len_alldata
-        }, names
-
-    # legacy
-    def get_chunk(
-        self, size: int | None = None
-    ) -> tuple[
-        Index | None, Sequence[Hashable] | MultiIndex, Mapping[Hashable, ArrayLike]
-    ]:
-        if size is None:
-            # error: "PythonParser" has no attribute "chunksize"
-            size = self.chunksize  # type: ignore[attr-defined]
-        return self.read(rows=size)
-
-    def _convert_data(
-        self,
-        data: Mapping[Hashable, np.ndarray],
-    ) -> Mapping[Hashable, ArrayLike]:
-        # apply converters
-        clean_conv = self._clean_mapping(self.converters)
-        clean_dtypes = self._clean_mapping(self.dtype)
-
-        # Apply NA values.
-        clean_na_values = {}
-        clean_na_fvalues = {}
-
-        if isinstance(self.na_values, dict):
-            for col in self.na_values:
-                na_value = self.na_values[col]
-                na_fvalue = self.na_fvalues[col]
-
-                if isinstance(col, int) and col not in self.orig_names:
-                    col = self.orig_names[col]
-
-                clean_na_values[col] = na_value
-                clean_na_fvalues[col] = na_fvalue
-        else:
-            clean_na_values = self.na_values
-            clean_na_fvalues = self.na_fvalues
-
-        return self._convert_to_ndarrays(
-            data,
-            clean_na_values,
-            clean_na_fvalues,
-            self.verbose,
-            clean_conv,
-            clean_dtypes,
-        )
-
-    @cache_readonly
-    def _have_mi_columns(self) -> bool:
-        if self.header is None:
-            return False
-
-        header = self.header
-        if isinstance(header, (list, tuple, np.ndarray)):
-            return len(header) > 1
-        else:
-            return False
-
-    def _infer_columns(
-        self,
-    ) -> tuple[list[list[Scalar | None]], int, set[Scalar | None]]:
-        names = self.names
-        num_original_columns = 0
-        clear_buffer = True
-        unnamed_cols: set[Scalar | None] = set()
-
-        if self.header is not None:
-            header = self.header
-            have_mi_columns = self._have_mi_columns
-
-            if isinstance(header, (list, tuple, np.ndarray)):
-                # we have a mi columns, so read an extra line
-                if have_mi_columns:
-                    header = list(header) + [header[-1] + 1]
-            else:
-                header = [header]
-
-            columns: list[list[Scalar | None]] = []
-            for level, hr in enumerate(header):
-                try:
-                    line = self._buffered_line()
-
-                    while self.line_pos <= hr:
-                        line = self._next_line()
-
-                except StopIteration as err:
-                    if 0 < self.line_pos <= hr and (
-                        not have_mi_columns or hr != header[-1]
-                    ):
-                        # If no rows we want to raise a different message and if
-                        # we have mi columns, the last line is not part of the header
-                        joi = list(map(str, header[:-1] if have_mi_columns else header))
-                        msg = f"[{','.join(joi)}], len of {len(joi)}, "
-                        raise ValueError(
-                            f"Passed header={msg}"
-                            f"but only {self.line_pos} lines in file"
-                        ) from err
-
-                    # We have an empty file, so check
-                    # if columns are provided. That will
-                    # serve as the 'line' for parsing
-                    if have_mi_columns and hr > 0:
-                        if clear_buffer:
-                            self._clear_buffer()
-                        columns.append([None] * len(columns[-1]))
-                        return columns, num_original_columns, unnamed_cols
-
-                    if not self.names:
-                        raise EmptyDataError("No columns to parse from file") from err
-
-                    line = self.names[:]
-
-                this_columns: list[Scalar | None] = []
-                this_unnamed_cols = []
-
-                for i, c in enumerate(line):
-                    if c == "":
-                        if have_mi_columns:
-                            col_name = f"Unnamed: {i}_level_{level}"
-                        else:
-                            col_name = f"Unnamed: {i}"
-
-                        this_unnamed_cols.append(i)
-                        this_columns.append(col_name)
-                    else:
-                        this_columns.append(c)
-
-                if not have_mi_columns:
-                    counts: DefaultDict = defaultdict(int)
-                    # Ensure that regular columns are used before unnamed ones
-                    # to keep given names and mangle unnamed columns
-                    col_loop_order = [
-                        i
-                        for i in range(len(this_columns))
-                        if i not in this_unnamed_cols
-                    ] + this_unnamed_cols
-
-                    # TODO: Use pandas.io.common.dedup_names instead (see #50371)
-                    for i in col_loop_order:
-                        col = this_columns[i]
-                        old_col = col
-                        cur_count = counts[col]
-
-                        if cur_count > 0:
-                            while cur_count > 0:
-                                counts[old_col] = cur_count + 1
-                                col = f"{old_col}.{cur_count}"
-                                if col in this_columns:
-                                    cur_count += 1
-                                else:
-                                    cur_count = counts[col]
-
-                            if (
-                                self.dtype is not None
-                                and is_dict_like(self.dtype)
-                                and self.dtype.get(old_col) is not None
-                                and self.dtype.get(col) is None
-                            ):
-                                self.dtype.update({col: self.dtype.get(old_col)})
-                        this_columns[i] = col
-                        counts[col] = cur_count + 1
-                elif have_mi_columns:
-                    # if we have grabbed an extra line, but its not in our
-                    # format so save in the buffer, and create an blank extra
-                    # line for the rest of the parsing code
-                    if hr == header[-1]:
-                        lc = len(this_columns)
-                        # error: Cannot determine type of 'index_col'
-                        sic = self.index_col  # type: ignore[has-type]
-                        ic = len(sic) if sic is not None else 0
-                        unnamed_count = len(this_unnamed_cols)
-
-                        # if wrong number of blanks or no index, not our format
-                        if (lc != unnamed_count and lc - ic > unnamed_count) or ic == 0:
-                            clear_buffer = False
-                            this_columns = [None] * lc
-                            self.buf = [self.buf[-1]]
-
-                columns.append(this_columns)
-                unnamed_cols.update({this_columns[i] for i in this_unnamed_cols})
-
-                if len(columns) == 1:
-                    num_original_columns = len(this_columns)
-
-            if clear_buffer:
-                self._clear_buffer()
-
-            first_line: list[Scalar] | None
-            if names is not None:
-                # Read first row after header to check if data are longer
-                try:
-                    first_line = self._next_line()
-                except StopIteration:
-                    first_line = None
-
-                len_first_data_row = 0 if first_line is None else len(first_line)
-
-                if len(names) > len(columns[0]) and len(names) > len_first_data_row:
-                    raise ValueError(
-                        "Number of passed names did not match "
-                        "number of header fields in the file"
-                    )
-                if len(columns) > 1:
-                    raise TypeError("Cannot pass names with multi-index columns")
-
-                if self.usecols is not None:
-                    # Set _use_cols. We don't store columns because they are
-                    # overwritten.
-                    self._handle_usecols(columns, names, num_original_columns)
-                else:
-                    num_original_columns = len(names)
-                if self._col_indices is not None and len(names) != len(
-                    self._col_indices
-                ):
-                    columns = [[names[i] for i in sorted(self._col_indices)]]
-                else:
-                    columns = [names]
-            else:
-                columns = self._handle_usecols(
-                    columns, columns[0], num_original_columns
-                )
-        else:
-            ncols = len(self._header_line)
-            num_original_columns = ncols
-
-            if not names:
-                columns = [list(range(ncols))]
-                columns = self._handle_usecols(columns, columns[0], ncols)
-            elif self.usecols is None or len(names) >= ncols:
-                columns = self._handle_usecols([names], names, ncols)
-                num_original_columns = len(names)
-            elif not callable(self.usecols) and len(names) != len(self.usecols):
-                raise ValueError(
-                    "Number of passed names did not match number of "
-                    "header fields in the file"
-                )
-            else:
-                # Ignore output but set used columns.
-                columns = [names]
-                self._handle_usecols(columns, columns[0], ncols)
-
-        return columns, num_original_columns, unnamed_cols
-
-    @cache_readonly
-    def _header_line(self):
-        # Store line for reuse in _get_index_name
-        if self.header is not None:
-            return None
-
-        try:
-            line = self._buffered_line()
-        except StopIteration as err:
-            if not self.names:
-                raise EmptyDataError("No columns to parse from file") from err
-
-            line = self.names[:]
-        return line
-
-    def _handle_usecols(
-        self,
-        columns: list[list[Scalar | None]],
-        usecols_key: list[Scalar | None],
-        num_original_columns: int,
-    ) -> list[list[Scalar | None]]:
-        """
-        Sets self._col_indices
-
-        usecols_key is used if there are string usecols.
-        """
-        col_indices: set[int] | list[int]
-        if self.usecols is not None:
-            if callable(self.usecols):
-                col_indices = self._evaluate_usecols(self.usecols, usecols_key)
-            elif any(isinstance(u, str) for u in self.usecols):
-                if len(columns) > 1:
-                    raise ValueError(
-                        "If using multiple headers, usecols must be integers."
-                    )
-                col_indices = []
-
-                for col in self.usecols:
-                    if isinstance(col, str):
-                        try:
-                            col_indices.append(usecols_key.index(col))
-                        except ValueError:
-                            self._validate_usecols_names(self.usecols, usecols_key)
-                    else:
-                        col_indices.append(col)
-            else:
-                missing_usecols = [
-                    col for col in self.usecols if col >= num_original_columns
-                ]
-                if missing_usecols:
-                    raise ParserError(
-                        "Defining usecols without of bounds indices is not allowed. "
-                        f"{missing_usecols} are out of bounds.",
-                    )
-                col_indices = self.usecols
-
-            columns = [
-                [n for i, n in enumerate(column) if i in col_indices]
-                for column in columns
-            ]
-            self._col_indices = sorted(col_indices)
-        return columns
-
-    def _buffered_line(self) -> list[Scalar]:
-        """
-        Return a line from buffer, filling buffer if required.
-        """
-        if len(self.buf) > 0:
-            return self.buf[0]
-        else:
-            return self._next_line()
-
-    def _check_for_bom(self, first_row: list[Scalar]) -> list[Scalar]:
-        """
-        Checks whether the file begins with the BOM character.
-        If it does, remove it. In addition, if there is quoting
-        in the field subsequent to the BOM, remove it as well
-        because it technically takes place at the beginning of
-        the name, not the middle of it.
-        """
-        # first_row will be a list, so we need to check
-        # that that list is not empty before proceeding.
-        if not first_row:
-            return first_row
-
-        # The first element of this row is the one that could have the
-        # BOM that we want to remove. Check that the first element is a
-        # string before proceeding.
-        if not isinstance(first_row[0], str):
-            return first_row
-
-        # Check that the string is not empty, as that would
-        # obviously not have a BOM at the start of it.
-        if not first_row[0]:
-            return first_row
-
-        # Since the string is non-empty, check that it does
-        # in fact begin with a BOM.
-        first_elt = first_row[0][0]
-        if first_elt != _BOM:
-            return first_row
-
-        first_row_bom = first_row[0]
-        new_row: str
-
-        if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar:
-            start = 2
-            quote = first_row_bom[1]
-            end = first_row_bom[2:].index(quote) + 2
-
-            # Extract the data between the quotation marks
-            new_row = first_row_bom[start:end]
-
-            # Extract any remaining data after the second
-            # quotation mark.
-            if len(first_row_bom) > end + 1:
-                new_row += first_row_bom[end + 1 :]
-
-        else:
-            # No quotation so just remove BOM from first element
-            new_row = first_row_bom[1:]
-
-        new_row_list: list[Scalar] = [new_row]
-        return new_row_list + first_row[1:]
-
-    def _is_line_empty(self, line: list[Scalar]) -> bool:
-        """
-        Check if a line is empty or not.
-
-        Parameters
-        ----------
-        line : str, array-like
-            The line of data to check.
-
-        Returns
-        -------
-        boolean : Whether or not the line is empty.
-        """
-        return not line or all(not x for x in line)
-
-    def _next_line(self) -> list[Scalar]:
-        if isinstance(self.data, list):
-            while self.skipfunc(self.pos):
-                if self.pos >= len(self.data):
-                    break
-                self.pos += 1
-
-            while True:
-                try:
-                    line = self._check_comments([self.data[self.pos]])[0]
-                    self.pos += 1
-                    # either uncommented or blank to begin with
-                    if not self.skip_blank_lines and (
-                        self._is_line_empty(self.data[self.pos - 1]) or line
-                    ):
-                        break
-                    if self.skip_blank_lines:
-                        ret = self._remove_empty_lines([line])
-                        if ret:
-                            line = ret[0]
-                            break
-                except IndexError:
-                    raise StopIteration
-        else:
-            while self.skipfunc(self.pos):
-                self.pos += 1
-                # assert for mypy, data is Iterator[str] or None, would error in next
-                assert self.data is not None
-                next(self.data)
-
-            while True:
-                orig_line = self._next_iter_line(row_num=self.pos + 1)
-                self.pos += 1
-
-                if orig_line is not None:
-                    line = self._check_comments([orig_line])[0]
-
-                    if self.skip_blank_lines:
-                        ret = self._remove_empty_lines([line])
-
-                        if ret:
-                            line = ret[0]
-                            break
-                    elif self._is_line_empty(orig_line) or line:
-                        break
-
-        # This was the first line of the file,
-        # which could contain the BOM at the
-        # beginning of it.
-        if self.pos == 1:
-            line = self._check_for_bom(line)
-
-        self.line_pos += 1
-        self.buf.append(line)
-        return line
-
-    def _alert_malformed(self, msg: str, row_num: int) -> None:
-        """
-        Alert a user about a malformed row, depending on value of
-        `self.on_bad_lines` enum.
-
-        If `self.on_bad_lines` is ERROR, the alert will be `ParserError`.
-        If `self.on_bad_lines` is WARN, the alert will be printed out.
-
-        Parameters
-        ----------
-        msg: str
-            The error message to display.
-        row_num: int
-            The row number where the parsing error occurred.
-            Because this row number is displayed, we 1-index,
-            even though we 0-index internally.
-        """
-        if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
-            raise ParserError(msg)
-        if self.on_bad_lines == self.BadLineHandleMethod.WARN:
-            base = f"Skipping line {row_num}: "
-            sys.stderr.write(base + msg + "\n")
-
-    def _next_iter_line(self, row_num: int) -> list[Scalar] | None:
-        """
-        Wrapper around iterating through `self.data` (CSV source).
-
-        When a CSV error is raised, we check for specific
-        error messages that allow us to customize the
-        error message displayed to the user.
-
-        Parameters
-        ----------
-        row_num: int
-            The row number of the line being parsed.
-        """
-        try:
-            # assert for mypy, data is Iterator[str] or None, would error in next
-            assert self.data is not None
-            line = next(self.data)
-            # for mypy
-            assert isinstance(line, list)
-            return line
-        except csv.Error as e:
-            if self.on_bad_lines in (
-                self.BadLineHandleMethod.ERROR,
-                self.BadLineHandleMethod.WARN,
-            ):
-                msg = str(e)
-
-                if "NULL byte" in msg or "line contains NUL" in msg:
-                    msg = (
-                        "NULL byte detected. This byte "
-                        "cannot be processed in Python's "
-                        "native csv library at the moment, "
-                        "so please pass in engine='c' instead"
-                    )
-
-                if self.skipfooter > 0:
-                    reason = (
-                        "Error could possibly be due to "
-                        "parsing errors in the skipped footer rows "
-                        "(the skipfooter keyword is only applied "
-                        "after Python's csv library has parsed "
-                        "all rows)."
-                    )
-                    msg += ". " + reason
-
-                self._alert_malformed(msg, row_num)
-            return None
-
-    def _check_comments(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
-        if self.comment is None:
-            return lines
-        ret = []
-        for line in lines:
-            rl = []
-            for x in line:
-                if (
-                    not isinstance(x, str)
-                    or self.comment not in x
-                    or x in self.na_values
-                ):
-                    rl.append(x)
-                else:
-                    x = x[: x.find(self.comment)]
-                    if len(x) > 0:
-                        rl.append(x)
-                    break
-            ret.append(rl)
-        return ret
-
-    def _remove_empty_lines(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
-        """
-        Iterate through the lines and remove any that are
-        either empty or contain only one whitespace value
-
-        Parameters
-        ----------
-        lines : list of list of Scalars
-            The array of lines that we are to filter.
-
-        Returns
-        -------
-        filtered_lines : list of list of Scalars
-            The same array of lines with the "empty" ones removed.
-        """
-        # Remove empty lines and lines with only one whitespace value
-        ret = [
-            line
-            for line in lines
-            if (
-                len(line) > 1
-                or len(line) == 1
-                and (not isinstance(line[0], str) or line[0].strip())
-            )
-        ]
-        return ret
-
-    def _check_thousands(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
-        if self.thousands is None:
-            return lines
-
-        return self._search_replace_num_columns(
-            lines=lines, search=self.thousands, replace=""
-        )
-
-    def _search_replace_num_columns(
-        self, lines: list[list[Scalar]], search: str, replace: str
-    ) -> list[list[Scalar]]:
-        ret = []
-        for line in lines:
-            rl = []
-            for i, x in enumerate(line):
-                if (
-                    not isinstance(x, str)
-                    or search not in x
-                    or i in self._no_thousands_columns
-                    or not self.num.search(x.strip())
-                ):
-                    rl.append(x)
-                else:
-                    rl.append(x.replace(search, replace))
-            ret.append(rl)
-        return ret
-
-    def _check_decimal(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
-        if self.decimal == parser_defaults["decimal"]:
-            return lines
-
-        return self._search_replace_num_columns(
-            lines=lines, search=self.decimal, replace="."
-        )
-
-    def _clear_buffer(self) -> None:
-        self.buf = []
-
-    def _get_index_name(
-        self,
-    ) -> tuple[Sequence[Hashable] | None, list[Hashable], list[Hashable]]:
-        """
-        Try several cases to get lines:
-
-        0) There are headers on row 0 and row 1 and their
-        total summed lengths equals the length of the next line.
-        Treat row 0 as columns and row 1 as indices
-        1) Look for implicit index: there are more columns
-        on row 1 than row 0. If this is true, assume that row
-        1 lists index columns and row 0 lists normal columns.
-        2) Get index from the columns if it was listed.
-        """
-        columns: Sequence[Hashable] = self.orig_names
-        orig_names = list(columns)
-        columns = list(columns)
-
-        line: list[Scalar] | None
-        if self._header_line is not None:
-            line = self._header_line
-        else:
-            try:
-                line = self._next_line()
-            except StopIteration:
-                line = None
-
-        next_line: list[Scalar] | None
-        try:
-            next_line = self._next_line()
-        except StopIteration:
-            next_line = None
-
-        # implicitly index_col=0 b/c 1 fewer column names
-        implicit_first_cols = 0
-        if line is not None:
-            # leave it 0, #2442
-            # Case 1
-            # error: Cannot determine type of 'index_col'
-            index_col = self.index_col  # type: ignore[has-type]
-            if index_col is not False:
-                implicit_first_cols = len(line) - self.num_original_columns
-
-            # Case 0
-            if (
-                next_line is not None
-                and self.header is not None
-                and index_col is not False
-            ):
-                if len(next_line) == len(line) + self.num_original_columns:
-                    # column and index names on diff rows
-                    self.index_col = list(range(len(line)))
-                    self.buf = self.buf[1:]
-
-                    for c in reversed(line):
-                        columns.insert(0, c)
-
-                    # Update list of original names to include all indices.
-                    orig_names = list(columns)
-                    self.num_original_columns = len(columns)
-                    return line, orig_names, columns
-
-        if implicit_first_cols > 0:
-            # Case 1
-            self._implicit_index = True
-            if self.index_col is None:
-                self.index_col = list(range(implicit_first_cols))
-
-            index_name = None
-
-        else:
-            # Case 2
-            (index_name, _, self.index_col) = self._clean_index_names(
-                columns, self.index_col
-            )
-
-        return index_name, orig_names, columns
-
-    def _rows_to_cols(self, content: list[list[Scalar]]) -> list[np.ndarray]:
-        col_len = self.num_original_columns
-
-        if self._implicit_index:
-            col_len += len(self.index_col)
-
-        max_len = max(len(row) for row in content)
-
-        # Check that there are no rows with too many
-        # elements in their row (rows with too few
-        # elements are padded with NaN).
-        # error: Non-overlapping identity check (left operand type: "List[int]",
-        # right operand type: "Literal[False]")
-        if (
-            max_len > col_len
-            and self.index_col is not False  # type: ignore[comparison-overlap]
-            and self.usecols is None
-        ):
-            footers = self.skipfooter if self.skipfooter else 0
-            bad_lines = []
-
-            iter_content = enumerate(content)
-            content_len = len(content)
-            content = []
-
-            for i, _content in iter_content:
-                actual_len = len(_content)
-
-                if actual_len > col_len:
-                    if callable(self.on_bad_lines):
-                        new_l = self.on_bad_lines(_content)
-                        if new_l is not None:
-                            content.append(new_l)
-                    elif self.on_bad_lines in (
-                        self.BadLineHandleMethod.ERROR,
-                        self.BadLineHandleMethod.WARN,
-                    ):
-                        row_num = self.pos - (content_len - i + footers)
-                        bad_lines.append((row_num, actual_len))
-
-                        if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
-                            break
-                else:
-                    content.append(_content)
-
-            for row_num, actual_len in bad_lines:
-                msg = (
-                    f"Expected {col_len} fields in line {row_num + 1}, saw "
-                    f"{actual_len}"
-                )
-                if (
-                    self.delimiter
-                    and len(self.delimiter) > 1
-                    and self.quoting != csv.QUOTE_NONE
-                ):
-                    # see gh-13374
-                    reason = (
-                        "Error could possibly be due to quotes being "
-                        "ignored when a multi-char delimiter is used."
-                    )
-                    msg += ". " + reason
-
-                self._alert_malformed(msg, row_num + 1)
-
-        # see gh-13320
-        zipped_content = list(lib.to_object_array(content, min_width=col_len).T)
-
-        if self.usecols:
-            assert self._col_indices is not None
-            col_indices = self._col_indices
-
-            if self._implicit_index:
-                zipped_content = [
-                    a
-                    for i, a in enumerate(zipped_content)
-                    if (
-                        i < len(self.index_col)
-                        or i - len(self.index_col) in col_indices
-                    )
-                ]
-            else:
-                zipped_content = [
-                    a for i, a in enumerate(zipped_content) if i in col_indices
-                ]
-        return zipped_content
-
-    def _get_lines(self, rows: int | None = None) -> list[list[Scalar]]:
-        lines = self.buf
-        new_rows = None
-
-        # already fetched some number
-        if rows is not None:
-            # we already have the lines in the buffer
-            if len(self.buf) >= rows:
-                new_rows, self.buf = self.buf[:rows], self.buf[rows:]
-
-            # need some lines
-            else:
-                rows -= len(self.buf)
-
-        if new_rows is None:
-            if isinstance(self.data, list):
-                if self.pos > len(self.data):
-                    raise StopIteration
-                if rows is None:
-                    new_rows = self.data[self.pos :]
-                    new_pos = len(self.data)
-                else:
-                    new_rows = self.data[self.pos : self.pos + rows]
-                    new_pos = self.pos + rows
-
-                new_rows = self._remove_skipped_rows(new_rows)
-                lines.extend(new_rows)
-                self.pos = new_pos
-
-            else:
-                new_rows = []
-                try:
-                    if rows is not None:
-                        rows_to_skip = 0
-                        if self.skiprows is not None and self.pos is not None:
-                            # Only read additional rows if pos is in skiprows
-                            rows_to_skip = len(
-                                set(self.skiprows) - set(range(self.pos))
-                            )
-
-                        for _ in range(rows + rows_to_skip):
-                            # assert for mypy, data is Iterator[str] or None, would
-                            # error in next
-                            assert self.data is not None
-                            new_rows.append(next(self.data))
-
-                        len_new_rows = len(new_rows)
-                        new_rows = self._remove_skipped_rows(new_rows)
-                        lines.extend(new_rows)
-                    else:
-                        rows = 0
-
-                        while True:
-                            new_row = self._next_iter_line(row_num=self.pos + rows + 1)
-                            rows += 1
-
-                            if new_row is not None:
-                                new_rows.append(new_row)
-                        len_new_rows = len(new_rows)
-
-                except StopIteration:
-                    len_new_rows = len(new_rows)
-                    new_rows = self._remove_skipped_rows(new_rows)
-                    lines.extend(new_rows)
-                    if len(lines) == 0:
-                        raise
-                self.pos += len_new_rows
-
-            self.buf = []
-        else:
-            lines = new_rows
-
-        if self.skipfooter:
-            lines = lines[: -self.skipfooter]
-
-        lines = self._check_comments(lines)
-        if self.skip_blank_lines:
-            lines = self._remove_empty_lines(lines)
-        lines = self._check_thousands(lines)
-        return self._check_decimal(lines)
-
-    def _remove_skipped_rows(self, new_rows: list[list[Scalar]]) -> list[list[Scalar]]:
-        if self.skiprows:
-            return [
-                row for i, row in enumerate(new_rows) if not self.skipfunc(i + self.pos)
-            ]
-        return new_rows
-
-    def _set_no_thousand_columns(self) -> set[int]:
-        no_thousands_columns: set[int] = set()
-        if self.columns and self.parse_dates:
-            assert self._col_indices is not None
-            no_thousands_columns = self._set_noconvert_dtype_columns(
-                self._col_indices, self.columns
-            )
-        if self.columns and self.dtype:
-            assert self._col_indices is not None
-            for i, col in zip(self._col_indices, self.columns):
-                if not isinstance(self.dtype, dict) and not is_numeric_dtype(
-                    self.dtype
-                ):
-                    no_thousands_columns.add(i)
-                if (
-                    isinstance(self.dtype, dict)
-                    and col in self.dtype
-                    and (
-                        not is_numeric_dtype(self.dtype[col])
-                        or is_bool_dtype(self.dtype[col])
-                    )
-                ):
-                    no_thousands_columns.add(i)
-        return no_thousands_columns
-
-
-class FixedWidthReader(abc.Iterator):
-    """
-    A reader of fixed-width lines.
-    """
-
-    def __init__(
-        self,
-        f: IO[str] | ReadCsvBuffer[str],
-        colspecs: list[tuple[int, int]] | Literal["infer"],
-        delimiter: str | None,
-        comment: str | None,
-        skiprows: set[int] | None = None,
-        infer_nrows: int = 100,
-    ) -> None:
-        self.f = f
-        self.buffer: Iterator | None = None
-        self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t "
-        self.comment = comment
-        if colspecs == "infer":
-            self.colspecs = self.detect_colspecs(
-                infer_nrows=infer_nrows, skiprows=skiprows
-            )
-        else:
-            self.colspecs = colspecs
-
-        if not isinstance(self.colspecs, (tuple, list)):
-            raise TypeError(
-                "column specifications must be a list or tuple, "
-                f"input was a {type(colspecs).__name__}"
-            )
-
-        for colspec in self.colspecs:
-            if not (
-                isinstance(colspec, (tuple, list))
-                and len(colspec) == 2
-                and isinstance(colspec[0], (int, np.integer, type(None)))
-                and isinstance(colspec[1], (int, np.integer, type(None)))
-            ):
-                raise TypeError(
-                    "Each column specification must be "
-                    "2 element tuple or list of integers"
-                )
-
-    def get_rows(self, infer_nrows: int, skiprows: set[int] | None = None) -> list[str]:
-        """
-        Read rows from self.f, skipping as specified.
-
-        We distinguish buffer_rows (the first <= infer_nrows
-        lines) from the rows returned to detect_colspecs
-        because it's simpler to leave the other locations
-        with skiprows logic alone than to modify them to
-        deal with the fact we skipped some rows here as
-        well.
-
-        Parameters
-        ----------
-        infer_nrows : int
-            Number of rows to read from self.f, not counting
-            rows that are skipped.
-        skiprows: set, optional
-            Indices of rows to skip.
-
-        Returns
-        -------
-        detect_rows : list of str
-            A list containing the rows to read.
-
-        """
-        if skiprows is None:
-            skiprows = set()
-        buffer_rows = []
-        detect_rows = []
-        for i, row in enumerate(self.f):
-            if i not in skiprows:
-                detect_rows.append(row)
-            buffer_rows.append(row)
-            if len(detect_rows) >= infer_nrows:
-                break
-        self.buffer = iter(buffer_rows)
-        return detect_rows
-
-    def detect_colspecs(
-        self, infer_nrows: int = 100, skiprows: set[int] | None = None
-    ) -> list[tuple[int, int]]:
-        # Regex escape the delimiters
-        delimiters = "".join([rf"\{x}" for x in self.delimiter])
-        pattern = re.compile(f"([^{delimiters}]+)")
-        rows = self.get_rows(infer_nrows, skiprows)
-        if not rows:
-            raise EmptyDataError("No rows from which to infer column width")
-        max_len = max(map(len, rows))
-        mask = np.zeros(max_len + 1, dtype=int)
-        if self.comment is not None:
-            rows = [row.partition(self.comment)[0] for row in rows]
-        for row in rows:
-            for m in pattern.finditer(row):
-                mask[m.start() : m.end()] = 1
-        shifted = np.roll(mask, 1)
-        shifted[0] = 0
-        edges = np.where((mask ^ shifted) == 1)[0]
-        edge_pairs = list(zip(edges[::2], edges[1::2]))
-        return edge_pairs
-
-    def __next__(self) -> list[str]:
-        # Argument 1 to "next" has incompatible type "Union[IO[str],
-        # ReadCsvBuffer[str]]"; expected "SupportsNext[str]"
-        if self.buffer is not None:
-            try:
-                line = next(self.buffer)
-            except StopIteration:
-                self.buffer = None
-                line = next(self.f)  # type: ignore[arg-type]
-        else:
-            line = next(self.f)  # type: ignore[arg-type]
-        # Note: 'colspecs' is a sequence of half-open intervals.
-        return [line[from_:to].strip(self.delimiter) for (from_, to) in self.colspecs]
-
-
-class FixedWidthFieldParser(PythonParser):
-    """
-    Specialization that Converts fixed-width fields into DataFrames.
-    See PythonParser for details.
-    """
-
-    def __init__(self, f: ReadCsvBuffer[str], **kwds) -> None:
-        # Support iterators, convert to a list.
-        self.colspecs = kwds.pop("colspecs")
-        self.infer_nrows = kwds.pop("infer_nrows")
-        PythonParser.__init__(self, f, **kwds)
-
-    def _make_reader(self, f: IO[str] | ReadCsvBuffer[str]) -> FixedWidthReader:
-        return FixedWidthReader(
-            f,
-            self.colspecs,
-            self.delimiter,
-            self.comment,
-            self.skiprows,
-            self.infer_nrows,
-        )
-
-    def _remove_empty_lines(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
-        """
-        Returns the list of lines without the empty ones. With fixed-width
-        fields, empty lines become arrays of empty strings.
-
-        See PythonParser._remove_empty_lines.
-        """
-        return [
-            line
-            for line in lines
-            if any(not isinstance(e, str) or e.strip() for e in line)
-        ]
-
-
-def count_empty_vals(vals) -> int:
-    return sum(1 for v in vals if v == "" or v is None)
-
-
-def _validate_skipfooter_arg(skipfooter: int) -> int:
-    """
-    Validate the 'skipfooter' parameter.
-
-    Checks whether 'skipfooter' is a non-negative integer.
-    Raises a ValueError if that is not the case.
-
-    Parameters
-    ----------
-    skipfooter : non-negative integer
-        The number of rows to skip at the end of the file.
-
-    Returns
-    -------
-    validated_skipfooter : non-negative integer
-        The original input if the validation succeeds.
-
-    Raises
-    ------
-    ValueError : 'skipfooter' was not a non-negative integer.
-    """
-    if not is_integer(skipfooter):
-        raise ValueError("skipfooter must be an integer")
-
-    if skipfooter < 0:
-        raise ValueError("skipfooter cannot be negative")
-
-    # Incompatible return value type (got "Union[int, integer[Any]]", expected "int")
-    return skipfooter  # type: ignore[return-value]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_transform.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_transform.py
deleted file mode 100644
index 2d57515882aed6c83535780a575b095d5f9b7489..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_transform.py
+++ /dev/null
@@ -1,264 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    MultiIndex,
-    Series,
-)
-import pandas._testing as tm
-from pandas.tests.apply.common import frame_transform_kernels
-from pandas.tests.frame.common import zip_frames
-
-
-def unpack_obj(obj, klass, axis):
-    """
-    Helper to ensure we have the right type of object for a test parametrized
-    over frame_or_series.
-    """
-    if klass is not DataFrame:
-        obj = obj["A"]
-        if axis != 0:
-            pytest.skip(f"Test is only for DataFrame with axis={axis}")
-    return obj
-
-
-def test_transform_ufunc(axis, float_frame, frame_or_series):
-    # GH 35964
-    obj = unpack_obj(float_frame, frame_or_series, axis)
-
-    with np.errstate(all="ignore"):
-        f_sqrt = np.sqrt(obj)
-
-    # ufunc
-    result = obj.transform(np.sqrt, axis=axis)
-    expected = f_sqrt
-    tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops, names",
-    [
-        ([np.sqrt], ["sqrt"]),
-        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
-        (np.array([np.sqrt]), ["sqrt"]),
-        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
-    ],
-)
-def test_transform_listlike(axis, float_frame, ops, names):
-    # GH 35964
-    other_axis = 1 if axis in {0, "index"} else 0
-    with np.errstate(all="ignore"):
-        expected = zip_frames([op(float_frame) for op in ops], axis=other_axis)
-    if axis in {0, "index"}:
-        expected.columns = MultiIndex.from_product([float_frame.columns, names])
-    else:
-        expected.index = MultiIndex.from_product([float_frame.index, names])
-    result = float_frame.transform(ops, axis=axis)
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("ops", [[], np.array([])])
-def test_transform_empty_listlike(float_frame, ops, frame_or_series):
-    obj = unpack_obj(float_frame, frame_or_series, 0)
-
-    with pytest.raises(ValueError, match="No transform functions were provided"):
-        obj.transform(ops)
-
-
-def test_transform_listlike_func_with_args():
-    # GH 50624
-    df = DataFrame({"x": [1, 2, 3]})
-
-    def foo1(x, a=1, c=0):
-        return x + a + c
-
-    def foo2(x, b=2, c=0):
-        return x + b + c
-
-    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
-    with pytest.raises(TypeError, match=msg):
-        df.transform([foo1, foo2], 0, 3, b=3, c=4)
-
-    result = df.transform([foo1, foo2], 0, 3, c=4)
-    expected = DataFrame(
-        [[8, 8], [9, 9], [10, 10]],
-        columns=MultiIndex.from_tuples([("x", "foo1"), ("x", "foo2")]),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("box", [dict, Series])
-def test_transform_dictlike(axis, float_frame, box):
-    # GH 35964
-    if axis in (0, "index"):
-        e = float_frame.columns[0]
-        expected = float_frame[[e]].transform(np.abs)
-    else:
-        e = float_frame.index[0]
-        expected = float_frame.iloc[[0]].transform(np.abs)
-    result = float_frame.transform(box({e: np.abs}), axis=axis)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_transform_dictlike_mixed():
-    # GH 40018 - mix of lists and non-lists in values of a dictionary
-    df = DataFrame({"a": [1, 2], "b": [1, 4], "c": [1, 4]})
-    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
-    expected = DataFrame(
-        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
-        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops",
-    [
-        {},
-        {"A": []},
-        {"A": [], "B": "cumsum"},
-        {"A": "cumsum", "B": []},
-        {"A": [], "B": ["cumsum"]},
-        {"A": ["cumsum"], "B": []},
-    ],
-)
-def test_transform_empty_dictlike(float_frame, ops, frame_or_series):
-    obj = unpack_obj(float_frame, frame_or_series, 0)
-
-    with pytest.raises(ValueError, match="No transform functions were provided"):
-        obj.transform(ops)
-
-
-@pytest.mark.parametrize("use_apply", [True, False])
-def test_transform_udf(axis, float_frame, use_apply, frame_or_series):
-    # GH 35964
-    obj = unpack_obj(float_frame, frame_or_series, axis)
-
-    # transform uses UDF either via apply or passing the entire DataFrame
-    def func(x):
-        # transform is using apply iff x is not a DataFrame
-        if use_apply == isinstance(x, frame_or_series):
-            # Force transform to fallback
-            raise ValueError
-        return x + 1
-
-    result = obj.transform(func, axis=axis)
-    expected = obj + 1
-    tm.assert_equal(result, expected)
-
-
-wont_fail = ["ffill", "bfill", "fillna", "pad", "backfill", "shift"]
-frame_kernels_raise = [x for x in frame_transform_kernels if x not in wont_fail]
-
-
-@pytest.mark.parametrize("op", [*frame_kernels_raise, lambda x: x + 1])
-def test_transform_bad_dtype(op, frame_or_series, request):
-    # GH 35964
-    if op == "ngroup":
-        request.node.add_marker(
-            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
-        )
-
-    obj = DataFrame({"A": 3 * [object]})  # DataFrame that will fail on most transforms
-    obj = tm.get_obj(obj, frame_or_series)
-    error = TypeError
-    msg = "|".join(
-        [
-            "not supported between instances of 'type' and 'type'",
-            "unsupported operand type",
-        ]
-    )
-
-    with pytest.raises(error, match=msg):
-        obj.transform(op)
-    with pytest.raises(error, match=msg):
-        obj.transform([op])
-    with pytest.raises(error, match=msg):
-        obj.transform({"A": op})
-    with pytest.raises(error, match=msg):
-        obj.transform({"A": [op]})
-
-
-@pytest.mark.parametrize("op", frame_kernels_raise)
-def test_transform_failure_typeerror(request, op):
-    # GH 35964
-
-    if op == "ngroup":
-        request.node.add_marker(
-            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
-        )
-
-    # Using object makes most transform kernels fail
-    df = DataFrame({"A": 3 * [object], "B": [1, 2, 3]})
-    error = TypeError
-    msg = "|".join(
-        [
-            "not supported between instances of 'type' and 'type'",
-            "unsupported operand type",
-        ]
-    )
-
-    with pytest.raises(error, match=msg):
-        df.transform([op])
-
-    with pytest.raises(error, match=msg):
-        df.transform({"A": op, "B": op})
-
-    with pytest.raises(error, match=msg):
-        df.transform({"A": [op], "B": [op]})
-
-    with pytest.raises(error, match=msg):
-        df.transform({"A": [op, "shift"], "B": [op]})
-
-
-def test_transform_failure_valueerror():
-    # GH 40211
-    def op(x):
-        if np.sum(np.sum(x)) < 10:
-            raise ValueError
-        return x
-
-    df = DataFrame({"A": [1, 2, 3], "B": [400, 500, 600]})
-    msg = "Transform function failed"
-
-    with pytest.raises(ValueError, match=msg):
-        df.transform([op])
-
-    with pytest.raises(ValueError, match=msg):
-        df.transform({"A": op, "B": op})
-
-    with pytest.raises(ValueError, match=msg):
-        df.transform({"A": [op], "B": [op]})
-
-    with pytest.raises(ValueError, match=msg):
-        df.transform({"A": [op, "shift"], "B": [op]})
-
-
-@pytest.mark.parametrize("use_apply", [True, False])
-def test_transform_passes_args(use_apply, frame_or_series):
-    # GH 35964
-    # transform uses UDF either via apply or passing the entire DataFrame
-    expected_args = [1, 2]
-    expected_kwargs = {"c": 3}
-
-    def f(x, a, b, c):
-        # transform is using apply iff x is not a DataFrame
-        if use_apply == isinstance(x, frame_or_series):
-            # Force transform to fallback
-            raise ValueError
-        assert [a, b] == expected_args
-        assert c == expected_kwargs["c"]
-        return x
-
-    frame_or_series([1]).transform(f, 0, *expected_args, **expected_kwargs)
-
-
-def test_transform_empty_dataframe():
-    # https://github.com/pandas-dev/pandas/issues/39636
-    df = DataFrame([], columns=["col1", "col2"])
-    result = df.transform(lambda x: x + 10)
-    tm.assert_frame_equal(result, df)
-
-    result = df["col1"].transform(lambda x: x + 10)
-    tm.assert_series_equal(result, df["col1"])
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_sample.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_sample.py
deleted file mode 100644
index 4dd474741740d4abdea1ebabf2b36c3b68d690ad..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_sample.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    Series,
-)
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
-def test_groupby_sample_balanced_groups_shape(n, frac):
-    values = [1] * 10 + [2] * 10
-    df = DataFrame({"a": values, "b": values})
-
-    result = df.groupby("a").sample(n=n, frac=frac)
-    values = [1] * 2 + [2] * 2
-    expected = DataFrame({"a": values, "b": values}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(n=n, frac=frac)
-    expected = Series(values, name="b", index=result.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_groupby_sample_unbalanced_groups_shape():
-    values = [1] * 10 + [2] * 20
-    df = DataFrame({"a": values, "b": values})
-
-    result = df.groupby("a").sample(n=5)
-    values = [1] * 5 + [2] * 5
-    expected = DataFrame({"a": values, "b": values}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(n=5)
-    expected = Series(values, name="b", index=result.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_groupby_sample_index_value_spans_groups():
-    values = [1] * 3 + [2] * 3
-    df = DataFrame({"a": values, "b": values}, index=[1, 2, 2, 2, 2, 2])
-
-    result = df.groupby("a").sample(n=2)
-    values = [1] * 2 + [2] * 2
-    expected = DataFrame({"a": values, "b": values}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(n=2)
-    expected = Series(values, name="b", index=result.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_groupby_sample_n_and_frac_raises():
-    df = DataFrame({"a": [1, 2], "b": [1, 2]})
-    msg = "Please enter a value for `frac` OR `n`, not both"
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a").sample(n=1, frac=1.0)
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a")["b"].sample(n=1, frac=1.0)
-
-
-def test_groupby_sample_frac_gt_one_without_replacement_raises():
-    df = DataFrame({"a": [1, 2], "b": [1, 2]})
-    msg = "Replace has to be set to `True` when upsampling the population `frac` > 1."
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a").sample(frac=1.5, replace=False)
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a")["b"].sample(frac=1.5, replace=False)
-
-
-@pytest.mark.parametrize("n", [-1, 1.5])
-def test_groupby_sample_invalid_n_raises(n):
-    df = DataFrame({"a": [1, 2], "b": [1, 2]})
-
-    if n < 0:
-        msg = "A negative number of rows requested. Please provide `n` >= 0."
-    else:
-        msg = "Only integers accepted as `n` values"
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a").sample(n=n)
-
-    with pytest.raises(ValueError, match=msg):
-        df.groupby("a")["b"].sample(n=n)
-
-
-def test_groupby_sample_oversample():
-    values = [1] * 10 + [2] * 10
-    df = DataFrame({"a": values, "b": values})
-
-    result = df.groupby("a").sample(frac=2.0, replace=True)
-    values = [1] * 20 + [2] * 20
-    expected = DataFrame({"a": values, "b": values}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(frac=2.0, replace=True)
-    expected = Series(values, name="b", index=result.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_groupby_sample_without_n_or_frac():
-    values = [1] * 10 + [2] * 10
-    df = DataFrame({"a": values, "b": values})
-
-    result = df.groupby("a").sample(n=None, frac=None)
-    expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(n=None, frac=None)
-    expected = Series([1, 2], name="b", index=result.index)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "index, expected_index",
-    [(["w", "x", "y", "z"], ["w", "w", "y", "y"]), ([3, 4, 5, 6], [3, 3, 5, 5])],
-)
-def test_groupby_sample_with_weights(index, expected_index):
-    # GH 39927 - tests for integer index needed
-    values = [1] * 2 + [2] * 2
-    df = DataFrame({"a": values, "b": values}, index=Index(index))
-
-    result = df.groupby("a").sample(n=2, replace=True, weights=[1, 0, 1, 0])
-    expected = DataFrame({"a": values, "b": values}, index=Index(expected_index))
-    tm.assert_frame_equal(result, expected)
-
-    result = df.groupby("a")["b"].sample(n=2, replace=True, weights=[1, 0, 1, 0])
-    expected = Series(values, name="b", index=Index(expected_index))
-    tm.assert_series_equal(result, expected)
-
-
-def test_groupby_sample_with_selections():
-    # GH 39928
-    values = [1] * 10 + [2] * 10
-    df = DataFrame({"a": values, "b": values, "c": values})
-
-    result = df.groupby("a")[["b", "c"]].sample(n=None, frac=None)
-    expected = DataFrame({"b": [1, 2], "c": [1, 2]}, index=result.index)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_groupby_sample_with_empty_inputs():
-    # GH48459
-    df = DataFrame({"a": [], "b": []})
-    groupby_df = df.groupby("a")
-
-    result = groupby_df.sample()
-    expected = df
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_json_table_schema.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_json_table_schema.py
deleted file mode 100644
index 0ee53572877a5d23f52e568ef8126078dcd7afab..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_json_table_schema.py
+++ /dev/null
@@ -1,847 +0,0 @@
-"""Tests for Table Schema integration."""
-from collections import OrderedDict
-from io import StringIO
-import json
-
-import numpy as np
-import pytest
-
-from pandas.core.dtypes.dtypes import (
-    CategoricalDtype,
-    DatetimeTZDtype,
-    PeriodDtype,
-)
-
-import pandas as pd
-from pandas import DataFrame
-import pandas._testing as tm
-
-from pandas.io.json._table_schema import (
-    as_json_table_type,
-    build_table_schema,
-    convert_json_field_to_pandas_type,
-    convert_pandas_type_to_json_field,
-    set_default_names,
-)
-
-
-@pytest.fixture
-def df_schema():
-    return DataFrame(
-        {
-            "A": [1, 2, 3, 4],
-            "B": ["a", "b", "c", "c"],
-            "C": pd.date_range("2016-01-01", freq="d", periods=4),
-            "D": pd.timedelta_range("1H", periods=4, freq="T"),
-        },
-        index=pd.Index(range(4), name="idx"),
-    )
-
-
-@pytest.fixture
-def df_table():
-    return DataFrame(
-        {
-            "A": [1, 2, 3, 4],
-            "B": ["a", "b", "c", "c"],
-            "C": pd.date_range("2016-01-01", freq="d", periods=4),
-            "D": pd.timedelta_range("1H", periods=4, freq="T"),
-            "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
-            "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
-            "G": [1.0, 2.0, 3, 4.0],
-            "H": pd.date_range("2016-01-01", freq="d", periods=4, tz="US/Central"),
-        },
-        index=pd.Index(range(4), name="idx"),
-    )
-
-
-class TestBuildSchema:
-    def test_build_table_schema(self, df_schema):
-        result = build_table_schema(df_schema, version=False)
-        expected = {
-            "fields": [
-                {"name": "idx", "type": "integer"},
-                {"name": "A", "type": "integer"},
-                {"name": "B", "type": "string"},
-                {"name": "C", "type": "datetime"},
-                {"name": "D", "type": "duration"},
-            ],
-            "primaryKey": ["idx"],
-        }
-        assert result == expected
-        result = build_table_schema(df_schema)
-        assert "pandas_version" in result
-
-    def test_series(self):
-        s = pd.Series([1, 2, 3], name="foo")
-        result = build_table_schema(s, version=False)
-        expected = {
-            "fields": [
-                {"name": "index", "type": "integer"},
-                {"name": "foo", "type": "integer"},
-            ],
-            "primaryKey": ["index"],
-        }
-        assert result == expected
-        result = build_table_schema(s)
-        assert "pandas_version" in result
-
-    def test_series_unnamed(self):
-        result = build_table_schema(pd.Series([1, 2, 3]), version=False)
-        expected = {
-            "fields": [
-                {"name": "index", "type": "integer"},
-                {"name": "values", "type": "integer"},
-            ],
-            "primaryKey": ["index"],
-        }
-        assert result == expected
-
-    def test_multiindex(self, df_schema):
-        df = df_schema
-        idx = pd.MultiIndex.from_product([("a", "b"), (1, 2)])
-        df.index = idx
-
-        result = build_table_schema(df, version=False)
-        expected = {
-            "fields": [
-                {"name": "level_0", "type": "string"},
-                {"name": "level_1", "type": "integer"},
-                {"name": "A", "type": "integer"},
-                {"name": "B", "type": "string"},
-                {"name": "C", "type": "datetime"},
-                {"name": "D", "type": "duration"},
-            ],
-            "primaryKey": ["level_0", "level_1"],
-        }
-        assert result == expected
-
-        df.index.names = ["idx0", None]
-        expected["fields"][0]["name"] = "idx0"
-        expected["primaryKey"] = ["idx0", "level_1"]
-        result = build_table_schema(df, version=False)
-        assert result == expected
-
-
-class TestTableSchemaType:
-    @pytest.mark.parametrize("int_type", [int, np.int16, np.int32, np.int64])
-    def test_as_json_table_type_int_data(self, int_type):
-        int_data = [1, 2, 3]
-        assert as_json_table_type(np.array(int_data, dtype=int_type).dtype) == "integer"
-
-    @pytest.mark.parametrize("float_type", [float, np.float16, np.float32, np.float64])
-    def test_as_json_table_type_float_data(self, float_type):
-        float_data = [1.0, 2.0, 3.0]
-        assert (
-            as_json_table_type(np.array(float_data, dtype=float_type).dtype) == "number"
-        )
-
-    @pytest.mark.parametrize("bool_type", [bool, np.bool_])
-    def test_as_json_table_type_bool_data(self, bool_type):
-        bool_data = [True, False]
-        assert (
-            as_json_table_type(np.array(bool_data, dtype=bool_type).dtype) == "boolean"
-        )
-
-    @pytest.mark.parametrize(
-        "date_data",
-        [
-            pd.to_datetime(["2016"]),
-            pd.to_datetime(["2016"], utc=True),
-            pd.Series(pd.to_datetime(["2016"])),
-            pd.Series(pd.to_datetime(["2016"], utc=True)),
-            pd.period_range("2016", freq="A", periods=3),
-        ],
-    )
-    def test_as_json_table_type_date_data(self, date_data):
-        assert as_json_table_type(date_data.dtype) == "datetime"
-
-    @pytest.mark.parametrize("str_data", [pd.Series(["a", "b"]), pd.Index(["a", "b"])])
-    def test_as_json_table_type_string_data(self, str_data):
-        assert as_json_table_type(str_data.dtype) == "string"
-
-    @pytest.mark.parametrize(
-        "cat_data",
-        [
-            pd.Categorical(["a"]),
-            pd.Categorical([1]),
-            pd.Series(pd.Categorical([1])),
-            pd.CategoricalIndex([1]),
-            pd.Categorical([1]),
-        ],
-    )
-    def test_as_json_table_type_categorical_data(self, cat_data):
-        assert as_json_table_type(cat_data.dtype) == "any"
-
-    # ------
-    # dtypes
-    # ------
-    @pytest.mark.parametrize("int_dtype", [int, np.int16, np.int32, np.int64])
-    def test_as_json_table_type_int_dtypes(self, int_dtype):
-        assert as_json_table_type(int_dtype) == "integer"
-
-    @pytest.mark.parametrize("float_dtype", [float, np.float16, np.float32, np.float64])
-    def test_as_json_table_type_float_dtypes(self, float_dtype):
-        assert as_json_table_type(float_dtype) == "number"
-
-    @pytest.mark.parametrize("bool_dtype", [bool, np.bool_])
-    def test_as_json_table_type_bool_dtypes(self, bool_dtype):
-        assert as_json_table_type(bool_dtype) == "boolean"
-
-    @pytest.mark.parametrize(
-        "date_dtype",
-        [
-            np.dtype("<M8[ns]"),
-            PeriodDtype("D"),
-            DatetimeTZDtype("ns", "US/Central"),
-        ],
-    )
-    def test_as_json_table_type_date_dtypes(self, date_dtype):
-        # TODO: datedate.date? datetime.time?
-        assert as_json_table_type(date_dtype) == "datetime"
-
-    @pytest.mark.parametrize("td_dtype", [np.dtype("<m8[ns]")])
-    def test_as_json_table_type_timedelta_dtypes(self, td_dtype):
-        assert as_json_table_type(td_dtype) == "duration"
-
-    @pytest.mark.parametrize("str_dtype", [object])  # TODO(GH#14904) flesh out dtypes?
-    def test_as_json_table_type_string_dtypes(self, str_dtype):
-        assert as_json_table_type(str_dtype) == "string"
-
-    def test_as_json_table_type_categorical_dtypes(self):
-        assert as_json_table_type(pd.Categorical(["a"]).dtype) == "any"
-        assert as_json_table_type(CategoricalDtype()) == "any"
-
-
-class TestTableOrient:
-    def test_build_series(self):
-        s = pd.Series([1, 2], name="a")
-        s.index.name = "id"
-        result = s.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-
-        assert "pandas_version" in result["schema"]
-        result["schema"].pop("pandas_version")
-
-        fields = [{"name": "id", "type": "integer"}, {"name": "a", "type": "integer"}]
-
-        schema = {"fields": fields, "primaryKey": ["id"]}
-
-        expected = OrderedDict(
-            [
-                ("schema", schema),
-                (
-                    "data",
-                    [
-                        OrderedDict([("id", 0), ("a", 1)]),
-                        OrderedDict([("id", 1), ("a", 2)]),
-                    ],
-                ),
-            ]
-        )
-
-        assert result == expected
-
-    def test_read_json_from_to_json_results(self):
-        # GH32383
-        df = DataFrame(
-            {
-                "_id": {"row_0": 0},
-                "category": {"row_0": "Goods"},
-                "recommender_id": {"row_0": 3},
-                "recommender_name_jp": {"row_0": "浦田"},
-                "recommender_name_en": {"row_0": "Urata"},
-                "name_jp": {"row_0": "博多人形(松尾吉将まつお よしまさ)"},
-                "name_en": {"row_0": "Hakata Dolls Matsuo"},
-            }
-        )
-
-        result1 = pd.read_json(StringIO(df.to_json()))
-        result2 = DataFrame.from_dict(json.loads(df.to_json()))
-        tm.assert_frame_equal(result1, df)
-        tm.assert_frame_equal(result2, df)
-
-    def test_to_json(self, df_table):
-        df = df_table
-        df.index.name = "idx"
-        result = df.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-
-        assert "pandas_version" in result["schema"]
-        result["schema"].pop("pandas_version")
-
-        fields = [
-            {"name": "idx", "type": "integer"},
-            {"name": "A", "type": "integer"},
-            {"name": "B", "type": "string"},
-            {"name": "C", "type": "datetime"},
-            {"name": "D", "type": "duration"},
-            {
-                "constraints": {"enum": ["a", "b", "c"]},
-                "name": "E",
-                "ordered": False,
-                "type": "any",
-            },
-            {
-                "constraints": {"enum": ["a", "b", "c"]},
-                "name": "F",
-                "ordered": True,
-                "type": "any",
-            },
-            {"name": "G", "type": "number"},
-            {"name": "H", "type": "datetime", "tz": "US/Central"},
-        ]
-
-        schema = {"fields": fields, "primaryKey": ["idx"]}
-        data = [
-            OrderedDict(
-                [
-                    ("idx", 0),
-                    ("A", 1),
-                    ("B", "a"),
-                    ("C", "2016-01-01T00:00:00.000"),
-                    ("D", "P0DT1H0M0S"),
-                    ("E", "a"),
-                    ("F", "a"),
-                    ("G", 1.0),
-                    ("H", "2016-01-01T06:00:00.000Z"),
-                ]
-            ),
-            OrderedDict(
-                [
-                    ("idx", 1),
-                    ("A", 2),
-                    ("B", "b"),
-                    ("C", "2016-01-02T00:00:00.000"),
-                    ("D", "P0DT1H1M0S"),
-                    ("E", "b"),
-                    ("F", "b"),
-                    ("G", 2.0),
-                    ("H", "2016-01-02T06:00:00.000Z"),
-                ]
-            ),
-            OrderedDict(
-                [
-                    ("idx", 2),
-                    ("A", 3),
-                    ("B", "c"),
-                    ("C", "2016-01-03T00:00:00.000"),
-                    ("D", "P0DT1H2M0S"),
-                    ("E", "c"),
-                    ("F", "c"),
-                    ("G", 3.0),
-                    ("H", "2016-01-03T06:00:00.000Z"),
-                ]
-            ),
-            OrderedDict(
-                [
-                    ("idx", 3),
-                    ("A", 4),
-                    ("B", "c"),
-                    ("C", "2016-01-04T00:00:00.000"),
-                    ("D", "P0DT1H3M0S"),
-                    ("E", "c"),
-                    ("F", "c"),
-                    ("G", 4.0),
-                    ("H", "2016-01-04T06:00:00.000Z"),
-                ]
-            ),
-        ]
-        expected = OrderedDict([("schema", schema), ("data", data)])
-
-        assert result == expected
-
-    def test_to_json_float_index(self):
-        data = pd.Series(1, index=[1.0, 2.0])
-        result = data.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-        result["schema"].pop("pandas_version")
-
-        expected = OrderedDict(
-            [
-                (
-                    "schema",
-                    {
-                        "fields": [
-                            {"name": "index", "type": "number"},
-                            {"name": "values", "type": "integer"},
-                        ],
-                        "primaryKey": ["index"],
-                    },
-                ),
-                (
-                    "data",
-                    [
-                        OrderedDict([("index", 1.0), ("values", 1)]),
-                        OrderedDict([("index", 2.0), ("values", 1)]),
-                    ],
-                ),
-            ]
-        )
-
-        assert result == expected
-
-    def test_to_json_period_index(self):
-        idx = pd.period_range("2016", freq="Q-JAN", periods=2)
-        data = pd.Series(1, idx)
-        result = data.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-        result["schema"].pop("pandas_version")
-
-        fields = [
-            {"freq": "Q-JAN", "name": "index", "type": "datetime"},
-            {"name": "values", "type": "integer"},
-        ]
-
-        schema = {"fields": fields, "primaryKey": ["index"]}
-        data = [
-            OrderedDict([("index", "2015-11-01T00:00:00.000"), ("values", 1)]),
-            OrderedDict([("index", "2016-02-01T00:00:00.000"), ("values", 1)]),
-        ]
-        expected = OrderedDict([("schema", schema), ("data", data)])
-
-        assert result == expected
-
-    def test_to_json_categorical_index(self):
-        data = pd.Series(1, pd.CategoricalIndex(["a", "b"]))
-        result = data.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-        result["schema"].pop("pandas_version")
-
-        expected = OrderedDict(
-            [
-                (
-                    "schema",
-                    {
-                        "fields": [
-                            {
-                                "name": "index",
-                                "type": "any",
-                                "constraints": {"enum": ["a", "b"]},
-                                "ordered": False,
-                            },
-                            {"name": "values", "type": "integer"},
-                        ],
-                        "primaryKey": ["index"],
-                    },
-                ),
-                (
-                    "data",
-                    [
-                        OrderedDict([("index", "a"), ("values", 1)]),
-                        OrderedDict([("index", "b"), ("values", 1)]),
-                    ],
-                ),
-            ]
-        )
-
-        assert result == expected
-
-    def test_date_format_raises(self, df_table):
-        msg = (
-            "Trying to write with `orient='table'` and `date_format='epoch'`. Table "
-            "Schema requires dates to be formatted with `date_format='iso'`"
-        )
-        with pytest.raises(ValueError, match=msg):
-            df_table.to_json(orient="table", date_format="epoch")
-
-        # others work
-        df_table.to_json(orient="table", date_format="iso")
-        df_table.to_json(orient="table")
-
-    def test_convert_pandas_type_to_json_field_int(self, index_or_series):
-        kind = index_or_series
-        data = [1, 2, 3]
-        result = convert_pandas_type_to_json_field(kind(data, name="name"))
-        expected = {"name": "name", "type": "integer"}
-        assert result == expected
-
-    def test_convert_pandas_type_to_json_field_float(self, index_or_series):
-        kind = index_or_series
-        data = [1.0, 2.0, 3.0]
-        result = convert_pandas_type_to_json_field(kind(data, name="name"))
-        expected = {"name": "name", "type": "number"}
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "dt_args,extra_exp", [({}, {}), ({"utc": True}, {"tz": "UTC"})]
-    )
-    @pytest.mark.parametrize("wrapper", [None, pd.Series])
-    def test_convert_pandas_type_to_json_field_datetime(
-        self, dt_args, extra_exp, wrapper
-    ):
-        data = [1.0, 2.0, 3.0]
-        data = pd.to_datetime(data, **dt_args)
-        if wrapper is pd.Series:
-            data = pd.Series(data, name="values")
-        result = convert_pandas_type_to_json_field(data)
-        expected = {"name": "values", "type": "datetime"}
-        expected.update(extra_exp)
-        assert result == expected
-
-    def test_convert_pandas_type_to_json_period_range(self):
-        arr = pd.period_range("2016", freq="A-DEC", periods=4)
-        result = convert_pandas_type_to_json_field(arr)
-        expected = {"name": "values", "type": "datetime", "freq": "A-DEC"}
-        assert result == expected
-
-    @pytest.mark.parametrize("kind", [pd.Categorical, pd.CategoricalIndex])
-    @pytest.mark.parametrize("ordered", [True, False])
-    def test_convert_pandas_type_to_json_field_categorical(self, kind, ordered):
-        data = ["a", "b", "c"]
-        if kind is pd.Categorical:
-            arr = pd.Series(kind(data, ordered=ordered), name="cats")
-        elif kind is pd.CategoricalIndex:
-            arr = kind(data, ordered=ordered, name="cats")
-
-        result = convert_pandas_type_to_json_field(arr)
-        expected = {
-            "name": "cats",
-            "type": "any",
-            "constraints": {"enum": data},
-            "ordered": ordered,
-        }
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "inp,exp",
-        [
-            ({"type": "integer"}, "int64"),
-            ({"type": "number"}, "float64"),
-            ({"type": "boolean"}, "bool"),
-            ({"type": "duration"}, "timedelta64"),
-            ({"type": "datetime"}, "datetime64[ns]"),
-            ({"type": "datetime", "tz": "US/Hawaii"}, "datetime64[ns, US/Hawaii]"),
-            ({"type": "any"}, "object"),
-            (
-                {
-                    "type": "any",
-                    "constraints": {"enum": ["a", "b", "c"]},
-                    "ordered": False,
-                },
-                CategoricalDtype(categories=["a", "b", "c"], ordered=False),
-            ),
-            (
-                {
-                    "type": "any",
-                    "constraints": {"enum": ["a", "b", "c"]},
-                    "ordered": True,
-                },
-                CategoricalDtype(categories=["a", "b", "c"], ordered=True),
-            ),
-            ({"type": "string"}, "object"),
-        ],
-    )
-    def test_convert_json_field_to_pandas_type(self, inp, exp):
-        field = {"name": "foo"}
-        field.update(inp)
-        assert convert_json_field_to_pandas_type(field) == exp
-
-    @pytest.mark.parametrize("inp", ["geopoint", "geojson", "fake_type"])
-    def test_convert_json_field_to_pandas_type_raises(self, inp):
-        field = {"type": inp}
-        with pytest.raises(
-            ValueError, match=f"Unsupported or invalid field type: {inp}"
-        ):
-            convert_json_field_to_pandas_type(field)
-
-    def test_categorical(self):
-        s = pd.Series(pd.Categorical(["a", "b", "a"]))
-        s.index.name = "idx"
-        result = s.to_json(orient="table", date_format="iso")
-        result = json.loads(result, object_pairs_hook=OrderedDict)
-        result["schema"].pop("pandas_version")
-
-        fields = [
-            {"name": "idx", "type": "integer"},
-            {
-                "constraints": {"enum": ["a", "b"]},
-                "name": "values",
-                "ordered": False,
-                "type": "any",
-            },
-        ]
-
-        expected = OrderedDict(
-            [
-                ("schema", {"fields": fields, "primaryKey": ["idx"]}),
-                (
-                    "data",
-                    [
-                        OrderedDict([("idx", 0), ("values", "a")]),
-                        OrderedDict([("idx", 1), ("values", "b")]),
-                        OrderedDict([("idx", 2), ("values", "a")]),
-                    ],
-                ),
-            ]
-        )
-
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "idx,nm,prop",
-        [
-            (pd.Index([1]), "index", "name"),
-            (pd.Index([1], name="myname"), "myname", "name"),
-            (
-                pd.MultiIndex.from_product([("a", "b"), ("c", "d")]),
-                ["level_0", "level_1"],
-                "names",
-            ),
-            (
-                pd.MultiIndex.from_product(
-                    [("a", "b"), ("c", "d")], names=["n1", "n2"]
-                ),
-                ["n1", "n2"],
-                "names",
-            ),
-            (
-                pd.MultiIndex.from_product(
-                    [("a", "b"), ("c", "d")], names=["n1", None]
-                ),
-                ["n1", "level_1"],
-                "names",
-            ),
-        ],
-    )
-    def test_set_names_unset(self, idx, nm, prop):
-        data = pd.Series(1, idx)
-        result = set_default_names(data)
-        assert getattr(result.index, prop) == nm
-
-    @pytest.mark.parametrize(
-        "idx",
-        [
-            pd.Index([], name="index"),
-            pd.MultiIndex.from_arrays([["foo"], ["bar"]], names=("level_0", "level_1")),
-            pd.MultiIndex.from_arrays([["foo"], ["bar"]], names=("foo", "level_1")),
-        ],
-    )
-    def test_warns_non_roundtrippable_names(self, idx):
-        # GH 19130
-        df = DataFrame(index=idx)
-        df.index.name = "index"
-        with tm.assert_produces_warning():
-            set_default_names(df)
-
-    def test_timestamp_in_columns(self):
-        df = DataFrame(
-            [[1, 2]], columns=[pd.Timestamp("2016"), pd.Timedelta(10, unit="s")]
-        )
-        result = df.to_json(orient="table")
-        js = json.loads(result)
-        assert js["schema"]["fields"][1]["name"] == "2016-01-01T00:00:00.000"
-        assert js["schema"]["fields"][2]["name"] == "P0DT0H0M10S"
-
-    @pytest.mark.parametrize(
-        "case",
-        [
-            pd.Series([1], index=pd.Index([1], name="a"), name="a"),
-            DataFrame({"A": [1]}, index=pd.Index([1], name="A")),
-            DataFrame(
-                {"A": [1]},
-                index=pd.MultiIndex.from_arrays([["a"], [1]], names=["A", "a"]),
-            ),
-        ],
-    )
-    def test_overlapping_names(self, case):
-        with pytest.raises(ValueError, match="Overlapping"):
-            case.to_json(orient="table")
-
-    def test_mi_falsey_name(self):
-        # GH 16203
-        df = DataFrame(
-            np.random.default_rng(2).standard_normal((4, 4)),
-            index=pd.MultiIndex.from_product([("A", "B"), ("a", "b")]),
-        )
-        result = [x["name"] for x in build_table_schema(df)["fields"]]
-        assert result == ["level_0", "level_1", 0, 1, 2, 3]
-
-
-class TestTableOrientReader:
-    @pytest.mark.parametrize(
-        "index_nm",
-        [None, "idx", pytest.param("index", marks=pytest.mark.xfail), "level_0"],
-    )
-    @pytest.mark.parametrize(
-        "vals",
-        [
-            {"ints": [1, 2, 3, 4]},
-            {"objects": ["a", "b", "c", "d"]},
-            {"objects": ["1", "2", "3", "4"]},
-            {"date_ranges": pd.date_range("2016-01-01", freq="d", periods=4)},
-            {"categoricals": pd.Series(pd.Categorical(["a", "b", "c", "c"]))},
-            {
-                "ordered_cats": pd.Series(
-                    pd.Categorical(["a", "b", "c", "c"], ordered=True)
-                )
-            },
-            {"floats": [1.0, 2.0, 3.0, 4.0]},
-            {"floats": [1.1, 2.2, 3.3, 4.4]},
-            {"bools": [True, False, False, True]},
-            {
-                "timezones": pd.date_range(
-                    "2016-01-01", freq="d", periods=4, tz="US/Central"
-                )  # added in # GH 35973
-            },
-        ],
-    )
-    def test_read_json_table_orient(self, index_nm, vals, recwarn):
-        df = DataFrame(vals, index=pd.Index(range(4), name=index_nm))
-        out = df.to_json(orient="table")
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(df, result)
-
-    @pytest.mark.parametrize("index_nm", [None, "idx", "index"])
-    @pytest.mark.parametrize(
-        "vals",
-        [{"timedeltas": pd.timedelta_range("1H", periods=4, freq="T")}],
-    )
-    def test_read_json_table_orient_raises(self, index_nm, vals, recwarn):
-        df = DataFrame(vals, index=pd.Index(range(4), name=index_nm))
-        out = df.to_json(orient="table")
-        with pytest.raises(NotImplementedError, match="can not yet read "):
-            pd.read_json(out, orient="table")
-
-    @pytest.mark.parametrize(
-        "index_nm",
-        [None, "idx", pytest.param("index", marks=pytest.mark.xfail), "level_0"],
-    )
-    @pytest.mark.parametrize(
-        "vals",
-        [
-            {"ints": [1, 2, 3, 4]},
-            {"objects": ["a", "b", "c", "d"]},
-            {"objects": ["1", "2", "3", "4"]},
-            {"date_ranges": pd.date_range("2016-01-01", freq="d", periods=4)},
-            {"categoricals": pd.Series(pd.Categorical(["a", "b", "c", "c"]))},
-            {
-                "ordered_cats": pd.Series(
-                    pd.Categorical(["a", "b", "c", "c"], ordered=True)
-                )
-            },
-            {"floats": [1.0, 2.0, 3.0, 4.0]},
-            {"floats": [1.1, 2.2, 3.3, 4.4]},
-            {"bools": [True, False, False, True]},
-            {
-                "timezones": pd.date_range(
-                    "2016-01-01", freq="d", periods=4, tz="US/Central"
-                )  # added in # GH 35973
-            },
-        ],
-    )
-    def test_read_json_table_period_orient(self, index_nm, vals, recwarn):
-        df = DataFrame(
-            vals,
-            index=pd.Index(
-                (pd.Period(f"2022Q{q}") for q in range(1, 5)), name=index_nm
-            ),
-        )
-        out = df.to_json(orient="table")
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(df, result)
-
-    @pytest.mark.parametrize(
-        "idx",
-        [
-            pd.Index(range(4)),
-            pd.date_range(
-                "2020-08-30",
-                freq="d",
-                periods=4,
-            )._with_freq(None),
-            pd.date_range(
-                "2020-08-30", freq="d", periods=4, tz="US/Central"
-            )._with_freq(None),
-            pd.MultiIndex.from_product(
-                [
-                    pd.date_range("2020-08-30", freq="d", periods=2, tz="US/Central"),
-                    ["x", "y"],
-                ],
-            ),
-        ],
-    )
-    @pytest.mark.parametrize(
-        "vals",
-        [
-            {"floats": [1.1, 2.2, 3.3, 4.4]},
-            {"dates": pd.date_range("2020-08-30", freq="d", periods=4)},
-            {
-                "timezones": pd.date_range(
-                    "2020-08-30", freq="d", periods=4, tz="Europe/London"
-                )
-            },
-        ],
-    )
-    def test_read_json_table_timezones_orient(self, idx, vals, recwarn):
-        # GH 35973
-        df = DataFrame(vals, index=idx)
-        out = df.to_json(orient="table")
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(df, result)
-
-    def test_comprehensive(self):
-        df = DataFrame(
-            {
-                "A": [1, 2, 3, 4],
-                "B": ["a", "b", "c", "c"],
-                "C": pd.date_range("2016-01-01", freq="d", periods=4),
-                # 'D': pd.timedelta_range('1H', periods=4, freq='T'),
-                "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
-                "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
-                "G": [1.1, 2.2, 3.3, 4.4],
-                "H": pd.date_range("2016-01-01", freq="d", periods=4, tz="US/Central"),
-                "I": [True, False, False, True],
-            },
-            index=pd.Index(range(4), name="idx"),
-        )
-
-        out = StringIO(df.to_json(orient="table"))
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(df, result)
-
-    @pytest.mark.parametrize(
-        "index_names",
-        [[None, None], ["foo", "bar"], ["foo", None], [None, "foo"], ["index", "foo"]],
-    )
-    def test_multiindex(self, index_names):
-        # GH 18912
-        df = DataFrame(
-            [["Arr", "alpha", [1, 2, 3, 4]], ["Bee", "Beta", [10, 20, 30, 40]]],
-            index=[["A", "B"], ["Null", "Eins"]],
-            columns=["Aussprache", "Griechisch", "Args"],
-        )
-        df.index.names = index_names
-        out = StringIO(df.to_json(orient="table"))
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(df, result)
-
-    def test_empty_frame_roundtrip(self):
-        # GH 21287
-        df = DataFrame(columns=["a", "b", "c"])
-        expected = df.copy()
-        out = StringIO(df.to_json(orient="table"))
-        result = pd.read_json(out, orient="table")
-        tm.assert_frame_equal(expected, result)
-
-    def test_read_json_orient_table_old_schema_version(self):
-        df_json = """
-        {
-            "schema":{
-                "fields":[
-                    {"name":"index","type":"integer"},
-                    {"name":"a","type":"string"}
-                ],
-                "primaryKey":["index"],
-                "pandas_version":"0.20.0"
-            },
-            "data":[
-                {"index":0,"a":1},
-                {"index":1,"a":2.0},
-                {"index":2,"a":"s"}
-            ]
-        }
-        """
-        expected = DataFrame({"a": [1, 2.0, "s"]})
-        result = pd.read_json(StringIO(df_json), orient="table")
-        tm.assert_frame_equal(expected, result)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/forth.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/forth.py
deleted file mode 100644
index 7feeaef53f4926d3d5d15c27768b7612fea2f4d6..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/forth.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""
-    pygments.lexers.forth
-    ~~~~~~~~~~~~~~~~~~~~~
-
-    Lexer for the Forth language.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import RegexLexer, bygroups
-from pygments.token import Text, Comment, Keyword, Name, String, Number, \
-    Whitespace
-
-
-__all__ = ['ForthLexer']
-
-
-class ForthLexer(RegexLexer):
-    """
-    Lexer for Forth files.
-
-    .. versionadded:: 2.2
-    """
-    name = 'Forth'
-    url = 'https://www.forth.com/forth/'
-    aliases = ['forth']
-    filenames = ['*.frt', '*.fs']
-    mimetypes = ['application/x-forth']
-
-    flags = re.IGNORECASE | re.MULTILINE
-
-    tokens = {
-        'root': [
-            (r'\s+', Whitespace),
-            # All comment types
-            (r'\\.*?$', Comment.Single),
-            (r'\([\s].*?\)', Comment.Single),
-            # defining words. The next word is a new command name
-            (r'(:|variable|constant|value|buffer:)(\s+)',
-             bygroups(Keyword.Namespace, Whitespace), 'worddef'),
-            # strings are rather simple
-            (r'([.sc]")(\s+?)', bygroups(String, Whitespace), 'stringdef'),
-            # keywords from the various wordsets
-            # *** Wordset BLOCK
-            (r'(blk|block|buffer|evaluate|flush|load|save-buffers|update|'
-             # *** Wordset BLOCK-EXT
-             r'empty-buffers|list|refill|scr|thru|'
-             # *** Wordset CORE
-             r'\#s|\*\/mod|\+loop|\/mod|0<|0=|1\+|1-|2!|'
-             r'2\*|2\/|2@|2drop|2dup|2over|2swap|>body|'
-             r'>in|>number|>r|\?dup|abort|abort\"|abs|'
-             r'accept|align|aligned|allot|and|base|begin|'
-             r'bl|c!|c,|c@|cell\+|cells|char|char\+|'
-             r'chars|constant|count|cr|create|decimal|'
-             r'depth|do|does>|drop|dup|else|emit|environment\?|'
-             r'evaluate|execute|exit|fill|find|fm\/mod|'
-             r'here|hold|i|if|immediate|invert|j|key|'
-             r'leave|literal|loop|lshift|m\*|max|min|'
-             r'mod|move|negate|or|over|postpone|quit|'
-             r'r>|r@|recurse|repeat|rot|rshift|s\"|s>d|'
-             r'sign|sm\/rem|source|space|spaces|state|swap|'
-             r'then|type|u\.|u\<|um\*|um\/mod|unloop|until|'
-             r'variable|while|word|xor|\[char\]|\[\'\]|'
-             r'@|!|\#|<\#|\#>|:|;|\+|-|\*|\/|,|<|>|\|1\+|1-|\.|'
-             # *** Wordset CORE-EXT
-             r'\.r|0<>|'
-             r'0>|2>r|2r>|2r@|:noname|\?do|again|c\"|'
-             r'case|compile,|endcase|endof|erase|false|'
-             r'hex|marker|nip|of|pad|parse|pick|refill|'
-             r'restore-input|roll|save-input|source-id|to|'
-             r'true|tuck|u\.r|u>|unused|value|within|'
-             r'\[compile\]|'
-             # *** Wordset CORE-EXT-obsolescent
-             r'\#tib|convert|expect|query|span|'
-             r'tib|'
-             # *** Wordset DOUBLE
-             r'2constant|2literal|2variable|d\+|d-|'
-             r'd\.|d\.r|d0<|d0=|d2\*|d2\/|d<|d=|d>s|'
-             r'dabs|dmax|dmin|dnegate|m\*\/|m\+|'
-             # *** Wordset DOUBLE-EXT
-             r'2rot|du<|'
-             # *** Wordset EXCEPTION
-             r'catch|throw|'
-             # *** Wordset EXCEPTION-EXT
-             r'abort|abort\"|'
-             # *** Wordset FACILITY
-             r'at-xy|key\?|page|'
-             # *** Wordset FACILITY-EXT
-             r'ekey|ekey>char|ekey\?|emit\?|ms|time&date|'
-             # *** Wordset FILE
-             r'BIN|CLOSE-FILE|CREATE-FILE|DELETE-FILE|FILE-POSITION|'
-             r'FILE-SIZE|INCLUDE-FILE|INCLUDED|OPEN-FILE|R\/O|'
-             r'R\/W|READ-FILE|READ-LINE|REPOSITION-FILE|RESIZE-FILE|'
-             r'S\"|SOURCE-ID|W/O|WRITE-FILE|WRITE-LINE|'
-             # *** Wordset FILE-EXT
-             r'FILE-STATUS|FLUSH-FILE|REFILL|RENAME-FILE|'
-             # *** Wordset FLOAT
-             r'>float|d>f|'
-             r'f!|f\*|f\+|f-|f\/|f0<|f0=|f<|f>d|f@|'
-             r'falign|faligned|fconstant|fdepth|fdrop|fdup|'
-             r'fliteral|float\+|floats|floor|fmax|fmin|'
-             r'fnegate|fover|frot|fround|fswap|fvariable|'
-             r'represent|'
-             # *** Wordset FLOAT-EXT
-             r'df!|df@|dfalign|dfaligned|dfloat\+|'
-             r'dfloats|f\*\*|f\.|fabs|facos|facosh|falog|'
-             r'fasin|fasinh|fatan|fatan2|fatanh|fcos|fcosh|'
-             r'fe\.|fexp|fexpm1|fln|flnp1|flog|fs\.|fsin|'
-             r'fsincos|fsinh|fsqrt|ftan|ftanh|f~|precision|'
-             r'set-precision|sf!|sf@|sfalign|sfaligned|sfloat\+|'
-             r'sfloats|'
-             # *** Wordset LOCAL
-             r'\(local\)|to|'
-             # *** Wordset LOCAL-EXT
-             r'locals\||'
-             # *** Wordset MEMORY
-             r'allocate|free|resize|'
-             # *** Wordset SEARCH
-             r'definitions|find|forth-wordlist|get-current|'
-             r'get-order|search-wordlist|set-current|set-order|'
-             r'wordlist|'
-             # *** Wordset SEARCH-EXT
-             r'also|forth|only|order|previous|'
-             # *** Wordset STRING
-             r'-trailing|\/string|blank|cmove|cmove>|compare|'
-             r'search|sliteral|'
-             # *** Wordset TOOLS
-             r'.s|dump|see|words|'
-             # *** Wordset TOOLS-EXT
-             r';code|'
-             r'ahead|assembler|bye|code|cs-pick|cs-roll|'
-             r'editor|state|\[else\]|\[if\]|\[then\]|'
-             # *** Wordset TOOLS-EXT-obsolescent
-             r'forget|'
-             # Forth 2012
-             r'defer|defer@|defer!|action-of|begin-structure|field:|buffer:|'
-             r'parse-name|buffer:|traverse-wordlist|n>r|nr>|2value|fvalue|'
-             r'name>interpret|name>compile|name>string|'
-             r'cfield:|end-structure)(?!\S)', Keyword),
-
-            # Numbers
-            (r'(\$[0-9A-F]+)', Number.Hex),
-            (r'(\#|%|&|\-|\+)?[0-9]+', Number.Integer),
-            (r'(\#|%|&|\-|\+)?[0-9.]+', Keyword.Type),
-            # amforth specific
-            (r'(@i|!i|@e|!e|pause|noop|turnkey|sleep|'
-             r'itype|icompare|sp@|sp!|rp@|rp!|up@|up!|'
-             r'>a|a>|a@|a!|a@+|a@-|>b|b>|b@|b!|b@+|b@-|'
-             r'find-name|1ms|'
-             r'sp0|rp0|\(evaluate\)|int-trap|int!)(?!\S)',
-             Name.Constant),
-            # a proposal
-            (r'(do-recognizer|r:fail|recognizer:|get-recognizers|'
-             r'set-recognizers|r:float|r>comp|r>int|r>post|'
-             r'r:name|r:word|r:dnum|r:num|recognizer|forth-recognizer|'
-             r'rec:num|rec:float|rec:word)(?!\S)', Name.Decorator),
-            # defining words. The next word is a new command name
-            (r'(Evalue|Rvalue|Uvalue|Edefer|Rdefer|Udefer)(\s+)',
-             bygroups(Keyword.Namespace, Text), 'worddef'),
-
-            (r'\S+', Name.Function),      # Anything else is executed
-
-        ],
-        'worddef': [
-            (r'\S+', Name.Class, '#pop'),
-        ],
-        'stringdef': [
-            (r'[^"]+', String, '#pop'),
-        ],
-    }
-
-    def analyse_text(text):
-        """Forth uses : COMMAND ; quite a lot in a single line, so we're trying
-        to find that."""
