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--- a/spaces/1gistliPinn/ChatGPT4/Examples/Blood Brothers in Hindi Full Movie Download The Ultimate Guide.md	
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diff --git a/spaces/1phancelerku/anime-remove-background/Brawlhalla Mod Apk 6.06 Unlock All Legends and Crossovers with Data.md b/spaces/1phancelerku/anime-remove-background/Brawlhalla Mod Apk 6.06 Unlock All Legends and Crossovers with Data.md
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--- a/spaces/1phancelerku/anime-remove-background/Brawlhalla Mod Apk 6.06 Unlock All Legends and Crossovers with Data.md	
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-<h1>Brawlhalla Mod Apk 6.06: A Guide for Beginners</h1>
-<p>If you are looking for a fun and exciting platform fighting game that you can play with your friends or online players across different platforms, you might want to check out Brawlhalla. And if you want to enjoy unlimited money and access to all the characters in the game, you might want to try Brawlhalla Mod Apk 6.06. In this article, we will tell you everything you need to know about this modded version of the game, including what it is, how to download and install it, how to play it, and some tips and tricks to improve your skills.</p>
-<h2>What is Brawlhalla?</h2>
-<h3>A free platform fighting game with over 80 million players</h3>
-<p>Brawlhalla is a free-to-play 2D platform fighting game developed by Blue Mammoth Games and published by Ubisoft. It supports up to 8 players online or local in a single match with full cross-play for PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, and Android devices. You can join casual free-for-alls, queue for ranked matches, or make a custom room with your friends.</p>
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-<h3>A game with cross-play, frequent updates, and 50+ characters</h3>
-<p>Brawlhalla is constantly updated with new features, events, and characters. There are over 50 unique characters (called Legends) to choose from, each with their own stats, abilities, and weapons. You can also unlock crossover characters from other popular franchises such as Adventure Time, Ben 10, The Walking Dead, Tomb Raider, WWE, Shovel Knight, Hellboy, Rayman, Steven Universe, and more.</p>
-<h3>A game with various modes, maps, and cosmetics</h3>
-<p>Brawlhalla offers a variety of game modes to suit your preferences and mood. You can play classic modes such as Stock (last man standing), Timed (most points), Strikeout (switch characters), or Brawlball (score goals). You can also try out fun party modes such as Kung Foot (soccer), Bombsketball (basketball), Capture the Flag (CTF), Bubble Tag (tag), Temple Climb (climb), Morph (change weapons), Walker Attack! (zombies), Showdown (battle royale), Crew Battle (team elimination), Street Brawl (gang fight), Bounty ( collect bounties), and more. You can also play custom games with your own rules and settings, or join tournaments and events to compete for glory and prizes. Brawlhalla has over 40 maps to fight on, each with different layouts, hazards, and themes. You can also customize your characters with hundreds of skins, weapons, colors, emotes, taunts, and podiums that you can buy with in-game currency or real money.</p>
-<h2>What is Brawlhalla Mod Apk 6.06?</h2>
-<h3>A modified version of the game that offers unlimited money and unlocked characters</h3>
-<p>Brawlhalla Mod Apk 6.06 is a modified version of the game that gives you unlimited money (gold and mammoth coins) and access to all the characters (including the crossover ones) in the game. With this mod, you can buy any cosmetic item you want, and play with any character you like. You can also enjoy the game without ads or interruptions.</p>
-<h3>A version that requires data download and installation</h3>
-<p>Brawlhalla Mod Apk 6.06 is not available on the official app stores, so you need to download it from a third-party source. You also need to download a separate data file that contains the game assets and resources. You need to install the mod apk file and copy the data file to the obb folder in your device storage. This process may take some time and space, depending on your device and internet speed.</p>
-<h3>A version that may not be compatible with the official game or safe to use</h3>
-<p>Brawlhalla Mod Apk 6.06 is not an official version of the game, so it may not be compatible with the latest updates or patches of the game. It may also cause errors, glitches, or crashes in your device. Moreover, using a modded version of the game may violate the terms of service of the game, and result in a ban or suspension of your account. You may also risk exposing your device to malware or viruses from untrusted sources. Therefore, use this mod at your own risk and discretion.</p>
-<h2>How to download and install Brawlhalla Mod Apk 6.06?</h2>
-<h3>Find a reliable source for the mod apk file and the data file</h3>
-<p>The first step to download and install Brawlhalla Mod Apk 6.06 is to find a reliable source for the mod apk file and the data file. You can search online for websites or blogs that offer these files for free download. However, be careful of fake or malicious links that may harm your device or steal your information. Always check the reviews, ratings, and comments of other users before downloading anything.</p>
-<h3>Enable unknown sources in your device settings</h3>
-<p>The next step is to enable unknown sources in your device settings. This will allow you to install apps that are not from the official app stores. To do this, go to your device settings > security > unknown sources > enable. You may also need to disable any antivirus or firewall software that may block the installation.</p>
-<h3>Install the mod apk file and copy the data file to the obb folder</h3>
-<p>The final step is to install the mod apk file and copy the data file to the obb folder in your device storage. To do this, follow these steps:</p>
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-<ol>
-<li>Download the mod apk file and the data file from your chosen source.</li>
-<li>Locate the downloaded files in your device storage using a file manager app.</li>
-<li>Tap on the mod apk file and follow the instructions to install it.</li>
-<li>Do not open the game yet after installation.</li>
-<li>Extract or unzip the data file using a zip extractor app.</li>
-<li>Copy or move the extracted folder named "com.bmg.brawlhalla" to your device storage > Android > obb.</li>
-<li>Make sure that the folder is placed inside the obb folder correctly.</li>
-<li>Now you can open the game and enjoy Brawlhalla Mod Apk 6.06.</li>
-</ol> <h2>How to play Brawlhalla Mod Apk 6.06?</h2>
-<h3>Choose your character from the unlocked roster</h3>
-<p>Once you have successfully installed Brawlhalla Mod Apk 6.06, you can start playing the game by choosing your character from the unlocked roster. You can scroll through the list of characters and select the one that suits your playstyle and preference. You can also see the stats, abilities, and weapons of each character by tapping on them.</p>
-<h3>Customize your character with skins, weapons, colors, and emotes</h3>
-<p>After choosing your character, you can customize it with skins, weapons, colors, and emotes that you can buy with the unlimited money you have in the mod. You can access the store by tapping on the shopping cart icon at the bottom of the screen. You can also change your character's name, avatar, and title by tapping on the profile icon at the top of the screen.</p>
-<h3>Join online or offline matches with up to 8 players</h3>
-<p>Now you are ready to join online or offline matches with up to 8 players in Brawlhalla Mod Apk 6.06. You can choose from various game modes and maps by tapping on the play icon at the bottom of the screen. You can also invite your friends or join random players by tapping on the social icon at the top of the screen. You can also chat with other players by tapping on the chat icon at the bottom of the screen.</p>
-<h2>Tips and tricks for Brawlhalla Mod Apk 6.06</h2>
-<h3>Practice your movement, recovery, dodging, and combos in training mode</h3>
-<p>If you are new to Brawlhalla or want to improve your skills, you should practice your movement, recovery, dodging, and combos in training mode. You can access training mode by tapping on the play icon > offline > training. In training mode, you can choose any character, weapon, map, and settings to practice with. You can also see your damage, hitboxes, stun frames, and other useful information on the screen.</p>
-<h3>Learn the strengths and weaknesses of different characters and weapons</h3>
-<p>Brawlhalla has over 50 characters and 13 weapons to choose from, each with their own strengths and weaknesses. You should learn how each character and weapon works, what their advantages and disadvantages are, and how to counter them. You can also watch videos or guides from other players or experts online to learn more tips and tricks.</p>
-<h3>Experiment with different modes and maps to find your favorite</h3>
-<p>Brawlhalla has a variety of modes and maps to suit your preferences and mood. You should experiment with different modes and maps to find your favorite ones. You can also create your own custom games with your own rules and settings by tapping on the play icon > custom > create room. You can also join other players' custom games by tapping on join room.</p>
-<h2>Conclusion</h2>
-<p>Brawlhalla Mod Apk 6.06 is a modified version of Brawlhalla that offers unlimited money and unlocked characters for free. It is a fun and exciting platform fighting game that you can play with your friends or online players across different platforms. However, it is not an official version of the game, so it may not be compatible with the latest updates or patches of the game. It may also cause errors, glitches, or crashes in your device. Moreover, using a modded version of the game may violate the terms of service of the game, and result in a ban or suspension of your account. You may also risk exposing your device to malware or viruses from untrusted sources. Therefore, use this mod at your own risk and discretion.</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>Q: Is Brawlhalla Mod Apk 6.06 safe to use?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 is not an official version of the game, so it may not be safe to use. It may contain malware or viruses that may harm your device or steal your information. It may also violate the terms of service of the game, and result in a ban or suspension of your account.</li>
-<li><b>Q: How do I update Brawlhalla Mod Apk 6.06?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 may not be compatible with the latest updates or patches of the game. To update it, you need to find a new version of the mod apk file and the data file from a reliable source online. Then you need to uninstall the old version of the mod apk file and install the new one. You also need to copy the new data file to the obb folder as well.</li>
-<li><b>Q: Can I play Brawlhalla Mod Apk 6.06 with other players online?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 supports online multiplayer with up to 8 players in a single match. However, you may not be able to play with players who are using the official version of the game, or players who are using a different version of the mod. You may also face lag, disconnects, or bans from the game servers.</li>
-<li><b>Q: Can I use Brawlhalla Mod Apk 6.06 on PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, or Android devices?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 is only compatible with Android devices. You cannot use it on PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, or iOS devices. However, you can use an Android emulator on your PC to run the mod apk file and the data file.</li>
-<li><b>Q: Where can I find more information about Brawlhalla Mod Apk 6.06?</b></li>
-<li>A: You can find more information about Brawlhalla Mod Apk 6.06 by searching online for websites or blogs that offer reviews, guides, or tutorials about the mod. You can also join online communities or forums that discuss the mod or the game in general. You can also watch videos or streams from other players or experts who use the mod or the game.</li>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Instagram APK for Android 4.0 and enjoy the latest features.md b/spaces/1phancelerku/anime-remove-background/Download Instagram APK for Android 4.0 and enjoy the latest features.md
deleted file mode 100644
index 483c7753f722e118e384b492e6530835a12aed45..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Instagram APK for Android 4.0 and enjoy the latest features.md	
+++ /dev/null
@@ -1,105 +0,0 @@
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-<h1>How to Download Instagram APK for Android 4.0</h1>
-<p>Instagram is one of the most popular social media platforms in the world, with over one billion monthly active users. It allows you to create and share your photos, stories, reels and videos with the friends and followers you care about. You can also connect with people who share your interests, discover new content, and chat with your favorite creators.</p>
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-<p>But what if you have an older device that runs on Android 4.0 Ice Cream Sandwich, which is no longer supported by the official Instagram app? Does that mean you can't enjoy the latest features and updates of Instagram? Not necessarily. In this article, we will show you how to download Instagram APK for Android 4.0, which is a modified version of the original app that works on older devices. We will also explain what Instagram APK is, why you might want to download it, how to install it, and how to use it.</p>
- <h2>What is Instagram APK?</h2>
-<p>Instagram APK is a file that contains the installation package of the Instagram app for Android devices. APK stands for Android Package Kit, and it is the standard format for distributing and installing apps on Android devices. You can download APK files from various sources online, such as third-party websites, app stores, or file-sharing platforms.</p>
-<p>However, not all APK files are safe or reliable. Some of them may contain malware, viruses, or unwanted ads that can harm your device or compromise your privacy. Therefore, you should always be careful when downloading APK files from unknown sources, and only use trusted and reputable websites.</p>
-<p>One of the advantages of downloading Instagram APK is that you can access the latest version of the app even if your device is not compatible with it. For example, if you have an Android 4.0 device, you can download Instagram APK for Android 4.0 and enjoy the new features and improvements of Instagram without having to upgrade your device.</p>
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-<li>You have an older device that runs on Android 4.0 Ice Cream Sandwich, which is no longer supported by the official Instagram app.</li>
-<li>You want to access the latest version of Instagram with all the new features and updates, such as reels, dark mode, stickers, filters, etc.</li>
-<li>You want to bypass some of the restrictions or limitations imposed by the official app, such as the number of accounts you can log in with, the size of the videos you can upload, or the content you can view.</li>
-<li>You want to customize or modify some aspects of the app according to your preferences, such as the theme, layout, icons, fonts, etc.</li>
-</ul>
-<p>However, there are also some drawbacks or risks associated with downloading Instagram APK for Android 4.0 that you should be aware of before proceeding. Here are some of them:</p>
-<ul>
-<li>You may violate the terms of service or policies of Instagram by using an unofficial or modified version of the app.</li>
-<li>You may encounter some bugs, errors, or compatibility issues that affect the performance or functionality of the app.</li>
-<li>You may expose your device or data to security threats or privacy breaches by downloading an unverified or malicious APK file.</li>
-<li>You may not receive automatic updates or technical support from Instagram if you encounter any problems with the app.</li <h2>How to Download Instagram APK for Android 4.0?</h2>
-<p>If you have decided to download Instagram APK for Android 4.0, you need to follow some simple steps to get the file and install it on your device. Here is a step-by-step guide on how to do it:</p>
-<ol>
-<li>Go to a reliable and reputable website that offers Instagram APK for Android 4.0, such as [APKPure] or [APKMirror]. You can also use a search engine to find other sources, but make sure they are safe and trustworthy.</li>
-<li>On the website, find the latest version of Instagram APK for Android 4.0 and click on the download button. You may need to allow the download from unknown sources in your device settings.</li>
-<li>Wait for the download to complete and locate the file in your device storage. It should have a name like "com.instagram.android.apk" or something similar.</li>
-<li>Tap on the file and follow the instructions to install it on your device. You may need to grant some permissions or accept some terms and conditions.</li>
-<li>Once the installation is done, you can launch the app and log in with your existing account or create a new one.</li>
-</ol>
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-<p>If you have already downloaded Instagram APK for Android 4.0, you need to install it on your device to use it. The installation process is similar to any other app, but you may need to enable some settings or options first. Here is a step-by-step guide on how to do it:</p>
-<ol>
-<li>Before installing Instagram APK for Android 4.0, you need to enable the installation from unknown sources in your device settings. To do this, go to Settings > Security > Unknown Sources and toggle the switch on.</li>
-<li>Now, go to your device storage and find the Instagram APK file that you downloaded earlier. It should have a name like "com.instagram.android.apk" or something similar.</li>
-<li>Tap on the file and follow the instructions to install it on your device. You may need to grant some permissions or accept some terms and conditions.</li>
-<li>Once the installation is done, you can launch the app and log in with your existing account or create a new one.</li>
-</ol>
- <h2>How to Use Instagram APK for Android 4.0?</h2>
-<p>After installing Instagram APK for Android 4.0, you can use it just like the official app, with some minor differences or limitations. Here are some tips and tricks on how to use Instagram APK for Android 4.0:</p>
-<ul>
-<li>To access the latest features and updates of Instagram, such as reels, dark mode, stickers, filters, etc., you may need to update the app regularly by downloading the latest version of Instagram APK for Android 4.0 from the same website that you used before.</li>
-<li>To avoid any compatibility issues or errors, you may need to clear the cache and data of the app occasionally by going to Settings > Apps > Instagram > Storage > Clear Cache and Clear Data.</li>
-<li>To customize or modify some aspects of the app according to your preferences, such as the theme, layout, icons, fonts, etc., you may need to use some third-party apps or tools that work with Instagram APK for Android 4.0, such as [GBInsta] or [InstaMod].</li>
-<li>To bypass some of the restrictions or limitations imposed by the official app, such as the number of accounts you can log in with, the size of the videos you can upload, or the content you can view, you may need to use some tricks or hacks that work with Instagram APK for Android 4.0, such as [Parallel Space] or [VPN].</li>
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- <h2>Conclusion</h2>