-        if re.search('\n:[^\n]+;\n', text):
-            return 0.3
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/graphviz.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/graphviz.py
deleted file mode 100644
index 1e4ba024365e625edc50bf8cb0eeefecd0338057..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/graphviz.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""
-    pygments.lexers.graphviz
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexer for the DOT language (graphviz).
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, bygroups
-from pygments.token import Comment, Keyword, Operator, Name, String, Number, \
-    Punctuation, Whitespace
-
-
-__all__ = ['GraphvizLexer']
-
-
-class GraphvizLexer(RegexLexer):
-    """
-    For graphviz DOT graph description language.
-
-    .. versionadded:: 2.8
-    """
-    name = 'Graphviz'
-    url = 'https://www.graphviz.org/doc/info/lang.html'
-    aliases = ['graphviz', 'dot']
-    filenames = ['*.gv', '*.dot']
-    mimetypes = ['text/x-graphviz', 'text/vnd.graphviz']
-    tokens = {
-        'root': [
-            (r'\s+', Whitespace),
-            (r'(#|//).*?$', Comment.Single),
-            (r'/(\\\n)?[*](.|\n)*?[*](\\\n)?/', Comment.Multiline),
-            (r'(?i)(node|edge|graph|digraph|subgraph|strict)\b', Keyword),
-            (r'--|->', Operator),
-            (r'[{}[\]:;,]', Punctuation),
-            (r'(\b\D\w*)(\s*)(=)(\s*)',
-                bygroups(Name.Attribute, Whitespace, Punctuation, Whitespace),
-                'attr_id'),
-            (r'\b(n|ne|e|se|s|sw|w|nw|c|_)\b', Name.Builtin),
-            (r'\b\D\w*', Name.Tag),  # node
-            (r'[-]?((\.[0-9]+)|([0-9]+(\.[0-9]*)?))', Number),
-            (r'"(\\"|[^"])*?"', Name.Tag),  # quoted node
-            (r'<', Punctuation, 'xml'),
-        ],
-        'attr_id': [
-            (r'\b\D\w*', String, '#pop'),
-            (r'[-]?((\.[0-9]+)|([0-9]+(\.[0-9]*)?))', Number, '#pop'),
-            (r'"(\\"|[^"])*?"', String.Double, '#pop'),
-            (r'<', Punctuation, ('#pop', 'xml')),
-        ],
-        'xml': [
-            (r'<', Punctuation, '#push'),
-            (r'>', Punctuation, '#pop'),
-            (r'\s+', Whitespace),
-            (r'[^<>\s]', Name.Tag),
-        ]
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/supercollider.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/supercollider.py
deleted file mode 100644
index ea7a1767648937c3d9066c8d8215943c1f396cde..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/supercollider.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""
-    pygments.lexers.supercollider
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexer for SuperCollider
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import RegexLexer, include, words, default
-from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation
-
-__all__ = ['SuperColliderLexer']
-
-
-class SuperColliderLexer(RegexLexer):
-    """
-    For SuperCollider source code.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'SuperCollider'
-    url = 'http://supercollider.github.io/'
-    aliases = ['supercollider', 'sc']
-    filenames = ['*.sc', '*.scd']
-    mimetypes = ['application/supercollider', 'text/supercollider']
-
-    flags = re.DOTALL | re.MULTILINE
-    tokens = {
-        'commentsandwhitespace': [
-            (r'\s+', Text),
-            (r'<!--', Comment),
-            (r'//.*?\n', Comment.Single),
-            (r'/\*.*?\*/', Comment.Multiline)
-        ],
-        'slashstartsregex': [
-            include('commentsandwhitespace'),
-            (r'/(\\.|[^[/\\\n]|\[(\\.|[^\]\\\n])*])+/'
-             r'([gim]+\b|\B)', String.Regex, '#pop'),
-            (r'(?=/)', Text, ('#pop', 'badregex')),
-            default('#pop'),
-        ],
-        'badregex': [
-            (r'\n', Text, '#pop')
-        ],
-        'root': [
-            (r'^(?=\s|/|<!--)', Text, 'slashstartsregex'),
-            include('commentsandwhitespace'),
-            (r'\+\+|--|~|&&|\?|:|\|\||\\(?=\n)|'
-             r'(<<|>>>?|==?|!=?|[-<>+*%&|^/])=?', Operator, 'slashstartsregex'),
-            (r'[{(\[;,]', Punctuation, 'slashstartsregex'),
-            (r'[})\].]', Punctuation),
-            (words((
-                'for', 'in', 'while', 'do', 'break', 'return', 'continue',
-                'switch', 'case', 'default', 'if', 'else', 'throw', 'try',
-                'catch', 'finally', 'new', 'delete', 'typeof', 'instanceof',
-                'void'), suffix=r'\b'),
-             Keyword, 'slashstartsregex'),
-            (words(('var', 'let', 'with', 'function', 'arg'), suffix=r'\b'),
-             Keyword.Declaration, 'slashstartsregex'),
-            (words((
-                '(abstract', 'boolean', 'byte', 'char', 'class', 'const',
-                'debugger', 'double', 'enum', 'export', 'extends', 'final',
-                'float', 'goto', 'implements', 'import', 'int', 'interface',
-                'long', 'native', 'package', 'private', 'protected', 'public',
-                'short', 'static', 'super', 'synchronized', 'throws',
-                'transient', 'volatile'), suffix=r'\b'),
-             Keyword.Reserved),
-            (words(('true', 'false', 'nil', 'inf'), suffix=r'\b'), Keyword.Constant),
-            (words((
-                'Array', 'Boolean', 'Date', 'Error', 'Function', 'Number',
-                'Object', 'Packages', 'RegExp', 'String',
-                'isFinite', 'isNaN', 'parseFloat', 'parseInt', 'super',
-                'thisFunctionDef', 'thisFunction', 'thisMethod', 'thisProcess',
-                'thisThread', 'this'), suffix=r'\b'),
-             Name.Builtin),
-            (r'[$a-zA-Z_]\w*', Name.Other),
-            (r'\\?[$a-zA-Z_]\w*', String.Symbol),
-            (r'[0-9][0-9]*\.[0-9]+([eE][0-9]+)?[fd]?', Number.Float),
-            (r'0x[0-9a-fA-F]+', Number.Hex),
-            (r'[0-9]+', Number.Integer),
-            (r'"(\\\\|\\[^\\]|[^"\\])*"', String.Double),
-            (r"'(\\\\|\\[^\\]|[^'\\])*'", String.Single),
-        ]
-    }
-
-    def analyse_text(text):
-        """We're searching for a common function and a unique keyword here."""
-        if 'SinOsc' in text or 'thisFunctionDef' in text:
-            return 0.1
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/ArtCAM 2018 X86-x64 Torrent EXCLUSIVE.md b/spaces/quidiaMuxgu/Expedit-SAM/ArtCAM 2018 X86-x64 Torrent EXCLUSIVE.md
deleted file mode 100644
index c1019391bdf0f2faf6a75cde756902386a2cb51b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/ArtCAM 2018 X86-x64 Torrent EXCLUSIVE.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<h2>ArtCAM 2018 x86-x64 torrent</h2><br /><p><b><b>Download Zip</b> &#10004;&#10004;&#10004; <a href="https://geags.com/2uCrSg">https://geags.com/2uCrSg</a></b></p><br /><br />
-
-Mar 20 , 2560 BC e. - After installing AutoCAD 2018, download and install the AutoCAD 2018 Language Pack to run AutoCAD in your preferred language. ✓ At the time of this writing, the language that AutoCAD supports depends on your region.
-This section describes how to install and use a version of AutoCAD 2018 that is supported in a language other than English and US English.
-You can choose any language to use in AutoCAD, but if you make language changes, you will no longer be able to create and view drawings in languages ​​other than those selected during installation. 8a78ff9644<br />
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Audi Navigation Plus Rns E 2016.rar.md b/spaces/quidiaMuxgu/Expedit-SAM/Audi Navigation Plus Rns E 2016.rar.md
deleted file mode 100644
index 1befe62e401f2f976bf546003466b5a896af6657..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Audi Navigation Plus Rns E 2016.rar.md	
+++ /dev/null
@@ -1,42 +0,0 @@
-<h2>Audi Navigation Plus Rns E 2016.rar</h2><br /><p><b><b>Download File</b> &#10001; &#10001; &#10001; <a href="https://geags.com/2uCrrj">https://geags.com/2uCrrj</a></b></p><br /><br />
-
-Pretty disappointing that they can’t even keep the previous generation of their navigation system working as the new updated one is just inferior to its previous version. Also for the upgraded navigation system, navigation directions is very weak. Not really what I expected from IFA. Overall, my overall opinion of the new GPS is still like what I had before. I still don’t find the new system cool at all. That said, if you want a cheaper navigation system for your car, the Garmin Nuvi 3415W Auto will be a good choice for you. If you are one of the IFA 2016 visitors, you can have a look at the new Garmin Nuvi 3415W Auto that is the upgraded version of the previous version of the Garmin Nuvi 3415W. So, I’ll talk about the new Garmin Nuvi 3415W Auto Navigation system in this article.
-
-Does the Garmin Nuvi 3415W Auto Navigation system have what I need?
-
-Price and availability:
-
-The new Garmin Nuvi 3415W Auto is now officially available in the US market. It is already available at $299.99.
-
-The new navigation system offers a 3.5-inch color touch screen.
-
-Navigation units is Garmin Nuvi line.
-
-Garmin Nuvi 8100 Auto.
-
-First generation Garmin Nuvi 3415W Auto.
-
-It also offers an expandable memory up to 7GB.
-
-It offers a lot of features like:
-
-It offers a user friendly interface.
-
-This system offers up to date maps of more than 180 countries.
-
-Also, this navigation system offers a built-in A2DP Bluetooth technology.
-
-Garmin Nuvi 8100 Auto GPS Navi Wireless System in Review
-
-Size and Weight:
-
-The navigation system is quite small and light weighing only 3.2 ounces. It is small enough that I can put it inside my glove box. Its overall size is 4.6 inches wide, 3 inches deep, and 6.5 inches tall. Overall, the new Garmin Nuvi 3415W Auto is a small navigation system.
-
-It also comes with a key ring attachment that can help you put the navigation system on your keychain.
-
-Performance and Features:
-
-The navigation system offers a very good performance. The navigation system is easy to use. When you start up the navigation system, you’ll get a quick start guide. It is a bit 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Blackberry Mep Left 0 Fixer V1 01 Rar.rar ((FULL)).md b/spaces/quidiaMuxgu/Expedit-SAM/Blackberry Mep Left 0 Fixer V1 01 Rar.rar ((FULL)).md
deleted file mode 100644
index 8964cdfdd1f5a9cfa5344d45777f3db57af21c32..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Blackberry Mep Left 0 Fixer V1 01 Rar.rar ((FULL)).md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<h2>blackberry mep left 0 fixer v1 01 rar.rar</h2><br /><p><b><b>Download Zip</b> --->>> <a href="https://geags.com/2uCsKS">https://geags.com/2uCsKS</a></b></p><br /><br />
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-blackberry mep left 0 fixer v1 01 rar.rar. download drivers for sound samsung android 2 1 samsung. download driver for canon 4018 printer how to install driver on.
-Download drivers for sound samsung android 2 1 samsung.
-Download drivers for sound samsung android 2 1 samsung. 8a78ff9644<br />
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (web Video Collection 6 Torrent 34).md b/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (web Video Collection 6 Torrent 34).md
deleted file mode 100644
index 07c62154ce524652cc315981c3f5b579ceb0243b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (web Video Collection 6 Torrent 34).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (web video collection 6 torrent 34)</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://geags.com/2uCrNs">https://geags.com/2uCrNs</a></b></p><br /><br />
-<br />
-https://torrentiumtv.app. Available on android TV and android mobile device. Torrentium allows you to enjoy online playback of content with the best audio-visual ... 4d29de3e1b<br />
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/model_param_init.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/model_param_init.py
deleted file mode 100644
index 5d818dbee4d4490b2884b3346c20c9370c0810fc..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/model_param_init.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import json
-import pathlib
-
-default_param = {}
-default_param["bins"] = 768
-default_param["unstable_bins"] = 9  # training only
-default_param["reduction_bins"] = 762  # training only
-default_param["sr"] = 44100
-default_param["pre_filter_start"] = 757
-default_param["pre_filter_stop"] = 768
-default_param["band"] = {}
-
-
-default_param["band"][1] = {
-    "sr": 11025,
-    "hl": 128,
-    "n_fft": 960,
-    "crop_start": 0,
-    "crop_stop": 245,
-    "lpf_start": 61,  # inference only
-    "res_type": "polyphase",
-}
-
-default_param["band"][2] = {
-    "sr": 44100,
-    "hl": 512,
-    "n_fft": 1536,
-    "crop_start": 24,
-    "crop_stop": 547,
-    "hpf_start": 81,  # inference only
-    "res_type": "sinc_best",
-}
-
-
-def int_keys(d):
-    r = {}
-    for k, v in d:
-        if k.isdigit():
-            k = int(k)
-        r[k] = v
-    return r
-
-
-class ModelParameters(object):
-    def __init__(self, config_path=""):
-        if ".pth" == pathlib.Path(config_path).suffix:
-            import zipfile
-
-            with zipfile.ZipFile(config_path, "r") as zip:
-                self.param = json.loads(
-                    zip.read("param.json"), object_pairs_hook=int_keys
-                )
-        elif ".json" == pathlib.Path(config_path).suffix:
-            with open(config_path, "r") as f:
-                self.param = json.loads(f.read(), object_pairs_hook=int_keys)
-        else:
-            self.param = default_param
-
-        for k in [
-            "mid_side",
-            "mid_side_b",
-            "mid_side_b2",
-            "stereo_w",
-            "stereo_n",
-            "reverse",
-        ]:
-            if not k in self.param:
-                self.param[k] = False
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/nets_new.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/nets_new.py
deleted file mode 100644
index 1c0f4fa96d921e979fe31bd4151701b7783fbcea..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/nets_new.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_new
-
-
-class BaseNet(nn.Module):
-    def __init__(
-        self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
-    ):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedNet(nn.Module):
-    def __init__(self, n_fft, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
-        )
-
-        self.stg1_high_band_net = BaseNet(
-            2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
-        )
-        self.stg2_high_band_net = BaseNet(
-            nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg3_full_band_net = BaseNet(
-            3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
-        )
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode="replicate",
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset : -self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x, aggressiveness=None):
-        mask = self.forward(x)
-        pred_mag = x * mask
-
-        if self.offset > 0:
-            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
-            assert pred_mag.size()[3] > 0
-
-        return pred_mag
diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/__version__.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/__version__.py
deleted file mode 100644
index 634eb62c3fadf4962ec55a8fc0ae4f62fffdcc7d..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/__version__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-VERSION = 'v5.5.1'
-PATCH = 'UVR_Patch_3_31_23_5_5'
-PATCH_MAC = 'UVR_Patch_01_10_12_6_50'
-PATCH_LINUX = 'UVR_Patch_01_01_23_6_50'
diff --git a/spaces/rachana219/MODT2/trackers/strongsort/sort/detection.py b/spaces/rachana219/MODT2/trackers/strongsort/sort/detection.py
deleted file mode 100644
index 1fb05f87999c5c911979e2031a2f08c867939c45..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/sort/detection.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-
-
-class Detection(object):
-    """
-    This class represents a bounding box detection in a single image.
-
-    Parameters
-    ----------
-    tlwh : array_like
-        Bounding box in format `(x, y, w, h)`.
-    confidence : float
-        Detector confidence score.
-    feature : array_like
-        A feature vector that describes the object contained in this image.
-
-    Attributes
-    ----------
-    tlwh : ndarray
-        Bounding box in format `(top left x, top left y, width, height)`.
-    confidence : ndarray
-        Detector confidence score.
-    feature : ndarray | NoneType
-        A feature vector that describes the object contained in this image.
-
-    """
-
-    def __init__(self, tlwh, confidence, feature):
-        self.tlwh = np.asarray(tlwh, dtype=np.float32)
-        self.confidence = float(confidence)
-        self.feature = np.asarray(feature.cpu(), dtype=np.float32)
-
-    def to_tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def to_xyah(self):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = self.tlwh.copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-    
-def to_xyah_ext(bbox):
-    """Convert bounding box to format `(center x, center y, aspect ratio,
-    height)`, where the aspect ratio is `width / height`.
-    """
-    ret = bbox.copy()
-    ret[:2] += ret[2:] / 2
-    ret[2] /= ret[3]
-    return ret
diff --git a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/latent_consistency_controlnet.py b/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/latent_consistency_controlnet.py
deleted file mode 100644
index c23bede05b2eafc964daefe7e81ed3ed097690c5..0000000000000000000000000000000000000000
--- a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/latent_consistency_controlnet.py
+++ /dev/null
@@ -1,1100 +0,0 @@
-# from https://github.com/taabata/LCM_Inpaint_Outpaint_Comfy/blob/main/LCM/pipeline_cn.py
-# Copyright 2023 Stanford University Team 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.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    ConfigMixin,
-    DiffusionPipeline,
-    SchedulerMixin,
-    UNet2DConditionModel,
-    ControlNetModel,
-    logging,
-)
-from diffusers.configuration_utils import register_to_config
-from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from diffusers.utils import BaseOutput
-
-from diffusers.utils.torch_utils import randn_tensor, is_compiled_module
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
-
-
-import PIL.Image
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(encoder_output, generator):
-    if hasattr(encoder_output, "latent_dist"):
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-
-
-class LatentConsistencyModelPipeline_controlnet(DiffusionPipeline):
-    _optional_components = ["scheduler"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        controlnet: Union[
-            ControlNetModel,
-            List[ControlNetModel],
-            Tuple[ControlNetModel],
-            MultiControlNetModel,
-        ],
-        unet: UNet2DConditionModel,
-        scheduler: "LCMScheduler",
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        scheduler = (
-            scheduler
-            if scheduler is not None
-            else LCMScheduler_X(
-                beta_start=0.00085,
-                beta_end=0.0120,
-                beta_schedule="scaled_linear",
-                prediction_type="epsilon",
-            )
-        )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            controlnet=controlnet,
-            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.control_image_processor = VaeImageProcessor(
-            vae_scale_factor=self.vae_scale_factor,
-            do_convert_rgb=True,
-            do_normalize=False,
-        )
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        prompt_embeds: 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
-            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.
-        """
-
-        if prompt is not None and isinstance(prompt, str):
-            pass
-        elif prompt is not None and isinstance(prompt, list):
-            len(prompt)
-        else:
-            prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            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]
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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
-        )
-
-        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
-        return prompt_embeds
-
-    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
-
-    def prepare_control_image(
-        self,
-        image,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-        image = self.control_image_processor.preprocess(
-            image, height=height, width=width
-        ).to(dtype=dtype)
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=dtype)
-
-        if do_classifier_free_guidance and not guess_mode:
-            image = torch.cat([image] * 2)
-
-        return image
-
-    def prepare_latents(
-        self,
-        image,
-        timestep,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        latents=None,
-        generator=None,
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        # batch_size = batch_size * num_images_per_prompt
-
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            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."
-                )
-
-            elif isinstance(generator, list):
-                init_latents = [
-                    retrieve_latents(
-                        self.vae.encode(image[i : i + 1]), generator=generator[i]
-                    )
-                    for i in range(batch_size)
-                ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                init_latents = retrieve_latents(
-                    self.vae.encode(image), generator=generator
-                )
-
-            init_latents = self.vae.config.scaling_factor * init_latents
-
-        if (
-            batch_size > init_latents.shape[0]
-            and batch_size % init_latents.shape[0] == 0
-        ):
-            # expand init_latents for batch_size
-            deprecation_message = (
-                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            # deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = torch.cat(
-                [init_latents] * additional_image_per_prompt, dim=0
-            )
-        elif (
-            batch_size > init_latents.shape[0]
-            and batch_size % init_latents.shape[0] != 0
-        ):
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        return latents
-
-        if latents is None:
-            latents = torch.randn(shape, dtype=dtype).to(device)
-        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
-
-    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
-        """
-        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-        Args:
-        timesteps: torch.Tensor: generate embedding vectors at these timesteps
-        embedding_dim: int: dimension of the embeddings to generate
-        dtype: data type of the generated embeddings
-        Returns:
-        embedding vectors with shape `(len(timesteps), embedding_dim)`
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.0
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, None] * emb[None, :]
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-        if embedding_dim % 2 == 1:  # zero pad
-            emb = torch.nn.functional.pad(emb, (0, 1))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        return timesteps, num_inference_steps - t_start
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: PipelineImageInput = None,
-        control_image: PipelineImageInput = None,
-        strength: float = 0.8,
-        height: Optional[int] = 768,
-        width: Optional[int] = 768,
-        guidance_scale: float = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        latents: Optional[torch.FloatTensor] = None,
-        generator: Optional[torch.Generator] = None,
-        num_inference_steps: int = 4,
-        lcm_origin_steps: int = 50,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
-        guess_mode: bool = True,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-    ):
-        controlnet = (
-            self.controlnet._orig_mod
-            if is_compiled_module(self.controlnet)
-            else self.controlnet
-        )
-        # 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
-        if not isinstance(control_guidance_start, list) and isinstance(
-            control_guidance_end, list
-        ):
-            control_guidance_start = len(control_guidance_end) * [
-                control_guidance_start
-            ]
-        elif not isinstance(control_guidance_end, list) and isinstance(
-            control_guidance_start, list
-        ):
-            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
-        elif not isinstance(control_guidance_start, list) and not isinstance(
-            control_guidance_end, list
-        ):
-            mult = (
-                len(controlnet.nets)
-                if isinstance(controlnet, MultiControlNetModel)
-                else 1
-            )
-            control_guidance_start, control_guidance_end = mult * [
-                control_guidance_start
-            ], mult * [control_guidance_end]
-        # 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
-        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
-        global_pool_conditions = (
-            controlnet.config.global_pool_conditions
-            if isinstance(controlnet, ControlNetModel)
-            else controlnet.nets[0].config.global_pool_conditions
-        )
-        guess_mode = guess_mode or global_pool_conditions
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            prompt_embeds=prompt_embeds,
-        )
-
-        # 3.5 encode image
-        image = self.image_processor.preprocess(image)
-
-        if isinstance(controlnet, ControlNetModel):
-            control_image = self.prepare_control_image(
-                image=control_image,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=controlnet.dtype,
-                guess_mode=guess_mode,
-            )
-        elif isinstance(controlnet, MultiControlNetModel):
-            control_images = []
-
-            for control_image_ in control_image:
-                control_image_ = self.prepare_control_image(
-                    image=control_image_,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size * num_images_per_prompt,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    dtype=controlnet.dtype,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    guess_mode=guess_mode,
-                )
-
-                control_images.append(control_image_)
-
-            control_image = control_images
-        else:
-            assert False
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(strength, num_inference_steps, lcm_origin_steps)
-        # timesteps = self.scheduler.timesteps
-        # timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1.0, device)
-        timesteps = self.scheduler.timesteps
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        print("timesteps: ", timesteps)
-
-        # 5. Prepare latent variable
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            latents,
-        )
-        bs = batch_size * num_images_per_prompt
-
-        # 6. Get Guidance Scale Embedding
-        w = torch.tensor(guidance_scale).repeat(bs)
-        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
-            device=device, dtype=latents.dtype
-        )
-        controlnet_keep = []
-        for i in range(len(timesteps)):
-            keeps = [
-                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
-                for s, e in zip(control_guidance_start, control_guidance_end)
-            ]
-            controlnet_keep.append(
-                keeps[0] if isinstance(controlnet, ControlNetModel) else keeps
-            )
-        # 7. LCM MultiStep Sampling Loop:
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                ts = torch.full((bs,), t, device=device, dtype=torch.long)
-                latents = latents.to(prompt_embeds.dtype)
-                if guess_mode:
-                    # Infer ControlNet only for the conditional batch.
-                    control_model_input = latents
-                    control_model_input = self.scheduler.scale_model_input(
-                        control_model_input, ts
-                    )
-                    controlnet_prompt_embeds = prompt_embeds
-                else:
-                    control_model_input = latents
-                    controlnet_prompt_embeds = prompt_embeds
-                if isinstance(controlnet_keep[i], list):
-                    cond_scale = [
-                        c * s
-                        for c, s in zip(
-                            controlnet_conditioning_scale, controlnet_keep[i]
-                        )
-                    ]
-                else:
-                    controlnet_cond_scale = controlnet_conditioning_scale
-                    if isinstance(controlnet_cond_scale, list):
-                        controlnet_cond_scale = controlnet_cond_scale[0]
-                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    control_model_input,
-                    ts,
-                    encoder_hidden_states=controlnet_prompt_embeds,
-                    controlnet_cond=control_image,
-                    conditioning_scale=cond_scale,
-                    guess_mode=guess_mode,
-                    return_dict=False,
-                )
-                # model prediction (v-prediction, eps, x)
-                model_pred = self.unet(
-                    latents,
-                    ts,
-                    timestep_cond=w_embedding,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                    return_dict=False,
-                )[0]
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents, denoised = self.scheduler.step(
-                    model_pred, i, t, latents, return_dict=False
-                )
-
-                # # call the callback, if provided
-                # if i == len(timesteps) - 1:
-                progress_bar.update()
-
-        denoised = denoised.to(prompt_embeds.dtype)
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.unet.to("cpu")
-            self.controlnet.to("cpu")
-            torch.cuda.empty_cache()
-        if not output_type == "latent":
-            image = self.vae.decode(
-                denoised / self.vae.config.scaling_factor, return_dict=False
-            )[0]
-            image, has_nsfw_concept = self.run_safety_checker(
-                image, device, prompt_embeds.dtype
-            )
-        else:
-            image = denoised
-            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
-        )
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(
-            images=image, nsfw_content_detected=has_nsfw_concept
-        )
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class LCMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    denoised: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-def rescale_zero_terminal_snr(betas):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-    Args:
-        betas (`torch.FloatTensor`):
-            the betas that the scheduler is being initialized with.
-    Returns:
-        `torch.FloatTensor`: rescaled betas with zero terminal SNR
-    """
-    # Convert betas to alphas_bar_sqrt
-    alphas = 1.0 - betas
-    alphas_cumprod = torch.cumprod(alphas, dim=0)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
-    alphas = torch.cat([alphas_bar[0:1], alphas])
-    betas = 1 - alphas
-
-    return betas
-
-
-class LCMScheduler_X(SchedulerMixin, ConfigMixin):
-    """
-    `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
-    non-Markovian guidance.
-    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
-    methods the library implements for all schedulers such as loading and saving.
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
-            Diffusion.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
-            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(
-                beta_start, beta_end, num_train_timesteps, dtype=torch.float32
-            )
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(
-                    beta_start**0.5,
-                    beta_end**0.5,
-                    num_train_timesteps,
-                    dtype=torch.float32,
-                )
-                ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(
-                f"{beta_schedule} does is not implemented for {self.__class__}"
-            )
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.betas = rescale_zero_terminal_snr(self.betas)
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        self.final_alpha_cumprod = (
-            torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-        )
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(
-            np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)
-        )
-
-    def scale_model_input(
-        self, sample: torch.FloatTensor, timestep: Optional[int] = None
-    ) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-        Args:
-            sample (`torch.FloatTensor`):
-                The input sample.
-            timestep (`int`, *optional*):
-                The current timestep in the diffusion chain.
-        Returns:
-            `torch.FloatTensor`:
-                A scaled input sample.
-        """
-        return sample
-
-    def _get_variance(self, timestep, prev_timestep):
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (
-            1 - alpha_prod_t / alpha_prod_t_prev
-        )
-
-        return variance
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
-    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        """
-        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
-        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
-        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
-        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
-        photorealism as well as better image-text alignment, especially when using very large guidance weights."
-        https://arxiv.org/abs/2205.11487
-        """
-        dtype = sample.dtype
-        batch_size, channels, height, width = sample.shape
-
-        if dtype not in (torch.float32, torch.float64):
-            sample = (
-                sample.float()
-            )  # upcast for quantile calculation, and clamp not implemented for cpu half
-
-        # Flatten sample for doing quantile calculation along each image
-        sample = sample.reshape(batch_size, channels * height * width)
-
-        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
-
-        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
-        s = torch.clamp(
-            s, min=1, max=self.config.sample_max_value
-        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
-
-        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = (
-            torch.clamp(sample, -s, s) / s
-        )  # "we threshold xt0 to the range [-s, s] and then divide by s"
-
-        sample = sample.reshape(batch_size, channels, height, width)
-        sample = sample.to(dtype)
-
-        return sample
-
-    def set_timesteps(
-        self,
-        stength,
-        num_inference_steps: int,
-        lcm_origin_steps: int,
-        device: Union[str, torch.device] = None,
-    ):
-        """
-        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # LCM Timesteps Setting:  # Linear Spacing
-        c = self.config.num_train_timesteps // lcm_origin_steps
-        lcm_origin_timesteps = (
-            np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1
-        )  # LCM Training  Steps Schedule
-        skipping_step = max(len(lcm_origin_timesteps) // num_inference_steps, 1)
-        timesteps = lcm_origin_timesteps[::-skipping_step][
-            :num_inference_steps
-        ]  # LCM Inference Steps Schedule
-
-        self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
-
-    def get_scalings_for_boundary_condition_discrete(self, t):
-        self.sigma_data = 0.5  # Default: 0.5
-
-        # By dividing 0.1: This is almost a delta function at t=0.
-        c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
-        c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
-        return c_skip, c_out
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timeindex: int,
-        timestep: int,
-        sample: torch.FloatTensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.FloatTensor] = None,
-        return_dict: bool = True,
-    ) -> Union[LCMSchedulerOutput, Tuple]:
-        """
-        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
-        process from the learned model outputs (most often the predicted noise).
-        Args:
-            model_output (`torch.FloatTensor`):
-                The direct output from learned diffusion model.
-            timestep (`float`):
-                The current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            generator (`torch.Generator`, *optional*):
-                A random number generator.
-            variance_noise (`torch.FloatTensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
-        Returns:
-            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
-                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # 1. get previous step value
-        prev_timeindex = timeindex + 1
-        if prev_timeindex < len(self.timesteps):
-            prev_timestep = self.timesteps[prev_timeindex]
-        else:
-            prev_timestep = timestep
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # 3. Get scalings for boundary conditions
-        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
-
-        # 4. Different Parameterization:
-        parameterization = self.config.prediction_type
-
-        if parameterization == "epsilon":  # noise-prediction
-            pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
-
-        elif parameterization == "sample":  # x-prediction
-            pred_x0 = model_output
-
-        elif parameterization == "v_prediction":  # v-prediction
-            pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
-
-        # 4. Denoise model output using boundary conditions
-        denoised = c_out * pred_x0 + c_skip * sample
-
-        # 5. Sample z ~ N(0, I), For MultiStep Inference
-        # Noise is not used for one-step sampling.
-        if len(self.timesteps) > 1:
-            noise = torch.randn(model_output.shape).to(model_output.device)
-            prev_sample = (
-                alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
-            )
-        else:
-            prev_sample = denoised
-
-        if not return_dict:
-            return (prev_sample, denoised)
-
-        return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=original_samples.device, dtype=original_samples.dtype
-        )
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = (
-            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        )
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(
-        self,
-        sample: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=sample.device, dtype=sample.dtype
-        )
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/scripts/train.py b/spaces/radames/UserControllableLT-Latent-Transformer/scripts/train.py
deleted file mode 100644
index abf1f59e7e51fa11048b3c8d52c257c00c9d7c1d..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/scripts/train.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""
-This file runs the main training/val loop
-"""
-import os
-import json
-import sys
-import pprint
-
-sys.path.append(".")
-sys.path.append("..")
-
-from options.train_options import TrainOptions
-from training.coach import Coach
-
-
-def main():
-	opts = TrainOptions().parse()
-	os.makedirs(opts.exp_dir, exist_ok=True)
-
-	opts_dict = vars(opts)
-	pprint.pprint(opts_dict)
-	with open(os.path.join(opts.exp_dir, 'opt.json'), 'w') as f:
-		json.dump(opts_dict, f, indent=4, sort_keys=True)
-
-	coach = Coach(opts)
-	coach.train()
-
-if __name__ == '__main__':
-	main()
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Folder Lock 7.8.0 Crack With Activation Number Protect Your Data and Privacy in 2020 with This Free Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Folder Lock 7.8.0 Crack With Activation Number Protect Your Data and Privacy in 2020 with This Free Download.md
deleted file mode 100644
index 667794daaf456bd93b20d36c53a0767a58d6a5b3..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Folder Lock 7.8.0 Crack With Activation Number Protect Your Data and Privacy in 2020 with This Free Download.md	
+++ /dev/null
@@ -1,119 +0,0 @@
-
-<h1>Folder Lock 7.8.0 Crack With Activation Number Free Download 2020</h1>
-<p>If you are looking for a way to keep your personal files protected from prying eyes, hackers, viruses, and spyware, then you might want to consider using Folder Lock 7.8.0 Crack With Activation Number Free Download 2020. This is a full suite solution for all your data security needs that lets you encrypt, backup, lock, shred, hide, and sync your files in a variety of ways.</p>
-<h2>Folder Lock 7.8.0 Crack With Activation Number Free Download 2020</h2><br /><p><b><b>DOWNLOAD</b> &#128504;&#128504;&#128504; <a href="https://tinourl.com/2uKZAL">https://tinourl.com/2uKZAL</a></b></p><br /><br />
-<p>Folder Lock is a powerful software that works under all flavors of Windows XP, Vista, Windows 7, Windows 8/8.1, and Windows 10. It can also protect your data on removable media such as USB drives, CDs, DVDs, memory cards, etc. In this article, we will explore the features of Folder Lock 7.8.0 Crack, how to download and install it for free, its pros and cons, and some FAQs that you might have.</p>
- <h2>Features of Folder Lock 7.8.0 Crack</h2>
-<p>Folder Lock 7.8.0 Crack offers a range of features that make it one of the best data security software in the market. Here are some of them:</p>
- <h3>Encrypt Files</h3>
-<p>Folder Lock allows you to encrypt your personal files using 256-bit on-the-fly encryption technology that is totally dynamic and fool-proof. You can create encrypted lockers that act as virtual safes where you can store your confidential documents, photos, videos, music, etc.</p>
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-<p>To encrypt your files, you just need to drag and drop them into the locker or use the right-click menu option. You can also create multiple lockers with different passwords for different purposes.</p>
-<p>The encrypted lockers are portable and can be copied or moved to any location without affecting their security.</p>
- <h3>Backup Online</h3>
-<p>Folder Lock also lets you back up all your encrypted files automatically to online storage such as Dropbox, Google Drive, OneDrive, etc. This way, you can easily restore them back in case of any data loss or theft.</p>
-<p>To backup your files online, you just need to sign up for a free account with Folder Lock and enable the backup option in the settings menu.</p>
-<p>You can also sync your encrypted files across multiple devices using the same account.</p>
- <h3>Protect USB / CD</h3>
-<p>Folder Lock can also convert your encrypted storage into executables that can run on any PC without requiring any installation or admin rights.</p>
-<p>This means that you can take your portable drives such as USB flash drives, CDs, DVDs, etc., anywhere with you and access your files securely without leaving any traces behind.</p>
-<p>To protect your USB / CD drives, you just need to use the protect USB / CD option in the main menu and follow the instructions.</p>
- <h3>Lock Files</h3>
-<p>Folder Lock can also protect important files from unwanted access by blocking visibility or access to files, folders, and drives.</p>
-<p>This means that no one can see or open your locked files unless they have the correct password.</p>
-<p>To lock your files, you just need to add them to the lock list using the add button or drag-and-drop method.</p>
-<p>You can also use hotkeys or context menu options to quickly lock or unlock your files.</p>
- <h3>Make Wallets</h3>
-<p>Folder Lock can also store sensitive information such as credit cards, bank accounts, passwords, licenses, etc., in encrypted form using its wallet feature.</p>
-<p>This way, you can keep all your personal information in one place and access it securely whenever you need it.</p>
-<p>To make wallets, you just need to use the make wallet option in the main menu and create a new wallet with a name and password.</p>
-<p>You can then add various types of cards or information using the add button or drag-and-drop method.</p>
- <h3>Shred Files</h3>
-<p>Folder Lock can also shred and permanently delete restorable files, folders, and drives using its shred feature.</p>
-<p>This means that no one can recover your deleted data using any recovery software or tool.</p>
-<p>To shred your files, you just need to use the shred option in the main menu and select the files, folders, or drives that you want to shred.</p>
-<p>You can also choose the shredding algorithm and the number of passes that you want to apply for maximum security.</p>
- <h3>Stealth Mode</h3>
-<p>Folder Lock can also run in complete stealth mode without anyone knowing that you ever installed it on your PC.</p>
-<p>This means that no one can see Folder Lock's icon on your desktop, taskbar, or start menu, or find it in the control panel, add/remove programs, or registry entries.</p>
-<p>To run Folder Lock in stealth mode, you just need to enable the stealth mode option in the settings menu and set a hotkey combination to launch Folder Lock whenever you want.</p>
- <h3>Hack Security</h3>
-<p>Folder Lock can also prevent hackers from using dictionary attacks or brute force methods to crack your passwords using its hack security feature.</p>
-<p>This means that Folder Lock will monitor, control, and perform actions on incorrect password attempts such as logging off, shutting down, or erasing all data.</p>
-<p>To enable hack security, you just need to use the hack security option in the settings menu and choose the action that you want to take in case of failed login attempts.</p>
- <h3>Password Security</h3>
-<p>Folder Lock can also restrict access to various parts of its application with the use of admin level master password using its password security feature.</p>
-<p>This means that only you can change Folder Lock's settings, uninstall it, or access its online backup account using your master password.</p>
-<p>To enable password security, you just need to use the password security option in the settings menu and set a master password for Folder Lock.</p>
- <h3>Auto Protection</h3>
-<p>Folder Lock can also set an inactivity-based security policy in order to secure everything when you're away from your PC using its auto protection feature.</p>
-<p>This means that Folder Lock will automatically lock or encrypt everything after a certain period of time or when certain events occur such as screen saver activation, power supply change, etc.</p>
-<p>To enable auto protection, you just need to use protection option in the settings menu and choose the time interval or the event that triggers the protection.</p>
- <h2>How to download and install Folder Lock 7.8.0 Crack With Activation Number Free</h2>
-<p>If you are interested in trying out Folder Lock 7.8.0 Crack With Activation Number Free Download 2020, then you can follow these simple steps to download and install it on your PC:</p>
-<ol>
-<li>Go to a reliable source that offers Folder Lock 7.8.0 Crack With Activation Number Free Download 2020 such as <a href="https://haxpc.net/folder-lock-2/">HaxPC</a> or <a href="https://crackingpatching.com/2019/09/folder-lock-7-8-0-final-keygen.html">CrackingPatching</a>.</li>
-<li>Click on the download link or button and wait for the download to complete.</li>
-<li>Extract the downloaded file using WinRAR or any other extraction tool.</li>
-<li>Run the setup file and follow the installation wizard.</li>
-<li>Copy the crack file from the crack folder and paste it into the installation directory of Folder Lock.</li>
-<li>Run Folder Lock and enter the activation number that you can find in the readme file or on the website.</li>
-<li>Enjoy Folder Lock 7.8.0 Crack With Activation Number Free Download 2020!</li>
-</ol>
- <h2>Pros and cons of Folder Lock 7.8.0 Crack</h2>
-<p>Folder Lock 7.8.0 Crack With Activation Number Free Download 2020 is a great software for data security, but it also has some pros and cons that you should be aware of before using it. Here is a table that compares them:</p>
- | Pros | Cons | | --- | --- | | Easy to use interface with drag-and-drop functionality | May slow down your PC performance if you encrypt or shred large files | | Offers a variety of features for encryption, backup, protection, shredding, hiding, and syncing files | May not be compatible with some antivirus or firewall software | | Supports multiple languages and platforms | May not be able to recover your data if you forget your password or lose your activation number | | Provides online backup and sync option with free account | May not be able to update to newer versions without losing your crack | | Allows you to run in stealth mode and prevent hacking attempts | May not be legal to use a cracked version of a paid software | <h2>Conclusion</h2>
-<p>In conclusion, Folder Lock 7.8.0 Crack With Activation Number Free Download 2020 is a powerful and versatile software that can help you secure your personal files from unauthorized access, theft, loss, or damage. It offers a range of features that let you encrypt, backup, lock, shred, hide, and sync your files in various ways. It also allows you to run in stealth mode and prevent hacking attempts.</p>
-<p>If you want to try out Folder Lock 7.8.0 Crack With Activation Number Free Download 2020, then you can download and install it from a reliable source following the steps mentioned above. However, you should also be aware of its pros and cons and make sure that you use it responsibly and legally.</p>
-<p>We hope that this article has provided you with useful information about Folder Lock 7.8.0 Crack With Activation Number Free Download 2020. If you have any questions or feedback, please feel free to leave them in the comments section below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about Folder Lock 7.8.0 Crack With Activation Number Free Download 2020:</p>
- <h3>Q: Is Folder Lock safe to use?</h3>
-<p>A: Folder Lock is safe to use as long as you download it from a trusted source and scan it with an antivirus software before installing it. However, you should also be careful not to lose your password or activation number as they are essential for accessing your encrypted files.</p>
- <h3>Q: How can I uninstall Folder Lock?</h3>
-<p>A: To uninstall Folder Lock, you need to enter your master password and go to the settings menu. Then, click on the uninstall option and follow the instructions. You should also delete any leftover files or folders related to Folder Lock from your PC.</p>
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-<p>A: Yes, you can use Folder Lock on multiple devices as long as you have the same activation number for each device. You can also sync your encrypted files across multiple devices using the online backup option.</p>
- <h3>Q: What if I forget my password or activation number?</h3>
-<p>A: If you forget your password or activation number, then you might not be able to access your encrypted files or use Folder Lock anymore. Therefore, you should always keep a backup of your password and activation number in a safe place or use the password recovery option if available.</p>
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-<p>A: Some alternatives to Folder Lock are VeraCrypt, AxCrypt, BitLocker, WinZip, 7-Zip, etc. These are also data security software that offer similar features such as encryption, compression, password protection, etc.</p>
- </p> 0a6ba089eb<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Free Ipcam Viewer For Mac.md b/spaces/raedeXanto/academic-chatgpt-beta/Free Ipcam Viewer For Mac.md
deleted file mode 100644
index 36a2c2706ee44df3ab36a0ab37227af5a623290f..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Free Ipcam Viewer For Mac.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>How to View IP Cameras on Mac for Free</h1>
-<p>If you have IP cameras at home or in your office, you might want to view them on your Mac computer. However, not all IP cameras are compatible with Mac OS, and some of them require proprietary software that is not free. In this article, we will show you how to view IP cameras on Mac for free using a web browser or a third-party app.</p>
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-<h2>View IP Cameras on Mac Using a Web Browser</h2>
-<p>One of the easiest ways to view IP cameras on Mac is to use a web browser. Most IP cameras have a web interface that allows you to access the live video stream and change some settings. To view IP cameras on Mac using a web browser, you need to know the IP address of the camera, the username and password (if any), and the port number (if any).</p>
-<p>Here are the steps to view IP cameras on Mac using a web browser:</p>
-<ol>
-<li>Open your web browser (such as Safari, Chrome, or Firefox) and type the IP address of the camera in the address bar. For example, if the IP address of the camera is 192.168.1.100, type http://192.168.1.100 in the address bar.</li>
-<li>If the camera requires a username and password, enter them in the pop-up window. The default username and password are usually admin or blank. You can check the user manual of the camera for more information.</li>
-<li>If the camera requires a port number, append it to the IP address with a colon. For example, if the port number is 8080, type http://192.168.1.100:8080 in the address bar.</li>
-<li>You should see the live video stream of the camera in your web browser. You can also change some settings such as resolution, frame rate, brightness, etc.</li>
-</ol>
-<h2>View IP Cameras on Mac Using a Third-Party App</h2>
-<p>Another way to view IP cameras on Mac is to use a third-party app that supports multiple brands and models of IP cameras. One of the most popular apps for this purpose is IPCam Viewer for Mac. IPCam Viewer for Mac is a free app that allows you to view up to 4 IP cameras simultaneously on your Mac screen. You can also record videos, take snapshots, zoom in and out, and control PTZ (pan-tilt-zoom) functions.</p>
-<p>Here are the steps to view IP cameras on Mac using IPCam Viewer for Mac:</p>
-<ol>
-<li>Download and install IPCam Viewer for Mac from <a href="https://apps.apple.com/us/app/ipcam-viewer/id1013455241?mt=12">here</a>.</li>
-<li>Open IPCam Viewer for Mac and click on the + button at the bottom left corner to add a new camera.</li>
-<li>Select the brand and model of your camera from the drop-down list. If your camera is not listed, you can select Generic Camera and enter the URL of the video stream manually.</li>
-<li>Enter the name, IP address, port number, username, and password of your camera. You can also adjust some settings such as resolution, frame rate, audio, etc.</li>
-<li>Click on Save to add the camera to your list.</li>
-<li>Repeat steps 2-5 to add more cameras if you want.</li>
-<li>You should see the live video stream of your cameras on your Mac screen. You can switch between different views by clicking on the icons at the bottom right corner.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to view IP cameras on Mac for free using a web browser or a third-party app. Both methods are easy and convenient, but they have some limitations as well. For example, using a web browser may not support all features of your camera, such as audio or PTZ control. Using a third-party app may require more bandwidth and CPU resources than using a web browser.</p>
-<p></p>
-<p>If you want to view IP cameras on Mac with more advanced features and performance, you may need to purchase a professional software such as SecuritySpy or Blue Iris. These software can support hundreds of IP cameras with high-resolution video streaming, motion detection, recording, alerts, remote access, etc.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rainfly/test_speed/ernie_vilg/module.py b/spaces/rainfly/test_speed/ernie_vilg/module.py
deleted file mode 100644
index 78c6c98cdf5653cc323b3a22da31eea615439d8e..0000000000000000000000000000000000000000
--- a/spaces/rainfly/test_speed/ernie_vilg/module.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import ast
-import argparse
-import os
-from functools import partial
-from typing import List, Optional
-import sys
-import base64
-from io import BytesIO
-
-import requests
-from tqdm.auto import tqdm
-import time
-from PIL import Image
-import re
-
-
-import paddlehub as hub
-from paddlehub.module.module import moduleinfo
-from paddlehub.module.module import runnable
-from paddlehub.module.module import serving
-
-
-@moduleinfo(
-    name="ernie_vilg",
-    version="1.0.0",
-    type="image/text_to_image",
-    summary="",
-    author="baidu-nlp",
-    author_email="paddle-dev@baidu.com")
-class ErnieVilG:
-    def __init__(self, ak=None, sk=None):
-      """
-      :param ak: ak for applying token to request wenxin api.
-      :param sk: sk for applying token to request wenxin api.
-      """
-      if ak is None or sk is None:
-        self.ak = 'G26BfAOLpGIRBN5XrOV2eyPA25CE01lE'
-        self.sk = 'txLZOWIjEqXYMU3lSm05ViW4p9DWGOWs'
-      else:
-        self.ak = ak
-        self.sk = sk
-      self.token_host = 'https://wenxin.baidu.com/younger/portal/api/oauth/token'
-      self.token = self._apply_token(self.ak, self.sk)
-    
-    def _apply_token(self, ak, sk):
-      if ak is None or sk is None:
-        ak = self.ak
-        sk = self.sk
-      response = requests.get(self.token_host, params={'grant_type':'client_credentials', 'client_id':ak, 'client_secret':sk})
-      if response:
-          res = response.json()
-          if res['code'] != 0:
-            print('Request access token error.')
-            raise RuntimeError("Request access token error.")
-      else:
-          print('Request access token error.')
-          raise RuntimeError("Request access token error.")
-      return res['data']
-
-
-    def generate_image(self,
-              text_prompts,
-              style: Optional[str] = "油画",
-              topk: Optional[int] = 6,
-              visualization: Optional[bool] = True,
-              output_dir: Optional[str] = 'ernievilg_output'):
-        """
-        Create image by text prompts using ErnieVilG model.
-
-        :param text_prompts: Phrase, sentence, or string of words and phrases describing what the image should look like.  
-        :param style: Image stype, currently supported 油画、水彩、粉笔画、卡通、儿童画、蜡笔画、探索无限。
-        :param topk: Top k images to save.
-        :param visualization: Whether to save images or not.
-        :output_dir: Output directory
-        """
-        if not os.path.exists(output_dir):
-          os.makedirs(output_dir, exist_ok=True)
-        token = self.token
-        task_create_start_time = time.time()
-        print('task_create_start_time', time.ctime(task_create_start_time))
-        create_url = 'https://wenxin.baidu.com/younger/portal/api/rest/1.0/ernievilg/v1/txt2img?from=paddlehub'
-        get_url = 'https://wenxin.baidu.com/younger/portal/api/rest/1.0/ernievilg/v1/getImg?from=paddlehub'
-        if isinstance(text_prompts, str):
-          text_prompts = [text_prompts]
-        taskids = []
-        for text_prompt in text_prompts:
-          res = requests.post(create_url, headers={'Content-Type':'application/x-www-form-urlencoded'}, data={'access_token': token,"text": text_prompt, "style": style})
-          res = res.json()
-          if res['code'] == 4001:
-            print('请求参数错误')
-            raise RuntimeError("请求参数错误")
-          elif res['code'] == 4002:
-            print('请求参数格式错误，请检查必传参数是否齐全，参数类型等')
-            raise RuntimeError("请求参数格式错误，请检查必传参数是否齐全，参数类型等")
-          elif res['code'] == 4003:
-            print('请求参数中，图片风格不在可选范围内')
-            raise RuntimeError("请求参数中，图片风格不在可选范围内")
-          elif res['code'] == 4004:
-            print('API服务内部错误，可能引起原因有请求超时、模型推理错误等')
-            raise RuntimeError("API服务内部错误，可能引起原因有请求超时、模型推理错误等")
-          elif res['code'] == 100 or res['code'] == 110 or res['code'] == 111:
-            token = self._apply_token(self.ak, self.sk)
-            res = requests.post(create_url, headers={'Content-Type':'application/x-www-form-urlencoded'}, data={'access_token': token,"text": text_prompt, "style": style})
-            res = res.json()
-            if res['code'] != 0:
-             print("Token失效重新请求后依然发生错误，请检查输入的参数")
-             raise RuntimeError("Token失效重新请求后依然发生错误，请检查输入的参数")
-          if res['msg'] == 'success':
-            taskids.append(res['data']["taskId"])
-          else:
-            print(res['msg'])
-            raise RuntimeError(res['msg'])
-        task_create_end_time = time.time()
-        print('task_create_end_time', time.ctime(task_create_end_time))
-        print('task create time:', task_create_end_time-task_create_start_time)
-        
-        start_time = time.time()
-        print('task poll start time', time.ctime(start_time))
-        process_bar = tqdm(total=100, unit='%')
-        results = {}
-        total_time = 60 * len(taskids)
-        
-        while True:
-          end_time = time.time()
-          duration = end_time - start_time
-          progress_rate = int((duration) / total_time*100)
-          if not taskids:
-            progress_rate = 100
-          if progress_rate > process_bar.n:
-            if progress_rate >= 100:
-              if not taskids:
-                increase_rate = 100 - process_bar.n
-              else:
-                increase_rate = 0
-            else:
-              increase_rate = progress_rate - process_bar.n
-          else:
-            increase_rate = 0
-          process_bar.update(increase_rate)
-          if duration < 30:
-            time.sleep(5)
-            print('increase_time', end_time - start_time)
-            print('increase_rate', increase_rate, flush=True)
-            continue
-          else:
-            time.sleep(1)
-            print('increase_time', end_time - start_time)
-            print('increase_rate', increase_rate, flush=True)
-          if not taskids:
-            break
-          has_done = []
-          for taskid in taskids:
-            res = requests.post(get_url, headers={'Content-Type':'application/x-www-form-urlencoded'}, data={'access_token': token,'taskId': {taskid}})
-            res = res.json()
-            if res['code'] == 4001:
-              print('请求参数错误')
-              raise RuntimeError("请求参数错误")
-            elif res['code'] == 4002:
-              print('请求参数格式错误，请检查必传参数是否齐全，参数类型等')
-              raise RuntimeError("请求参数格式错误，请检查必传参数是否齐全，参数类型等")
-            elif res['code'] == 4003:
-              print('请求参数中，图片风格不在可选范围内')
-              raise RuntimeError("请求参数中，图片风格不在可选范围内")
-            elif res['code'] == 4004:
-              print('API服务内部错误，可能引起原因有请求超时、模型推理错误等')
-              raise RuntimeError("API服务内部错误，可能引起原因有请求超时、模型推理错误等")
-            elif res['code'] == 100 or res['code'] == 110 or res['code'] == 111:
-              token = self._apply_token(self.ak, self.sk)
-              res = requests.post(get_url, headers={'Content-Type':'application/x-www-form-urlencoded'}, data={'access_token': token,'taskId': {taskid}})
-              res = res.json()
-              if res['code'] != 0:
-                print("Token失效重新请求后依然发生错误，请检查输入的参数")
-                raise RuntimeError("Token失效重新请求后依然发生错误，请检查输入的参数")
-            if res['msg'] == 'success':
-              if res['data']['status'] == 1:
-                print('I am finished', flush=True)
-                has_done.append(res['data']['taskId'])
-                results[res['data']['text']] = {
-                    'imgUrls': res['data']['imgUrls'],
-                    'waiting': res['data']['waiting'],
-                    'taskId': res['data']['taskId']
-                  }
-            else:
-              print(res['msg'])
-              raise RuntimeError(res['msg'])
-          for taskid in has_done:
-            taskids.remove(taskid)
-        end_time = time.time() #debug code
-        print('task poll end time', time.ctime(end_time)) #debug code
-        print('poll time: ',  end_time-start_time)
-        start_save_time = time.time() #debug code
-        print('Saving Images...')
-        print('download_start_time', time.ctime(start_save_time)) #debug code
-        result_images = []
-        for text, data in results.items():
-          for idx, imgdata in enumerate(data['imgUrls']):
-            try:
-              image = Image.open(BytesIO(requests.get(imgdata['image']).content))
-            except Exception as e:
-              print('Download generated images error, retry one time')
-              try:
-                image = Image.open(BytesIO(requests.get(imgdata['image']).content))
-              except Exception:
-                raise RuntimeError('Download generated images failed.')
-            if visualization:
-              image.save(os.path.join(output_dir, '{}_{}.png'.format(text, idx)))
-            result_images.append(image)
-            if idx + 1 >= topk:
-              break
-        print('Done')
-        end_save_time = time.time() #debug code
-        print('download_end_time', time.ctime(end_save_time)) #debug code
-        print('Download and decode images time', end_save_time-start_save_time)
-        return result_images
-
-    @runnable
-    def run_cmd(self, argvs):
-        """
-        Run as a command.
-        """
-        self.parser = argparse.ArgumentParser(
-            description="Run the {} module.".format(self.name),
-            prog='hub run {}'.format(self.name),
-            usage='%(prog)s',
-            add_help=True)
-        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
-        self.add_module_input_arg()
-        args = self.parser.parse_args(argvs)
-        if args.ak is not None and args.sk is not None:
-          self.ak = args.ak
-          self.sk = args.sk
-          self.token = self._apply_token(self.ak, self.sk)
-        results = self.generate_image(
-              text_prompts=args.text_prompts, 
-              style=args.style,
-              topk=args.topk,
-              visualization=args.visualization,
-              output_dir=args.output_dir
-            )
-        return results
-    
-    @serving
-    def serving_method(self, text_prompts, **kwargs):
-        """
-        Run as a service.
-        """
-        results_base64encoded = []
-        results = self.generate_image(text_prompts=text_prompts, **kwargs)
-        for result in results:
-          buffered = BytesIO()
-          result.save(buffered, format="png")
-          img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-          results_base64encoded.append(img_str)
-        return results_base64encoded
-      
-
-    def add_module_input_arg(self):
-        """
-        Add the command input options.
-        """
-        self.arg_input_group.add_argument('--text_prompts', type=str)
-        self.arg_input_group.add_argument('--style', type=str, default='油画', choices=['油画','水彩','粉笔画','卡通','儿童画','蜡笔画','探索无限'], help="绘画风格")
-        self.arg_input_group.add_argument('--topk', type=int, default=6, help="选取保存前多少张图，最多10张")
-        self.arg_input_group.add_argument('--ak', type=str, default=None, help="申请文心api使用token的ak")
-        self.arg_input_group.add_argument('--sk', type=str, default=None, help="申请文心api使用token的sk")
-        self.arg_input_group.add_argument('--visualization', type=bool, default=True, help="是否保存生成的图片")
-        self.arg_input_group.add_argument('--output_dir', type=str, default='ernievilg_output')
-     
diff --git a/spaces/ramiin2/AutoGPT/autogpt/permanent_memory/__init__.py b/spaces/ramiin2/AutoGPT/autogpt/permanent_memory/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ravisingh15/ligand_distance/README.md b/spaces/ravisingh15/ligand_distance/README.md
deleted file mode 100644
index a44163af97de688fe05d261d00c1cfd05047afdb..0000000000000000000000000000000000000000
--- a/spaces/ravisingh15/ligand_distance/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: App1
-emoji: 🏃
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/BricsCad Platinum 20.1.04.1 Crack License Key Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/BricsCad Platinum 20.1.04.1 Crack License Key Download.md
deleted file mode 100644
index ac4ad5df9a80b9e9511998b47ff31e5b87429fbb..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/BricsCad Platinum 20.1.04.1 Crack License Key Download.md	
+++ /dev/null
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-
-<h1>BricsCad Platinum 20.1.04.1 Crack License Key Download: A Powerful Alternative to AutoCAD</h1>
-<p>If you are looking for a 2D and 3D computer-aided design (CAD) software that is compatible with DWG format and offers advanced features at an affordable price, you might want to check out BricsCad Platinum 20.1.04.1 Crack License Key Download. This software is a reliable alternative to AutoCAD that can handle various design, illustration and engineering tasks with ease and efficiency.</p>
-<p>In this article, we will review some of the key features and benefits of BricsCad Platinum 20.1.04.1 Crack License Key Download and show you how to get it for free.</p>
-<h2>BricsCad Platinum 20.1.04.1 Crack License Key Download</h2><br /><p><b><b>Download Zip</b> &gt;&gt;&gt; <a href="https://urlgoal.com/2uCN6p">https://urlgoal.com/2uCN6p</a></b></p><br /><br />
-<h2>What is BricsCad Platinum 20.1.04.1 Crack License Key Download?</h2>
-<p>BricsCad Platinum 20.1.04.1 Crack License Key Download is the latest version of BricsCad Platinum, a comprehensive CAD software that supports all the main CAD formats, including DWG, DXF, DWF, BMP and ECW[^1^]. It can perform 3D direct modelling, assembly editing, 3D constraints, building information modelling (BIM), sheet metal design and more[^2^].</p>
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Soccer Highly Compressed To 5 Mb.rar NEW.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Soccer Highly Compressed To 5 Mb.rar NEW.md
deleted file mode 100644
index 389ca7361e5fc95c890413b08f51c0c3d920abbf..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Soccer Highly Compressed To 5 Mb.rar NEW.md	
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diff --git a/spaces/rgres/Seg2Sat/frontend/.svelte-kit/generated/client-manifest.js b/spaces/rgres/Seg2Sat/frontend/.svelte-kit/generated/client-manifest.js
deleted file mode 100644
index 8635adf5d10b0aeb60f7376fae6dbe8fcf17bc9c..0000000000000000000000000000000000000000
--- a/spaces/rgres/Seg2Sat/frontend/.svelte-kit/generated/client-manifest.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export { matchers } from './client-matchers.js';
-
-export const components = [
-	() => import("../../src/routes/__layout.svelte"),
-	() => import("../runtime/components/error.svelte"),
-	() => import("../../src/routes/index.svelte")
-];
-
-export const dictionary = {
-	"": [[0, 2], [1]]
-};
\ No newline at end of file
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/focalnet_dino/models/dino/swin_transformer.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/focalnet_dino/models/dino/swin_transformer.py
deleted file mode 100644
index d9fb7c6d4a2e2c2a4ea6035dbffa90796d582610..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/focalnet_dino/models/dino/swin_transformer.py
+++ /dev/null
@@ -1,729 +0,0 @@
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# --------------------------------------------------------
-# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from .util.misc import NestedTensor
-
-
-class Mlp(nn.Module):
-    """ Multilayer perceptron."""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """ Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """ Forward function.
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """ Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """ Patch Merging Layer
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    """ Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
-    """
-
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=4,
-                 in_chans=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.2,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 out_indices=(0, 1, 2, 3),
-                 frozen_stages=-1,
-                 dilation=False,
-                 use_checkpoint=False):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.dilation = dilation
-
-        if use_checkpoint:
-            print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        # prepare downsample list
-        downsamplelist = [PatchMerging for i in range(self.num_layers)]
-        downsamplelist[-1] = None
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        if self.dilation:
-            downsamplelist[-2] = None
-            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                # dim=int(embed_dim * 2 ** i_layer),
-                dim=num_features[i_layer],
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                norm_layer=norm_layer,
-                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                downsample=downsamplelist[i_layer],
-                use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f'norm{i_layer}'
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    # def init_weights(self, pretrained=None):
-    #     """Initialize the weights in backbone.
-    #     Args:
-    #         pretrained (str, optional): Path to pre-trained weights.
-    #             Defaults to None.
-    #     """
-
-    #     def _init_weights(m):
-    #         if isinstance(m, nn.Linear):
-    #             trunc_normal_(m.weight, std=.02)
-    #             if isinstance(m, nn.Linear) and m.bias is not None:
-    #                 nn.init.constant_(m.bias, 0)
-    #         elif isinstance(m, nn.LayerNorm):
-    #             nn.init.constant_(m.bias, 0)
-    #             nn.init.constant_(m.weight, 1.0)
-
-    #     if isinstance(pretrained, str):
-    #         self.apply(_init_weights)
-    #         logger = get_root_logger()
-    #         load_checkpoint(self, pretrained, strict=False, logger=logger)
-    #     elif pretrained is None:
-    #         self.apply(_init_weights)
-    #     else:
-    #         raise TypeError('pretrained must be a str or None')
-
-
-    def forward_raw(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-            # import ipdb; ipdb.set_trace()
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-        # in:
-        #   torch.Size([2, 3, 1024, 1024])
-        # outs:
-        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
-        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
-        return tuple(outs)
-
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-        # in:
-        #   torch.Size([2, 3, 1024, 1024])
-        # out:
-        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
-        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
-
-        # collect for nesttensors        
-        outs_dict = {}
-        for idx, out_i in enumerate(outs):
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
-            outs_dict[idx] = NestedTensor(out_i, mask)
-
-        return outs_dict
-
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-
-
-def build_swin_transformer(modelname, pretrain_img_size, **kw):
-    assert modelname in ['swin_T_224_1k', 'swin_B_224_22k', 'swin_B_384_22k', 'swin_L_224_22k', 'swin_L_384_22k']
-
-    model_para_dict = {
-        'swin_T_224_1k': dict(
-            embed_dim=96,
-            depths=[ 2, 2, 6, 2 ],
-            num_heads=[ 3, 6, 12, 24],
-            window_size=7
-        ),        
-        'swin_B_224_22k': dict(
-            embed_dim=128,
-            depths=[ 2, 2, 18, 2 ],
-            num_heads=[ 4, 8, 16, 32 ],
-            window_size=7
-        ),
-        'swin_B_384_22k': dict(
-            embed_dim=128,
-            depths=[ 2, 2, 18, 2 ],
-            num_heads=[ 4, 8, 16, 32 ],
-            window_size=12
-        ),
-        'swin_L_224_22k': dict(
-            embed_dim=192,
-            depths=[ 2, 2, 18, 2 ],
-            num_heads=[ 6, 12, 24, 48 ],
-            window_size=7
-        ),
-        'swin_L_384_22k': dict(
-            embed_dim=192,
-            depths=[ 2, 2, 18, 2 ],
-            num_heads=[ 6, 12, 24, 48 ],
-            window_size=12
-        ),
-    }
-    kw_cgf = model_para_dict[modelname]
-    kw_cgf.update(kw)
-    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
-    return model
-
-if __name__ == "__main__":
-    model = build_swin_transformer('swin_L_384_22k', 384, dilation=True)
-    x = torch.rand(2, 3, 1024, 1024)
-    y = model.forward_raw(x)
-    import ipdb; ipdb.set_trace()
-    x = torch.rand(2, 3, 384, 384)
-    y = model.forward_raw(x)
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/1z0 591 Dumps PDF Achieve Your Oracle BI Certification Goals with This Comprehensive Study Material.md b/spaces/rorallitri/biomedical-language-models/logs/1z0 591 Dumps PDF Achieve Your Oracle BI Certification Goals with This Comprehensive Study Material.md
deleted file mode 100644
index dfb5d3bead3d5152804dcba6a920571aeeb54a69..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/1z0 591 Dumps PDF Achieve Your Oracle BI Certification Goals with This Comprehensive Study Material.md	
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deleted file mode 100644
index 58f966cc1b51d23ac871fa7bf13c578c941032d1..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Lichelle Marie Takes Two Dicks At The Same Time.md	
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diff --git a/spaces/runa91/bite_gradio/scripts/gradio_demo.py b/spaces/runa91/bite_gradio/scripts/gradio_demo.py
deleted file mode 100644
index e5e53bb1a2161042ce85fac74d5e80ff476fe3ae..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/scripts/gradio_demo.py
+++ /dev/null
@@ -1,671 +0,0 @@
-
-import os
-os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"  
-os.environ["CUDA_VISIBLE_DEVICES"]="0"
-try:
-    # os.system("pip install --upgrade  torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html")
-    os.system("pip install --upgrade  torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/cu101/torch_stable.html")
-except Exception as e:
-    print(e)
-
-import argparse
-import os.path
-import json
-import numpy as np
-import pickle as pkl
-import csv
-from distutils.util import strtobool
-import torch
-from torch import nn
-import torch.backends.cudnn
-from torch.nn import DataParallel
-from torch.utils.data import DataLoader
-from collections import OrderedDict
-import glob
-from tqdm import tqdm
-from dominate import document
-from dominate.tags import *
-from PIL import Image
-from matplotlib import pyplot as plt
-import trimesh
-import cv2
-import shutil
-import random
-from datetime import datetime
-import gradio as gr
-
-import torchvision
-from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
-import torchvision.transforms as T
-from pytorch3d.structures import Meshes
-from pytorch3d.loss import mesh_edge_loss, mesh_laplacian_smoothing, mesh_normal_consistency
-
-
-import sys
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
-
-from combined_model.train_main_image_to_3d_wbr_withref import do_validation_epoch
-from combined_model.model_shape_v7_withref_withgraphcnn import ModelImageTo3d_withshape_withproj 
-
-from configs.barc_cfg_defaults import get_cfg_defaults, update_cfg_global_with_yaml, get_cfg_global_updated
-
-from lifting_to_3d.utils.geometry_utils import rot6d_to_rotmat, rotmat_to_rot6d  
-from stacked_hourglass.datasets.utils_dataset_selection import get_evaluation_dataset, get_sketchfab_evaluation_dataset, get_crop_evaluation_dataset, get_norm_dict, get_single_crop_dataset_from_image
-
-from test_time_optimization.bite_inference_model_for_ttopt import BITEInferenceModel
-from smal_pytorch.smal_model.smal_torch_new import SMAL
-from configs.SMAL_configs import SMAL_MODEL_CONFIG
-from smal_pytorch.renderer.differentiable_renderer import SilhRenderer
-from test_time_optimization.utils.utils_ttopt import reset_loss_values, get_optimed_pose_with_glob
-
-from combined_model.loss_utils.loss_utils import leg_sideway_error, leg_torsion_error, tail_sideway_error, tail_torsion_error, spine_torsion_error, spine_sideway_error
-from combined_model.loss_utils.loss_utils_gc import LossGConMesh, calculate_plane_errors_batch
-from combined_model.loss_utils.loss_arap import Arap_Loss
-from combined_model.loss_utils.loss_laplacian_mesh_comparison import LaplacianCTF     # (coarse to fine animal)
-from graph_networks import graphcmr     # .utils_mesh import Mesh
-from stacked_hourglass.utils.visualization import save_input_image_with_keypoints, save_input_image
-
-random.seed(2)
-
-print(
-    "torch: ", torch.__version__,
-    "\ntorchvision: ", torchvision.__version__,
-)
-
-
-def get_prediction(model, img_path_or_img, confidence=0.5):
-    """
-    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
-    get_prediction
-        parameters:
-        - img_path - path of the input image
-        - confidence - threshold value for prediction score
-        method:
-        - Image is obtained from the image path
-        - the image is converted to image tensor using PyTorch's Transforms
-        - image is passed through the model to get the predictions
-        - class, box coordinates are obtained, but only prediction score > threshold
-            are chosen.
-    """
-    if isinstance(img_path_or_img, str):
-        img = Image.open(img_path_or_img).convert('RGB')
-    else:
-        img = img_path_or_img
-    transform = T.Compose([T.ToTensor()])
-    img = transform(img)
-    pred = model([img])
-    # pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]
-    pred_class = list(pred[0]['labels'].numpy())
-    pred_boxes = [[(int(i[0]), int(i[1])), (int(i[2]), int(i[3]))] for i in list(pred[0]['boxes'].detach().numpy())]
-    pred_score = list(pred[0]['scores'].detach().numpy())
-    try:
-        pred_t = [pred_score.index(x) for x in pred_score if x>confidence][-1]
-        pred_boxes = pred_boxes[:pred_t+1]
-        pred_class = pred_class[:pred_t+1]
-        return pred_boxes, pred_class, pred_score
-    except:
-        print('no bounding box with a score that is high enough found! -> work on full image')
-        return None, None, None
-
-
-def detect_object(model, img_path_or_img, confidence=0.5, rect_th=2, text_size=0.5, text_th=1):
-    """
-    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
-    object_detection_api
-        parameters:
-        - img_path_or_img - path of the input image
-        - confidence - threshold value for prediction score
-        - rect_th - thickness of bounding box
-        - text_size - size of the class label text
-        - text_th - thichness of the text
-        method:
-        - prediction is obtained from get_prediction method
-        - for each prediction, bounding box is drawn and text is written 
-            with opencv
-        - the final image is displayed
-    """
-    boxes, pred_cls, pred_scores = get_prediction(model, img_path_or_img, confidence)
-    if isinstance(img_path_or_img, str):
-        img = cv2.imread(img_path_or_img)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    else:
-        img = img_path_or_img
-    is_first = True
-    bbox = None
-    if boxes is not None:
-        for i in range(len(boxes)):
-            cls = pred_cls[i]
-            if cls == 18 and bbox is None:
-                cv2.rectangle(img, boxes[i][0], boxes[i][1],color=(0, 255, 0), thickness=rect_th)
-                # cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
-                # cv2.putText(img, str(pred_scores[i]), boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
-                bbox = boxes[i]
-    return img, bbox
-
-
-# -------------------------------------------------------------------------------------------------------------------- #
-model_bbox = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
-model_bbox.eval()
-
-def run_bbox_inference(input_image):
-    # load configs
-    cfg = get_cfg_global_updated()
-    out_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples', 'test2.png')
-    img, bbox = detect_object(model=model_bbox, img_path_or_img=input_image, confidence=0.5)
-    # fig = plt.figure()   #  plt.figure(figsize=(20,30))
-    # plt.imsave(out_path, img)
-    return img, bbox
-
-
-
-# -------------------------------------------------------------------------------------------------------------------- #
-args_config = "refinement_cfg_test_withvertexwisegc_csaddnonflat.yaml"
-# args_model_file_complete = "cvpr23_dm39dnnv3barcv2b_refwithgcpervertisflat0morestanding0/checkpoint.pth.tar"
-args_model_file_complete = "cvpr23_dm39dnnv3barcv2b_refwithgcpervertisflat0morestanding0_forrelease_v0/checkpoint.pth.tar"
-args_suffix = "ttopt_v0"
-args_loss_weight_ttopt_path = "bite_loss_weights_ttopt.json"
-args_workers = 12
-# -------------------------------------------------------------------------------------------------------------------- #
-
-
-
-# load configs
-#   step 1: load default configs
-#   step 2: load updates from .yaml file
-path_config = os.path.join(get_cfg_defaults().barc_dir, 'src', 'configs', args_config)
-update_cfg_global_with_yaml(path_config)
-cfg = get_cfg_global_updated()
-
-# define path to load the trained model
-path_model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH, args_model_file_complete) 
-
-# define and create paths to save results
-out_sub_name = cfg.data.VAL_OPT + '_' + cfg.data.DATASET + '_' + args_suffix + '/'
-root_out_path = os.path.join(os.path.dirname(path_model_file_complete).replace(cfg.paths.ROOT_CHECKPOINT_PATH, cfg.paths.ROOT_OUT_PATH + 'results_gradio/'), out_sub_name)
-root_out_path_details = root_out_path + 'details/'
-if not os.path.exists(root_out_path): os.makedirs(root_out_path)
-if not os.path.exists(root_out_path_details): os.makedirs(root_out_path_details)
-print('root_out_path: ' + root_out_path)
-
-# other paths
-root_data_path = os.path.join(os.path.dirname(__file__), '../', 'data')
-# downsampling as used in graph neural network
-root_smal_downsampling = os.path.join(root_data_path, 'graphcmr_data')   
-# remeshing as used for ground contact
-remeshing_path = os.path.join(root_data_path, 'smal_data_remeshed', 'uniform_surface_sampling', 'my_smpl_39dogsnorm_Jr_4_dog_remesh4000_info.pkl')
-
-loss_weight_path = os.path.join(os.path.dirname(__file__), '../', 'src', 'configs', 'ttopt_loss_weights', args_loss_weight_ttopt_path)  
-print(loss_weight_path)
-
-
-# Select the hardware device to use for training.
-if torch.cuda.is_available() and cfg.device=='cuda':
-    device = torch.device('cuda', torch.cuda.current_device())
-    torch.backends.cudnn.benchmark = False      # True
-else:
-    device = torch.device('cpu')
-
-print('structure_pose_net: ' + cfg.params.STRUCTURE_POSE_NET)
-print('refinement network type: ' + cfg.params.REF_NET_TYPE)
-print('smal_model_type: ' + cfg.smal.SMAL_MODEL_TYPE)
-
-# prepare complete model
-norm_dict = get_norm_dict(data_info=None, device=device)
-bite_model = BITEInferenceModel(cfg, path_model_file_complete, norm_dict)
-smal_model_type = bite_model.smal_model_type
-logscale_part_list = SMAL_MODEL_CONFIG[smal_model_type]['logscale_part_list']       # ['legs_l', 'legs_f', 'tail_l', 'tail_f', 'ears_y', 'ears_l', 'head_l'] 
-smal = SMAL(smal_model_type=smal_model_type, template_name='neutral', logscale_part_list=logscale_part_list).to(device)    
-silh_renderer = SilhRenderer(image_size=256).to(device)    
-
-# load loss modules -> not necessary!
-# loss_module = Loss(smal_model_type=cfg.smal.SMAL_MODEL_TYPE, data_info=StanExt.DATA_INFO, nf_version=cfg.params.NF_VERSION).to(device)    
-# loss_module_ref = LossRef(smal_model_type=cfg.smal.SMAL_MODEL_TYPE, data_info=StanExt.DATA_INFO, nf_version=cfg.params.NF_VERSION).to(device)    
-
-# remeshing utils
-with open(remeshing_path, 'rb') as fp: 
-    remeshing_dict = pkl.load(fp)
-remeshing_relevant_faces = torch.tensor(remeshing_dict['smal_faces'][remeshing_dict['faceid_closest']], dtype=torch.long, device=device)
-remeshing_relevant_barys = torch.tensor(remeshing_dict['barys_closest'], dtype=torch.float32, device=device)
-
-
-
-
-# create path for output files
-save_imgs_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples')
-if not os.path.exists(save_imgs_path):
-    os.makedirs(save_imgs_path)
-
-
-
-
-
-def run_bite_inference(input_image, bbox=None, apply_ttopt=True,  dog_name="dog_model"):
-
-    with open(loss_weight_path, 'r') as j: 
-        losses = json.loads(j.read())
-    shutil.copyfile(loss_weight_path, root_out_path_details + os.path.basename(loss_weight_path))
-
-    # prepare dataset and dataset loader
-    val_dataset, val_loader, len_val_dataset, test_name_list, stanext_data_info, stanext_acc_joints = get_single_crop_dataset_from_image(input_image, bbox=bbox)
-
-    # summarize information for normalization 
-    norm_dict = get_norm_dict(stanext_data_info, device)
-    # get keypoint weights
-    keypoint_weights = torch.tensor(stanext_data_info.keypoint_weights, dtype=torch.float)[None, :].to(device) 
-
-
-    # prepare progress bar
-    iterable = enumerate(val_loader) # the length of this iterator should be 1
-    progress = None
-    if False:        # not quiet:
-        progress = tqdm(iterable, desc='Train', total=len(val_loader), ascii=True, leave=False)
-        iterable = progress
-    ind_img_tot = 0
-
-    for i, (input, target_dict) in iterable:
-        batch_size = input.shape[0]
-        # prepare variables, put them on the right device
-        for key in target_dict.keys(): 
-            if key == 'breed_index':
-                target_dict[key] = target_dict[key].long().to(device)
-            elif key in ['index', 'pts', 'tpts', 'target_weight', 'silh', 'silh_distmat_tofg', 'silh_distmat_tobg', 'sim_breed_index', 'img_border_mask']:
-                target_dict[key] = target_dict[key].float().to(device)
-            elif key == 'has_seg':
-                target_dict[key] = target_dict[key].to(device)
-            else:
-                pass
-        input = input.float().to(device)
-
-        # get starting values for the optimization 
-        preds_dict = bite_model.get_all_results(input)
-        # res_normal_and_ref = bite_model.get_selected_results(preds_dict=preds_dict, result_networks=['normal', 'ref'])
-        res = bite_model.get_selected_results(preds_dict=preds_dict, result_networks=['ref'])['ref']
-        bs = res['pose_rotmat'].shape[0]
-        all_pose_6d = rotmat_to_rot6d(res['pose_rotmat'][:, None, 1:, :, :].clone().reshape((-1, 3, 3))).reshape((bs, -1, 6))       # [bs, 34, 6]
-        all_orient_6d = rotmat_to_rot6d(res['pose_rotmat'][:, None, :1, :, :].clone().reshape((-1, 3, 3))).reshape((bs, -1, 6))     # [bs, 1, 6]
-        
-
-    ind_img = 0
-    name = (test_name_list[target_dict['index'][ind_img].long()]).replace('/', '__').split('.')[0]
-
-    ind_img_tot += 1
-    batch_size = 1
-
-    # initialize the variables over which we want to optimize
-    optimed_pose_6d = all_pose_6d[ind_img, None, :, :].to(device).clone().detach().requires_grad_(True)
-    optimed_orient_6d = all_orient_6d[ind_img, None, :, :].to(device).clone().detach().requires_grad_(True)  # [1, 1, 6]
-    optimed_betas = res['betas'][ind_img, None, :].to(device).clone().detach().requires_grad_(True)   # [1,30]
-    optimed_trans_xy = res['trans'][ind_img, None, :2].to(device).clone().detach().requires_grad_(True)
-    optimed_trans_z =res['trans'][ind_img, None, 2:3].to(device).clone().detach().requires_grad_(True)
-    optimed_camera_flength = res['flength'][ind_img, None, :].to(device).clone().detach().requires_grad_(True)  # [1,1]
-    n_vert_comp = 2*smal.n_center + 3*smal.n_left
-    optimed_vert_off_compact = torch.tensor(np.zeros((batch_size, n_vert_comp)), dtype=torch.float,
-                                device=device,
-                                requires_grad=True) 
-    assert len(logscale_part_list) == 7
-    new_betas_limb_lengths = res['betas_limbs'][ind_img, None, :]
-    optimed_betas_limbs = new_betas_limb_lengths.to(device).clone().detach().requires_grad_(True)  # [1,7]
-
-    # define the optimizers 
-    optimizer = torch.optim.SGD(
-        # [optimed_pose, optimed_trans_xy, optimed_betas, optimed_betas_limbs, optimed_orient, optimed_vert_off_compact],
-        [optimed_camera_flength, optimed_trans_z, optimed_trans_xy, optimed_pose_6d, optimed_orient_6d, optimed_betas, optimed_betas_limbs],
-        lr=5*1e-4,        # 1e-3,
-        momentum=0.9)
-    optimizer_vshift = torch.optim.SGD(
-        [optimed_camera_flength, optimed_trans_z, optimed_trans_xy, optimed_pose_6d, optimed_orient_6d, optimed_betas, optimed_betas_limbs, optimed_vert_off_compact],
-        lr=1e-4,  # 1e-4,
-        momentum=0.9)
-    nopose_optimizer = torch.optim.SGD(
-        # [optimed_pose, optimed_trans_xy, optimed_betas, optimed_betas_limbs, optimed_orient, optimed_vert_off_compact],
-        [optimed_camera_flength, optimed_trans_z, optimed_trans_xy, optimed_orient_6d, optimed_betas, optimed_betas_limbs],
-        lr=5*1e-4,        # 1e-3,
-        momentum=0.9)
-    nopose_optimizer_vshift = torch.optim.SGD(
-        [optimed_camera_flength, optimed_trans_z, optimed_trans_xy, optimed_orient_6d, optimed_betas, optimed_betas_limbs, optimed_vert_off_compact],
-        lr=1e-4,  # 1e-4,
-        momentum=0.9)
-    # define schedulers
-    patience = 5
-    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-        optimizer,
-        mode='min',
-        factor=0.5,
-        verbose=0,
-        min_lr=1e-5,
-        patience=patience)
-    scheduler_vshift = torch.optim.lr_scheduler.ReduceLROnPlateau(
-        optimizer_vshift,
-        mode='min',
-        factor=0.5,
-        verbose=0,
-        min_lr=1e-5,
-        patience=patience)
-
-    # set all loss values to 0
-    losses = reset_loss_values(losses)
-
-    # prepare all the target labels: keypoints, silhouette, ground contact, ...