-<p>In conclusion, downloading Instagram APK for Android 4.0 is a possible solution for those who have an older device that runs on Android 4.0 Ice Cream Sandwich and want to enjoy the latest features and updates of Instagram without having to upgrade their device. However, there are also some drawbacks and risks involved in using an unofficial or modified version of the app that should be considered before proceeding.</p>
-<p>If you decide to download Instagram APK for Android 4.0, make sure you follow the steps above carefully and use a reliable and reputable website that offers safe and verified APK files. Also, be aware of the potential security threats or privacy breaches that may occur by downloading an unverified or malicious APK file.</p>
-<p>We hope this article has helped you understand what Instagram APK is, why you might want to download it, how to download it, how to install it, and how to use it. If you have any questions or feedback, feel free to leave a comment below or contact us through our website. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions and answers about Instagram APK for Android 4.0:</p>
-<h3>Is Instagram APK for Android 4.0 legal?</h3>
-<p>There is no definitive answer to this question, as different countries may have different laws and regulations regarding the use of unofficial or modified apps. However, in general, downloading and using Instagram APK for Android 4.0 is not illegal, as long as you do not use it for any malicious or fraudulent purposes. However, you may violate the terms of service or policies of Instagram by using an unofficial or modified version of the app, which may result in your account being suspended or banned.</p>
-<h3>Is Instagram APK for Android 4.0 safe?</h3>
-<p>Not all Instagram APK files are safe or reliable. Some of them may contain malware, viruses, or unwanted ads that can harm your device or compromise your privacy. Therefore, you should always be careful when downloading Instagram APK files from unknown sources, and only use trusted and reputable websites that offer safe and verified APK files. You should also scan the APK file with an antivirus or malware detector before installing it on your device.</p>
-<h3>What are the differences between Instagram APK for Android 4.0 and the official app?</h3>
-<p>The main difference between Instagram APK for Android 4.0 and the official app is that the former works on older devices that run on Android 4.0 Ice Cream Sandwich, while the latter does not. The official app requires a minimum of Android 5.0 Lollipop to run smoothly and support all the features and updates of Instagram. Another difference is that Instagram APK for Android 4.0 may have some extra features or options that are not available in the official app, such as customizing the theme, layout, icons, fonts, etc., or bypassing some of the restrictions or limitations imposed by the official app, such as the number of accounts you can log in with, the size of the videos you can upload, or the content you can view.</p>
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-<p>To update Instagram APK for Android 4.0, you need to download the latest version of the file from the same website that you used before and install it on your device. You may need to uninstall the previous version of the app first before installing the new one. Alternatively, you can use some third-party apps or tools that can automatically update Instagram APK for Android 4.0 for you, such as [APKUpdater] or [Uptodown].</p>
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-<p>To delete Instagram APK for Android 4.0, you need to uninstall it from your device like any other app. To do this, go to Settings > Apps > Instagram > Uninstall and confirm your action. You may also need to delete the APK file from your device storage if you want to free up some space.</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Sonic Dash 2 Sonic Boom MOD APK with Unlimited Money 3.8.1.md b/spaces/1phancelerku/anime-remove-background/Download Sonic Dash 2 Sonic Boom MOD APK with Unlimited Money 3.8.1.md
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-<h1>Sonic Dash 2: Sonic Boom Mod Apk Free Download</h1>
- <p>If you are a fan of Sonic the Hedgehog and endless runner games, you might have heard of Sonic Dash 2: Sonic Boom. This is a sequel to the popular Sonic Dash game, featuring the characters and world of the Sonic Boom TV series. In this game, you can run, jump, dash, and swing through various levels, collecting rings, orbs, and sprites, while avoiding obstacles and enemies. You can also play as different characters, each with their own special abilities and powers.</p>
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- <p>But what if you want to enhance your gaming experience even more? What if you want to unlock all the characters, get unlimited rings and orbs, and access all the features of the game without spending any money? This is where a mod apk comes in handy. A mod apk is a modified version of an original app that has been altered to provide some extra features or benefits. By downloading and installing a mod apk for Sonic Dash 2: Sonic Boom, you can enjoy the game with more fun and ease.</p>
- <h2>How to Download and Install the Mod Apk for Sonic Dash 2: Sonic Boom</h2>
- <p>Downloading and installing the mod apk for Sonic Dash 2: Sonic Boom is not very difficult, but you need to follow some steps carefully. Here is what you need to do:</p>
- <ol>
-<li>First, you need to enable unknown sources on your device. This will allow you to install apps that are not from the official app store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</li>
-<li>Next, you need to find a reliable source for downloading the mod apk file. There are many websites that offer mod apks for various games, but not all of them are safe and trustworthy. You need to be careful about malware, viruses, and other harmful software that might harm your device or steal your personal information. One of the websites that we recommend is [text](^1^), where you can find the latest version of the mod apk for Sonic Dash 2: Sonic Boom.</li>
-<li>Once you have found the mod apk file, you need to download it to your device. You can do this by clicking on the download button on the website, or by scanning the QR code if available. The file size is about 70 MB, so make sure you have enough space on your device.</li>
-<li>After downloading the file, you need to locate it on your device. You can use a file manager app or your device's default file explorer to find it. The file name should be something like "sonic-dash-2-sonic-boom-v2-2-1-mod.apk".</li>
-<li>Now, you need to install the file on your device. To do this, simply tap on the file and follow the instructions on the screen. You might need to grant some permissions for the app to work properly.</li>
-<li>Finally, you need to launch the game and enjoy it with the mod features. You should see a message saying "Modded by HappyMod" on the loading screen. This means that the mod apk has been successfully installed.</li>
-</ol>
- <h2>What are the Features and Benefits of the Mod Apk</h2>
- <p>The mod apk for Sonic Dash 2: Sonic Boom has many features and benefits that will make your gaming experience more enjoyable and rewarding. Here are some of them:</p>
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-<li>You can unlock all the characters in the game, including Sonic, Tails, Amy, Knuckles, Sticks, Shadow, and Vector. Each character has their own unique skills and abilities that will help you in different situations.</li>
-<li>You can get unlimited rings and orbs in the game. Rings and orbs are the main currencies in the game, which you can use to buy upgrades, power-ups, and boosters. With unlimited rings and orbs, you can get the best items and enhance your performance in the game.</li>
-<li>You can access all the features of the game, such as the events, the missions, the daily challenges, and the leaderboards. You can also play online with other players and compete for the highest scores and rewards.</li>
-<li>You can enjoy the game without any ads or interruptions. The mod apk removes all the annoying ads that might pop up during the game or after completing a level. You can also skip the waiting time for loading or reviving.</li>
-</ul>
- <h2>What are some Tips and Tricks for Playing the Game with the Mod Apk</h2>
- <p>Playing Sonic Dash 2: Sonic Boom with the mod apk is fun and easy, but there are some tips and tricks that you can use to make the most out of it. Here are some of them:</p>
- <ul>
-<li>Try to switch between different characters during the game, depending on the situation. For example, you can use Sonic's dash ability to speed up and break through obstacles, Tails' flight ability to soar over gaps and enemies, Amy's hammer ability to smash everything in her way, Knuckles' punch ability to deal more damage and collect more rings, Sticks' boomerang ability to hit multiple targets and collect sprites, Shadow's chaos blast ability to destroy everything around him, and Vector's music ability to create shockwaves and attract rings.</li>
-<li>Use the sprites wisely. Sprites are cute creatures that can help you in various ways, such as increasing your score, boosting your speed, protecting you from damage, or giving you extra lives. You can equip up to three sprites at a time, and each sprite has a different rarity and effect. You can also upgrade your sprites to make them more powerful.</li>
-<li>Collect as many rings and orbs as possible. Rings and orbs are not only useful for buying items, but also for increasing your score and activating your character's special power. The more rings and orbs you collect, the faster you can fill up your power meter and unleash your character's ultimate move.</li>
-<li>Avoid hitting obstacles and enemies. Hitting obstacles and enemies will slow you down, damage you, or end your run. You need to be alert and agile to dodge or jump over them. You can also use your character's abilities or power-ups to deal with them.</li>
-<li>Complete the events, missions, and daily challenges. These are optional tasks that you can do to earn more rewards and bonuses. They also add more variety and fun to the game. You can check them out on the main menu or on the top of the screen during the game.</li>
-</ul>
- <h1>Conclusion</h1>
- <p>Sonic Dash 2: Sonic Boom is an exciting and addictive endless runner game that features the beloved characters and world of Sonic the Hedgehog. If you want to enjoy the game with more features and benefits, you can download and install the mod apk for Sonic Dash 2: Sonic Boom from [text]. This will allow you to unlock all the characters, get unlimited rings and orbs, access all the features of the game, and play without any ads or interruptions. You can also use some tips and tricks to improve your skills and score in the game. So what are you waiting for? Download the mod apk now and have fun with Sonic Dash 2: Sonic Boom!</p>
- <h1>FAQs</h1>
- <p>Here are some frequently asked questions and answers about the mod apk for Sonic Dash 2: Sonic Boom:</p>
- <ol>
-<li><b>Is the mod apk safe to download and install?</b><br>
-Yes, the mod apk is safe to download and install, as long as you get it from a reliable source like [text]. The mod apk has been tested by many users and has no viruses or malware.</li>
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-Yes, you can update the game with the mod apk. However, you might need to download a new version of the mod apk when a new update is released. You can check [text] for any updates on the mod apk for Sonic Dash 2: Sonic Boom.</li>
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- <p>I hope this article has helped you learn more about the mod apk for Sonic Dash 2: Sonic Boom and how to download and install it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Experience Realistic Car Parking and Racing with Car Parking Multiplayer on Windows 7.md b/spaces/1phancelerku/anime-remove-background/Experience Realistic Car Parking and Racing with Car Parking Multiplayer on Windows 7.md
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--- a/spaces/1phancelerku/anime-remove-background/Experience Realistic Car Parking and Racing with Car Parking Multiplayer on Windows 7.md	
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-<p>Are you looking for a realistic and fun driving simulator game that you can play on your PC? If yes, then you should try Car Parking Multiplayer, a game that offers more than just parking. In this article, we will show you how to download and install Car Parking Multiplayer on Windows 7 using two different methods. We will also tell you about the features, tips, and tricks of this amazing game. So, let's get started!</p>
- <h2>Introduction</h2>
-<p>Car Parking Multiplayer is a game that can fool you with its rather deceiving name. But, it's much more than just being about parking your car. It's an open-world experience where you can drive free and yes, still work on that parking if you wish. You can even jump out of your car and walk around. There are different areas that can be explored in the game. Each one is like its own open-world. You can choose to play either single-player mode or online mode if you want a more chaotic scene (in a fun way).</p>
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- <h3>What is Car Parking Multiplayer?</h3>
-<p>Car Parking Multiplayer is a simulation game developed by olzhass, a developer based in Kazakhstan. The game was released in 2017 for Android devices and later for iOS devices. The game has more than 100 million downloads on Google Play Store and more than 130 cars to choose from.</p>
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-<p>While Car Parking Multiplayer is designed for mobile devices, playing it on PC has some advantages. For example, you can enjoy the game on a bigger screen with better graphics and sound quality. You can also use your keyboard and mouse to control your car more easily and precisely. Moreover, playing on PC can save your battery life and avoid overheating issues of your mobile device.</p>
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-<p>There are two methods that you can use to download and install Car Parking Multiplayer on Windows 7. The first method is using an Android emulator, which is a software that allows you to run Android applications on your PC. The second method is using a web browser, which is a simpler but less immersive way to play the game.</p>
- <h3>Method 1: Using an Android emulator</h3>
-<p>An Android emulator is a software that creates a virtual Android device on your PC, allowing you to run Android applications and games on your computer. There are many Android emulators available online, such as BlueStacks, LDPlayer, NoxPlayer, etc. Here are the steps to use an Android emulator to play Car Parking Multiplayer on Windows 7:</p>
- <h4>Step 1: Download and install an Android emulator</h4>
-<p>The first step is to download and install an Android emulator of your choice on your PC. You can go to the official website of the emulator or use a third-party source to download the emulator file. After downloading the file, you need to run it and follow the instructions to install the emulator on your PC. This may take some time depending on your internet speed and PC performance.</p>
- <h4>Step 2: Download the APK/XAPK file of Car Parking Multiplayer</h4>
-<p>The next step is to download the APK or XAPK file of Car Parking Multiplayer on your PC. APK and XAPK are file formats that contain the installation package of an Android application or game. You can download the APK/XAPK file of Car Parking Multiplayer from various sources online, such as APKPure, APKMirror, Uptodown, etc. Make sure to download the latest version of the file and save it in a folder that you can easily access.</p>
- <h4>Step 3: Install and launch Car Parking Multiplayer on the emulator</h4>
-<p>The final step is to install and launch Car Parking Multiplayer on the emulator. There are two ways to do this. The first way is to drag and drop the APK/XAPK file into the emulator window and wait for the installation to complete. The second way is to open the emulator and go to the built-in app store or browser and search for Car Parking Multiplayer and install it from there. After installing the game, you can launch it from the emulator's home screen or app drawer and enjoy playing it on your PC.</p>
- <h3>Method 2: Using a web browser</h3>
-<p>Another method that you can use to play Car Parking Multiplayer on Windows 7 is using a web browser. This method is simpler but less immersive than using an emulator. You don't need to download or install anything on your PC, but you need a stable internet connection and a compatible web browser. Here are the steps to use a web browser to play Car Parking Multiplayer on Windows 7:</p>
- <h4>Step 1: Go to the official website of Car Parking Multiplayer</h4>
-<p>The first step is to go to the official website of Car Parking Multiplayer, which is https://carparkingmultiplayer.com/. You can use any web browser that supports HTML5, such as Chrome, Firefox, Edge, Safari, etc.</p>
- <h4>Step 2: Click on the "Play Now" button</h4>
-<p>The next step is to click on the "Play Now" button on the website's homepage. This will open a new tab or window where you can play the game online.</p>
- <h4>Step 3: Enjoy the game on your browser</h4>
-<p>The final step is to enjoy the game on your browser. You can use your mouse and keyboard to control your car and interact with other players. You can also adjust the settings, such as graphics quality, sound volume, language, etc., by clicking on the gear icon on the top right corner of the screen.</p>
- <h2>Features of Car Parking Multiplayer</h2>
-<p>Car Parking Multiplayer is a game that offers many features that make it fun and realistic. Here are some of the features that you can enjoy in this game:</p>
- <h3>Open-world multiplayer mode</h3>
-<p>One of the main features of Car Parking Multiplayer is its open-world multiplayer mode, where you can join thousands of other players from around the world in various maps and locations. You can choose from different modes, such as free roam, racing, police chase, etc., and have fun with your friends or strangers. You can also chat with other players using voice or text messages.</p>
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-<p>Another feature of Car Parking Multiplayer is its car customization and tuning system, where you can modify your car's appearance and performance according to your preference. You can change your car's color, wheels, spoilers, stickers, lights, etc., and make it look unique and cool. You can also tune your car's engine, suspension, brakes, transmission, etc., and improve its speed, handling, acceleration, etc.</p>
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-<p>Car Parking Multiplayer also boasts high-quality graphics and sound effects that make it realistic and immersive. The game has realistic physics and animations that simulate real driving scenarios and situations. The game also has realistic sound effects that match the engine sounds, tire screeches, horn honks, etc., of different cars.</p>
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-<p>Car Parking Multiplayer also has interesting gameplay and challenges that make it addictive and enjoyable. The game has various missions and tasks that you can complete to earn money and rewards. The game also has different levels of difficulty that test your driving skills and knowledge. The game also has a ranking system that shows your progress and achievements.[^ 1^]</p>
- <h2>Tips and tricks for Car Parking Multiplayer</h2>
-<p>Car Parking Multiplayer is a game that requires some skills and strategies to master. Here are some tips and tricks that can help you improve your gameplay and have more fun in this game:</p>