-    with torch.no_grad():
-        thr_kp = 0.2
-        kp_weights = res['hg_keyp_scores']
-        kp_weights[res['hg_keyp_scores']<thr_kp] = 0
-        weights_resh = kp_weights[ind_img, None, :, :].reshape((-1))  # target_dict['tpts'][:, :, 2].reshape((-1)) 
-        keyp_w_resh = keypoint_weights.repeat((batch_size, 1)).reshape((-1))
-        # prepare predicted ground contact labels
-        sm = nn.Softmax(dim=1)
-        target_gc_class = sm(res['vertexwise_ground_contact'][ind_img, :, :])[None, :, 1]       # values between 0 and 1
-        target_gc_class_remeshed = torch.einsum('ij,aij->ai', remeshing_relevant_barys, target_gc_class[:, remeshing_relevant_faces].to(device=device, dtype=torch.float32))
-        target_gc_class_remeshed_prep = torch.round(target_gc_class_remeshed).to(torch.long)
-        # vert_colors = np.repeat(255*target_gc_class.detach().cpu().numpy()[0, :, None], 3, 1)
-        # vert_colors[:, 2] = 255
-        vert_colors = np.ones_like(np.repeat(target_gc_class.detach().cpu().numpy()[0, :, None], 3, 1)) * 255
-        faces_prep = smal.faces.unsqueeze(0).expand((batch_size, -1, -1))
-        # prepare target silhouette and keypoints, from stacked hourglass predictions
-        target_hg_silh = res['hg_silh_prep'][ind_img, :, :].detach()
-        target_kp_resh = res['hg_keyp_256'][ind_img, None, :, :].reshape((-1, 2)).detach()
-        # find out if ground contact constraints should be used for the image at hand
-        if res['isflat_prep'][ind_img] >= 0.5: # threshold should probably be set higher
-            isflat = [True]
-        else:
-            isflat = [False] 
-        if target_gc_class_remeshed_prep.sum() > 3:
-            istouching = [True]
-        else:
-            istouching = [False]
-        ignore_pose_optimization = False
-
-
-    if not apply_ttopt:
-        # get 3d smal model
-        optimed_pose_with_glob = get_optimed_pose_with_glob(optimed_orient_6d, optimed_pose_6d)
-        optimed_trans = torch.cat((optimed_trans_xy, optimed_trans_z), dim=1)
-        smal_verts, keyp_3d, _ = smal(beta=optimed_betas, betas_limbs=optimed_betas_limbs, pose=optimed_pose_with_glob, vert_off_compact=optimed_vert_off_compact, trans=optimed_trans, keyp_conf='olive', get_skin=True)
-
-        # save mesh
-        my_mesh_tri = trimesh.Trimesh(vertices=smal_verts[0, ...].detach().cpu().numpy(), faces=faces_prep[0, ...].detach().cpu().numpy(), process=False,  maintain_order=True)
-        my_mesh_tri.visual.vertex_colors = vert_colors
-        # my_mesh_tri.export(root_out_path +  name + '_res_e000' + '.obj')
-
-    else: 
-
-        ##########################################################################################################
-        # start optimizing for this image
-        n_iter = 301    # how many iterations are desired? (+1)
-        loop = range(n_iter)
-        per_loop_lst = []
-        list_error_procrustes = []
-        for i in loop:
-            # for the first 150 iterations steps we don't allow vertex shifts
-            if i == 0:          
-                current_i = 0
-                if ignore_pose_optimization:
-                    current_optimizer = nopose_optimizer
-                else:
-                    current_optimizer = optimizer
-                current_scheduler = scheduler
-                current_weight_name = 'weight' 
-            # after 150 iteration steps we start with vertex shifts    
-            elif i == 150:      
-                current_i = 0
-                if ignore_pose_optimization:
-                    current_optimizer = nopose_optimizer_vshift
-                else:
-                    current_optimizer = optimizer_vshift
-                current_scheduler = scheduler_vshift
-                current_weight_name = 'weight_vshift'    
-                # set up arap loss
-                if losses["arap"]['weight_vshift'] > 0.0:
-                    with torch.no_grad():
-                        torch_mesh_comparison = Meshes(smal_verts.detach(), faces_prep.detach())
-                    arap_loss = Arap_Loss(meshes=torch_mesh_comparison, device=device)  
-                #  is there a laplacian loss similar as in coarse-to-fine?
-                if losses["lapctf"]['weight_vshift'] > 0.0:
-                    torch_verts_comparison = smal_verts.detach().clone()
-                    smal_model_type_downsampling = '39dogs_norm'
-                    smal_downsampling_npz_name = 'mesh_downsampling_' + os.path.basename(SMAL_MODEL_CONFIG[smal_model_type_downsampling]['smal_model_path']).replace('.pkl', '_template.npz')
-                    smal_downsampling_npz_path = os.path.join(root_smal_downsampling, smal_downsampling_npz_name)  
-                    data = np.load(smal_downsampling_npz_path, encoding='latin1', allow_pickle=True)
-                    adjmat = data['A'][0]
-                    laplacian_ctf = LaplacianCTF(adjmat, device=device)
-            else:
-                pass
-
-
-            current_optimizer.zero_grad()
-
-            # get 3d smal model
-            optimed_pose_with_glob = get_optimed_pose_with_glob(optimed_orient_6d, optimed_pose_6d)
-            optimed_trans = torch.cat((optimed_trans_xy, optimed_trans_z), dim=1)
-            smal_verts, keyp_3d, _ = smal(beta=optimed_betas, betas_limbs=optimed_betas_limbs, pose=optimed_pose_with_glob, vert_off_compact=optimed_vert_off_compact, trans=optimed_trans, keyp_conf='olive', get_skin=True)
-
-            # render silhouette and keypoints
-            pred_silh_images, pred_keyp_raw = silh_renderer(vertices=smal_verts, points=keyp_3d, faces=faces_prep, focal_lengths=optimed_camera_flength)
-            pred_keyp = pred_keyp_raw[:, :24, :]
-
-            # save silhouette reprojection visualization
-            """
-            if i==0:
-                img_silh = Image.fromarray(np.uint8(255*pred_silh_images[0, 0, :, :].detach().cpu().numpy())).convert('RGB')
-                img_silh.save(root_out_path_details +  name + '_silh_ainit.png')
-                my_mesh_tri = trimesh.Trimesh(vertices=smal_verts[0, ...].detach().cpu().numpy(), faces=faces_prep[0, ...].detach().cpu().numpy(), process=False,  maintain_order=True)
-                my_mesh_tri.export(root_out_path_details +  name + '_res_ainit.obj')
-            """
-
-            # silhouette loss
-            diff_silh = torch.abs(pred_silh_images[0, 0, :, :] - target_hg_silh)
-            losses['silhouette']['value'] = diff_silh.mean()
-
-            # keypoint_loss
-            output_kp_resh = (pred_keyp[0, :, :]).reshape((-1, 2))    
-            losses['keyp']['value'] = ((((output_kp_resh - target_kp_resh)[weights_resh>0]**2).sum(axis=1).sqrt() * \
-                weights_resh[weights_resh>0])*keyp_w_resh[weights_resh>0]).sum() / \
-                max((weights_resh[weights_resh>0]*keyp_w_resh[weights_resh>0]).sum(), 1e-5)
-            # losses['keyp']['value'] = ((((output_kp_resh - target_kp_resh)[weights_resh>0]**2).sum(axis=1).sqrt()*weights_resh[weights_resh>0])*keyp_w_resh[weights_resh>0]).sum() / max((weights_resh[weights_resh>0]*keyp_w_resh[weights_resh>0]).sum(), 1e-5)
-
-            # pose priors on refined pose
-            losses['pose_legs_side']['value'] = leg_sideway_error(optimed_pose_with_glob)
-            losses['pose_legs_tors']['value'] = leg_torsion_error(optimed_pose_with_glob)
-            losses['pose_tail_side']['value'] = tail_sideway_error(optimed_pose_with_glob)
-            losses['pose_tail_tors']['value'] = tail_torsion_error(optimed_pose_with_glob)
-            losses['pose_spine_side']['value'] = spine_sideway_error(optimed_pose_with_glob)
-            losses['pose_spine_tors']['value'] = spine_torsion_error(optimed_pose_with_glob)
-
-            # ground contact loss
-            sel_verts = torch.index_select(smal_verts, dim=1, index=remeshing_relevant_faces.reshape((-1))).reshape((batch_size, remeshing_relevant_faces.shape[0], 3, 3))
-            verts_remeshed = torch.einsum('ij,aijk->aik', remeshing_relevant_barys, sel_verts)
-
-            # gc_errors_plane, gc_errors_under_plane = calculate_plane_errors_batch(verts_remeshed, target_gc_class_remeshed_prep, target_dict['has_gc'], target_dict['has_gc_is_touching'])
-            gc_errors_plane, gc_errors_under_plane = calculate_plane_errors_batch(verts_remeshed, target_gc_class_remeshed_prep, isflat, istouching)
-
-            losses['gc_plane']['value'] = torch.mean(gc_errors_plane) 
-            losses['gc_belowplane']['value'] = torch.mean(gc_errors_under_plane)
-
-            # edge length of the predicted mesh
-            if (losses["edge"][current_weight_name] + losses["normal"][ current_weight_name] + losses["laplacian"][ current_weight_name]) > 0:
-                torch_mesh = Meshes(smal_verts, faces_prep.detach())
-                losses["edge"]['value'] = mesh_edge_loss(torch_mesh)
-                # mesh normal consistency
-                losses["normal"]['value'] = mesh_normal_consistency(torch_mesh)
-                # mesh laplacian smoothing
-                losses["laplacian"]['value'] = mesh_laplacian_smoothing(torch_mesh, method="uniform")
-
-            # arap loss
-            if losses["arap"][current_weight_name] > 0.0:
-                torch_mesh = Meshes(smal_verts, faces_prep.detach())
-                losses["arap"]['value'] =  arap_loss(torch_mesh)
-
-            # laplacian loss for comparison (from coarse-to-fine paper)
-            if losses["lapctf"][current_weight_name] > 0.0:
-                verts_refine = smal_verts
-                loss_almost_arap, loss_smooth = laplacian_ctf(verts_refine, torch_verts_comparison)
-                losses["lapctf"]['value'] =  loss_almost_arap
-
-            # Weighted sum of the losses
-            total_loss = 0.0 
-            for k in ['keyp', 'silhouette', 'pose_legs_side', 'pose_legs_tors', 'pose_tail_side', 'pose_tail_tors', 'pose_spine_tors', 'pose_spine_side', 'gc_plane', 'gc_belowplane', 'edge', 'normal', 'laplacian', 'arap', 'lapctf']:
-                if losses[k][current_weight_name] > 0.0:
-                    total_loss += losses[k]['value'] * losses[k][current_weight_name]
-
-            # calculate gradient and make optimization step
-            total_loss.backward(retain_graph=True)  #  
-            current_optimizer.step()
-            current_scheduler.step(total_loss)
-            # loop.set_description(f"Body Fitting = {total_loss.item():.3f}")
-
-            # save the result three times (0, 150, 300)
-            if i == 300:  # if i % 150 == 0:    
-                # save silhouette image
-                img_silh = Image.fromarray(np.uint8(255*pred_silh_images[0, 0, :, :].detach().cpu().numpy())).convert('RGB')
-                img_silh.save(root_out_path_details +  name + '_silh_e' + format(i, '03d') + '.png')
-                # save image overlay
-                visualizations = silh_renderer.get_visualization_nograd(smal_verts, faces_prep, optimed_camera_flength, color=0)
-                pred_tex = visualizations[0, :, :, :].permute((1, 2, 0)).cpu().detach().numpy() / 256
-                # out_path = root_out_path_details +  name + '_tex_pred_e' + format(i, '03d') + '.png'
-                # plt.imsave(out_path, pred_tex)
-                pred_tex_max = np.max(pred_tex, axis=2)
-                out_path = root_out_path +  name + '_comp_pred_e' + format(i, '03d') + '.png'
-                # save mesh
-                my_mesh_tri = trimesh.Trimesh(vertices=smal_verts[0, ...].detach().cpu().numpy(), faces=faces_prep[0, ...].detach().cpu().numpy(), process=False,  maintain_order=True)
-                my_mesh_tri.visual.vertex_colors = vert_colors
-                # my_mesh_tri.export(root_out_path +  name + '_res_e' + format(i, '03d') + '.obj')
-                # save focal length (together with the mesh this is enough to create an overlay in blender)
-                # out_file_flength = root_out_path_details +  name + '_flength_e' + format(i, '03d') # + '.npz'
-                # np.save(out_file_flength, optimed_camera_flength.detach().cpu().numpy())
-            current_i += 1
-
-    # prepare output mesh
-    mesh = my_mesh_tri  # all_results[0]['mesh_posed']
-    mesh.apply_transform([[-1, 0, 0, 0],
-                            [0, -1, 0, 0],
-                            [0, 0, 1, 1],
-                            [0, 0, 0, 1]])
-    result_path = os.path.join(save_imgs_path, dog_name)
-    mesh.export(file_obj=result_path + '.glb')
-    result_gltf = result_path + '.glb'
-    return result_gltf
-
-
-
-
-
-# -------------------------------------------------------------------------------------------------------------------- #
-
-total_count = 0
-
-def run_complete_inference(img_path_or_img, crop_choice, use_ttopt):
-    now = datetime.now()
-    dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
-    global total_count
-    total_count += 1
-    print(dt_string + ' total count: ' + str(total_count))
-    # depending on crop_choice: run faster r-cnn or take the input image directly
-    if crop_choice == "input image is cropped":
-        if isinstance(img_path_or_img, str):
-            img = cv2.imread(img_path_or_img)
-            output_interm_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        else:
-            output_interm_image = img_path_or_img
-        output_interm_bbox = None
-    else:
-        output_interm_image, output_interm_bbox = run_bbox_inference(img_path_or_img.copy())
-    if use_ttopt == "enable test-time optimization":
-        apply_ttopt = True
-    else:
-        apply_ttopt = False
-    # run barc inference
-    if img_path_or_img.dtype == str:
-        dog_name = os.path.basename(img_path_or_img).split(".")[0]
-    else:
-        dog_name = "dog"
-
-    result_gltf = run_bite_inference(img_path_or_img, output_interm_bbox, apply_ttopt, dog_name=dog_name)
-    # add white border to image for nicer alignment
-    output_interm_image_vis = np.concatenate((255*np.ones_like(output_interm_image), output_interm_image, 255*np.ones_like(output_interm_image)), axis=1)
-    return [result_gltf, result_gltf, output_interm_image_vis]
-
-
-
-
-########################################################################################################################
-
-# see: https://huggingface.co/spaces/radames/PIFu-Clothed-Human-Digitization/blob/main/PIFu/spaces.py
-
-description = '''
-# BITE
-
-#### Project Page
-* https://bite.is.tue.mpg.de/
-
-#### Description
-This is a demo for BITE (*B*eyond Priors for *I*mproved *T*hree-{D} Dog Pose *E*stimation). 
-To run inference on one of the examples below, click on the desired image and push the submit button. Alternatively, you may upload one of your own images.
-You can either submit a cropped image or choose the option to run a pretrained Faster R-CNN in order to obtain a bounding box. 
-While we recommend enabeling test-time optimization (computation can take up to a minute), you have the possibility to skip it, which will lead to faster calculation (a few seconds) at the cost of less accurate results. 
-<details>
-
-<summary>More</summary>
-
-#### Citation
-
-```
-@inproceedings{bite2023rueegg,
-        title = {{BITE}: Beyond Priors for Improved Three-{D} Dog Pose Estimation},
-        author = {R\"uegg, Nadine and Tripathi, Shashank and Schindler, Konrad and Black, Michael J. and Zuffi, Silvia},
-        booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
-        pages = {8867-8876},
-        year = {2023},
-}
-```
-
-#### Image Sources
-* Stanford extra image dataset
-* Images from google search engine
-    * https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRnx2sHnnLU3zy1XnJB7BvGUR9spmAh5bxTUg&usqp=CAU
-    * https://www.westend61.de/en/imageView/CAVF56467/portrait-of-dog-lying-on-floor-at-home
-
-#### Disclosure
-The results shown in this demo are slightly improved compared to the ones depicted within our paper, as we apply a regularizer on the tail.
-
-</details>
-
-'''
-
-
-
-
-
-
-example_images = sorted(glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.jpg')) + glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.jpeg')) + glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.png')))  
-random.shuffle(example_images)
-# example_images.reverse()
-# examples = [[img, "input image is cropped"] for img in example_images]
-examples = []
-for img in example_images:
-    if os.path.basename(img)[:2] == 'z_':
-        examples.append([img, "use Faster R-CNN to get a bounding box", "enable test-time optimization"])
-    else:
-        examples.append([img, "input image is cropped", "enable test-time optimization"])
-
-demo = gr.Interface(
-    fn=run_complete_inference,
-    description=description,
-    inputs=[gr.Image(label="Input Image"),
-        gr.Radio(["input image is cropped", "use Faster R-CNN to get a bounding box"], value="use Faster R-CNN to get a bounding box", label="Crop Choice"),
-        gr.Radio(["enable test-time optimization", "skip test-time optimization"], value="enable test-time optimization", label="Test Time Optimization"),
-    ],
-    outputs=[
-        gr.Model3D(
-            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
-        gr.File(label="Download 3D Model"),
-        gr.Image(label="Bounding Box (Faster R-CNN prediction)"),
-
-    ],
-    examples=examples,
-    thumbnail="bite_thumbnail.png",
-    allow_flagging="never",
-    cache_examples=True,
-    examples_per_page=14,
-)
-
-demo.launch()   # share=True)
\ No newline at end of file
diff --git a/spaces/rupeshs/fastsdcpu/app.py b/spaces/rupeshs/fastsdcpu/app.py
deleted file mode 100644
index bec7fa45023e4b167a5d6b1e332b066ad713bd00..0000000000000000000000000000000000000000
--- a/spaces/rupeshs/fastsdcpu/app.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from app_settings import AppSettings
-from utils import show_system_info
-import constants
-from argparse import ArgumentParser
-from context import Context
-from constants import APP_VERSION, LCM_DEFAULT_MODEL_OPENVINO
-from models.interface_types import InterfaceType
-from constants import DEVICE
-from frontend.webui.ui import start_webui
-parser = ArgumentParser(description=f"FAST SD CPU {constants.APP_VERSION}")
-parser.add_argument(
-    "-s",
-    "--share",
-    action="store_true",
-    help="Create sharable link(Web UI)",
-    required=False,
-)
-group = parser.add_mutually_exclusive_group(required=False)
-group.add_argument(
-    "-g",
-    "--gui",
-    action="store_true",
-    help="Start desktop GUI",
-)
-group.add_argument(
-    "-w",
-    "--webui",
-    action="store_true",
-    help="Start Web UI",
-)
-group.add_argument(
-    "-v",
-    "--version",
-    action="store_true",
-    help="Version",
-)
-parser.add_argument(
-    "--lcm_model_id",
-    type=str,
-    help="Model ID or path,Default SimianLuo/LCM_Dreamshaper_v7",
-    default="SimianLuo/LCM_Dreamshaper_v7",
-)
-parser.add_argument(
-    "--prompt",
-    type=str,
-    help="Describe the image you want to generate",
-)
-parser.add_argument(
-    "--image_height",
-    type=int,
-    help="Height of the image",
-    default=512,
-)
-parser.add_argument(
-    "--image_width",
-    type=int,
-    help="Width of the image",
-    default=512,
-)
-parser.add_argument(
-    "--inference_steps",
-    type=int,
-    help="Number of steps,default : 4",
-    default=4,
-)
-parser.add_argument(
-    "--guidance_scale",
-    type=int,
-    help="Guidance scale,default : 8.0",
-    default=8.0,
-)
-
-parser.add_argument(
-    "--number_of_images",
-    type=int,
-    help="Number of images to generate ,default : 1",
-    default=1,
-)
-parser.add_argument(
-    "--seed",
-    type=int,
-    help="Seed,default : -1 (disabled) ",
-    default=-1,
-)
-parser.add_argument(
-    "--use_openvino",
-    action="store_true",
-    help="Use OpenVINO model",
-)
-
-parser.add_argument(
-    "--use_offline_model",
-    action="store_true",
-    help="Use offline model",
-)
-parser.add_argument(
-    "--use_safety_checker",
-    action="store_false",
-    help="Use safety checker",
-)
-
-parser.add_argument(
-    "-i",
-    "--interactive",
-    action="store_true",
-    help="Interactive CLI mode",
-)
-
-args = parser.parse_args()
-
-if args.version:
-    print(APP_VERSION)
-    exit()
-
-parser.print_help()
-show_system_info()
-print(f"Using device : {constants.DEVICE}")
-
-app_settings = AppSettings()
-app_settings.load()
-
-
-start_webui(
-        app_settings,
-        args.share,)
\ No newline at end of file
diff --git a/spaces/saas18/minidellayeni/README.md b/spaces/saas18/minidellayeni/README.md
deleted file mode 100644
index cdc929d6e22c8424223f58054fef342d9ac589ef..0000000000000000000000000000000000000000
--- a/spaces/saas18/minidellayeni/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Minidellayeni
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: static
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sayakpaul/raindrop-deraining-maxim/app.py b/spaces/sayakpaul/raindrop-deraining-maxim/app.py
deleted file mode 100644
index e8e7d987175df69c02069be322937e447511ac24..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/raindrop-deraining-maxim/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""
-Some preprocessing utilities have been taken from:
-https://github.com/google-research/maxim/blob/main/maxim/run_eval.py
-"""
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from huggingface_hub.keras_mixin import from_pretrained_keras
-from PIL import Image
-
-from create_maxim_model import Model
-from maxim.configs import MAXIM_CONFIGS
-
-CKPT = "google/maxim-s2-deraining-raindrop"
-VARIANT = CKPT.split("/")[-1].split("-")[1]
-VARIANT = VARIANT[0].upper() + "-" + VARIANT[1]
-_MODEL = from_pretrained_keras(CKPT)
-
-
-def mod_padding_symmetric(image, factor=64):
-    """Padding the image to be divided by factor."""
-    height, width = image.shape[0], image.shape[1]
-    height_pad, width_pad = ((height + factor) // factor) * factor, (
-        (width + factor) // factor
-    ) * factor
-    padh = height_pad - height if height % factor != 0 else 0
-    padw = width_pad - width if width % factor != 0 else 0
-    image = tf.pad(
-        image, [(padh // 2, padh // 2), (padw // 2, padw // 2), (0, 0)], mode="REFLECT"
-    )
-    return image
-
-
-def make_shape_even(image):
-    """Pad the image to have even shapes."""
-    height, width = image.shape[0], image.shape[1]
-    padh = 1 if height % 2 != 0 else 0
-    padw = 1 if width % 2 != 0 else 0
-    image = tf.pad(image, [(0, padh), (0, padw), (0, 0)], mode="REFLECT")
-    return image
-
-
-def process_image(image: Image):
-    input_img = np.asarray(image) / 255.0
-    height, width = input_img.shape[0], input_img.shape[1]
-
-    # Padding images to have even shapes
-    input_img = make_shape_even(input_img)
-    height_even, width_even = input_img.shape[0], input_img.shape[1]
-
-    # padding images to be multiplies of 64
-    input_img = mod_padding_symmetric(input_img, factor=64)
-    input_img = tf.expand_dims(input_img, axis=0)
-    return input_img, height, width, height_even, width_even
-
-
-def init_new_model(input_img):
-    configs = MAXIM_CONFIGS.get(VARIANT)
-    configs.update(
-        {
-            "variant": VARIANT,
-            "dropout_rate": 0.0,
-            "num_outputs": 3,
-            "use_bias": True,
-            "num_supervision_scales": 3,
-        }
-    )
-    configs.update({"input_resolution": (input_img.shape[1], input_img.shape[2])})
-    new_model = Model(**configs)
-    new_model.set_weights(_MODEL.get_weights())
-    return new_model
-
-
-def infer(image):
-    preprocessed_image, height, width, height_even, width_even = process_image(image)
-    new_model = init_new_model(preprocessed_image)
-
-    preds = new_model.predict(preprocessed_image)
-    if isinstance(preds, list):
-        preds = preds[-1]
-        if isinstance(preds, list):
-            preds = preds[-1]
-
-    preds = np.array(preds[0], np.float32)
-
-    new_height, new_width = preds.shape[0], preds.shape[1]
-    h_start = new_height // 2 - height_even // 2
-    h_end = h_start + height
-    w_start = new_width // 2 - width_even // 2
-    w_end = w_start + width
-    preds = preds[h_start:h_end, w_start:w_end, :]
-
-    return Image.fromarray(np.array((np.clip(preds, 0.0, 1.0) * 255.0).astype(np.uint8)))
-
-
-title = "Derain images containing rain drops or stripes."
-description = f"The underlying model is [this](https://huggingface.co/{CKPT}). You can use the model to derain images containing rain drops or stripes. This variant is particularly suited for draining image containing rain drops. If your images contain rain streaks, then use [this variant](https://huggingface.co/google/maxim-s2-deraining-rain13k). To quickly try out the model, you can choose from the available sample images below, or you can submit your own image. Not that, internally, the model is re-initialized based on the spatial dimensions of the input image and this process is time-consuming."
-
-iface = gr.Interface(
-    infer,
-    inputs="image",
-    outputs=gr.Image().style(height=242),
-    title=title,
-    description=description,
-    allow_flagging="never",
-    examples=[["0.jpg"], ["1.png"], ["15.png"], ["55.png"]],
-)
-iface.launch(debug=True)
diff --git a/spaces/scedlatioru/img-to-music/example/Monster Hunter World - The Handlers Guildmarm Costume Full Fixed Crack [Xforce].md b/spaces/scedlatioru/img-to-music/example/Monster Hunter World - The Handlers Guildmarm Costume Full Fixed Crack [Xforce].md
deleted file mode 100644
index 479da719a8710c32604b0aa0690db6b52624a706..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Monster Hunter World - The Handlers Guildmarm Costume Full Fixed Crack [Xforce].md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>Monster Hunter: World - The Handler's Guildmarm Costume full crack [Xforce]</h2><br /><p><b><b>Download Zip</b> &#10003; <a href="https://gohhs.com/2uEAb6">https://gohhs.com/2uEAb6</a></b></p><br /><br />
-<br />
-It is recommended that you equip your appropriate weapon(s) before accessing this content.
-
-◆Contents
-
-■Costume
-
-Monster Hunter: World Costume for the Handler
-
-Monster Hunter: World
-
-Handler Suit
-
-This content adds a costume for the Handler to Monster Hunter: World. You can access it from the Housekeeper menu in Your Room. It is recommended that you equip your appropriate weapon(s) before accessing this content.
-
-Monster Hunter: World 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Street.Legal.Racing.Redline.2.2.1.M.FULL.Version.rar !!HOT!!.md b/spaces/scedlatioru/img-to-music/example/Street.Legal.Racing.Redline.2.2.1.M.FULL.Version.rar !!HOT!!.md
deleted file mode 100644
index c5bdaa6dea134d1ddddc7d3ea4fa15556ccb4852..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Street.Legal.Racing.Redline.2.2.1.M.FULL.Version.rar !!HOT!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Street.Legal.Racing.Redline.2.2.1.M.FULL.Version.rar</h2><br /><p><b><b>Download</b> &#187;&#187;&#187; <a href="https://gohhs.com/2uEAHS">https://gohhs.com/2uEAHS</a></b></p><br /><br />
-<br />
-The method is described and evaluated on data from the DSS 13 34-m beam wave ... a calibration evaluation, and a full user interface evaluation. o The UMS can be ... of the CardioMEMS (CardioMEMS Heart Failure System, St Jude Medical Inc, ... redline system used in the Space Shuttle Main Engine Controller (SSMEC). 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/sczhou/ProPainter/core/trainer_flow_w_edge.py b/spaces/sczhou/ProPainter/core/trainer_flow_w_edge.py
deleted file mode 100644
index d4eba04c8a5fa56bce3e335e6036bc0e0a1e848a..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/core/trainer_flow_w_edge.py
+++ /dev/null
@@ -1,380 +0,0 @@
-import os
-import glob
-import logging
-import importlib
-from tqdm import tqdm
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from core.prefetch_dataloader import PrefetchDataLoader, CPUPrefetcher
-from torch.utils.data.distributed import DistributedSampler
-from torch.nn.parallel import DistributedDataParallel as DDP
-
-from torch.utils.tensorboard import SummaryWriter
-
-from core.lr_scheduler import MultiStepRestartLR, CosineAnnealingRestartLR
-from core.dataset import TrainDataset
-
-from model.modules.flow_comp_raft import RAFT_bi, FlowLoss, EdgeLoss
-
-# from skimage.feature import canny
-from model.canny.canny_filter import Canny
-from RAFT.utils.flow_viz_pt import flow_to_image
-
-
-class Trainer:
-    def __init__(self, config):
-        self.config = config
-        self.epoch = 0
-        self.iteration = 0
-        self.num_local_frames = config['train_data_loader']['num_local_frames']
-        self.num_ref_frames = config['train_data_loader']['num_ref_frames']
-
-        # setup data set and data loader
-        self.train_dataset = TrainDataset(config['train_data_loader'])
-
-        self.train_sampler = None
-        self.train_args = config['trainer']
-        if config['distributed']:
-            self.train_sampler = DistributedSampler(
-                self.train_dataset,
-                num_replicas=config['world_size'],
-                rank=config['global_rank'])
-
-        dataloader_args = dict(
-            dataset=self.train_dataset,
-            batch_size=self.train_args['batch_size'] // config['world_size'],
-            shuffle=(self.train_sampler is None),
-            num_workers=self.train_args['num_workers'],
-            sampler=self.train_sampler,
-            drop_last=True)
-
-        self.train_loader = PrefetchDataLoader(self.train_args['num_prefetch_queue'], **dataloader_args)
-        self.prefetcher = CPUPrefetcher(self.train_loader)
-
-        # set raft
-        self.fix_raft = RAFT_bi(device = self.config['device'])
-        self.flow_loss = FlowLoss()
-        self.edge_loss = EdgeLoss()
-        self.canny = Canny(sigma=(2,2), low_threshold=0.1, high_threshold=0.2)
-
-        # setup models including generator and discriminator
-        net = importlib.import_module('model.' + config['model']['net'])
-        self.netG = net.RecurrentFlowCompleteNet()
-        # print(self.netG)
-        self.netG = self.netG.to(self.config['device'])
-
-        # setup optimizers and schedulers
-        self.setup_optimizers()
-        self.setup_schedulers()
-        self.load()
-
-        if config['distributed']:
-            self.netG = DDP(self.netG,
-                            device_ids=[self.config['local_rank']],
-                            output_device=self.config['local_rank'],
-                            broadcast_buffers=True,
-                            find_unused_parameters=True)
-
-        # set summary writer
-        self.dis_writer = None
-        self.gen_writer = None
-        self.summary = {}
-        if self.config['global_rank'] == 0 or (not config['distributed']):
-            self.gen_writer = SummaryWriter(
-                os.path.join(config['save_dir'], 'gen'))
-
-    def setup_optimizers(self):
-        """Set up optimizers."""
-        backbone_params = []
-        for name, param in self.netG.named_parameters():
-            if param.requires_grad:
-                backbone_params.append(param)
-            else:
-                print(f'Params {name} will not be optimized.')
-                
-        optim_params = [
-            {
-                'params': backbone_params,
-                'lr': self.config['trainer']['lr']
-            },
-        ]
-
-        self.optimG = torch.optim.Adam(optim_params,
-                                       betas=(self.config['trainer']['beta1'],
-                                              self.config['trainer']['beta2']))
-
-
-    def setup_schedulers(self):
-        """Set up schedulers."""
-        scheduler_opt = self.config['trainer']['scheduler']
-        scheduler_type = scheduler_opt.pop('type')
-
-        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
-            self.scheG = MultiStepRestartLR(
-                self.optimG,
-                milestones=scheduler_opt['milestones'],
-                gamma=scheduler_opt['gamma'])
-        elif scheduler_type == 'CosineAnnealingRestartLR':
-            self.scheG = CosineAnnealingRestartLR(
-                self.optimG,
-                periods=scheduler_opt['periods'],
-                restart_weights=scheduler_opt['restart_weights'])
-        else:
-            raise NotImplementedError(
-                f'Scheduler {scheduler_type} is not implemented yet.')
-
-    def update_learning_rate(self):
-        """Update learning rate."""
-        self.scheG.step()
-
-    def get_lr(self):
-        """Get current learning rate."""
-        return self.optimG.param_groups[0]['lr']
-
-    def add_summary(self, writer, name, val):
-        """Add tensorboard summary."""
-        if name not in self.summary:
-            self.summary[name] = 0
-        self.summary[name] += val
-        n = self.train_args['log_freq']
-        if writer is not None and self.iteration % n == 0:
-            writer.add_scalar(name, self.summary[name] / n, self.iteration)
-            self.summary[name] = 0
-
-    def load(self):
-        """Load netG."""
-        # get the latest checkpoint
-        model_path = self.config['save_dir']
-        if os.path.isfile(os.path.join(model_path, 'latest.ckpt')):
-            latest_epoch = open(os.path.join(model_path, 'latest.ckpt'),
-                                'r').read().splitlines()[-1]
-        else:
-            ckpts = [
-                os.path.basename(i).split('.pth')[0]
-                for i in glob.glob(os.path.join(model_path, '*.pth'))
-            ]
-            ckpts.sort()
-            latest_epoch = ckpts[-1][4:] if len(ckpts) > 0 else None
-
-        if latest_epoch is not None:
-            gen_path = os.path.join(model_path, f'gen_{int(latest_epoch):06d}.pth')
-            opt_path = os.path.join(model_path,f'opt_{int(latest_epoch):06d}.pth')
-
-            if self.config['global_rank'] == 0:
-                print(f'Loading model from {gen_path}...')
-            dataG = torch.load(gen_path, map_location=self.config['device'])
-            self.netG.load_state_dict(dataG)
-
-
-            data_opt = torch.load(opt_path, map_location=self.config['device'])
-            self.optimG.load_state_dict(data_opt['optimG'])
-            self.scheG.load_state_dict(data_opt['scheG'])
-
-            self.epoch = data_opt['epoch']
-            self.iteration = data_opt['iteration']
-
-        else:
-            if self.config['global_rank'] == 0:
-                print('Warnning: There is no trained model found.'
-                      'An initialized model will be used.')
-
-    def save(self, it):
-        """Save parameters every eval_epoch"""
-        if self.config['global_rank'] == 0:
-            # configure path
-            gen_path = os.path.join(self.config['save_dir'],
-                                    f'gen_{it:06d}.pth')
-            opt_path = os.path.join(self.config['save_dir'],
-                                    f'opt_{it:06d}.pth')
-            print(f'\nsaving model to {gen_path} ...')
-
-            # remove .module for saving
-            if isinstance(self.netG, torch.nn.DataParallel) or isinstance(self.netG, DDP):
-                netG = self.netG.module
-            else:
-                netG = self.netG
-
-            # save checkpoints
-            torch.save(netG.state_dict(), gen_path)
-            torch.save(
-                {
-                    'epoch': self.epoch,
-                    'iteration': self.iteration,
-                    'optimG': self.optimG.state_dict(),
-                    'scheG': self.scheG.state_dict()
-                }, opt_path)
-
-            latest_path = os.path.join(self.config['save_dir'], 'latest.ckpt')
-            os.system(f"echo {it:06d} > {latest_path}")
-
-    def train(self):
-        """training entry"""
-        pbar = range(int(self.train_args['iterations']))
-        if self.config['global_rank'] == 0:
-            pbar = tqdm(pbar,
-                        initial=self.iteration,
-                        dynamic_ncols=True,
-                        smoothing=0.01)
-
-        os.makedirs('logs', exist_ok=True)
-
-        logging.basicConfig(
-            level=logging.INFO,
-            format="%(asctime)s %(filename)s[line:%(lineno)d]"
-            "%(levelname)s %(message)s",
-            datefmt="%a, %d %b %Y %H:%M:%S",
-            filename=f"logs/{self.config['save_dir'].split('/')[-1]}.log",
-            filemode='w')
-
-        while True:
-            self.epoch += 1
-            self.prefetcher.reset()
-            if self.config['distributed']:
-                self.train_sampler.set_epoch(self.epoch)
-            self._train_epoch(pbar)
-            if self.iteration > self.train_args['iterations']:
-                break
-        print('\nEnd training....')
-
-    # def get_edges(self, flows): # fgvc
-    #     # (b, t, 2, H, W)
-    #     b, t, _, h, w = flows.shape
-    #     flows = flows.view(-1, 2, h, w)
-    #     flows_list = flows.permute(0, 2, 3, 1).cpu().numpy()
-    #     edges = []
-    #     for f in list(flows_list):
-    #         flows_gray = (f[:, :, 0] ** 2 + f[:, :, 1] ** 2) ** 0.5
-    #         if flows_gray.max() < 1:
-    #             flows_gray = flows_gray*0
-    #         else:
-    #             flows_gray = flows_gray / flows_gray.max()
-            
-    #         edge = canny(flows_gray, sigma=2, low_threshold=0.1, high_threshold=0.2) # fgvc
-    #         edge = torch.from_numpy(edge).view(1, 1, h, w).float()
-    #         edges.append(edge)
-    #     edges = torch.stack(edges, dim=0).to(self.config['device'])
-    #     edges = edges.view(b, t, 1, h, w)
-    #     return edges
-
-    def get_edges(self, flows): 
-        # (b, t, 2, H, W)
-        b, t, _, h, w = flows.shape
-        flows = flows.view(-1, 2, h, w)
-        flows_gray = (flows[:, 0, None] ** 2 + flows[:, 1, None] ** 2) ** 0.5
-        if flows_gray.max() < 1:
-            flows_gray = flows_gray*0
-        else:
-            flows_gray = flows_gray / flows_gray.max()
-            
-        magnitude, edges = self.canny(flows_gray.float())
-        edges = edges.view(b, t, 1, h, w)
-        return edges
-        
-    def _train_epoch(self, pbar):
-        """Process input and calculate loss every training epoch"""
-        device = self.config['device']
-        train_data = self.prefetcher.next()
-        while train_data is not None:
-            self.iteration += 1
-            frames, masks, flows_f, flows_b, _ = train_data
-            frames, masks = frames.to(device), masks.to(device)
-            masks = masks.float()
-
-            l_t = self.num_local_frames
-            b, t, c, h, w = frames.size()
-            gt_local_frames = frames[:, :l_t, ...]
-            local_masks = masks[:, :l_t, ...].contiguous()
-
-            # get gt optical flow
-            if flows_f[0] == 'None' or flows_b[0] == 'None':
-                gt_flows_bi = self.fix_raft(gt_local_frames)
-            else:
-                gt_flows_bi = (flows_f.to(device), flows_b.to(device))
-
-            # get gt edge
-            gt_edges_forward = self.get_edges(gt_flows_bi[0])
-            gt_edges_backward = self.get_edges(gt_flows_bi[1])
-            gt_edges_bi = [gt_edges_forward, gt_edges_backward]
-
-            # complete flow
-            pred_flows_bi, pred_edges_bi = self.netG.module.forward_bidirect_flow(gt_flows_bi, local_masks)
-
-            # optimize net_g
-            self.optimG.zero_grad()
-
-            # compulte flow_loss
-            flow_loss, warp_loss = self.flow_loss(pred_flows_bi, gt_flows_bi, local_masks, gt_local_frames)
-            flow_loss = flow_loss * self.config['losses']['flow_weight']
-            warp_loss = warp_loss * 0.01
-            self.add_summary(self.gen_writer, 'loss/flow_loss', flow_loss.item())
-            self.add_summary(self.gen_writer, 'loss/warp_loss', warp_loss.item())
-
-            # compute edge loss
-            edge_loss = self.edge_loss(pred_edges_bi, gt_edges_bi, local_masks)
-            edge_loss = edge_loss*1.0
-            self.add_summary(self.gen_writer, 'loss/edge_loss', edge_loss.item())
-
-            loss = flow_loss + warp_loss + edge_loss
-            loss.backward()
-            self.optimG.step()
-            self.update_learning_rate()
-
-            # write image to tensorboard
-            # if self.iteration % 200 == 0:             
-            if self.iteration % 200 == 0 and self.gen_writer is not None:        
-                t = 5     
-                # forward to cpu
-                gt_flows_forward_cpu = flow_to_image(gt_flows_bi[0][0]).cpu()
-                masked_flows_forward_cpu = (gt_flows_forward_cpu[t] * (1-local_masks[0][t].cpu())).to(gt_flows_forward_cpu)
-                pred_flows_forward_cpu = flow_to_image(pred_flows_bi[0][0]).cpu()
-
-                flow_results = torch.cat([gt_flows_forward_cpu[t], masked_flows_forward_cpu, pred_flows_forward_cpu[t]], 1)
-                self.gen_writer.add_image('img/flow-f:gt-pred', flow_results, self.iteration)
-
-                # backward to cpu
-                gt_flows_backward_cpu = flow_to_image(gt_flows_bi[1][0]).cpu()
-                masked_flows_backward_cpu = (gt_flows_backward_cpu[t] * (1-local_masks[0][t+1].cpu())).to(gt_flows_backward_cpu)
-                pred_flows_backward_cpu = flow_to_image(pred_flows_bi[1][0]).cpu()
-
-                flow_results = torch.cat([gt_flows_backward_cpu[t], masked_flows_backward_cpu, pred_flows_backward_cpu[t]], 1)
-                self.gen_writer.add_image('img/flow-b:gt-pred', flow_results, self.iteration)
-
-                # TODO: show edge
-                # forward
-                gt_edges_forward_cpu = gt_edges_bi[0][0].cpu()
-                masked_edges_forward_cpu = (gt_edges_forward_cpu[t] * (1-local_masks[0][t].cpu())).to(gt_edges_forward_cpu)
-                pred_edges_forward_cpu = pred_edges_bi[0][0].cpu()
-
-                edge_results = torch.cat([gt_edges_forward_cpu[t], masked_edges_forward_cpu, pred_edges_forward_cpu[t]], 1)
-                self.gen_writer.add_image('img/edge-f:gt-pred', edge_results, self.iteration)
-                # backward
-                gt_edges_backward_cpu = gt_edges_bi[1][0].cpu()
-                masked_edges_backward_cpu = (gt_edges_backward_cpu[t] * (1-local_masks[0][t+1].cpu())).to(gt_edges_backward_cpu)
-                pred_edges_backward_cpu = pred_edges_bi[1][0].cpu()
-
-                edge_results = torch.cat([gt_edges_backward_cpu[t], masked_edges_backward_cpu, pred_edges_backward_cpu[t]], 1)
-                self.gen_writer.add_image('img/edge-b:gt-pred', edge_results, self.iteration)
-                
-            # console logs
-            if self.config['global_rank'] == 0:
-                pbar.update(1)
-                pbar.set_description((f"flow: {flow_loss.item():.3f}; "
-                                      f"warp: {warp_loss.item():.3f}; "
-                                      f"edge: {edge_loss.item():.3f}; "
-                                      f"lr: {self.get_lr()}"))
-
-                if self.iteration % self.train_args['log_freq'] == 0:
-                    logging.info(f"[Iter {self.iteration}] "
-                                 f"flow: {flow_loss.item():.4f}; "
-                                 f"warp: {warp_loss.item():.4f}")
-
-            # saving models
-            if self.iteration % self.train_args['save_freq'] == 0:
-                self.save(int(self.iteration))
-
-            if self.iteration > self.train_args['iterations']:
-                break
-
-            train_data = self.prefetcher.next()
\ No newline at end of file
diff --git a/spaces/sdhsdhk/bingosjj/src/components/ui/button.tsx b/spaces/sdhsdhk/bingosjj/src/components/ui/button.tsx
deleted file mode 100644
index 281da005124fa94c89a9a9db7605748a92b60865..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingosjj/src/components/ui/button.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-import * as React from 'react'
-import { Slot } from '@radix-ui/react-slot'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const buttonVariants = cva(
-  'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50',
-  {
-    variants: {
-      variant: {
-        default:
-          'bg-primary text-primary-foreground shadow-md hover:bg-primary/90',
-        destructive:
-          'bg-destructive text-destructive-foreground hover:bg-destructive/90',
-        outline:
-          'border border-input hover:bg-accent hover:text-accent-foreground',
-        secondary:
-          'bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground',
-        link: 'text-primary underline-offset-4 shadow-none hover:underline'
-      },
-      size: {
-        default: 'h-8 px-4 py-2',
-        sm: 'h-8 rounded-md px-3',
-        lg: 'h-11 rounded-md px-8',
-        icon: 'h-8 w-8 p-0'
-      }
-    },
-    defaultVariants: {
-      variant: 'default',
-      size: 'default'
-    }
-  }
-)
-
-export interface ButtonProps
-  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
-    VariantProps<typeof buttonVariants> {
-  asChild?: boolean
-}
-
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
-  ({ className, variant, size, asChild = false, ...props }, ref) => {
-    const Comp = asChild ? Slot : 'button'
-    return (
-      <Comp
-        className={cn(buttonVariants({ variant, size, className }))}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Button.displayName = 'Button'
-
-export { Button, buttonVariants }
diff --git a/spaces/seanshahkarami/clip-explorer/app.py b/spaces/seanshahkarami/clip-explorer/app.py
deleted file mode 100644
index 2518e038f6d0d01d02369634544b36c1c002132e..0000000000000000000000000000000000000000
--- a/spaces/seanshahkarami/clip-explorer/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import torch
-import gradio as gr
-from transformers import CLIPProcessor, CLIPModel
-
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-
-def similarity(image, text, threshold, order):
-    lines = list(map(str.strip, text.splitlines()))
-    if len(lines) == 0:
-        return "", ""
-    inputs = processor(text=lines, images=image, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    similarities = outputs.logits_per_image.view(-1)
-
-    # convert to plain list of floats for display
-    similarities = [s.item() for s in similarities]
-
-    if order:
-        tfm = lambda xs: sorted(xs, reverse=True)
-    else:
-        tfm = lambda xs: xs
-
-    detections = [(f"{line}: {similarity:0.2f}", "yes" if similarity > threshold else "no") for similarity, line in tfm(zip(similarities, lines))]
-
-    return detections
-
-
-demo = gr.Interface(
-    title="CLIP Explorer",
-    description="Input an image and lines of text then press submit to output the image-text similarity scores.",
-    fn=similarity,
-    inputs=[
-        gr.Image(label="Image"),
-        gr.TextArea(label="Text descriptions"),
-        gr.Slider(0, 40, 26, label="Similarity threshold"),
-        gr.Checkbox(value=True, label="Order by similarity score?"),
-    ],
-    outputs=gr.HighlightedText(label="Image-text similarity scores", color_map={
-        "yes": "green",
-        "no": "red",
-    }),
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/segments-tobias/conex/espnet2/fileio/__init__.py b/spaces/segments-tobias/conex/espnet2/fileio/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/extract_feature_print.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/extract_feature_print.py
deleted file mode 100644
index 987daabb9cf8a3259f673dc9bd7d24a15dadfde6..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/extract_feature_print.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os, sys, traceback
-
-# device=sys.argv[1]
-n_part = int(sys.argv[2])
-i_part = int(sys.argv[3])
-if len(sys.argv) == 5:
-    exp_dir = sys.argv[4]
-else:
-    i_gpu = sys.argv[4]
-    exp_dir = sys.argv[5]
-    os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
-
-import torch
-import torch.nn.functional as F
-import soundfile as sf
-import numpy as np
-from fairseq import checkpoint_utils
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
-
-
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-
-
-printt(sys.argv)
-model_path = "hubert_base.pt"
-
-printt(exp_dir)
-wavPath = "%s/1_16k_wavs" % exp_dir
-outPath = "%s/3_feature256" % exp_dir
-os.makedirs(outPath, exist_ok=True)
-
-
-# wave must be 16k, hop_size=320
-def readwave(wav_path, normalize=False):
-    wav, sr = sf.read(wav_path)
-    assert sr == 16000
-    feats = torch.from_numpy(wav).float()
-    if feats.dim() == 2:  # double channels
-        feats = feats.mean(-1)
-    assert feats.dim() == 1, feats.dim()
-    if normalize:
-        with torch.no_grad():
-            feats = F.layer_norm(feats, feats.shape)
-    feats = feats.view(1, -1)
-    return feats
-
-
-# HuBERT model
-printt("load model(s) from {}".format(model_path))
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-printt("move model to %s" % device)
-if device != "cpu":
-    model = model.half()
-model.eval()
-
-todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
-n = max(1, len(todo) // 10)  # 最多打印十条
-if len(todo) == 0:
-    printt("no-feature-todo")
-else:
-    printt("all-feature-%s" % len(todo))
-    for idx, file in enumerate(todo):
-        try:
-            if file.endswith(".wav"):
-                wav_path = "%s/%s" % (wavPath, file)
-                out_path = "%s/%s" % (outPath, file.replace("wav", "npy"))
-
-                if os.path.exists(out_path):
-                    continue
-
-                feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
-                padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-                inputs = {
-                    "source": feats.half().to(device)
-                    if device != "cpu"
-                    else feats.to(device),
-                    "padding_mask": padding_mask.to(device),
-                    "output_layer": 9,  # layer 9
-                }
-                with torch.no_grad():
-                    logits = model.extract_features(**inputs)
-                    feats = model.final_proj(logits[0])
-
-                feats = feats.squeeze(0).float().cpu().numpy()
-                if np.isnan(feats).sum() == 0:
-                    np.save(out_path, feats, allow_pickle=False)
-                else:
-                    printt("%s-contains nan" % file)
-                if idx % n == 0:
-                    printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape))
-        except:
-            printt(traceback.format_exc())
-    printt("all-feature-done")
diff --git a/spaces/sidphbot/Researcher/tests/test_survey_files.py b/spaces/sidphbot/Researcher/tests/test_survey_files.py
deleted file mode 100644
index a73682d2819f747e7223b8014d22930de5084739..0000000000000000000000000000000000000000
--- a/spaces/sidphbot/Researcher/tests/test_survey_files.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-from src.Surveyor import Surveyor
-
-def test_files():
-    surveyor = Surveyor()
-    sample_query = 'quantum entanglement'
-    zip_file, survey_file = surveyor.survey(sample_query, max_search=10, num_papers=6,
-                    debug=False, weigh_authors=False)
-    assert os.path.exists(zip_file)
-    assert os.path.exists(survey_file)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Cardfight!! Vanguard EX A Nintendo Switch Game that Brings the Card Battles to Life.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Cardfight!! Vanguard EX A Nintendo Switch Game that Brings the Card Battles to Life.md
deleted file mode 100644
index fe336a5330bf987059ca787ce3e824bc43c4c2c6..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Cardfight!! Vanguard EX A Nintendo Switch Game that Brings the Card Battles to Life.md	
+++ /dev/null
@@ -1,60 +0,0 @@
-
-<h1>How to Download Cardfight!! Vanguard EX on Nintendo Switch</h1>
- <p>If you are a fan of card games and anime, you might have heard of Cardfight!! Vanguard, a popular franchise that has spawned multiple manga, anime, and video game adaptations. Cardfight!! Vanguard EX is the latest video game based on this franchise, and it is available for both PlayStation 4 and Nintendo Switch. In this article, we will focus on how to download Cardfight!! Vanguard EX on Nintendo Switch, and why you should give it a try.</p>
- <p>Cardfight!! Vanguard EX is a strategy game that lets you experience the thrill of cardfighting with your favorite characters from the anime series. You can choose from over 30 characters, each with their own deck and playstyle, and challenge them in various modes, such as story mode, free fight mode, or online mode. You can also customize your own deck with over 2000 cards from the series, including Imaginary Gifts, up until September 19, 2019. You can also enjoy an original story supervised by Akira Ito, the creator of Cardfight!! Vanguard, and character designs by Morimoto Yuki, the illustrator of Future Card Buddyfight.</p>
-<h2>download cardfight vanguard ex</h2><br /><p><b><b>Download</b> &#10026;&#10026;&#10026; <a href="https://ssurll.com/2uNWJt">https://ssurll.com/2uNWJt</a></b></p><br /><br />
- <p>But what makes Cardfight!! Vanguard EX stand out from other card games is its unique feature called EX Gift. This feature allows you to use the special abilities of characters that you have defeated in story mode. For example, you can use PSYqualia to sense your opponent's triggers, Final Turn to increase your chances of getting critical triggers, or Resurrection to heal yourself when you are about to lose. There are many more EX Gifts to discover and use in your cardfights.</p>
- <p>If you are interested in downloading Cardfight!! Vanguard EX on Nintendo Switch, here are some tips and tricks that will help you get started.</p>
- <h2>What You Need to Download Cardfight!! Vanguard EX</h2>
- <p>Before you can download Cardfight!! Vanguard EX on Nintendo Switch, you need to have a few things ready. First, you need a Nintendo Switch console with a stable internet connection and enough storage space. The game requires 3.1 GB of free space to download. Second, you need a Japanese Nintendo eShop account, since the game is only available in Japan. You can create a Japanese Nintendo eShop account by following these steps: - Go to the Nintendo website and create a new Nintendo account. - Choose Japan as your country/region of residence and enter a valid email address and password. - Verify your email address and log in to your new Nintendo account. - On your Nintendo Switch, go to System Settings and select Users. - Add a new user and link it to your new Nintendo account. - Access the eShop with your new user and select Japan as your region. You can also watch this video tutorial for more details. Once you have set up your Japanese Nintendo eShop account, you are ready to download Cardfight!! Vanguard EX. The game costs 6,980 yen (about 63 USD) to purchase, but you can also download a free demo version to try it out first. <h2>How to Find Cardfight!! Vanguard EX on the eShop</h2>
- <p>Now that you have your Japanese Nintendo eShop account, you can easily find Cardfight!! Vanguard EX on the eShop. Here are some screenshots and instructions on how to do it:</p>
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- - On the eShop home screen, select the magnifying glass icon on the top right corner to open the search menu. - Type in "カードファイト!! ヴァンガード EX" (the Japanese title of the game) or "Cardfight!! Vanguard EX" (the English title of the game) and press the + button to start the search. - You should see the game icon on the search results. Select it to open the game page. - On the game page, you can see more information about the game, such as screenshots, videos, ratings, and reviews. You can also choose between the physical version or the digital version of the game. The physical version requires a cartridge to play, while the digital version can be downloaded and played without a cartridge. The digital version also has a faster loading time and does not take up any space on your Switch console. However, the physical version has some advantages, such as being able to resell it or lend it to someone else. The choice is up to you. - To download the game, select the orange button that says "購入する" (purchase) or "体験版をダウンロードする" (download demo). You will need to enter your credit card information or use a prepaid eShop card to pay for the game. You can buy prepaid eShop cards online or at convenience stores in Japan. - After you have completed the payment, the game will start downloading automatically. You can check the progress of the download on the home screen or on the eShop menu. Once the download is finished, you can start playing Cardfight!! Vanguard EX. <h2>How to Play Cardfight!! Vanguard EX</h2>
- <p>Cardfight!! Vanguard EX is a fun and challenging game that will test your strategic skills and cardfighting spirit. The game has several modes and features that will keep you entertained for hours. Here are some of them:</p>
- - Story Mode: This is where you can follow an original story that features characters from different seasons of the anime series, such as Aichi Sendou, Toshiki Kai, Ren Suzugamori, Kouji Ibuki, Chrono Shindou, Shion Kiba, Tokoha Anjou, Kazuma Shouji, and many more. You can also create your own original character and interact with them. The story mode has over 50 episodes that will take you through different scenarios and cardfights. You can also unlock new cards and EX Gifts by completing episodes. - Free Fight Mode: This is where you can choose any character and deck that you have unlocked and challenge them in a cardfight. You can also customize the rules and settings of the cardfight, such as turn order, damage zone limit, trigger effects, etc. You can also play against another player locally or online using wireless communication or LAN play. - Deck Edit Mode: This is where you can create your own deck using over 2000 cards from the series. You can also edit your character's appearance, voice, and name. You can also test your deck against a CPU opponent or view your deck statistics. - Gallery Mode: This is where you can view various content related to Cardfight!! Vanguard EX, such as illustrations, voice clips, music tracks, card images, etc. Cardfight!! Vanguard EX has a lot of gameplay mechanics and modes that will appeal to both beginners and veterans of card games. However, if you are new to Cardfight!! Vanguard or need some help with improving your skills, here are some tips and tricks that will help you get the most out of the game: - How to beat the Extreme Fight rules challenge: This is a special challenge that you can unlock after completing the story mode. In this challenge, you have to face all the characters in the game using the Extreme Fight rules, which allow you to use any clan and any card in your deck. This means that you can mix and match cards from different clans and create powerful combinations. However, this also means that your opponents can do the same, so you have to be prepared for anything. To beat this challenge, you need to have a well-balanced deck that can handle different situations and strategies. You also need to know your opponent's deck and playstyle, and anticipate their moves and counters. You also need to use your EX Gifts wisely and at the right time. This challenge is not for the faint of heart, but it is very rewarding if you manage to clear it. - How to use Tsumujibashou to manipulate your opponent's triggers: Tsumujibashou is a grade 2 unit from the Nubatama clan that has a unique ability that can mess with your opponent's triggers. When it attacks, you can choose one of your opponent's rear-guards and bind it face down until the end of their next turn. Then, you can look at the top card of their deck and put it on the top or bottom of their deck. This means that you can control what kind of trigger they will get on their next drive check, or prevent them from getting one at all. You can also use this ability to disrupt their strategy or combo by binding their key units or interceptors. Tsumujibashou is a very useful unit that can give you an edge in cardfights, especially if you use it with other Nubatama cards that can bind or retire your opponent's units. - How to build a Nubatama deck with Stealth Fiend, Tsumujibashou: If you want to use Tsumujibashou in your deck, you might want to consider building a Nubatama deck that focuses on binding and retiring your opponent's units. This way, you can weaken their field and hand, and gain advantage over them. Here is an example of a Nubatama deck that uses Tsumujibashou as one of its main cards: | Grade | Card Name | Quantity | | ----- | --------- | -------- | | 0 | Stealth Beast, Katarigitsune (V Series) (Starter) | 1 | | 0 | Stealth Dragon, Magatsu Wind (Critical) | 4 | | 0 | Stealth Fiend, Zashikihime (Critical) | 4 | | 0 | Stealth Fiend, Oboro Cart (Draw/Perfect Guard) | 4 | | 0 | Stealth Fiend, Yukihime (Heal) | 4 | | 1 | Stealth Dragon, Magatsu Gale (V Series) | 4 | | 1 | Stealth Beast, Million Rat (V Series) | 4 | | 1 | Stealth Rogue of Night Fog, Agitomaru (V Series) | 3 | | 1 | Stealth Dragon, Dual Weapon (V Series) | 2 | | 2 | Stealth Fiend, Tsumujibashou (V Series) | 4 | | 2 | Stealth Dragon, Magatsu Breath (V Series) | 3 | | 2 | Stealth Rogue of Concealment, Tanba (V Series) | 3 | | 3 | Evil-eye Hades Emperor, Shiranui "Mukuro" (V Series) | 4 | | 3 | Evil-eye Sovereign King, Shiranui "Rinne" (V Series) | 3 | | 3 | Evil-eye Vidya Emperor, Shiranui "Zanki" (V Series) | 1 | This deck uses Shiranui "Mukuro" as its main vanguard, which has an ability that can bind all of your opponent's rear-guards when it attacks. It also has an ability that can retire one of your opponent's bound cards every time they ride a vanguard. This means that you can prevent them from calling back their bound units or using them for other purposes. You can also use Shiranui "Rinne" or Shiranui "Zanki" as backup vanguards, which have abilities that can bind or retire your opponent's units as well. The rest of the deck consists of units that can support your binding and retiring strategy, such as Magatsu Gale, Million Rat, Agitomaru, Dual Weapon, Tsumujibashou, Magatsu Breath, and Tanba. You can also use Magatsu Wind and Zashikihime as critical triggers to increase your pressure and damage output. - How to use PSYqualia EX to increase your chances of getting triggers: PSYqualia EX is one of the EX Gifts that you can use in Cardfight!! Vanguard EX. It is based on the ability of Aichi Sendou, the main protagonist of the anime series, who can sense the cards in his deck and manipulate them to his advantage. PSYqualia EX allows you to look at the top card of your deck before you drive check, and decide whether to keep it or put it on the bottom of your deck. This means that you can increase your chances of getting triggers, such as critical, heal, draw, or front, that can boost your power, heal your damage, draw more cards, or strengthen your front row. You can also avoid getting triggers that are not useful at the moment, such as heal triggers when you have less damage than your opponent, or draw triggers when you have a full hand. PSYqualia EX is a very powerful EX Gift that can give you an edge in cardfights, especially if you use it with decks that have high trigger ratios or rely on trigger effects. <h2>Conclusion</h2>
- <p>Cardfight!! Vanguard EX is a game that will appeal to fans of card games and anime alike. It has a lot of features and modes that will keep you entertained for hours, such as an original story, over 30 characters, over 2000 cards, online and local multiplayer, and EX Gifts. It is also easy to download and play on Nintendo Switch, as long as you have a Japanese Nintendo eShop account and enough storage space. If you are looking for a fun and challenging game that will test your strategic skills and cardfighting spirit, you should definitely give Cardfight!! Vanguard EX a try.</p>
- <p>Thank you for reading this article. I hope you found it helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. And if you enjoyed this article, please share it with your friends and fellow cardfighters. Happy cardfighting!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Cardfight!! Vanguard EX:</p>
- - Q: Is Cardfight!! Vanguard EX available in English? - A: No, Cardfight!! Vanguard EX is only available in Japanese. However, the game is not very difficult to understand or play even if you don't know Japanese, as long as you are familiar with the rules and cards of Cardfight!! Vanguard. You can also use online guides or translators to help you with some of the menus or texts. - Q: Is Cardfight!! Vanguard EX compatible with other regions or versions of Nintendo Switch? - A: Yes, Cardfight!! Vanguard EX is compatible with any region or version of Nintendo Switch, as long as you have a Japanese Nintendo eShop account and enough storage space. You can also play online or locally with other players who have the same game. - Q: How do I unlock more characters and cards in Cardfight!! Vanguard EX? - A: You can unlock more characters and cards by completing episodes in story mode, winning cardfights in free fight mode, or buying card packs from the shop. You can also use codes from real-life Cardfight!! Vanguard products to get exclusive cards in the game. - Q: How do I update Cardfight!! Vanguard EX to get the latest cards and features? - A: You can update Cardfight!! Vanguard EX by accessing the eShop with your Japanese Nintendo eShop account and downloading the latest patch for the game. The game will automatically update when you start it. - Q: Where can I find more information or help about Cardfight!! Vanguard EX? - A: You can find more information or help about Cardfight!! Vanguard EX by visiting the official website of the game, or by joining online communities and forums dedicated to Cardfight!! Vanguard.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/transformer.py b/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/transformer.py
deleted file mode 100644
index be6a5e420fc53eebe9947aa5dde7bfebd3cb4dad..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/transformer.py
+++ /dev/null
@@ -1,704 +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.
-
-"""
-Transformer model, with streaming support, xformer attention support
-and easy causal attention with a potentially finite receptive field.
-
-See `StreamingTransformer` for more information.
-
-Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
-"""
-
-import typing as tp
-
-from einops import rearrange
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint as torch_checkpoint
-from xformers import ops
-
-from .rope import RotaryEmbedding
-from .streaming import StreamingModule
-
-
-def _is_profiled() -> bool:
-    # Return true if we are currently running with a xformers profiler activated.
-    try:
-        from xformers.profiler import profiler
-    except ImportError:
-        return False
-    return profiler._Profiler._CURRENT_PROFILER is not None
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
-    """Create normalization module for transformer encoder layer.
-
-    Args:
-        norm_type (str): Normalization method.
-        dim (int): Dimension of the normalized layer.
-        **kwargs (dict): Additional parameters for normalization layer.
-    Returns:
-        nn.Module: Normalization module.
-    """
-    if norm_type == 'layer_norm':
-        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
-    else:
-        raise ValueError(f"Unknown norm type: {norm_type}")
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
-                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
-    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
-    Args:
-        positions (torch.Tensor): LongTensor of positions.
-        dim (int): Dimension of the embedding.
-        max_period (float): Maximum period of the cosine/sine functions.
-        dtype (torch.dtype or str): dtype to use to generate the embedding.
-    Returns:
-        torch.Tensor: Sinusoidal positional embedding.
-    """
-    # We aim for BTC format
-    assert dim % 2 == 0
-    half_dim = dim // 2
-    positions = positions.to(dtype)
-    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
-    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
-    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
-    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers"""
-    bs, slen, n_kv_heads, head_dim = x.shape
-    if n_rep == 1:
-        return x
-    return (
-        x[:, :, :, None, :]
-        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
-        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
-    )
-
-
-class LayerScale(nn.Module):
-    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
-    This rescales diagonaly the residual outputs close to 0, with a learnt scale.
-
-    Args:
-        channels (int): Number of channels.
-        init (float): Initial scale.
-        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype or None): dtype to use to initialize the module.
-    """
-    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
-                 device=None, dtype=None):
-        super().__init__()
-        self.channel_last = channel_last
-        self.scale = nn.Parameter(
-            torch.full((channels,), init,
-                       requires_grad=True, device=device, dtype=dtype))
-
-    def forward(self, x: torch.Tensor):
-        if self.channel_last:
-            return self.scale * x
-        else:
-            return self.scale[:, None] * x
-
-
-class StreamingMultiheadAttention(StreamingModule):
-    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
-
-    Args:
-        embed_dim (int): Dimension to project to.
-        num_heads (int): Number of heads.
-        dropout (float): Dropout level.
-        bias (bool): Use bias in projections.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        cross_attention: Should be true when used as a cross attention.
-            All keys and values must be available at once, streaming is only for the queries.
-            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
-            intepret the time steps in the keys relative to those in the queries).
-        safe_streaming (bool): Bug fix, will go away with xformers update.
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Sevice on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-    """
-    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
-                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
-                 device=None, dtype=None):
-        super().__init__()
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        if past_context is not None:
-            assert causal
-
-        self.embed_dim = embed_dim
-        self.causal = causal
-        self.past_context = past_context
-        self.memory_efficient = memory_efficient
-        self.attention_as_float32 = attention_as_float32
-        self.rope = rope
-        self.cross_attention = cross_attention
-        self.safe_streaming = safe_streaming
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.kv_repeat = kv_repeat
-        if cross_attention:
-            assert not causal, "Causal cannot work with cross attention."
-            assert rope is None, "Rope cannot work with cross attention."
-
-        if memory_efficient:
-            _verify_xformers_memory_efficient_compat()
-
-        self.custom = _is_custom(custom, memory_efficient)
-        if self.custom:
-            out_dim = embed_dim
-            assert num_heads % kv_repeat == 0
-            assert not cross_attention or kv_repeat == 1
-            num_kv = num_heads // kv_repeat
-            kv_dim = (embed_dim // num_heads) * num_kv
-            out_dim += 2 * kv_dim
-            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
-            # We try to follow the default PyTorch MHA convention, to easily compare results.
-            self.in_proj_weight = in_proj.weight
-            self.in_proj_bias = in_proj.bias
-            if bias:
-                self.in_proj_bias.data.zero_()  # Following Pytorch convention
-            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
-            if bias:
-                self.out_proj.bias.data.zero_()
-        else:
-            assert not qk_layer_norm
-            assert kv_repeat == 1
-            self.mha = nn.MultiheadAttention(
-                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
-                **factory_kwargs)
-        self.qk_layer_norm = qk_layer_norm
-        if qk_layer_norm:
-            assert self.custom
-            assert kv_repeat == 1
-            ln_dim = embed_dim
-            self.q_layer_norm = nn.LayerNorm(ln_dim)
-            self.k_layer_norm = nn.LayerNorm(ln_dim)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        if not self.custom:
-            # Support compat with regular MHA
-            keys = [n for n, _ in self.mha.named_parameters()]
-            for key in keys:
-                if prefix + key in state_dict:
-                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
-        # Return a causal mask, accounting for potentially stored past keys/values
-        # We actually return a bias for the attention score, as this has the same
-        # convention both in the builtin MHA in Pytorch, and Xformers functions.
-        if self.memory_efficient:
-            from xformers.ops import LowerTriangularMask
-            if current_steps == 1:
-                # If we only have one step, then we do not need a mask.
-                return None
-            elif 'past_keys' in self._streaming_state:
-                raise RuntimeError('Not supported at the moment')
-            else:
-                # Then we can safely use a lower triangular mask
-                return LowerTriangularMask()
-        if self._streaming_state:
-            past_keys = self._streaming_state['past_keys']
-            past_steps = past_keys.shape[1]
-        else:
-            past_steps = 0
-
-        queries_pos = torch.arange(
-            past_steps, current_steps + past_steps, device=device).view(-1, 1)
-        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
-        delta = queries_pos - keys_pos
-        valid = delta >= 0
-        if self.past_context is not None:
-            valid &= (delta <= self.past_context)
-        return torch.where(
-            valid,
-            torch.zeros([], device=device, dtype=dtype),
-            torch.full([], float('-inf'), device=device, dtype=dtype))
-
-    def _complete_kv(self, k, v):
-        if self.cross_attention:
-            # With cross attention we assume all keys and values
-            # are already available, and streaming is with respect
-            # to the queries only.
-            return k, v
-        # Complete the key/value pair using the streaming state.
-        if self._streaming_state:
-            pk = self._streaming_state['past_keys']
-            nk = torch.cat([pk, k], dim=1)
-            if v is k:
-                nv = nk
-            else:
-                pv = self._streaming_state['past_values']
-                nv = torch.cat([pv, v], dim=1)
-        else:
-            nk = k
-            nv = v
-
-        assert nk.shape[1] == nv.shape[1]
-        offset = 0
-        if self.past_context is not None:
-            offset = max(0, nk.shape[1] - self.past_context)
-        if self._is_streaming:
-            self._streaming_state['past_keys'] = nk[:, offset:]
-            if v is not k:
-                self._streaming_state['past_values'] = nv[:, offset:]
-            if 'offset' in self._streaming_state:
-                self._streaming_state['offset'] += offset
-            else:
-                self._streaming_state['offset'] = torch.tensor(0)
-        return nk, nv
-
-    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
-        # Apply rope embeddings to query and key tensors.
-        assert self.rope is not None
-        if 'past_keys' in self._streaming_state:
-            past_keys_offset = self._streaming_state['past_keys'].shape[1]
-        else:
-            past_keys_offset = 0
-        if 'offset' in self._streaming_state:
-            past_context_offset = int(self._streaming_state['offset'].item())
-        else:
-            past_context_offset = 0
-        streaming_offset = past_context_offset + past_keys_offset
-        return self.rope.rotate_qk(query, key, start=streaming_offset)
-
-    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
-                key_padding_mask=None, need_weights=False, attn_mask=None,
-                average_attn_weights=True, is_causal=False):
-        assert attn_mask is None
-        assert not is_causal, ("new param added in torch 2.0.1 not supported, "
-                               "use the causal args in the constructor.")
-
-        dtype = query.dtype
-        if self._is_streaming:
-            assert self.causal or self.cross_attention, \
-                "Streaming only available for causal or cross attention"
-
-        if self.causal:
-            # At the moment we specialize only for the self-attention case.
-            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
-
-        if self.custom:
-            # custom implementation
-            assert need_weights is False
-            assert key_padding_mask is None
-            if self.cross_attention:
-                # Different queries, keys, values, we have to spit manually the weights
-                # before applying the linear.
-                dim = self.in_proj_weight.shape[0] // 3
-                if self.in_proj_bias is None:
-                    bias_q, bias_k, bias_v = None, None, None
-                else:
-                    bias_q = self.in_proj_bias[:dim]
-                    bias_k = self.in_proj_bias[dim: 2 * dim]
-                    bias_v = self.in_proj_bias[2 * dim:]
-                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
-                # todo: when streaming, we could actually save k, v and check the shape actually match.
-                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
-                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
-                if self.qk_layer_norm is True:
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                # q, k, v = [rearrange(x, "b t (h d) -> (b h) t d", h=self.num_heads) for x in [q, k, v]]
-                q, k, v = [rearrange(x, "b t (h d) -> b t h d", h=self.num_heads) for x in [q, k, v]]
-            else:
-                if not _is_profiled():
-                    # profiling breaks that propertysomehow.
-                    assert query is key, "specialized implementation"
-                    assert value is key, "specialized implementation"
-                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
-                if self.kv_repeat == 1:
-                    packed = rearrange(projected, "b t (p h d) -> b t p h d", p=3, h=self.num_heads)
-                    q, k, v = ops.unbind(packed, dim=2)
-                else:
-                    embed_dim = self.embed_dim
-                    per_head_dim = (embed_dim // self.num_heads)
-                    kv_heads = self.num_heads // self.kv_repeat
-                    q = projected[:, :, :embed_dim]
-                    start = embed_dim
-                    end = start + per_head_dim * kv_heads
-                    k = projected[:, :, start: end]
-                    v = projected[:, :, end:]
-                    q = rearrange(q, "b t (h d) -> b t h d", h=self.num_heads)
-                    k = rearrange(k, "b t (h d) -> b t h d", h=kv_heads)
-                    v = rearrange(v, "b t (h d) -> b t h d", h=kv_heads)
-
-                if self.qk_layer_norm is True:
-                    assert self.kv_repeat == 1
-                    q, k = [rearrange(x, "b t h d -> b t (h d)") for x in [q, k]]
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                    q, k = [rearrange(x, "b t (h d) -> b t h d", h=self.num_heads) for x in [q, k]]
-                if self.rope:
-                    q, k = self._apply_rope(q, k)
-                k, v = self._complete_kv(k, v)
-                if self.kv_repeat > 1:
-                    k = expand_repeated_kv(k, self.kv_repeat)
-                    v = expand_repeated_kv(v, self.kv_repeat)
-            if self.attention_as_float32:
-                q, k, v = [x.float() for x in [q, k, v]]
-            if self.memory_efficient:
-                p = self.dropout if self.training else 0
-                x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
-            else:
-                # We include the dot product as float32, for consistency
-                # with the other implementations that include that step
-                # as part of the attention. Note that when using `autocast`,
-                # the einsums would be done as bfloat16, but the softmax
-                # would be done as bfloat16, so `attention_as_float32` will
-                # extend a bit the range of operations done in float32,
-                # although this should make no difference.
-                q = q / q.shape[-1] ** 0.5
-                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
-                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
-                        pre_w = torch.einsum("bqhc,bkhc->bhqk", q, k)
-                else:
-                    pre_w = torch.einsum("bqhc,bkhc->bhqk", q, k)
-                if attn_mask is not None:
-                    pre_w = pre_w + attn_mask
-                w = torch.softmax(pre_w, dim=-1)
-                w = F.dropout(w, self.dropout, training=self.training).to(v)
-                x = torch.einsum("bhqk,bkhc->bqhc", w, v)
-            x = x.to(dtype)
-            x = rearrange(x, "b t h d -> b t (h d)", h=self.num_heads)
-            x = self.out_proj(x)
-        else:
-            key, value = self._complete_kv(key, value)
-            if self.attention_as_float32:
-                query, key, value = [x.float() for x in [query, key, value]]
-            x, _ = self.mha(
-                query, key, value, key_padding_mask,
-                need_weights, attn_mask, average_attn_weights)
-            x = x.to(dtype)
-
-        return x, None
-
-
-class StreamingTransformerLayer(nn.TransformerEncoderLayer):
-    """TransformerLayer with Streaming / Causal support.
-    This also integrates cross_attention, when passing `cross_attention=True`,
-    rather than having two separate classes like in PyTorch.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
-        qk_layer_norm_cross (bool): Same for the cross attention.
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-            Cross attention will use the default MHA, as it typically won't require
-            special treatment.
-        layer_scale (float or None): If not None, LayerScale will be used with
-            the given value as initial scale.
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        attention_dropout (float or None): If not None, separate the value of the dimension dropout
-            in FFN and of the attention dropout.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
-                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
-                 past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
-                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
-        super().__init__(d_model, num_heads, dim_feedforward, dropout,
-                         device=device, dtype=dtype, batch_first=True, **kwargs)
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        # Redefine self_attn to our streaming multi-head attention
-        attn_kwargs: tp.Dict[str, tp.Any] = {
-            'embed_dim': d_model,
-            'num_heads': num_heads,
-            'dropout': dropout if attention_dropout is None else attention_dropout,
-            'bias': bias_attn,
-            'custom': custom,
-            'memory_efficient': memory_efficient,
-            'attention_as_float32': attention_as_float32,
-        }
-        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
-            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
-            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
-        # Redefine feedforward layers to expose bias parameter
-        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
-        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
-
-        self.layer_scale_1: nn.Module
-        self.layer_scale_2: nn.Module
-        if layer_scale is None:
-            self.layer_scale_1 = nn.Identity()
-            self.layer_scale_2 = nn.Identity()
-        else:
-            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
-            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
-
-        self.cross_attention: tp.Optional[nn.Module] = None
-        if cross_attention:
-            self.cross_attention = StreamingMultiheadAttention(
-                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
-                **attn_kwargs, **factory_kwargs)
-            # Norm and dropout
-            self.dropout_cross = nn.Dropout(dropout)
-            # eps value matching that used in PyTorch reference implementation.
-            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
-            self.layer_scale_cross: nn.Module
-            if layer_scale is None:
-                self.layer_scale_cross = nn.Identity()
-            else:
-                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
-        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-
-    def _cross_attention_block(self, src: torch.Tensor,
-                               cross_attention_src: torch.Tensor) -> torch.Tensor:
-        assert self.cross_attention is not None
-        # queries are from src, keys and values from cross_attention_src.
-        x = self.cross_attention(
-            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
-        return self.dropout_cross(x)  # type: ignore
-
-    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
-                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
-                cross_attention_src: tp.Optional[torch.Tensor] = None):
-        if self.cross_attention is None:
-            assert cross_attention_src is None
-        else:
-            assert cross_attention_src is not None
-        x = src
-        if self.norm_first:
-            x = x + self.layer_scale_1(
-                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
-            if cross_attention_src is not None:
-                x = x + self.layer_scale_cross(
-                    self._cross_attention_block(
-                        self.norm_cross(x), cross_attention_src))
-            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
-        else:
-            x = self.norm1(x + self.layer_scale_1(
-                self._sa_block(x, src_mask, src_key_padding_mask)))
-            if cross_attention_src is not None:
-                x = self.norm_cross(
-                    x + self.layer_scale_cross(
-                        self._cross_attention_block(src, cross_attention_src)))
-            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
-        return x
-
-
-class StreamingTransformer(StreamingModule):
-    """Transformer with Streaming / Causal support.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-        layer_scale (float or None): If not None, LayerScale will be used
-            with the given value as initial scale.
-        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
-        max_period (float): Maximum period of the time embedding.
-        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
-        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
-        lr (float or None): learning rate override through the `make_optim_group` API.
-        weight_decay (float or None): Weight_decay override through the `make_optim_group` API.
-        layer_class: (subclass of `StreamingTransformerLayer): class to use
-            to initialize the layers, allowing further customization outside of Audiocraft.
-        checkpointing (str): Checkpointing strategy to reduce memory usage.
-            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
-            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
-            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
-            a policy for opting-out some operations of the checkpointing like
-            linear layers and attention, providing a middle ground between speed and memory.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
-                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None,
-                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
-                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
-                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
-                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
-        super().__init__()
-        assert d_model % num_heads == 0
-
-        self.positional_embedding = positional_embedding
-        self.max_period = max_period
-        self.positional_scale = positional_scale
-        self.weight_decay = weight_decay
-        self.lr = lr
-
-        assert positional_embedding in ['sin', 'rope', 'sin_rope']
-        self.rope: tp.Optional[RotaryEmbedding] = None
-        if self.positional_embedding in ['rope', 'sin_rope']:
-            assert _is_custom(custom, memory_efficient)
-            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
-                                        xpos=xpos, scale=positional_scale, device=device)
-
-        self.checkpointing = checkpointing
-
-        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
-        if self.checkpointing.startswith('xformers'):
-            _verify_xformers_internal_compat()
-
-        self.layers = nn.ModuleList()
-        for idx in range(num_layers):
-            self.layers.append(
-                layer_class(
-                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
-                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
-                    causal=causal, past_context=past_context, custom=custom,
-                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
-                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
-                    device=device, dtype=dtype, **kwargs))
-
-        if self.checkpointing != 'none':
-            for layer in self.layers:
-                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
-                # backward hook inside of FSDP...
-                layer._magma_checkpointed = True  # type: ignore
-                assert layer.layer_drop == 0., "Need further checking"  # type: ignore
-
-    def _apply_layer(self, layer, *args, **kwargs):
-        method = self.checkpointing
-        if method == 'none':
-            return layer(*args, **kwargs)
-        elif method == 'torch':
-            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
-        elif method.startswith('xformers'):
-            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
-            if method == 'xformers_default':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "xformers.efficient_attention_forward_cutlass.default",
-                    "xformers_flash.flash_fwd.default",
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            elif method == 'xformers_mm':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            else:
-                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
-            policy_fn = _get_default_policy(allow_list)
-            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
-        else:
-            raise ValueError(f"Checkpointing method {method} is unknown.")
-
-    def forward(self, x: torch.Tensor, *args, **kwargs):
-        B, T, C = x.shape
-
-        if 'offsets' in self._streaming_state:
-            offsets = self._streaming_state['offsets']
-        else:
-            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
-
-        if self.positional_embedding in ['sin', 'sin_rope']:
-            positions = torch.arange(T, device=x.device).view(1, -1, 1)
-            positions = positions + offsets.view(-1, 1, 1)
-            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
-            x = x + self.positional_scale * pos_emb
-
-        for layer in self.layers:
-            x = self._apply_layer(layer, x, *args, **kwargs)
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return x
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        if self.weight_decay is not None:
-            group["weight_decay"] = self.weight_decay
-        return group
-
-
-# special attention attention related function
-
-def _verify_xformers_memory_efficient_compat():
-    try:
-        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
-    except ImportError:
-        raise ImportError(
-            "xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _verify_xformers_internal_compat():
-    try:
-        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
-    except ImportError:
-        raise ImportError(
-            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _is_custom(custom: bool, memory_efficient: bool):
-    return custom or memory_efficient
diff --git a/spaces/skf15963/summary/fengshen/models/deltalm/modeling_deltalm.py b/spaces/skf15963/summary/fengshen/models/deltalm/modeling_deltalm.py
deleted file mode 100644
index 2cdd65f3e106e9433dd5116419dfd50cd8a33b85..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/deltalm/modeling_deltalm.py
+++ /dev/null
@@ -1,1551 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-
-import copy
-import math
-import random
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss
-from typing import List, Optional, Tuple, Union
-
-from transformers.modeling_utils import PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    Seq2SeqModelOutput,
-    Seq2SeqLMOutput,
-)
-from transformers.file_utils import (
-    add_end_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-
-import logging
-from .configuration_deltalm import DeltalmConfig
-logger = logging.getLogger(__name__)
-
-_CHECKPOINT_FOR_DOC = "IDEA-CCNL/Randeng-Deltalm-362M-En-Zn"
-_CONFIG_FOR_DOC = "DeltalmConfig"
-_TOKENIZER_FOR_DOC = "DeltalmTokenizer"
-
-# Base model docstring
-_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]
-
-
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
-    """
-    Shift input ids one token to the right.
-    """
-    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
-    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
-    shifted_input_ids[:, 0] = decoder_start_token_id
-
-    if pad_token_id is None:
-        raise ValueError("self.model.config.pad_token_id has to be defined.")
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
-    return shifted_input_ids
-
-
-def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.tensor(float("-inf")))
-    mask_cond = torch.arange(mask.size(-1))
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-class DeltalmLearnedPositionalEmbedding(nn.Embedding):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    """
-
-    def __init__(self, num_embeddings: int, embedding_dim: int):
-        # Deltalm is set up so that if padding_idx is specified then offset the embedding ids by 2
-        # and adjust num_embeddings appropriately. Other models don't have this hack
-        self.offset = 2
-        super().__init__(num_embeddings + self.offset, embedding_dim)
-
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
-        positions = torch.arange(
-            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
-        return super().forward(positions + self.offset)
-
-
-class DeltalmAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-
-        if (self.head_dim * num_heads) != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
-                f" and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        if is_cross_attention and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned aross GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
-
-
-class DeltalmEncoderLayer(nn.Module):
-    def __init__(self, config: DeltalmConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = DeltalmAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        layer_head_mask: torch.FloatTensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-        hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        if hidden_states.dtype == torch.float16 and (
-            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
-        ):
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class DeltalmDecoderLayer(nn.Module):
-    def __init__(self, config: DeltalmConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        self.self_attn = DeltalmAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-            is_decoder=True,
-        )
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.encoder_attn = DeltalmAttention(
-            self.embed_dim,
-            config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-            is_decoder=True,
-        )
-        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-        self.fc3 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc4 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-
-        self.ffn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = True,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            encoder_hidden_states (`torch.FloatTensor`):
-                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
-            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
-                size `(decoder_attention_heads,)`.
-            past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        # Self Attention
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        # add present self-attn cache to positions 1,2 of present_key_value tuple
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            past_key_value=self_attn_past_key_value,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        # Add another ffn after self-attention to keep the structure same to encoder-layer
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc3(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc4(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.ffn_layer_norm(hidden_states)
-
-        # Cross-Attention Block
-        cross_attn_present_key_value = None
-        cross_attn_weights = None
-        if encoder_hidden_states is not None:
-            residual = hidden_states
-
-            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
-            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
-            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
-                hidden_states=hidden_states,
-                key_value_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                layer_head_mask=cross_attn_layer_head_mask,
-                past_key_value=cross_attn_past_key_value,
-                output_attentions=output_attentions,
-            )
-            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-            hidden_states = residual + hidden_states
-            hidden_states = self.encoder_attn_layer_norm(hidden_states)
-
-            # add cross-attn to positions 3,4 of present_key_value tuple
-            present_key_value = present_key_value + cross_attn_present_key_value
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights, cross_attn_weights)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-class DeltalmPretrainedModel(PreTrainedModel):
-    config_class = DeltalmConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (DeltalmDecoder, DeltalmEncoder)):
-            module.gradient_checkpointing = value
-
-
-class DeltalmDecoder(DeltalmPretrainedModel):
-    """
-    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DeltalmDecoderLayer`]
-    Args:
-        config: DeltalmConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
-        super().__init__(config)
-        self.dropout = config.dropout
-        self.layerdrop = config.decoder_layerdrop
-        self.padding_idx = config.pad_token_id
-        self.max_target_positions = config.max_position_embeddings
-        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
-
-        if embed_tokens is not None:
-            self.embed_tokens = embed_tokens
-        else:
-            self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
-
-        self.embed_positions = DeltalmLearnedPositionalEmbedding(
-            config.max_position_embeddings,
-            config.d_model,
-        )
-        self.layers = nn.ModuleList([DeltalmDecoderLayer(config) for _ in range(config.decoder_layers)])
-        self.layernorm_embedding = nn.LayerNorm(config.d_model)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # fairseq实现了一个 nn.init.normal_(self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5) 对最后的output权重做正态分布转换？
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
-            ).to(inputs_embeds.device)
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-                Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                of the decoder.
-            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
-                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
-                selected in `[0, 1]`:
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
-                cross-attention on hidden heads. Mask values selected in `[0, 1]`:
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
-                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
-                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
-                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
-                all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
-                shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
-                `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
-                control over how to convert `input_ids` indices into associated vectors than the model's internal
-                embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask, input_shape, inputs_embeds, past_key_values_length
-        )
-
-        # expand encoder attention mask
-        if encoder_hidden_states is not None and encoder_attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
-
-        # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
-
-        hidden_states = inputs_embeds + positions
-        hidden_states = self.layernorm_embedding(hidden_states)
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
-        next_decoder_cache = () if use_cache else None
-
-        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
-        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
-            if attn_mask is not None:
-                if attn_mask.size()[0] != (len(self.layers)):
-                    raise ValueError(
-                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
-                        f" {head_mask.size()[0]}."
-                    )
-
-        for idx, decoder_layer in enumerate(self.layers):
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            dropout_probability = random.uniform(0, 1)
-            if self.training and (dropout_probability < self.layerdrop):
-                continue
-
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                if use_cache:
-                    logger.warning(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, use_cache)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    head_mask[idx] if head_mask is not None else None,
-                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
-                    None,
-                )
-            else:
-
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_attention_mask,
-                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                    cross_attn_layer_head_mask=(
-                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
-                    ),
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-                if encoder_hidden_states is not None:
-                    all_cross_attentions += (layer_outputs[2],)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-DELTALM_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-    Parameters:
-        config ([`DeltalmConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-DELTALM_GENERATION_EXAMPLE = r"""
-    Summarization example:
-    ```python
-    >>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
-    >>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-large-cnn")
-    >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-large-cnn")
-    >>> ARTICLE_TO_SUMMARIZE = (
-    ...     "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
-    ...     "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
-    ...     "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
-    ... )
-    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")
-    >>> # Generate Summary
-    >>> summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
-    >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions'
-    ```
-    Mask filling example:
-    ```python
-    >>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
-    >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
-    >>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-base")
-    >>> TXT = "My friends are <mask> but they eat too many carbs."
-    >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
-    >>> logits = model(input_ids).logits
-    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
-    >>> probs = logits[0, masked_index].softmax(dim=0)
-    >>> values, predictions = probs.topk(5)
-    >>> tokenizer.decode(predictions).split()
-    ['not', 'good', 'healthy', 'great', 'very']
-    ```
-"""
-
-DELTALM_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-            Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-            [What are attention masks?](../glossary#attention-mask)
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-            Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-            [What are decoder input IDs?](../glossary#decoder-input-ids)
-            Deltalm uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
-            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
-            For translation and summarization training, `decoder_input_ids` should be provided. If no
-            `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
-            for denoising pre-training following the paper.
-        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-            If you want to change padding behavior, you should read [`modeling_deltalm._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
-            1]`:
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
-            `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
-            can choose to directly pass an embedded representation. This is useful if you want more control over how to
-            convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
-            input (see `past_key_values`). This is useful if you want more control over how to convert
-            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
-            of `inputs_embeds`.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class DeltalmEncoder(DeltalmPretrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
-    [`DeltalmEncoderLayer`].
-    Args:
-        config: DeltalmConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-        self.layerdrop = config.encoder_layerdrop
-
-        embed_dim = config.d_model
-        self.padding_idx = config.pad_token_id
-        self.max_source_positions = config.max_position_embeddings
-        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
-
-        if embed_tokens is not None:
-            self.embed_tokens = embed_tokens
-        else:
-            self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
-
-        self.embed_positions = DeltalmLearnedPositionalEmbedding(
-            config.max_position_embeddings,
-            embed_dim,
-        )
-        self.layers = nn.ModuleList([DeltalmEncoderLayer(config) for _ in range(config.encoder_layers)])
-        self.layernorm_embedding = nn.LayerNorm(embed_dim)
-
-        self.gradient_checkpointing = False
-        if config.encoder_normalize_before:
-            self.layer_norm = nn.LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-                Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
-        embed_pos = self.embed_positions(input_shape)
-
-        hidden_states = inputs_embeds + embed_pos
-        hidden_states = self.layernorm_embedding(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        # check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            if head_mask.size()[0] != (len(self.layers)):
-                raise ValueError(
-                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
-                    f" {head_mask.size()[0]}."
-                )
-
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            dropout_probability = random.uniform(0, 1)
-            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
-                layer_outputs = (None, None)
-            else:
-                if self.gradient_checkpointing and self.training:
-
-                    def create_custom_forward(module):
-                        def custom_forward(*inputs):
-                            return module(*inputs, output_attentions)
-
-                        return custom_forward
-
-                    layer_outputs = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(encoder_layer),
-                        hidden_states,
-                        attention_mask,
-                        (head_mask[idx] if head_mask is not None else None),
-                    )
-                else:
-                    layer_outputs = encoder_layer(
-                        hidden_states,
-                        attention_mask,
-                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                        output_attentions=output_attentions,
-                    )
-
-                hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if self.layer_norm is not None:
-            hidden_states = self.layer_norm(hidden_states)
-        # hidden_states = self.layernorm_embedding(hidden_states)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class DeltalmModel(DeltalmPretrainedModel):
-    def __init__(self, config: DeltalmConfig):
-        super().__init__(config)
-
-        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
-        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
-
-        self.encoder = DeltalmEncoder(config, self.shared)
-        self.decoder = DeltalmDecoder(config, self.shared)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, value):
-        self.shared = value
-        self.encoder.embed_tokens = self.shared
-        self.decoder.embed_tokens = self.shared
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    @add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
-    # @add_code_sample_docstrings(
-    #     processor_class=_TOKENIZER_FOR_DOC,
-    #     checkpoint=_CHECKPOINT_FOR_DOC,
-    #     output_type=Seq2SeqModelOutput,
-    #     config_class=_CONFIG_FOR_DOC,
-    #     expected_output=_EXPECTED_OUTPUT_SHAPE,
-    # )
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[List[torch.FloatTensor]] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, Seq2SeqModelOutput]:
-
-        # different to other models, Deltalm automatically creates decoder_input_ids from
-        # input_ids if no decoder_input_ids are provided
-        if decoder_input_ids is None and decoder_inputs_embeds is None:
-            if input_ids is None:
-                raise ValueError(
-                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
-                    "passed, `input_ids` cannot be `None`. Please pass either "
-                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
-                )
-
-            decoder_input_ids = shift_tokens_right(
-                input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
-            )
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                head_mask=head_mask,
-                inputs_embeds=inputs_embeds,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            encoder_hidden_states=encoder_outputs[0],
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=decoder_inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            return decoder_outputs + encoder_outputs
-
-        logger.debug("last_hidden_state.size:  %s", decoder_outputs.last_hidden_state)
-        return Seq2SeqModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    "The DELTALM Model with a language modeling head. Can be used for translation.", DELTALM_START_DOCSTRING
-)
-class DeltalmForConditionalGeneration(DeltalmPretrainedModel):
-    base_model_prefix = "model"
-    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head.weight"]
-
-    def __init__(self, config: DeltalmConfig):
-        super().__init__(config)
-        self.model = DeltalmModel(config)
-        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
-        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_encoder(self):
-        return self.model.get_encoder()
-
-    def get_decoder(self):
-        return self.model.get_decoder()
-
-    def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens)
-        self._resize_final_logits_bias(new_num_tokens)
-        return new_embeddings
-
-    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
-        logger.debug("Debug: coming to _resize_final_logits_bias")
-        old_num_tokens = self.final_logits_bias.shape[-1]
-        if new_num_tokens <= old_num_tokens:
-            new_bias = self.final_logits_bias[:, :new_num_tokens]
-        else:
-            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
-            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
-        self.register_buffer("final_logits_bias", new_bias)
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    @add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
-    @add_end_docstrings(DELTALM_GENERATION_EXAMPLE)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[List[torch.FloatTensor]] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, Seq2SeqLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-        Returns:
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        logger.debug("Comming to Generation!")
-
-        if labels is not None:
-            logger.debug("Debug: *************** Before label ***************** ")
-            logger.debug("Debug: %s", labels.size())
-            if use_cache:
-                logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
-            use_cache = False
-            if decoder_input_ids is None and decoder_inputs_embeds is None:
-                decoder_input_ids = shift_tokens_right(
-                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
-                )
-
-            logger.debug("Debug: ************ After labels ************")
-            logger.debug("Debug: %s", labels.size())
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            encoder_outputs=encoder_outputs,
-            decoder_attention_mask=decoder_attention_mask,
-            head_mask=head_mask,
-            decoder_head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
-        # print(self.lm_head)
-        logger.debug("Debug: logit_size: %s", lm_logits.size())
-
-        # logger.debug("Debug: change logit size: ", lm_logits.view(-1, self.config.vocab_size).size())
-        # logger.debug("Debug: change label size: ", labels.view(-1).size())
-        masked_lm_loss = None
-
-        if labels is not None:
-            # logger.debug("Debug: model label_size: %s", labels.size())
-            # loss_fct = CrossEntropyLoss()
-            # masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-            # label_smoothing = self.config.label_smoothing
-            # # logger.debug("Debug: label.size: ", )
-            # if label_smoothing == 0:
-            #     # compute label smoothed loss
-            #     loss_fct = CrossEntropyLoss()
-            #     masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-            # else:
-            #     m = torch.nn.LogSoftmax(dim=-1)
-            #     lprobs = m(lm_logits.float())
-            #     # lprobs = m(lm_logits)
-            #     # # torch.set_printoptions(linewidth=200)
-            #     loss_fn = label_smoothed_nll_loss
-            #     masked_lm_loss, _ = loss_fn(lprobs.view(-1, lprobs.size(-1)), labels.view(-1), label_smoothing, self.config.pad_token_id)
-
-        if not return_dict:
-            logger.debug("Debug: not return dict")
-            output = (lm_logits,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=masked_lm_loss,
-            logits=lm_logits,
-            past_key_values=outputs.past_key_values,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        decoder_input_ids,
-        past=None,
-        attention_mask=None,
-        head_mask=None,
-        decoder_head_mask=None,
-        cross_attn_head_mask=None,
-        use_cache=None,
-        encoder_outputs=None,
-        **kwargs
-    ):
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            decoder_input_ids = decoder_input_ids[:, -1:]
-
-        return {
-            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
-            "encoder_outputs": encoder_outputs,
-            "past_key_values": past,
-            "decoder_input_ids": decoder_input_ids,
-            "attention_mask": attention_mask,
-            "head_mask": head_mask,
-            "decoder_head_mask": decoder_head_mask,
-            "cross_attn_head_mask": cross_attn_head_mask,
-            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
-        }
-
-    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
-        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
-
-    @staticmethod
-    def _reorder_cache(past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            # cached cross_attention states don't have to be reordered -> they are always the same
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
-            )
-        return reordered_past
-
-
-class DeltalmDecoderWrapper(DeltalmPretrainedModel):
-    """
-    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
-    used in combination with the [`EncoderDecoderModel`] framework.
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.decoder = DeltalmDecoder(config)
-
-    def forward(self, *args, **kwargs):
-        return self.decoder(*args, **kwargs)
-
-
-class DeltalmForCausalLM(DeltalmPretrainedModel):
-    def __init__(self, config):
-        config = copy.deepcopy(config)
-        config.is_decoder = True
-        config.is_encoder_decoder = False
-        super().__init__(config)
-        self.model = DeltalmDecoderWrapper(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.decoder.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.decoder.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model.decoder = decoder
-
-    def get_decoder(self):
-        return self.model.decoder
-
-    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-                Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                if the model is configured as a decoder.
-            encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
-                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
-            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
-                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
-                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
-                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
-                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
-                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
-                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        Returns:
-        Example:
-        ```python
-        >>> from transformers import DeltalmTokenizer, DeltalmForCausalLM
-        >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
-        >>> model = DeltalmForCausalLM.from_pretrained("facebook/deltalm-base", add_cross_attention=False)
-        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(**inputs)
-        >>> logits = outputs.logits
-        >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
-        >>> list(logits.shape) == expected_shape
-        True
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model.decoder(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            head_mask=head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        logits = self.lm_head(outputs[0])
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_ids.shape)
-
-        if past:
-            input_ids = input_ids[:, -1:]
-        # first step, decoder_cached_states are empty
-        return {
-            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
-            "attention_mask": attention_mask,
-            "past_key_values": past,
-            "use_cache": use_cache,
-        }
-
-    @staticmethod
-    def _reorder_cache(past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past
diff --git a/spaces/skytnt/midi-composer/javascript/app.js b/spaces/skytnt/midi-composer/javascript/app.js
deleted file mode 100644
index 0a8d121b50c116434442101ec8232c722ab7c7e9..0000000000000000000000000000000000000000
--- a/spaces/skytnt/midi-composer/javascript/app.js
+++ /dev/null
@@ -1,389 +0,0 @@
-function gradioApp() {
-    const elems = document.getElementsByTagName('gradio-app')
-    const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
-    return !!gradioShadowRoot ? gradioShadowRoot : document;
-}
-
-uiUpdateCallbacks = []
-msgReceiveCallbacks = []
-
-function onUiUpdate(callback){
-    uiUpdateCallbacks.push(callback)
-}
-
-function onMsgReceive(callback){
-    msgReceiveCallbacks.push(callback)
-}
-
-function runCallback(x, m){
-    try {
-        x(m)
-    } catch (e) {
-        (console.error || console.log).call(console, e.message, e);
-    }
-}
-function executeCallbacks(queue, m) {
-    queue.forEach(function(x){runCallback(x, m)})
-}
-
-document.addEventListener("DOMContentLoaded", function() {
-    var mutationObserver = new MutationObserver(function(m){
-        executeCallbacks(uiUpdateCallbacks, m);
-    });
-    mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
-});
-
-(()=>{
-    let mse_receiver_inited = null
-    onUiUpdate(()=>{
-        let app = gradioApp()
-        let msg_receiver = app.querySelector("#msg_receiver");
-        if(!!msg_receiver && mse_receiver_inited !== msg_receiver){
-            let mutationObserver = new MutationObserver(function(ms){
-                ms.forEach((m)=>{
-                    m.addedNodes.forEach((node)=>{
-                        if(node.nodeName === "P"){
-                            let obj = JSON.parse(node.innerText);
-                            if(obj instanceof Array){
-                                obj.forEach((o)=>{executeCallbacks(msgReceiveCallbacks, o);});
-                            }else{
-                                executeCallbacks(msgReceiveCallbacks, obj);
-                            }
-                        }
-                    })
-                })
-            });
-            mutationObserver.observe( msg_receiver, {childList:true, subtree:true, characterData:true})
-            console.log("receiver init");
-            mse_receiver_inited = msg_receiver;
-        }
-    })
-})()
-
-function HSVtoRGB(h, s, v) {
-    let r, g, b, i, f, p, q, t;
-    i = Math.floor(h * 6);
-    f = h * 6 - i;
-    p = v * (1 - s);
-    q = v * (1 - f * s);
-    t = v * (1 - (1 - f) * s);
-    switch (i % 6) {
-        case 0: r = v; g = t; b = p; break;
-        case 1: r = q; g = v; b = p; break;
-        case 2: r = p; g = v; b = t; break;
-        case 3: r = p; g = q; b = v; break;
-        case 4: r = t; g = p; b = v; break;
-        case 5: r = v; g = p; b = q; break;
-    }
-    return {
-        r: Math.round(r * 255),
-        g: Math.round(g * 255),
-        b: Math.round(b * 255)
-    };
-}
-
-class MidiVisualizer extends HTMLElement{
-    constructor() {
-        super();
-        this.midiEvents = [];
-        this.activeNotes = [];
-        this.midiTimes = [];
-        this.wrapper = null;
-        this.svg = null;
-        this.timeLine = null;
-        this.config = {
-            noteHeight : 4,
-            beatWidth: 32
-        }
-        this.timePreBeat = 16
-        this.svgWidth = 0;
-        this.t1 = 0;
-        this.playTime = 0
-        this.playTimeMs = 0
-        this.colorMap = new Map();
-        this.playing = false;
-        this.timer = null;
-        this.init();
-    }
-
-    init(){
-        this.innerHTML=''
-        const shadow = this.attachShadow({mode: 'open'});
-        const style = document.createElement("style");
-        const wrapper = document.createElement('div');
-        style.textContent = ".note.active {stroke: black;stroke-width: 0.75;stroke-opacity: 0.75;}";
-        wrapper.style.overflowX= "scroll"
-        const svg = document.createElementNS('http://www.w3.org/2000/svg', 'svg');
-        svg.style.height = `${this.config.noteHeight*128}px`;
-        svg.style.width = `${this.svgWidth}px`;
-        const timeLine = document.createElementNS('http://www.w3.org/2000/svg', 'line');
-        timeLine.style.stroke = "green"
-        timeLine.style.strokeWidth = 2;
-        shadow.appendChild(style)
-        shadow.appendChild(wrapper);
-        wrapper.appendChild(svg);
-        svg.appendChild(timeLine)
-        this.wrapper = wrapper;
-        this.svg = svg;
-        this.timeLine= timeLine;
-        this.setPlayTime(0);
-    }
-
-    clearMidiEvents(){
-        this.pause()
-        this.midiEvents = [];
-        this.activeNotes = [];
-        this.midiTimes = [];
-        this.t1 = 0
-        this.colorMap.clear()
-        this.setPlayTime(0);
-        this.playTimeMs = 0
-        this.svgWidth = 0
-        this.svg.innerHTML = ''
-        this.svg.style.width = `${this.svgWidth}px`;
-        this.svg.appendChild(this.timeLine)
-    }
-
-    appendMidiEvent(midiEvent){
-        if(midiEvent instanceof Array && midiEvent.length > 0){
-
-            this.t1 += midiEvent[1]
-            let t = this.t1*this.timePreBeat + midiEvent[2]
-            midiEvent = [midiEvent[0], t].concat(midiEvent.slice(3))
-            if(midiEvent[0] === "note"){
-                let track = midiEvent[2]
-                let duration = midiEvent[3]
-                let channel = midiEvent[4]
-                let pitch = midiEvent[5]
-                let velocity = midiEvent[6]
-                let x = (t/this.timePreBeat)*this.config.beatWidth
-                let y = (127 - pitch)*this.config.noteHeight
-                let w = (duration/this.timePreBeat)*this.config.beatWidth
-                let h = this.config.noteHeight
-                this.svgWidth = Math.ceil(Math.max(x + w, this.svgWidth))
-                let color = this.getColor(track, channel)
-                let opacity = Math.min(1, velocity/127 + 0.1).toFixed(2)
-                let rect = this.drawNote(x,y,w,h, `rgba(${color.r}, ${color.g}, ${color.b}, ${opacity})`)
-                midiEvent.push(rect)
-                this.setPlayTime(t);
-                this.wrapper.scrollTo(this.svgWidth - this.wrapper.offsetWidth, 0)
-            }
-            this.midiEvents.push(midiEvent);
-            this.svg.style.width = `${this.svgWidth}px`;
-        }
-
-    }
-
-    getColor(track, channel){
-        let key = `${track},${channel}`;
-        let color = this.colorMap.get(key);
-        if(!!color){
-            return color;
-        }
-        color = HSVtoRGB(Math.random(),Math.random()*0.5 + 0.5,1);
-        this.colorMap.set(key, color);
-        return color;
-    }
-
-    drawNote(x, y, w, h, fill) {
-        if (!this.svg) {
-          return null;
-        }
-        const rect = document.createElementNS('http://www.w3.org/2000/svg', 'rect');
-        rect.classList.add('note');
-        rect.setAttribute('fill', fill);
-        // Round values to the nearest integer to avoid partially filled pixels.
-        rect.setAttribute('x', `${Math.round(x)}`);
-        rect.setAttribute('y', `${Math.round(y)}`);
-        rect.setAttribute('width', `${Math.round(w)}`);
-        rect.setAttribute('height', `${Math.round(h)}`);
-        this.svg.appendChild(rect);
-        return rect
-    }
-
-    finishAppendMidiEvent(){
-        this.pause()
-        let midiEvents = this.midiEvents.sort((a, b)=>a[1]-b[1])
-        let tempo = (60 / 120) * 10 ** 3
-        let ms = 0
-        let lastT = 0
-        this.midiTimes.push({ms:ms, t: 0, tempo: tempo})
-        midiEvents.forEach((midiEvent)=>{
-            let t = midiEvent[1]
-            ms += ((t- lastT) / this.timePreBeat) * tempo
-            if(midiEvent[0]==="set_tempo"){
-                tempo = (60 / midiEvent[3]) * 10 ** 3
-                this.midiTimes.push({ms:ms, t: t, tempo: tempo})
-            }
-            lastT = t
-        })
-    }
-
-    setPlayTime(t){
-        this.playTime = t
-        let x = Math.round((t/this.timePreBeat)*this.config.beatWidth)
-        this.timeLine.setAttribute('x1', `${x}`);
-        this.timeLine.setAttribute('y1', '0');
-        this.timeLine.setAttribute('x2', `${x}`);
-        this.timeLine.setAttribute('y2', `${this.config.noteHeight*128}`);
-
-        this.wrapper.scrollTo(Math.max(0, x - this.wrapper.offsetWidth/2), 0)
-
-        if(this.playing){
-            let activeNotes = []
-            this.removeActiveNotes(this.activeNotes)
-            this.midiEvents.forEach((midiEvent)=>{
-                if(midiEvent[0] === "note"){
-                    let time = midiEvent[1]
-                    let duration = midiEvent[3]
-                    let note = midiEvent[midiEvent.length - 1]
-                    if(time <=this.playTime && time+duration>= this.playTime){
-                        activeNotes.push(note)
-                    }
-                }
-            })
-            this.addActiveNotes(activeNotes)
-        }
-    }
-
-    setPlayTimeMs(ms){
-        this.playTimeMs = ms
-        let playTime = 0
-        for(let i =0;i<this.midiTimes.length;i++){
-            let midiTime = this.midiTimes[i]
-            if(midiTime.ms>=ms){
-                break;
-            }
-            playTime = midiTime.t + (ms-midiTime.ms) * this.timePreBeat / midiTime.tempo
-        }
-        this.setPlayTime(playTime)
-    }
-
-    addActiveNotes(notes){
-        notes.forEach((note)=>{
-            this.activeNotes.push(note)
-            note.classList.add('active');
-        });
-    }
-
-    removeActiveNotes(notes){
-        notes.forEach((note)=>{
-            let idx = this.activeNotes.indexOf(note)
-            if(idx>-1)
-                this.activeNotes.splice(idx, 1);
-            note.classList.remove('active');
-        });
-    }
-
-    play(){
-        this.playing = true;
-        this.timer = setInterval(() => {
-            this.setPlayTimeMs(this.playTimeMs + 10)
-        }, 10);
-    }
-
-    pause(){
-        if(!!this.timer)
-            clearInterval(this.timer)
-        this.removeActiveNotes(this.activeNotes)
-        this.timer = null;
-        this.playing = false;
-    }
-
-
-    bindAudioPlayer(audio){
-        this.pause()
-        audio.addEventListener("play", (event)=>{
-            this.play()
-        })
-        audio.addEventListener("pause", (event)=>{
-            this.pause()
-        })
-        audio.addEventListener("timeupdate", (event)=>{
-            this.setPlayTimeMs(event.target.currentTime*10**3)
-        })
-    }
-}
-
-customElements.define('midi-visualizer', MidiVisualizer);
-
-(()=>{
-    let midi_visualizer_container_inited = null
-    let midi_audio_inited = null;
-    let midi_visualizer = document.createElement('midi-visualizer')
-    onUiUpdate((m)=>{
-        let app = gradioApp()
-        let midi_visualizer_container = app.querySelector("#midi_visualizer_container");
-        if(!!midi_visualizer_container && midi_visualizer_container_inited!== midi_visualizer_container){
-            midi_visualizer_container.appendChild(midi_visualizer)
-            midi_visualizer_container_inited = midi_visualizer_container;
-        }
-        let midi_audio = app.querySelector("#midi_audio > audio");
-        if(!!midi_audio && midi_audio_inited!==midi_audio){
-            midi_visualizer.bindAudioPlayer(midi_audio)
-            midi_audio_inited = midi_audio
-        }
-    })
-
-    function createProgressBar(progressbarContainer){
-        let parentProgressbar = progressbarContainer.parentNode;
-        let divProgress = document.createElement('div');
-        divProgress.className='progressDiv';
-        let rect = progressbarContainer.getBoundingClientRect();
-        divProgress.style.width = rect.width + "px";
-        divProgress.style.background = "#b4c0cc";
-        divProgress.style.borderRadius = "8px";
-        let divInner = document.createElement('div');
-        divInner.className='progress';
-        divInner.style.color = "white";
-        divInner.style.background = "#0060df";
-        divInner.style.textAlign = "right";
-        divInner.style.fontWeight = "bold";
-        divInner.style.borderRadius = "8px";
-        divInner.style.height = "20px";
-        divInner.style.lineHeight = "20px";
-        divInner.style.paddingRight = "8px"
-        divInner.style.width = "0%";
-        divProgress.appendChild(divInner);
-        parentProgressbar.insertBefore(divProgress, progressbarContainer);
-    }
-
-    function removeProgressBar(progressbarContainer){
-        let parentProgressbar = progressbarContainer.parentNode;
-        let divProgress = parentProgressbar.querySelector(".progressDiv");
-        parentProgressbar.removeChild(divProgress);
-    }
-
-    function setProgressBar(progressbarContainer, progress, total){
-        let parentProgressbar = progressbarContainer.parentNode;
-        let divProgress = parentProgressbar.querySelector(".progressDiv");
-        let divInner = parentProgressbar.querySelector(".progress");
-        if(total===0)
-            total = 1;
-        divInner.style.width = `${(progress/total)*100}%`;
-        divInner.textContent = `${progress}/${total}`;
-    }
-
-    onMsgReceive((msg)=>{
-        switch (msg.name) {
-            case "visualizer_clear":
-                midi_visualizer.clearMidiEvents();
-                createProgressBar(midi_visualizer_container_inited)
-                break;
-            case "visualizer_append":
-                midi_visualizer.appendMidiEvent(msg.data);
-                break;
-            case "progress":
-                let progress = msg.data[0]
-                let total = msg.data[1]
-                setProgressBar(midi_visualizer_container_inited, progress, total)
-                break;
-            case "visualizer_end":
-                midi_visualizer.finishAppendMidiEvent()
-                midi_visualizer.setPlayTime(0);
-                removeProgressBar(midi_visualizer_container_inited);
-                break;
-            default:
-        }
-    })
-})();
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py
deleted file mode 100644
index e4b5887f825df36f4e1e0384f38fefe790e485e6..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py
+++ /dev/null
@@ -1,365 +0,0 @@
-from dataclasses import dataclass, field
-import os
-
-import torch
-import torch.nn as nn
-
-from fairseq import utils
-from fairseq.dataclass import ChoiceEnum, FairseqDataclass
-from fairseq.models import (
-    BaseFairseqModel,
-    register_model,
-)
-
-from fairseq.models.roberta.model import RobertaClassificationHead
-
-from fairseq.modules import (
-    LayerNorm,
-    TransformerSentenceEncoder,
-    TransformerSentenceEncoderLayer,
-)
-
-
-ACTIVATION_FN_CHOICES = ChoiceEnum(utils.get_available_activation_fns())
-JOINT_CLASSIFICATION_CHOICES = ChoiceEnum(["none", "sent"])
-SENTENCE_REP_CHOICES = ChoiceEnum(["head", "meanpool", "maxpool"])
-
-
-def update_init_roberta_model_state(state):
-    """
-   update the state_dict of a Roberta model for initializing
-   weights of the BertRanker
-   """
-    for k in list(state.keys()):
-        if ".lm_head." in k or "version" in k:
-            del state[k]
-            continue
-        # remove 'encoder/decoder.sentence_encoder.' from the key
-        assert k.startswith("encoder.sentence_encoder.") or k.startswith(
-            "decoder.sentence_encoder."
-        ), f"Cannot recognize parameter name {k}"
-        if "layernorm_embedding" in k:
-            new_k = k.replace(".layernorm_embedding.", ".emb_layer_norm.")
-            state[new_k[25:]] = state[k]
-        else:
-            state[k[25:]] = state[k]
-        del state[k]
-
-
-class BaseRanker(nn.Module):
-    def __init__(self, args, task):
-        super().__init__()
-
-        self.separator_token = task.dictionary.eos()
-        self.padding_idx = task.dictionary.pad()
-
-    def forward(self, src_tokens):
-        raise NotImplementedError
-
-    def get_segment_labels(self, src_tokens):
-        segment_boundary = (src_tokens == self.separator_token).long()
-        segment_labels = (
-            segment_boundary.cumsum(dim=1)
-            - segment_boundary
-            - (src_tokens == self.padding_idx).long()
-        )
-
-        return segment_labels
-
-    def get_positions(self, src_tokens, segment_labels):
-        segment_positions = (
-            torch.arange(src_tokens.shape[1])
-            .to(src_tokens.device)
-            .repeat(src_tokens.shape[0], 1)
-        )
-        segment_boundary = (src_tokens == self.separator_token).long()
-        _, col_idx = (segment_positions * segment_boundary).nonzero(as_tuple=True)
-        col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx])
-        offset = torch.cat(
-            [
-                torch.zeros(1).type_as(segment_boundary),
-                segment_boundary.sum(dim=1).cumsum(dim=0)[:-1],
-            ]
-        )
-        segment_positions -= col_idx[segment_labels + offset.unsqueeze(1)] * (
-            segment_labels != 0
-        )
-
-        padding_mask = src_tokens.ne(self.padding_idx)
-        segment_positions = (segment_positions + 1) * padding_mask.type_as(
-            segment_positions
-        ) + self.padding_idx
-
-        return segment_positions
-
-
-class BertRanker(BaseRanker):
-    def __init__(self, args, task):
-        super(BertRanker, self).__init__(args, task)
-
-        init_model = getattr(args, "pretrained_model", "")
-        self.joint_layers = nn.ModuleList()
-        if os.path.isfile(init_model):
-            print(f"initialize weight from {init_model}")
-
-            from fairseq import hub_utils
-
-            x = hub_utils.from_pretrained(
-                os.path.dirname(init_model),
-                checkpoint_file=os.path.basename(init_model),
-            )
-
-            in_state_dict = x["models"][0].state_dict()
-            init_args = x["args"].model
-
-            num_positional_emb = init_args.max_positions + task.dictionary.pad() + 1
-
-            # follow the setup in roberta
-            self.model = TransformerSentenceEncoder(
-                padding_idx=task.dictionary.pad(),
-                vocab_size=len(task.dictionary),
-                num_encoder_layers=getattr(
-                    args, "encoder_layers", init_args.encoder_layers
-                ),
-                embedding_dim=init_args.encoder_embed_dim,
-                ffn_embedding_dim=init_args.encoder_ffn_embed_dim,
-                num_attention_heads=init_args.encoder_attention_heads,
-                dropout=init_args.dropout,
-                attention_dropout=init_args.attention_dropout,
-                activation_dropout=init_args.activation_dropout,
-                num_segments=2,  # add language embeddings
-                max_seq_len=num_positional_emb,
-                offset_positions_by_padding=False,
-                encoder_normalize_before=True,
-                apply_bert_init=True,
-                activation_fn=init_args.activation_fn,
-                freeze_embeddings=args.freeze_embeddings,
-                n_trans_layers_to_freeze=args.n_trans_layers_to_freeze,
-            )
-
-            # still need to learn segment embeddings as we added a second language embedding
-            if args.freeze_embeddings:
-                for p in self.model.segment_embeddings.parameters():
-                    p.requires_grad = False
-
-            update_init_roberta_model_state(in_state_dict)
-            print("loading weights from the pretrained model")
-            self.model.load_state_dict(
-                in_state_dict, strict=False
-            )  # ignore mismatch in language embeddings
-
-            ffn_embedding_dim = init_args.encoder_ffn_embed_dim
-            num_attention_heads = init_args.encoder_attention_heads
-            dropout = init_args.dropout
-            attention_dropout = init_args.attention_dropout
-            activation_dropout = init_args.activation_dropout
-            activation_fn = init_args.activation_fn
-
-            classifier_embed_dim = getattr(
-                args, "embed_dim", init_args.encoder_embed_dim
-            )
-            if classifier_embed_dim != init_args.encoder_embed_dim:
-                self.transform_layer = nn.Linear(
-                    init_args.encoder_embed_dim, classifier_embed_dim
-                )
-        else:
-            self.model = TransformerSentenceEncoder(
-                padding_idx=task.dictionary.pad(),
-                vocab_size=len(task.dictionary),
-                num_encoder_layers=args.encoder_layers,
-                embedding_dim=args.embed_dim,
-                ffn_embedding_dim=args.ffn_embed_dim,
-                num_attention_heads=args.attention_heads,
-                dropout=args.dropout,
-                attention_dropout=args.attention_dropout,
-                activation_dropout=args.activation_dropout,
-                max_seq_len=task.max_positions()
-                if task.max_positions()
-                else args.tokens_per_sample,
-                num_segments=2,
-                offset_positions_by_padding=False,
-                encoder_normalize_before=args.encoder_normalize_before,
-                apply_bert_init=args.apply_bert_init,
-                activation_fn=args.activation_fn,
-            )
-
-            classifier_embed_dim = args.embed_dim
-            ffn_embedding_dim = args.ffn_embed_dim
-            num_attention_heads = args.attention_heads
-            dropout = args.dropout
-            attention_dropout = args.attention_dropout
-            activation_dropout = args.activation_dropout
-            activation_fn = args.activation_fn
-
-        self.joint_classification = args.joint_classification
-        if args.joint_classification == "sent":
-            if args.joint_normalize_before:
-                self.joint_layer_norm = LayerNorm(classifier_embed_dim)
-            else:
-                self.joint_layer_norm = None
-
-            self.joint_layers = nn.ModuleList(
-                [
-                    TransformerSentenceEncoderLayer(
-                        embedding_dim=classifier_embed_dim,
-                        ffn_embedding_dim=ffn_embedding_dim,
-                        num_attention_heads=num_attention_heads,
-                        dropout=dropout,
-                        attention_dropout=attention_dropout,
-                        activation_dropout=activation_dropout,
-                        activation_fn=activation_fn,
-                    )
-                    for _ in range(args.num_joint_layers)
-                ]
-            )
-
-        self.classifier = RobertaClassificationHead(
-            classifier_embed_dim,
-            classifier_embed_dim,
-            1,  # num_classes
-            "tanh",
-            args.classifier_dropout,
-        )
-
-    def forward(self, src_tokens, src_lengths):
-        segment_labels = self.get_segment_labels(src_tokens)
-        positions = self.get_positions(src_tokens, segment_labels)
-
-        inner_states, _ = self.model(
-            tokens=src_tokens,
-            segment_labels=segment_labels,
-            last_state_only=True,
-            positions=positions,
-        )
-
-        return inner_states[-1].transpose(0, 1)  # T x B x C -> B x T x C
-
-    def sentence_forward(self, encoder_out, src_tokens=None, sentence_rep="head"):
-        # encoder_out: B x T x C
-        if sentence_rep == "head":
-            x = encoder_out[:, :1, :]
-        else:  # 'meanpool', 'maxpool'
-            assert src_tokens is not None, "meanpool requires src_tokens input"
-            segment_labels = self.get_segment_labels(src_tokens)
-            padding_mask = src_tokens.ne(self.padding_idx)
-            encoder_mask = segment_labels * padding_mask.type_as(segment_labels)
-
-            if sentence_rep == "meanpool":
-                ntokens = torch.sum(encoder_mask, dim=1, keepdim=True)
-                x = torch.sum(
-                    encoder_out * encoder_mask.unsqueeze(2), dim=1, keepdim=True
-                ) / ntokens.unsqueeze(2).type_as(encoder_out)
-            else:  # 'maxpool'
-                encoder_out[
-                    (encoder_mask == 0).unsqueeze(2).repeat(1, 1, encoder_out.shape[-1])
-                ] = -float("inf")
-                x, _ = torch.max(encoder_out, dim=1, keepdim=True)
-
-        if hasattr(self, "transform_layer"):
-            x = self.transform_layer(x)
-
-        return x  # B x 1 x C
-
-    def joint_forward(self, x):
-        # x: T x B x C
-        if self.joint_layer_norm:
-            x = self.joint_layer_norm(x.transpose(0, 1))
-            x = x.transpose(0, 1)
-
-        for layer in self.joint_layers:
-            x, _ = layer(x, self_attn_padding_mask=None)
-        return x
-
-    def classification_forward(self, x):
-        # x: B x T x C
-        return self.classifier(x)
-
-
-@dataclass
-class DiscriminativeNMTRerankerConfig(FairseqDataclass):
-    pretrained_model: str = field(
-        default="", metadata={"help": "pretrained model to load"}
-    )
-    sentence_rep: SENTENCE_REP_CHOICES = field(
-        default="head",
-        metadata={
-            "help": "method to transform the output of the transformer stack to a sentence-level representation"
-        },
-    )
-
-    dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
-    attention_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability for attention weights"}
-    )
-    activation_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability after activation in FFN"}
-    )
-    classifier_dropout: float = field(
-        default=0.0, metadata={"help": "classifier dropout probability"}
-    )
-    embed_dim: int = field(default=768, metadata={"help": "embedding dimension"})
-    ffn_embed_dim: int = field(
-        default=2048, metadata={"help": "embedding dimension for FFN"}
-    )
-    encoder_layers: int = field(default=12, metadata={"help": "num encoder layers"})
-    attention_heads: int = field(default=8, metadata={"help": "num attention heads"})
-    encoder_normalize_before: bool = field(
-        default=False, metadata={"help": "apply layernorm before each encoder block"}
-    )
-    apply_bert_init: bool = field(
-        default=False, metadata={"help": "use custom param initialization for BERT"}
-    )
-    activation_fn: ACTIVATION_FN_CHOICES = field(
-        default="relu", metadata={"help": "activation function to use"}
-    )
-    freeze_embeddings: bool = field(
-        default=False, metadata={"help": "freeze embeddings in the pretrained model"}
-    )
-    n_trans_layers_to_freeze: int = field(
-        default=0,
-        metadata={
-            "help": "number of layers to freeze in the pretrained transformer model"
-        },
-    )
-
-    # joint classfication
-    joint_classification: JOINT_CLASSIFICATION_CHOICES = field(
-        default="none",
-        metadata={"help": "method to compute joint features for classification"},
-    )
-    num_joint_layers: int = field(
-        default=1, metadata={"help": "number of joint layers"}
-    )
-    joint_normalize_before: bool = field(
-        default=False,
-        metadata={"help": "apply layer norm on the input to the joint layer"},
-    )
-
-
-@register_model(
-    "discriminative_nmt_reranker", dataclass=DiscriminativeNMTRerankerConfig
-)
-class DiscriminativeNMTReranker(BaseFairseqModel):
-    @classmethod
-    def build_model(cls, args, task):
-        model = BertRanker(args, task)
-        return DiscriminativeNMTReranker(args, model)
-
-    def __init__(self, args, model):
-        super().__init__()
-
-        self.model = model
-        self.sentence_rep = args.sentence_rep
-        self.joint_classification = args.joint_classification
-
-    def forward(self, src_tokens, src_lengths, **kwargs):
-        return self.model(src_tokens, src_lengths)
-
-    def sentence_forward(self, encoder_out, src_tokens):
-        return self.model.sentence_forward(encoder_out, src_tokens, self.sentence_rep)
-
-    def joint_forward(self, x):
-        return self.model.joint_forward(x)
-
-    def classification_forward(self, x):
-        return self.model.classification_forward(x)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/pointer_generator/postprocess.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/pointer_generator/postprocess.py
deleted file mode 100644
index b213aed80fd1e3d86f975256fcb7d9d4c16ca857..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/pointer_generator/postprocess.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import re
-import sys
-
-
-class OOVIndexError(IndexError):
-    def __init__(self, pos, source_seq, target_seq):
-        super(OOVIndexError, self).__init__(
-            "A <unk-N> tag in the target sequence refers to a position that is "
-            "outside the source sequence. Most likely there was a mismatch in "
-            "provided source and target sequences. Otherwise this would mean that "
-            "the pointing mechanism somehow attended to a position that is past "
-            "the actual sequence end."
-        )
-        self.source_pos = pos
-        self.source_seq = source_seq
-        self.target_seq = target_seq
-
-
-def replace_oovs(source_in, target_in, target_out):
-    """Replaces <unk-N> tokens in the target text with the corresponding word in
-    the source text.
-    """
-
-    oov_re = re.compile("^<unk-([0-9]+)>$")
-
-    for source_seq, target_seq in zip(source_in, target_in):
-        target_seq_out = []
-
-        pos_to_word = source_seq.strip().split()
-        for token in target_seq.strip().split():
-            m = oov_re.match(token)
-            if m:
-                pos = int(m.group(1))
-                if pos >= len(pos_to_word):
-                    raise OOVIndexError(pos, source_seq, target_seq)
-                token_out = pos_to_word[pos]
-            else:
-                token_out = token
-            target_seq_out.append(token_out)
-        target_out.write(" ".join(target_seq_out) + "\n")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Replaces <unk-N> tokens in target sequences with words from "
-        "the corresponding position in the source sequence."
-    )
-    parser.add_argument(
-        "--source", type=str, help="text file with source sequences", required=True
-    )
-    parser.add_argument(
-        "--target", type=str, help="text file with target sequences", required=True
-    )
-    parser.add_argument(
-        "--target-out",
-        type=str,
-        help="where to write target sequences without <unk-N> " "entries",
-        required=True,
-    )
-    args = parser.parse_args()
-
-    target_in = (
-        open(args.target, "r", encoding="utf-8") if args.target is not None else None
-    )
-    target_out = (
-        open(args.target_out, "w", encoding="utf-8")
-        if args.target_out is not None
-        else None
-    )
-    with open(args.source, "r", encoding="utf-8") as source_in, open(
-        args.target, "r", encoding="utf-8"
-    ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out:
-        replace_oovs(source_in, target_in, target_out)
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except OOVIndexError as e:
-        print(e, file=sys.stderr)
-        print("Source sequence:", e.source_seq.strip(), file=sys.stderr)
-        print("Target sequence:", e.target_seq.strip(), file=sys.stderr)
-        print(
-            "Source sequence length:",
-            len(e.source_seq.strip().split()),
-            file=sys.stderr,
-        )
-        print("The offending tag points to:", e.source_pos)
-        sys.exit(2)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_recognition/data/asr_dataset.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_recognition/data/asr_dataset.py
deleted file mode 100644
index 63a6fcac85d73b1fce8e4d044b4209b1b67fa8ce..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_recognition/data/asr_dataset.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-
-import numpy as np
-from fairseq.data import FairseqDataset
-
-from . import data_utils
-from .collaters import Seq2SeqCollater
-
-
-class AsrDataset(FairseqDataset):
-    """
-    A dataset representing speech and corresponding transcription.
-
-    Args:
-        aud_paths: (List[str]): A list of str with paths to audio files.
-        aud_durations_ms (List[int]): A list of int containing the durations of
-            audio files.
-        tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
-            of target transcriptions.
-        tgt_dict (~fairseq.data.Dictionary): target vocabulary.
-        ids (List[str]): A list of utterance IDs.
-        speakers (List[str]): A list of speakers corresponding to utterances.
-        num_mel_bins (int): Number of triangular mel-frequency bins (default: 80)
-        frame_length (float): Frame length in milliseconds (default: 25.0)
-        frame_shift (float): Frame shift in milliseconds (default: 10.0)
-    """
-
-    def __init__(
-        self,
-        aud_paths,
-        aud_durations_ms,
-        tgt,
-        tgt_dict,
-        ids,
-        speakers,
-        num_mel_bins=80,
-        frame_length=25.0,
-        frame_shift=10.0,
-    ):
-        assert frame_length > 0
-        assert frame_shift > 0
-        assert all(x > frame_length for x in aud_durations_ms)
-        self.frame_sizes = [
-            int(1 + (d - frame_length) / frame_shift) for d in aud_durations_ms
-        ]
-
-        assert len(aud_paths) > 0
-        assert len(aud_paths) == len(aud_durations_ms)
-        assert len(aud_paths) == len(tgt)
-        assert len(aud_paths) == len(ids)
-        assert len(aud_paths) == len(speakers)
-        self.aud_paths = aud_paths
-        self.tgt_dict = tgt_dict
-        self.tgt = tgt
-        self.ids = ids
-        self.speakers = speakers
-        self.num_mel_bins = num_mel_bins
-        self.frame_length = frame_length
-        self.frame_shift = frame_shift
-
-        self.s2s_collater = Seq2SeqCollater(
-            0,
-            1,
-            pad_index=self.tgt_dict.pad(),
-            eos_index=self.tgt_dict.eos(),
-            move_eos_to_beginning=True,
-        )
-
-    def __getitem__(self, index):
-        import torchaudio
-        import torchaudio.compliance.kaldi as kaldi
-
-        tgt_item = self.tgt[index] if self.tgt is not None else None
-
-        path = self.aud_paths[index]
-        if not os.path.exists(path):
-            raise FileNotFoundError("Audio file not found: {}".format(path))
-        sound, sample_rate = torchaudio.load_wav(path)
-        output = kaldi.fbank(
-            sound,
-            num_mel_bins=self.num_mel_bins,
-            frame_length=self.frame_length,
-            frame_shift=self.frame_shift,
-        )
-        output_cmvn = data_utils.apply_mv_norm(output)
-
-        return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
-
-    def __len__(self):
-        return len(self.aud_paths)
-
-    def collater(self, samples):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[int]): sample indices to collate
-
-        Returns:
-            dict: a mini-batch suitable for forwarding with a Model
-        """
-        return self.s2s_collater.collate(samples)
-
-    def num_tokens(self, index):
-        return self.frame_sizes[index]
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return (
-            self.frame_sizes[index],
-            len(self.tgt[index]) if self.tgt is not None else 0,
-        )
-
-    def ordered_indices(self):
-        """Return an ordered list of indices. Batches will be constructed based
-        on this order."""
-        return np.arange(len(self))
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py
deleted file mode 100644
index 5f0d70fdad92ba4f554d971710b60f2f9e8d9298..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py
+++ /dev/null
@@ -1,18 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-
-The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
-from . import cmudict
-
-_pad        = '_'
-_punctuation = '!\'(),.:;? '
-_special = '-'
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-
-# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
-_arpabet = ['@' + s for s in cmudict.valid_symbols]
-
-# Export all symbols:
-symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
diff --git a/spaces/sriramelango/Social_Classification_Public/models/ofa/unify_transformer.py b/spaces/sriramelango/Social_Classification_Public/models/ofa/unify_transformer.py
deleted file mode 100644
index d03a5d332e5fcd7ff0085b026b2eb79e8acfd4f8..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/models/ofa/unify_transformer.py
+++ /dev/null
@@ -1,1510 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import random
-from typing import Any, Dict, List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.distributed import fsdp_wrap
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    FairseqIncrementalDecoder,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.modules import (
-    AdaptiveSoftmax,
-    BaseLayer,
-    FairseqDropout,
-    LayerDropModuleList,
-    LayerNorm,
-    SinusoidalPositionalEmbedding,
-    GradMultiply
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from torch import Tensor
-
-from .unify_transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
-from .resnet import ResNet
-
-
-DEFAULT_MAX_SOURCE_POSITIONS = 1024
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
-
-
-def BatchNorm2d(out_chan, momentum=0.1, eps=1e-3):
-    return nn.SyncBatchNorm.convert_sync_batchnorm(
-        nn.BatchNorm2d(out_chan, momentum=momentum, eps=eps)
-    )
-
-
-def make_token_bucket_position(bucket_size, max_position=DEFAULT_MAX_SOURCE_POSITIONS):
-    context_pos = torch.arange(max_position, dtype=torch.long)[:, None]
-    memory_pos = torch.arange(max_position, dtype=torch.long)[None, :]
-    relative_pos = context_pos - memory_pos
-    sign = torch.sign(relative_pos)
-    mid = bucket_size // 2
-    abs_pos = torch.where((relative_pos<mid) & (relative_pos > -mid), mid-1, torch.abs(relative_pos))
-    log_pos = torch.ceil(torch.log(abs_pos/mid)/math.log((max_position-1)/mid) * (mid-1)) + mid
-    log_pos = log_pos.int()
-    bucket_pos = torch.where(abs_pos.le(mid), relative_pos, log_pos*sign).long()
-    return bucket_pos + bucket_size - 1
-
-
-def make_image_bucket_position(bucket_size, num_relative_distance):
-    coords_h = torch.arange(bucket_size)
-    coords_w = torch.arange(bucket_size)
-    coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-    coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-    relative_coords[:, :, 0] += bucket_size - 1  # shift to start from 0
-    relative_coords[:, :, 1] += bucket_size - 1
-    relative_coords[:, :, 0] *= 2 * bucket_size - 1
-    relative_position_index = torch.zeros(size=(bucket_size * bucket_size + 1,) * 2, dtype=relative_coords.dtype)
-    relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-    relative_position_index[0, 0:] = num_relative_distance - 3
-    relative_position_index[0:, 0] = num_relative_distance - 2
-    relative_position_index[0, 0] = num_relative_distance - 1
-    return relative_position_index
-
-
-@register_model("unify_transformer")
-class TransformerModel(FairseqEncoderDecoderModel):
-    """
-    Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
-    <https://arxiv.org/abs/1706.03762>`_.
-
-    Args:
-        encoder (TransformerEncoder): the encoder
-        decoder (TransformerDecoder): the decoder
-
-    The Transformer model provides the following named architectures and
-    command-line arguments:
-
-    .. argparse::
-        :ref: fairseq.models.transformer_parser
-        :prog:
-    """
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(encoder, decoder)
-        self.args = args
-        self.supports_align_args = True
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--activation-fn',
-                            choices=utils.get_available_activation_fns(),
-                            help='activation function to use')
-        parser.add_argument('--dropout', type=float, metavar='D',
-                            help='dropout probability')
-        parser.add_argument('--attention-dropout', type=float, metavar='D',
-                            help='dropout probability for attention weights')
-        parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
-                            help='dropout probability after activation in FFN.')
-        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained encoder embedding')
-        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension')
-        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension for FFN')
-        parser.add_argument('--encoder-layers', type=int, metavar='N',
-                            help='num encoder layers')
-        parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
-                            help='num encoder attention heads')
-        parser.add_argument('--encoder-normalize-before', action='store_true',
-                            help='apply layernorm before each encoder block')
-        parser.add_argument('--encoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the encoder')
-        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained decoder embedding')
-        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension')
-        parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension for FFN')
-        parser.add_argument('--decoder-layers', type=int, metavar='N',
-                            help='num decoder layers')
-        parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
-                            help='num decoder attention heads')
-        parser.add_argument('--decoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the decoder')
-        parser.add_argument('--decoder-normalize-before', action='store_true',
-                            help='apply layernorm before each decoder block')
-        parser.add_argument('--decoder-output-dim', type=int, metavar='N',
-                            help='decoder output dimension (extra linear layer '
-                                 'if different from decoder embed dim')
-        parser.add_argument('--share-decoder-input-output-embed', action='store_true',
-                            help='share decoder input and output embeddings')
-        parser.add_argument('--share-all-embeddings', action='store_true',
-                            help='share encoder, decoder and output embeddings'
-                                 ' (requires shared dictionary and embed dim)')
-        parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
-                            help='if set, disables positional embeddings (outside self attention)')
-        parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
-                            help='comma separated list of adaptive softmax cutoff points. '
-                                 'Must be used with adaptive_loss criterion'),
-        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
-                            help='sets adaptive softmax dropout for the tail projections')
-        parser.add_argument('--layernorm-embedding', action='store_true',
-                            help='add layernorm to embedding')
-        parser.add_argument('--no-scale-embedding', action='store_true',
-                            help='if True, dont scale embeddings')
-        parser.add_argument('--checkpoint-activations', action='store_true',
-                            help='checkpoint activations at each layer, which saves GPU '
-                                 'memory usage at the cost of some additional compute')
-        parser.add_argument('--offload-activations', action='store_true',
-                            help='checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations.')
-        # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
-        parser.add_argument('--no-cross-attention', default=False, action='store_true',
-                            help='do not perform cross-attention')
-        parser.add_argument('--cross-self-attention', default=False, action='store_true',
-                            help='perform cross+self-attention')
-        # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
-        parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for encoder')
-        parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for decoder')
-        parser.add_argument('--encoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        parser.add_argument('--decoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
-        parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0,
-                            help='iterative PQ quantization noise at training time')
-        parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8,
-                            help='block size of quantization noise at training time')
-        parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0,
-                            help='scalar quantization noise and scalar quantization at training time')
-        # args for Fully Sharded Data Parallel (FSDP) training
-        parser.add_argument(
-            '--min-params-to-wrap', type=int, metavar='D', default=DEFAULT_MIN_PARAMS_TO_WRAP,
-            help=(
-                'minimum number of params for a layer to be wrapped with FSDP() when '
-                'training with --ddp-backend=fully_sharded. Smaller values will '
-                'improve memory efficiency, but may make torch.distributed '
-                'communication less efficient due to smaller input sizes. This option '
-                'is set to 0 (i.e., always wrap) when --checkpoint-activations or '
-                '--offload-activations are passed.'
-            )
-        )
-
-        parser.add_argument('--resnet-drop-path-rate', type=float,
-                            help='resnet drop path rate')
-        parser.add_argument('--encoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-        parser.add_argument('--decoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-
-        parser.add_argument('--token-bucket-size', type=int,
-                            help='token bucket size')
-        parser.add_argument('--image-bucket-size', type=int,
-                            help='image bucket size')
-
-        parser.add_argument('--attn-scale-factor', type=float,
-                            help='attention scale factor')
-        parser.add_argument('--freeze-resnet', action='store_true',
-                            help='freeze resnet')
-        parser.add_argument('--freeze-encoder-embedding', action='store_true',
-                            help='freeze encoder token embedding')
-        parser.add_argument('--freeze-decoder-embedding', action='store_true',
-                            help='freeze decoder token embedding')
-        parser.add_argument('--add-type-embedding', action='store_true',
-                            help='add source/region/patch type embedding')
-
-        parser.add_argument('--resnet-type', choices=['resnet50', 'resnet101', 'resnet152'],
-                            help='resnet type')
-        parser.add_argument('--resnet-model-path', type=str, metavar='STR',
-                            help='path to load resnet')
-        parser.add_argument('--code-image-size', type=int,
-                            help='code image size')
-        parser.add_argument('--patch-layernorm-embedding', action='store_true',
-                            help='add layernorm to patch embedding')
-        parser.add_argument('--code-layernorm-embedding', action='store_true',
-                            help='add layernorm to code embedding')
-        parser.add_argument('--entangle-position-embedding', action='store_true',
-                            help='entangle position embedding')
-        parser.add_argument('--disable-entangle', action='store_true',
-                            help='disable entangle')
-        parser.add_argument('--sync-bn', action='store_true',
-                            help='sync batchnorm')
-
-        parser.add_argument('--scale-attn', action='store_true',
-                            help='scale attn')
-        parser.add_argument('--scale-fc', action='store_true',
-                            help='scale fc')
-        parser.add_argument('--scale-heads', action='store_true',
-                            help='scale heads')
-        parser.add_argument('--scale-resids', action='store_true',
-                            help='scale resids')
-        # fmt: on
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present in older models
-        base_architecture(args)
-
-        if args.encoder_layers_to_keep:
-            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if getattr(args, "max_source_positions", None) is None:
-            args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
-        if getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
-
-        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
-
-        if args.share_all_embeddings:
-            if src_dict != tgt_dict:
-                raise ValueError("--share-all-embeddings requires a joined dictionary")
-            if args.encoder_embed_dim != args.decoder_embed_dim:
-                raise ValueError(
-                    "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
-                )
-            if args.decoder_embed_path and (
-                args.decoder_embed_path != args.encoder_embed_path
-            ):
-                raise ValueError(
-                    "--share-all-embeddings not compatible with --decoder-embed-path"
-                )
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = encoder_embed_tokens
-            args.share_decoder_input_output_embed = True
-        else:
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = cls.build_embedding(
-                args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
-            )
-        if getattr(args, "freeze_encoder_embedding", False):
-            encoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "freeze_decoder_embedding", False):
-            decoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "offload_activations", False):
-            args.checkpoint_activations = True  # offloading implies checkpointing
-        encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
-        decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
-        if not args.share_all_embeddings:
-            min_params_to_wrap = getattr(
-                args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
-            )
-            # fsdp_wrap is a no-op when --ddp-backend != fully_sharded
-            encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)
-            decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)
-        return cls(args, encoder, decoder)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-
-        emb = Embedding(num_embeddings, embed_dim, padding_idx)
-        # if provided, load from preloaded dictionaries
-        if path:
-            embed_dict = utils.parse_embedding(path)
-            utils.load_embedding(embed_dict, dictionary, emb)
-        return emb
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerEncoder(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return TransformerDecoder(
-            args,
-            tgt_dict,
-            embed_tokens,
-            no_encoder_attn=getattr(args, "no_cross_attention", False),
-        )
-
-    # TorchScript doesn't support optional arguments with variable length (**kwargs).
-    # Current workaround is to add union of all arguments in child classes.
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        return_all_hiddens: bool = True,
-        features_only: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Run the forward pass for an encoder-decoder model.
-
-        Copied from the base class, but without ``**kwargs``,
-        which are not supported by TorchScript.
-        """
-        encoder_out = self.encoder(
-            src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
-        )
-        decoder_out = self.decoder(
-            prev_output_tokens,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-        return decoder_out
-
-    # Since get_normalized_probs is in the Fairseq Model which is not scriptable,
-    # I rewrite the get_normalized_probs from Base Class to call the
-    # helper function in the Base Class.
-    @torch.jit.export
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        """Get normalized probabilities (or log probs) from a net's output."""
-        return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
-
-
-class TransformerEncoder(FairseqEncoder):
-    """
-    Transformer encoder consisting of *args.encoder_layers* layers. Each layer
-    is a :class:`TransformerEncoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): encoding dictionary
-        embed_tokens (torch.nn.Embedding): input embedding
-    """
-
-    def __init__(self, args, dictionary, embed_tokens):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.encoder_layerdrop = args.encoder_layerdrop
-
-        embed_dim = embed_tokens.embedding_dim
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_source_positions = args.max_source_positions
-        self.num_attention_heads = args.encoder_attention_heads
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        if getattr(args, "add_type_embedding", False):
-            self.type_embedding = Embedding(2, embed_dim, padding_idx=None)
-        else:
-            self.type_embedding = None
-
-        if getattr(args, "sync_bn", False):
-            norm_layer = BatchNorm2d
-        else:
-            norm_layer = None
-
-        if args.resnet_type == 'resnet101':
-            self.embed_images = ResNet([3, 4, 23], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-        elif args.resnet_type == 'resnet152':
-            self.embed_images = ResNet([3, 8, 36], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-        else:
-            raise NotImplementedError
-        self.image_proj = Linear(1024, embed_dim)
-        if getattr(args, "resnet_model_path", None):
-            print("load resnet {}".format(args.resnet_model_path))
-            resnet_state_dict = torch.load(self.args.resnet_model_path)
-            self.embed_images.load_state_dict(resnet_state_dict)
-        if getattr(args, "patch_layernorm_embedding", False):
-            self.patch_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.patch_layernorm_embedding = None
-
-        self.embed_positions = Embedding(args.max_source_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / args.encoder_attention_heads * args.attn_scale_factor) ** -0.5
-        self.pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        if self.encoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.encoder_drop_path_rate, args.encoder_layers)]
-        self.layers.extend(
-            [self.build_encoder_layer(args, drop_path_rate=dpr[i]) for i in range(args.encoder_layers)]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-    def train(self, mode=True):
-        super(TransformerEncoder, self).train(mode)
-        if getattr(self.args, "freeze_resnet", False):
-            for m in self.embed_images.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                    m.weight.requires_grad = False
-                    m.bias.requires_grad = False
-
-    def build_encoder_layer(self, args, drop_path_rate=0.0):
-        layer = TransformerEncoderLayer(args, drop_path_rate=drop_path_rate)
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.unsqueeze(0).expand(x.size(0), -1, -1, -1)
-        values = values.permute([0, 3, 1, 2])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, image_position_ids, idx):
-        bsz, seq_len = image_position_ids.shape
-        rp_bucket_size = self.image_rp_bucket.size(1)
-
-        rp_bucket = self.image_rp_bucket.unsqueeze(0).expand(
-            bsz, rp_bucket_size, rp_bucket_size
-        ).gather(1, image_position_ids[:, :, None].expand(bsz, seq_len, rp_bucket_size)
-        ).gather(2, image_position_ids[:, None, :].expand(bsz, seq_len, seq_len))
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(0, 3, 1, 2)
-        return values
-
-    def get_patch_images_info(self, patch_images, sample_patch_num, device):
-        image_embed = self.embed_images(patch_images)
-        h, w = image_embed.shape[-2:]
-        image_num_patches = h * w
-        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
-        image_position_idx = torch.arange(w).unsqueeze(0).expand(h, w) + \
-                             torch.arange(h).unsqueeze(1) * self.args.image_bucket_size + 1
-        image_position_idx = image_position_idx.view(-1).to(device)
-        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)
-
-        image_embed = image_embed.flatten(2).transpose(1, 2)
-        if sample_patch_num is not None:
-            patch_orders = [
-                random.sample(range(image_num_patches), k=sample_patch_num)
-                for _ in range(patch_images.size(0))
-            ]
-            patch_orders = torch.LongTensor(patch_orders).to(device)
-            image_embed = image_embed.gather(
-                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
-            )
-            image_num_patches = sample_patch_num
-            image_padding_mask = image_padding_mask.gather(1, patch_orders)
-            image_position_ids = image_position_ids.gather(1, patch_orders)
-        image_pos_embed = self.embed_image_positions(image_position_ids)
-
-        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed
-
-    def forward_embedding(
-        self,
-        src_tokens,
-        image_embed: Optional[torch.Tensor] = None,
-        image_embed_2: Optional[torch.Tensor] = None,
-        token_embedding: Optional[torch.Tensor] = None,
-        pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed_2: Optional[torch.Tensor] = None
-    ):
-        # embed tokens and positions
-        if token_embedding is None:
-            token_embedding = self.embed_tokens(src_tokens)
-        x = embed = self.embed_scale * token_embedding
-        if self.entangle_position_embedding and pos_embed is not None:
-            x += pos_embed
-        if self.type_embedding is not None:
-            x += self.type_embedding(src_tokens.new_zeros(x.size()[:2]))
-        if self.layernorm_embedding is not None:
-            x = self.layernorm_embedding(x)
-        x = self.dropout_module(x)
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        # embed raw images
-        if image_embed is not None:
-            image_embed = self.image_proj(image_embed)
-            image_x = image_embed = self.embed_scale * image_embed
-            if self.entangle_position_embedding and image_pos_embed is not None:
-                image_x += image_pos_embed
-            if self.type_embedding is not None:
-                image_x += self.type_embedding(src_tokens.new_ones(image_x.size()[:2]))
-            if self.patch_layernorm_embedding is not None:
-                image_x = self.patch_layernorm_embedding(image_x)
-            image_x = self.dropout_module(image_x)
-            if self.quant_noise is not None:
-                image_x = self.quant_noise(image_x)
-            x = torch.cat([image_x, x], dim=1)
-            embed = torch.cat([image_embed, embed], dim=1)
-
-        if image_embed_2 is not None:
-            assert self.type_embedding is not None
-            image_embed_2 = self.image_proj(image_embed_2)
-            image_x_2 = image_embed_2 = self.embed_scale * image_embed_2
-            if self.entangle_position_embedding and image_pos_embed_2 is not None:
-                image_x_2 += image_pos_embed_2
-            if self.type_embedding is not None:
-                image_x_2 += self.type_embedding(src_tokens.new_full(image_x_2.size()[:2], fill_value=2))
-            if self.patch_layernorm_embedding is not None:
-                image_x_2 = self.patch_layernorm_embedding(image_x_2)
-            image_x_2 = self.dropout_module(image_x_2)
-            if self.quant_noise is not None:
-                image_x_2 = self.quant_noise(image_x_2)
-            x = torch.cat([image_x_2, x], dim=1)
-            embed = torch.cat([image_embed_2, embed], dim=1)
-
-        return x, embed
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        code_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        return self.forward_scriptable(src_tokens,
-                                       src_lengths,
-                                       patch_images,
-                                       patch_images_2,
-                                       patch_masks,
-                                       return_all_hiddens,
-                                       token_embeddings,
-                                       sample_patch_num)
-
-    # TorchScript doesn't support super() method so that the scriptable Subclass
-    # can't access the base class model in Torchscript.
-    # Current workaround is to add a helper function with different name and
-    # call the helper function from scriptable Subclass.
-    def forward_scriptable(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        image_embed = None
-        image_embed_2 = None
-        image_pos_embed = None
-        image_pos_embed_2 = None
-        if patch_images is not None:
-            image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-                self.get_patch_images_info(patch_images, sample_patch_num, src_tokens.device)
-            image_padding_mask[~patch_masks] = True
-        if patch_images_2 is not None:
-            image_embed_2, image_num_patches_2, image_padding_mask_2, image_position_ids_2, image_pos_embed_2 = \
-                self.get_patch_images_info(patch_images_2, sample_patch_num, src_tokens.device)
-            image_padding_mask_2[~patch_masks] = True
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-        if patch_images is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask, encoder_padding_mask], dim=1)
-        if patch_images_2 is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask_2, encoder_padding_mask], dim=1)
-        has_pads = (src_tokens.device.type == "xla" or encoder_padding_mask.any())
-
-        pos_embed = self.embed_positions(utils.new_arange(src_tokens))
-        x, encoder_embedding = self.forward_embedding(
-            src_tokens, image_embed, image_embed_2, token_embeddings,
-            pos_embed, image_pos_embed, image_pos_embed_2
-        )
-
-        # account for padding while computing the representation
-        if has_pads:
-            x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        pos_embed = self.pos_ln(pos_embed)
-        if patch_images is not None:
-            image_pos_embed = self.image_pos_ln(image_pos_embed)
-            pos_embed = torch.cat([image_pos_embed, pos_embed], dim=1)
-        if patch_images_2 is not None:
-            image_pos_embed_2 = self.image_pos_ln(image_pos_embed_2)
-            pos_embed = torch.cat([image_pos_embed_2, pos_embed], dim=1)
-
-        pos_q = self.pos_q_linear(pos_embed).view(
-            x.size(1), x.size(0), self.num_attention_heads, -1
-        ).transpose(1, 2) * self.pos_scaling
-        pos_k = self.pos_k_linear(pos_embed).view(
-            x.size(1), x.size(0), self.num_attention_heads, -1
-        ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-
-        encoder_states = []
-
-        if return_all_hiddens:
-            encoder_states.append(x)
-
-        # encoder layers
-        for idx, layer in enumerate(self.layers):
-            self_attn_bias = abs_pos_bias.clone()
-            self_attn_bias[:, :, -src_tokens.size(1):, -src_tokens.size(1):] += self.get_rel_pos_bias(src_tokens, idx)
-            if patch_images_2 is not None:
-                self_attn_bias[:, :, :image_num_patches_2, :image_num_patches_2] += \
-                    self.get_image_rel_pos_bias(image_position_ids_2, idx)
-                self_attn_bias[:, :, image_num_patches_2:image_num_patches_2+image_num_patches, image_num_patches_2:image_num_patches_2+image_num_patches] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-            elif patch_images is not None:
-                self_attn_bias[:, :, :x.size(0) - src_tokens.size(1), :x.size(0) - src_tokens.size(1)] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-            self_attn_bias = self_attn_bias.reshape(-1, x.size(0), x.size(0))
-
-            x = layer(
-                x, encoder_padding_mask=encoder_padding_mask if has_pads else None, self_attn_bias=self_attn_bias
-            )
-            if return_all_hiddens:
-                assert encoder_states is not None
-                encoder_states.append(x)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
-        # `forward` so we use a dictionary instead.
-        # TorchScript does not support mixed values so the values are all lists.
-        # The empty list is equivalent to None.
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-            "position_embeddings": [pos_embed],  # B x T x C
-        }
-
-    @torch.jit.export
-    def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
-        """
-        Reorder encoder output according to *new_order*.
-
-        Args:
-            encoder_out: output from the ``forward()`` method
-            new_order (LongTensor): desired order
-
-        Returns:
-            *encoder_out* rearranged according to *new_order*
-        """
-        if len(encoder_out["encoder_out"]) == 0:
-            new_encoder_out = []
-        else:
-            new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
-        if len(encoder_out["encoder_padding_mask"]) == 0:
-            new_encoder_padding_mask = []
-        else:
-            new_encoder_padding_mask = [
-                encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
-            ]
-        if len(encoder_out["encoder_embedding"]) == 0:
-            new_encoder_embedding = []
-        else:
-            new_encoder_embedding = [
-                encoder_out["encoder_embedding"][0].index_select(0, new_order)
-            ]
-
-        if len(encoder_out["src_tokens"]) == 0:
-            new_src_tokens = []
-        else:
-            new_src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["src_lengths"]) == 0:
-            new_src_lengths = []
-        else:
-            new_src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["position_embeddings"]) == 0:
-            new_position_embeddings = []
-        else:
-            new_position_embeddings = [(encoder_out["position_embeddings"][0]).index_select(0, new_order)]
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": new_src_tokens,  # B x T
-            "src_lengths": new_src_lengths,  # B x 1
-            "position_embeddings": new_position_embeddings,  # B x T x C
-        }
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        if self.embed_positions is None:
-            return self.max_source_positions
-        return self.max_source_positions
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                print("deleting {0}".format(weights_key))
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        # version_key = "{}.version".format(name)
-        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
-        #     # earlier checkpoints did not normalize after the stack of layers
-        #     self.layer_norm = None
-        #     self.normalize = False
-        #     state_dict[version_key] = torch.Tensor([1])
-
-        prefix = name + "." if name != "" else ""
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["encoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["encoder.embed_image_positions.weight"])
-            embed_dim = state_dict["encoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["encoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["encoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["encoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-class TransformerDecoder(FairseqIncrementalDecoder):
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): decoding dictionary
-        embed_tokens (torch.nn.Embedding): output embedding
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(
-        self,
-        args,
-        dictionary,
-        embed_tokens,
-        no_encoder_attn=False,
-        output_projection=None,
-    ):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-        self._future_mask = torch.empty(0)
-
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.decoder_layerdrop = args.decoder_layerdrop
-        self.share_input_output_embed = args.share_decoder_input_output_embed
-        self.num_attention_heads = args.decoder_attention_heads
-
-        input_embed_dim = embed_tokens.embedding_dim
-        embed_dim = args.decoder_embed_dim
-        self.embed_dim = embed_dim
-        self.output_embed_dim = args.decoder_output_dim
-
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_target_positions = args.max_target_positions
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        self.project_in_dim = (
-            Linear(input_embed_dim, embed_dim, bias=False)
-            if embed_dim != input_embed_dim
-            else None
-        )
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        self.window_size = args.code_image_size // 8
-
-        self.embed_positions = Embedding(args.max_target_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / self.num_attention_heads * args.attn_scale_factor) ** -0.5
-        self.self_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.self_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if getattr(args, "code_layernorm_embedding", False):
-            self.code_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.code_layernorm_embedding = None
-
-        self.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-        if self.decoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.decoder_drop_path_rate, args.decoder_layers)]
-        self.layers.extend(
-            [
-                self.build_decoder_layer(args, no_encoder_attn, drop_path_rate=dpr[i])
-                for i in range(args.decoder_layers)
-            ]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.decoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        self.project_out_dim = (
-            Linear(embed_dim, self.output_embed_dim, bias=False)
-            if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
-            else None
-        )
-
-        self.adaptive_softmax = None
-        self.output_projection = output_projection
-        if self.output_projection is None:
-            self.build_output_projection(args, dictionary, embed_tokens)
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        image_position_idx = torch.arange(self.window_size).unsqueeze(0).expand(self.window_size, self.window_size) + \
-                             torch.arange(self.window_size).unsqueeze(1) * image_bucket_size + 1
-        image_position_idx = torch.cat([torch.tensor([0]), image_position_idx.view(-1)])
-        image_position_idx = torch.cat([image_position_idx, torch.tensor([1024] * 768)])
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.register_buffer("image_position_idx", image_position_idx)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-    def build_output_projection(self, args, dictionary, embed_tokens):
-        if args.adaptive_softmax_cutoff is not None:
-            self.adaptive_softmax = AdaptiveSoftmax(
-                len(dictionary),
-                self.output_embed_dim,
-                utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
-                dropout=args.adaptive_softmax_dropout,
-                adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
-                factor=args.adaptive_softmax_factor,
-                tie_proj=args.tie_adaptive_proj,
-            )
-        elif self.share_input_output_embed:
-            self.output_projection = nn.Linear(
-                self.embed_tokens.weight.shape[1],
-                self.embed_tokens.weight.shape[0],
-                bias=False,
-            )
-            self.output_projection.weight = self.embed_tokens.weight
-        else:
-            self.output_projection = nn.Linear(
-                self.output_embed_dim, len(dictionary), bias=False
-            )
-            nn.init.normal_(
-                self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5
-            )
-        num_base_layers = getattr(args, "base_layers", 0)
-        for i in range(num_base_layers):
-            self.layers.insert(((i+1) * args.decoder_layers) // (num_base_layers + 1), BaseLayer(args))
-
-    def build_decoder_layer(self, args, no_encoder_attn=False, drop_path_rate=0.0):
-        layer = TransformerDecoderLayer(args, no_encoder_attn, drop_path_rate=drop_path_rate)
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.permute([2, 0, 1])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        image_position_idx = self.image_position_idx[:seq_len]
-        rp_bucket = self.image_rp_bucket[image_position_idx][:, image_position_idx]
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(2, 0, 1)
-        return values
-
-    def get_pos_info(self, tokens, tgt_pos_embed, src_pos_embed=None, use_image=False):
-        batch_size = tokens.size(0)
-        tgt_len = tokens.size(1)
-        tgt_pos_embed = self.image_pos_ln(tgt_pos_embed) if use_image else self.pos_ln(tgt_pos_embed)
-        if src_pos_embed is not None:
-            src_len = src_pos_embed.size(1)
-            pos_q = self.cross_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.cross_pos_k_linear(src_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        else:
-            src_len = tgt_pos_embed.size(1)
-            pos_q = self.self_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.self_pos_k_linear(tgt_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-        return abs_pos_bias
-
-    def forward(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor] = None,
-        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        features_only: bool = False,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-        src_lengths: Optional[Any] = None,
-        return_all_hiddens: bool = False,
-    ):
-        """
-        Args:
-            prev_output_tokens (LongTensor): previous decoder outputs of shape
-                `(batch, tgt_len)`, for teacher forcing
-            encoder_out (optional): output from the encoder, used for
-                encoder-side attention, should be of size T x B x C
-            incremental_state (dict): dictionary used for storing state during
-                :ref:`Incremental decoding`
-            features_only (bool, optional): only return features without
-                applying output layer (default: False).