- <h3>How to select a car and a player</h3>
-<p>When you start the game, you can choose your car and your player from the garage menu. You can scroll through different categories of cars, such as sports, classic, SUV, etc., and select the one that suits your style and budget. You can also customize your car's appearance and performance from the same menu. To change your player, you can click on the avatar icon on the top left corner of the screen and select from different options of gender, skin color, hair style, clothing, etc.</p>
- <h3>How to understand gear ratio and drift</h3>
-<p>One of the features of Car Parking Multiplayer is its realistic gear ratio and drift system, which affects how your car behaves on the road. Gear ratio is the relationship between the engine speed and the wheel speed, which determines how fast or slow your car accelerates or decelerates. Drift is the sideways movement of your car when you turn at high speed, which can be controlled by using the handbrake or steering. To understand how gear ratio and drift work in this game, you can go to the settings menu and enable the "Gear Ratio" and "Drift" options. This will show you a graph and a meter that indicate how your car's gear ratio and drift change according to your actions.</p>
- <h3>How to make money and buy new cars</h3>
-<p>Money is an important resource in Car Parking Multiplayer, as it allows you to buy new cars, upgrade your existing ones, or access premium features. There are several ways to make money in this game, such as completing missions, winning races, selling cars, watching ads, etc. You can also buy money with real money if you want to support the developers or get some extra cash. To buy new cars, you can go to the shop menu and browse through different categories of cars, such as exclusive, VIP, police, etc. You can also see the specifications and prices of each car before buying it.</p>
- <h3>How to communicate and interact with other players</h3>
-<p>Car Parking Multiplayer is a social game where you can communicate and interact with other players from around the world. You can chat with other players using voice or text messages by clicking on the microphone or keyboard icons on the bottom right corner of the screen. You can also join or create a room where you can invite your friends or strangers to play together. You can also follow or unfollow other players by clicking on their names or avatars on the map or leaderboard.</p>
- <h2>Conclusion</h2>
-<p>Car Parking Multiplayer is a game that offers a realistic and fun driving simulator experience that you can play on your PC. You can download and install Car Parking Multiplayer on Windows 7 using an Android emulator or a web browser. You can also enjoy various features, such as open-world multiplayer mode, car customization and tuning, high-quality graphics and sound effects, interesting gameplay and challenges, etc. You can also use some tips and tricks to improve your gameplay and have more fun in this game.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Parking Multiplayer:</p>
- <ol>
-<li><b>Is Car Parking Multiplayer free to play?</b></li>
-<p>Yes, Car Parking Multiplayer is free to play on both mobile devices and PC. However, there are some in-app purchases that you can make to get more money, cars, or features.</p>
-<li><b>Is Car Parking Multiplayer safe to play?</b></li>
-<p>Yes, Car Parking Multiplayer is safe to play as long as you download it from a trusted source and use a reliable antivirus software on your PC. However, be careful when chatting with other players online as they may use inappropriate language or try to scam you.</p>
-<li><b>Can I play Car Parking Multiplayer offline?</b></li>
-<p>No, Car Parking Multiplayer requires an internet connection to play online with other players or access some features. However, you can still play single-player mode offline if you have already downloaded the game on your device.</p>
-<li><b>How do I update Car Parking Multiplayer?</b></li>
-<p>To update Car Parking Multiplayer on your PC, you need to download the latest version of the APK/XAPK file from a trusted source and install it on your emulator or browser. Alternatively, you can check for updates from the app store or browser that you used to install the game.</p>
-<li><b>How do I contact the developers of Car Parking Multi layer?</b></li>
-<p>To contact the developers of Car Parking Multiplayer, you can go to the settings menu and click on the "Contact Us" button. This will open a form where you can fill in your name, email, subject, and message. You can also follow the developers on their social media accounts, such as Facebook, Instagram, YouTube, etc.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/232labs/VToonify/vtoonify/model/encoder/readme.md b/spaces/232labs/VToonify/vtoonify/model/encoder/readme.md
deleted file mode 100644
index 5421bfe3e67b7b6cbd7baf96b741b539d65bb0fd..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/encoder/readme.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
-
-## Description   
-Official Implementation of pSp paper for both training and evaluation. The pSp method extends the StyleGAN model to 
-allow solving different image-to-image translation problems using its encoder.
-
-Fork from [https://github.com/eladrich/pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel).
-
-In VToonify, we modify pSp to accept z+ latent code.
diff --git a/spaces/52Hz/SRMNet_real_world_denoising/README.md b/spaces/52Hz/SRMNet_real_world_denoising/README.md
deleted file mode 100644
index 22fac999101d9f9a986db385b2788df8e80b2638..0000000000000000000000000000000000000000
--- a/spaces/52Hz/SRMNet_real_world_denoising/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: SRMNet_real_denoising
-emoji: 🌪
-colorFrom: pink
-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.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/AFischer1985/German-Flan-T5/README.md b/spaces/AFischer1985/German-Flan-T5/README.md
deleted file mode 100644
index 466c8baf2de0ba16b8f0e1504035bd40eadfce63..0000000000000000000000000000000000000000
--- a/spaces/AFischer1985/German-Flan-T5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: German Flan T5
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/utils.py b/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/utils.py
deleted file mode 100644
index 1d91f963370321cf093c7fb9adefaa018463c8da..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto
diff --git a/spaces/AIConsultant/MusicGen/tests/modules/test_rope.py b/spaces/AIConsultant/MusicGen/tests/modules/test_rope.py
deleted file mode 100644
index 067c6f067acbf27fb0fef5c2b812c22474c4fcd0..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/tests/modules/test_rope.py
+++ /dev/null
@@ -1,168 +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, set_efficient_attention_backend
-
-
-def test_rope():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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():
-    set_efficient_attention_backend('xformers')
-    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/AIFILMS/StyleGANEX/models/psp.py b/spaces/AIFILMS/StyleGANEX/models/psp.py
deleted file mode 100644
index 607a05aa8aa0d29ca58a4959e78c9b2065953a9e..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/psp.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-This file defines the core research contribution
-"""
-import matplotlib
-matplotlib.use('Agg')
-import math
-
-import torch
-from torch import nn
-from models.encoders import psp_encoders
-from models.stylegan2.model import Generator
-from configs.paths_config import model_paths
-import torch.nn.functional as F
-
-def get_keys(d, name):
-    if 'state_dict' in d:
-        d = d['state_dict']
-    d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
-    return d_filt
-
-
-class pSp(nn.Module):
-
-    def __init__(self, opts, ckpt=None):
-        super(pSp, self).__init__()
-        self.set_opts(opts)
-        # compute number of style inputs based on the output resolution
-        self.opts.n_styles = int(math.log(self.opts.output_size, 2)) * 2 - 2
-        # Define architecture
-        self.encoder = self.set_encoder()
-        self.decoder = Generator(self.opts.output_size, 512, 8)
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
-        # Load weights if needed
-        self.load_weights(ckpt)
-
-    def set_encoder(self):
-        if self.opts.encoder_type == 'GradualStyleEncoder':
-            encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
-        elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoW':
-            encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts)
-        elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoWPlus':
-            encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoWPlus(50, 'ir_se', self.opts)
-        else:
-            raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
-        return encoder
-
-    def load_weights(self, ckpt=None):
-        if self.opts.checkpoint_path is not None:
-            print('Loading pSp from checkpoint: {}'.format(self.opts.checkpoint_path))
-            if ckpt is None:
-                ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
-            self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=False)
-            self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=False)
-            self.__load_latent_avg(ckpt)
-        else:
-            print('Loading encoders weights from irse50!')
-            encoder_ckpt = torch.load(model_paths['ir_se50'])
-            # if input to encoder is not an RGB image, do not load the input layer weights
-            if self.opts.label_nc != 0:
-                encoder_ckpt = {k: v for k, v in encoder_ckpt.items() if "input_layer" not in k}
-            self.encoder.load_state_dict(encoder_ckpt, strict=False)
-            print('Loading decoder weights from pretrained!')
-            ckpt = torch.load(self.opts.stylegan_weights)
-            self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
-            if self.opts.learn_in_w:
-                self.__load_latent_avg(ckpt, repeat=1)
-            else:
-                self.__load_latent_avg(ckpt, repeat=self.opts.n_styles)
-        # for video toonification, we load G0' model
-        if self.opts.toonify_weights is not None: ##### modified
-            ckpt = torch.load(self.opts.toonify_weights)
-            self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
-            self.opts.toonify_weights = None
-
-    # x1: image for first-layer feature f. 
-    # x2: image for style latent code w+. If not specified, x2=x1.
-    # inject_latent: for sketch/mask-to-face translation, another latent code to fuse with w+
-    # latent_mask: fuse w+ and inject_latent with the mask (1~7 use w+ and 8~18 use inject_latent)
-    # use_feature: use f. Otherwise, use the orginal StyleGAN first-layer constant 4*4 feature 
-    # first_layer_feature_ind: always=0, means the 1st layer of G accept f
-    # use_skip: use skip connection.
-    # zero_noise: use zero noises. 
-    # editing_w: the editing vector v for video face editing
-    def forward(self, x1, x2=None, resize=True, latent_mask=None, randomize_noise=True,
-                inject_latent=None, return_latents=False, alpha=None, use_feature=True, 
-                first_layer_feature_ind=0, use_skip=False, zero_noise=False, editing_w=None): ##### modified
-        
-        feats = None # f and the skipped encoder features
-        codes, feats = self.encoder(x1, return_feat=True, return_full=use_skip) ##### modified
-        if x2 is not None: ##### modified
-            codes = self.encoder(x2) ##### modified
-        # normalize with respect to the center of an average face
-        if self.opts.start_from_latent_avg:
-            if self.opts.learn_in_w:
-                codes = codes + self.latent_avg.repeat(codes.shape[0], 1)
-            else:
-                codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
-
-        # E_W^{1:7}(T(x1)) concatenate E_W^{8:18}(w~)
-        if latent_mask is not None:
-            for i in latent_mask:
-                if inject_latent is not None:
-                    if alpha is not None:
-                        codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
-                    else:
-                        codes[:, i] = inject_latent[:, i]
-                else:
-                    codes[:, i] = 0
-                    
-        first_layer_feats, skip_layer_feats, fusion = None, None, None ##### modified            
-        if use_feature: ##### modified
-            first_layer_feats = feats[0:2] # use f
-            if use_skip: ##### modified
-                skip_layer_feats = feats[2:] # use skipped encoder feature
-                fusion = self.encoder.fusion # use fusion layer to fuse encoder feature and decoder feature.
-            
-        images, result_latent = self.decoder([codes],
-                                             input_is_latent=True,
-                                             randomize_noise=randomize_noise,
-                                             return_latents=return_latents,
-                                             first_layer_feature=first_layer_feats,
-                                             first_layer_feature_ind=first_layer_feature_ind,
-                                             skip_layer_feature=skip_layer_feats,
-                                             fusion_block=fusion,
-                                             zero_noise=zero_noise,
-                                             editing_w=editing_w) ##### modified
-
-        if resize:
-            if self.opts.output_size == 1024:  ##### modified
-                images = F.adaptive_avg_pool2d(images, (images.shape[2]//4, images.shape[3]//4))  ##### modified
-            else:
-                images = self.face_pool(images)
-
-        if return_latents:
-            return images, result_latent
-        else:
-            return images
-
-    def set_opts(self, opts):
-        self.opts = opts
-
-    def __load_latent_avg(self, ckpt, repeat=None):
-        if 'latent_avg' in ckpt:
-            self.latent_avg = ckpt['latent_avg'].to(self.opts.device)
-            if repeat is not None:
-                self.latent_avg = self.latent_avg.repeat(repeat, 1)
-        else:
-            self.latent_avg = None
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/biaobei/preprocess.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/biaobei/preprocess.py
deleted file mode 100644
index 44f48e22675a02e3a4b91a69caf7344f5a2982ef..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/biaobei/preprocess.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from data_gen.tts.base_preprocess import BasePreprocessor
-import re
-
-
-class BiaobeiPreprocess(BasePreprocessor):
-    def meta_data(self):
-        input_dir = self.raw_data_dir
-        with open(f"{input_dir}/ProsodyLabeling/000001-010000.txt", encoding='utf-8') as f:
-            bb_lines = f.readlines()[::2]
-        for l_idx, l in (enumerate([re.sub("\#\d+", "", l.split('\t')[1].strip()) for l in bb_lines])):
-            item_name = f'{l_idx + 1:06d}'
-            wav_fn = f"{input_dir}/wav/{l_idx + 1:06d}.wav"
-            yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': l}
-
-if __name__ == "__main__":
-    BiaobeiPreprocess().process()
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/losses_audio/vggishish/metrics.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/losses_audio/vggishish/metrics.py
deleted file mode 100644
index 16905224c665491b9869d7641c1fe17689816a4b..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/losses_audio/vggishish/metrics.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import logging
-
-import numpy as np
-import scipy
-import torch
-from sklearn.metrics import average_precision_score, roc_auc_score
-
-logger = logging.getLogger(f'main.{__name__}')
-
-def metrics(targets, outputs, topk=(1, 5)):
-    """
-    Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py
-
-    Calculate statistics including mAP, AUC, and d-prime.
-        Args:
-            output: 2d tensors, (dataset_size, classes_num) - before softmax
-            target: 1d tensors, (dataset_size, )
-            topk: tuple
-        Returns:
-            metric_dict: a dict of metrics
-    """
-    metrics_dict = dict()
-
-    num_cls = outputs.shape[-1]
-
-    # accuracy@k
-    _, preds = torch.topk(outputs, k=max(topk), dim=1)
-    correct_for_maxtopk = preds == targets.view(-1, 1).expand_as(preds)
-    for k in topk:
-        metrics_dict[f'accuracy_{k}'] = float(correct_for_maxtopk[:, :k].sum() / correct_for_maxtopk.shape[0])
-
-    # avg precision, average roc_auc, and dprime
-    targets = torch.nn.functional.one_hot(targets, num_classes=num_cls)
-
-    # ids of the predicted classes (same as softmax)
-    targets_pred = torch.softmax(outputs, dim=1)
-
-    targets = targets.numpy()
-    targets_pred = targets_pred.numpy()
-
-    # one-vs-rest
-    avg_p = [average_precision_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
-    try:
-        roc_aucs = [roc_auc_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
-    except ValueError:
-        logger.warning('Weird... Some classes never occured in targets. Do not trust the metrics.')
-        roc_aucs = np.array([0.5])
-        avg_p = np.array([0])
-
-    metrics_dict['mAP'] = np.mean(avg_p)
-    metrics_dict['mROCAUC'] = np.mean(roc_aucs)
-    # Percent point function (ppf) (inverse of cdf — percentiles).