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-
-        Returns:
-            tuple:
-                - the decoder's output of shape `(batch, tgt_len, vocab)`
-                - a dictionary with any model-specific outputs
-        """
-
-        x, extra = self.extract_features(
-            prev_output_tokens,
-            code_masks=code_masks,
-            encoder_out=encoder_out,
-            incremental_state=incremental_state,
-            full_context_alignment=full_context_alignment,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-        )
-
-        if not features_only:
-            x = self.output_layer(x)
-        return x, extra
-
-    def extract_features(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        return self.extract_features_scriptable(
-            prev_output_tokens,
-            code_masks,
-            encoder_out,
-            incremental_state,
-            full_context_alignment,
-            alignment_layer,
-            alignment_heads,
-        )
-
-    """
-    A scriptable subclass of this class has an extract_features method and calls
-    super().extract_features, but super() is not supported in torchscript. A copy of
-    this function is made to be used in the subclass instead.
-    """
-
-    def extract_features_scriptable(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Similar to *forward* but only return features.
-
-        Includes several features from "Jointly Learning to Align and
-        Translate with Transformer Models" (Garg et al., EMNLP 2019).
-
-        Args:
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-            alignment_layer (int, optional): return mean alignment over
-                heads at this layer (default: last layer).
-            alignment_heads (int, optional): only average alignment over
-                this many heads (default: all heads).
-
-        Returns:
-            tuple:
-                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
-                - a dictionary with any model-specific outputs
-        """
-        bs, slen = prev_output_tokens.size()
-        if alignment_layer is None:
-            alignment_layer = self.num_layers - 1
-
-        enc: Optional[Tensor] = None
-        padding_mask: Optional[Tensor] = None
-        if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
-            enc = encoder_out["encoder_out"][0]
-            assert (
-                enc.size()[1] == bs
-            ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
-        if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
-            padding_mask = encoder_out["encoder_padding_mask"][0]
-
-        bsz, tgt_len = prev_output_tokens.shape
-        token_position_idx = utils.new_arange(prev_output_tokens)
-        tgt_pos_embed = self.embed_positions(token_position_idx)
-        if code_masks is not None and torch.any(code_masks):
-            image_position_idx = self.image_position_idx[:prev_output_tokens.size(1)].unsqueeze(0).expand(bsz, tgt_len)
-            tgt_pos_embed[code_masks] = self.embed_image_positions(image_position_idx)[code_masks]
-
-        # self attn position bias
-        self_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=False)
-        if code_masks is not None and torch.any(code_masks):
-            self_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=True)
-            self_abs_pos_bias[code_masks] = self_image_abs_pos_bias[code_masks]
-        # cross attn position bias
-        src_pos_embed = encoder_out['position_embeddings'][0]
-        cross_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed)
-        if code_masks is not None and torch.any(code_masks):
-            cross_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed, use_image=True)
-            cross_abs_pos_bias[code_masks] = cross_image_abs_pos_bias[code_masks]
-        cross_abs_pos_bias = cross_abs_pos_bias.reshape(-1, *cross_abs_pos_bias.size()[-2:])
-
-        all_prev_output_tokens = prev_output_tokens.clone()
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-            cross_abs_pos_bias = cross_abs_pos_bias[:, -1:, :]
-            tgt_pos_embed = tgt_pos_embed[:, -1:, :]
-
-        # embed tokens and positions
-        x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        if self.project_in_dim is not None:
-            x = self.project_in_dim(x)
-
-        if self.entangle_position_embedding is not None and not self.args.disable_entangle:
-            x += tgt_pos_embed
-
-        if self.layernorm_embedding is not None:
-            if code_masks is None or not code_masks.any() or not getattr(self, "code_layernorm_embedding", False):
-                x = self.layernorm_embedding(x)
-            elif code_masks is not None and code_masks.all():
-                x = self.code_layernorm_embedding(x)
-            else:
-                x[~code_masks] = self.layernorm_embedding(x[~code_masks])
-                x[code_masks] = self.code_layernorm_embedding(x[code_masks])
-
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        self_attn_padding_mask: Optional[Tensor] = None
-        if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
-            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
-
-        # decoder layers
-        attn: Optional[Tensor] = None
-        inner_states: List[Optional[Tensor]] = [x]
-        for idx, layer in enumerate(self.layers):
-            if incremental_state is None and not full_context_alignment:
-                self_attn_mask = self.buffered_future_mask(x)
-            else:
-                self_attn_mask = None
-
-            self_attn_bias = self_abs_pos_bias.clone()
-            if code_masks is None or not code_masks.any():
-                self_attn_bias += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            elif code_masks is not None and code_masks.all():
-                self_attn_bias += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            else:
-                self_attn_bias[~code_masks] += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-                self_attn_bias[code_masks] += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            self_attn_bias = self_attn_bias.reshape(-1, *self_attn_bias.size()[-2:])
-            if incremental_state is not None:
-                self_attn_bias = self_attn_bias[:, -1:, :]
-
-            x, layer_attn, _ = layer(
-                x,
-                enc,
-                padding_mask,
-                incremental_state,
-                self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask,
-                need_attn=bool((idx == alignment_layer)),
-                need_head_weights=bool((idx == alignment_layer)),
-                self_attn_bias=self_attn_bias,
-                cross_attn_bias=cross_abs_pos_bias
-            )
-            inner_states.append(x)
-            if layer_attn is not None and idx == alignment_layer:
-                attn = layer_attn.float().to(x)
-
-        if attn is not None:
-            if alignment_heads is not None:
-                attn = attn[:alignment_heads]
-
-            # average probabilities over heads
-            attn = attn.mean(dim=0)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if self.project_out_dim is not None:
-            x = self.project_out_dim(x)
-
-        return x, {"attn": [attn], "inner_states": inner_states}
-
-    def output_layer(self, features):
-        """Project features to the vocabulary size."""
-        if self.adaptive_softmax is None:
-            # project back to size of vocabulary
-            return self.output_projection(features)
-        else:
-            return features
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        if self.embed_positions is None:
-            return self.max_target_positions
-        return self.max_target_positions
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
-        if (
-            self._future_mask.size(0) == 0
-            or (not self._future_mask.device == tensor.device)
-            or self._future_mask.size(0) < dim
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
-            )
-        self._future_mask = self._future_mask.to(tensor)
-        return self._future_mask[:dim, :dim]
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-
-        if f"{name}.output_projection.weight" not in state_dict:
-            if self.share_input_output_embed:
-                embed_out_key = f"{name}.embed_tokens.weight"
-            else:
-                embed_out_key = f"{name}.embed_out"
-            if embed_out_key in state_dict:
-                state_dict[f"{name}.output_projection.weight"] = state_dict[
-                    embed_out_key
-                ]
-                if not self.share_input_output_embed:
-                    del state_dict[embed_out_key]
-
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        # version_key = "{}.version".format(name)
-        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
-        #     # earlier checkpoints did not normalize after the stack of layers
-        #     self.layer_norm = None
-        #     self.normalize = False
-        #     state_dict[version_key] = torch.Tensor([1])
-
-        prefix = name + "." if name != "" else ""
-        image_params = ["image_position_idx"]
-        for image_param in image_params:
-            state_dict[prefix + image_param] = self.state_dict()[image_param]
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["decoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["decoder.embed_image_positions.weight"])
-            embed_dim = state_dict["decoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["decoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["decoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["decoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None, zero_init=False):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    if zero_init:
-        nn.init.constant_(m.weight, 0)
-    return m
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.0)
-    return m
-
-
-@register_model_architecture("unify_transformer", "unify_transformer")
-def base_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.no_cross_attention = getattr(args, "no_cross_attention", False)
-    args.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
-    args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
-    args.offload_activations = getattr(args, "offload_activations", False)
-    if args.offload_activations:
-        args.checkpoint_activations = True
-    args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
-    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
-    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)
\ No newline at end of file
diff --git a/spaces/starlit7/KorPoliticsTTS/text/__init__.py b/spaces/starlit7/KorPoliticsTTS/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/starlit7/KorPoliticsTTS/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/stomexserde/gpt4-ui/Examples/FairUse Wizard 2.9 Full Edition(Tutorial Included)(Pre-Cracked)[ Serial Key.md b/spaces/stomexserde/gpt4-ui/Examples/FairUse Wizard 2.9 Full Edition(Tutorial Included)(Pre-Cracked)[ Serial Key.md
deleted file mode 100644
index 1ba2de9f02dd8368b497385e6f5e037bfc616b56..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/FairUse Wizard 2.9 Full Edition(Tutorial Included)(Pre-Cracked)[ Serial Key.md	
+++ /dev/null
@@ -1,239 +0,0 @@
-
-<h1>How to Convert a DVD Movie to AVI with FairUse Wizard 2.9 Full Edition</h1>
- <p>Do you have a collection of DVDs that you want to back up or watch on your computer or mobile device? Do you want a simple and easy way to convert your DVDs to AVI files without losing quality or spending hours on encoding? If so, you should try FairUse Wizard 2.9 Full Edition, a powerful and user-friendly DVD ripper that can help you achieve your goals.</p>
- <h2>Introduction</h2>
- <p>FairUse Wizard is a software program that uses the DivX, Xvid, or h.264 codec to convert DVD movies to AVI files. It comes in both a free version and a commercial version. The free, or "Light" version, can create files up to 700MB while the commercial version can output a 1400MB file. This will allow you to back up your movies to CD, or even multiple movies on a single DVD.</p>
-<h2>FairUse Wizard 2.9 Full Edition(Tutorial Included)(Pre-Cracked)[ Serial Key</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734; <a href="https://urlgoal.com/2uIbls">https://urlgoal.com/2uIbls</a></b></p><br /><br />
- <p>Converting DVD movies to AVI files has many benefits. You can save space on your hard drive or external storage device, as AVI files are much smaller than DVD files. You can also play your movies on any device that supports AVI format, such as computers, smartphones, tablets, media players, game consoles, etc. You can also edit your movies with video editing software, such as adding subtitles, cropping, trimming, merging, etc.</p>
- <p>FairUse Wizard 2.9 Full Edition is the latest version of FairUse Wizard that offers many features and improvements over the previous versions. Some of the features include:</p>
- <ul>
-<li>Support for Windows XP, Vista, 7, 8, and 10</li>
-<li>Support for DVD sources from discs, ISO images, or folders</li>
-<li>Support for full auto mode or manual mode</li>
-<li>Support for previewing and editing video segments</li>
-<li>Support for two-pass encoding for higher quality</li>
-<li>Support for batch mode for converting multiple DVDs</li>
-<li>Support for customizing video resolution, bitrate, framerate, aspect ratio, audio track, etc.</li>
-<li>Support for adding subtitles from SRT files</li>
-<li>Support for creating chapters from IFO files</li>
-<li>Support for creating menus from VOB files</li>
-</ul>
- <p>In this article, we will show you how to use FairUse Wizard 2.9 Full Edition (Pre-Cracked) with serial key to convert a DVD movie to AVI in a few simple steps. We will also provide some tips and tricks on how to optimize your conversion process and deal with some common issues.</p>
- <h2>Requirements <h2>Requirements</h2>
- <p>Before you start converting your DVD movie to AVI with FairUse Wizard 2.9 Full Edition, you need to make sure you have the following requirements:</p>
- <ul>
-<li>A DVD drive that can read your DVD movie</li>
-<li>A computer that meets the minimum system requirements of FairUse Wizard 2.9 Full Edition, which are:</li>
-    <ul>
-    <li>Windows XP, Vista, 7, 8, or 10</li>
-    <li>1 GHz processor or higher</li>
-    <li>512 MB of RAM or higher</li>
-    <li>At least 5 GB of free hard disk space</li>
-    <li>A monitor that supports at least 800x600 resolution</li>
-    </ul>
-<li>A copy of FairUse Wizard 2.9 Full Edition (Pre-Cracked) with serial key, which you can download from this link: </li>
-<li>A valid serial key that you can enter during the installation process, which is: XXXX-XXXX-XXXX-XXXX (replace the Xs with the actual key)</li>
-</ul>
- <p>Once you have all the requirements, you can proceed to install FairUse Wizard 2.9 Full Edition on your computer.</p>
- <h2>How to install FairUse Wizard 2.9 Full Edition</h2>
- <p>Installing FairUse Wizard 2.9 Full Edition is very easy and straightforward. Just follow these steps:</p>
-<p></p>
- <ol>
-<li>Download the FairUse Wizard 2.9 Full Edition (Pre-Cracked) setup file from the link provided above and save it to your preferred location.</li>
-<li>Double-click on the setup file to launch the installation wizard.</li>
-<li>Follow the instructions on the screen and accept the terms and conditions.</li>
-<li>When prompted, enter the serial key that you received with the download link and click Next.</li>
-<li>Choose the destination folder where you want to install FairUse Wizard 2.9 Full Edition and click Next.</li>
-<li>Wait for the installation process to complete and click Finish.</li>
-<li>You can now launch FairUse Wizard 2.9 Full Edition from your desktop or start menu.</li>
-</ol>
- <p>Congratulations! You have successfully installed FairUse Wizard 2.9 Full Edition on your computer. You are now ready to convert your DVD movie to AVI.</p>
- <h2>Step-by-step tutorial</h2>
- <p>In this section, we will show you how to use FairUse Wizard 2.9 Full Edition to convert a DVD movie to AVI in a few simple steps. We will use an example DVD movie called "The Matrix" for demonstration purposes, but you can use any DVD movie that you own or have access to.</p>
- <h3>How to create a new project and set the options</h3>
- <p>The first thing you need to do is create a new project and set the options for your conversion. Here is how:</p>
- <ol>
-<li>Launch FairUse Wizard 2.9 Full Edition from your desktop or start menu.</li>
-<li>You will see the main interface of FairUse Wizard 2.9 Full Edition, which consists of four tabs: Project Options, Video Segments, Conversion Progress, and Batch Mode.</li>
-<li>Click on the Project Options tab to access the settings for your conversion.</li>
-<li>You will see a window with several sections: General Options, Video Options, Audio Options, Subtitle Options, Chapter Options, and Menu Options.</li>
-<li>In the General Options section, you can choose between full auto mode and manual mode. Full auto mode will automatically detect and select the best settings for your conversion based on your DVD source and output file size. Manual mode will allow you to customize every aspect of your conversion according to your preferences.</li>
-<li>We recommend using full auto mode for beginners or users who want a quick and easy conversion. However, if you want more control over your conversion, you can switch to manual mode by unchecking the box next to "Full Auto Mode".</li>
-<li>In this tutorial, we will use full auto mode for simplicity.</li>
-<li>In the Video Options section, you can choose the output file size for your AVI file. You can either enter a custom value in megabytes (MB) or choose from one of the predefined values: CD-R (700 MB), CD-RW (650 MB), DVD-R (4400 MB), DVD-RW (4200 MB), etc.</li>
-<li>We recommend choosing a file size that matches your intended storage device or media player. For example, if you want to burn your AVI file to a CD-R disc, you should choose CD-R (700 MB) as your output file size.</li>
-<li>In this tutorial, we will choose CD-R (700 MB) as our output file size.</li>
-<li>In the Audio Options section, you can choose the audio track and the audio codec for your AVI file. You can either let FairUse Wizard automatically select the best audio track and codec for you, or you can manually choose them from the drop-down menus.</li>
-<li>We recommend letting FairUse Wizard automatically select the audio track and codec for you, unless you have a specific preference or need. For example, if you want to keep the original language and sound quality of your DVD movie, you should choose the same audio track and codec as your DVD source.</li>
-<li>In this tutorial, we will let FairUse Wizard automatically select the audio track and codec for us.</li>
-<li>In the Subtitle Options section, you can choose whether to add subtitles to your AVI file or not. You can either let FairUse Wizard automatically select the best subtitle track for you, or you can manually choose one from the drop-down menu. You can also add external subtitles from SRT files by clicking on the "Add Subtitle" button and browsing for the SRT file on your computer.</li>
-<li>We recommend adding subtitles to your AVI file if you want to watch your movie in a different language or with captions for the hearing impaired. However, if you don't need subtitles, you can skip this step.</li>
-<li>In this tutorial, we will let FairUse Wizard automatically select the subtitle track for us.</li>
-<li>In the Chapter Options section, you can choose whether to create chapters for your AVI file or not. Chapters are useful for navigating through your movie and skipping to specific scenes. You can either let FairUse Wizard automatically create chapters for you based on the IFO file of your DVD source, or you can manually create chapters by clicking on the "Add Chapter" button and entering the start time and end time of each chapter.</li>
-<li>We recommend creating chapters for your AVI file if you want to have more control over your movie playback. However, if you don't need chapters, you can skip this step.</li>
-<li>In this tutorial, we will let FairUse Wizard automatically create chapters for us based on the IFO file of our DVD source.</li>
-<li>In the Menu Options section, you can choose whether to create a menu for your AVI file or not. A menu is a graphical interface that allows you to select different options for your movie, such as play, pause, stop, chapters, subtitles, etc. You can either let FairUse Wizard automatically create a menu for you based on the VOB file of your DVD source, or you can manually create a menu by clicking on the "Add Menu" button and choosing a template and a background image.</li>
-<li>We recommend creating a menu for your AVI file if you want to have a more professional and interactive movie experience. However, if you don't need a menu, you can skip this step.</li>
-<li>In this tutorial, we will let FairUse Wizard automatically create a menu for us based on the VOB file of our DVD source.</li>
-</ol>
- <p>After you have set all the options for your conversion, click on the "Next" button at the bottom right corner of the window to proceed to the next step.</p>
- <h3>How to choose between full auto mode and manual mode</h3>
- <p>The next step is to choose between full auto mode and manual mode for your conversion. Full auto mode will automatically detect and select the best video segments for your conversion based on your DVD source and output file size. Manual mode will allow you to manually select and edit the video segments according to your preferences.</p>
- <p>We recommend using full auto mode for beginners or users who want a quick and easy conversion. However, if you want more control over your conversion, you can switch to manual mode by checking the box next to "Manual Mode".</p>
- <p>In this tutorial, we will use full auto mode for simplicity.</p>
- <ol>
-<li>Click on the "Next" button at the bottom right corner of the window to start full auto mode.</li>
-<li>FairUse Wizard will scan your DVD source and analyze its structure. This may take a few minutes depending on the size and complexity of your DVD source.</li>
-<li>FairUse Wizard will then display a list of video segments that it has detected and selected for your conversion. Each video segment represents a part of your DVD movie that has a consistent video quality and aspect ratio. You can see the details of each video segment, such as its title, duration, resolution, bitrate, etc., by clicking on it.</li>
-<li>If you are satisfied with the video segments that FairUse Wizard has selected for you, click on the "Next" button at the bottom right corner of the window to proceed to the next step.</li>
-<li>If you are not satisfied with the video segments that FairUse Wizard has selected for you, you can switch to manual mode by checking the box next to "Manual Mode". This will allow you to manually select and edit the video segments according to your preferences. You can do the following actions in manual mode:</li>
-    <ul>
-    <li>Add a video segment by clicking on the "Add Segment" button and choosing a title from the drop-down menu.</li>
-    <li>Delete a video segment by clicking on the "Delete Segment" button and confirming your choice.</li>
-    <li>Move a video segment up or down in the list by clicking on the "Move Up" or "Move Down" button.</li>
-    <li>Edit a video segment by clicking on the "Edit Segment" button and adjusting the start time and end time of the segment. You can also preview the segment by clicking on the "Preview Segment" button.</li>
-    </ul>
-<li>After you have selected and edited the video segments that you want to convert, click on the "Next" button at the bottom right corner of the window to proceed to the next step.</li>
-</ol>
- <h3>How to select the DVD source and the output folder</h3>
- <p>The next step is to select the DVD source and the output folder for your conversion. The DVD source is where FairUse Wizard will read your DVD movie from, and the output folder is where FairUse Wizard will save your AVI file to. Here is how:</p>
- <ol>
-<li>Click on the "Next" button at the bottom right corner of the window to access the DVD source and output folder selection window.</li>
-<li>You will see two sections: DVD Source and Output Folder.</li>
-<li>In the DVD Source section, you can choose between three options: DVD Disc, DVD Folder, or ISO Image. DVD Disc means that your DVD movie is inserted in your DVD drive. DVD Folder means that your DVD movie is copied to a folder on your hard drive. ISO Image means that your DVD movie is compressed into a single file on your hard drive.</li>
-<li>We recommend choosing DVD Disc if you have a physical copy of your DVD movie. However, if you have a digital copy of your DVD movie, you can choose either DVD Folder or ISO Image depending on how you stored it.</li>
-<li>In this tutorial, we will choose DVD Disc as our DVD source.</li>
-<li>In the Output Folder section, you can choose where you want to save your AVI file. You can either enter a custom path in the text box or browse for a folder on your computer by clicking on the "Browse" button.</li>
-<li>We recommend choosing a folder that has enough space to store your AVI file and that is easy to access and locate. For example, you can create a new folder on your desktop or in your documents folder and name it after your movie title.</li>
-<li>In this tutorial, we will choose C:\Users\YourName\Desktop\The Matrix as our output folder.</li>
-</ol>
- <p>After you have selected the DVD source and the output folder for your conversion, click on the "Next" button at the bottom right corner of the window to proceed to the next step.</p>
- <h3>How to preview and edit the video segments</h3>
- <p>The next step is to preview and edit the video segments for your conversion. This step is optional, but it can help you improve the quality and accuracy of your conversion. You can preview each video segment and make adjustments to its resolution, aspect ratio, cropping, deinterlacing, etc. Here is how:</p>
- <ol>
-<li>Click on the "Next" button at the bottom right corner of the window to access the video segment preview and editing window.</li>
-<li>You will see a list of video segments that you have selected for your conversion on the left side of the window. You can click on each video segment to preview it on the right side of the window.</li>
-<li>Below the preview window, you will see a toolbar with several buttons and sliders that allow you to edit the video segment. You can do the following actions:</li>
-    <ul>
-    <li>Change the resolution of the video segment by clicking on the "Resolution" button and choosing a preset or a custom value.</li>
-    <li>Change the aspect ratio of the video segment by clicking on the "Aspect Ratio" button and choosing a preset or a custom value.</li>
-    <li>Crop the video segment by dragging the sliders on the top, bottom, left, and right edges of the preview window.</li>
-    <li>Deinterlace the video segment by clicking on the "Deinterlace" button and choosing a method.</li>
-    <li>Adjust the brightness, contrast, saturation, and gamma of the video segment by dragging the sliders below the preview window.</li>
-    </ul>
-<li>You can also compare the original and edited video segments by clicking on the "Compare" button at the bottom left corner of the window.</li>
-<li>If you are satisfied with your edits, click on the "Apply" button at the bottom right corner of the window to save your changes. If you want to undo your edits, click on the "Reset" button at the bottom right corner of the window to restore the original settings.</li>
-<li>You can repeat this process for each video segment that you want to edit. You can also skip this step if you don't want to edit any video segments.</li>
-</ol>
- <p>After you have previewed and edited the video segments for your conversion, click on the "Next" button at the bottom right corner of the window to proceed to the next step.</p>
- <h3>How to start the conversion process and monitor the progress</h3>
- <p>The final step is to start the conversion process and monitor its progress. This is where FairUse Wizard will use its powerful engine to convert your DVD movie to AVI file. Here is how:</p>
- <ol>
-<li>Click on the "Next" button at the bottom right corner of the window to start the conversion process.</li>
-<li>You will see a new window with a progress bar and some information about the conversion process, such as the current video segment, the elapsed time, the remaining time, the current bitrate, etc.</li>
-<li>You can also see a preview of the output AVI file by clicking on the "Preview" button at the bottom left corner of the window.</li>
-<li>You can pause or cancel the conversion process by clicking on the "Pause" or "Cancel" button at the bottom right corner of the window.</li>
-<li>The conversion process may take a long time depending on the size and quality of your DVD movie and your computer specifications. We recommend that you do not use your computer for other tasks while the conversion process is running, as this may slow down or interfere with the conversion process.</li>
-<li>When the conversion process is complete, you will see a message saying "Conversion Complete" and a summary of the conversion statistics, such as the output file size, the average bitrate, the total time, etc.</li>
-<li>Click on the "OK" button to close the window and return to the main interface of FairUse Wizard 2.9 Full Edition.</li>
-</ol>
- <p>Congratulations! You have successfully converted your DVD movie to AVI file with FairUse Wizard 2.9 Full Edition. You can now find your AVI file in the output folder that you selected earlier. You can also play your AVI file with any media player that supports AVI format, such as VLC Media Player, Windows Media Player, etc.</p>
- <h2>Tips and tricks</h2>
- <p>In this section, we will provide some tips and tricks on how to optimize your conversion process and deal with some common issues that you may encounter while using FairUse Wizard 2.9 Full Edition.</p>
- <h3>How to optimize the video quality and file size</h3>
- <p>One of the main challenges of converting DVD movies to AVI files is finding the right balance between video quality and file size. You want your AVI file to have a high video quality that preserves the original details and colors of your DVD movie, but you also want your AVI file to have a small file size that saves space and bandwidth. Here are some tips on how to optimize the video quality and file size of your AVI file:</p>
- <ul>
-<li>Use full auto mode if you are not sure about what settings to use for your conversion. Full auto mode will automatically detect and select the best settings for your conversion based on your DVD source and output file size.</li>
-<li>Use two-pass encoding if you want to achieve a higher video quality with a lower bitrate. Two-pass encoding will analyze your DVD movie twice and adjust the bitrate accordingly to avoid wasting bits on simple scenes and allocate more bits on complex scenes. To enable two-pass encoding, check the box next to "Two Pass Encoding" in the Video Options section of the Project Options tab.</li>
-<li>Use a lower resolution or a lower aspect ratio if you want to reduce the file size of your AVI file. A lower resolution or a lower aspect ratio will result in a smaller video frame that requires less bits to encode. However, this may also result in a loss of video quality or a distorted video image. To change the resolution or the aspect ratio of your video segment, click on the "Edit Segment" button in the Video Segments tab and choose a preset or a custom value from the drop-down menus.</li>
-<li>Use a higher bitrate or a higher framerate if you want to increase the video quality of your AVI file. A higher bitrate or a higher framerate will result in a smoother and sharper video image that preserves more details and colors. However, this may also result in a larger file size that consumes more space and bandwidth. To change the bitrate or the framerate of your video segment, click on the "Edit Segment" button in the Video Segments tab and drag the sliders to adjust the values.</li>
-<li>Use cropping and deinterlacing if you want to improve the video appearance of your AVI file. Cropping will remove the black bars or unwanted edges from your video image, making it more focused and clear. Deinterlacing will remove the horizontal lines or artifacts from your video image, making it more smooth and natural. To crop or deinterlace your video segment, click on the "Edit Segment" button in the Video Segments tab and drag the sliders or choose a method from the drop-down menu.</li>
-</ul>
- <p>By following these tips, you can optimize the video quality and file size of your AVI file according to your needs and preferences.</p>
- <h3>How to deal with copy-protected DVDs</h3>
- <p>Another challenge of converting DVD movies to AVI files is dealing with copy-protected DVDs. Copy-protected DVDs are DVDs that have encryption or other mechanisms that prevent unauthorized copying or playback. Some examples of copy-protection methods are CSS, ARccOS, RipGuard, etc. If you try to convert a copy-protected DVD with FairUse Wizard 2.9 Full Edition, you may encounter errors or failures.</p>
- <p>To deal with copy-protected DVDs, you need to use a third-party software program that can decrypt or bypass the copy-protection methods. Some examples of such software programs are DVD Decrypter, AnyDVD, DVDFab, etc. You can use these software programs to either rip your copy-protected DVD to your hard drive as an ISO image or a DVD folder, or run them in the background while using FairUse Wizard 2.9 Full Edition to convert your DVD movie to AVI file.</p>
- <p>We recommend using DVD Decrypter as it is free, easy to use, and compatible with FairUse Wizard 2.9 Full Edition. Here are some steps on how to use DVD Decrypter to rip your copy-protected DVD to your hard drive as an ISO image:</p>
- <ol>
-<li>Download DVD Decrypter from this link: </li>
-<li>Install DVD Decrypter on your computer and launch it.</li>
-<li>Insert your copy-protected DVD into your DVD drive and select it as the source in DVD Decrypter.</li>
-<li>Select "Mode" from the menu bar and choose "ISO" then "Read".</li>
-<li>Select a destination folder where you want to save your ISO image on your hard drive.</li>
-<li>Click on the "Decrypt" button at the bottom right corner of the window to start ripping your DVD.</li>
-<li>Wait for the ripping process to complete and close DVD Decrypter.</li>
-</ol>
- <p>You can now use FairUse Wizard 2.9 Full Edition to convert your ISO image to AVI file by choosing ISO Image as your DVD source in the Project Options tab.</p>
- <h3>How to convert multiple DVDs in batch mode</h3>
- <p>If you have multiple DVDs that you want to convert to AVI files, you can use FairUse Wizard 2.9 Full Edition's batch mode feature. Batch mode allows you to create multiple projects and convert them one after another without any user intervention. This can save you time and effort as you don't have to manually start each conversion process.</p>
- <p>To use batch mode, you need to create each project separately and save it as a FUP file (FairUse Project File). You can then load all the FUP files into FairUse Wizard 2.9 Full Edition's batch mode window and start the batch conversion process. Here are some steps on how to use batch mode:</p>
- <ol>
-<li>Create each project separately by following the steps in the previous sections of this article.</li>
-<li>Repeat this process for each DVD that you want to convert to AVI file.</li>
-<li>After you have created and saved all the projects that you want to convert, click on the "Batch Mode" tab in the main interface of FairUse Wizard 2.9 Full Edition.</li>
-<li>You will see a window with a list of projects that you can load into batch mode. You can either drag and drop your FUP files into the window, or click on the "Add Project" button and browse for your FUP files on your computer.</li>
-<li>You can also delete a project from the list by clicking on the "Delete Project" button, or move a project up or down in the list by clicking on the "Move Up" or "Move Down" button.</li>
-<li>After you have loaded all the projects that you want to convert, click on the "Start Batch" button at the bottom right corner of the window to start the batch conversion process.</li>
-<li>FairUse Wizard 2.9 Full Edition will convert each project one after another without any user intervention. You can see the progress and details of each conversion process in the Conversion Progress tab.</li>
-<li>When the batch conversion process is complete, you will see a message saying "Batch Complete" and a summary of the batch conversion statistics, such as the number of projects converted, the total output file size, the average bitrate, the total time, etc.</li>
-<li>Click on the "OK" button to close the window and return to the main interface of FairUse Wizard 2.9 Full Edition.</li>
-</ol>
- <p>Congratulations! You have successfully converted multiple DVDs to AVI files with FairUse Wizard 2.9 Full Edition's batch mode feature. You can now find your AVI files in the output folders that you selected earlier. You can also play your AVI files with any media player that supports AVI format, such as VLC Media Player, Windows Media Player, etc.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to use FairUse Wizard 2.9 Full Edition (Pre-Cracked) with serial key to convert a DVD movie to AVI file in a few simple steps. We have also provided some tips and tricks on how to optimize your conversion process and deal with some common issues. We hope that you have found this article helpful and informative, and that you have enjoyed using FairUse Wizard 2.9 Full Edition to convert your DVD movies to AVI files.</p>
- <p>FairUse Wizard 2.9 Full Edition is a powerful and user-friendly DVD ripper that can help you back up or watch your DVD movies on any device that supports AVI format. It offers many features and improvements over the previous versions, such as support for Windows 10, two-pass encoding, batch mode, subtitle options, chapter options, menu options, etc. It also comes with a pre-cracked version that does not require any activation or registration.</p>
- <p>If you want to download FairUse Wizard 2.9 Full Edition (Pre-Cracked) with serial key, you can use this link: </p>
- <p>If you want to learn more about FairUse Wizard 2.9 Full Edition or contact its support team, you can visit its official website: </p>
- <p>If you have any feedback or questions about this article or FairUse Wizard 2.9 Full Edition, please feel free to leave a comment below or send us an email at </p>
- <p>Thank you for reading this article and happy converting!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about FairUse Wizard 2.9 Full Edition and their answers:</p>
- <h3>What are the differences between the free version and the full version of FairUse Wizard?</h3>
- <p>The free version of FairUse Wizard is also known as the "Light" version. It has some limitations compared to the full version of FairUse Wizard, such as:</p>
- <ul>
-<li>The maximum output file size is 700 MB</li>
-<li>The maximum resolution is 720x576</li>
-<li>The maximum framerate is 25 fps</li>
-<li>The maximum bitrate is 1000 kbps</li>
-<li>The subtitle options are disabled</li>
-<li>The chapter options are disabled</li>
-<li>The menu options are disabled</li>
-</ul>
- <p>The full version of FairUse Wizard does not have these limitations and offers more features and options for your conversion process.</p>
- <h3>What are the supported video codecs and output formats of FairUse Wizard?</h3>
- <p>FairUse Wizard supports three video codecs: DivX, Xvid , and h.264. These are the most popular and widely supported video codecs that can produce high-quality video files with low file sizes. You can choose the video codec that you prefer in the Project Options tab. FairUse Wizard only supports one output format: AVI. AVI is a container format that can store video and audio data encoded with different codecs. AVI is a common and compatible format that can be played on most devices and platforms. However, if you want to convert your AVI file to another format, such as MP4, MKV, MOV, etc., you can use a third-party software program, such as HandBrake, VLC Media Player, Format Factory, etc.</p>
- <h3>How to play the converted AVI files on different devices and platforms?</h3>
- <p>The converted AVI files can be played on any device or platform that supports AVI format and the video codec that you used for your conversion. However, some devices or platforms may have specific requirements or limitations for playing AVI files, such as resolution, bitrate, framerate, aspect ratio, etc. To ensure that your AVI files can be played smoothly and correctly on your desired device or platform, you may need to adjust some settings or use a compatible media player.</p>
- <p>Here are some examples of how to play the converted AVI files on different devices and platforms:</p>
- <ul>
-<li>To play the converted AVI files on Windows PC, you can use any media player that supports AVI format and the video codec that you used for your conversion, such as Windows Media Player, VLC Media Player, Media Player Classic, etc. You may need to install the appropriate codec pack or plugin if your media player does not support the video codec that you used for your conversion.</li>
-<li>To play the converted AVI files on Mac OS X, you can use VLC Media Player, which is a free and versatile media player that can play almost any video format and codec. You can download VLC Media Player from this link: </li>
-<li>To play the converted AVI files on iOS devices, such as iPhone, iPad, iPod Touch, etc., you can use VLC for iOS, which is a free and powerful media player that can play most video formats and codecs. You can download VLC for iOS from this link: </li>
-<li>To play the converted AVI files on Android devices, such as smartphones, tablets, etc., you can use MX Player, which is a free and popular media player that can play most video formats and codecs. You can download MX Player from this link: </li>
-<li>To play the converted AVI files on game consoles, such as PlayStation 4, Xbox One, Nintendo Switch, etc., you may need to convert your AVI file to a compatible format, such as MP4 or MKV. You can use a third-party software program, such as HandBrake, to convert your AVI file to a compatible format. You can download HandBrake from this link: </li>
-</ul>
- <p>By following these examples, you can play your converted AVI files on different devices and platforms with ease and convenience.</p>
- <h3>How to update FairUse Wizard to the latest version?</h3>
- <p>FairUse Wizard is constantly updated with new features and improvements to enhance its performance and compatibility. To update FairUse Wizard to the latest version, you can do the following:</p>
- <ol>
-<li>Launch FairUse Wizard 2.9 Full Edition from your desktop or start menu.</li>
-<li>Select "Help" from the menu bar and choose "Check for Updates".</li>
-<li>If there is a new version available, you will see a message saying "A new version of FairUse Wizard is available".</li>
-<li>Click on the "Download" button to download the new version of FairUse Wizard.</li>
-<li>Follow the instructions on the screen to install the new version of FairUse Wizard.</li>
-<li>You may need to enter your serial key again during the installation process.</li>
-</ol>
- <p>You have now updated FairUse Wizard to the latest version. You can enjoy the new features and improvements of FairUse Wizard.</p>
- <h3>How to contact FairUse Wizard support team?</h3>
- <p>If you have any questions or problems regarding FairUse Wizard 2.9 Full Edition or its usage, you can contact FairUse Wizard support team by doing the following:</p>
- <ol>
-<li>Launch FairUse Wizard 2.9 Full Edition from your desktop or start menu.</li>
-<li>Select "Help" from the menu bar and choose "Contact Support".</li>
-<li>You will see a window with a form where you can enter your name, email address, subject, and message.</li>
-<li>Fill in the form with your details and describe your question or problem clearly and concisely.</li>
-<li>Click on the "Send" button to send your message to FairUse Wizard support team.</li>
-</ol>
- <p>You will receive a confirmation email that your message has been sent. You will also receive a reply from FairUse Wizard support team within 24 hours.</p>
- <p>FairUse Wizard support team is friendly and helpful, and they will try their best to answer your questions or solve your problems.</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/JetBrains AppCode 2019.2 MacOS Free Download.md b/spaces/stomexserde/gpt4-ui/Examples/JetBrains AppCode 2019.2 MacOS Free Download.md
deleted file mode 100644
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--- a/spaces/stomexserde/gpt4-ui/Examples/JetBrains AppCode 2019.2 MacOS Free Download.md	
+++ /dev/null
@@ -1,143 +0,0 @@
-
-<h1>JetBrains AppCode 2019.2 macOS Free Download</h1>
-<p>If you are looking for a smart and powerful integrated development environment (IDE) for macOS development, you should consider JetBrains AppCode. In this article, we will tell you what JetBrains AppCode is, how to download it for free, how to use it, and what are its pros and cons.</p>
-<h2>JetBrains AppCode 2019.2 macOS Free Download</h2><br /><p><b><b>Download File</b> &gt; <a href="https://urlgoal.com/2uI9XE">https://urlgoal.com/2uI9XE</a></b></p><br /><br />
- <h2>Introduction</h2>
-<p>JetBrains AppCode is an IDE for Swift, Objective-C, C, C++, and JavaScript development on macOS. It is based on the IntelliJ IDEA platform and supports iOS, macOS, watchOS, tvOS, Linux, and Windows applications.</p>
-<p>JetBrains AppCode has many features that make it a great choice for macOS development, such as:</p>
-<ul>
-<li>Smart code completion and navigation</li>
-<li>Advanced code analysis and refactoring</li>
-<li>Powerful debugging and testing tools</li>
-<li>Seamless integration with Xcode, CocoaPods, Carthage, Git, SVN, and other tools</li>
-<li>Customizable user interface and editor</li>
-<li>Cross-platform compatibility and support for multiple languages</li>
-</ul>
-<p>With JetBrains AppCode, you can write high-quality code faster, easier, and more efficiently. You can also enjoy a smooth and productive development experience with a reliable and user-friendly IDE.</p>
-<p></p>
- <h2>How to download JetBrains AppCode 2019.2 for macOS for free</h2>
-<p>If you want to download JetBrains AppCode 2019.2 for macOS for free, you can do so from the official website of JetBrains. Here is the link to the download page:</p>
-<p><a href="https://www.jetbrains.com/objc/download/#section=mac">https://www.jetbrains.com/objc/download/#section=mac</a></p>
-<p>On this page, you can choose between two options: a free trial or a free community edition. The free trial gives you access to all the features of JetBrains AppCode for 30 days, while the free community edition gives you access to a limited set of features for non-commercial use.</p>
-<p>To download JetBrains AppCode 2019.2 for macOS for free, you need to meet the following system requirements:</p>
-<ul>
-<li>macOS 10.13 or higher</li>
-<li>4 GB RAM minimum, 8 GB RAM recommended</li>
-<li>2.5 GB hard disk space minimum, SSD recommended</li>
-<li>1024x768 minimum screen resolution</li>
-</ul>
-<p>To install JetBrains AppCode 2019.2 for macOS, you need to follow these steps:</p>
-<ol>
-<li>Download the .dmg file from the link above</li>
-<li>Open the .dmg file and drag the AppCode.app icon to the Applications folder</li>
-<li>Launch the AppCode.app from the Applications folder or the Launchpad</li>
-<li>Follow the instructions on the screen to complete the installation process</li>
-<li>Enjoy using JetBrains AppCode for macOS development</li>
-</ol>
- <h2>How to use JetBrains AppCode 2019.2 for macOS</h2>
-<p>Once you have installed JetBrains AppCode 2019.2 for macOS, you can start using it to create and develop your macOS applications. Here are some of the main features and tools that you can use with JetBrains AppCode 2019.2 for macOS:</p>
- <h3>The user interface and the main features</h3>
-<p>The user interface of JetBrains AppCode 2019.2 for macOS is similar to other JetBrains IDEs, such as IntelliJ IDEA or PyCharm. It consists of several components, such as:</p>
-<ul>
-<li>The menu bar, which contains the main menus and commands</li>
-<li>The toolbar, which contains the frequently used buttons and actions</li>
-<li>The tool window bar, which contains the icons for the various tool windows</li>
-<li>The editor area, which displays the code editor and the tabs for the open files</li>
-<li>The status bar, which shows the information about the current file, project, and IDE state</li>
-</ul>
-<p>You can customize the user interface of JetBrains AppCode 2019.2 for macOS according to your preferences and needs. You can change the theme, the font size, the layout, the shortcuts, and the plugins. You can also switch between different modes, such as full screen mode, distraction free mode, presentation mode, or zen mode.</p>
-<p>Some of the main features that you can access from the user interface of JetBrains AppCode 2019.2 for macOS are:</p>
-<ul>
-<li>The project view, which shows the structure and the files of your project</li>
-<li>The navigation bar, which allows you to navigate through your project and files</li>
-<li>The search and replace functionality, which lets you find and modify any text or code in your project</li>
-<li>The run and debug functionality, which lets you execute and debug your application</li>
-<li>The code generation functionality, which lets you create new classes, methods, variables, and other code elements</li>
-<li>The refactoring functionality, which lets you change the structure and the name of your code elements</li>
-<li>The code formatting functionality, which lets you apply a consistent style and indentation to your code</li>
-<li>The code documentation functionality, which lets you add comments and annotations to your code</li>
-</ul>
- <h3>The code editor and the code analysis tools</h3>
-<p>The code editor of JetBrains AppCode 2019.2 for macOS is where you write and edit your code. It supports syntax highlighting, auto-completion, auto-indentation, code folding, code snippets, and multiple cursors. It also supports multiple languages, such as Swift, Objective-C, C, C++, JavaScript, HTML, CSS, XML, JSON, YAML, and more.</p>
-<p>JetBrains AppCode 2019.2 for macOS also provides several code analysis tools that help you write better code. These tools include:</p>
-<ul>
-<li>The code inspection tool, which detects and highlights errors, warnings, typos, and other issues in your code</li>
-<li>The quick-fix tool, which suggests and applies solutions to fix the detected issues in your code</li>
-<li>The intention action tool, which suggests and performs context-sensitive actions to improve your code</li>
-<li>The code completion tool, which suggests and inserts relevant code elements based on your context and preferences</li>
-<li>The parameter info tool, which shows the information about the parameters of a method or a function</li>
-<li>The documentation info tool, which shows the documentation of a code element or a symbol</li>
-<li>The type info tool, which shows the type of a variable or an expression</li>
-<li>The rename tool, which changes the name of a code element or a symbol across your project</li>
-<li>The extract tool, which extracts a code fragment into a variable, a method, or a function</li>
-<li>The inline tool, which replaces a variable, a method, or a function with its code fragment</li>
-<li>The move tool, which moves a code element or a symbol to another file, class, or module</li>
-<li>The change signature tool, which changes the parameters, the return type, or the name of a method or a function</li>
-<li>The optimize imports tool, which organizes and removes unused imports in your code</li>
-</ul>
- <h3>The debugging and testing tools</h3>
-<p>JetBrains AppCode 2019.2 for macOS also provides several debugging and testing tools that help you find and fix errors and bugs in your code. These tools include:</p>
-<ul>
-<li>The debugger tool, which lets you run your application in debug mode and inspect its state and behavior</li>
-<li>The breakpoint tool, which lets you set and manage breakpoints in your code to pause the execution and examine the values</li>
-<li>The watch tool, which lets you monitor the values of variables and expressions during the debugging session</li>
-<li>The evaluate expression tool, which lets you execute any code or expression in the context of the current frame</li>
-<li>The console tool, which lets you interact with your application using standard input and output</li>
-<li>The testing tool, which lets you create and run unit tests and UI tests for your application using various frameworks, such as XCTest, Quick, Kiwi, Specta, and more</li>
-<li>The test runner tool, which lets you manage and execute your tests and view the results and the coverage</li>
-<li>The test recorder tool, which lets you record UI actions and generate UI tests automatically</li>
-</ul>
- <h3>The project management and version control tools</h3>
-<p>JetBrains AppCode 2019.2 for macOS also provides several project management and version control tools that help you organize and maintain your code. These tools include:</p>
-<ul>
-<li>The project tool, which lets you create and configure your project settings, such as the SDK, the target platform, the build system, the dependencies, and more</li>
-<li>The module tool, which lets you create and manage sub-projects within your project</li>
-<li>The file template tool, which lets you create new files using predefined or custom templates</li>
-<li>The live template tool, which lets you insert common code snippets using abbreviations</li>
-<li>The file comparison tool, which lets you compare and merge two or more files or directories</li>
-<li>The version control tool, which lets you integrate your project with various version control systems, such as Git, SVN, Mercurial, Perforce, and more</li>
-<li>The commit tool, which lets you review and commit your changes to the version control system</li>
-<li>The push tool, which lets you upload your commits to the remote repository</li>
-<li>The pull tool, which lets you download and merge the changes from the remote repository</li>
-<li>The branch tool, which lets you create and switch between different branches of your project</li>
-<li>The merge tool, which lets you resolve conflicts and merge branches</li>
-<li>The history tool, which lets you view and manage the history of your project and files</li>
-<li>The annotate tool, which lets you view the author, date, and revision of each line of code</li>
-</ul>
- <h2>Pros and cons of JetBrains AppCode 2019.2 for macOS</h2>
-<p>JetBrains AppCode 2019.2 for macOS is a powerful and smart IDE for macOS development, but it also has some pros and cons that you should be aware of. Here are some of them:</p>
- <h3>Pros</h3>
-<ul>
-<li>It supports multiple languages and platforms, such as Swift, Objective-C, C, C++, JavaScript, iOS, macOS, watchOS, tvOS, Linux, and Windows</li>
-<li>It has advanced code analysis and refactoring tools that help you write high-quality code faster and easier</li>
-<li>It has powerful debugging and testing tools that help you find and fix errors and bugs in your code</li>
-<li>It has seamless integration with Xcode, CocoaPods, Carthage, Git, SVN, and other tools that you need for macOS development</li>
-<li>It has a customizable user interface and editor that you can adjust to your preferences and needs</li>
-<li>It has a free trial and a free community edition that you can download and use for free</li>
-</ul>
- <h3>Cons</h3>
-<ul>
-<li>It requires more RAM and disk space than other IDEs, which may affect your system performance</li>
-<li>It may have some compatibility issues with the latest versions of Xcode or macOS</li>
-<li>It may have some bugs or glitches that may affect your development experience</li>
-<li>It has a steep learning curve for beginners or users who are used to other IDEs</li>
-<li>It has a limited set of features in the free community edition compared to the paid professional edition</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, JetBrains AppCode 2019.2 for macOS is a smart and powerful IDE for macOS development that offers many features and tools that can help you create and develop your macOS applications. It supports multiple languages and platforms, such as Swift, Objective-C, C, C++, JavaScript, iOS, macOS, watchOS, tvOS, Linux, and Windows. It also integrates with Xcode, CocoaPods, Carthage, Git, SVN, and other tools that you need for macOS development.</p>
-<p>If you want to try JetBrains AppCode 2019.2 for macOS for free, you can download it from the official website of JetBrains. You can choose between a free trial or a free community edition. The free trial gives you access to all the features of JetBrains AppCode for 30 days, while the free community edition gives you access to a limited set of features for non-commercial use.</p>
-<p>We hope that this article has given you some useful information about JetBrains AppCode 2019.2 for macOS and how to download it for free. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers related to JetBrains AppCode 2019.2 for macOS:</p>
- <h4>Q: How do I update JetBrains AppCode 2019.2 for macOS?</h4>
-<p>A: You can update JetBrains AppCode 2019.2 for macOS by using the built-in update functionality. To do so, go to AppCode > Check for Updates in the menu bar. If there is a new version available, you can download and install it from there.</p>
- <h4>Q: How do I uninstall JetBrains AppCode 2019.2 for macOS?</h4>
-<p>A: You can uninstall JetBrains AppCode 2019.2 for macOS by using the standard uninstall procedure. To do so, go to the Applications folder and drag the AppCode.app icon to the Trash. Alternatively, you can use a third-party app cleaner tool to remove all the associated files and folders of JetBrains AppCode 2019.2 for macOS.</p>
- <h4>Q: How do I activate JetBrains AppCode 2019.2 for macOS?</h4>
-<p>A: You can activate JetBrains AppCode 2019.2 for macOS by using a license key or a JetBrains account. To do so, go to AppCode > Register in the menu bar. You can enter your license key or sign in with your JetBrains account to activate your product.</p>
- <h4>Q: How do I contact JetBrains support for JetBrains AppCode 2019.2 for macOS?</h4>
-<p>A: You can contact JetBrains support for JetBrains AppCode 2019.2 for macOS by using the online form, the email, or the phone. To do so, go to Help > Contact Support in the menu bar. You can fill out the online form, send an email to appcode-support@jetbrains.com, or call +1 617 398 7999.</p>
- <h4>Q: How do I get more information about JetBrains AppCode 2019.2 for macOS?</h4>
-<p>A: You can get more information about JetBrains AppCode 2019.2 for macOS by visiting the official website, the documentation, the blog, or the forum of JetBrains. To do so, go to Help > AppCode Help in the menu bar. You can access the official website, the documentation, the blog, or the forum of JetBrains from there.</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/sub314xxl/MusicGen/tests/__init__.py b/spaces/sub314xxl/MusicGen/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/tests/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Alan.Wake.v1.05.16.5341.Update-EyePatch Fixed Patch.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Alan.Wake.v1.05.16.5341.Update-EyePatch Fixed Patch.md
deleted file mode 100644
index e5c17f9757ee9aefcb0d1d235b891165bb516c4f..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Alan.Wake.v1.05.16.5341.Update-EyePatch Fixed Patch.md	
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download NEW! Ntsd 2.6 Full Version 108 Tokyo Maquetas Rocket Regaetton}.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download NEW! Ntsd 2.6 Full Version 108 Tokyo Maquetas Rocket Regaetton}.md
deleted file mode 100644
index d25a6134ac54eba1c7555e4ab2afd1030ca76365..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download NEW! Ntsd 2.6 Full Version 108 Tokyo Maquetas Rocket Regaetton}.md	
+++ /dev/null
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Arnaldo Antunes - DiscografiaArnaldo Antunes - Discografia.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Arnaldo Antunes - DiscografiaArnaldo Antunes - Discografia.md
deleted file mode 100644
index 39d074df596433540c87fab0d60dd15e522e3046..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Arnaldo Antunes - DiscografiaArnaldo Antunes - Discografia.md	
+++ /dev/null
@@ -1,44 +0,0 @@
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-<h1>Arnaldo Antunes - Discografia</h1>
-<p>Arnaldo Antunes is a Brazilian writer and composer who has been active since the late 1970s. He is known for his eclectic musical style, blending rock, pop, samba, rap, and experimental sounds. He has also collaborated with many other artists, such as Marisa Monte, Carlinhos Brown, Nando Reis, and Caetano Veloso.</p>
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-<p>His discography consists of 16 solo albums, 10 albums with the rock band Titãs, 4 albums with the trio Tribalistas, and several other projects and compilations. Here is a list of his main albums and their release dates:</p>
-<ul>
-<li>Aguilar e Banda Performática (1979) - with Aguilar e Banda Performática</li>
-<li>Titãs (1984) - with Titãs</li>
-<li>Televisão (1985) - with Titãs</li>
-<li>Cabeça Dinossauro (1986) - with Titãs</li>
-<li>Jesus Não Tem Dentes no País dos Banguelas (1987) - with Titãs</li>
-<li>Õ Blésq Blom (1989) - with Titãs</li>
-<li>Tudo ao Mesmo Tempo Agora (1991) - with Titãs</li>
-<li>Nome (1993) - solo debut</li>
-<li>Titanomaquia (1993) - with Titãs</li>
-<li>Ninguém (1995) - solo</li>
-<li>Domingo (1995) - with Titãs</li>
-<li>O Silêncio (1996) - solo</li>
-<li>Um Sonho (1998) - solo</li>
-<li>Volume Dois (1998) - with Titãs</li>
-<li>O Corpo (2000) - solo</li>
-<li>As Canções de Eu Tu Eles (2000) - soundtrack for the movie Eu Tu Eles</li>
-<li>Paradeiro (2001) - solo</li>
-<li>Tribalistas (2002) - with Tribalistas</li>
-<li>Saiba (2004) - solo</li>
-<li>A Curva da Cintura (2005) - with Edgard Scandurra and Toumani Diabaté</li>
-<li>Qualquer (2006) - solo</li>
-<li>Ao Vivo no Estúdio (2007) - live album</li>
-<li>Iê Iê Iê (2009) - solo</li>
-<li>Ao Vivo Lá em Casa (2010) - live album and DVD</li>
-<li>Tribalistas ao Vivo (2012) - live album and DVD with Tribalistas</li>
-<li>Disco (2012) - solo</li>
-<li>Já É (2015) - solo</li>
-<li>Nheengatu ao Vivo (2015) - live album and DVD with Titãs</li>
-<li>RSTUVXZ (2018) - solo</li>
-<li>Tribalistas 2017/2018 Tour (2019) - live album and DVD with Tribalistas</li>
-</ul>
-
-<p>If you want to know more about Arnaldo Antunes and his discography, you can visit his official website[^1^] or his page on Discogs. He is also active on social media platforms such as Instagram, Facebook, Twitter, and YouTube.</p><p>Arnaldo Antunes is not only a musician, but also a poet, visual artist, and performer. He has published several books of poetry, such as Psia (1986), Tudos (1990), As Coisas (1992), 2 ou + Corpos no Mesmo Espaço (1997), and Palavra Desordem (2012). He has also exhibited his artworks in galleries and museums, such as the São Paulo Museum of Modern Art, the Pinacoteca do Estado de São Paulo, and the Centro Cultural Banco do Brasil. His works explore the relationship between language, image, sound, and space.</p>
-<p>He has received many awards and honors for his artistic achievements, such as the Prêmio Jabuti for literature in 1993 and 2013, the Prêmio Multishow for music in 2007 and 2013, the Prêmio da Música Brasileira in 2013 and 2016, and the Latin Grammy Award for Best Portuguese Language Contemporary Pop Album in 2019. He has also been nominated for several other awards, such as the MTV Video Music Brasil, the Troféu Imprensa, and the APCA Award.</p>
-<p>Arnaldo Antunes is one of the most influential and versatile artists in Brazil. He has contributed to the development and innovation of Brazilian culture and art. He continues to create new works and collaborate with other artists from different fields and backgrounds. He is a true icon of Brazilian music and poetry.</p>
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Auto Clicker Murgee 1.9 Crack.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Auto Clicker Murgee 1.9 Crack.md
deleted file mode 100644
index a1e369a5a2b283066f8dc17b017f435203c123ac..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Auto Clicker Murgee 1.9 Crack.md	
+++ /dev/null
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diff --git a/spaces/sxunwashere/rvc-voice/README.md b/spaces/sxunwashere/rvc-voice/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/sxunwashere/rvc-voice/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/taesiri/ChatGPT-ImageCaptioner/demo.py b/spaces/taesiri/ChatGPT-ImageCaptioner/demo.py
deleted file mode 100644
index 183f6c44c4ca8cdf32d1c4b57bd75a11a07dcbde..0000000000000000000000000000000000000000
--- a/spaces/taesiri/ChatGPT-ImageCaptioner/demo.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import glob
-import multiprocessing as mp
-import numpy as np
-import os
-import tempfile
-import time
-import warnings
-import cv2
-import tqdm
-import sys
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.utils.logger import setup_logger
-
-sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/')
-sys.path.insert(0, 'third_party/CenterNet2/')
-
-from centernet.config import add_centernet_config
-from detic.config import add_detic_config
-
-from detic.predictor import VisualizationDemo
-
-
-# constants
-WINDOW_NAME = "Detic"
-
-def setup_cfg(args):
-    cfg = get_cfg()
-    add_centernet_config(cfg)
-    add_detic_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    # Set score_threshold for builtin models
-    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
-    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
-    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
-    cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'rand' # load later
-    if not args.pred_all_class:
-        cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = True
-    cfg.freeze()
-    return cfg
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
-    parser.add_argument(
-        "--config-file",
-        default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml",
-        metavar="FILE",
-        help="path to config file",
-    )
-    parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
-    parser.add_argument("--video-input", help="Path to video file.")
-    parser.add_argument(
-        "--input",
-        nargs="+",
-        help="A list of space separated input images; "
-        "or a single glob pattern such as 'directory/*.jpg'",
-    )
-    parser.add_argument(
-        "--output",
-        help="A file or directory to save output visualizations. "
-        "If not given, will show output in an OpenCV window.",
-    )
-    parser.add_argument(
-        "--vocabulary",
-        default="lvis",
-        choices=['lvis', 'openimages', 'objects365', 'coco', 'custom'],
-        help="",
-    )
-    parser.add_argument(
-        "--custom_vocabulary",
-        default="",
-        help="",
-    )
-    parser.add_argument("--pred_all_class", action='store_true')
-    parser.add_argument(
-        "--confidence-threshold",
-        type=float,
-        default=0.5,
-        help="Minimum score for instance predictions to be shown",
-    )
-    parser.add_argument(
-        "--opts",
-        help="Modify config options using the command-line 'KEY VALUE' pairs",
-        default=[],
-        nargs=argparse.REMAINDER,
-    )
-    return parser
-
-
-def test_opencv_video_format(codec, file_ext):
-    with tempfile.TemporaryDirectory(prefix="video_format_test") as dir:
-        filename = os.path.join(dir, "test_file" + file_ext)
-        writer = cv2.VideoWriter(
-            filename=filename,
-            fourcc=cv2.VideoWriter_fourcc(*codec),
-            fps=float(30),
-            frameSize=(10, 10),
-            isColor=True,
-        )
-        [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)]
-        writer.release()
-        if os.path.isfile(filename):
-            return True
-        return False
-
-
-if __name__ == "__main__":
-    mp.set_start_method("spawn", force=True)
-    args = get_parser().parse_args()
-    setup_logger(name="fvcore")
-    logger = setup_logger()
-    logger.info("Arguments: " + str(args))
-
-    cfg = setup_cfg(args)
-
-    demo = VisualizationDemo(cfg, args)
-
-    if args.input:
-        if len(args.input) == 1:
-            args.input = glob.glob(os.path.expanduser(args.input[0]))
-            assert args.input, "The input path(s) was not found"
-        for path in tqdm.tqdm(args.input, disable=not args.output):
-            img = read_image(path, format="BGR")
-            start_time = time.time()
-            predictions, visualized_output = demo.run_on_image(img)
-            logger.info(
-                "{}: {} in {:.2f}s".format(
-                    path,
-                    "detected {} instances".format(len(predictions["instances"]))
-                    if "instances" in predictions
-                    else "finished",
-                    time.time() - start_time,
-                )
-            )
-
-            if args.output:
-                if os.path.isdir(args.output):
-                    assert os.path.isdir(args.output), args.output
-                    out_filename = os.path.join(args.output, os.path.basename(path))
-                else:
-                    assert len(args.input) == 1, "Please specify a directory with args.output"
-                    out_filename = args.output
-                visualized_output.save(out_filename)
-            else:
-                cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
-                cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
-                if cv2.waitKey(0) == 27:
-                    break  # esc to quit
-    elif args.webcam:
-        assert args.input is None, "Cannot have both --input and --webcam!"
-        assert args.output is None, "output not yet supported with --webcam!"
-        cam = cv2.VideoCapture(0)
-        for vis in tqdm.tqdm(demo.run_on_video(cam)):
-            cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
-            cv2.imshow(WINDOW_NAME, vis)
-            if cv2.waitKey(1) == 27:
-                break  # esc to quit
-        cam.release()
-        cv2.destroyAllWindows()
-    elif args.video_input:
-        video = cv2.VideoCapture(args.video_input)
-        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        frames_per_second = video.get(cv2.CAP_PROP_FPS)
-        num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-        basename = os.path.basename(args.video_input)
-        codec, file_ext = (
-            ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
-        )
-        if codec == ".mp4v":
-            warnings.warn("x264 codec not available, switching to mp4v")
-        if args.output:
-            if os.path.isdir(args.output):
-                output_fname = os.path.join(args.output, basename)
-                output_fname = os.path.splitext(output_fname)[0] + file_ext
-            else:
-                output_fname = args.output
-            assert not os.path.isfile(output_fname), output_fname
-            output_file = cv2.VideoWriter(
-                filename=output_fname,
-                # some installation of opencv may not support x264 (due to its license),
-                # you can try other format (e.g. MPEG)
-                fourcc=cv2.VideoWriter_fourcc(*codec),
-                fps=float(frames_per_second),
-                frameSize=(width, height),
-                isColor=True,
-            )
-        assert os.path.isfile(args.video_input)
-        for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
-            if args.output:
-                output_file.write(vis_frame)
-            else:
-                cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
-                cv2.imshow(basename, vis_frame)
-                if cv2.waitKey(1) == 27:
-                    break  # esc to quit
-        video.release()
-        if args.output:
-            output_file.release()
-        else:
-            cv2.destroyAllWindows()
diff --git a/spaces/tdeshane/artists-of-data-science-chainlit/Dockerfile b/spaces/tdeshane/artists-of-data-science-chainlit/Dockerfile
deleted file mode 100644
index 67bc06e9dc2c3b1262e35d4fcba3187c9893b9d0..0000000000000000000000000000000000000000
--- a/spaces/tdeshane/artists-of-data-science-chainlit/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM python:3.9
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-WORKDIR $HOME/notebooks
-COPY --chown=user . $HOME/app
-COPY ./requirements.txt $HOME/app/requirements.txt
-RUN pip install -r $HOME/app/requirements.txt
-COPY . .
-CMD ["chainlit", "run", "notebooks/app.py", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Chirita In Provincie.pdf.md b/spaces/terfces0erbo/CollegeProjectV2/Chirita In Provincie.pdf.md
deleted file mode 100644
index 2b7f951afb84d40c7e9fd9cf6adf80d6ffcbc99c..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Chirita In Provincie.pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Chirita In Provincie.pdf</h2><br /><p><b><b>Download Zip</b> &#9881; <a href="https://bytlly.com/2uGjF4">https://bytlly.com/2uGjF4</a></b></p><br /><br />
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-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Faronics Anti-executable Standard 5.3 Full VERIFIED Serial Number.md b/spaces/terfces0erbo/CollegeProjectV2/Faronics Anti-executable Standard 5.3 Full VERIFIED Serial Number.md
deleted file mode 100644
index b94797ecea2019a628aac8ffcbdb8a20b0c9a3a5..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Faronics Anti-executable Standard 5.3 Full VERIFIED Serial Number.md	
+++ /dev/null
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diff --git a/spaces/tfwang/PITI-Synthesis/glide_text2im/train_util.py b/spaces/tfwang/PITI-Synthesis/glide_text2im/train_util.py
deleted file mode 100644
index 2b101fd029fd55cbea410c1cd97c67a0a30ce9d6..0000000000000000000000000000000000000000
--- a/spaces/tfwang/PITI-Synthesis/glide_text2im/train_util.py
+++ /dev/null
@@ -1,475 +0,0 @@
-import copy
-import functools
-import os
-
-import blobfile as bf
-import numpy as np
-import torch as th
-import torch.distributed as dist
-from torch.nn.parallel.distributed import DistributedDataParallel as DDP
-from torch.optim import AdamW
-from .glide_util import sample
-from . import  logger
-from .fp16_util import (
-    make_master_params,
-    master_params_to_model_params,
-    model_grads_to_master_grads,
-    unflatten_master_params,
-    zero_grad,
-)
-from .nn import update_ema
-from .vgg import VGG
-from .adv import AdversarialLoss
-from .resample import LossAwareSampler, UniformSampler
-import glob
-import torchvision.utils as tvu
-import PIL.Image as Image
-# For ImageNet experiments, this was a good default value.
-# We found that the lg_loss_scale quickly climbed to
-# 20-21 within the first ~1K steps of training.
-INITIAL_LOG_LOSS_SCALE = 20.0
- 
- 
-
-class TrainLoop:
-    def __init__(
-        self,
-        model,
-        glide_options,
-        diffusion,
-        data,
-        val_data,
-        batch_size,
-        microbatch,
-        lr,
-        ema_rate,
-        log_interval,
-        save_interval,
-        resume_checkpoint,
-        use_fp16=False,
-        fp16_scale_growth=1e-3,
-        schedule_sampler=None,
-        weight_decay=0.0,
-        lr_anneal_steps=0,
-        finetune_decoder = False,
-        mode = '',
-        use_vgg = False,
-        use_gan = False,
-        uncond_p = 0,
-        super_res = 0,
-    ):
-        self.model = model
-        self.glide_options=glide_options
-        self.diffusion = diffusion
-        self.data = data
-        self.val_data=val_data
-        self.batch_size = batch_size
-        self.microbatch = microbatch if microbatch > 0 else batch_size
-        self.lr = lr
-        self.ema_rate = (
-            [ema_rate]
-            if isinstance(ema_rate, float)
-            else [float(x) for x in ema_rate.split(",")]
-        )
-        self.log_interval = log_interval
-        self.save_interval = save_interval
-        self.resume_checkpoint = find_resume_checkpoint(resume_checkpoint)
-        self.use_fp16 = use_fp16
-        self.fp16_scale_growth = fp16_scale_growth
-        self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
-        self.weight_decay = weight_decay
-        self.lr_anneal_steps = lr_anneal_steps
-        self.step = 0
-        self.resume_step = 0
-        self.global_batch = self.batch_size * dist.get_world_size()
-
-        if use_vgg:
-            self.vgg = VGG(conv_index='22').cuda()
-            print('use perc')
-        else:
-            self.vgg = None
-
-        if use_gan:
-            self.adv = AdversarialLoss()
-            print('use adv')
-        else:
-            self.adv = None
-
-        self.super_res = super_res
-         
-        self.uncond_p =uncond_p
-        self.mode = mode
-
-        self.finetune_decoder = finetune_decoder
-        if finetune_decoder:
-            self.optimize_model = self.model
-        else:          
-            self.optimize_model = self.model.encoder
-         
-        self.model_params = list(self.optimize_model.parameters())
-        self.master_params = self.model_params
-        self.lg_loss_scale = INITIAL_LOG_LOSS_SCALE
-        self.sync_cuda = th.cuda.is_available()
-        self._load_and_sync_parameters()
-        if self.use_fp16:
-            self._setup_fp16()
-
-        self.opt = AdamW(self.master_params, lr=self.lr, weight_decay=self.weight_decay)
-        if self.resume_step:
-            self._load_optimizer_state()
-            # Model was resumed, either due to a restart or a checkpoint
-            # being specified at the command line.
-            self.ema_params = [
-                self._load_ema_parameters(rate) for rate in self.ema_rate
-            ]
-        else:
-            self.ema_params = [
-                copy.deepcopy(self.master_params) for _ in range(len(self.ema_rate))
-            ]
-
-        if th.cuda.is_available():
-            self.use_ddp = True
-            self.ddp_model = DDP(
-                self.model,
-                device_ids=[torch.device('cuda')],
-                output_device=torch.device('cuda'),
-                broadcast_buffers=False,
-                bucket_cap_mb=128,
-                find_unused_parameters=False,
-            )
-        else:
-            if dist.get_world_size() > 1:
-                logger.warn(
-                    "Distributed training requires CUDA. "
-                    "Gradients will not be synchronized properly!"
-                )
-            self.use_ddp = False
-            self.ddp_model = self.model
-
-    def _load_and_sync_parameters(self):
-        resume_checkpoint = self.resume_checkpoint
-
-        if resume_checkpoint:
-            self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
-            if dist.get_rank() == 0:
-                logger.log(f"loading model from checkpoint: {resume_checkpoint}...")
-                self.model.load_state_dict(th.load(resume_checkpoint, map_location="cpu"),strict=False)
-
-        #dist_util.sync_params(self.model.parameters())
-
-    def _load_ema_parameters(self, rate):
-        ema_params = copy.deepcopy(self.master_params)
-
-        main_checkpoint = self.resume_checkpoint
-        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
-        if ema_checkpoint:
-            if dist.get_rank() == 0:
-                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
-                state_dict = th.load(ema_checkpoint, map_location=torch.device('cuda'))
-                ema_params = self._state_dict_to_master_params(state_dict)
-
-        #dist_util.sync_params(ema_params)
-        return ema_params
-
-    def _load_optimizer_state(self):
-        main_checkpoint = self.resume_checkpoint
-        opt_checkpoint = bf.join(
-            bf.dirname(main_checkpoint), f"opt{self.resume_step:06}.pt"
-        )
-        if bf.exists(opt_checkpoint):
-            logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
-            state_dict = th.load(opt_checkpoint, map_location="cpu")
-            try:
-                self.opt.load_state_dict(state_dict)
-            except:
-                pass
-
-    def _setup_fp16(self):
-        self.master_params = make_master_params(self.model_params)
-        self.model.convert_to_fp16()
-
-    def run_loop(self):
-        while (
-            not self.lr_anneal_steps
-            or self.step   <= self.lr_anneal_steps
-        ):
-
-            batch, model_kwargs = next(self.data)
-
-            # uncond_p = 0
-            # if self.super_res:
-            #     uncond_p = 0
-            # elif   self.finetune_decoder:
-            #     uncond_p = self.uncond_p
-            # elif  self.step > self.lr_anneal_steps - 40000:
-            #     uncond_p = self.uncond_p
-
-            self.run_step(batch, model_kwargs)
-            if self.step % self.log_interval == 0:
-                logger.dumpkvs()
-            if self.step % self.save_interval == 0:
-                self.save()
-                self.val(self.step)
-            self.step += 1
-         
-        if (self.step - 1) % self.save_interval != 0:
-            self.save()
-
-
-    def run_step(self, batch, model_kwargs):
-        self.forward_backward(batch, model_kwargs)
-        if self.use_fp16:
-            self.optimize_fp16()
-        else:
-            self.optimize_normal()
-        self.log_step()
-
-    def forward_backward(self, batch, model_kwargs):
-        zero_grad(self.model_params)
-        for i in range(0, batch.shape[0], self.microbatch):
-            micro = batch[i : i + self.microbatch].cuda()
-            micro_cond={n:model_kwargs[n][i:i+self.microbatch].cuda() for n in model_kwargs if n  in ['ref', 'low_res']}
-            last_batch = (i + self.microbatch) >= batch.shape[0]
-            t, weights = self.schedule_sampler.sample(micro.shape[0], torch.device('cuda'))
-             
-            if self.step <100:
-                vgg_loss = None
-                adv_loss = None
-            else:
-                vgg_loss = self.vgg
-                adv_loss = self.adv
-            compute_losses = functools.partial(
-                self.diffusion.training_losses,
-                self.ddp_model,
-                micro,
-                t,
-                vgg_loss,
-                adv_loss,
-                model_kwargs=micro_cond,
-            )
-            
-            if last_batch or not self.use_ddp:
-                losses = compute_losses()
-            else:
-                with self.ddp_model.no_sync():
-                    losses = compute_losses()
-           
-            if isinstance(self.schedule_sampler, LossAwareSampler):
-                self.schedule_sampler.update_with_local_losses(
-                    t, losses["loss"].detach()
-                )
-            
-            loss = (losses["loss"] * weights).mean()
-            log_loss_dict(
-                self.diffusion, t, {k: v * weights for k, v in losses.items()}
-            )
-            if self.use_fp16:
-                loss_scale = 2 ** self.lg_loss_scale
-                (loss * loss_scale).backward()
-            else:
-                loss.backward()
-
-    def val(self, step):
-        inner_model=self.ddp_model.module
-        inner_model.eval()
-        if dist.get_rank() == 0:
-            print("sampling...")   
-
-        s_path = os.path.join(logger.get_dir(), 'results')
-        os.makedirs(s_path,exist_ok=True)
-        img_id = 0
-        guidance_scale=self.glide_options['sample_c']
-       
-
-        while (True):
-            if img_id >= self.glide_options['num_samples']:
-                break
-
-            batch, model_kwargs = next(self.val_data)              
-            with th.no_grad():
-                samples=sample(
-                    glide_model=inner_model,
-                    glide_options=self.glide_options,
-                    side_x=self.glide_options['image_size'],
-                    side_y=self.glide_options['image_size'],
-                    prompt=model_kwargs,
-                    batch_size=self.glide_options['batch_size']//2,
-                    guidance_scale=guidance_scale,
-                    device=torch.device('cuda'),
-                    prediction_respacing=self.glide_options['sample_respacing'],
-                    upsample_enabled=self.glide_options['super_res'],
-                    upsample_temp=0.997,
-                    mode = self.mode,
-                )
-                
-                samples = samples.cpu()
-     
-                ref = model_kwargs['ref_ori']
-                # LR = model_kwargs['low_res'].cpu()
-
-                for i in range(samples.size(0)):
-                    out_path = os.path.join(s_path, f"{dist.get_rank()}_{img_id}_step{step}_{guidance_scale}_output.png")
-                    tvu.save_image(
-                        (samples[i]+1)*0.5, out_path)
-
-                    out_path = os.path.join(s_path, f"{dist.get_rank()}_{img_id}_step{step}_{guidance_scale}_gt.png")
-                    tvu.save_image(
-                        (batch[i]+1)*0.5, out_path)
-
-                    out_path = os.path.join(s_path, f"{dist.get_rank()}_{img_id}_step{step}_{guidance_scale}_ref.png")
-                    tvu.save_image(
-                        (ref[i]+1)*0.5, out_path)
-
-                    # out_path = os.path.join(s_path, f"{dist.get_rank()}_{img_id}_step{step}_{guidance_scale}_lr.png")
-                    # tvu.save_image(
-                    #     (LR[i]+1)*0.5, out_path)
-
-                    img_id += 1     
-        inner_model.train() 
-
-
-    def optimize_fp16(self):
-        if any(not th.isfinite(p.grad).all() for p in self.model_params):
-            self.lg_loss_scale -= 1
-            logger.log(f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
-            return
-
-        model_grads_to_master_grads(self.model_params, self.master_params)
-        self.master_params[0].grad.mul_(1.0 / (2 ** self.lg_loss_scale))
-        self._log_grad_norm()
-        self._anneal_lr()
-        self.opt.step()
-        for rate, params in zip(self.ema_rate, self.ema_params):
-            update_ema(params, self.master_params, rate=rate)
-        master_params_to_model_params(self.model_params, self.master_params)
-        self.lg_loss_scale += self.fp16_scale_growth
-
-    def optimize_normal(self):
-        self._log_grad_norm()
-        self._anneal_lr()
-        self.opt.step()
-        for rate, params in zip(self.ema_rate, self.ema_params):
-            update_ema(params, self.master_params, rate=rate)
-
-    def _log_grad_norm(self):
-        sqsum = 0.0
-        for p in self.master_params:
-            sqsum += (p.grad ** 2).sum().item()
-        logger.logkv_mean("grad_norm", np.sqrt(sqsum))
-
-    def _anneal_lr(self):
-        return
-   
-
-    def log_step(self):
-        logger.logkv("step", self.step + self.resume_step)
-        logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)
-        if self.use_fp16:
-            logger.logkv("lg_loss_scale", self.lg_loss_scale)
-
-    def save(self):
-        def save_checkpoint(rate, params):
-            state_dict = self._master_params_to_state_dict(params)
-            if dist.get_rank() == 0:
-                logger.log(f"saving model {rate}...")
-                if not rate:
-                    filename = f"model{(self.step+self.resume_step):06d}.pt"
-                else:
-                    filename = f"ema_{rate}_{(self.step+self.resume_step):06d}.pt"
-                with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
-                    th.save(state_dict, f)
-
-        save_checkpoint(0, self.master_params)
-        for rate, params in zip(self.ema_rate, self.ema_params):
-            save_checkpoint(rate, params)
-
-        if dist.get_rank() == 0:
-            with bf.BlobFile(
-                bf.join(get_blob_logdir(), f"opt{(self.step+self.resume_step):06d}.pt"),
-                "wb",
-            ) as f:
-                th.save(self.opt.state_dict(), f)
-
-        dist.barrier()
-
-    def _master_params_to_state_dict(self, master_params):
-        if self.use_fp16:
-            master_params = unflatten_master_params(
-                list(self.optimize_model.parameters()), master_params
-            )
-        state_dict = self.optimize_model.state_dict()
-        for i, (name, _value) in enumerate(self.optimize_model.named_parameters()):
-            assert name in state_dict
-            state_dict[name] = master_params[i]
-        return state_dict
-
-    def _state_dict_to_master_params(self, state_dict):
-        params = [state_dict[name] for name, _ in self.optimize_model.named_parameters()]
-        if self.use_fp16:
-            return make_master_params(params)
-        else:
-            return params
-
-
-def parse_resume_step_from_filename(filename):
-    """
-    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
-    checkpoint's number of steps.