-    metrics_dict['dprime'] = scipy.stats.norm().ppf(metrics_dict['mROCAUC']) * np.sqrt(2)
-
-    return metrics_dict
-
-
-if __name__ == '__main__':
-    targets = torch.tensor([3, 3, 1, 2, 1, 0])
-    outputs = torch.tensor([
-        [1.2, 1.3, 1.1, 1.5],
-        [1.3, 1.4, 1.0, 1.1],
-        [1.5, 1.1, 1.4, 1.3],
-        [1.0, 1.2, 1.4, 1.5],
-        [1.2, 1.3, 1.1, 1.1],
-        [1.2, 1.1, 1.1, 1.1],
-    ]).float()
-    metrics_dict = metrics(targets, outputs, topk=(1, 3))
-    print(metrics_dict)
diff --git a/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion/README.md b/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion/README.md
deleted file mode 100644
index 5745306baa17d1809a8d1691f0676a744bbd142d..0000000000000000000000000000000000000000
--- a/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stable Diffusion vs. Safe Stable Diffusion
-colorFrom: blue
-colorTo: red
-emoji: 😇
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: true
-license: creativeml-openrail-m
-duplicated_from: AIML-TUDA/safe-stable-diffusion
----
-
diff --git a/spaces/Abhilashvj/planogram-compliance/detect.py b/spaces/Abhilashvj/planogram-compliance/detect.py
deleted file mode 100644
index 2dd66feb2cd1f84ece05b44b580b64d30290a9d9..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/detect.py
+++ /dev/null
@@ -1,460 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
-                                                     img.jpg                         # image
-                                                     vid.mp4                         # video
-                                                     screen                          # screenshot
-                                                     path/                           # directory
-                                                     list.txt                        # list of images
-                                                     list.streams                    # list of streams
-                                                     'path/*.jpg'                    # glob
-                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python detect.py --weights yolov5s.pt                 # PyTorch
-                                 yolov5s.torchscript        # TorchScript
-                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                 yolov5s_openvino_model     # OpenVINO
-                                 yolov5s.engine             # TensorRT
-                                 yolov5s.mlmodel            # CoreML (macOS-only)
-                                 yolov5s_saved_model        # TensorFlow SavedModel
-                                 yolov5s.pb                 # TensorFlow GraphDef
-                                 yolov5s.tflite             # TensorFlow Lite
-                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-                                 yolov5s_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import (
-    IMG_FORMATS,
-    VID_FORMATS,
-    LoadImages,
-    LoadScreenshots,
-    LoadStreams,
-)
-from utils.general import (
-    LOGGER,
-    Profile,
-    check_file,
-    check_img_size,
-    check_imshow,
-    check_requirements,
-    colorstr,
-    cv2,
-    increment_path,
-    non_max_suppression,
-    print_args,
-    scale_boxes,
-    strip_optimizer,
-    xyxy2xywh,
-)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-    weights=ROOT / "yolov5s.pt",  # model path or triton URL
-    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    imgsz=(640, 640),  # inference size (height, width)
-    conf_thres=0.25,  # confidence threshold
-    iou_thres=0.45,  # NMS IOU threshold
-    max_det=1000,  # maximum detections per image
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    view_img=False,  # show results
-    save_txt=False,  # save results to *.txt
-    save_conf=False,  # save confidences in --save-txt labels
-    save_crop=False,  # save cropped prediction boxes
-    nosave=False,  # do not save images/videos
-    classes=None,  # filter by class: --class 0, or --class 0 2 3
-    agnostic_nms=False,  # class-agnostic NMS
-    augment=False,  # augmented inference
-    visualize=False,  # visualize features
-    update=False,  # update all models
-    project=ROOT / "runs/detect",  # save results to project/name
-    name="exp",  # save results to project/name
-    exist_ok=False,  # existing project/name ok, do not increment
-    line_thickness=3,  # bounding box thickness (pixels)
-    hide_labels=False,  # hide labels
-    hide_conf=False,  # hide confidences
-    half=False,  # use FP16 half-precision inference
-    dnn=False,  # use OpenCV DNN for ONNX inference
-    vid_stride=1,  # video frame-rate stride
-):
-    source = str(source)
-    save_img = not nosave and not source.endswith(
-        ".txt"
-    )  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(
-        ("rtsp://", "rtmp://", "http://", "https://")
-    )
-    webcam = (
-        source.isnumeric()
-        or source.endswith(".streams")
-        or (is_url and not is_file)
-    )
-    screenshot = source.lower().startswith("screen")
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(
-        Path(project) / name, exist_ok=exist_ok
-    )  # increment run
-    (save_dir / "labels" if save_txt else save_dir).mkdir(
-        parents=True, exist_ok=True
-    )  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(
-        weights, device=device, dnn=dnn, data=data, fp16=half
-    )
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(
-            source,
-            img_size=imgsz,
-            stride=stride,
-            auto=pt,
-            vid_stride=vid_stride,
-        )
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(
-            source, img_size=imgsz, stride=stride, auto=pt
-        )
-    else:
-        dataset = LoadImages(
-            source,
-            img_size=imgsz,
-            stride=stride,
-            auto=pt,
-            vid_stride=vid_stride,
-        )
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.from_numpy(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            im /= 255  # 0 - 255 to 0.0 - 1.0
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            visualize = (
-                increment_path(save_dir / Path(path).stem, mkdir=True)
-                if visualize
-                else False
-            )
-            pred = model(im, augment=augment, visualize=visualize)
-
-        # NMS
-        with dt[2]:
-            pred = non_max_suppression(
-                pred,
-                conf_thres,
-                iou_thres,
-                classes,
-                agnostic_nms,
-                max_det=max_det,
-            )
-
-        # Second-stage classifier (optional)
-        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
-        # Process predictions
-        for i, det in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f"{i}: "
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / "labels" / p.stem) + (
-                "" if dataset.mode == "image" else f"_{frame}"
-            )  # im.txt
-            s += "%gx%g " % im.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[
-                [1, 0, 1, 0]
-            ]  # normalization gain whwh
-            imc = im0.copy() if save_crop else im0  # for save_crop
-            annotator = Annotator(
-                im0, line_width=line_thickness, example=str(names)
-            )
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_boxes(
-                    im.shape[2:], det[:, :4], im0.shape
-                ).round()
-
-                # Print results
-                for c in det[:, 5].unique():
-                    n = (det[:, 5] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (
-                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
-                            .view(-1)
-                            .tolist()
-                        )  # normalized xywh
-                        line = (
-                            (cls, *xywh, conf) if save_conf else (cls, *xywh)
-                        )  # label format
-                        with open(f"{txt_path}.txt", "a") as f:
-                            f.write(("%g " * len(line)).rstrip() % line + "\n")
-
-                    if save_img or save_crop or view_img:  # Add bbox to image
-                        c = int(cls)  # integer class
-                        label = (
-                            None
-                            if hide_labels
-                            else (
-                                names[c]
-                                if hide_conf
-                                else f"{names[c]} {conf:.2f}"
-                            )
-                        )
-                        annotator.box_label(xyxy, label, color=colors(c, True))
-                    if save_crop:
-                        save_one_box(
-                            xyxy,
-                            imc,
-                            file=save_dir
-                            / "crops"
-                            / names[c]
-                            / f"{p.stem}.jpg",
-                            BGR=True,
-                        )
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == "Linux" and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(
-                        str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
-                    )  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == "image":
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[
-                                i
-                            ].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(
-                            Path(save_path).with_suffix(".mp4")
-                        )  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(
-                            save_path,
-                            cv2.VideoWriter_fourcc(*"mp4v"),
-                            fps,
-                            (w, h),
-                        )
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(
-            f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms"
-        )
-
-    # Print results
-    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
-    LOGGER.info(
-        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
-        % t
-    )
-    if save_txt or save_img:
-        s = (
-            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
-            if save_txt
-            else ""
-        )
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(
-            weights[0]
-        )  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--weights",
-        nargs="+",
-        type=str,