-    """
-    filename=filename.split('/')[-1]
-    assert(filename.endswith(".pt"))
-    filename=filename[:-3]
-    if filename.startswith("model"):
-        split = filename[5:]
-    elif filename.startswith("ema"):
-        split = filename.split("_")[-1]
-    else:
-        return 0
-    try:
-        return int(split)
-    except ValueError:
-        return 0
-
-
-def get_blob_logdir():
-    p=os.path.join(logger.get_dir(),"checkpoints")
-    os.makedirs(p,exist_ok=True)
-    return p
-
-def find_resume_checkpoint(resume_checkpoint):
-    # On your infrastructure, you may want to override this to automatically
-    # discover the latest checkpoint on your blob storage, etc.
-    if not resume_checkpoint:
-        return None
-    if "ROOT" in resume_checkpoint:
-        maybe_root=os.environ.get("AMLT_MAP_INPUT_DIR")
-        maybe_root="OUTPUT/log" if not maybe_root else maybe_root
-        root=os.path.join(maybe_root,"checkpoints")
-        resume_checkpoint=resume_checkpoint.replace("ROOT",root)
-    if "LATEST" in resume_checkpoint:
-        files=glob.glob(resume_checkpoint.replace("LATEST","*.pt"))
-        if not files:
-            return None
-        return max(files,key=parse_resume_step_from_filename)
-    return resume_checkpoint
-
-
-
-def find_ema_checkpoint(main_checkpoint, step, rate):
-    if main_checkpoint is None:
-        return None
-    filename = f"ema_{rate}_{(step):06d}.pt"
-    path = bf.join(bf.dirname(main_checkpoint), filename)
-    if bf.exists(path):
-        return path
-    return None
-
-
-def log_loss_dict(diffusion, ts, losses):
-    for key, values in losses.items():
-        logger.logkv_mean(key, values.mean().item())
-        # Log the quantiles (four quartiles, in particular).
-        for sub_t, sub_loss in zip(ts.cpu().numpy(), values.detach().cpu().numpy()):
-            quartile = int(4 * sub_t / diffusion.num_timesteps)
-            logger.logkv_mean(f"{key}_q{quartile}", sub_loss)
-
diff --git a/spaces/theaster/imoitari/README.md b/spaces/theaster/imoitari/README.md
deleted file mode 100644
index d23015aac4d15a255932f848e3bacd8152240130..0000000000000000000000000000000000000000
--- a/spaces/theaster/imoitari/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: imoitari
-emoji: 🏆
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: username84/g
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/thiagolira/ChatPequenoPrincipe/app.py b/spaces/thiagolira/ChatPequenoPrincipe/app.py
deleted file mode 100644
index 913b998a65c948c1aeb357b1098264130fe3cd12..0000000000000000000000000000000000000000
--- a/spaces/thiagolira/ChatPequenoPrincipe/app.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import os
-from typing import Optional, Tuple
-
-import gradio as gr
-from query_data import get_chain
-from threading import Lock
-from langchain.vectorstores.faiss import FAISS
-from langchain.embeddings import OpenAIEmbeddings
-
-embeddings = OpenAIEmbeddings()
-vectorstore = FAISS.load_local('.', embeddings)
-
-# init  chain
-chain = get_chain(vectorstore)
-
-class ChatWrapper:
-
-    def __init__(self):
-        self.lock = Lock()
-    def __call__(
-        self, inp: str, history: Optional[Tuple[str, str]]
-    ):
-        """Execute the chat functionality."""
-        self.lock.acquire()
-        try:
-            history = history or []
-            # Set OpenAI key
-            import openai
-            openai.api_key = os.environ['OPENAI_API_KEY']
-            # Run chain and append input.
-            output = chain({"question": inp, "chat_history": history})["answer"]
-            history.append((inp, output))
-        except Exception as e:
-            raise e
-        finally:
-            self.lock.release()
-        return history, history
-
-chat = ChatWrapper()
-
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-
-with block:
-
-    markdown = '''
-    <h3><center>Chat with The Little Prince!</center></h3>
-    <h4><center>With this app you can ask questions directly to the book The Little Prince.</center></h4>
-    <h4><center>This app is powered by <a href="https://openai.com/blog/openai-api/">OpenAI embeddings and completion models</a>. And <a href="https://github.com/hwchase17/">LangChain🦜️🔗.</a></center></h4>
-    '''
-    gr.Markdown(markdown)
-
-    chatbot = gr.Chatbot()
-
-    with gr.Row():
-        message = gr.Textbox(
-            label="What's your question?",
-            placeholder="Ask questions about The Little Prince",
-            lines=1,
-        )
-        submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
-
-    gr.Examples(
-        examples=[
-            "How can you befriend the little fox?"
-        ],
-        inputs=message,
-    )
-
-    gr.HTML(
-        "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
-    )
-
-    state = gr.State()
-
-    submit.click(chat, inputs=[message, state], outputs=[chatbot, state])
-    message.submit(chat, inputs=[message, state], outputs=[chatbot, state])
-
-
-block.launch(debug=True)
diff --git a/spaces/threadxl/bingo/README.md b/spaces/threadxl/bingo/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/threadxl/bingo/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Assassins creed iv black flag patch 1.07 Whats new and how to download it.md b/spaces/tialenAdioni/chat-gpt-api/logs/Assassins creed iv black flag patch 1.07 Whats new and how to download it.md
deleted file mode 100644
index 0df9bb42be3ae1ff6084f2ccb01a470d246e4ac2..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Assassins creed iv black flag patch 1.07 Whats new and how to download it.md	
+++ /dev/null
@@ -1,93 +0,0 @@
-
-<h1>Assassins Creed IV Black Flag Patch 1.07: What's New and How to Download</h1>
-
-<p>Assassins Creed IV Black Flag is one of the most popular action-adventure games set in the Caribbean during the Golden Age of Piracy. The game features an open world that players can explore as Edward Kenway, a pirate and assassin who gets involved in a conflict between the Assassins and the Templars.</p>
-<h2>Assassins creed iv black flag patch 1.07</h2><br /><p><b><b>Download</b> &#10027; <a href="https://urlcod.com/2uKb1a">https://urlcod.com/2uKb1a</a></b></p><br /><br />
-
-<p>If you are a fan of this game, you might be interested in the latest update that Ubisoft has released for it. Assassins Creed IV Black Flag Patch 1.07 is a sizeable single-player update that weighs in at 2.3GB and fixes several elements of the campaign[^1^]. Here are some of the highlights of this patch and how you can download it for your PC or console.</p>
-
-<h2>What's New in Assassins Creed IV Black Flag Patch 1.07?</h2>
-
-<p>This patch addresses various issues and bugs that affected the gameplay, graphics, sound, user interface, and other aspects of the game. Some of the most notable changes are:</p>
-
-<ul>
-<li>Fixed the situation where the user could not kill or stun while both assassin and target were near a corner</li>
-<li>Fixed the situation where character freezed for a brief moment before getting killed by an assassin hidden in a haystack</li>
-<li>Fixed the situation where some zones could not be captured from an inclined terrain</li>
-<li>Fixed inconsistencies on the score gauge in Wolfpack mode</li>
-<li>Fixed various issues causing crashes and freezes</li>
-<li>Fixed issues pushing players into limited mode</li>
-<li>Fixed various issues on the HUD, menus, contract system, death cam, etc.</li>
-<li>Improved graphics and sound effects</li>
-</ul>
-
-<p>You can find the full patch notes on the Ubisoft forum[^1^]. This patch applies to the PC, PS3, PS4, Xbox 360 and Xbox One versions of the game.</p>
-
-<h2>How to Download Assassins Creed IV Black Flag Patch 1.07?</h2>
-
-<p>If you want to enjoy the improved performance and stability of Assassins Creed IV Black Flag, you need to download and install this patch on your device. The process is simple and depends on your platform:</p>
-
-<ul>
-<li>For PC users, you need to launch Uplay and log in to your account. The patch will be automatically downloaded and installed when you start the game.</li>
-<li>For PS3 and PS4 users, you need to connect your console to the internet and launch the game. The patch will be automatically downloaded and installed when you start the game.</li>
-<li>For Xbox 360 and Xbox One users, you need to connect your console to the internet and launch the game. The patch will be automatically downloaded and installed when you start the game.</li>
-</ul>
-
-<p>If you have any problems with downloading or installing this patch, you can contact Ubisoft support for assistance.</p>
-
-<h2>Conclusion</h2>
-
-<p>Assassins Creed IV Black Flag Patch 1.07 is a welcome update that enhances the gameplay experience and fixes various issues that players encountered. If you are a fan of this game, you should download this patch as soon as possible and enjoy the new features and improvements.</p>
-<p>How to download and install Assassins creed iv black flag patch 1.07<br />
-Assassins creed iv black flag patch 1.07 fixes and improvements<br />
-Assassins creed iv black flag patch 1.07 release date and notes<br />
-Assassins creed iv black flag patch 1.07 gameplay and performance<br />
-Assassins creed iv black flag patch 1.07 compatibility and requirements<br />
-Assassins creed iv black flag patch 1.07 review and rating<br />
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-Assassins creed iv black flag patch 1.07 developer and publisher<br />
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-Assassins creed iv black flag patch 1.07 remake and remaster possibilities<br />
-Assassins creed iv black flag patch 1.07 VR and AR support<br />
-How to play assassins creed IV Black Flag without Patching it to version 107?<br />
-What are the benefits of updating assassins Creed IV Black Flag to Patch version one point zero seven?<br />
-Where can I find the official changelog for assassins Creed IV Black Flag Patch one point zero seven?<br />
-How to fix assassins Creed IV Black Flag crashing after installing Patch one point zero seven?<br />
-How to enable assassins Creed IV Black Flag DLCs with Patch one point zero seven?<br />
-How to disable assassins Creed IV Black Flag auto-update feature for Patch one point zero seven?<br />
-How to revert assassins Creed IV Black Flag to an earlier version before Patch one point zero seven?<br />
-How to troubleshoot assassins Creed IV Black Flag Patch one point zero seven installation issues?<br />
-How to optimize assassins Creed IV Black Flag for better performance after Patch one point zero seven?</p>
-
-<p>If you want to learn more about Assassins Creed IV Black Flag, you can check out our other articles on this game or visit our website for more information.</p> e753bf7129<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Awm 2725 Vw-1 60 C 30v USB Cable Driver Why You Should Buy It Now.md b/spaces/tialenAdioni/chat-gpt-api/logs/Awm 2725 Vw-1 60 C 30v USB Cable Driver Why You Should Buy It Now.md
deleted file mode 100644
index 2afb31d38f6cdc3d6d0d67c32146ec8c3741c700..0000000000000000000000000000000000000000
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@@ -1,89 +0,0 @@
-
-<h1>Awm 2725 Vw-1 60 C 30v USB Cable Driver: What You Need to Know</h1>
-<p>If you have a USB device that requires a specific cable driver, you might have come across the term Awm 2725 Vw-1 60 C 30v USB Cable Driver. This is a type of USB cable that has certain specifications and features that make it compatible with certain devices. In this article, we will explain what Awm 2725 Vw-1 60 C 30v USB Cable Driver is, how to find and install it, and what are the benefits of using it. Let's get started!</p>
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-<h2>What is Awm 2725 Vw-1 60 C 30v USB Cable Driver?</h2>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver is a type of USB cable that has the following characteristics:</p>
-<ul>
-<li>It has a standard USB A male connector on one end and a micro USB connector on the other end.</li>
-<li>It has a high speed 2.0 cable that is compliant with USB 1.1, USB 2.0, and USB 3.0 standards.</li>
-<li>It has one 28awg twisted pair for data transmission and two power conductors for power supply.</li>
-<li>It has an overall shield that consists of an aluminized polyester shield with a drain wire and a tinned copper braid shield.</li>
-<li>It has a rating of Awm 2725 Vw-1 60 C 30v, which means that it can withstand a voltage of 30 volts and a temperature of 60 degrees Celsius.</li>
-</ul>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver is designed to work with certain devices that require this type of cable driver. Some examples of these devices are webcams, digital cameras, hard drives, printers, scanners, and more. You can check the compatibility of your device with this cable driver by looking at the label or the manual of your device.</p>
-<h2>How to Find and Install Awm 2725 Vw-1 60 C 30v USB Cable Driver?</h2>
-<p>If you need to use Awm 2725 Vw-1 60 C 30v USB Cable Driver for your device, you need to find and install it on your computer. Here are some steps on how to do that:</p>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver download<br />
-Awm 2725 Vw-1 60 C 30v USB Cable Driver installation<br />
-Awm 2725 Vw-1 60 C 30v USB Cable Driver compatibility<br />
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-Awm 2725 Vw-1 60 C 30v USB Cable Driver error<br />
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-Awm 2725 Vw-1 60 C 30v USB Cable Driver specifications<br />
-Awm 2725 Vw-1 60 C 30v USB Cable Driver price<br />
-Awm 2725 Vw-1 60 C 30v USB Cable Driver review<br />
-Awm 2725 Vw-1 60 C 30v USB Cable Driver replacement<br />
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-<ol>
-<li>First, you need to find a website that offers Awm 2725 Vw-1 60 C 30v USB Cable Driver for download. You can use Google or other search engines to look for such websites. Some examples of these websites are Tom's Hardware Forum, Ko-fi, Totek International, Amazon, and more.</li>
-<li>Second, you need to download the cable driver from the website. You can click on the download link or button and wait for the file to be saved on your computer.</li>
-<li>Third, you need to extract the file using a program like WinRAR or 7-Zip. You can right-click on the file and choose Extract Here or Extract to Folder.</li>
-<li>Fourth, you need to open the folder and double-click on the executable file to start the installation process. You can follow the instructions on the screen and agree to the terms and conditions.</li>
-<li>Fifth, you need to restart your computer after the installation is complete. This will ensure that the cable driver is properly installed and ready to use.</li>
-</ol>
-<h2>What are the Benefits of Using Awm 2725 Vw-1 60 C 30v USB Cable Driver?</h2>
-<p>Using Awm 2725 Vw-1 60 C 30v USB Cable Driver has some benefits that can enhance your experience with your device. Here are some of them:</p>
-<ul>
-<li>You can enjoy a fast and stable data transfer between your device and your computer. The high speed 2.0 cable and the twisted pair ensure that your data is transmitted without any interference or loss.</li>
-<li>You can provide enough power supply for your device without any risk of damage. The two power conductors and the overall shield protect your device from overvoltage and overheating.</li>
-<li>You can use your device with any operating system that supports USB standards. The cable driver is compatible with Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, Mac OS X, Linux, and more.</li>
-<li>You can use your device with any computer that has a standard USB port. The cable driver has a universal USB A male connector that can fit into any USB port.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver is a type of USB cable that has specific specifications and features that make it compatible with certain devices. You can find and install it on your computer by following some simple steps. You can also enjoy some benefits of using it, such as fast data transfer, safe power supply, wide compatibility, and easy connection. If you have a device that requires Awm 2725 Vw-1 60 C 30v USB Cable Driver, you should definitely try it out!</p>
-<h2>How to Get More Out of Awm 2725 Vw-1 60 C 30v USB Cable Driver</h2>
-<p>If you want to get more out of Awm 2725 Vw-1 60 C 30v USB Cable Driver and enhance your experience with your device, you can try some of these tips:</p>
-<ul>
-<li>You can adjust the settings of Awm 2725 Vw-1 60 C 30v USB Cable Driver to suit your preferences. You can change the resolution, the sound volume, the language, and the difficulty level. You can also enable or disable some features, such as the online mode, the hints, or the fullscreen mode.</li>
-<li>You can explore the different modes and options of Awm 2725 Vw-1 60 C 30v USB Cable Driver. Besides the normal mode, you can also try the arcade mode, where you have to use Awm 2725 Vw-1 60 C 30v USB Cable Driver as fast as possible. You can also try the bonus mode, where you have to use Awm 2725 Vw-1 60 C 30v USB Cable Driver with different devices and challenges.</li>
-<li>You can challenge yourself and try to achieve all the achievements and trophies of Awm 2725 Vw-1 60 C 30v USB Cable Driver. You can see your progress and statistics in the main menu. You can also compare your score and rank with other users in the online leaderboard.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver is a type of USB cable that has specific specifications and features that make it compatible with certain devices. You can find and install it on your computer by following some simple steps. You can also enjoy some benefits of using it, such as fast data transfer, safe power supply, wide compatibility, and easy connection. You can also share it with your friends, troubleshoot it if you have any problems, and get more out of it by trying some tips. If you have a device that requires Awm 2725 Vw-1 60 C 30v USB Cable Driver, you should definitely try it out!</p>
-<h2>How to Review Awm 2725 Vw-1 60 C 30v USB Cable Driver</h2>
-<p>If you have used Awm 2725 Vw-1 60 C 30v USB Cable Driver and want to share your opinion and feedback with other users, you can write a review of Awm 2725 Vw-1 60 C 30v USB Cable Driver. Here are some tips on how to write a good review:</p>
-<ul>
-<li>Be honest and objective. Don't exaggerate or lie about your experience with Awm 2725 Vw-1 60 C 30v USB Cable Driver. Tell the truth about what you liked and disliked about Awm 2725 Vw-1 60 C 30v USB Cable Driver.</li>
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-<li>Be helpful and informative. Don't just write a review for yourself. Write a review for other users who might be interested in Awm 2725 Vw-1 60 C 30v USB Cable Driver. Provide useful information and tips that can help them make a better decision.</li>
-<li>Be respectful and polite. Don't use offensive or abusive language in your review. Don't insult or attack other users or the developer of Awm 2725 Vw-1 60 C 30v USB Cable Driver. Be courteous and constructive in your criticism.</li>
-</ul>
-<p>Here is an example of a possible review of Awm 2725 Vw-1 60 C 30v USB Cable Driver:</p>
-<p>I have been using Awm 2725 Vw-1 60 C 30v USB Cable Driver for a few months now and I am very satisfied with it. It is a very reliable and durable cable that works perfectly with my webcam and printer. It has a high speed and stable data transfer that never fails or slows down. It also provides enough power supply for my devices without any risk of damage or overheating. It is compatible with my Windows 10 operating system and my USB port. It is easy to install and use, and it has some nice features, such as the online mode, the arcade mode, and the bonus mode. It also has some achievements and trophies that make it more fun and challenging. I have also shared it with some of my friends and they also love it. I would highly recommend Awm 2725 Vw-1 60 C 30v USB Cable Driver to anyone who needs a good USB cable for their devices.</p>
-<h2>How to Compare Awm 2725 Vw-1 60 C 30v USB Cable Driver with Other USB Cables</h2>
-<p>If you want to know how Awm 2725 Vw-1 60 C 30v USB Cable Driver stands out from other USB cables, you can compare Awm 2725 Vw-1 60 C 30v USB Cable Driver with other USB cables based on some criteria. Here are some of them:</p>
-<ul>
-<li>The type of connectors. Awm 2725 Vw-1 60 C 30v USB Cable Driver has a standard USB A male connector on one end and a micro USB connector on the other end. This makes it suitable for devices that have a micro USB port, such as webcams, digital cameras, hard drives, printers, scanners, and more. Other USB cables might have different types of connectors, such as mini USB, USB B, USB C, or lightning.</li>
-<li>The speed and quality of data transfer. Awm 2725 Vw-1 60 C 30v USB Cable Driver has a high speed 2.0 cable that is compliant with USB 1.1, USB 2.0, and USB 3.0 standards. This means that it can transfer data at up to 480 Mbps without any interference or loss. Other USB cables might have lower or higher speeds depending on their standards and specifications.</li>
-<li>The power supply and safety. Awm 2725 Vw-1 60 C 30v USB Cable Driver has two power conductors that can provide enough power supply for your device without any risk of damage or overheating. It also has an overall shield that protects your device from overvoltage and electromagnetic interference. Other USB cables might have different power ratings and shielding methods depending on their designs and materials.</li>
-<li>The compatibility and versatility. Awm 2725 Vw-1 60 C 30v USB Cable Driver is compatible with any operating system that supports USB standards, such as Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, Mac OS X, Linux, and more. It is also compatible with any computer that has a standard USB port. It is also versatile in terms of its modes and options, such as the online mode, the arcade mode, the bonus mode, the achievements and trophies, and more. Other USB cables might have different compatibility and versatility depending on their features and functions.</li>
-</ul>
-<p>By comparing Awm 2725 Vw-1 60 C 30v USB Cable Driver with other USB cables based on these criteria, you can see how Awm
-<h2>Conclusion</h2>
-<p>Awm 2725 Vw-1 60 C 30v USB Cable Driver is a type of USB cable that has specific specifications and features that make it compatible with certain devices. You can find and install it on your computer by following some simple steps. You can also enjoy some benefits of using it, such as fast data transfer, safe power supply, wide compatibility, and easy connection. You can also share it with your friends, troubleshoot it if you have any problems, get more out of it by trying some tips, and compare it with other USB cables based on some criteria. If you have a device that requires Awm 2725 Vw-1 60 C 30v USB Cable Driver, you should definitely try it out!</p> 679dcb208e<br />
-<br />
-<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Crazytalk Animator Combo Pack Power Tools Vol 4 Mediafire 339.md b/spaces/tialenAdioni/chat-gpt-api/logs/Crazytalk Animator Combo Pack Power Tools Vol 4 Mediafire 339.md
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+++ /dev/null
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-<br />
-<h1>Crazytalk Animator Combo Pack Power Tools Vol 4 Mediafire 339</h1>
-<p>Crazytalk Animator is a software that allows you to create 2D animations with ease. You can use pre-made characters, props, motions, and backgrounds, or create your own with the powerful tools and features. One of the most popular content packs for Crazytalk Animator is Power Tools Vol 4, which includes five packs of assets for different themes and genres.</p>
-<p>Power Tools Vol 4 contains the following packs:</p>
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-<ul>
-<li>Real Life Character Bodies - Action Figures: This pack contains six realistic human characters with different body shapes and outfits. You can mix and match their body parts and accessories to create your own custom characters.</li>
-<li>Historical Series - Titans of Time: This pack contains six historical characters from different eras, such as Julius Caesar, Cleopatra, Napoleon, and Genghis Khan. You can use them to recreate historical scenes or make your own stories.</li>
-<li>Cosmic Hero: This pack contains five comic-style characters with superpowers and costumes. You can use them to create action-packed scenes or humorous situations.</li>
-<li>Transportation Series - Modern Vehicles Vol 1: This pack contains six modern vehicles, such as a car, a motorcycle, a helicopter, and a jet. You can use them to add realism and movement to your scenes.</li>
-<li>Fighting Motion Library Vol 3: This pack contains over 100 fighting motions for your characters, such as punches, kicks, blocks, and combos. You can use them to create dynamic and exciting fight scenes.</li>
-</ul>
-<p>Power Tools Vol 4 is a great addition to your Crazytalk Animator library, as it gives you more options and possibilities for your animations. You can download it from Mediafire for free using this link: <a href="https://www.mediafire.com/file/339/Crazytalk_Animator_Combo_Pack_Power_Tools_Vol_4.zip/file">https://www.mediafire.com/file/339/Crazytalk_Animator_Combo_Pack_Power_Tools_Vol_4.zip/file</a></p>
-<p>Enjoy creating amazing animations with Crazytalk Animator and Power Tools Vol 4!</p><p>If you want to see some examples of animations made with Power Tools Vol 4, you can check out some of these links:</p>
-<p></p>
-<ul>
-<li><a href="https://www.youtube.com/watch?v=uKri7tGDdUM">CrazyTalk Animator Power Tools Vol.4</a>: This is a video that showcases the features and assets of Power Tools Vol 4, with some sample animations and scenes.</li>
-<li><a href="https://www.youtube.com/watch?v=vPngvyBzeiA">Cartoon Animator 4 - Content Pack: Power Tools Vol.2 - Campus Hero Bundle</a>: This is a video that showcases another content pack for Cartoon Animator 4 (the successor of CrazyTalk Animator), which is similar to Power Tools Vol 4 in terms of style and functionality.</li>
-<li><a href="https://www.reallusion.com/ContentStore/promo/CTA2_G2PowerTools_V4/default.aspx">CrazyTalk Animator 2 Content - G2 Power Tools 4 - Buddy's World</a>: This is a webpage that introduces another content pack for CrazyTalk Animator 2, which is also similar to Power Tools Vol 4 in terms of style and functionality.</li>
-</ul>
-<p>These examples will give you an idea of what you can do with Power Tools Vol 4, and how you can use it to create your own animations. You can also find more examples and tutorials on the official website of Reallusion, the developer of CrazyTalk Animator and Cartoon Animator.</p> 7196e7f11a<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Descargar enciclopedia universal micronet 2012 18 en espaol y con licencia.md b/spaces/tialenAdioni/chat-gpt-api/logs/Descargar enciclopedia universal micronet 2012 18 en espaol y con licencia.md
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-
-<h1>How to Download and Install Azurewave Aw-nb041h Driver for Windows 10</h1>
-<p>If you are looking for a wireless network adapter that can provide fast and stable internet connection, you may want to consider the Azurewave Aw-nb041h. This device is compatible with Windows 10 and supports 802.11n Wi-Fi standards. However, to use it properly, you need to install the correct driver for your system.</p>
-<p>A driver is a software that allows your computer to communicate with your hardware devices. Without a driver, your device may not work properly or at all. Therefore, it is important to keep your drivers updated and avoid any compatibility issues.</p>
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-<p>In this article, we will show you how to download and install the Azurewave Aw-nb041h driver for Windows 10 in a few simple steps. Follow the instructions below and enjoy your wireless internet connection.</p>
-<h2>Step 1: Download the Azurewave Aw-nb041h Driver from the Official Website</h2>
-<p>The best way to get the latest and most compatible driver for your device is to download it from the official website of the manufacturer. In this case, you can visit the Azurewave website and find the driver for your model.</p>
-<p>Here are the steps to download the driver:</p>
-<ul>
-<li>Go to <a href="http://www.azurewave.com/product_Wireless%20LAN_1.asp">http://www.azurewave.com/product_Wireless%20LAN_1.asp</a> and scroll down to find the Aw-nb041h model.</li>
-<li>Click on the "Download" button next to the model name and select your operating system (Windows 10).</li>
-<li>Save the file to your computer and remember its location.</li>
-</ul>
-<h2>Step 2: Install the Azurewave Aw-nb041h Driver on Your Computer</h2>
-<p>After downloading the driver file, you need to install it on your computer. This will update your device driver and enable it to work properly.</p>
-<p>Here are the steps to install the driver:</p>
-<ul>
-<li>Locate the downloaded file and double-click on it to run it.</li>
-<li>Follow the on-screen instructions and agree to the terms and conditions.</li>
-<li>Wait for the installation process to complete and restart your computer if prompted.</li>
-</ul>
-<h2>Step 3: Check if Your Device is Working Properly</h2>
-<p>Once you have installed the driver, you can check if your device is working properly by connecting to a wireless network. You can also use the Device Manager to verify if your driver is updated and functioning.</p>
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-Azurewave Aw-nb041h Bluetooth Driver for Windows 10<br />
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-How to Download the Latest Drivers and Manuals for Your AzureWave Device from the Official Website[^1^]<br />
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-What is the Difference Between AzureWave AW-NB047H and AW-NB041H Drivers?<br />
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-What are the Benefits of Using AW-NB041H Drivers over Generic or Third-party Drivers?<br />
-What are the Common Problems and Errors that Users Encounter when Using AW-NB041H Drivers?<br />
-What are the Best Practices and Tips for Using AW-NB041H Drivers Safely and Effectively?</p>
-<p>Here are the steps to check your device status:</p>
-<ul>
-<li>Press Windows + X keys on your keyboard and select Device Manager from the menu.</li>
-<li>Expand the Network adapters category and look for your Azurewave Aw-nb041h device.</li>
-<li>Right-click on it and select Properties.</li>
-<li>Go to the Driver tab and check if the driver version and date are correct.</li>
-<li>If there are no errors or warnings, your device is working properly.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>The Azurewave Aw-nb041h is a reliable wireless network adapter that can provide fast and stable internet connection for your Windows 10 computer. However, you need to install the correct driver for it to work properly. By following the steps above, you can easily download and install the Azurewave Aw-nb041h driver for Windows 10 and enjoy your wireless internet connection.</p> e753bf7129<br />
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-<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Black Yellow Icon Pack A Unique and Elegant Icon Set for Your Device.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Black Yellow Icon Pack A Unique and Elegant Icon Set for Your Device.md
deleted file mode 100644
index f83cf0dd59c89df6b9ed75b70e39d348129ab3c4..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Black Yellow Icon Pack A Unique and Elegant Icon Set for Your Device.md	
+++ /dev/null
@@ -1,106 +0,0 @@
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-<h1>Black Yellow Icon Pack APK: A Stylish and Vibrant Theme for Your Android Device</h1>
-<p>If you are looking for a way to spice up your Android device's appearance, you might want to try out Black Yellow Icon Pack APK. This is a stunning theme that features black and yellow icons, wallpapers, and widgets that will give your device a fresh and modern look. In this article, we will tell you everything you need to know about this icon pack, including what it is, how to download and install it, why you should choose it, and how to use it. Let's get started!</p>
- <h2>What is Black Yellow Icon Pack APK?</h2>
-<h3>A brief introduction to the icon pack and its features</h3>
-<p>Black Yellow Icon Pack APK is a theme that changes the appearance of your device's icons, wallpapers, and widgets. It is designed by <a href="(^1^)">XDA Developers</a>, a popular online community for Android enthusiasts. The icon pack consists of more than 1180+ icons that are inspired by Apple's iOS 16 design. The icons have a rounded square shape and bold colors that contrast well with the black background. The icon pack also comes with 10 high-quality wallpapers that match the theme, as well as 5 custom widgets that show the time, date, weather, battery, and music.</p>
-<h2>black yellow icon pack apk</h2><br /><p><b><b>DOWNLOAD</b> &mdash; <a href="https://bltlly.com/2uOnIm">https://bltlly.com/2uOnIm</a></b></p><br /><br />
-<h3>How to download and install the icon pack on your device</h3>
-<p>To download and install Black Yellow Icon Pack APK on your device, you need to follow these simple steps:</p>
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- <h2>Why Choose Black Yellow Icon Pack APK?</h2>
-<h3>The benefits of using this icon pack over others</h3>
-<p>There are many reasons why you should choose Black Yellow Icon Pack APK over other icon packs available on the market. Here are some of them:</p>
-<ul>
-<li>It is free and easy to use. You don't have to pay anything or register for anything to use this icon pack. You just need to download it, install it, and apply it.</li>
-<li>It is stylish and vibrant. The black and yellow colors create a striking contrast that makes your device stand out from the crowd. The icons are also sleek and elegant, giving your device a professional look.</li>
-<li>It is updated regularly. The developer of this icon pack is constantly adding new icons, wallpapers, and widgets to keep up with the latest trends and requests from users. You can always expect new content and improvements from this icon pack.</li>
-</ul>
-<h3>The compatibility and customization options of the icon pack</h3>
-<p>Another advantage of using Black Yellow Icon Pack APK is that it is compatible with most Android devices and launchers. You can use this icon pack with any launcher that supports icon packs, such as Nova Launcher, Apex Launcher, ADW Launcher, and more. You can also customize the icon pack to suit your preferences. You can change the icon size, shape, color, and label. You can also apply different wallpapers and widgets to create your own unique theme.</p>
- <h2>How to Use Black Yellow Icon Pack APK?</h2>
-<h3>How to apply the icon pack to your launcher</h3>
-<p>After you have installed the icon pack on your device, you need to apply it to your launcher. Here is how you can do that:</p>
-<ol>
-<li>Open the icon pack app from your app drawer and tap on the Apply button.</li>
-<li>Select the launcher that you are using from the list of supported launchers.</li>
-<li>Wait for the launcher to apply the icon pack and then go back to your home screen.</li>
-<li>You should see the new icons, wallpapers, and widgets on your device.</li>
-</ol>
-<h3>How to change the icons, wallpapers, and widgets of the icon pack</h3>
-<p>If you want to change the icons, wallpapers, and widgets of the icon pack, you can do that easily from the icon pack app. Here is how you can do that:</p>
-<ol>
-<li>Open the icon pack app from your app drawer and tap on the Icons, Wallpapers, or Widgets button.</li>
-<li>Browse through the available icons, wallpapers, or widgets and select the ones that you like.</li>
-<li>Tap on the Apply button to apply the changes to your device.</li>
-<li>You can also use the Search button to find specific icons, wallpapers, or widgets by name or category.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Black Yellow Icon Pack APK is a great way to give your Android device a stylish and vibrant makeover. It is a free and easy-to-use theme that features black and yellow icons, wallpapers, and widgets that are inspired by Apple's iOS 16 design. It is compatible with most Android devices and launchers and offers a lot of customization options. If you are looking for a new theme for your device, you should definitely give Black Yellow Icon Pack APK a try. You won't regret it!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Black Yellow Icon Pack APK free?</h3>
-<p>A1: Yes, Black Yellow Icon Pack APK is completely free to download and use. You don't have to pay anything or register for anything to use this theme.</p>
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-<p>A2: No, Black Yellow Icon Pack APK does not require root access or any special permissions to work on your device. You just need to enable the installation from unknown sources if prompted.</p>
- <h3>Q3: What launchers are supported by Black Yellow Icon Pack APK?</h3>
-<p>A3: Black Yellow Icon Pack APK supports most launchers that support icon packs, such as Nova Launcher, Apex Launcher, ADW Launcher, and more. You can check the list of supported launchers in the icon pack app.</p>
- <h3>Q4: How can I request new icons for Black Yellow Icon Pack APK?</h3>
-<p>A4: If you want to request new icons for Black Yellow Icon Pack APK, you can do that by contacting the developer of this theme. You can find their email address in the icon pack app or on their <a href="">XDA Developers profile</a>. You can also join their <a href="">Telegram group</a> to get updates and support.</p>
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-<p>A5: If you have any questions, suggestions, or feedback about Black Yellow Icon Pack APK, you can contact the developer of this theme by email or Telegram. You can find their contact details in the icon pack app or on their <a href="">XDA Developers profile</a>.</p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Daz 3D - Toon Generations Mega-1.md b/spaces/tioseFevbu/cartoon-converter/scripts/Daz 3D - Toon Generations Mega-1.md
deleted file mode 100644
index 147b5178f4c7fbcdc116a037a11fa1e1b0618afe..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Daz 3D - Toon Generations Mega-1.md	
+++ /dev/null
@@ -1,37 +0,0 @@
-<br />
-<h1>How to Create Fun and Unique Cartoon Characters with Daz 3D - Toon Generations Mega-1</h1>
-
-<p>If you love cartoons and want to create your own characters, you might be interested in Daz 3D - Toon Generations Mega-1. This is a product set that allows you to create cartoon characters of all different ages, sizes and genders using the Daz Studio software. You can customize their appearance, clothing, hair and personality with a few clicks and sliders. Whether you want to make a cute baby, a grumpy grandpa, a sassy teenager or anything in between, you can do it with Toon Generations Mega-1.</p>
-<h2>Daz 3D - Toon Generations Mega-1</h2><br /><p><b><b>Download Zip</b> &#10004;&#10004;&#10004; <a href="https://urlcod.com/2uHxDX">https://urlcod.com/2uHxDX</a></b></p><br /><br />
-
-<h2>What is Daz 3D - Toon Generations Mega-1?</h2>
-
-<p>Daz 3D - Toon Generations Mega-1 is a bundle of products that includes the following items[^1^]:</p>
-
-<ul>
-<li>Toon Generations Base - This is the core product that contains the base figures for male and female toons. You can use them as they are or modify them with morphs and presets.</li>
-<li>Toon Generations Clothing - This is a collection of clothing items for both male and female toons. You can mix and match them to create different outfits and styles.</li>
-<li>Toon Generations Hair - This is a set of hair styles for both male and female toons. You can choose from different colors, lengths and shapes.</li>
-<li>Toon Generations Personalities - This is a set of expressions and poses for both male and female toons. You can use them to give your characters different moods and emotions.</li>
-</ul>
-
-<p>With these products, you can create unlimited combinations of cartoon characters for your projects. You can also use them with other Daz 3D products and assets to enhance your scenes and animations.</p>
-
-<h2>How to Use Daz 3D - Toon Generations Mega-1?</h2>
-
-<p>To use Daz 3D - Toon Generations Mega-1, you need to have Daz Studio installed on your computer. Daz Studio is a free 3D software that lets you create and render 3D scenes and animations. You can download it from <a href="https://www.daz3d.com/get_studio">here</a>.</p>
-
-<p>Once you have Daz Studio, you need to install the Toon Generations Mega-1 products. You can do this by using the Daz Install Manager (DIM) or by manually downloading and extracting the files. For more information on how to install Daz 3D products, please refer to <a href="https://helpdaz.zendesk.com/hc/en-us/articles/207530243-How-to-install-with-DIM">this article</a>.</p>
-
-<p>After installing the products, you can find them in your Daz Studio content library under People > Toon Generations. You can load the base figures by double-clicking on them or dragging them into your scene. You can then apply clothing, hair and personality presets by selecting the figure and choosing the desired options from the content library.</p>
-<p></p>
-
-<p>You can also adjust the age, size and gender of your characters by using the sliders in the parameters tab. The age slider ranges from 0 (baby) to 100 (elderly), while the size slider ranges from -100 (tiny) to 100 (huge). The gender slider ranges from -100 (female) to 100 (male), but you can also use other morphs to fine-tune the features of your characters.</p>
-
-<p>You can pose your characters by using the pose presets or by manually manipulating their body parts. You can also change their expressions by using the expression presets or by adjusting their facial features. You can use the camera tools to change the perspective and angle of your scene. You can also add lights, backgrounds, props and other elements to make your scene more interesting.</p>
-
-<p>When you are happy with your scene, you can render it by clicking on the render button or by pressing Ctrl+R. You can choose from different render settings and formats depending on your needs. You can also save your scene as a .duf file for future use or editing.</p>
-
-<h2>Why Choose Daz 3D</p> 81aa517590<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Final Destination 2 Full Movie In Hindi Free 20 HOT!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Final Destination 2 Full Movie In Hindi Free 20 HOT!.md
deleted file mode 100644
index a29e32f467e969cb22732106db05601aaf0b6c67..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Final Destination 2 Full Movie In Hindi Free 20 HOT!.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-```html
-<h1>How to Watch Final Destination 2 Full Movie in Hindi for Free</h1>
-<p>If you are a fan of horror movies, you might be interested in watching Final Destination 2, the sequel to the popular 2000 film that follows a group of people who cheat death by escaping a deadly highway pileup. But what if you want to watch it in Hindi and for free? Is it possible to find a legal and safe way to do so?</p>
-<h2>Final Destination 2 Full Movie In Hindi Free 20</h2><br /><p><b><b>Download File</b> &bull; <a href="https://urlcod.com/2uHykS">https://urlcod.com/2uHykS</a></b></p><br /><br />
-<p>In this article, we will show you how to watch Final Destination 2 full movie in Hindi for free using some of the best online streaming platforms available. We will also explain why you should avoid illegal and risky sites that offer pirated copies of the movie.</p>
-<h2>Why You Should Avoid Pirated Sites</h2>
-<p>Before we get into the details of how to watch Final Destination 2 full movie in Hindi for free, we want to warn you about the dangers of using pirated sites. These are websites that offer free downloads or streaming of movies and TV shows without the permission of the creators or distributors.</p>
-<p>While it might be tempting to use these sites to save money and time, there are several reasons why you should avoid them:</p>
-<ul>
-<li>They are illegal. Downloading or streaming copyrighted content without authorization is a violation of intellectual property laws and can result in fines or legal action.</li>
-<li>They are unsafe. Pirated sites often contain malware, viruses, or spyware that can harm your device or steal your personal information. They may also redirect you to phishing or scam sites that can trick you into giving up your credit card details or other sensitive data.</li>
-<li>They are low-quality. Pirated sites usually offer poor-quality copies of movies and TV shows that have been compressed, cropped, or edited. They may also have annoying ads, pop-ups, or buffering issues that ruin your viewing experience.</li>
-</ul>
-<p>Therefore, we recommend that you stay away from pirated sites and use only legal and reputable platforms to watch Final Destination 2 full movie in Hindi for free.</p>
-<h2>How to Watch Final Destination 2 Full Movie in Hindi for Free</h2>
-<p>Now that we have established why you should avoid pirated sites, let's look at some of the best options to watch Final Destination 2 full movie in Hindi for free. These are platforms that offer legitimate and high-quality streaming of movies and TV shows in various languages and genres.</p>
-<p></p>
-<p>Here are some of the platforms that we suggest:</p>
-<ol>
-<li><strong>Netflix</strong>. Netflix is one of the most popular and widely used streaming services in the world. It offers a huge library of movies and TV shows, including Final Destination 2, that you can watch on any device with an internet connection. You can also choose from different audio and subtitle options, including Hindi. Netflix offers a 30-day free trial for new users, so you can watch Final Destination 2 full movie in Hindi for free without paying anything.</li>
-<li><strong>Amazon Prime Video</strong>. Amazon Prime Video is another great streaming service that offers a variety of movies and TV shows, including Final Destination 2, that you can watch on any device with an internet connection. You can also choose from different audio and subtitle options, including Hindi. Amazon Prime Video offers a 30-day free trial for new users, so you can watch Final Destination 2 full movie in Hindi for free without paying anything.</li>
-<li><strong>Internet Archive</strong>. Internet Archive is a non-profit organization that preserves and provides access to digital media, including movies and TV shows. It has a collection of public domain and creative commons licensed content that you can watch for free online or download for offline viewing. You can find Final Destination 2 full movie in Hindi on Internet Archive and watch it for free without any ads or registration.</li>
-</ol>
-<p>These are some of the best ways to watch Final Destination 2 full movie in Hindi for free legally and safely. We hope you enjoy watching this thrilling horror movie and have a great time.</p>
-```</p> cec2833e83<br />
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Get 100 Facebook Like WORK.md b/spaces/tioseFevbu/cartoon-converter/scripts/Get 100 Facebook Like WORK.md
deleted file mode 100644
index e7f09c4534688be7cc2b2256d09876292d97ca60..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Get 100 Facebook Like WORK.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-```
-<h1>How to Get 100 Facebook Like in a Day: A Simple Guide</h1>
-<p>Facebook is one of the most popular social media platforms in the world, with over 2.8 billion monthly active users. Having a strong presence on Facebook can help you grow your brand awareness, reach new customers, and increase your sales. But how do you get more people to like your page and posts?</p>
-<h2>get 100 facebook like</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlcod.com/2uHvYL">https://urlcod.com/2uHvYL</a></b></p><br /><br />
-<p>In this article, we will show you how to get 100 Facebook like in a day, using some simple and effective strategies. Whether you are a business owner, a blogger, or an influencer, you can use these tips to boost your Facebook engagement and visibility.</p>
-<h2>Why are Facebook likes important?</h2>
-<p>Facebook likes are more than just numbers. They are a way of showing your audience that you care about their opinions and feedback. They are also a way of measuring your content's performance and popularity. The more likes you have, the more likely your posts are to be seen by more people, thanks to Facebook's algorithm.</p>
-<p>Getting more likes can also help you build trust and credibility with your potential customers. According to a study by BrightLocal, 88% of consumers trust online reviews as much as personal recommendations. If your page and posts have a lot of likes, it means that people find your content valuable and relevant.</p>
-<p>Moreover, getting more likes can help you generate more traffic to your website or blog. By adding a call-to-action button or a link to your posts, you can encourage your followers to visit your site and learn more about your products or services.</p>
-<p></p>
-<h2>How to get 100 Facebook like in a day</h2>
-<p>Now that you know why Facebook likes are important, let's see how you can get 100 of them in a day. Here are some proven strategies that you can implement right away:</p>
-<ul>
-<li><b>Create high-quality content</b>. The first and most important step to get more likes is to create content that your audience will love. Your content should be relevant, informative, entertaining, or inspiring. It should also be original and unique, not copied from other sources. You can use different types of content, such as images, videos, live streams, polls, quizzes, infographics, etc., to keep your followers engaged and interested.</li>
-<li><b>Post at optimal times</b>. The second step is to post your content at the best times for your audience. This will ensure that your posts get maximum exposure and reach. To find out when your followers are most active on Facebook, you can use the Insights tool on your page. It will show you the days and hours when your fans are online. You can then schedule your posts accordingly using tools like Buffer or Hootsuite.</li>
-<li><b>Use hashtags</b>. The third step is to use hashtags to increase your visibility and discoverability on Facebook. Hashtags are keywords or phrases that describe your content and help people find it. You can use up to 30 hashtags per post, but make sure they are relevant and specific to your niche. You can also use popular or trending hashtags that relate to your topic or industry.</li>
-<li><b>Engage with your followers</b>. The fourth step is to interact with your followers and show them that you appreciate their support. You can do this by liking, commenting, and replying to their messages and feedback. You can also ask them questions, solicit their opinions, or run contests and giveaways to encourage them to participate. By engaging with your followers, you will build a loyal and active community around your page.</li>
-<li><b>Promote your page</b>. The fifth and final step is to promote your page and posts to reach more people who might be interested in your content. You can do this by sharing your page and posts on other social media platforms, such as Instagram, Twitter, or LinkedIn. You can also invite your friends, family, and contacts to like your page and posts. Another option is to use Facebook ads to target your ideal audience based on their demographics, interests, behaviors, and location.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Getting 100 Facebook like in a day is not impossible if you follow these simple steps. By creating high-quality content, posting at optimal times, using</p> 7b8c122e87<br />
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Manual De Fallas Yamaha Xtz 250 __TOP__.md b/spaces/tioseFevbu/cartoon-converter/scripts/Manual De Fallas Yamaha Xtz 250 __TOP__.md
deleted file mode 100644
index 0efde1a369dbddfd2a962120e042c331991ffed8..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Manual De Fallas Yamaha Xtz 250 __TOP__.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-<h1>Manual De Fallas Yamaha Xtz 250: Cómo Solucionar Los Problemas Más Comunes</h1>
-<p>La Yamaha Xtz 250 es una motocicleta versátil y confiable que se adapta tanto al asfalto como al off-road. Sin embargo, como cualquier vehículo, puede presentar algunas fallas que afecten su rendimiento o seguridad. En este artículo te mostraremos cómo identificar y solucionar los problemas más comunes que puede tener tu Yamaha Xtz 250, según el manual de taller oficial.</p>
-<h2>Código De Fallas</h2>
-<p>La Yamaha Xtz 250 cuenta con un sistema de autodiagnóstico que te permite saber si hay algún problema en el motor o en los sensores. Para acceder al código de fallas, debes seguir estos pasos:</p>
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-<ol>
-<li>Apaga el interruptor de encendido.</li>
-<li>Conecta el conector de diagnóstico que se encuentra debajo del asiento.</li>
-<li>Enciende el interruptor de encendido y observa el indicador de diagnóstico en el panel de instrumentos.</li>
-<li>Anota el número que aparece en el indicador, que corresponde al código de falla.</li>
-<li>Consulta la tabla de código de fallas que se encuentra en el manual de taller o en la página 311 del siguiente enlace[^1^].</li>
-</ol>
-<h2>Problemas Más Comunes</h2>
-<p>A continuación te presentamos algunos de los problemas más comunes que puede tener tu Yamaha Xtz 250 y cómo solucionarlos.</p>
-<h3>Falla 12: Ninguna señal recibida del sensor de posición del cigüeñal</h3>
-<p>Este problema puede causar que el motor no arranque o se detenga repentinamente. Las posibles causas son:</p>
-<ul>
-<li>Circuito abierto o en corto en el cableado.</li>
-<li>Sensor de posición del cigüeñal defectuoso.</li>
-<li>Mala conexión del conector del sensor.</li>
-</ul>
-<p>Para solucionarlo, debes revisar el cableado y el sensor, y reemplazarlos si es necesario. También debes asegurarte de que el conector esté bien conectado y limpio.</p>
-<h3>Falla 19: Ninguna señal recibida del sensor de posición del acelerador</h3>
-<p>Este problema puede causar que el motor funcione de forma irregular o tenga una aceleración deficiente. Las posibles causas son:</p>
-<ul>
-<li>Circuito abierto o en corto en el cableado.</li>
-<li>Sensor de posición del acelerador defectuoso.</li>
-<li>Mala conexión del conector del sensor.</li>
-</ul>
-<p>Para solucionarlo, debes revisar el cableado y el sensor, y reemplazarlos si es necesario. También debes asegurarte de que el conector esté bien conectado y limpio.</p>
-<h3>Falla 30: Señal anormal recibida del sensor de inclinación</h3>
-<p>Este problema puede causar que el motor se apague cuando la motocicleta se inclina demasiado. Las posibles causas son:</p>
-<ul>
-<li>Circuito abierto o en corto en el cableado.</li>
-<li>Sensor de inclinación defectuoso.</li>
-<li>Mala conexión del conector del sensor.</li>
-<li>Motocicleta inclinada más de 65 grados.</li>
-</ul>
-<p>Para solucionarlo, debes revisar el cableado y el sensor, y reemplazarlos si es necesario. También debes asegurarte de que el conector esté bien conectado y limpio. Además, debes evitar inclinar la motocicleta más de lo necesario.</p>
-<p></p>
-
-<h2>Conclusión</h2>
-
-<p>La Yamaha Xtz 250 es una motocicleta que ofrece un buen rendimiento tanto en la ciudad como en el campo. Sin embargo, como cualquier vehículo, puede presentar algunas fallas que requieren atención y mantenimiento. En este artículo te hemos mostrado cómo identificar y sol</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py
deleted file mode 100644
index 201b3c3283218f45d5cfa192a07c9e9d991eaaff..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py
+++ /dev/null
@@ -1,338 +0,0 @@
-"""
-    pygments.formatters.terminal256
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for 256-color terminal output with ANSI sequences.
-
-    RGB-to-XTERM color conversion routines adapted from xterm256-conv
-    tool (http://frexx.de/xterm-256-notes/data/xterm256-conv2.tar.bz2)
-    by Wolfgang Frisch.
-
-    Formatter version 1.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-# TODO:
-#  - Options to map style's bold/underline/italic/border attributes
-#    to some ANSI attrbutes (something like 'italic=underline')
-#  - An option to output "style RGB to xterm RGB/index" conversion table
-#  - An option to indicate that we are running in "reverse background"
-#    xterm. This means that default colors are white-on-black, not
-#    black-on-while, so colors like "white background" need to be converted
-#    to "white background, black foreground", etc...
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.console import codes
-from pip._vendor.pygments.style import ansicolors
-
-
-__all__ = ['Terminal256Formatter', 'TerminalTrueColorFormatter']
-
-
-class EscapeSequence:
-    def __init__(self, fg=None, bg=None, bold=False, underline=False, italic=False):
-        self.fg = fg
-        self.bg = bg
-        self.bold = bold
-        self.underline = underline
-        self.italic = italic
-
-    def escape(self, attrs):
-        if len(attrs):
-            return "\x1b[" + ";".join(attrs) + "m"
-        return ""
-
-    def color_string(self):
-        attrs = []
-        if self.fg is not None:
-            if self.fg in ansicolors:
-                esc = codes[self.fg.replace('ansi','')]
-                if ';01m' in esc:
-                    self.bold = True
-                # extract fg color code.
-                attrs.append(esc[2:4])
-            else:
-                attrs.extend(("38", "5", "%i" % self.fg))
-        if self.bg is not None:
-            if self.bg in ansicolors:
-                esc = codes[self.bg.replace('ansi','')]
-                # extract fg color code, add 10 for bg.
-                attrs.append(str(int(esc[2:4])+10))
-            else:
-                attrs.extend(("48", "5", "%i" % self.bg))
-        if self.bold:
-            attrs.append("01")
-        if self.underline:
-            attrs.append("04")
-        if self.italic:
-            attrs.append("03")
-        return self.escape(attrs)
-
-    def true_color_string(self):
-        attrs = []
-        if self.fg:
-            attrs.extend(("38", "2", str(self.fg[0]), str(self.fg[1]), str(self.fg[2])))
-        if self.bg:
-            attrs.extend(("48", "2", str(self.bg[0]), str(self.bg[1]), str(self.bg[2])))
-        if self.bold:
-            attrs.append("01")
-        if self.underline:
-            attrs.append("04")
-        if self.italic:
-            attrs.append("03")
-        return self.escape(attrs)
-
-    def reset_string(self):
-        attrs = []
-        if self.fg is not None:
-            attrs.append("39")
-        if self.bg is not None:
-            attrs.append("49")
-        if self.bold or self.underline or self.italic:
-            attrs.append("00")
-        return self.escape(attrs)
-
-
-class Terminal256Formatter(Formatter):
-    """
-    Format tokens with ANSI color sequences, for output in a 256-color
-    terminal or console.  Like in `TerminalFormatter` color sequences
-    are terminated at newlines, so that paging the output works correctly.
-
-    The formatter takes colors from a style defined by the `style` option
-    and converts them to nearest ANSI 256-color escape sequences. Bold and
-    underline attributes from the style are preserved (and displayed).
-
-    .. versionadded:: 0.9
-
-    .. versionchanged:: 2.2
-       If the used style defines foreground colors in the form ``#ansi*``, then
-       `Terminal256Formatter` will map these to non extended foreground color.
-       See :ref:`AnsiTerminalStyle` for more information.
-
-    .. versionchanged:: 2.4
-       The ANSI color names have been updated with names that are easier to
-       understand and align with colornames of other projects and terminals.
-       See :ref:`this table <new-ansi-color-names>` for more information.
-
-
-    Options accepted:
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-
-    `linenos`
-        Set to ``True`` to have line numbers on the terminal output as well
-        (default: ``False`` = no line numbers).
-    """
-    name = 'Terminal256'
-    aliases = ['terminal256', 'console256', '256']
-    filenames = []
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-
-        self.xterm_colors = []
-        self.best_match = {}
-        self.style_string = {}
-
-        self.usebold = 'nobold' not in options
-        self.useunderline = 'nounderline' not in options
-        self.useitalic = 'noitalic' not in options
-
-        self._build_color_table()  # build an RGB-to-256 color conversion table
-        self._setup_styles()  # convert selected style's colors to term. colors
-
-        self.linenos = options.get('linenos', False)
-        self._lineno = 0
-
-    def _build_color_table(self):
-        # colors 0..15: 16 basic colors
-
-        self.xterm_colors.append((0x00, 0x00, 0x00))  # 0
-        self.xterm_colors.append((0xcd, 0x00, 0x00))  # 1
-        self.xterm_colors.append((0x00, 0xcd, 0x00))  # 2
-        self.xterm_colors.append((0xcd, 0xcd, 0x00))  # 3
-        self.xterm_colors.append((0x00, 0x00, 0xee))  # 4
-        self.xterm_colors.append((0xcd, 0x00, 0xcd))  # 5
-        self.xterm_colors.append((0x00, 0xcd, 0xcd))  # 6
-        self.xterm_colors.append((0xe5, 0xe5, 0xe5))  # 7
-        self.xterm_colors.append((0x7f, 0x7f, 0x7f))  # 8
-        self.xterm_colors.append((0xff, 0x00, 0x00))  # 9
-        self.xterm_colors.append((0x00, 0xff, 0x00))  # 10
-        self.xterm_colors.append((0xff, 0xff, 0x00))  # 11
-        self.xterm_colors.append((0x5c, 0x5c, 0xff))  # 12
-        self.xterm_colors.append((0xff, 0x00, 0xff))  # 13
-        self.xterm_colors.append((0x00, 0xff, 0xff))  # 14
-        self.xterm_colors.append((0xff, 0xff, 0xff))  # 15
-
-        # colors 16..232: the 6x6x6 color cube
-
-        valuerange = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff)
-
-        for i in range(217):
-            r = valuerange[(i // 36) % 6]
-            g = valuerange[(i // 6) % 6]
-            b = valuerange[i % 6]
-            self.xterm_colors.append((r, g, b))
-
-        # colors 233..253: grayscale
-
-        for i in range(1, 22):
-            v = 8 + i * 10
-            self.xterm_colors.append((v, v, v))
-
-    def _closest_color(self, r, g, b):
-        distance = 257*257*3  # "infinity" (>distance from #000000 to #ffffff)
-        match = 0
-
-        for i in range(0, 254):
-            values = self.xterm_colors[i]
-
-            rd = r - values[0]
-            gd = g - values[1]
-            bd = b - values[2]
-            d = rd*rd + gd*gd + bd*bd
-
-            if d < distance:
-                match = i
-                distance = d
-        return match
-
-    def _color_index(self, color):
-        index = self.best_match.get(color, None)
-        if color in ansicolors:
-            # strip the `ansi/#ansi` part and look up code
-            index = color
-            self.best_match[color] = index
-        if index is None:
-            try:
-                rgb = int(str(color), 16)
-            except ValueError:
-                rgb = 0
-
-            r = (rgb >> 16) & 0xff
-            g = (rgb >> 8) & 0xff
-            b = rgb & 0xff
-            index = self._closest_color(r, g, b)
-            self.best_match[color] = index
-        return index
-
-    def _setup_styles(self):
-        for ttype, ndef in self.style:
-            escape = EscapeSequence()
-            # get foreground from ansicolor if set
-            if ndef['ansicolor']:
-                escape.fg = self._color_index(ndef['ansicolor'])
-            elif ndef['color']:
-                escape.fg = self._color_index(ndef['color'])
-            if ndef['bgansicolor']:
-                escape.bg = self._color_index(ndef['bgansicolor'])
-            elif ndef['bgcolor']:
-                escape.bg = self._color_index(ndef['bgcolor'])
-            if self.usebold and ndef['bold']:
-                escape.bold = True
-            if self.useunderline and ndef['underline']:
-                escape.underline = True
-            if self.useitalic and ndef['italic']:
-                escape.italic = True
-            self.style_string[str(ttype)] = (escape.color_string(),
-                                             escape.reset_string())
-
-    def _write_lineno(self, outfile):
-        self._lineno += 1
-        outfile.write("%s%04d: " % (self._lineno != 1 and '\n' or '', self._lineno))
-
-    def format(self, tokensource, outfile):
-        return Formatter.format(self, tokensource, outfile)
-
-    def format_unencoded(self, tokensource, outfile):
-        if self.linenos:
-            self._write_lineno(outfile)
-
-        for ttype, value in tokensource:
-            not_found = True
-            while ttype and not_found:
-                try:
-                    # outfile.write( "<" + str(ttype) + ">" )
-                    on, off = self.style_string[str(ttype)]
-
-                    # Like TerminalFormatter, add "reset colors" escape sequence
-                    # on newline.
-                    spl = value.split('\n')
-                    for line in spl[:-1]:
-                        if line:
-                            outfile.write(on + line + off)
-                        if self.linenos:
-                            self._write_lineno(outfile)
-                        else:
-                            outfile.write('\n')
-
-                    if spl[-1]:
-                        outfile.write(on + spl[-1] + off)
-
-                    not_found = False
-                    # outfile.write( '#' + str(ttype) + '#' )
-
-                except KeyError:
-                    # ottype = ttype
-                    ttype = ttype.parent
-                    # outfile.write( '!' + str(ottype) + '->' + str(ttype) + '!' )
-
-            if not_found:
-                outfile.write(value)
-
-        if self.linenos:
-            outfile.write("\n")
-
-
-
-class TerminalTrueColorFormatter(Terminal256Formatter):
-    r"""
-    Format tokens with ANSI color sequences, for output in a true-color
-    terminal or console.  Like in `TerminalFormatter` color sequences
-    are terminated at newlines, so that paging the output works correctly.
-
-    .. versionadded:: 2.1
-
-    Options accepted:
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-    """
-    name = 'TerminalTrueColor'
-    aliases = ['terminal16m', 'console16m', '16m']
-    filenames = []
-
-    def _build_color_table(self):
-        pass
-
-    def _color_tuple(self, color):
-        try:
-            rgb = int(str(color), 16)
-        except ValueError:
-            return None
-        r = (rgb >> 16) & 0xff
-        g = (rgb >> 8) & 0xff
-        b = rgb & 0xff
-        return (r, g, b)
-
-    def _setup_styles(self):
-        for ttype, ndef in self.style:
-            escape = EscapeSequence()
-            if ndef['color']:
-                escape.fg = self._color_tuple(ndef['color'])
-            if ndef['bgcolor']:
-                escape.bg = self._color_tuple(ndef['bgcolor'])
-            if self.usebold and ndef['bold']:
-                escape.bold = True
-            if self.useunderline and ndef['underline']:
-                escape.underline = True
-            if self.useitalic and ndef['italic']:
-                escape.italic = True
-            self.style_string[str(ttype)] = (escape.true_color_string(),
-                                             escape.reset_string())
diff --git a/spaces/tmaham/DS-Fusion-Express/ldm/modules/diffusionmodules/util.py b/spaces/tmaham/DS-Fusion-Express/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index a952e6c40308c33edd422da0ce6a60f47e73661b..0000000000000000000000000000000000000000
--- a/spaces/tmaham/DS-Fusion-Express/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad():
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/tomofi/ABINet-OCR/modules/model.py b/spaces/tomofi/ABINet-OCR/modules/model.py
deleted file mode 100644
index dc19b937690f82d388fc2f9bd8127567618df5e7..0000000000000000000000000000000000000000
--- a/spaces/tomofi/ABINet-OCR/modules/model.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import torch.nn as nn
-
-from utils import CharsetMapper
-
-
-_default_tfmer_cfg = dict(d_model=512, nhead=8, d_inner=2048, # 1024
-                          dropout=0.1, activation='relu')
-
-class Model(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-        self.max_length = config.dataset_max_length + 1
-        self.charset = CharsetMapper(config.dataset_charset_path, max_length=self.max_length)
-    
-    def load(self, source, device=None, strict=True):
-        state = torch.load(source, map_location=device)
-        self.load_state_dict(state['model'], strict=strict)
-
-    def _get_length(self, logit, dim=-1):
-        """ Greed decoder to obtain length from logit"""
-        out = (logit.argmax(dim=-1) == self.charset.null_label)
-        abn = out.any(dim)
-        out = ((out.cumsum(dim) == 1) & out).max(dim)[1]
-        out = out + 1  # additional end token
-        out = torch.where(abn, out, out.new_tensor(logit.shape[1]))
-        return out
-
-    @staticmethod
-    def _get_padding_mask(length, max_length):
-        length = length.unsqueeze(-1)
-        grid = torch.arange(0, max_length, device=length.device).unsqueeze(0)
-        return grid >= length
-
-    @staticmethod
-    def _get_square_subsequent_mask(sz, device, diagonal=0, fw=True):
-        r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
-            Unmasked positions are filled with float(0.0).
-        """
-        mask = (torch.triu(torch.ones(sz, sz, device=device), diagonal=diagonal) == 1)
-        if fw: mask = mask.transpose(0, 1)
-        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-        return mask
-
-    @staticmethod
-    def _get_location_mask(sz, device=None):
-        mask = torch.eye(sz, device=device)
-        mask = mask.float().masked_fill(mask == 1, float('-inf'))
-        return mask
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/base.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/base.py
deleted file mode 100644
index 976299d9947dd1b3d32af37fd0ce03040b15c419..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/base.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import CONVERTORS
-from mmocr.utils import list_from_file
-
-
-@CONVERTORS.register_module()
-class BaseConvertor:
-    """Convert between text, index and tensor for text recognize pipeline.
-
-    Args:
-        dict_type (str): Type of dict, should be either 'DICT36' or 'DICT90'.
-        dict_file (None|str): Character dict file path. If not none,
-            the dict_file is of higher priority than dict_type.
-        dict_list (None|list[str]): Character list. If not none, the list
-            is of higher priority than dict_type, but lower than dict_file.
-    """
-    start_idx = end_idx = padding_idx = 0
-    unknown_idx = None
-    lower = False
-
-    DICT36 = tuple('0123456789abcdefghijklmnopqrstuvwxyz')
-    DICT90 = tuple('0123456789abcdefghijklmnopqrstuvwxyz'
-                   'ABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()'
-                   '*+,-./:;<=>?@[\\]_`~')
-
-    def __init__(self, dict_type='DICT90', dict_file=None, dict_list=None):
-        assert dict_type in ('DICT36', 'DICT90')
-        assert dict_file is None or isinstance(dict_file, str)
-        assert dict_list is None or isinstance(dict_list, list)
-        self.idx2char = []
-        if dict_file is not None:
-            for line in list_from_file(dict_file):
-                line = line.strip()
-                if line != '':
-                    self.idx2char.append(line)
-        elif dict_list is not None:
-            self.idx2char = dict_list
-        else:
-            if dict_type == 'DICT36':
-                self.idx2char = list(self.DICT36)
-            else:
-                self.idx2char = list(self.DICT90)
-
-        self.char2idx = {}
-        for idx, char in enumerate(self.idx2char):
-            self.char2idx[char] = idx
-
-    def num_classes(self):
-        """Number of output classes."""
-        return len(self.idx2char)
-
-    def str2idx(self, strings):
-        """Convert strings to indexes.
-
-        Args:
-            strings (list[str]): ['hello', 'world'].
-        Returns:
-            indexes (list[list[int]]): [[1,2,3,3,4], [5,4,6,3,7]].
-        """
-        assert isinstance(strings, list)
-
-        indexes = []
-        for string in strings:
-            if self.lower:
-                string = string.lower()
-            index = []
-            for char in string:
-                char_idx = self.char2idx.get(char, self.unknown_idx)
-                if char_idx is None:
-                    raise Exception(f'Chararcter: {char} not in dict,'
-                                    f' please check gt_label and use'
-                                    f' custom dict file,'
-                                    f' or set "with_unknown=True"')
-                index.append(char_idx)
-            indexes.append(index)
-
-        return indexes
-
-    def str2tensor(self, strings):
-        """Convert text-string to input tensor.
-
-        Args:
-            strings (list[str]): ['hello', 'world'].
-        Returns:
-            tensors (list[torch.Tensor]): [torch.Tensor([1,2,3,3,4]),
-                torch.Tensor([5,4,6,3,7])].
-        """
-        raise NotImplementedError
-
-    def idx2str(self, indexes):
-        """Convert indexes to text strings.
-
-        Args:
-            indexes (list[list[int]]): [[1,2,3,3,4], [5,4,6,3,7]].
-        Returns:
-            strings (list[str]): ['hello', 'world'].
-        """
-        assert isinstance(indexes, list)
-
-        strings = []
-        for index in indexes:
-            string = [self.idx2char[i] for i in index]
-            strings.append(''.join(string))
-
-        return strings
-
-    def tensor2idx(self, output):
-        """Convert model output tensor to character indexes and scores.
-        Args:
-            output (tensor): The model outputs with size: N * T * C
-        Returns:
-            indexes (list[list[int]]): [[1,2,3,3,4], [5,4,6,3,7]].
-            scores (list[list[float]]): [[0.9,0.8,0.95,0.97,0.94],
-                [0.9,0.9,0.98,0.97,0.96]].
-        """
-        raise NotImplementedError
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
deleted file mode 100644
index f8ef6ec092db2e454ca5359b6df89d31365672c0..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
+++ /dev/null
@@ -1,14 +0,0 @@
-_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(
-        type='ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch',
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/mask/structures.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/mask/structures.py
deleted file mode 100644
index d9ec5775f281ab8b76cb873e71a4edd9969ab905..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/mask/structures.py
+++ /dev/null
@@ -1,1024 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import cv2
-import mmcv
-import numpy as np
-import pycocotools.mask as maskUtils
-import torch
-from mmcv.ops.roi_align import roi_align
-
-
-class BaseInstanceMasks(metaclass=ABCMeta):
-    """Base class for instance masks."""
-
-    @abstractmethod
-    def rescale(self, scale, interpolation='nearest'):
-        """Rescale masks as large as possible while keeping the aspect ratio.
-        For details can refer to `mmcv.imrescale`.
-
-        Args:
-            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
-            interpolation (str): Same as :func:`mmcv.imrescale`.
-
-        Returns:
-            BaseInstanceMasks: The rescaled masks.
-        """
-
-    @abstractmethod
-    def resize(self, out_shape, interpolation='nearest'):
-        """Resize masks to the given out_shape.
-
-        Args:
-            out_shape: Target (h, w) of resized mask.
-            interpolation (str): See :func:`mmcv.imresize`.
-
-        Returns:
-            BaseInstanceMasks: The resized masks.
-        """
-
-    @abstractmethod
-    def flip(self, flip_direction='horizontal'):
-        """Flip masks alone the given direction.
-
-        Args:
-            flip_direction (str): Either 'horizontal' or 'vertical'.
-
-        Returns:
-            BaseInstanceMasks: The flipped masks.
-        """
-
-    @abstractmethod
-    def pad(self, out_shape, pad_val):
-        """Pad masks to the given size of (h, w).
-
-        Args:
-            out_shape (tuple[int]): Target (h, w) of padded mask.
-            pad_val (int): The padded value.
-
-        Returns:
-            BaseInstanceMasks: The padded masks.
-        """
-
-    @abstractmethod
-    def crop(self, bbox):
-        """Crop each mask by the given bbox.
-
-        Args:
-            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).
-
-        Return:
-            BaseInstanceMasks: The cropped masks.
-        """
-
-    @abstractmethod
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device,
-                        interpolation='bilinear'):
-        """Crop and resize masks by the given bboxes.
-
-        This function is mainly used in mask targets computation.
-        It firstly align mask to bboxes by assigned_inds, then crop mask by the
-        assigned bbox and resize to the size of (mask_h, mask_w)
-
-        Args:
-            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
-            out_shape (tuple[int]): Target (h, w) of resized mask
-            inds (ndarray): Indexes to assign masks to each bbox,
-                shape (N,) and values should be between [0, num_masks - 1].
-            device (str): Device of bboxes
-            interpolation (str): See `mmcv.imresize`
-
-        Return:
-            BaseInstanceMasks: the cropped and resized masks.
-        """
-
-    @abstractmethod
-    def expand(self, expanded_h, expanded_w, top, left):
-        """see :class:`Expand`."""
-
-    @property
-    @abstractmethod
-    def areas(self):
-        """ndarray: areas of each instance."""
-
-    @abstractmethod
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray.
-
-        Return:
-            ndarray: Converted masks in the format of ndarray.
-        """
-
-    @abstractmethod
-    def to_tensor(self, dtype, device):
-        """Convert masks to the format of Tensor.
-
-        Args:
-            dtype (str): Dtype of converted mask.
-            device (torch.device): Device of converted masks.
-
-        Returns:
-            Tensor: Converted masks in the format of Tensor.
-        """
-
-    @abstractmethod
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            Translated masks.
-        """
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border. Default 0.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            ndarray: Sheared masks.
-        """
-
-    @abstractmethod
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            Rotated masks.
-        """
-
-
-class BitmapMasks(BaseInstanceMasks):
-    """This class represents masks in the form of bitmaps.
-
-    Args:
-        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
-            the number of objects.
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> num_masks, H, W = 3, 32, 32
-        >>> rng = np.random.RandomState(0)
-        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
-        >>> self = BitmapMasks(masks, height=H, width=W)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 5
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (14, 14)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        self.height = height
-        self.width = width
-        if len(masks) == 0:
-            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
-        else:
-            assert isinstance(masks, (list, np.ndarray))
-            if isinstance(masks, list):
-                assert isinstance(masks[0], np.ndarray)
-                assert masks[0].ndim == 2  # (H, W)
-            else:
-                assert masks.ndim == 3  # (N, H, W)
-
-            self.masks = np.stack(masks).reshape(-1, height, width)
-            assert self.masks.shape[1] == self.height
-            assert self.masks.shape[2] == self.width
-
-    def __getitem__(self, index):
-        """Index the BitmapMask.
-
-        Args:
-            index (int | ndarray): Indices in the format of integer or ndarray.
-
-        Returns:
-            :obj:`BitmapMasks`: Indexed bitmap masks.
-        """
-        masks = self.masks[index].reshape(-1, self.height, self.width)
-        return BitmapMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.rescale`."""
-        if len(self.masks) == 0:
-            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
-        else:
-            rescaled_masks = np.stack([
-                mmcv.imrescale(mask, scale, interpolation=interpolation)
-                for mask in self.masks
-            ])
-        height, width = rescaled_masks.shape[1:]
-        return BitmapMasks(rescaled_masks, height, width)
-
-    def resize(self, out_shape, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.resize`."""
-        if len(self.masks) == 0:
-            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            resized_masks = np.stack([
-                mmcv.imresize(
-                    mask, out_shape[::-1], interpolation=interpolation)
-                for mask in self.masks
-            ])
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def flip(self, flip_direction='horizontal'):
-        """See :func:`BaseInstanceMasks.flip`."""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-
-        if len(self.masks) == 0:
-            flipped_masks = self.masks
-        else:
-            flipped_masks = np.stack([
-                mmcv.imflip(mask, direction=flip_direction)
-                for mask in self.masks
-            ])
-        return BitmapMasks(flipped_masks, self.height, self.width)
-
-    def pad(self, out_shape, pad_val=0):
-        """See :func:`BaseInstanceMasks.pad`."""
-        if len(self.masks) == 0:
-            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            padded_masks = np.stack([
-                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
-                for mask in self.masks
-            ])
-        return BitmapMasks(padded_masks, *out_shape)
-
-    def crop(self, bbox):
-        """See :func:`BaseInstanceMasks.crop`."""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
-        else:
-            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
-        return BitmapMasks(cropped_masks, h, w)
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.crop_and_resize`."""
-        if len(self.masks) == 0:
-            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
-            return BitmapMasks(empty_masks, *out_shape)
-
-        # convert bboxes to tensor
-        if isinstance(bboxes, np.ndarray):
-            bboxes = torch.from_numpy(bboxes).to(device=device)
-        if isinstance(inds, np.ndarray):
-            inds = torch.from_numpy(inds).to(device=device)
-
-        num_bbox = bboxes.shape[0]
-        fake_inds = torch.arange(
-            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
-        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
-        rois = rois.to(device=device)
-        if num_bbox > 0:
-            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
-                0, inds).to(dtype=rois.dtype)
-            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
-                                1.0, 0, 'avg', True).squeeze(1)
-            resized_masks = (targets >= 0.5).cpu().numpy()
-        else:
-            resized_masks = []
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def expand(self, expanded_h, expanded_w, top, left):
-        """See :func:`BaseInstanceMasks.expand`."""
-        if len(self.masks) == 0:
-            expanded_mask = np.empty((0, expanded_h, expanded_w),
-                                     dtype=np.uint8)
-        else:
-            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
-                                     dtype=np.uint8)
-            expanded_mask[:, top:top + self.height,
-                          left:left + self.width] = self.masks
-        return BitmapMasks(expanded_mask, expanded_h, expanded_w)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0 for masks.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            BitmapMasks: Translated BitmapMasks.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random(dtype=np.uint8)
-            >>> out_shape = (32, 32)
-            >>> offset = 4
-            >>> direction = 'horizontal'
-            >>> fill_val = 0
-            >>> interpolation = 'bilinear'
-            >>> # Note, There seem to be issues when:
-            >>> # * out_shape is different than self's shape
-            >>> # * the mask dtype is not supported by cv2.AffineWarp
-            >>> new = self.translate(out_shape, offset, direction, fill_val,
-            >>>                      interpolation)
-            >>> assert len(new) == len(self)
-            >>> assert new.height, new.width == out_shape
-        """
-        if len(self.masks) == 0:
-            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            translated_masks = mmcv.imtranslate(
-                self.masks.transpose((1, 2, 0)),
-                offset,
-                direction,
-                border_value=fill_val,
-                interpolation=interpolation)
-            if translated_masks.ndim == 2:
-                translated_masks = translated_masks[:, :, None]
-            translated_masks = translated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(translated_masks, *out_shape)
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            BitmapMasks: The sheared masks.
-        """
-        if len(self.masks) == 0:
-            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            sheared_masks = mmcv.imshear(
-                self.masks.transpose((1, 2, 0)),
-                magnitude,
-                direction,
-                border_value=border_value,
-                interpolation=interpolation)
-            if sheared_masks.ndim == 2:
-                sheared_masks = sheared_masks[:, :, None]
-            sheared_masks = sheared_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(sheared_masks, *out_shape)
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            BitmapMasks: Rotated BitmapMasks.
-        """
-        if len(self.masks) == 0:
-            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
-        else:
-            rotated_masks = mmcv.imrotate(
-                self.masks.transpose((1, 2, 0)),
-                angle,
-                center=center,
-                scale=scale,
-                border_value=fill_val)
-            if rotated_masks.ndim == 2:
-                # case when only one mask, (h, w)
-                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
-            rotated_masks = rotated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(rotated_masks, *out_shape)
-
-    @property
-    def areas(self):
-        """See :py:attr:`BaseInstanceMasks.areas`."""
-        return self.masks.sum((1, 2))
-
-    def to_ndarray(self):
-        """See :func:`BaseInstanceMasks.to_ndarray`."""
-        return self.masks
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        return torch.tensor(self.masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               dtype=np.uint8,
-               rng=None):
-        """Generate random bitmap masks for demo / testing purposes.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random()
-            >>> print('self = {}'.format(self))
-            self = BitmapMasks(num_masks=3, height=32, width=32)
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
-        self = cls(masks, height=height, width=width)
-        return self
-
-
-class PolygonMasks(BaseInstanceMasks):
-    """This class represents masks in the form of polygons.
-
-    Polygons is a list of three levels. The first level of the list
-    corresponds to objects, the second level to the polys that compose the
-    object, the third level to the poly coordinates
-
-    Args:
-        masks (list[list[ndarray]]): The first level of the list
-            corresponds to objects, the second level to the polys that
-            compose the object, the third level to the poly coordinates
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> masks = [
-        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
-        >>> ]
-        >>> height, width = 16, 16
-        >>> self = PolygonMasks(masks, height, width)
-
-        >>> # demo translate
-        >>> new = self.translate((16, 16), 4., direction='horizontal')
-        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
-        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 3
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (16, 16)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        assert isinstance(masks, list)
-        if len(masks) > 0:
-            assert isinstance(masks[0], list)
-            assert isinstance(masks[0][0], np.ndarray)
-
-        self.height = height
-        self.width = width
-        self.masks = masks
-
-    def __getitem__(self, index):
-        """Index the polygon masks.
-
-        Args:
-            index (ndarray | List): The indices.
-
-        Returns:
-            :obj:`PolygonMasks`: The indexed polygon masks.
-        """
-        if isinstance(index, np.ndarray):
-            index = index.tolist()
-        if isinstance(index, list):
-            masks = [self.masks[i] for i in index]
-        else:
-            try:
-                masks = self.masks[index]
-            except Exception:
-                raise ValueError(
-                    f'Unsupported input of type {type(index)} for indexing!')
-        if len(masks) and isinstance(masks[0], np.ndarray):
-            masks = [masks]  # ensure a list of three levels
-        return PolygonMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation=None):
-        """see :func:`BaseInstanceMasks.rescale`"""
-        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-        if len(self.masks) == 0:
-            rescaled_masks = PolygonMasks([], new_h, new_w)
-        else:
-            rescaled_masks = self.resize((new_h, new_w))
-        return rescaled_masks
-
-    def resize(self, out_shape, interpolation=None):
-        """see :func:`BaseInstanceMasks.resize`"""
-        if len(self.masks) == 0:
-            resized_masks = PolygonMasks([], *out_shape)
-        else:
-            h_scale = out_shape[0] / self.height
-            w_scale = out_shape[1] / self.width
-            resized_masks = []
-            for poly_per_obj in self.masks:
-                resized_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    p[0::2] *= w_scale
-                    p[1::2] *= h_scale
-                    resized_poly.append(p)
-                resized_masks.append(resized_poly)
-            resized_masks = PolygonMasks(resized_masks, *out_shape)
-        return resized_masks
-
-    def flip(self, flip_direction='horizontal'):
-        """see :func:`BaseInstanceMasks.flip`"""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-        if len(self.masks) == 0:
-            flipped_masks = PolygonMasks([], self.height, self.width)
-        else:
-            flipped_masks = []
-            for poly_per_obj in self.masks:
-                flipped_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if flip_direction == 'horizontal':
-                        p[0::2] = self.width - p[0::2]
-                    elif flip_direction == 'vertical':
-                        p[1::2] = self.height - p[1::2]
-                    else:
-                        p[0::2] = self.width - p[0::2]
-                        p[1::2] = self.height - p[1::2]
-                    flipped_poly_per_obj.append(p)
-                flipped_masks.append(flipped_poly_per_obj)
-            flipped_masks = PolygonMasks(flipped_masks, self.height,
-                                         self.width)
-        return flipped_masks
-
-    def crop(self, bbox):
-        """see :func:`BaseInstanceMasks.crop`"""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = PolygonMasks([], h, w)
-        else:
-            cropped_masks = []
-            for poly_per_obj in self.masks:
-                cropped_poly_per_obj = []
-                for p in poly_per_obj:
-                    # pycocotools will clip the boundary
-                    p = p.copy()
-                    p[0::2] -= bbox[0]
-                    p[1::2] -= bbox[1]
-                    cropped_poly_per_obj.append(p)
-                cropped_masks.append(cropped_poly_per_obj)
-            cropped_masks = PolygonMasks(cropped_masks, h, w)
-        return cropped_masks
-
-    def pad(self, out_shape, pad_val=0):
-        """padding has no effect on polygons`"""
-        return PolygonMasks(self.masks, *out_shape)
-
-    def expand(self, *args, **kwargs):
-        """TODO: Add expand for polygon"""
-        raise NotImplementedError
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """see :func:`BaseInstanceMasks.crop_and_resize`"""
-        out_h, out_w = out_shape
-        if len(self.masks) == 0:
-            return PolygonMasks([], out_h, out_w)
-
-        resized_masks = []
-        for i in range(len(bboxes)):
-            mask = self.masks[inds[i]]
-            bbox = bboxes[i, :]
-            x1, y1, x2, y2 = bbox
-            w = np.maximum(x2 - x1, 1)
-            h = np.maximum(y2 - y1, 1)
-            h_scale = out_h / max(h, 0.1)  # avoid too large scale
-            w_scale = out_w / max(w, 0.1)
-
-            resized_mask = []
-            for p in mask:
-                p = p.copy()
-                # crop
-                # pycocotools will clip the boundary
-                p[0::2] -= bbox[0]
-                p[1::2] -= bbox[1]
-
-                # resize
-                p[0::2] *= w_scale
-                p[1::2] *= h_scale
-                resized_mask.append(p)
-            resized_masks.append(resized_mask)
-        return PolygonMasks(resized_masks, *out_shape)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=None,
-                  interpolation=None):
-        """Translate the PolygonMasks.
-
-        Example:
-            >>> self = PolygonMasks.random(dtype=np.int)
-            >>> out_shape = (self.height, self.width)
-            >>> new = self.translate(out_shape, 4., direction='horizontal')
-            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
-            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
-        """
-        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
-            f'used, and defaultly should be None or 0. got {fill_val}.'
-        if len(self.masks) == 0:
-            translated_masks = PolygonMasks([], *out_shape)
-        else:
-            translated_masks = []
-            for poly_per_obj in self.masks:
-                translated_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if direction == 'horizontal':
-                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
-                    elif direction == 'vertical':
-                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
-                    translated_poly_per_obj.append(p)
-                translated_masks.append(translated_poly_per_obj)
-            translated_masks = PolygonMasks(translated_masks, *out_shape)
-        return translated_masks
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.shear`."""
-        if len(self.masks) == 0:
-            sheared_masks = PolygonMasks([], *out_shape)
-        else:
-            sheared_masks = []
-            if direction == 'horizontal':
-                shear_matrix = np.stack([[1, magnitude],
-                                         [0, 1]]).astype(np.float32)
-            elif direction == 'vertical':
-                shear_matrix = np.stack([[1, 0], [magnitude,
-                                                  1]]).astype(np.float32)
-            for poly_per_obj in self.masks:
-                sheared_poly = []
-                for p in poly_per_obj:
-                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
-                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
-                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
-                                               out_shape[1])
-                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
-                                               out_shape[0])
-                    sheared_poly.append(
-                        new_coords.transpose((1, 0)).reshape(-1))
-                sheared_masks.append(sheared_poly)
-            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
-        return sheared_masks
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """See :func:`BaseInstanceMasks.rotate`."""
-        if len(self.masks) == 0:
-            rotated_masks = PolygonMasks([], *out_shape)
-        else:
-            rotated_masks = []
-            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
-            for poly_per_obj in self.masks:
-                rotated_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
-                    # pad 1 to convert from format [x, y] to homogeneous
-                    # coordinates format [x, y, 1]
-                    coords = np.concatenate(
-                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
-                        axis=1)  # [n, 3]
-                    rotated_coords = np.matmul(
-                        rotate_matrix[None, :, :],
-                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
-                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
-                                                   out_shape[1])
-                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
-                                                   out_shape[0])
-                    rotated_poly.append(rotated_coords.reshape(-1))
-                rotated_masks.append(rotated_poly)
-            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
-        return rotated_masks
-
-    def to_bitmap(self):
-        """convert polygon masks to bitmap masks."""
-        bitmap_masks = self.to_ndarray()
-        return BitmapMasks(bitmap_masks, self.height, self.width)
-
-    @property
-    def areas(self):
-        """Compute areas of masks.
-
-        This func is modified from `detectron2
-        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
-        The function only works with Polygons using the shoelace formula.
-
-        Return:
-            ndarray: areas of each instance
-        """  # noqa: W501
-        area = []
-        for polygons_per_obj in self.masks:
-            area_per_obj = 0
-            for p in polygons_per_obj:
-                area_per_obj += self._polygon_area(p[0::2], p[1::2])
-            area.append(area_per_obj)
-        return np.asarray(area)
-
-    def _polygon_area(self, x, y):
-        """Compute the area of a component of a polygon.
-
-        Using the shoelace formula:
-        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
-
-        Args:
-            x (ndarray): x coordinates of the component
-            y (ndarray): y coordinates of the component
-
-        Return:
-            float: the are of the component
-        """  # noqa: 501
-        return 0.5 * np.abs(
-            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
-
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray."""
-        if len(self.masks) == 0:
-            return np.empty((0, self.height, self.width), dtype=np.uint8)
-        bitmap_masks = []
-        for poly_per_obj in self.masks:
-            bitmap_masks.append(
-                polygon_to_bitmap(poly_per_obj, self.height, self.width))
-        return np.stack(bitmap_masks)
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        if len(self.masks) == 0:
-            return torch.empty((0, self.height, self.width),
-                               dtype=dtype,
-                               device=device)
-        ndarray_masks = self.to_ndarray()
-        return torch.tensor(ndarray_masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               n_verts=5,
-               dtype=np.float32,
-               rng=None):
-        """Generate random polygon masks for demo / testing purposes.
-
-        Adapted from [1]_
-
-        References:
-            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501
-
-        Example:
-            >>> from mmdet.core.mask.structures import PolygonMasks
-            >>> self = PolygonMasks.random()
-            >>> print('self = {}'.format(self))
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-
-        def _gen_polygon(n, irregularity, spikeyness):
-            """Creates the polygon by sampling points on a circle around the
-            centre.  Random noise is added by varying the angular spacing
-            between sequential points, and by varying the radial distance of
-            each point from the centre.
-
-            Based on original code by Mike Ounsworth
-
-            Args:
-                n (int): number of vertices
-                irregularity (float): [0,1] indicating how much variance there
-                    is in the angular spacing of vertices. [0,1] will map to
-                    [0, 2pi/numberOfVerts]
-                spikeyness (float): [0,1] indicating how much variance there is
-                    in each vertex from the circle of radius aveRadius. [0,1]
-                    will map to [0, aveRadius]
-
-            Returns:
-                a list of vertices, in CCW order.
-            """
-            from scipy.stats import truncnorm
-            # Generate around the unit circle
-            cx, cy = (0.0, 0.0)
-            radius = 1
-
-            tau = np.pi * 2
-
-            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
-            spikeyness = np.clip(spikeyness, 1e-9, 1)
-
-            # generate n angle steps
-            lower = (tau / n) - irregularity
-            upper = (tau / n) + irregularity
-            angle_steps = rng.uniform(lower, upper, n)
-
-            # normalize the steps so that point 0 and point n+1 are the same
-            k = angle_steps.sum() / (2 * np.pi)
-            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)
-
-            # Convert high and low values to be wrt the standard normal range
-            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
-            low = 0
-            high = 2 * radius
-            mean = radius
-            std = spikeyness
-            a = (low - mean) / std
-            b = (high - mean) / std
-            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)
-
-            # now generate the points
-            radii = tnorm.rvs(n, random_state=rng)
-            x_pts = cx + radii * np.cos(angles)
-            y_pts = cy + radii * np.sin(angles)
-
-            points = np.hstack([x_pts[:, None], y_pts[:, None]])
-
-            # Scale to 0-1 space
-            points = points - points.min(axis=0)
-            points = points / points.max(axis=0)
-
-            # Randomly place within 0-1 space
-            points = points * (rng.rand() * .8 + .2)
-            min_pt = points.min(axis=0)
-            max_pt = points.max(axis=0)
-
-            high = (1 - max_pt)
-            low = (0 - min_pt)
-            offset = (rng.rand(2) * (high - low)) + low
-            points = points + offset
-            return points
-
-        def _order_vertices(verts):
-            """
-            References:
-                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
-            """
-            mlat = verts.T[0].sum() / len(verts)
-            mlng = verts.T[1].sum() / len(verts)
-
-            tau = np.pi * 2
-            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
-                     tau) % tau
-            sortx = angle.argsort()
-            verts = verts.take(sortx, axis=0)
-            return verts
-
-        # Generate a random exterior for each requested mask
-        masks = []
-        for _ in range(num_masks):
-            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
-            exterior = (exterior * [(width, height)]).astype(dtype)
-            masks.append([exterior.ravel()])
-
-        self = cls(masks, height, width)
-        return self
-
-
-def polygon_to_bitmap(polygons, height, width):
-    """Convert masks from the form of polygons to bitmaps.
-
-    Args:
-        polygons (list[ndarray]): masks in polygon representation
-        height (int): mask height
-        width (int): mask width
-
-    Return:
-        ndarray: the converted masks in bitmap representation
-    """
-    rles = maskUtils.frPyObjects(polygons, height, width)
-    rle = maskUtils.merge(rles)
-    bitmap_mask = maskUtils.decode(rle).astype(np.bool)
-    return bitmap_mask
diff --git a/spaces/tsi-org/LLaVA/docs/Customize_Component.md b/spaces/tsi-org/LLaVA/docs/Customize_Component.md
deleted file mode 100644
index e99a60879920b389799fb3a0baf1fd864ee0bccc..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/docs/Customize_Component.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Customize Components in LLaVA
-
-This is an initial guide on how to replace the LLMs, visual encoders, etc. with your choice of components.
-
-## LLM
-
-It is quite simple to swap out LLaMA to any other LLMs.  You can refer to our implementation of [`llava_llama.py`](https://raw.githubusercontent.com/haotian-liu/LLaVA/main/llava/model/language_model/llava_llama.py) for an example of how to replace the LLM.
-
-Although it may seem that it still needs ~100 lines of code, most of them are copied from the original `llama.py` from HF.  The only part that is different is to insert some lines for processing the multimodal inputs.
-
-In `forward` function, you can see that we call `self.prepare_inputs_labels_for_multimodal` to process the multimodal inputs.  This function is defined in `LlavaMetaForCausalLM` and you just need to insert it into the `forward` function of your LLM.
-
-In `prepare_inputs_for_generation` function, you can see that we add `images` to the `model_inputs`.  This is because we need to pass the images to the LLM during generation.
-
-These are basically all the changes you need to make to replace the LLM.
-
-## Visual Encoder
-
-You can check out [`clip_encoder.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/clip_encoder.py) on how we implement the CLIP visual encoder.
-
diff --git a/spaces/ttt246/brain/Brain/src/rising_plugin/guardrails-config/__init__.py b/spaces/ttt246/brain/Brain/src/rising_plugin/guardrails-config/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ucalyptus/DragGAN-unofficial/stylegan2/_init__.py b/spaces/ucalyptus/DragGAN-unofficial/stylegan2/_init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/utils.py b/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/utils.py
deleted file mode 100644
index 6ad5a37507987bd6e1200cb9241edf97989e3ead..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/scripts/prompt_matrix.py b/spaces/user238921933/stable-diffusion-webui/scripts/prompt_matrix.py
deleted file mode 100644
index 51c70998866d4b0853a46e4de73d86c3d9ec9b93..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/scripts/prompt_matrix.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-from collections import namedtuple
-from copy import copy
-import random
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import images
-from modules.processing import process_images, Processed
-from modules.shared import opts, cmd_opts, state
-import modules.sd_samplers
-
-
-def draw_xy_grid(xs, ys, x_label, y_label, cell):
-    res = []
-
-    ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
-    hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
-
-    first_processed = None
-
-    state.job_count = len(xs) * len(ys)
-
-    for iy, y in enumerate(ys):
-        for ix, x in enumerate(xs):
-            state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
-
-            processed = cell(x, y)
-            if first_processed is None:
-                first_processed = processed
-
-            res.append(processed.images[0])
-
-    grid = images.image_grid(res, rows=len(ys))
-    grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
-
-    first_processed.images = [grid]
-
-    return first_processed
-
-
-class Script(scripts.Script):
-    def title(self):
-        return "Prompt matrix"
-
-    def ui(self, is_img2img):
-        gr.HTML('<br />')
-        with gr.Row():
-            with gr.Column():
-                put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
-                different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
-            with gr.Column():
-                prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
-                variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
-            with gr.Column():
-                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
-
-        return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
-
-    def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size):
-        modules.processing.fix_seed(p)
-        # Raise error if promp type is not positive or negative
-        if prompt_type not in ["positive", "negative"]:
-            raise ValueError(f"Unknown prompt type {prompt_type}")
-        # Raise error if variations delimiter is not comma or space
-        if variations_delimiter not in ["comma", "space"]:
-            raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
-
-        prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
-        original_prompt = prompt[0] if type(prompt) == list else prompt
-        positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
-
-        delimiter = ", " if variations_delimiter == "comma" else " "
-
-        all_prompts = []
-        prompt_matrix_parts = original_prompt.split("|")
-        combination_count = 2 ** (len(prompt_matrix_parts) - 1)
-        for combination_num in range(combination_count):
-            selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
-
-            if put_at_start:
-                selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
-            else:
-                selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
-
-            all_prompts.append(delimiter.join(selected_prompts))
-
-        p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
-        p.do_not_save_grid = True
-
-        print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
-
-        if prompt_type == "positive":
-            p.prompt = all_prompts
-        else:
-            p.negative_prompt = all_prompts
-        p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
-        p.prompt_for_display = positive_prompt
-        processed = process_images(p)
-
-        grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) 
-        grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[1].height, prompt_matrix_parts, margin_size)
-        processed.images.insert(0, grid)
-        processed.index_of_first_image = 1
-        processed.infotexts.insert(0, processed.infotexts[0])
-
-        if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
-
-        return processed
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/export.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/export.md
deleted file mode 100644
index 54518b2961086b716ab06074336404691026cd67..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/export.md
+++ /dev/null
@@ -1,88 +0,0 @@
----
-comments: true
-description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
-keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, PaddlePaddle, TorchScript, ONNX format, OpenVINO format, TensorRT format, CoreML format, TF SavedModel format, PaddlePaddle format, Tencent NCNN, NCNN
----
-
-<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
-
-**Export mode** is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the
-model is converted to a format that can be used by other software applications or hardware devices. This mode is useful
-when deploying the model to production environments.
-
-!!! tip "Tip"
-
-    * Export to ONNX or OpenVINO for up to 3x CPU speedup.
-    * Export to TensorRT for up to 5x GPU speedup.
-
-## Usage Examples
-
-Export a YOLOv8n model to a different format like ONNX or TensorRT. See Arguments section below for a full list of
-export arguments.
-
-!!! example ""
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO('yolov8n.pt')  # load an official model
-        model = YOLO('path/to/best.pt')  # load a custom trained
-        
-        # Export the model
-        model.export(format='onnx')
-        ```
-    === "CLI"
-    
-        ```bash
-        yolo export model=yolov8n.pt format=onnx  # export official model
-        yolo export model=path/to/best.pt format=onnx  # export custom trained model
-        ```
-
-## Arguments
-
-Export settings for YOLO models refer to the various configurations and options used to save or
-export the model for use in other environments or platforms. These settings can affect the model's performance, size,
-and compatibility with different systems. Some common YOLO export settings include the format of the exported model
-file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
-additional features such as masks or multiple labels per box. Other factors that may affect the export process include
-the specific task the model is being used for and the requirements or constraints of the target environment or platform.
-It is important to carefully consider and configure these settings to ensure that the exported model is optimized for
-the intended use case and can be used effectively in the target environment.
-
-| Key         | Value           | Description                                          |
-|-------------|-----------------|------------------------------------------------------|
-| `format`    | `'torchscript'` | format to export to                                  |
-| `imgsz`     | `640`           | image size as scalar or (h, w) list, i.e. (640, 480) |
-| `keras`     | `False`         | use Keras for TF SavedModel export                   |
-| `optimize`  | `False`         | TorchScript: optimize for mobile                     |
-| `half`      | `False`         | FP16 quantization                                    |
-| `int8`      | `False`         | INT8 quantization                                    |
-| `dynamic`   | `False`         | ONNX/TensorRT: dynamic axes                          |
-| `simplify`  | `False`         | ONNX/TensorRT: simplify model                        |
-| `opset`     | `None`          | ONNX: opset version (optional, defaults to latest)   |
-| `workspace` | `4`             | TensorRT: workspace size (GB)                        |
-| `nms`       | `False`         | CoreML: add NMS                                      |
-
-## Export Formats
-
-Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument,
-i.e. `format='onnx'` or `format='engine'`.
-
-| Format                                                             | `format` Argument | Model                     | Metadata | Arguments                                           |
-|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
-| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
-| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
-| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`                                     |
-| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
-| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
-| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
-| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
-| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
-| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
-| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`                                             |
-| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
-| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
\ No newline at end of file
diff --git a/spaces/vishnusureshperumbavoor/vspbot-falcon-langchain/app.py b/spaces/vishnusureshperumbavoor/vspbot-falcon-langchain/app.py
deleted file mode 100644
index 4fa6a7e045d97fdbfde67d61c32d8f9bc41b2cfe..0000000000000000000000000000000000000000
--- a/spaces/vishnusureshperumbavoor/vspbot-falcon-langchain/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-from langchain import PromptTemplate, LLMChain
-from langchain import HuggingFaceHub
-import os
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
-repo_id = "tiiuae/falcon-7b-instruct"
-llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, 
-                     repo_id=repo_id, 
-                     model_kwargs={"temperature":0.7, "max_new_tokens":700})
-
-template = """
-You are a helpful AI assistant and provide the answer for the question asked politely.
-
-{question}
-Answer: Let's think step by step.
-"""
-prompt = PromptTemplate(template=template, input_variables=["question"])
-llm_chain = LLMChain(prompt=prompt, llm=llm)
-
-# Define the function that will be used in Gradio
-def generate_answer(question):
-    answer = llm_chain.run(question)
-    return answer
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=generate_answer,
-    inputs=gr.inputs.Textbox(),
-    outputs=gr.outputs.Textbox(),
-    title="VSP Bot",
-    description="Created by VSP",
-)
-
-# Launch the Gradio interface
-iface.launch()
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/sync_bn.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/sync_bn.py
deleted file mode 100644
index c9b016fcbe860989c56cd1040034bcfa60e146d2..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/sync_bn.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.module import Module
-from torch.nn.parameter import Parameter
-
-from annotator.uniformer.mmcv.cnn import NORM_LAYERS
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'sync_bn_forward_mean', 'sync_bn_forward_var', 'sync_bn_forward_output',
-    'sync_bn_backward_param', 'sync_bn_backward_data'
-])
-
-
-class SyncBatchNormFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, running_mean, running_var, weight, bias, momentum,
-                 eps, group, group_size, stats_mode):
-        return g.op(
-            'mmcv::MMCVSyncBatchNorm',
-            input,
-            running_mean,
-            running_var,
-            weight,
-            bias,
-            momentum_f=momentum,
-            eps_f=eps,
-            group_i=group,
-            group_size_i=group_size,
-            stats_mode=stats_mode)
-
-    @staticmethod
-    def forward(self, input, running_mean, running_var, weight, bias, momentum,
-                eps, group, group_size, stats_mode):
-        self.momentum = momentum
-        self.eps = eps
-        self.group = group
-        self.group_size = group_size
-        self.stats_mode = stats_mode
-
-        assert isinstance(
-                   input, (torch.HalfTensor, torch.FloatTensor,
-                           torch.cuda.HalfTensor, torch.cuda.FloatTensor)), \
-               f'only support Half or Float Tensor, but {input.type()}'
-        output = torch.zeros_like(input)
-        input3d = input.flatten(start_dim=2)
-        output3d = output.view_as(input3d)
-        num_channels = input3d.size(1)
-
-        # ensure mean/var/norm/std are initialized as zeros
-        # ``torch.empty()`` does not guarantee that
-        mean = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-        var = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-        norm = torch.zeros_like(
-            input3d, dtype=torch.float, device=input3d.device)
-        std = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-
-        batch_size = input3d.size(0)
-        if batch_size > 0:
-            ext_module.sync_bn_forward_mean(input3d, mean)
-            batch_flag = torch.ones([1], device=mean.device, dtype=mean.dtype)
-        else:
-            # skip updating mean and leave it as zeros when the input is empty
-            batch_flag = torch.zeros([1], device=mean.device, dtype=mean.dtype)
-
-        # synchronize mean and the batch flag
-        vec = torch.cat([mean, batch_flag])
-        if self.stats_mode == 'N':
-            vec *= batch_size
-        if self.group_size > 1:
-            dist.all_reduce(vec, group=self.group)
-        total_batch = vec[-1].detach()
-        mean = vec[:num_channels]
-
-        if self.stats_mode == 'default':
-            mean = mean / self.group_size
-        elif self.stats_mode == 'N':
-            mean = mean / total_batch.clamp(min=1)
-        else:
-            raise NotImplementedError
-
-        # leave var as zeros when the input is empty
-        if batch_size > 0:
-            ext_module.sync_bn_forward_var(input3d, mean, var)
-
-        if self.stats_mode == 'N':
-            var *= batch_size
-        if self.group_size > 1:
-            dist.all_reduce(var, group=self.group)
-
-        if self.stats_mode == 'default':
-            var /= self.group_size
-        elif self.stats_mode == 'N':
-            var /= total_batch.clamp(min=1)
-        else:
-            raise NotImplementedError
-
-        # if the total batch size over all the ranks is zero,
-        # we should not update the statistics in the current batch
-        update_flag = total_batch.clamp(max=1)
-        momentum = update_flag * self.momentum
-        ext_module.sync_bn_forward_output(
-            input3d,
-            mean,
-            var,
-            weight,
-            bias,
-            running_mean,
-            running_var,
-            norm,
-            std,
-            output3d,
-            eps=self.eps,
-            momentum=momentum,
-            group_size=self.group_size)
-        self.save_for_backward(norm, std, weight)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(self, grad_output):
-        norm, std, weight = self.saved_tensors
-        grad_weight = torch.zeros_like(weight)
-        grad_bias = torch.zeros_like(weight)
-        grad_input = torch.zeros_like(grad_output)
-        grad_output3d = grad_output.flatten(start_dim=2)
-        grad_input3d = grad_input.view_as(grad_output3d)
-
-        batch_size = grad_input3d.size(0)
-        if batch_size > 0:
-            ext_module.sync_bn_backward_param(grad_output3d, norm, grad_weight,
-                                              grad_bias)
-
-        # all reduce
-        if self.group_size > 1:
-            dist.all_reduce(grad_weight, group=self.group)
-            dist.all_reduce(grad_bias, group=self.group)
-            grad_weight /= self.group_size
-            grad_bias /= self.group_size
-
-        if batch_size > 0:
-            ext_module.sync_bn_backward_data(grad_output3d, weight,
-                                             grad_weight, grad_bias, norm, std,
-                                             grad_input3d)
-
-        return grad_input, None, None, grad_weight, grad_bias, \
-            None, None, None, None, None
-
-
-@NORM_LAYERS.register_module(name='MMSyncBN')
-class SyncBatchNorm(Module):
-    """Synchronized Batch Normalization.
-
-    Args:
-        num_features (int): number of features/chennels in input tensor
-        eps (float, optional): a value added to the denominator for numerical
-            stability. Defaults to 1e-5.
-        momentum (float, optional): the value used for the running_mean and
-            running_var computation. Defaults to 0.1.
-        affine (bool, optional): whether to use learnable affine parameters.
-            Defaults to True.
-        track_running_stats (bool, optional): whether to track the running
-            mean and variance during training. When set to False, this
-            module does not track such statistics, and initializes statistics
-            buffers ``running_mean`` and ``running_var`` as ``None``. When
-            these buffers are ``None``, this module always uses batch
-            statistics in both training and eval modes. Defaults to True.
-        group (int, optional): synchronization of stats happen within
-            each process group individually. By default it is synchronization
-            across the whole world. Defaults to None.
-        stats_mode (str, optional): The statistical mode. Available options
-            includes ``'default'`` and ``'N'``. Defaults to 'default'.
-            When ``stats_mode=='default'``, it computes the overall statistics
-            using those from each worker with equal weight, i.e., the
-            statistics are synchronized and simply divied by ``group``. This
-            mode will produce inaccurate statistics when empty tensors occur.
-            When ``stats_mode=='N'``, it compute the overall statistics using
-            the total number of batches in each worker ignoring the number of
-            group, i.e., the statistics are synchronized and then divied by
-            the total batch ``N``. This mode is beneficial when empty tensors
-            occur during training, as it average the total mean by the real
-            number of batch.
-    """
-
-    def __init__(self,
-                 num_features,
-                 eps=1e-5,
-                 momentum=0.1,
-                 affine=True,
-                 track_running_stats=True,
-                 group=None,
-                 stats_mode='default'):
-        super(SyncBatchNorm, self).__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.momentum = momentum
-        self.affine = affine
-        self.track_running_stats = track_running_stats
-        group = dist.group.WORLD if group is None else group
-        self.group = group
-        self.group_size = dist.get_world_size(group)
-        assert stats_mode in ['default', 'N'], \
-            f'"stats_mode" only accepts "default" and "N", got "{stats_mode}"'
-        self.stats_mode = stats_mode
-        if self.affine:
-            self.weight = Parameter(torch.Tensor(num_features))
-            self.bias = Parameter(torch.Tensor(num_features))
-        else:
-            self.register_parameter('weight', None)
-            self.register_parameter('bias', None)
-        if self.track_running_stats:
-            self.register_buffer('running_mean', torch.zeros(num_features))
-            self.register_buffer('running_var', torch.ones(num_features))
-            self.register_buffer('num_batches_tracked',
-                                 torch.tensor(0, dtype=torch.long))
-        else:
-            self.register_buffer('running_mean', None)
-            self.register_buffer('running_var', None)
-            self.register_buffer('num_batches_tracked', None)
-        self.reset_parameters()
-
-    def reset_running_stats(self):
-        if self.track_running_stats:
-            self.running_mean.zero_()
-            self.running_var.fill_(1)
-            self.num_batches_tracked.zero_()
-
-    def reset_parameters(self):
-        self.reset_running_stats()
-        if self.affine:
-            self.weight.data.uniform_()  # pytorch use ones_()
-            self.bias.data.zero_()
-
-    def forward(self, input):
-        if input.dim() < 2:
-            raise ValueError(
-                f'expected at least 2D input, got {input.dim()}D input')
-        if self.momentum is None:
-            exponential_average_factor = 0.0
-        else:
-            exponential_average_factor = self.momentum
-
-        if self.training and self.track_running_stats:
-            if self.num_batches_tracked is not None:
-                self.num_batches_tracked += 1
-                if self.momentum is None:  # use cumulative moving average
-                    exponential_average_factor = 1.0 / float(
-                        self.num_batches_tracked)
-                else:  # use exponential moving average
-                    exponential_average_factor = self.momentum
-
-        if self.training or not self.track_running_stats:
-            return SyncBatchNormFunction.apply(
-                input, self.running_mean, self.running_var, self.weight,
-                self.bias, exponential_average_factor, self.eps, self.group,
-                self.group_size, self.stats_mode)
-        else:
-            return F.batch_norm(input, self.running_mean, self.running_var,
-                                self.weight, self.bias, False,
-                                exponential_average_factor, self.eps)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'({self.num_features}, '
-        s += f'eps={self.eps}, '
-        s += f'momentum={self.momentum}, '
-        s += f'affine={self.affine}, '
-        s += f'track_running_stats={self.track_running_stats}, '
-        s += f'group_size={self.group_size},'
-        s += f'stats_mode={self.stats_mode})'
-        return s
diff --git a/spaces/whisper-event/leaderboard/README.md b/spaces/whisper-event/leaderboard/README.md
deleted file mode 100644
index 104cbe7ee27fcd201a2c8744735541d99355a7bb..0000000000000000000000000000000000000000
--- a/spaces/whisper-event/leaderboard/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Leaderboard
-emoji: 📊
-colorFrom: green
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wonderit-safeai/tts-announcer/models.py b/spaces/wonderit-safeai/tts-announcer/models.py
deleted file mode 100644
index f5acdeb2bedd47897348407c0ae55c9a160da881..0000000000000000000000000000000000000000
--- a/spaces/wonderit-safeai/tts-announcer/models.py
+++ /dev/null
@@ -1,534 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/wuhuik/bingo/src/components/ui/select.tsx b/spaces/wuhuik/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
-  IconArrowDown,
-  IconCheck,
-  IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Trigger>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Trigger>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Trigger
-    ref={ref}
-    className={cn(
-      'flex h-9 w-full items-center justify-between rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow ring-offset-background placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <SelectPrimitive.Icon asChild>
-      <IconChevronUpDown className="opacity-50" />
-    </SelectPrimitive.Icon>
-  </SelectPrimitive.Trigger>
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Content>
->(({ className, children, position = 'popper', ...props }, ref) => (
-  <SelectPrimitive.Portal>
-    <SelectPrimitive.Content
-      ref={ref}
-      className={cn(
-        'relative z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover text-popover-foreground shadow-md animate-in fade-in-80',
-        position === 'popper' && 'translate-y-1',
-        className
-      )}
-      position={position}
-      {...props}
-    >
-      <SelectPrimitive.Viewport
-        className={cn(
-          'p-1',
-          position === 'popper' &&
-            'h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]'
-        )}
-      >
-        {children}
-      </SelectPrimitive.Viewport>
-    </SelectPrimitive.Content>
-  </SelectPrimitive.Portal>
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Label>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Label
-    ref={ref}
-    className={cn('py-1.5 pl-8 pr-2 text-sm font-semibold', className)}
-    {...props}
-  />
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
-
-const SelectItem = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Item>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex w-full cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      className
-    )}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <SelectPrimitive.ItemIndicator>
-        <IconCheck className="h-4 w-4" />
-      </SelectPrimitive.ItemIndicator>
-    </span>
-    <SelectPrimitive.ItemText>{children}</SelectPrimitive.ItemText>
-  </SelectPrimitive.Item>
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
-
-export {
-  Select,
-  SelectGroup,
-  SelectValue,
-  SelectTrigger,
-  SelectContent,
-  SelectLabel,
-  SelectItem,
-  SelectSeparator
-}
diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/backbone/build.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/backbone/build.py
deleted file mode 100644
index a559fa6a010d3379ff5fcbeb43c510122988735f..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/backbone/build.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .registry import model_entrypoints
-from .registry import is_model
-
-from .backbone import *
-
-def build_backbone(config, **kwargs):
-    model_name = config['MODEL']['BACKBONE']['NAME']
-    if not is_model(model_name):
-        raise ValueError(f'Unkown model: {model_name}')
-
-    return model_entrypoints(model_name)(config, **kwargs)
\ No newline at end of file
diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/build.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/build.py
deleted file mode 100644
index 8d9acdf9766e3bc1184c4200ef4dace3437617e4..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/build.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .registry import model_entrypoints
-from .registry import is_model
-
-
-def build_language_encoder(config, **kwargs):
-    model_name = config['MODEL']['TEXT']['ARCH']
-
-    if not is_model(model_name):
-        raise ValueError(f'Unkown model: {model_name}')
-
-    return model_entrypoints(model_name)(config, **kwargs)
\ No newline at end of file
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/scripts/main.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/scripts/main.py
deleted file mode 100644
index 61aa49ddd3cb8ba9c9bf1e4c281b765b8d971c10..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/scripts/main.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import sys
-import time
-import os.path as osp
-import argparse
-import torch
-import torch.nn as nn
-
-import torchreid
-from torchreid.utils import (
-    Logger, check_isfile, set_random_seed, collect_env_info,
-    resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
-)
-
-from default_config import (
-    imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
-    get_default_config, lr_scheduler_kwargs
-)
-
-
-def build_datamanager(cfg):
-    if cfg.data.type == 'image':
-        return torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
-    else:
-        return torchreid.data.VideoDataManager(**videodata_kwargs(cfg))
-
-
-def build_engine(cfg, datamanager, model, optimizer, scheduler):
-    if cfg.data.type == 'image':
-        if cfg.loss.name == 'softmax':
-            engine = torchreid.engine.ImageSoftmaxEngine(
-                datamanager,
-                model,
-                optimizer=optimizer,
-                scheduler=scheduler,
-                use_gpu=cfg.use_gpu,
-                label_smooth=cfg.loss.softmax.label_smooth
-            )
-
-        else:
-            engine = torchreid.engine.ImageTripletEngine(
-                datamanager,
-                model,
-                optimizer=optimizer,
-                margin=cfg.loss.triplet.margin,
-                weight_t=cfg.loss.triplet.weight_t,
-                weight_x=cfg.loss.triplet.weight_x,
-                scheduler=scheduler,
-                use_gpu=cfg.use_gpu,
-                label_smooth=cfg.loss.softmax.label_smooth
-            )
-
-    else:
-        if cfg.loss.name == 'softmax':
-            engine = torchreid.engine.VideoSoftmaxEngine(
-                datamanager,
-                model,
-                optimizer=optimizer,
-                scheduler=scheduler,
-                use_gpu=cfg.use_gpu,
-                label_smooth=cfg.loss.softmax.label_smooth,
-                pooling_method=cfg.video.pooling_method
-            )
-
-        else:
-            engine = torchreid.engine.VideoTripletEngine(
-                datamanager,
-                model,
-                optimizer=optimizer,
-                margin=cfg.loss.triplet.margin,
-                weight_t=cfg.loss.triplet.weight_t,
-                weight_x=cfg.loss.triplet.weight_x,
-                scheduler=scheduler,
-                use_gpu=cfg.use_gpu,
-                label_smooth=cfg.loss.softmax.label_smooth
-            )
-
-    return engine
-
-
-def reset_config(cfg, args):
-    if args.root:
-        cfg.data.root = args.root
-    if args.sources:
-        cfg.data.sources = args.sources
-    if args.targets:
-        cfg.data.targets = args.targets
-    if args.transforms:
-        cfg.data.transforms = args.transforms
-
-
-def check_cfg(cfg):
-    if cfg.loss.name == 'triplet' and cfg.loss.triplet.weight_x == 0:
-        assert cfg.train.fixbase_epoch == 0, \
-            'The output of classifier is not included in the computational graph'
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument(
-        '--config-file', type=str, default='', help='path to config file'
-    )
-    parser.add_argument(
-        '-s',
-        '--sources',
-        type=str,
-        nargs='+',
-        help='source datasets (delimited by space)'
-    )
-    parser.add_argument(
-        '-t',
-        '--targets',
-        type=str,
-        nargs='+',
-        help='target datasets (delimited by space)'
-    )
-    parser.add_argument(
-        '--transforms', type=str, nargs='+', help='data augmentation'
-    )
-    parser.add_argument(
-        '--root', type=str, default='', help='path to data root'
-    )
-    parser.add_argument(
-        'opts',
-        default=None,
-        nargs=argparse.REMAINDER,
-        help='Modify config options using the command-line'
-    )
-    args = parser.parse_args()
-
-    cfg = get_default_config()
-    cfg.use_gpu = torch.cuda.is_available()
-    if args.config_file:
-        cfg.merge_from_file(args.config_file)
-    reset_config(cfg, args)
-    cfg.merge_from_list(args.opts)
-    set_random_seed(cfg.train.seed)
-    check_cfg(cfg)
-
-    log_name = 'test.log' if cfg.test.evaluate else 'train.log'
-    log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
-    sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
-
-    print('Show configuration\n{}\n'.format(cfg))
-    print('Collecting env info ...')
-    print('** System info **\n{}\n'.format(collect_env_info()))
-
-    if cfg.use_gpu:
-        torch.backends.cudnn.benchmark = True
-
-    datamanager = build_datamanager(cfg)
-
-    print('Building model: {}'.format(cfg.model.name))
-    model = torchreid.models.build_model(
-        name=cfg.model.name,
-        num_classes=datamanager.num_train_pids,
-        loss=cfg.loss.name,
-        pretrained=cfg.model.pretrained,
-        use_gpu=cfg.use_gpu
-    )
-    num_params, flops = compute_model_complexity(
-        model, (1, 3, cfg.data.height, cfg.data.width)
-    )
-    print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
-
-    if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
-        load_pretrained_weights(model, cfg.model.load_weights)
-
-    if cfg.use_gpu:
-        model = nn.DataParallel(model).cuda()
-
-    optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
-    scheduler = torchreid.optim.build_lr_scheduler(
-        optimizer, **lr_scheduler_kwargs(cfg)
-    )
-
-    if cfg.model.resume and check_isfile(cfg.model.resume):
-        cfg.train.start_epoch = resume_from_checkpoint(
-            cfg.model.resume, model, optimizer=optimizer, scheduler=scheduler
-        )
-
-    print(
-        'Building {}-engine for {}-reid'.format(cfg.loss.name, cfg.data.type)
-    )
-    engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
-    engine.run(**engine_run_kwargs(cfg))
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/xnetba/Chat_advance/modules/models/inspurai.py b/spaces/xnetba/Chat_advance/modules/models/inspurai.py
deleted file mode 100644
index c590859fa7717d032290ccc490d22f4494541576..0000000000000000000000000000000000000000
--- a/spaces/xnetba/Chat_advance/modules/models/inspurai.py
+++ /dev/null
@@ -1,345 +0,0 @@
-# 代码主要来源于 https://github.com/Shawn-Inspur/Yuan-1.0/blob/main/yuan_api/inspurai.py
-
-import hashlib
-import json
-import os
-import time
-import uuid
-from datetime import datetime
-
-import pytz
-import requests
-
-from modules.presets import NO_APIKEY_MSG
-from modules.models.base_model import BaseLLMModel
-
-
-class Example:
-    """ store some examples(input, output pairs and formats) for few-shots to prime the model."""
-
-    def __init__(self, inp, out):
-        self.input = inp
-        self.output = out
-        self.id = uuid.uuid4().hex
-
-    def get_input(self):
-        """return the input of the example."""
-        return self.input
-
-    def get_output(self):
-        """Return the output of the example."""
-        return self.output
-
-    def get_id(self):
-        """Returns the unique ID of the example."""
-        return self.id
-
-    def as_dict(self):
-        return {
-            "input": self.get_input(),
-            "output": self.get_output(),
-            "id": self.get_id(),
-        }
-
-
-class Yuan:
-    """The main class for a user to interface with the Inspur Yuan API.
-    A user can set account info and add examples of the API request.
-    """
-
-    def __init__(self,
-                 engine='base_10B',
-                 temperature=0.9,
-                 max_tokens=100,
-                 input_prefix='',
-                 input_suffix='\n',
-                 output_prefix='答:',
-                 output_suffix='\n\n',
-                 append_output_prefix_to_query=False,
-                 topK=1,
-                 topP=0.9,
-                 frequencyPenalty=1.2,
-                 responsePenalty=1.2,
-                 noRepeatNgramSize=2):
-
-        self.examples = {}
-        self.engine = engine
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-        self.topK = topK
-        self.topP = topP
-        self.frequencyPenalty = frequencyPenalty
-        self.responsePenalty = responsePenalty
-        self.noRepeatNgramSize = noRepeatNgramSize
-        self.input_prefix = input_prefix
-        self.input_suffix = input_suffix
-        self.output_prefix = output_prefix
-        self.output_suffix = output_suffix
-        self.append_output_prefix_to_query = append_output_prefix_to_query
-        self.stop = (output_suffix + input_prefix).strip()
-        self.api = None
-
-        # if self.engine not in ['base_10B','translate','dialog']:
-        #     raise Exception('engine must be one of [\'base_10B\',\'translate\',\'dialog\'] ')
-    def set_account(self, api_key):
-        account = api_key.split('||')
-        self.api = YuanAPI(user=account[0], phone=account[1])
-
-    def add_example(self, ex):
-        """Add an example to the object.
-        Example must be an instance of the Example class."""
-        assert isinstance(ex, Example), "Please create an Example object."
-        self.examples[ex.get_id()] = ex
-
-    def delete_example(self, id):
-        """Delete example with the specific id."""
-        if id in self.examples:
-            del self.examples[id]
-
-    def get_example(self, id):
-        """Get a single example."""
-        return self.examples.get(id, None)
-
-    def get_all_examples(self):
-        """Returns all examples as a list of dicts."""
-        return {k: v.as_dict() for k, v in self.examples.items()}
-
-    def get_prime_text(self):
-        """Formats all examples to prime the model."""
-        return "".join(
-            [self.format_example(ex) for ex in self.examples.values()])
-
-    def get_engine(self):
-        """Returns the engine specified for the API."""
-        return self.engine
-
-    def get_temperature(self):
-        """Returns the temperature specified for the API."""
-        return self.temperature
-
-    def get_max_tokens(self):
-        """Returns the max tokens specified for the API."""
-        return self.max_tokens
-
-    def craft_query(self, prompt):
-        """Creates the query for the API request."""
-        q = self.get_prime_text(
-        ) + self.input_prefix + prompt + self.input_suffix
-        if self.append_output_prefix_to_query:
-            q = q + self.output_prefix
-
-        return q
-
-    def format_example(self, ex):
-        """Formats the input, output pair."""
-        return self.input_prefix + ex.get_input(
-        ) + self.input_suffix + self.output_prefix + ex.get_output(
-        ) + self.output_suffix
-
-    def response(self,
-                 query,
-                 engine='base_10B',
-                 max_tokens=20,
-                 temperature=0.9,
-                 topP=0.1,
-                 topK=1,
-                 frequencyPenalty=1.0,
-                 responsePenalty=1.0,
-                 noRepeatNgramSize=0):
-        """Obtains the original result returned by the API."""
-
-        if self.api is None:
-            return NO_APIKEY_MSG
-        try:
-            # requestId = submit_request(query,temperature,topP,topK,max_tokens, engine)
-            requestId = self.api.submit_request(query, temperature, topP, topK, max_tokens, engine, frequencyPenalty,
-                                       responsePenalty, noRepeatNgramSize)
-            response_text = self.api.reply_request(requestId)
-        except Exception as e:
-            raise e
-
-        return response_text
-
-    def del_special_chars(self, msg):
-        special_chars = ['<unk>', '<eod>', '#', '▃', '▁', '▂', '　']
-        for char in special_chars:
-            msg = msg.replace(char, '')
-        return msg
-
-    def submit_API(self, prompt, trun=[]):
-        """Submit prompt to yuan API interface and obtain an pure text reply.
-        :prompt: Question or any content a user may input.
-        :return: pure text response."""
-        query = self.craft_query(prompt)
-        res = self.response(query, engine=self.engine,
-                            max_tokens=self.max_tokens,
-                            temperature=self.temperature,
-                            topP=self.topP,
-                            topK=self.topK,
-                            frequencyPenalty=self.frequencyPenalty,
-                            responsePenalty=self.responsePenalty,
-                            noRepeatNgramSize=self.noRepeatNgramSize)
-        if 'resData' in res and res['resData'] != None:
-            txt = res['resData']
-        else:
-            txt = '模型返回为空，请尝试修改输入'
-        # 单独针对翻译模型的后处理
-        if self.engine == 'translate':
-            txt = txt.replace(' ##', '').replace(' "', '"').replace(": ", ":").replace(" ,", ",") \
-                .replace('英文：', '').replace('文：', '').replace("( ", "(").replace(" )", ")")
-        else:
-            txt = txt.replace(' ', '')
-        txt = self.del_special_chars(txt)
-
-        # trun多结束符截断模型输出
-        if isinstance(trun, str):
-            trun = [trun]
-        try:
-            if trun != None and isinstance(trun, list) and trun != []:
-                for tr in trun:
-                    if tr in txt and tr != "":
-                        txt = txt[:txt.index(tr)]
-                    else:
-                        continue
-        except:
-            return txt
-        return txt
-
-
-class YuanAPI:
-    ACCOUNT = ''
-    PHONE = ''
-
-    SUBMIT_URL = "http://api.airyuan.cn:32102/v1/interface/api/infer/getRequestId?"
-    REPLY_URL = "http://api.airyuan.cn:32102/v1/interface/api/result?"
-
-    def __init__(self, user, phone):
-        self.ACCOUNT = user
-        self.PHONE = phone
-
-    @staticmethod
-    def code_md5(str):
-        code = str.encode("utf-8")
-        m = hashlib.md5()
-        m.update(code)
-        result = m.hexdigest()
-        return result
-
-    @staticmethod
-    def rest_get(url, header, timeout, show_error=False):
-        '''Call rest get method'''
-        try:
-            response = requests.get(url, headers=header, timeout=timeout, verify=False)
-            return response
-        except Exception as exception:
-            if show_error:
-                print(exception)
-            return None
-
-    def header_generation(self):
-        """Generate header for API request."""
-        t = datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d")
-        token = self.code_md5(self.ACCOUNT + self.PHONE + t)
-        headers = {'token': token}
-        return headers
-
-    def submit_request(self, query, temperature, topP, topK, max_tokens, engine, frequencyPenalty, responsePenalty,
-                       noRepeatNgramSize):
-        """Submit query to the backend server and get requestID."""
-        headers = self.header_generation()
-        # url=SUBMIT_URL + "account={0}&data={1}&temperature={2}&topP={3}&topK={4}&tokensToGenerate={5}&type={6}".format(ACCOUNT,query,temperature,topP,topK,max_tokens,"api")
-        # url=SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \
-        #                  "&type={7}".format(engine,ACCOUNT,query,temperature,topP,topK, max_tokens,"api")
-        url = self.SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \
-                                "&type={7}&frequencyPenalty={8}&responsePenalty={9}&noRepeatNgramSize={10}". \
-            format(engine, self.ACCOUNT, query, temperature, topP, topK, max_tokens, "api", frequencyPenalty,
-                   responsePenalty, noRepeatNgramSize)
-        response = self.rest_get(url, headers, 30)
-        response_text = json.loads(response.text)
-        if response_text["flag"]:
-            requestId = response_text["resData"]
-            return requestId
-        else:
-            raise RuntimeWarning(response_text)
-
-    def reply_request(self, requestId, cycle_count=5):
-        """Check reply API to get the inference response."""
-        url = self.REPLY_URL + "account={0}&requestId={1}".format(self.ACCOUNT, requestId)
-        headers = self.header_generation()
-        response_text = {"flag": True, "resData": None}
-        for i in range(cycle_count):
-            response = self.rest_get(url, headers, 30, show_error=True)
-            response_text = json.loads(response.text)
-            if response_text["resData"] is not None:
-                return response_text
-            if response_text["flag"] is False and i == cycle_count - 1:
-                raise RuntimeWarning(response_text)
-            time.sleep(3)
-        return response_text
-
-
-class Yuan_Client(BaseLLMModel):
-
-    def __init__(self, model_name, api_key, user_name="", system_prompt=None):
-        super().__init__(model_name=model_name, user=user_name)
-        self.history = []
-        self.api_key = api_key
-        self.system_prompt = system_prompt
-
-        self.input_prefix = ""
-        self.output_prefix = ""
-
-    def set_text_prefix(self, option, value):
-        if option == 'input_prefix':
-            self.input_prefix = value
-        elif option == 'output_prefix':
-            self.output_prefix = value
-
-    def get_answer_at_once(self):
-        # yuan temperature is (0,1] and base model temperature is [0,2], and yuan 0.9 == base 1 so need to convert
-        temperature = self.temperature if self.temperature <= 1 else 0.9 + (self.temperature - 1) / 10
-        topP = self.top_p
-        topK = self.n_choices
-        # max_tokens should be in [1,200]
-        max_tokens = self.max_generation_token if self.max_generation_token is not None else 50
-        if max_tokens > 200:
-            max_tokens = 200
-        stop = self.stop_sequence if self.stop_sequence is not None else []
-        examples = []
-        system_prompt = self.system_prompt
-        if system_prompt is not None:
-            lines = system_prompt.splitlines()
-            # TODO: support prefixes in system prompt or settings
-            """
-            if lines[0].startswith('-'):
-                prefixes = lines.pop()[1:].split('|')
-                self.input_prefix = prefixes[0]
-                if len(prefixes) > 1:
-                    self.output_prefix = prefixes[1]
-                if len(prefixes) > 2:
-                    stop = prefixes[2].split(',')
-            """
-            for i in range(0, len(lines), 2):
-                in_line = lines[i]
-                out_line = lines[i + 1] if i + 1 < len(lines) else ""
-                examples.append((in_line, out_line))
-        yuan = Yuan(engine=self.model_name.replace('yuanai-1.0-', ''),
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                    topK=topK,
-                    topP=topP,
-                    input_prefix=self.input_prefix,
-                    input_suffix="",
-                    output_prefix=self.output_prefix,
-                    output_suffix="".join(stop),
-                    )
-        if not self.api_key:
-            return NO_APIKEY_MSG, 0
-        yuan.set_account(self.api_key)
-
-        for in_line, out_line in examples:
-            yuan.add_example(Example(inp=in_line, out=out_line))
-
-        prompt = self.history[-1]["content"]
-        answer = yuan.submit_API(prompt, trun=stop)
-        return answer, len(answer)
diff --git "a/spaces/xwsm/gpt/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py" "b/spaces/xwsm/gpt/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py"
deleted file mode 100644
index 5bf8bc4ba95864dc53f98b7335e654f58c4fed54..0000000000000000000000000000000000000000
--- "a/spaces/xwsm/gpt/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py"
+++ /dev/null
@@ -1,67 +0,0 @@
-from toolbox import CatchException, update_ui, get_conf, select_api_key
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-
-
-def gen_image(llm_kwargs, prompt, resolution="256x256"):
-    import requests, json, time, os
-    from request_llm.bridge_all import model_info
-
-    proxies, = get_conf('proxies')
-    # Set up OpenAI API key and model 
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-    # 'https://api.openai.com/v1/chat/completions'
-    img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
-    # # Generate the image
-    url = img_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}",
-        'Content-Type': 'application/json'
-    }
-    data = {
-        'prompt': prompt,
-        'n': 1,
-        'size': resolution,
-        'response_format': 'url'
-    }
-    response = requests.post(url, headers=headers, json=data, proxies=proxies)
-    print(response.content)
-    image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-
-    # 文件保存到本地
-    r = requests.get(image_url, proxies=proxies)
-    file_path = 'gpt_log/image_gen/'
-    os.makedirs(file_path, exist_ok=True)
-    file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
-    with open(file_path+file_name, 'wb+') as f: f.write(r.content)
-
-
-    return image_url, file_path+file_name
-
-
-
-@CatchException
-def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-xxxx或者api2d-xxxx。如果中文效果不理想, 尝试Prompt。正在处理中 ....."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    resolution = plugin_kwargs.get("advanced_arg", '256x256')
-    image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
-    chatbot.append([prompt,  
-        f'图像中转网址: <br/>`{image_url}`<br/>'+
-        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
-        f'本地文件地址: <br/>`{image_path}`<br/>'+
-        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
diff --git a/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/longcode/jpgd.cpp b/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/longcode/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/longcode/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include <string.h>
-
-#include <assert.h>
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
-	static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-	static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
-	// stolen from IImageWrapper.h
-	enum ERGBFormatJPG
-	{
-		Invalid = -1,
-		RGBA =  0,
-		BGRA =  1,
-		Gray =  2,
-	};
-	static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
-	// DCT coefficients are stored in this sequence.
-	static int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
-	enum JPEG_MARKER
-	{
-		M_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,
-		M_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,
-		M_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,
-		M_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,
-		M_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0
-	};
-
-	enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS  13
-#define PASS1_BITS  2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */
-#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */
-#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */
-#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */
-#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */
-#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */
-#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */
-#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */
-#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */
-#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */
-#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */
-#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */
-
-#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst)  ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
-	// Compiler creates a fast path 1D IDCT for X non-zero columns
-	template <int NONZERO_COLS>
-	struct Row
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			// ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
-			const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
-			pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
-		}
-	};
-
-	template <>
-	struct Row<0>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-#ifdef _MSC_VER
-			pTemp; pSrc;
-#endif
-		}
-	};
-
-	template <>
-	struct Row<1>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			const int dcval = (pSrc[0] << PASS1_BITS);
-
-			pTemp[0] = dcval;
-			pTemp[1] = dcval;
-			pTemp[2] = dcval;
-			pTemp[3] = dcval;
-			pTemp[4] = dcval;
-			pTemp[5] = dcval;
-			pTemp[6] = dcval;
-			pTemp[7] = dcval;
-		}
-	};
-
-	// Compiler creates a fast path 1D IDCT for X non-zero rows
-	template <int NONZERO_ROWS>
-	struct Col
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
-			const int z2 = ACCESS_ROW(2);
-			const int z3 = ACCESS_ROW(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*0] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*7] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*1] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*6] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*2] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*5] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*3] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*4] = (uint8)CLAMP(i);
-		}
-	};
-
-	template <>
-	struct Col<1>
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
-			const uint8 dcval_clamped = (uint8)CLAMP(dcval);
-			pDst_ptr[0*8] = dcval_clamped;
-			pDst_ptr[1*8] = dcval_clamped;
-			pDst_ptr[2*8] = dcval_clamped;
-			pDst_ptr[3*8] = dcval_clamped;
-			pDst_ptr[4*8] = dcval_clamped;
-			pDst_ptr[5*8] = dcval_clamped;
-			pDst_ptr[6*8] = dcval_clamped;
-			pDst_ptr[7*8] = dcval_clamped;
-		}
-	};
-
-	static const uint8 s_idct_row_table[] =
-	{
-		1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
-		4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
-		6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
-		6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
-		8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
-		8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
-		8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
-		8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
-	};
-
-	static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
-	void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
-	{
-		JPGD_ASSERT(block_max_zag >= 1);
-		JPGD_ASSERT(block_max_zag <= 64);
-
-		if (block_max_zag == 1)
-		{
-			int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
-			k = CLAMP(k);
-			k = k | (k<<8);
-			k = k | (k<<16);
-
-			for (int i = 8; i > 0; i--)
-			{
-				*(int*)&pDst_ptr[0] = k;
-				*(int*)&pDst_ptr[4] = k;
-				pDst_ptr += 8;
-			}
-			return;
-		}
-
-		int temp[64];
-
-		const jpgd_block_t* pSrc = pSrc_ptr;
-		int* pTemp = temp;
-
-		const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
-		int i;
-		for (i = 8; i > 0; i--, pRow_tab++)
-		{
-			switch (*pRow_tab)
-			{
-			case 0: Row<0>::idct(pTemp, pSrc); break;
-			case 1: Row<1>::idct(pTemp, pSrc); break;
-			case 2: Row<2>::idct(pTemp, pSrc); break;
-			case 3: Row<3>::idct(pTemp, pSrc); break;
-			case 4: Row<4>::idct(pTemp, pSrc); break;
-			case 5: Row<5>::idct(pTemp, pSrc); break;
-			case 6: Row<6>::idct(pTemp, pSrc); break;
-			case 7: Row<7>::idct(pTemp, pSrc); break;
-			case 8: Row<8>::idct(pTemp, pSrc); break;
-			}
-
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-
-		const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
-		for (i = 8; i > 0; i--)
-		{
-			switch (nonzero_rows)
-			{
-			case 1: Col<1>::idct(pDst_ptr, pTemp); break;
-			case 2: Col<2>::idct(pDst_ptr, pTemp); break;
-			case 3: Col<3>::idct(pDst_ptr, pTemp); break;
-			case 4: Col<4>::idct(pDst_ptr, pTemp); break;
-			case 5: Col<5>::idct(pDst_ptr, pTemp); break;
-			case 6: Col<6>::idct(pDst_ptr, pTemp); break;
-			case 7: Col<7>::idct(pDst_ptr, pTemp); break;
-			case 8: Col<8>::idct(pDst_ptr, pTemp); break;
-			}
-
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
-	{
-		int temp[64];
-		int* pTemp = temp;
-		const jpgd_block_t* pSrc = pSrc_ptr;
-
-		for (int i = 4; i > 0; i--)
-		{
-			Row<4>::idct(pTemp, pSrc);
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-		for (int i = 8; i > 0; i--)
-		{
-			Col<4>::idct(pDst_ptr, pTemp);
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	// Retrieve one character from the input stream.
-	inline uint jpeg_decoder::get_char()
-	{
-		// Any bytes remaining in buffer?
-		if (!m_in_buf_left)
-		{
-			// Try to get more bytes.
-			prep_in_buffer();
-			// Still nothing to get?
-			if (!m_in_buf_left)
-			{
-				// Pad the end of the stream with 0xFF 0xD9 (EOI marker)
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Same as previous method, except can indicate if the character is a pad character or not.
-	inline uint jpeg_decoder::get_char(bool *pPadding_flag)
-	{
-		if (!m_in_buf_left)
-		{
-			prep_in_buffer();
-			if (!m_in_buf_left)
-			{
-				*pPadding_flag = true;
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		*pPadding_flag = false;
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Inserts a previously retrieved character back into the input buffer.
-	inline void jpeg_decoder::stuff_char(uint8 q)
-	{
-		*(--m_pIn_buf_ofs) = q;
-		m_in_buf_left++;
-	}
-
-	// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
-	inline uint8 jpeg_decoder::get_octet()
-	{
-		bool padding_flag;
-		int c = get_char(&padding_flag);
-
-		if (c == 0xFF)
-		{
-			if (padding_flag)
-				return 0xFF;
-
-			c = get_char(&padding_flag);
-			if (padding_flag)
-			{
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-
-			if (c == 0x00)
-				return 0xFF;
-			else
-			{
-				stuff_char(static_cast<uint8>(c));
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-		}
-
-		return static_cast<uint8>(c);
-	}
-
-	// Retrieves a variable number of bits from the input stream. Does not recognize markers.
-	inline uint jpeg_decoder::get_bits(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			uint c1 = get_char();
-			uint c2 = get_char();
-			m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
-	inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
-			{
-				uint c1 = get_octet();
-				uint c2 = get_octet();
-				m_bit_buf |= (c1 << 8) | c2;
-			}
-			else
-			{
-				m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
-				m_in_buf_left -= 2;
-				m_pIn_buf_ofs += 2;
-			}
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
-		{
-			// Decode more bits, use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-		}
-		else
-			get_bits_no_markers(pH->code_size[symbol]);
-
-		return symbol;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
-		{
-			// Use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-
-			extra_bits = get_bits_no_markers(symbol & 0xF);
-		}
-		else
-		{
-			JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
-			if (symbol & 0x8000)
-			{
-				get_bits_no_markers((symbol >> 8) & 31);
-				extra_bits = symbol >> 16;
-			}
-			else
-			{
-				int code_size = (symbol >> 8) & 31;
-				int num_extra_bits = symbol & 0xF;
-				int bits = code_size + num_extra_bits;
-				if (bits <= (m_bits_left + 16))
-					extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
-				else
-				{
-					get_bits_no_markers(code_size);
-					extra_bits = get_bits_no_markers(num_extra_bits);
-				}
-			}
-
-			symbol &= 0xFF;
-		}
-
-		return symbol;
-	}
-
-	// Tables and macro used to fully decode the DPCM differences.
-	static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
-	static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
-	static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
-	// Clamps a value between 0-255.
-	inline uint8 jpeg_decoder::clamp(int i)
-	{
-		if (static_cast<uint>(i) > 255)
-			i = (((~i) >> 31) & 0xFF);
-
-		return static_cast<uint8>(i);
-	}
-
-	namespace DCT_Upsample
-	{
-		struct Matrix44
-		{
-			typedef int Element_Type;
-			enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
-			Element_Type v[NUM_ROWS][NUM_COLS];
-
-			inline int rows() const { return NUM_ROWS; }
-			inline int cols() const { return NUM_COLS; }
-
-			inline const Element_Type & at(int r, int c) const { return v[r][c]; }
-			inline       Element_Type & at(int r, int c)       { return v[r][c]; }
-
-			inline Matrix44() { }
-
-			inline Matrix44& operator += (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) += a.at(r, 0);
-					at(r, 1) += a.at(r, 1);
-					at(r, 2) += a.at(r, 2);
-					at(r, 3) += a.at(r, 3);
-				}
-				return *this;
-			}
-
-			inline Matrix44& operator -= (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) -= a.at(r, 0);
-					at(r, 1) -= a.at(r, 1);
-					at(r, 2) -= a.at(r, 2);
-					at(r, 3) -= a.at(r, 3);
-				}
-				return *this;
-			}
-
-			friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
-				}
-				return ret;
-			}
-
-			friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
-				}
-				return ret;
-			}
-
-			static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));
-				}
-			}
-
-			static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));
-				}
-			}
-		};
-
-		const int FRACT_BITS = 10;
-		const int SCALE = 1 << FRACT_BITS;
-
-		typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
-		// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
-		// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
-		template<int NUM_ROWS, int NUM_COLS>
-		struct P_Q
-		{
-			static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X000 = AT(0, 0);
-				const Temp_Type X001 = AT(0, 1);
-				const Temp_Type X002 = AT(0, 2);
-				const Temp_Type X003 = AT(0, 3);
-				const Temp_Type X004 = AT(0, 4);
-				const Temp_Type X005 = AT(0, 5);
-				const Temp_Type X006 = AT(0, 6);
-				const Temp_Type X007 = AT(0, 7);
-				const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
-				const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
-				const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
-				const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
-				const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
-				const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
-				const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
-				const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
-				const Temp_Type X020 = AT(4, 0);
-				const Temp_Type X021 = AT(4, 1);
-				const Temp_Type X022 = AT(4, 2);
-				const Temp_Type X023 = AT(4, 3);
-				const Temp_Type X024 = AT(4, 4);
-				const Temp_Type X025 = AT(4, 5);
-				const Temp_Type X026 = AT(4, 6);
-				const Temp_Type X027 = AT(4, 7);
-				const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
-				const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
-				const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
-				const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
-				const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
-				const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
-				const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
-				const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				P.at(0, 0) = X000;
-				P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
-				P.at(0, 2) = X004;
-				P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
-				P.at(1, 0) = X010;
-				P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
-				P.at(1, 2) = X014;
-				P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
-				P.at(2, 0) = X020;
-				P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
-				P.at(2, 2) = X024;
-				P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
-				P.at(3, 0) = X030;
-				P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
-				P.at(3, 2) = X034;
-				P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
-				// 40 muls 24 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
-				Q.at(0, 1) = X002;
-				Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
-				Q.at(0, 3) = X006;
-				Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
-				Q.at(1, 1) = X012;
-				Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
-				Q.at(1, 3) = X016;
-				Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
-				Q.at(2, 1) = X022;
-				Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
-				Q.at(2, 3) = X026;
-				Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
-				Q.at(3, 1) = X032;
-				Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
-				Q.at(3, 3) = X036;
-				// 40 muls 24 adds
-			}
-		};
-
-		template<int NUM_ROWS, int NUM_COLS>
-		struct R_S
-		{
-			static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
-				const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
-				const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
-				const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
-				const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
-				const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
-				const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
-				const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
-				const Temp_Type X110 = AT(2, 0);
-				const Temp_Type X111 = AT(2, 1);
-				const Temp_Type X112 = AT(2, 2);
-				const Temp_Type X113 = AT(2, 3);
-				const Temp_Type X114 = AT(2, 4);
-				const Temp_Type X115 = AT(2, 5);
-				const Temp_Type X116 = AT(2, 6);
-				const Temp_Type X117 = AT(2, 7);
-				const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
-				const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
-				const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
-				const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
-				const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
-				const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
-				const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
-				const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
-				const Temp_Type X130 = AT(6, 0);
-				const Temp_Type X131 = AT(6, 1);
-				const Temp_Type X132 = AT(6, 2);
-				const Temp_Type X133 = AT(6, 3);
-				const Temp_Type X134 = AT(6, 4);
-				const Temp_Type X135 = AT(6, 5);
-				const Temp_Type X136 = AT(6, 6);
-				const Temp_Type X137 = AT(6, 7);
-				// 80 muls 48 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				R.at(0, 0) = X100;
-				R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
-				R.at(0, 2) = X104;
-				R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
-				R.at(1, 0) = X110;
-				R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
-				R.at(1, 2) = X114;
-				R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
-				R.at(2, 0) = X120;
-				R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
-				R.at(2, 2) = X124;
-				R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
-				R.at(3, 0) = X130;
-				R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
-				R.at(3, 2) = X134;
-				R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
-				// 40 muls 24 adds
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
-				S.at(0, 1) = X102;
-				S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
-				S.at(0, 3) = X106;
-				S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
-				S.at(1, 1) = X112;
-				S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
-				S.at(1, 3) = X116;
-				S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
-				S.at(2, 1) = X122;
-				S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
-				S.at(2, 3) = X126;
-				S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
-				S.at(3, 1) = X132;
-				S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
-				S.at(3, 3) = X136;
-				// 40 muls 24 adds
-			}
-		};
-	} // end namespace DCT_Upsample
-
-	// Unconditionally frees all allocated m_blocks.
-	void jpeg_decoder::free_all_blocks()
-	{
-		m_pStream = NULL;
-		for (mem_block *b = m_pMem_blocks; b; )
-		{
-			mem_block *n = b->m_pNext;
-			jpgd_free(b);
-			b = n;
-		}
-		m_pMem_blocks = NULL;
-	}
-
-	// This method handles all errors.
-	// It could easily be changed to use C++ exceptions.
-	void jpeg_decoder::stop_decoding(jpgd_status status)
-	{
-		m_error_code = status;
-		free_all_blocks();
-		longjmp(m_jmp_state, status);
-
-		// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
-		// that this function doesn't return, otherwise we get this error:
-		// 
-		// error : function declared 'noreturn' should not return
-		exit(1);
-	}
-
-	void *jpeg_decoder::alloc(size_t nSize, bool zero)
-	{
-		nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
-		char *rv = NULL;
-		for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
-		{
-			if ((b->m_used_count + nSize) <= b->m_size)
-			{
-				rv = b->m_data + b->m_used_count;
-				b->m_used_count += nSize;
-				break;
-			}
-		}
-		if (!rv)
-		{
-			int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
-			mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
-			if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
-			b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
-			b->m_used_count = nSize;
-			b->m_size = capacity;
-			rv = b->m_data;
-		}
-		if (zero) memset(rv, 0, nSize);
-		return rv;
-	}
-
-	void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
-	{
-		uint8 *pD = (uint8*)p;
-		const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
-		while (n)
-		{
-			pD[0] = l; pD[1] = h; pD += 2;
-			n--;
-		}
-	}
-
-	// Refill the input buffer.
-	// This method will sit in a loop until (A) the buffer is full or (B)
-	// the stream's read() method reports and end of file condition.
-	void jpeg_decoder::prep_in_buffer()
-	{
-		m_in_buf_left = 0;
-		m_pIn_buf_ofs = m_in_buf;
-
-		if (m_eof_flag)
-			return;
-
-		do
-		{
-			int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
-			if (bytes_read == -1)
-				stop_decoding(JPGD_STREAM_READ);
-
-			m_in_buf_left += bytes_read;
-		} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
-		m_total_bytes_read += m_in_buf_left;
-
-		// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
-		// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
-		word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
-	}
-
-	// Read a Huffman code table.
-	void jpeg_decoder::read_dht_marker()
-	{
-		int i, index, count;
-		uint8 huff_num[17];
-		uint8 huff_val[256];
-
-		uint num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DHT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			index = get_bits(8);
-
-			huff_num[0] = 0;
-
-			count = 0;
-
-			for (i = 1; i <= 16; i++)
-			{
-				huff_num[i] = static_cast<uint8>(get_bits(8));
-				count += huff_num[i];
-			}
-
-			if (count > 255)
-				stop_decoding(JPGD_BAD_DHT_COUNTS);
-
-			for (i = 0; i < count; i++)
-				huff_val[i] = static_cast<uint8>(get_bits(8));
-
-			i = 1 + 16 + count;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DHT_MARKER);
-
-			num_left -= i;
-
-			if ((index & 0x10) > 0x10)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
-			if (index >= JPGD_MAX_HUFF_TABLES)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			if (!m_huff_num[index])
-				m_huff_num[index] = (uint8 *)alloc(17);
-
-			if (!m_huff_val[index])
-				m_huff_val[index] = (uint8 *)alloc(256);
-
-			m_huff_ac[index] = (index & 0x10) != 0;
-			memcpy(m_huff_num[index], huff_num, 17);
-			memcpy(m_huff_val[index], huff_val, 256);
-		}
-	}
-
-	// Read a quantization table.
-	void jpeg_decoder::read_dqt_marker()
-	{
-		int n, i, prec;
-		uint num_left;
-		uint temp;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DQT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			n = get_bits(8);
-			prec = n >> 4;
-			n &= 0x0F;
-
-			if (n >= JPGD_MAX_QUANT_TABLES)
-				stop_decoding(JPGD_BAD_DQT_TABLE);
-
-			if (!m_quant[n])
-				m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
-			// read quantization entries, in zag order
-			for (i = 0; i < 64; i++)
-			{
-				temp = get_bits(8);
-
-				if (prec)
-					temp = (temp << 8) + get_bits(8);
-
-				m_quant[n][i] = static_cast<jpgd_quant_t>(temp);
-			}
-
-			i = 64 + 1;
-
-			if (prec)
-				i += 64;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DQT_LENGTH);
-
-			num_left -= i;
-		}
-	}
-
-	// Read the start of frame (SOF) marker.
-	void jpeg_decoder::read_sof_marker()
-	{
-		int i;
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */
-			stop_decoding(JPGD_BAD_PRECISION);
-
-		m_image_y_size = get_bits(16);
-
-		if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
-			stop_decoding(JPGD_BAD_HEIGHT);
-
-		m_image_x_size = get_bits(16);
-
-		if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
-			stop_decoding(JPGD_BAD_WIDTH);
-
-		m_comps_in_frame = get_bits(8);
-
-		if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
-			stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
-		if (num_left != (uint)(m_comps_in_frame * 3 + 8))
-			stop_decoding(JPGD_BAD_SOF_LENGTH);
-
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_comp_ident[i]  = get_bits(8);
-			m_comp_h_samp[i] = get_bits(4);
-			m_comp_v_samp[i] = get_bits(4);
-			m_comp_quant[i]  = get_bits(8);
-		}
-	}
-
-	// Used to skip unrecognized markers.
-	void jpeg_decoder::skip_variable_marker()
-	{
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Read a define restart interval (DRI) marker.
-	void jpeg_decoder::read_dri_marker()
-	{
-		if (get_bits(16) != 4)
-			stop_decoding(JPGD_BAD_DRI_LENGTH);
-
-		m_restart_interval = get_bits(16);
-	}
-
-	// Read a start of scan (SOS) marker.
-	void jpeg_decoder::read_sos_marker()
-	{
-		uint num_left;
-		int i, ci, n, c, cc;
-
-		num_left = get_bits(16);
-
-		n = get_bits(8);
-
-		m_comps_in_scan = n;
-
-		num_left -= 3;
-
-		if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
-			stop_decoding(JPGD_BAD_SOS_LENGTH);
-
-		for (i = 0; i < n; i++)
-		{
-			cc = get_bits(8);
-			c = get_bits(8);
-			num_left -= 2;
-
-			for (ci = 0; ci < m_comps_in_frame; ci++)
-				if (cc == m_comp_ident[ci])
-					break;
-
-			if (ci >= m_comps_in_frame)
-				stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
-			m_comp_list[i]    = ci;
-			m_comp_dc_tab[ci] = (c >> 4) & 15;
-			m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
-		}
-
-		m_spectral_start  = get_bits(8);
-		m_spectral_end    = get_bits(8);
-		m_successive_high = get_bits(4);
-		m_successive_low  = get_bits(4);
-
-		if (!m_progressive_flag)
-		{
-			m_spectral_start = 0;
-			m_spectral_end = 63;
-		}
-
-		num_left -= 3;
-
-		while (num_left)                  /* read past whatever is num_left */
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Finds the next marker.
-	int jpeg_decoder::next_marker()
-	{
-		uint c, bytes;
-
-		bytes = 0;
-
-		do
-		{
-			do
-			{
-				bytes++;
-				c = get_bits(8);
-			} while (c != 0xFF);
-
-			do
-			{
-				c = get_bits(8);
-			} while (c == 0xFF);
-
-		} while (c == 0);
-
-		// If bytes > 0 here, there where extra bytes before the marker (not good).
-
-		return c;
-	}
-
-	// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
-	// encountered.
-	int jpeg_decoder::process_markers()
-	{
-		int c;
-
-		for ( ; ; )
-		{
-			c = next_marker();
-
-			switch (c)
-			{
-			case M_SOF0:
-			case M_SOF1:
-			case M_SOF2:
-			case M_SOF3:
-			case M_SOF5:
-			case M_SOF6:
-			case M_SOF7:
-				//      case M_JPG:
-			case M_SOF9:
-			case M_SOF10:
-			case M_SOF11:
-			case M_SOF13:
-			case M_SOF14:
-			case M_SOF15:
-			case M_SOI:
-			case M_EOI:
-			case M_SOS:
-				{
-					return c;
-				}
-			case M_DHT:
-				{
-					read_dht_marker();
-					break;
-				}
-				// No arithmitic support - dumb patents!
-			case M_DAC:
-				{
-					stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-					break;
-				}
-			case M_DQT:
-				{
-					read_dqt_marker();
-					break;
-				}
-			case M_DRI:
-				{
-					read_dri_marker();
-					break;
-				}
-				//case M_APP0:  /* no need to read the JFIF marker */
-
-			case M_JPG:
-			case M_RST0:    /* no parameters */
-			case M_RST1:
-			case M_RST2:
-			case M_RST3:
-			case M_RST4:
-			case M_RST5:
-			case M_RST6:
-			case M_RST7:
-			case M_TEM:
-				{
-					stop_decoding(JPGD_UNEXPECTED_MARKER);
-					break;
-				}
-			default:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
-				{
-					skip_variable_marker();
-					break;
-				}
-			}
-		}
-	}
-
-	// Finds the start of image (SOI) marker.
-	// This code is rather defensive: it only checks the first 512 bytes to avoid
-	// false positives.
-	void jpeg_decoder::locate_soi_marker()
-	{
-		uint lastchar, thischar;
-		uint bytesleft;
-
-		lastchar = get_bits(8);
-
-		thischar = get_bits(8);
-
-		/* ok if it's a normal JPEG file without a special header */
-
-		if ((lastchar == 0xFF) && (thischar == M_SOI))
-			return;
-
-		bytesleft = 4096; //512;
-
-		for ( ; ; )
-		{
-			if (--bytesleft == 0)
-				stop_decoding(JPGD_NOT_JPEG);
-
-			lastchar = thischar;
-
-			thischar = get_bits(8);
-
-			if (lastchar == 0xFF)
-			{
-				if (thischar == M_SOI)
-					break;
-				else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
-					stop_decoding(JPGD_NOT_JPEG);
-			}
-		}
-
-		// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
-		thischar = (m_bit_buf >> 24) & 0xFF;
-
-		if (thischar != 0xFF)
-			stop_decoding(JPGD_NOT_JPEG);
-	}
-
-	// Find a start of frame (SOF) marker.
-	void jpeg_decoder::locate_sof_marker()
-	{
-		locate_soi_marker();
-
-		int c = process_markers();
-
-		switch (c)
-		{
-		case M_SOF2:
-			m_progressive_flag = JPGD_TRUE;
-		case M_SOF0:  /* baseline DCT */
-		case M_SOF1:  /* extended sequential DCT */
-			{
-				read_sof_marker();
-				break;
-			}
-		case M_SOF9:  /* Arithmitic coding */
-			{
-				stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-				break;
-			}
-		default:
-			{
-				stop_decoding(JPGD_UNSUPPORTED_MARKER);
-				break;
-			}
-		}
-	}
-
-	// Find a start of scan (SOS) marker.
-	int jpeg_decoder::locate_sos_marker()
-	{
-		int c;
-
-		c = process_markers();
-
-		if (c == M_EOI)
-			return JPGD_FALSE;
-		else if (c != M_SOS)
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-
-		read_sos_marker();
-
-		return JPGD_TRUE;
-	}
-
-	// Reset everything to default/uninitialized state.
-	void jpeg_decoder::init(jpeg_decoder_stream *pStream)
-	{
-		m_pMem_blocks = NULL;
-		m_error_code = JPGD_SUCCESS;
-		m_ready_flag = false;
-		m_image_x_size = m_image_y_size = 0;
-		m_pStream = pStream;
-		m_progressive_flag = JPGD_FALSE;
-
-		memset(m_huff_ac, 0, sizeof(m_huff_ac));
-		memset(m_huff_num, 0, sizeof(m_huff_num));
-		memset(m_huff_val, 0, sizeof(m_huff_val));
-		memset(m_quant, 0, sizeof(m_quant));
-
-		m_scan_type = 0;
-		m_comps_in_frame = 0;
-
-		memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
-		memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
-		memset(m_comp_quant, 0, sizeof(m_comp_quant));
-		memset(m_comp_ident, 0, sizeof(m_comp_ident));
-		memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
-		memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
-		m_comps_in_scan = 0;
-		memset(m_comp_list, 0, sizeof(m_comp_list));
-		memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
-		memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
-		m_spectral_start = 0;
-		m_spectral_end = 0;
-		m_successive_low = 0;
-		m_successive_high = 0;
-		m_max_mcu_x_size = 0;
-		m_max_mcu_y_size = 0;
-		m_blocks_per_mcu = 0;
-		m_max_blocks_per_row = 0;
-		m_mcus_per_row = 0;
-		m_mcus_per_col = 0;
-		m_expanded_blocks_per_component = 0;
-		m_expanded_blocks_per_mcu = 0;
-		m_expanded_blocks_per_row = 0;
-		m_freq_domain_chroma_upsample = false;
-
-		memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
-		m_total_lines_left = 0;
-		m_mcu_lines_left = 0;
-		m_real_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_pixel = 0;
-
-		memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
-		memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
-		memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_eob_run = 0;
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_pIn_buf_ofs = m_in_buf;
-		m_in_buf_left = 0;
-		m_eof_flag = false;
-		m_tem_flag = 0;
-
-		memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
-		memset(m_in_buf, 0, sizeof(m_in_buf));
-		memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
-		m_restart_interval = 0;
-		m_restarts_left    = 0;
-		m_next_restart_num = 0;
-
-		m_max_mcus_per_row = 0;
-		m_max_blocks_per_mcu = 0;
-		m_max_mcus_per_col = 0;
-
-		memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
-		m_pMCU_coefficients = NULL;
-		m_pSample_buf = NULL;
-
-		m_total_bytes_read = 0;
-
-		m_pScan_line_0 = NULL;
-		m_pScan_line_1 = NULL;
-
-		// Ready the input buffer.
-		prep_in_buffer();
-
-		// Prime the bit buffer.
-		m_bits_left = 16;
-		m_bit_buf = 0;
-
-		get_bits(16);
-		get_bits(16);
-
-		for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
-			m_mcu_block_max_zag[i] = 64;
-	}
-
-#define SCALEBITS 16
-#define ONE_HALF  ((int) 1 << (SCALEBITS-1))
-#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))
-
-	// Create a few tables that allow us to quickly convert YCbCr to RGB.
-	void jpeg_decoder::create_look_ups()
-	{
-		for (int i = 0; i <= 255; i++)
-		{
-			int k = i - 128;
-			m_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_crg[i] = (-FIX(0.71414f)) * k;
-			m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
-		}
-	}
-
-	// This method throws back into the stream any bytes that where read
-	// into the bit buffer during initial marker scanning.
-	void jpeg_decoder::fix_in_buffer()
-	{
-		// In case any 0xFF's where pulled into the buffer during marker scanning.
-		JPGD_ASSERT((m_bits_left & 7) == 0);
-
-		if (m_bits_left == 16)
-			stuff_char( (uint8)(m_bit_buf & 0xFF));
-
-		if (m_bits_left >= 8)
-			stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
-		stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
-		stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	void jpeg_decoder::transform_mcu(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
-		for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-	}
-
-	static const uint8 s_max_rc[64] =
-	{
-		17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
-		102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
-	};
-
-	void jpeg_decoder::transform_mcu_expand(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
-		// Y IDCT
-		int mcu_block;
-		for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-
-		// Chroma IDCT, with upsampling
-		jpgd_block_t temp_block[64];
-
-		for (int i = 0; i < 2; i++)
-		{
-			DCT_Upsample::Matrix44 P, Q, R, S;
-
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
-			switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
-			{
-			case 1*16+1:
-				DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
-				break;
-			case 1*16+2:
-				DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 2*16+2:
-				DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+2:
-				DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+3:
-				DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+4:
-				DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 4*16+4:
-				DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+4:
-				DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+5:
-				DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+6:
-				DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 6*16+6:
-				DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+6:
-				DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+7:
-				DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+8:
-				DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
-				break;
-			case 8*16+8:
-				DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
-				break;
-			default:
-				JPGD_ASSERT(false);
-			}
-
-			DCT_Upsample::Matrix44 a(P + Q); P -= Q;
-			DCT_Upsample::Matrix44& b = P;
-			DCT_Upsample::Matrix44 c(R + S); R -= S;
-			DCT_Upsample::Matrix44& d = R;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			pSrc_ptr += 64;
-		}
-	}
-
-	// Loads and dequantizes the next row of (already decoded) coefficients.
-	// Progressive images only.
-	void jpeg_decoder::load_next_row()
-	{
-		int i;
-		jpgd_block_t *p;
-		jpgd_quant_t *q;
-		int mcu_row, mcu_block, row_block = 0;
-		int component_num, component_id;
-		int block_x_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
-		for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-			for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-			{
-				component_id = m_mcu_org[mcu_block];
-				q = m_quant[m_comp_quant[component_id]];
-
-				p = m_pMCU_coefficients + 64 * mcu_block;
-
-				jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				p[0] = pDC[0];
-				memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
-				for (i = 63; i > 0; i--)
-					if (p[g_ZAG[i]])
-						break;
-
-				m_mcu_block_max_zag[mcu_block] = i + 1;
-
-				for ( ; i >= 0; i--)
-					if (p[g_ZAG[i]])
-						p[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);
-
-				row_block++;
-
-				if (m_comps_in_scan == 1)
-					block_x_mcu[component_id]++;
-				else
-				{
-					if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-					{
-						block_x_mcu_ofs = 0;
-
-						if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-						{
-							block_y_mcu_ofs = 0;
-
-							block_x_mcu[component_id] += m_comp_h_samp[component_id];
-						}
-					}
-				}
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-		}
-
-		if (m_comps_in_scan == 1)
-			m_block_y_mcu[m_comp_list[0]]++;
-		else
-		{
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				component_id = m_comp_list[component_num];
-
-				m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-			}
-		}
-	}
-
-	// Restart interval processing.
-	void jpeg_decoder::process_restart()
-	{
-		int i;
-		int c = 0;
-
-		// Align to a byte boundry
-		// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
-		//get_bits_no_markers(m_bits_left & 7);
-
-		// Let's scan a little bit to find the marker, but not _too_ far.
-		// 1536 is a "fudge factor" that determines how much to scan.
-		for (i = 1536; i > 0; i--)
-			if (get_char() == 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		for ( ; i > 0; i--)
-			if ((c = get_char()) != 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Is it the expected marker? If not, something bad happened.
-		if (c != (m_next_restart_num + M_RST0))
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Reset each component's DC prediction values.
-		memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		m_restarts_left = m_restart_interval;
-
-		m_next_restart_num = (m_next_restart_num + 1) & 7;
-
-		// Get the bit buffer going again...
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	static inline int dequantize_ac(int c, int q) {	c *= q;	return c; }
-
-	// Decodes and dequantizes the next row of coefficients.
-	void jpeg_decoder::decode_next_row()
-	{
-		int row_block = 0;
-
-		for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			if ((m_restart_interval) && (m_restarts_left == 0))
-				process_restart();
-
-			jpgd_block_t* p = m_pMCU_coefficients;
-			for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
-			{
-				int component_id = m_mcu_org[mcu_block];
-				jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
-				int r, s;
-				s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
-				s = HUFF_EXTEND(r, s);
-
-				m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
-				p[0] = static_cast<jpgd_block_t>(s * q[0]);
-
-				int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
-				huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
-				int k;
-				for (k = 1; k < 64; k++)
-				{
-					int extra_bits;
-					s = huff_decode(pH, extra_bits);
-
-					r = s >> 4;
-					s &= 15;
-
-					if (s)
-					{
-						if (r)
-						{
-							if ((k + r) > 63)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(r, prev_num_set - k);
-								int kt = k;
-								while (n--)
-									p[g_ZAG[kt++]] = 0;
-							}
-
-							k += r;
-						}
-
-						s = HUFF_EXTEND(extra_bits, s);
-
-						JPGD_ASSERT(k < 64);
-
-						p[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];
-					}
-					else
-					{
-						if (r == 15)
-						{
-							if ((k + 16) > 64)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(16, prev_num_set - k);
-								int kt = k;
-								while (n--)
-								{
-									JPGD_ASSERT(kt <= 63);
-									p[g_ZAG[kt++]] = 0;
-								}
-							}
-
-							k += 16 - 1; // - 1 because the loop counter is k
-							// BEGIN EPIC MOD
-							JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
-							// END EPIC MOD
-						}
-						else
-							break;
-					}
-				}
-
-				if (k < prev_num_set)
-				{
-					int kt = k;
-					while (kt < prev_num_set)
-						p[g_ZAG[kt++]] = 0;
-				}
-
-				m_mcu_block_max_zag[mcu_block] = k;
-
-				row_block++;
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-
-			m_restarts_left--;
-		}
-	}
-
-	// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int y = s[j];
-				int cb = s[64+j];
-				int cr = s[128+j];
-
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d[0] = clamp(y + m_cbb[cb]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_crr[cr]);
-					d[3] = 255;
-				}
-				else
-				{
-					d[0] = clamp(y + m_crr[cr]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_cbb[cb]);
-					d[3] = 255;
-				}
-				d += 4;
-			}
-
-			s += 64*3;
-		}
-	}
-
-	// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *y = m_pSample_buf + row * 8;
-		uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 4; j++)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j<<1];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-					}
-
-					d0 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*4 - 64*2;
-			c += 64*4 - 8;
-		}
-	}
-
-	// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int cb = c[0+j];
-				int cr = c[64+j];
-
-				int rc = m_crr[cr];
-				int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-				int bc = m_cbb[cb];
-
-				int yy = y[j];
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d0[0] = clamp(yy+bc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+rc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+bc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+rc);
-					d1[3] = 255;
-				}
-				else
-				{
-					d0[0] = clamp(yy+rc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+bc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+rc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+bc);
-					d1[3] = 255;
-				}
-
-				d0 += 4;
-				d1 += 4;
-			}
-
-			y += 64*4;
-			c += 64*4;
-		}
-	}
-
-	// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 8; j += 2)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+bc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+rc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+bc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+rc);
-						d1[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+rc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+bc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+rc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+bc);
-						d1[7] = 255;
-					}
-
-					d0 += 8;
-					d1 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*6 - 64*2;
-			c += 64*6 - 8;
-		}
-	}
-
-	// Y (1 block per MCU) to 8-bit grayscale
-	void jpeg_decoder::gray_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			*(uint *)d = *(uint *)s;
-			*(uint *)(&d[4]) = *(uint *)(&s[4]);
-
-			s += 64;
-			d += 8;
-		}
-	}
-
-	void jpeg_decoder::expanded_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-
-		uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
-		uint8* d = m_pScan_line_0;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int k = 0; k < m_max_mcu_x_size; k += 8)
-			{
-				const int Y_ofs = k * 8;
-				const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
-				const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
-				for (int j = 0; j < 8; j++)
-				{
-					int y = Py[Y_ofs + j];
-					int cb = Py[Cb_ofs + j];
-					int cr = Py[Cr_ofs + j];
-
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d[0] = clamp(y + m_cbb[cb]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_crr[cr]);
-						d[3] = 255;
-					}
-					else
-					{
-						d[0] = clamp(y + m_crr[cr]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_cbb[cb]);
-						d[3] = 255;
-					}
-
-					d += 4;
-				}
-			}
-
-			Py += 64 * m_expanded_blocks_per_mcu;
-		}
-	}
-
-	// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
-	void jpeg_decoder::find_eoi()
-	{
-		if (!m_progressive_flag)
-		{
-			// Attempt to read the EOI marker.
-			//get_bits_no_markers(m_bits_left & 7);
-
-			// Prime the bit buffer
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-
-			// The next marker _should_ be EOI
-			process_markers();
-		}
-
-		m_total_bytes_read -= m_in_buf_left;
-	}
-
-	int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
-	{
-		if ((m_error_code) || (!m_ready_flag))
-			return JPGD_FAILED;
-
-		if (m_total_lines_left == 0)
-			return JPGD_DONE;
-
-		if (m_mcu_lines_left == 0)
-		{
-			if (setjmp(m_jmp_state))
-				return JPGD_FAILED;
-
-			if (m_progressive_flag)
-				load_next_row();
-			else
-				decode_next_row();
-
-			// Find the EOI marker if that was the last row.
-			if (m_total_lines_left <= m_max_mcu_y_size)
-				find_eoi();
-
-			m_mcu_lines_left = m_max_mcu_y_size;
-		}
-
-		if (m_freq_domain_chroma_upsample)
-		{
-			expanded_convert();
-			*pScan_line = m_pScan_line_0;
-		}
-		else
-		{
-			switch (m_scan_type)
-			{
-			case JPGD_YH2V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H2V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH2V1:
-				{
-					H2V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_YH1V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H1V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH1V1:
-				{
-					H1V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_GRAYSCALE:
-				{
-					gray_convert();
-					*pScan_line = m_pScan_line_0;
-
-					break;
-				}
-			}
-		}
-
-		*pScan_line_len = m_real_dest_bytes_per_scan_line;
-
-		m_mcu_lines_left--;
-		m_total_lines_left--;
-
-		return JPGD_SUCCESS;
-	}
-
-	// Creates the tables needed for efficient Huffman decoding.
-	void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
-	{
-		int p, i, l, si;
-		uint8 huffsize[257];
-		uint huffcode[257];
-		uint code;
-		uint subtree;
-		int code_size;
-		int lastp;
-		int nextfreeentry;
-		int currententry;
-
-		pH->ac_table = m_huff_ac[index] != 0;
-
-		p = 0;
-
-		for (l = 1; l <= 16; l++)
-		{
-			for (i = 1; i <= m_huff_num[index][l]; i++)
-				huffsize[p++] = static_cast<uint8>(l);
-		}
-
-		huffsize[p] = 0;
-
-		lastp = p;
-
-		code = 0;
-		si = huffsize[0];
-		p = 0;
-
-		while (huffsize[p])
-		{
-			while (huffsize[p] == si)
-			{
-				huffcode[p++] = code;
-				code++;
-			}
-
-			code <<= 1;
-			si++;
-		}
-
-		memset(pH->look_up, 0, sizeof(pH->look_up));
-		memset(pH->look_up2, 0, sizeof(pH->look_up2));
-		memset(pH->tree, 0, sizeof(pH->tree));
-		memset(pH->code_size, 0, sizeof(pH->code_size));
-
-		nextfreeentry = -1;
-
-		p = 0;
-
-		while (p < lastp)
-		{
-			i = m_huff_val[index][p];
-			code = huffcode[p];
-			code_size = huffsize[p];
-
-			pH->code_size[i] = static_cast<uint8>(code_size);
-
-			if (code_size <= 8)
-			{
-				code <<= (8 - code_size);
-
-				for (l = 1 << (8 - code_size); l > 0; l--)
-				{
-					JPGD_ASSERT(i < 256);
-
-					pH->look_up[code] = i;
-
-					bool has_extrabits = false;
-					int extra_bits = 0;
-					int num_extra_bits = i & 15;
-
-					int bits_to_fetch = code_size;
-					if (num_extra_bits)
-					{
-						int total_codesize = code_size + num_extra_bits;
-						if (total_codesize <= 8)
-						{
-							has_extrabits = true;
-							extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
-							JPGD_ASSERT(extra_bits <= 0x7FFF);
-							bits_to_fetch += num_extra_bits;
-						}
-					}
-
-					if (!has_extrabits)
-						pH->look_up2[code] = i | (bits_to_fetch << 8);
-					else
-						pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
-					code++;
-				}
-			}
-			else
-			{
-				subtree = (code >> (code_size - 8)) & 0xFF;
-
-				currententry = pH->look_up[subtree];
-
-				if (currententry == 0)
-				{
-					pH->look_up[subtree] = currententry = nextfreeentry;
-					pH->look_up2[subtree] = currententry = nextfreeentry;
-
-					nextfreeentry -= 2;
-				}
-
-				code <<= (16 - (code_size - 8));
-
-				for (l = code_size; l > 9; l--)
-				{
-					if ((code & 0x8000) == 0)
-						currententry--;
-
-					if (pH->tree[-currententry - 1] == 0)
-					{
-						pH->tree[-currententry - 1] = nextfreeentry;
-
-						currententry = nextfreeentry;
-
-						nextfreeentry -= 2;
-					}
-					else
-						currententry = pH->tree[-currententry - 1];
-
-					code <<= 1;
-				}
-
-				if ((code & 0x8000) == 0)
-					currententry--;
-
-				pH->tree[-currententry - 1] = i;
-			}
-
-			p++;
-		}
-	}
-
-	// Verifies the quantization tables needed for this scan are available.
-	void jpeg_decoder::check_quant_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-			if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
-				stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
-	}
-
-	// Verifies that all the Huffman tables needed for this scan are available.
-	void jpeg_decoder::check_huff_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-		{
-			if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
-			if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-		}
-
-		for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
-			if (m_huff_num[i])
-			{
-				if (!m_pHuff_tabs[i])
-					m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
-				make_huff_table(i, m_pHuff_tabs[i]);
-			}
-	}
-
-	// Determines the component order inside each MCU.
-	// Also calcs how many MCU's are on each row, etc.
-	void jpeg_decoder::calc_mcu_block_order()
-	{
-		int component_num, component_id;
-		int max_h_samp = 0, max_v_samp = 0;
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			if (m_comp_h_samp[component_id] > max_h_samp)
-				max_h_samp = m_comp_h_samp[component_id];
-
-			if (m_comp_v_samp[component_id] > max_v_samp)
-				max_v_samp = m_comp_v_samp[component_id];
-		}
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
-			m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
-			m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
-		}
-		else
-		{
-			m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
-			m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcu_org[0] = m_comp_list[0];
-
-			m_blocks_per_mcu = 1;
-		}
-		else
-		{
-			m_blocks_per_mcu = 0;
-
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				int num_blocks;
-
-				component_id = m_comp_list[component_num];
-
-				num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
-				while (num_blocks--)
-					m_mcu_org[m_blocks_per_mcu++] = component_id;
-			}
-		}
-	}
-
-	// Starts a new scan.
-	int jpeg_decoder::init_scan()
-	{
-		if (!locate_sos_marker())
-			return JPGD_FALSE;
-
-		calc_mcu_block_order();
-
-		check_huff_tables();
-
-		check_quant_tables();
-
-		memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		if (m_restart_interval)
-		{
-			m_restarts_left = m_restart_interval;
-			m_next_restart_num = 0;
-		}
-
-		fix_in_buffer();
-
-		return JPGD_TRUE;
-	}
-
-	// Starts a frame. Determines if the number of components or sampling factors
-	// are supported.
-	void jpeg_decoder::init_frame()
-	{
-		int i;
-
-		if (m_comps_in_frame == 1)
-		{
-			if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			m_scan_type = JPGD_GRAYSCALE;
-			m_max_blocks_per_mcu = 1;
-			m_max_mcu_x_size = 8;
-			m_max_mcu_y_size = 8;
-		}
-		else if (m_comps_in_frame == 3)
-		{
-			if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
-				((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH1V1;
-
-				m_max_blocks_per_mcu = 3;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH2V1;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH1V2;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 16;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH2V2;
-				m_max_blocks_per_mcu = 6;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 16;
-			}
-			else
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-		}
-		else
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
-		m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
-		// These values are for the *destination* pixels: after conversion.
-		if (m_scan_type == JPGD_GRAYSCALE)
-			m_dest_bytes_per_pixel = 1;
-		else
-			m_dest_bytes_per_pixel = 4;
-
-		m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
-		m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
-		// Initialize two scan line buffers.
-		m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-		if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
-			m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
-		m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
-		// Should never happen
-		if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
-			stop_decoding(JPGD_ASSERTION_ERROR);
-
-		// Allocate the coefficient buffer, enough for one MCU
-		m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
-		for (i = 0; i < m_max_blocks_per_mcu; i++)
-			m_mcu_block_max_zag[i] = 64;
-
-		m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
-		m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
-		m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
-		// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
-		m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
-		m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
-		if (m_freq_domain_chroma_upsample)
-			m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
-		else
-			m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
-		m_total_lines_left = m_image_y_size;
-
-		m_mcu_lines_left = 0;
-
-		create_look_ups();
-	}
-
-	// The coeff_buf series of methods originally stored the coefficients
-	// into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
-	// was used to make this process more efficient. Now, we can store the entire
-	// thing in RAM.
-	jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
-	{
-		coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
-		cb->block_num_x = block_num_x;
-		cb->block_num_y = block_num_y;
-		cb->block_len_x = block_len_x;
-		cb->block_len_y = block_len_y;
-		cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
-		cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
-		return cb;
-	}
-
-	inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
-	{
-		JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
-		return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
-	}
-
-	// The following methods decode the various types of m_blocks encountered
-	// in progressively encoded images.
-	void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, r;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-		if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
-		{
-			r = pD->get_bits_no_markers(s);
-			s = HUFF_EXTEND(r, s);
-		}
-
-		pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
-		p[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-	}
-
-	void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		if (pD->get_bits_no_markers(1))
-		{
-			jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-			p[0] |= (1 << pD->m_successive_low);
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int k, s, r;
-
-		if (pD->m_eob_run)
-		{
-			pD->m_eob_run--;
-			return;
-		}
-
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
-		{
-			s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-			r = s >> 4;
-			s &= 15;
-
-			if (s)
-			{
-				if ((k += r) > 63)
-					pD->stop_decoding(JPGD_DECODE_ERROR);
-
-				r = pD->get_bits_no_markers(s);
-				s = HUFF_EXTEND(r, s);
-
-				p[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-			}
-			else
-			{
-				if (r == 15)
-				{
-					if ((k += 15) > 63)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-				}
-				else
-				{
-					pD->m_eob_run = 1 << r;
-
-					if (r)
-						pD->m_eob_run += pD->get_bits_no_markers(r);
-
-					pD->m_eob_run--;
-
-					break;
-				}
-			}
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, k, r;
-		int p1 = 1 << pD->m_successive_low;
-		int m1 = (-1) << pD->m_successive_low;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		k = pD->m_spectral_start;
-
-		if (pD->m_eob_run == 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-				r = s >> 4;
-				s &= 15;
-
-				if (s)
-				{
-					if (s != 1)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-
-					if (pD->get_bits_no_markers(1))
-						s = p1;
-					else
-						s = m1;
-				}
-				else
-				{
-					if (r != 15)
-					{
-						pD->m_eob_run = 1 << r;
-
-						if (r)
-							pD->m_eob_run += pD->get_bits_no_markers(r);
-
-						break;
-					}
-				}
-
-				do
-				{
-					// BEGIN EPIC MOD
-					JPGD_ASSERT(k < 64);
-					// END EPIC MOD
-
-					jpgd_block_t *this_coef = p + g_ZAG[k];
-
-					if (*this_coef != 0)
-					{
-						if (pD->get_bits_no_markers(1))
-						{
-							if ((*this_coef & p1) == 0)
-							{
-								if (*this_coef >= 0)
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-								else
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-							}
-						}
-					}
-					else
-					{
-						if (--r < 0)
-							break;
-					}
-
-					k++;
-
-				} while (k <= pD->m_spectral_end);
-
-				if ((s) && (k < 64))
-				{
-					p[g_ZAG[k]] = static_cast<jpgd_block_t>(s);
-				}
-			}
-		}
-
-		if (pD->m_eob_run > 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				// BEGIN EPIC MOD
-				JPGD_ASSERT(k < 64);
-				// END EPIC MOD
-
-				jpgd_block_t *this_coef = p + g_ZAG[k];
-
-				if (*this_coef != 0)
-				{
-					if (pD->get_bits_no_markers(1))
-					{
-						if ((*this_coef & p1) == 0)
-						{
-							if (*this_coef >= 0)
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-							else
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-						}
-					}
-				}
-			}
-
-			pD->m_eob_run--;
-		}
-	}
-
-	// Decode a scan in a progressively encoded image.
-	void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
-	{
-		int mcu_row, mcu_col, mcu_block;
-		int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
-		{
-			int component_num, component_id;
-
-			memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
-			for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-			{
-				int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-				if ((m_restart_interval) && (m_restarts_left == 0))
-					process_restart();
-
-				for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-				{
-					component_id = m_mcu_org[mcu_block];
-
-					decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
-					if (m_comps_in_scan == 1)
-						block_x_mcu[component_id]++;
-					else
-					{
-						if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-						{
-							block_x_mcu_ofs = 0;
-
-							if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-							{
-								block_y_mcu_ofs = 0;
-								block_x_mcu[component_id] += m_comp_h_samp[component_id];
-							}
-						}
-					}
-				}
-
-				m_restarts_left--;
-			}
-
-			if (m_comps_in_scan == 1)
-				m_block_y_mcu[m_comp_list[0]]++;
-			else
-			{
-				for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-				{
-					component_id = m_comp_list[component_num];
-					m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-				}
-			}
-		}
-	}
-
-	// Decode a progressively encoded image.
-	void jpeg_decoder::init_progressive()
-	{
-		int i;
-
-		if (m_comps_in_frame == 4)
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		// Allocate the coefficient buffers.
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
-			m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
-		}
-
-		for ( ; ; )
-		{
-			int dc_only_scan, refinement_scan;
-			pDecode_block_func decode_block_func;
-
-			if (!init_scan())
-				break;
-
-			dc_only_scan = (m_spectral_start == 0);
-			refinement_scan = (m_successive_high != 0);
-
-			if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if (dc_only_scan)
-			{
-				if (m_spectral_end)
-					stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-			}
-			else if (m_comps_in_scan != 1)  /* AC scans can only contain one component */
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
-				stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
-			if (dc_only_scan)
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_dc_refine;
-				else
-					decode_block_func = decode_block_dc_first;
-			}
-			else
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_ac_refine;
-				else
-					decode_block_func = decode_block_ac_first;
-			}
-
-			decode_scan(decode_block_func);
-
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-		}
-
-		m_comps_in_scan = m_comps_in_frame;
-
-		for (i = 0; i < m_comps_in_frame; i++)
-			m_comp_list[i] = i;
-
-		calc_mcu_block_order();
-	}
-
-	void jpeg_decoder::init_sequential()
-	{
-		if (!init_scan())
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-	}
-
-	void jpeg_decoder::decode_start()
-	{
-		init_frame();
-
-		if (m_progressive_flag)
-			init_progressive();
-		else
-			init_sequential();
-	}
-
-	void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
-	{
-		init(pStream);
-		locate_sof_marker();
-	}
-
-	jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
-	{
-		if (setjmp(m_jmp_state))
-			return;
-		decode_init(pStream);
-	}
-
-	int jpeg_decoder::begin_decoding()
-	{
-		if (m_ready_flag)
-			return JPGD_SUCCESS;
-
-		if (m_error_code)
-			return JPGD_FAILED;
-
-		if (setjmp(m_jmp_state))
-			return JPGD_FAILED;
-
-		decode_start();
-
-		m_ready_flag = true;
-
-		return JPGD_SUCCESS;
-	}
-
-	jpeg_decoder::~jpeg_decoder()
-	{
-		free_all_blocks();
-	}
-
-	jpeg_decoder_file_stream::jpeg_decoder_file_stream()
-	{
-		m_pFile = NULL;
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	void jpeg_decoder_file_stream::close()
-	{
-		if (m_pFile)
-		{
-			fclose(m_pFile);
-			m_pFile = NULL;
-		}
-
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
-	{
-		close();
-	}
-
-	bool jpeg_decoder_file_stream::open(const char *Pfilename)
-	{
-		close();
-
-		m_eof_flag = false;
-		m_error_flag = false;
-
-#if defined(_MSC_VER)
-		m_pFile = NULL;
-		fopen_s(&m_pFile, Pfilename, "rb");
-#else
-		m_pFile = fopen(Pfilename, "rb");
-#endif
-		return m_pFile != NULL;
-	}
-
-	int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		if (!m_pFile)
-			return -1;
-
-		if (m_eof_flag)
-		{
-			*pEOF_flag = true;
-			return 0;
-		}
-
-		if (m_error_flag)
-			return -1;
-
-		int bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));
-		if (bytes_read < max_bytes_to_read)
-		{
-			if (ferror(m_pFile))
-			{
-				m_error_flag = true;
-				return -1;
-			}
-
-			m_eof_flag = true;
-			*pEOF_flag = true;
-		}
-
-		return bytes_read;
-	}
-
-	bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
-	{
-		close();
-		m_pSrc_data = pSrc_data;
-		m_ofs = 0;
-		m_size = size;
-		return true;
-	}
-
-	int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		*pEOF_flag = false;
-
-		if (!m_pSrc_data)
-			return -1;
-
-		uint bytes_remaining = m_size - m_ofs;
-		if ((uint)max_bytes_to_read > bytes_remaining)
-		{
-			max_bytes_to_read = bytes_remaining;
-			*pEOF_flag = true;
-		}
-
-		memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
-		m_ofs += max_bytes_to_read;
-
-		return max_bytes_to_read;
-	}
-
-	unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		if (!actual_comps)
-			return NULL;
-		*actual_comps = 0;
-
-		if ((!pStream) || (!width) || (!height) || (!req_comps))
-			return NULL;
-
-		if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
-			return NULL;
-
-		jpeg_decoder decoder(pStream);
-		if (decoder.get_error_code() != JPGD_SUCCESS)
-			return NULL;
-
-		const int image_width = decoder.get_width(), image_height = decoder.get_height();
-		*width = image_width;
-		*height = image_height;
-		*actual_comps = decoder.get_num_components();
-
-		if (decoder.begin_decoding() != JPGD_SUCCESS)
-			return NULL;
-
-		const int dst_bpl = image_width * req_comps;
-
-		uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
-		if (!pImage_data)
-			return NULL;
-
-		for (int y = 0; y < image_height; y++)
-		{
-			const uint8* pScan_line = 0;
-			uint scan_line_len;
-			if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
-			{
-				jpgd_free(pImage_data);
-				return NULL;
-			}
-
-			uint8 *pDst = pImage_data + y * dst_bpl;
-
-			if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
-				((req_comps == 1) && (decoder.get_num_components() == 1)))
-			{
-				memcpy(pDst, pScan_line, dst_bpl);
-			}
-			else if (decoder.get_num_components() == 1)
-			{
-				if (req_comps == 3)
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst += 3;
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst[3] = 255;
-						pDst += 4;
-					}
-				}
-			}
-			else if (decoder.get_num_components() == 3)
-			{
-				if (req_comps == 1)
-				{
-					const int YR = 19595, YG = 38470, YB = 7471;
-					for (int x = 0; x < image_width; x++)
-					{
-						int r = pScan_line[x*4+0];
-						int g = pScan_line[x*4+1];
-						int b = pScan_line[x*4+2];
-						*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						pDst[0] = pScan_line[x*4+0];
-						pDst[1] = pScan_line[x*4+1];
-						pDst[2] = pScan_line[x*4+2];
-						pDst += 3;
-					}
-				}
-			}
-		}
-
-		return pImage_data;
-	}
-
-// BEGIN EPIC MOD
-	unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
-	{
-		jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
-		jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
-		return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
-	}
-
-	unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		jpgd::jpeg_decoder_file_stream file_stream;
-		if (!file_stream.open(pSrc_filename))
-			return NULL;
-		return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
-	}
-
-} // namespace jpgd
diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/helpers/mapBeats.ts b/spaces/yderre-aubay/midi-player-demo/src/common/helpers/mapBeats.ts
deleted file mode 100644
index d93d298b6d0d3a24789090deb6e03781c304b358..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/helpers/mapBeats.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-import { Measure } from "../measure/Measure"
-
-export interface Beat {
-  measure: number
-  beat: number
-  tick: number
-}
-
-export type BeatWithX = Beat & {
-  x: number
-}
-
-// 範囲内の measure を探す。最初の要素は startTick 以前のものも含む
-// Find Measure within the range.The first element also includes something before StartTick
-const getMeasuresInRange = (
-  measures: Measure[],
-  startTick: number,
-  endTick: number,
-) => {
-  let i = 0
-  const result: Measure[] = []
-
-  for (const measure of measures) {
-    const nextMeasure = measures[i + 1]
-    i++
-
-    // 最初の measure を探す
-    // Find the first MEASURE
-    if (result.length === 0) {
-      if (nextMeasure !== undefined && nextMeasure.startTick <= startTick) {
-        continue // 次の measure が最初になりうる場合はスキップ
-        // Skip if the next Measure can be the first
-      }
-      if (measure.startTick > startTick) {
-        console.warn("There is no initial time signature. Use 4/4 by default")
-        result.push({ startTick: 0, measure: 0, numerator: 4, denominator: 4 })
-      } else {
-        result.push(measure)
-      }
-    }
-
-    // 残りの measure を探す. 最初の measure がない場合に正しく処理できるように else ではなくもう一度最初の measure があるか調べる
-    // Find the remaining MEASURE. If you can handle correctly if you do not have the first MEASURE, check if you do the first MEASURE, rather than ELSE
-    if (result.length !== 0) {
-      if (measure.startTick <= endTick) {
-        result.push(measure)
-      } else {
-        break
-      }
-    }
-  }
-
-  return result
-}
-
-export const createBeatsInRange = (
-  allMeasures: Measure[],
-  timebase: number,
-  startTick: number,
-  endTick: number,
-): Beat[] => {
-  const beats: Beat[] = []
-  const measures = getMeasuresInRange(allMeasures, startTick, endTick)
-
-  measures.forEach((measure, i) => {
-    const nextMeasure = measures[i + 1]
-
-    const ticksPerBeat = (timebase * 4) / measure.denominator
-
-    // 次の小節か曲の endTick まで拍を作る
-    // Make a beat up to the next bar or song EndTick
-    const lastTick = nextMeasure ? nextMeasure.startTick : endTick
-
-    const startBeat = Math.max(
-      0,
-      Math.floor((startTick - measure.startTick) / ticksPerBeat),
-    )
-    const endBeat = (lastTick - measure.startTick) / ticksPerBeat
-
-    for (let beat = startBeat; beat < endBeat; beat++) {
-      const tick = measure.startTick + ticksPerBeat * beat
-      beats.push({
-        measure: measure.measure + Math.floor(beat / measure.numerator),
-        beat: beat % measure.numerator,
-        tick,
-      })
-    }
-  })
-
-  return beats
-}
-
-export const createBeatsWithXInRange = (
-  allMeasures: Measure[],
-  pixelsPerTick: number,
-  timebase: number,
-  startTick: number,
-  width: number,
-): BeatWithX[] => {
-  const endTick = startTick + width / pixelsPerTick
-  return createBeatsInRange(allMeasures, timebase, startTick, endTick).map(
-    (b) => ({
-      ...b,
-      x: Math.round(b.tick * pixelsPerTick),
-    }),
-  )
-}
diff --git a/spaces/yeqingmei123/face-test/e4e/models/stylegan2/op/fused_act.py b/spaces/yeqingmei123/face-test/e4e/models/stylegan2/op/fused_act.py
deleted file mode 100644
index 973a84fffde53668d31397da5fb993bbc95f7be0..0000000000000000000000000000000000000000
--- a/spaces/yeqingmei123/face-test/e4e/models/stylegan2/op/fused_act.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-module_path = os.path.dirname(__file__)
-fused = load(
-    'fused',
-    sources=[
-        os.path.join(module_path, 'fused_bias_act.cpp'),
-        os.path.join(module_path, 'fused_bias_act_kernel.cu'),
-    ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused.fused_bias_act(
-            grad_output, empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused.fused_bias_act(
-            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.negative_slope, ctx.scale
-        )
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blip_2/processing_blip_2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blip_2/processing_blip_2.py
deleted file mode 100644
index 837056f88891181da608b013bfcb11703e139217..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blip_2/processing_blip_2.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Processor class for BLIP-2.
-"""
-
-from typing import List, Optional, Union
-
-from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
-from ...utils import TensorType
-
-
-class Blip2Processor(ProcessorMixin):
-    r"""
-    Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.
-
-    [`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring
-    of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
-
-    Args:
-        image_processor (`BlipImageProcessor`):
-            An instance of [`BlipImageProcessor`]. The image processor is a required input.
-        tokenizer (`AutoTokenizer`):
-            An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
-    """
-    attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "BlipImageProcessor"
-    tokenizer_class = "AutoTokenizer"
-
-    # Copied from transformers.models.blip.processing_blip.BlipProcessor.__init__
-    def __init__(self, image_processor, tokenizer):
-        tokenizer.return_token_type_ids = False
-        super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-
-    # Copied from transformers.models.blip.processing_blip.BlipProcessor.__call__
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_token_type_ids: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
-        [`BertTokenizerFast.__call__`] to prepare text for the model.
-
-        Please refer to the docstring of the above two methods for more information.
-        """
-        if images is None and text is None:
-            raise ValueError("You have to specify either images or text.")
-
-        # Get only text
-        if images is None:
-            self.current_processor = self.tokenizer
-            text_encoding = self.tokenizer(
-                text=text,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
-                **kwargs,
-            )
-            return text_encoding
-
-        # add pixel_values
-        encoding_image_processor = self.image_processor(images, return_tensors=return_tensors)
-
-        if text is not None:
-            text_encoding = self.tokenizer(
-                text=text,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
-                **kwargs,
-            )
-        else:
-            text_encoding = None
-
-        if text_encoding is not None:
-            encoding_image_processor.update(text_encoding)
-
-        return encoding_image_processor
-
-    # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
-        to the docstring of this method for more information.
-        """
-        return self.tokenizer.decode(*args, **kwargs)
-
-    @property
-    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lilt/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lilt/__init__.py
deleted file mode 100644
index 50c493e352bc75f0a72cbda074c4b060cea1b087..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lilt/__init__.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import TYPE_CHECKING
-
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_lilt"] = [
-        "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "LiltForQuestionAnswering",
-        "LiltForSequenceClassification",
-        "LiltForTokenClassification",
-        "LiltModel",
-        "LiltPreTrainedModel",
-    ]
-
-if TYPE_CHECKING:
-    from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_lilt import (
-            LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            LiltForQuestionAnswering,
-            LiltForSequenceClassification,
-            LiltForTokenClassification,
-            LiltModel,
-            LiltPreTrainedModel,
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py
deleted file mode 100644
index 4985e0ff22d79c2a3d79b0553a553e16e7a7089f..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert MobileNetV1 checkpoints from the tensorflow/models library."""
-
-
-import argparse
-import json
-import re
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import (
-    MobileNetV1Config,
-    MobileNetV1ForImageClassification,
-    MobileNetV1ImageProcessor,
-    load_tf_weights_in_mobilenet_v1,
-)
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_mobilenet_v1_config(model_name):
-    config = MobileNetV1Config(layer_norm_eps=0.001)
-
-    if "_quant" in model_name:
-        raise ValueError("Quantized models are not supported.")
-
-    matches = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$", model_name)
-    if matches:
-        config.depth_multiplier = float(matches[1])
-        config.image_size = int(matches[2])
-
-    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
-    # the usual 1000. The first class (index 0) is "background".
-    config.num_labels = 1001
-    filename = "imagenet-1k-id2label.json"
-    repo_id = "huggingface/label-files"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k) + 1: v for k, v in id2label.items()}
-    id2label[0] = "background"
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    return config
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
-    """
-    Copy/paste/tweak model's weights to our MobileNetV1 structure.
-    """
-    config = get_mobilenet_v1_config(model_name)
-
-    # Load 🤗 model
-    model = MobileNetV1ForImageClassification(config).eval()
-
-    # Load weights from TensorFlow checkpoint
-    load_tf_weights_in_mobilenet_v1(model, config, checkpoint_path)
-
-    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
-    image_processor = MobileNetV1ImageProcessor(
-        crop_size={"width": config.image_size, "height": config.image_size},
-        size={"shortest_edge": config.image_size + 32},
-    )
-    encoding = image_processor(images=prepare_img(), return_tensors="pt")
-    outputs = model(**encoding)
-    logits = outputs.logits
-
-    assert logits.shape == (1, 1001)
-
-    if model_name == "mobilenet_v1_1.0_224":
-        expected_logits = torch.tensor([-4.1739, -1.1233, 3.1205])
-    elif model_name == "mobilenet_v1_0.75_192":
-        expected_logits = torch.tensor([-3.9440, -2.3141, -0.3333])
-    else:
-        expected_logits = None
-
-    if expected_logits is not None:
-        assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Saving image processor to {pytorch_dump_folder_path}")
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print("Pushing to the hub...")
-        repo_id = "google/" + model_name
-        image_processor.push_to_hub(repo_id)
-        model.push_to_hub(repo_id)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="mobilenet_v1_1.0_224",
-        type=str,
-        help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
-    )
-    parser.add_argument(
-        "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-
-    args = parser.parse_args()
-    convert_movilevit_checkpoint(
-        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
-    )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevitv2/modeling_mobilevitv2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevitv2/modeling_mobilevitv2.py
deleted file mode 100644
index 5a0e08d7344dc7c9590f494f22477aed1901abdc..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevitv2/modeling_mobilevitv2.py
+++ /dev/null
@@ -1,1044 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
-""" PyTorch MobileViTV2 model."""
-
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutputWithNoAttention,
-    BaseModelOutputWithPoolingAndNoAttention,
-    ImageClassifierOutputWithNoAttention,
-    SemanticSegmenterOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_mobilevitv2 import MobileViTV2Config
-
-
-logger = logging.get_logger(__name__)
-
-
-# General docstring
-_CONFIG_FOR_DOC = "MobileViTV2Config"
-
-# Base docstring
-_CHECKPOINT_FOR_DOC = "apple/mobilevitv2-1.0-imagenet1k-256"
-_EXPECTED_OUTPUT_SHAPE = [1, 512, 8, 8]
-
-# Image classification docstring
-_IMAGE_CLASS_CHECKPOINT = "apple/mobilevitv2-1.0-imagenet1k-256"
-_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
-
-
-MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "apple/mobilevitv2-1.0-imagenet1k-256"
-    # See all MobileViTV2 models at https://huggingface.co/models?filter=mobilevitv2
-]
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.make_divisible
-def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
-    """
-    Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
-    original TensorFlow repo. It can be seen here:
-    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
-    """
-    if min_value is None:
-        min_value = divisor
-    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_value < 0.9 * value:
-        new_value += divisor
-    return int(new_value)
-
-
-def clip(value: float, min_val: float = float("-inf"), max_val: float = float("inf")) -> float:
-    return max(min_val, min(max_val, value))
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTConvLayer with MobileViT->MobileViTV2
-class MobileViTV2ConvLayer(nn.Module):
-    def __init__(
-        self,
-        config: MobileViTV2Config,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: int,
-        stride: int = 1,
-        groups: int = 1,
-        bias: bool = False,
-        dilation: int = 1,
-        use_normalization: bool = True,
-        use_activation: Union[bool, str] = True,
-    ) -> None:
-        super().__init__()
-        padding = int((kernel_size - 1) / 2) * dilation
-
-        if in_channels % groups != 0:
-            raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
-        if out_channels % groups != 0:
-            raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
-
-        self.convolution = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-            bias=bias,
-            padding_mode="zeros",
-        )
-
-        if use_normalization:
-            self.normalization = nn.BatchNorm2d(
-                num_features=out_channels,
-                eps=1e-5,
-                momentum=0.1,
-                affine=True,
-                track_running_stats=True,
-            )
-        else:
-            self.normalization = None
-
-        if use_activation:
-            if isinstance(use_activation, str):
-                self.activation = ACT2FN[use_activation]
-            elif isinstance(config.hidden_act, str):
-                self.activation = ACT2FN[config.hidden_act]
-            else:
-                self.activation = config.hidden_act
-        else:
-            self.activation = None
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        features = self.convolution(features)
-        if self.normalization is not None:
-            features = self.normalization(features)
-        if self.activation is not None:
-            features = self.activation(features)
-        return features
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTInvertedResidual with MobileViT->MobileViTV2
-class MobileViTV2InvertedResidual(nn.Module):
-    """
-    Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
-    """
-
-    def __init__(
-        self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
-    ) -> None:
-        super().__init__()
-        expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
-
-        if stride not in [1, 2]:
-            raise ValueError(f"Invalid stride {stride}.")
-
-        self.use_residual = (stride == 1) and (in_channels == out_channels)
-
-        self.expand_1x1 = MobileViTV2ConvLayer(
-            config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
-        )
-
-        self.conv_3x3 = MobileViTV2ConvLayer(
-            config,
-            in_channels=expanded_channels,
-            out_channels=expanded_channels,
-            kernel_size=3,
-            stride=stride,
-            groups=expanded_channels,
-            dilation=dilation,
-        )
-
-        self.reduce_1x1 = MobileViTV2ConvLayer(
-            config,
-            in_channels=expanded_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            use_activation=False,
-        )
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        residual = features
-
-        features = self.expand_1x1(features)
-        features = self.conv_3x3(features)
-        features = self.reduce_1x1(features)
-
-        return residual + features if self.use_residual else features
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTMobileNetLayer with MobileViT->MobileViTV2
-class MobileViTV2MobileNetLayer(nn.Module):
-    def __init__(
-        self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
-    ) -> None:
-        super().__init__()
-
-        self.layer = nn.ModuleList()
-        for i in range(num_stages):
-            layer = MobileViTV2InvertedResidual(
-                config,
-                in_channels=in_channels,
-                out_channels=out_channels,
-                stride=stride if i == 0 else 1,
-            )
-            self.layer.append(layer)
-            in_channels = out_channels
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        for layer_module in self.layer:
-            features = layer_module(features)
-        return features
-
-
-class MobileViTV2LinearSelfAttention(nn.Module):
-    """
-    This layer applies a self-attention with linear complexity, as described in MobileViTV2 paper:
-    https://arxiv.org/abs/2206.02680
-
-    Args:
-        config (`MobileVitv2Config`):
-             Model configuration object
-        embed_dim (`int`):
-            `input_channels` from an expected input of size :math:`(batch_size, input_channels, height, width)`
-    """
-
-    def __init__(self, config: MobileViTV2Config, embed_dim: int) -> None:
-        super().__init__()
-
-        self.qkv_proj = MobileViTV2ConvLayer(
-            config=config,
-            in_channels=embed_dim,
-            out_channels=1 + (2 * embed_dim),
-            bias=True,
-            kernel_size=1,
-            use_normalization=False,
-            use_activation=False,
-        )
-
-        self.attn_dropout = nn.Dropout(p=config.attn_dropout)
-        self.out_proj = MobileViTV2ConvLayer(
-            config=config,
-            in_channels=embed_dim,
-            out_channels=embed_dim,
-            bias=True,
-            kernel_size=1,
-            use_normalization=False,
-            use_activation=False,
-        )
-        self.embed_dim = embed_dim
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        # (batch_size, embed_dim, num_pixels_in_patch, num_patches) --> (batch_size, 1+2*embed_dim, num_pixels_in_patch, num_patches)
-        qkv = self.qkv_proj(hidden_states)
-
-        # Project hidden_states into query, key and value
-        # Query --> [batch_size, 1, num_pixels_in_patch, num_patches]
-        # value, key --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
-        query, key, value = torch.split(qkv, split_size_or_sections=[1, self.embed_dim, self.embed_dim], dim=1)
-
-        # apply softmax along num_patches dimension
-        context_scores = torch.nn.functional.softmax(query, dim=-1)
-        context_scores = self.attn_dropout(context_scores)
-
-        # Compute context vector
-        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] x [batch_size, 1, num_pixels_in_patch, num_patches] -> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
-        context_vector = key * context_scores
-        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] --> [batch_size, embed_dim, num_pixels_in_patch, 1]
-        context_vector = torch.sum(context_vector, dim=-1, keepdim=True)
-
-        # combine context vector with values
-        # [batch_size, embed_dim, num_pixels_in_patch, num_patches] * [batch_size, embed_dim, num_pixels_in_patch, 1] --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
-        out = torch.nn.functional.relu(value) * context_vector.expand_as(value)
-        out = self.out_proj(out)
-        return out
-
-
-class MobileViTV2FFN(nn.Module):
-    def __init__(
-        self,
-        config: MobileViTV2Config,
-        embed_dim: int,
-        ffn_latent_dim: int,
-        ffn_dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-        self.conv1 = MobileViTV2ConvLayer(
-            config=config,
-            in_channels=embed_dim,
-            out_channels=ffn_latent_dim,
-            kernel_size=1,
-            stride=1,
-            bias=True,
-            use_normalization=False,
-            use_activation=True,
-        )
-        self.dropout1 = nn.Dropout(ffn_dropout)
-
-        self.conv2 = MobileViTV2ConvLayer(
-            config=config,
-            in_channels=ffn_latent_dim,
-            out_channels=embed_dim,
-            kernel_size=1,
-            stride=1,
-            bias=True,
-            use_normalization=False,
-            use_activation=False,
-        )
-        self.dropout2 = nn.Dropout(ffn_dropout)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.conv1(hidden_states)
-        hidden_states = self.dropout1(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-        hidden_states = self.dropout2(hidden_states)
-        return hidden_states
-
-
-class MobileViTV2TransformerLayer(nn.Module):
-    def __init__(
-        self,
-        config: MobileViTV2Config,
-        embed_dim: int,
-        ffn_latent_dim: int,
-        dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-        self.layernorm_before = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
-        self.attention = MobileViTV2LinearSelfAttention(config, embed_dim)
-        self.dropout1 = nn.Dropout(p=dropout)
-        self.layernorm_after = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
-        self.ffn = MobileViTV2FFN(config, embed_dim, ffn_latent_dim, config.ffn_dropout)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        layernorm_1_out = self.layernorm_before(hidden_states)
-        attention_output = self.attention(layernorm_1_out)
-        hidden_states = attention_output + hidden_states
-
-        layer_output = self.layernorm_after(hidden_states)
-        layer_output = self.ffn(layer_output)
-
-        layer_output = layer_output + hidden_states
-        return layer_output
-
-
-class MobileViTV2Transformer(nn.Module):
-    def __init__(self, config: MobileViTV2Config, n_layers: int, d_model: int) -> None:
-        super().__init__()
-
-        ffn_multiplier = config.ffn_multiplier
-
-        ffn_dims = [ffn_multiplier * d_model] * n_layers
-
-        # ensure that dims are multiple of 16
-        ffn_dims = [int((d // 16) * 16) for d in ffn_dims]
-
-        self.layer = nn.ModuleList()
-        for block_idx in range(n_layers):
-            transformer_layer = MobileViTV2TransformerLayer(
-                config, embed_dim=d_model, ffn_latent_dim=ffn_dims[block_idx]
-            )
-            self.layer.append(transformer_layer)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states)
-        return hidden_states
-
-
-class MobileViTV2Layer(nn.Module):
-    """
-    MobileViTV2 layer: https://arxiv.org/abs/2206.02680
-    """
-
-    def __init__(
-        self,
-        config: MobileViTV2Config,
-        in_channels: int,
-        out_channels: int,
-        attn_unit_dim: int,
-        n_attn_blocks: int = 2,
-        dilation: int = 1,
-        stride: int = 2,
-    ) -> None:
-        super().__init__()
-        self.patch_width = config.patch_size
-        self.patch_height = config.patch_size
-
-        cnn_out_dim = attn_unit_dim
-
-        if stride == 2:
-            self.downsampling_layer = MobileViTV2InvertedResidual(
-                config,
-                in_channels=in_channels,
-                out_channels=out_channels,
-                stride=stride if dilation == 1 else 1,
-                dilation=dilation // 2 if dilation > 1 else 1,
-            )
-            in_channels = out_channels
-        else:
-            self.downsampling_layer = None
-
-        # Local representations
-        self.conv_kxk = MobileViTV2ConvLayer(
-            config,
-            in_channels=in_channels,
-            out_channels=in_channels,
-            kernel_size=config.conv_kernel_size,
-            groups=in_channels,
-        )
-        self.conv_1x1 = MobileViTV2ConvLayer(
-            config,
-            in_channels=in_channels,
-            out_channels=cnn_out_dim,
-            kernel_size=1,
-            use_normalization=False,
-            use_activation=False,
-        )
-
-        # Global representations
-        self.transformer = MobileViTV2Transformer(config, d_model=attn_unit_dim, n_layers=n_attn_blocks)
-
-        # self.layernorm = MobileViTV2LayerNorm2D(attn_unit_dim, eps=config.layer_norm_eps)
-        self.layernorm = nn.GroupNorm(num_groups=1, num_channels=attn_unit_dim, eps=config.layer_norm_eps)
-
-        # Fusion
-        self.conv_projection = MobileViTV2ConvLayer(
-            config,
-            in_channels=cnn_out_dim,
-            out_channels=in_channels,
-            kernel_size=1,
-            use_normalization=True,
-            use_activation=False,
-        )
-
-    def unfolding(self, feature_map: torch.Tensor) -> Tuple[torch.Tensor, Tuple[int, int]]:
-        batch_size, in_channels, img_height, img_width = feature_map.shape
-        patches = nn.functional.unfold(
-            feature_map,
-            kernel_size=(self.patch_height, self.patch_width),
-            stride=(self.patch_height, self.patch_width),
-        )
-        patches = patches.reshape(batch_size, in_channels, self.patch_height * self.patch_width, -1)
-
-        return patches, (img_height, img_width)
-
-    def folding(self, patches: torch.Tensor, output_size: Tuple[int, int]) -> torch.Tensor:
-        batch_size, in_dim, patch_size, n_patches = patches.shape
-        patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)
-
-        feature_map = nn.functional.fold(
-            patches,
-            output_size=output_size,
-            kernel_size=(self.patch_height, self.patch_width),
-            stride=(self.patch_height, self.patch_width),
-        )
-
-        return feature_map
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        # reduce spatial dimensions if needed
-        if self.downsampling_layer:
-            features = self.downsampling_layer(features)
-
-        # local representation
-        features = self.conv_kxk(features)
-        features = self.conv_1x1(features)
-
-        # convert feature map to patches
-        patches, output_size = self.unfolding(features)
-
-        # learn global representations
-        patches = self.transformer(patches)
-        patches = self.layernorm(patches)
-
-        # convert patches back to feature maps
-        # [batch_size, patch_height, patch_width, input_dim] --> [batch_size, input_dim, patch_height, patch_width]
-        features = self.folding(patches, output_size)
-
-        features = self.conv_projection(features)
-        return features
-
-
-class MobileViTV2Encoder(nn.Module):
-    def __init__(self, config: MobileViTV2Config) -> None:
-        super().__init__()
-        self.config = config
-
-        self.layer = nn.ModuleList()
-        self.gradient_checkpointing = False
-
-        # segmentation architectures like DeepLab and PSPNet modify the strides
-        # of the classification backbones
-        dilate_layer_4 = dilate_layer_5 = False
-        if config.output_stride == 8:
-            dilate_layer_4 = True
-            dilate_layer_5 = True
-        elif config.output_stride == 16:
-            dilate_layer_5 = True
-
-        dilation = 1
-
-        layer_0_dim = make_divisible(
-            clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
-        )
-
-        layer_1_dim = make_divisible(64 * config.width_multiplier, divisor=16)
-        layer_2_dim = make_divisible(128 * config.width_multiplier, divisor=8)
-        layer_3_dim = make_divisible(256 * config.width_multiplier, divisor=8)
-        layer_4_dim = make_divisible(384 * config.width_multiplier, divisor=8)
-        layer_5_dim = make_divisible(512 * config.width_multiplier, divisor=8)
-
-        layer_1 = MobileViTV2MobileNetLayer(
-            config,
-            in_channels=layer_0_dim,
-            out_channels=layer_1_dim,
-            stride=1,
-            num_stages=1,
-        )
-        self.layer.append(layer_1)
-
-        layer_2 = MobileViTV2MobileNetLayer(
-            config,
-            in_channels=layer_1_dim,
-            out_channels=layer_2_dim,
-            stride=2,
-            num_stages=2,
-        )
-        self.layer.append(layer_2)
-
-        layer_3 = MobileViTV2Layer(
-            config,
-            in_channels=layer_2_dim,
-            out_channels=layer_3_dim,
-            attn_unit_dim=make_divisible(config.base_attn_unit_dims[0] * config.width_multiplier, divisor=8),
-            n_attn_blocks=config.n_attn_blocks[0],
-        )
-        self.layer.append(layer_3)
-
-        if dilate_layer_4:
-            dilation *= 2
-
-        layer_4 = MobileViTV2Layer(
-            config,
-            in_channels=layer_3_dim,
-            out_channels=layer_4_dim,
-            attn_unit_dim=make_divisible(config.base_attn_unit_dims[1] * config.width_multiplier, divisor=8),
-            n_attn_blocks=config.n_attn_blocks[1],
-            dilation=dilation,
-        )
-        self.layer.append(layer_4)
-
-        if dilate_layer_5:
-            dilation *= 2
-
-        layer_5 = MobileViTV2Layer(
-            config,
-            in_channels=layer_4_dim,
-            out_channels=layer_5_dim,
-            attn_unit_dim=make_divisible(config.base_attn_unit_dims[2] * config.width_multiplier, divisor=8),
-            n_attn_blocks=config.n_attn_blocks[2],
-            dilation=dilation,
-        )
-        self.layer.append(layer_5)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ) -> Union[tuple, BaseModelOutputWithNoAttention]:
-        all_hidden_states = () if output_hidden_states else None
-
-        for i, layer_module in enumerate(self.layer):
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                )
-            else:
-                hidden_states = layer_module(hidden_states)
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTPreTrainedModel with MobileViT->MobileViTV2,mobilevit->mobilevitv2
-class MobileViTV2PreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = MobileViTV2Config
-    base_model_prefix = "mobilevitv2"
-    main_input_name = "pixel_values"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, MobileViTV2Encoder):
-            module.gradient_checkpointing = value
-
-
-MOBILEVITV2_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`MobileViTV2Config`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-MOBILEVITV2_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
-            [`MobileViTImageProcessor.__call__`] for details.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare MobileViTV2 model outputting raw hidden-states without any specific head on top.",
-    MOBILEVITV2_START_DOCSTRING,
-)
-class MobileViTV2Model(MobileViTV2PreTrainedModel):
-    def __init__(self, config: MobileViTV2Config, expand_output: bool = True):
-        super().__init__(config)
-        self.config = config
-        self.expand_output = expand_output
-
-        layer_0_dim = make_divisible(
-            clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
-        )
-
-        self.conv_stem = MobileViTV2ConvLayer(
-            config,
-            in_channels=config.num_channels,
-            out_channels=layer_0_dim,
-            kernel_size=3,
-            stride=2,
-            use_normalization=True,
-            use_activation=True,
-        )
-        self.encoder = MobileViTV2Encoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def _prune_heads(self, heads_to_prune):
-        """Prunes heads of the model.
-        heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
-        """
-        for layer_index, heads in heads_to_prune.items():
-            mobilevitv2_layer = self.encoder.layer[layer_index]
-            if isinstance(mobilevitv2_layer, MobileViTV2Layer):
-                for transformer_layer in mobilevitv2_layer.transformer.layer:
-                    transformer_layer.attention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPoolingAndNoAttention,
-        config_class=_CONFIG_FOR_DOC,
-        modality="vision",
-        expected_output=_EXPECTED_OUTPUT_SHAPE,
-    )
-    def forward(
-        self,
-        pixel_values: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        embedding_output = self.conv_stem(pixel_values)
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if self.expand_output:
-            last_hidden_state = encoder_outputs[0]
-
-            # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
-            pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
-        else:
-            last_hidden_state = encoder_outputs[0]
-            pooled_output = None
-
-        if not return_dict:
-            output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
-            return output + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndNoAttention(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(
-    """
-    MobileViTV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
-    ImageNet.
-    """,
-    MOBILEVITV2_START_DOCSTRING,
-)
-class MobileViTV2ForImageClassification(MobileViTV2PreTrainedModel):
-    def __init__(self, config: MobileViTV2Config) -> None:
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-        self.mobilevitv2 = MobileViTV2Model(config)
-
-        out_channels = make_divisible(512 * config.width_multiplier, divisor=8)  # layer 5 output dimension
-        # Classifier head
-        self.classifier = (
-            nn.Linear(in_features=out_channels, out_features=config.num_labels)
-            if config.num_labels > 0
-            else nn.Identity()
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=ImageClassifierOutputWithNoAttention,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
-    )
-    def forward(
-        self,
-        pixel_values: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.mobilevitv2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
-
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
-
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return ImageClassifierOutputWithNoAttention(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-        )
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTASPPPooling with MobileViT->MobileViTV2
-class MobileViTV2ASPPPooling(nn.Module):
-    def __init__(self, config: MobileViTV2Config, in_channels: int, out_channels: int) -> None:
-        super().__init__()
-
-        self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
-
-        self.conv_1x1 = MobileViTV2ConvLayer(
-            config,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            stride=1,
-            use_normalization=True,
-            use_activation="relu",
-        )
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        spatial_size = features.shape[-2:]
-        features = self.global_pool(features)
-        features = self.conv_1x1(features)
-        features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
-        return features
-
-
-class MobileViTV2ASPP(nn.Module):
-    """
-    ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
-    """
-
-    def __init__(self, config: MobileViTV2Config) -> None:
-        super().__init__()
-
-        encoder_out_channels = make_divisible(512 * config.width_multiplier, divisor=8)  # layer 5 output dimension
-        in_channels = encoder_out_channels
-        out_channels = config.aspp_out_channels
-
-        if len(config.atrous_rates) != 3:
-            raise ValueError("Expected 3 values for atrous_rates")
-
-        self.convs = nn.ModuleList()
-
-        in_projection = MobileViTV2ConvLayer(
-            config,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            use_activation="relu",
-        )
-        self.convs.append(in_projection)
-
-        self.convs.extend(
-            [
-                MobileViTV2ConvLayer(
-                    config,
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_size=3,
-                    dilation=rate,
-                    use_activation="relu",
-                )
-                for rate in config.atrous_rates
-            ]
-        )
-
-        pool_layer = MobileViTV2ASPPPooling(config, in_channels, out_channels)
-        self.convs.append(pool_layer)
-
-        self.project = MobileViTV2ConvLayer(
-            config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
-        )
-
-        self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
-
-    def forward(self, features: torch.Tensor) -> torch.Tensor:
-        pyramid = []
-        for conv in self.convs:
-            pyramid.append(conv(features))
-        pyramid = torch.cat(pyramid, dim=1)
-
-        pooled_features = self.project(pyramid)
-        pooled_features = self.dropout(pooled_features)
-        return pooled_features
-
-
-# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTDeepLabV3 with MobileViT->MobileViTV2
-class MobileViTV2DeepLabV3(nn.Module):
-    """
-    DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
-    """
-
-    def __init__(self, config: MobileViTV2Config) -> None:
-        super().__init__()
-        self.aspp = MobileViTV2ASPP(config)
-
-        self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
-
-        self.classifier = MobileViTV2ConvLayer(
-            config,
-            in_channels=config.aspp_out_channels,
-            out_channels=config.num_labels,
-            kernel_size=1,
-            use_normalization=False,
-            use_activation=False,
-            bias=True,
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        features = self.aspp(hidden_states[-1])
-        features = self.dropout(features)
-        features = self.classifier(features)
-        return features
-
-
-@add_start_docstrings(
-    """
-    MobileViTV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
-    """,
-    MOBILEVITV2_START_DOCSTRING,
-)
-class MobileViTV2ForSemanticSegmentation(MobileViTV2PreTrainedModel):
-    def __init__(self, config: MobileViTV2Config) -> None:
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-        self.mobilevitv2 = MobileViTV2Model(config, expand_output=False)
-        self.segmentation_head = MobileViTV2DeepLabV3(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, SemanticSegmenterOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
-            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> import requests
-        >>> import torch
-        >>> from PIL import Image
-        >>> from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
-        >>> model = MobileViTV2ForSemanticSegmentation.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-
-        >>> with torch.no_grad():
-        ...     outputs = model(**inputs)
-
-        >>> # logits are of shape (batch_size, num_labels, height, width)
-        >>> logits = outputs.logits
-        ```"""
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.mobilevitv2(
-            pixel_values,
-            output_hidden_states=True,  # we need the intermediate hidden states
-            return_dict=return_dict,
-        )
-
-        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
-
-        logits = self.segmentation_head(encoder_hidden_states)
-
-        loss = None
-        if labels is not None:
-            if self.config.num_labels == 1:
-                raise ValueError("The number of labels should be greater than one")
-            else:
-                # upsample logits to the images' original size
-                upsampled_logits = nn.functional.interpolate(
-                    logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
-                )
-                loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
-                loss = loss_fct(upsampled_logits, labels)
-
-        if not return_dict:
-            if output_hidden_states:
-                output = (logits,) + outputs[1:]
-            else:
-                output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SemanticSegmenterOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
-            attentions=None,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mpt/configuration_mpt.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mpt/configuration_mpt.py
deleted file mode 100644
index cc91966b6b0d0181e500db2ccf5f20e082f315eb..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mpt/configuration_mpt.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc. team and MosaicML NLP team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Mpt configuration"""
-from typing import TYPE_CHECKING, Optional, Union
-
-
-if TYPE_CHECKING:
-    pass
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-MPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "mosaicml/mpt-7b": "https://huggingface.co/mosaicml/mpt-7b/resolve/main/config.json",
-}
-
-
-class MptAttentionConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`MptAttention`] class. It is used to instantiate
-    attention layers according to the specified arguments, defining the layers architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the MPT
-    [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) architecture. Most of the arguments are kept for backward
-    compatibility with previous MPT models that are hosted on the Hub (previously with `trust_remote_code=True`).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        attn_type (`str`, *optional*, defaults to `"multihead_attention"`):
-            type of attention to use. Options: `"multihead_attention"`, `"multiquery_attention"`.
-        attn_pdrop (`float`, *optional*, defaults to 0.0):
-            The dropout probability for the attention layers.
-        attn_impl (`str`, *optional*, defaults to `"torch"`):
-            The attention implementation to use. One of `"torch"`, `"flash"`, or `"triton"`.
-        clip_qkv (`float`, *optional*):
-            If not `None`, clip the queries, keys, and values in the attention layer to this value.
-        softmax_scale (`float`, *optional*, defaults to `None`):
-            If not `None`, scale the softmax in the attention layer by this value. If `None`, will default to
-            `1/sqrt(hidden_size)`.
-        prefix_lm (`bool`, *optional*, defaults to `False`)):
-            Whether the model should operate as a Prefix LM. This requires passing an extra `prefix_mask` argument
-            which indicates which tokens belong to the prefix. Tokens in the prefix can attend to one another
-            bi-directionally. Tokens outside the prefix use causal attention.
-        qk_ln (`bool`, *optional*, defaults to `False`):
-            Whether to apply layer normalization to the queries and keys in the attention layer.
-        attn_uses_sequence_id (`bool`, *optional*, defaults to `False`)):
-            Whether to restrict attention to tokens that have the same token_type_ids. When the model is in `train`
-            mode, this requires passing an extra *token_type_ids* argument which indicates which sub-sequence each
-            token belongs to. Defaults to `False` meaning any provided *token_type_ids* will be ignored.
-        alibi (`bool`, *optional*, defaults to `True`):
-            Whether or not to use the alibi bias instead of positional embedding.
-        alibi_bias_max (`int`, *optional*, defaults to 8):
-            The maximum value of the alibi bias.
-    """
-
-    def __init__(
-        self,
-        attn_type="multihead_attention",
-        attn_pdrop=0,
-        attn_impl="torch",
-        clip_qkv=None,
-        softmax_scale=None,
-        prefix_lm=False,
-        qk_ln=False,
-        attn_uses_sequence_id=False,
-        alibi=True,
-        alibi_bias_max=8,
-        **kwargs,
-    ):
-        super().__init__()
-        self.attn_type = attn_type
-        self.attn_pdrop = attn_pdrop
-        self.attn_impl = attn_impl
-        self.clip_qkv = clip_qkv
-        self.softmax_scale = softmax_scale
-        self.prefix_lm = prefix_lm
-        self.attn_uses_sequence_id = attn_uses_sequence_id
-        self.alibi = alibi
-        self.qk_ln = qk_ln
-        self.alibi_bias_max = alibi_bias_max
-
-        if attn_type not in ["multihead_attention", "multiquery_attention"]:
-            raise ValueError(
-                f"`attn_type` has to be either `multihead_attention` or `multiquery_attention`. Received: {attn_type}"
-            )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        if config_dict.get("model_type") == "mpt":
-            config_dict = config_dict["attn_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class MptConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`MptModel`]. It is used to instantiate a Mpt model
-    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
-    defaults will yield a similar configuration to the Mpt-7b architecture
-    [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        d_model (`int`, *optional*, defaults to 2048):
-            Dimensionality of the embeddings and hidden states.
-        n_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        n_layers (`int`, *optional*, defaults to 24):
-            Number of hidden layers in the Transformer encoder.
-        expansion_ratio (`int`, *optional*, defaults to 4):
-            The ratio of the up/down scale in the MLP.
-        max_seq_len (`int`, *optional*, defaults to 2048):
-            The maximum sequence length of the model.
-        vocab_size (`int`, *optional*, defaults to 50368):
-            Vocabulary size of the Mpt model. Defines the maximum number of different tokens that can be represented by
-            the `inputs_ids` passed when calling [`MptModel`]. Check [this
-            discussion](https://huggingface.co/bigscience/mpt/discussions/120#633d28389addb8530b406c2a) on how the
-            `vocab_size` has been defined.
-        resid_pdrop (`float`, *optional*, defaults to 0.0):
-            The dropout probability applied to the attention output before combining with residual.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
-            The epsilon to use in the layer normalization layers.
-        emb_pdrop (`float`, *optional*, defaults to 0.0):
-            The dropout probability for the embedding layer.
-        learned_pos_emb (`bool`, *optional*, defaults to `True`):
-            Whether to use learned positional embeddings.
-        attn_config (`dict`, *optional*):
-            A dictionary used to configure the model's attention module.
-        init_device (`str`, *optional*, defaults to `"cpu"`):
-            The device to use for parameter initialization. Defined for backward compatibility
-        logit_scale (`float`, *optional*):
-            If not None, scale the logits by this value.
-        no_bias (`bool`, *optional*, defaults to `True`):
-            Whether to use bias in all linear layers.
-        verbose (`int`, *optional*, defaults to 0):
-            The verbosity level to use for logging. Used in the previous versions of MPT models for logging. This
-            argument is deprecated.
-        embedding_fraction (`float`, *optional*, defaults to 1.0):
-            The fraction to scale the gradients of the embedding layer by.
-        norm_type (`str`, *optional*, defaults to `"low_precision_layernorm"`):
-            Type of layer norm to use. All MPT models uses the same layer norm implementation. Defined for backward
-            compatibility.
-        use_cache (`bool`, *optional*, defaults to `False`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-
-    Example:
-
-    ```python
-    >>> from transformers import MptConfig, MptModel
-
-    >>> # Initializing a Mpt configuration
-    >>> configuration = MptConfig()
-
-    >>> # Initializing a model (with random weights) from the configuration
-    >>> model = MptModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```
-    """
-
-    model_type = "mpt"
-    attribute_map = {
-        "num_attention_heads": "n_heads",
-        "hidden_size": "d_model",
-        "num_hidden_layers": "n_layers",
-    }
-
-    def __init__(
-        self,
-        d_model: int = 2048,
-        n_heads: int = 16,
-        n_layers: int = 24,
-        expansion_ratio: int = 4,
-        max_seq_len: int = 2048,
-        vocab_size: int = 50368,
-        resid_pdrop: float = 0.0,
-        layer_norm_epsilon: float = 1e-5,
-        emb_pdrop: float = 0.0,
-        learned_pos_emb: bool = True,
-        attn_config: MptAttentionConfig = None,
-        init_device: str = "cpu",
-        logit_scale: Optional[Union[float, str]] = None,
-        no_bias: bool = True,
-        verbose: int = 0,
-        embedding_fraction: float = 1.0,
-        norm_type: str = "low_precision_layernorm",
-        use_cache: bool = False,
-        initializer_range=0.02,
-        **kwargs,
-    ):
-        if attn_config is None:
-            self.attn_config = MptAttentionConfig()
-        elif isinstance(attn_config, dict):
-            self.attn_config = MptAttentionConfig(**attn_config)
-        else:
-            self.attn_config = attn_config
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.expansion_ratio = expansion_ratio
-        self.max_seq_len = max_seq_len
-        self.vocab_size = vocab_size
-        self.resid_pdrop = resid_pdrop
-        self.emb_pdrop = emb_pdrop
-        self.learned_pos_emb = learned_pos_emb
-        self.init_device = init_device
-        self.logit_scale = logit_scale
-        self.no_bias = no_bias
-        self.verbose = verbose
-        self.embedding_fraction = embedding_fraction
-        self.norm_type = norm_type
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.use_cache = use_cache
-        self.initializer_range = initializer_range
-        super().__init__(**kwargs)
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vdecoder/hifiganwithsnake/alias/resample.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/vdecoder/hifiganwithsnake/alias/resample.py
deleted file mode 100644
index 750e6c3402cc5ac939c4b9d075246562e0e1d1a7..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/vdecoder/hifiganwithsnake/alias/resample.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from torch.nn import functional as F
-from .filter import LowPassFilter1d
-from .filter import kaiser_sinc_filter1d
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
-                                      half_width=0.6 / ratio,
-                                      kernel_size=self.kernel_size)
-        self.register_buffer("filter", filter)
-
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        x = F.pad(x, (self.pad, self.pad), mode='replicate')
-        x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
-        x = x[..., self.pad_left:-self.pad_right]
-
-        return x
-
-
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
-                                       half_width=0.6 / ratio,
-                                       stride=ratio,
-                                       kernel_size=self.kernel_size)
-
-    def forward(self, x):
-        xx = self.lowpass(x)
-
-        return xx
\ No newline at end of file
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/README.md b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/README.md
deleted file mode 100644
index ea709f33b007abd2de044a0338659ec003330725..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docker/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-## Use the container (with docker ≥ 19.03)
-
-```
-cd docker/
-# Build:
-docker build --build-arg USER_ID=$UID -t detectron2:v0 .
-# Launch (require GPUs):
-docker run --gpus all -it \
-  --shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
-  --name=detectron2 detectron2:v0
-
-# Grant docker access to host X server to show images
-xhost +local:`docker inspect --format='{{ .Config.Hostname }}' detectron2`
-```
-
-## Use the container (with docker-compose ≥ 1.28.0)
-
-Install docker-compose and nvidia-docker-toolkit, then run:
-```
-cd docker && USER_ID=$UID docker-compose run detectron2
-```
-
-## Use the deployment container (to test C++ examples)
-After building the base detectron2 container as above, do:
-```
-# Build:
-docker build -t detectron2-deploy:v0 -f deploy.Dockerfile .
-# Launch:
-docker run --gpus all -it detectron2-deploy:v0
-```
-
-#### Using a persistent cache directory
-
-You can prevent models from being re-downloaded on every run,
-by storing them in a cache directory.
-
-To do this, add `--volume=$HOME/.torch/fvcore_cache:/tmp:rw` in the run command.
-
-## Install new dependencies
-Add the following to `Dockerfile` to make persistent changes.
-```
-RUN sudo apt-get update && sudo apt-get install -y vim
-```
-Or run them in the container to make temporary changes.
diff --git a/spaces/zhan66/vits-simple-api/logger.py b/spaces/zhan66/vits-simple-api/logger.py
deleted file mode 100644
index d8e83f51f8aa5079b8ceaba0530668c5a7c0bba5..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/logger.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import sys
-import logging
-import logzero
-import config
-from logging.handlers import TimedRotatingFileHandler
-
-logzero.loglevel(logging.WARNING)
-logger = logging.getLogger("vits-simple-api")
-level = getattr(config, "LOGGING_LEVEL", "DEBUG")
-level_dict = {'DEBUG': logging.DEBUG, 'INFO': logging.INFO, 'WARNING': logging.WARNING, 'ERROR': logging.ERROR,
-              'CRITICAL': logging.CRITICAL}
-logging.basicConfig(level=level_dict[level])
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger("langid.langid").setLevel(logging.INFO)
-logging.getLogger("apscheduler.scheduler").setLevel(logging.INFO)
-
-os.makedirs(config.LOGS_PATH, exist_ok=True)
-log_file = os.path.join(config.LOGS_PATH, 'latest.log')
-backup_count = getattr(config, "LOGS_BACKUPCOUNT", 30)
-handler = TimedRotatingFileHandler(log_file, when="midnight", interval=1, backupCount=backup_count, encoding='utf-8')
-handler.suffix = "%Y-%m-%d.log"
-formatter = logging.Formatter('%(levelname)s:%(name)s %(message)s')
-handler.setFormatter(formatter)
-logger.addHandler(handler)
-
-logging.getLogger("werkzeug").addHandler(handler)
-logging.getLogger("apscheduler.scheduler").addHandler(handler)
-
-
-# Custom function to handle uncaught exceptions
-def handle_exception(exc_type, exc_value, exc_traceback):
-    # If it's a keyboard interrupt, don't handle it, just return
-    if issubclass(exc_type, KeyboardInterrupt):
-        sys.__excepthook__(exc_type, exc_value, exc_traceback)
-        return
-
-    logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
-
-
-# Set the global exception handler in Python
-sys.excepthook = handle_exception
diff --git a/spaces/zhan66/vits-simple-api/utils/data_utils.py b/spaces/zhan66/vits-simple-api/utils/data_utils.py
deleted file mode 100644
index f1abf47484f4705d00f7e9a719e0f2577cffda22..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/utils/data_utils.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import os
-import logging
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
-
-
-def load_checkpoint(checkpoint_path, model):
-    from torch import load
-    checkpoint_dict = load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict.get('iteration', None)
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logging.info(f"{k} is not in the checkpoint")
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    if iteration:
-        logging.info(f"Loaded checkpoint '{checkpoint_path}' (iteration {iteration})")
-    else:
-        logging.info(f"Loaded checkpoint '{checkpoint_path}'")
-    return
-
-
-def get_hparams_from_file(config_path):
-    from json import loads
-    with open(config_path, 'r', encoding='utf-8') as f:
-        data = f.read()
-    config = loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-    import librosa
-    from torch import FloatTensor
-    from numpy import float32
-    audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-    return FloatTensor(audio.astype(float32))
-
-
-def clean_folder(folder_path):
-    for filename in os.listdir(folder_path):
-        file_path = os.path.join(folder_path, filename)
-        # 如果是文件，则删除文件。如果是文件夹则跳过。
-        if os.path.isfile(file_path):
-            os.remove(file_path)
-
-
-def check_is_none(item) -> bool:
-    # none -> True, not none -> False
-    return item is None or (isinstance(item, str) and str(item).isspace()) or str(item) == ""
-
-
-def save_audio(audio, path):
-    with open(path, "wb") as f:
-        f.write(audio)
diff --git a/spaces/zhang-wei-jian/docker/node_modules/supports-color/readme.md b/spaces/zhang-wei-jian/docker/node_modules/supports-color/readme.md
deleted file mode 100644
index f6e40195730ae8c665bdd898cfe0442e9ac08105..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/supports-color/readme.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# supports-color [![Build Status](https://travis-ci.org/chalk/supports-color.svg?branch=master)](https://travis-ci.org/chalk/supports-color)
-
-> Detect whether a terminal supports color
-
-
-## Install
-
-```
-$ npm install supports-color
-```
-
-
-## Usage
-
-```js
-const supportsColor = require('supports-color');
-
-if (supportsColor.stdout) {
-	console.log('Terminal stdout supports color');
-}
-
-if (supportsColor.stdout.has256) {
-	console.log('Terminal stdout supports 256 colors');
-}
-
-if (supportsColor.stderr.has16m) {
-	console.log('Terminal stderr supports 16 million colors (truecolor)');
-}
-```
-
-
-## API
-
-Returns an `Object` with a `stdout` and `stderr` property for testing either streams. Each property is an `Object`, or `false` if color is not supported.
-
-The `stdout`/`stderr` objects specifies a level of support for color through a `.level` property and a corresponding flag:
-
-- `.level = 1` and `.hasBasic = true`: Basic color support (16 colors)
-- `.level = 2` and `.has256 = true`: 256 color support
-- `.level = 3` and `.has16m = true`: Truecolor support (16 million colors)
-
-
-## Info
-
-It obeys the `--color` and `--no-color` CLI flags.
-
-Can be overridden by the user with the flags `--color` and `--no-color`. For situations where using `--color` is not possible, add the environment variable `FORCE_COLOR=1` to forcefully enable color or `FORCE_COLOR=0` to forcefully disable. The use of `FORCE_COLOR` overrides all other color support checks.
-
-Explicit 256/Truecolor mode can be enabled using the `--color=256` and `--color=16m` flags, respectively.
-
-
-## Related
-
-- [supports-color-cli](https://github.com/chalk/supports-color-cli) - CLI for this module
-- [chalk](https://github.com/chalk/chalk) - Terminal string styling done right
-
-
-## Maintainers
-
-- [Sindre Sorhus](https://github.com/sindresorhus)
-- [Josh Junon](https://github.com/qix-)
-
-
-## License
-
-MIT
diff --git a/spaces/zixian/Zhenhuan-VITS/models_infer.py b/spaces/zixian/Zhenhuan-VITS/models_infer.py
deleted file mode 100644
index 4b9bb82bf5831c5264f3e1e52b23e8e875f5fd9e..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/models_infer.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1)
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self,
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock,
-    resblock_kernel_sizes,
-    resblock_dilation_sizes,
-    upsample_rates,
-    upsample_initial_channel,
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/zomehwh/sovits-models/app.py b/spaces/zomehwh/sovits-models/app.py
deleted file mode 100644
index 2d6c236a564fa5283fa0385477be02d87c8aee35..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-models/app.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import io
-import gradio as gr
-import librosa
-import numpy as np
-import utils
-from inference.infer_tool import Svc
-import logging
-import soundfile
-import asyncio
-import argparse
-import edge_tts
-import gradio.processing_utils as gr_processing_utils
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-audio_postprocess_ori = gr.Audio.postprocess
-
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-
-
-gr.Audio.postprocess = audio_postprocess
-def create_vc_fn(model, sid):
-    def vc_fn(input_audio, vc_transform, auto_f0, tts_text, tts_voice, tts_mode):
-        if tts_mode:
-            if len(tts_text) > 100 and limitation:
-                return "Text is too long", None
-            if tts_text is None or tts_voice is None:
-                return "You need to enter text and select a voice", None
-            asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-            audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, audio, 16000, format="wav")
-            raw_path.seek(0)
-            out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
-                                            auto_predict_f0=auto_f0,
-                                            )
-            return "Success", (44100, out_audio.cpu().numpy())
-        if input_audio is None:
-            return "You need to upload an audio", None
-        sampling_rate, audio = input_audio
-        duration = audio.shape[0] / sampling_rate
-        if duration > 20 and limitation:
-            return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != 16000:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-        raw_path = io.BytesIO()
-        soundfile.write(raw_path, audio, 16000, format="wav")
-        raw_path.seek(0)
-        out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
-                                       auto_predict_f0=auto_f0,
-                                       )
-        return "Success", (44100, out_audio.cpu().numpy())
-    return vc_fn
-
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), gr.Checkbox.update(value=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Checkbox.update(value=False)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    hubert_model = utils.get_hubert_model().to(args.device)
-    models = []
-    others = {
-        "rudolf": "https://huggingface.co/spaces/sayashi/sovits-rudolf",
-        "teio": "https://huggingface.co/spaces/sayashi/sovits-teio",
-        "goldship": "https://huggingface.co/spaces/sayashi/sovits-goldship",
-        "tannhauser": "https://huggingface.co/spaces/sayashi/sovits-tannhauser"
-    }
-    voices = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    for r in tts_voice_list:
-        voices.append(f"{r['ShortName']}-{r['Gender']}")
-    for f in os.listdir("models"):
-        name = f
-        model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device)
-        cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
-        models.append((name, cover, create_vc_fn(model, name)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> Sovits Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n"
-            "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/sovits-models?duplicate=true)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/svc-develop-team/so-vits-svc)"
-
-        )
-        with gr.Tabs():
-            for (name, cover, vc_fn) in models:
-                with gr.TabItem(name):
-                    with gr.Row():
-                        gr.Markdown(
-                            '<div align="center">'
-                            f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                            '</div>'
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
-                            vc_transform = gr.Number(label="vc_transform", value=0)
-                            auto_f0 = gr.Checkbox(label="auto_f0", value=False)
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False, label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(choices=voices, visible=False)
-                            vc_submit = gr.Button("Generate", variant="primary")
-                        with gr.Column():
-                            vc_output1 = gr.Textbox(label="Output Message")
-                            vc_output2 = gr.Audio(label="Output Audio")
-                vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice, auto_f0])
-            for category, link in others.items():
-                with gr.TabItem(category):
-                    gr.Markdown(
-                        f'''
-                        <center>
-                          <h2>Click to Go</h2>
-                          <a href="{link}">
-                            <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg"
-                          </a>
-                        </center>
-                        '''
-                    )
-        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
diff --git a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/utils.py b/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/utils.py
deleted file mode 100644
index 9c93c996d3cc73c30d71c1fc47056e4230f35c0f..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-