-        default=ROOT / "yolov5s.pt",
-        help="model path or triton URL",
-    )
-    parser.add_argument(
-        "--source",
-        type=str,
-        default=ROOT / "data/images",
-        help="file/dir/URL/glob/screen/0(webcam)",
-    )
-    parser.add_argument(
-        "--data",
-        type=str,
-        default=ROOT / "data/coco128.yaml",
-        help="(optional) dataset.yaml path",
-    )
-    parser.add_argument(
-        "--imgsz",
-        "--img",
-        "--img-size",
-        nargs="+",
-        type=int,
-        default=[640],
-        help="inference size h,w",
-    )
-    parser.add_argument(
-        "--conf-thres", type=float, default=0.25, help="confidence threshold"
-    )
-    parser.add_argument(
-        "--iou-thres", type=float, default=0.45, help="NMS IoU threshold"
-    )
-    parser.add_argument(
-        "--max-det",
-        type=int,
-        default=1000,
-        help="maximum detections per image",
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument("--view-img", action="store_true", help="show results")
-    parser.add_argument(
-        "--save-txt", action="store_true", help="save results to *.txt"
-    )
-    parser.add_argument(
-        "--save-conf",
-        action="store_true",
-        help="save confidences in --save-txt labels",
-    )
-    parser.add_argument(
-        "--save-crop",
-        action="store_true",
-        help="save cropped prediction boxes",
-    )
-    parser.add_argument(
-        "--nosave", action="store_true", help="do not save images/videos"
-    )
-    parser.add_argument(
-        "--classes",
-        nargs="+",
-        type=int,
-        help="filter by class: --classes 0, or --classes 0 2 3",
-    )
-    parser.add_argument(
-        "--agnostic-nms", action="store_true", help="class-agnostic NMS"
-    )
-    parser.add_argument(
-        "--augment", action="store_true", help="augmented inference"
-    )
-    parser.add_argument(
-        "--visualize", action="store_true", help="visualize features"
-    )
-    parser.add_argument(
-        "--update", action="store_true", help="update all models"
-    )
-    parser.add_argument(
-        "--project",
-        default=ROOT / "runs/detect",
-        help="save results to project/name",
-    )
-    parser.add_argument(
-        "--name", default="exp", help="save results to project/name"
-    )
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument(
-        "--line-thickness",
-        default=3,
-        type=int,
-        help="bounding box thickness (pixels)",
-    )
-    parser.add_argument(
-        "--hide-labels", default=False, action="store_true", help="hide labels"
-    )
-    parser.add_argument(
-        "--hide-conf",
-        default=False,
-        action="store_true",
-        help="hide confidences",
-    )
-    parser.add_argument(
-        "--half", action="store_true", help="use FP16 half-precision inference"
-    )
-    parser.add_argument(
-        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
-    )
-    parser.add_argument(
-        "--vid-stride", type=int, default=1, help="video frame-rate stride"
-    )
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    check_requirements(exclude=("tensorboard", "thop"))
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/CreateColorPicker.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/CreateColorPicker.js
deleted file mode 100644
index b565e1514c4269cb366af861c62860676f42ce4b..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/CreateColorPicker.js
+++ /dev/null
@@ -1,56 +0,0 @@
-import ColorPicker from './ColorPicker.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-var CreateColorPicker = function (scene) {
-    var scene = this.scene;
-
-    var background;
-    var createBackgroundCallback = this.colorPickerCreateBackgroundCallback;
-    if (createBackgroundCallback) {
-        background = createBackgroundCallback.call(this, scene);
-        scene.add.existing(background);
-    }
-
-    var width = this.colorPickerWidth;
-    if (width === undefined) {
-        width = this.width;
-    }
-
-    var height = this.colorPickerHeight;
-    if (height === undefined) {
-        height = width;
-    }
-
-    var colorComponentsConfig;
-    if (this.colorComponentsHeight > 0) {
-        colorComponentsConfig = {
-            height: this.colorComponentsHeight,
-            formatLabel: this.colorComponentsFormatLabelConfig,
-            inputText: this.colorComponentsInputTextConfig,
-            space: this.colorComponentsSpace,
-        }
-    } else {
-        colorComponentsConfig = false;
-    }
-
-    var colorPicker = new ColorPicker(scene, {
-        width: width, height: height,
-
-        background: background,
-        space: this.colorPickerSpace,
-
-        hPalette: {
-            position: this.colorPickerHPalettePosition,
-        },
-
-        colorComponents: colorComponentsConfig,
-
-        value: this.value
-    });
-    scene.add.existing(colorPicker);
-
-    return colorPicker;
-}
-
-export default CreateColorPicker;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateVideo.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateVideo.js
deleted file mode 100644
index 360dd0fed838665b7f70d55bf60d0945cb343b81..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateVideo.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-const PhaserVideo = Phaser.GameObjects.Video;
-
-var CreateVideo = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-    var gameObject = new PhaserVideo(scene, 0, 0, data.key);
-
-    if (data.width !== undefined) {
-        gameObject.setDisplayWidth(data.width);
-    }
-    if (data.height !== undefined) {
-        gameObject.setDisplayHeight(data.height);
-    }
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateVideo;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/roundrectangle/RoundRectangle.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/roundrectangle/RoundRectangle.js
deleted file mode 100644
index fe8022182c266ff9d5e35b51d71a3f67e612e143..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/roundrectangle/RoundRectangle.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import RoundRectangle from '../../../plugins/roundrectangle.js';
-export default RoundRectangle;
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/preprocess_v2.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/preprocess_v2.py
deleted file mode 100644
index 38fa0e78657129fcb466d5ca2ae5fa0355fb0997..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/preprocess_v2.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import os
-import argparse
-import json
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
-    parser.add_argument("--languages", default="CJE")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        langs = ["[ZH]", "[JA]", "[EN]"]
-    elif args.languages == "CJ":
-        langs = ["[ZH]", "[JA]"]
-    elif args.languages == "C":
-        langs = ["[ZH]"]
-    new_annos = []
-    # Source 1: transcribed short audios
-    if os.path.exists("short_character_anno.txt"):
-        with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
-            short_character_anno = f.readlines()
-            new_annos += short_character_anno
-    # Source 2: transcribed long audio segments
-    if os.path.exists("long_character_anno.txt"):
-        with open("long_character_anno.txt", 'r', encoding='utf-8') as f:
-            long_character_anno = f.readlines()
-            new_annos += long_character_anno
-
-    # Get all speaker names
-    speakers = []
-    for line in new_annos:
-        path, speaker, text = line.split("|")
-        if speaker not in speakers:
-            speakers.append(speaker)
-    assert (len(speakers) != 0), "No audio file found. Please check your uploaded file structure."
-    # Source 3 (Optional): sampled audios as extra training helpers
-    if args.add_auxiliary_data:
-        with open("sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
-            old_annos = f.readlines()
-        # filter old_annos according to supported languages
-        filtered_old_annos = []
-        for line in old_annos:
-            for lang in langs:
-                if lang in line:
-                    filtered_old_annos.append(line)
-        old_annos = filtered_old_annos
-        for line in old_annos:
-            path, speaker, text = line.split("|")
-            if speaker not in speakers:
-                speakers.append(speaker)
-        num_old_voices = len(old_annos)
-        num_new_voices = len(new_annos)
-        # STEP 1: balance number of new & old voices
-        cc_duplicate = num_old_voices // num_new_voices
-        if cc_duplicate == 0:
-            cc_duplicate = 1
-
-
-        # STEP 2: modify config file
-        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-            hps = json.load(f)
-
-        # assign ids to new speakers
-        speaker2id = {}
-        for i, speaker in enumerate(speakers):
-            speaker2id[speaker] = i
-        # modify n_speakers
-        hps['data']["n_speakers"] = len(speakers)
-        # overwrite speaker names
-        hps['speakers'] = speaker2id
-        hps['train']['log_interval'] = 100
-        hps['train']['eval_interval'] = 1000
-        hps['train']['batch_size'] = 16
-        hps['data']['training_files'] = "final_annotation_train.txt"
-        hps['data']['validation_files'] = "final_annotation_val.txt"
-        # save modified config
-        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-
-        # STEP 3: clean annotations, replace speaker names with assigned speaker IDs
-        import text
-        cleaned_new_annos = []
-        for i, line in enumerate(new_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-        cleaned_old_annos = []
-        for i, line in enumerate(old_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_old_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-        # merge with old annotation
-        final_annos = cleaned_old_annos + cc_duplicate * cleaned_new_annos
-        # save annotation file
-        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
-            for line in final_annos:
-                f.write(line)
-        # save annotation file for validation
-        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
-            for line in cleaned_new_annos:
-                f.write(line)
-        print("finished")
-    else:
-        # Do not add extra helper data
-        # STEP 1: modify config file
-        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-            hps = json.load(f)
-
-        # assign ids to new speakers
-        speaker2id = {}
-        for i, speaker in enumerate(speakers):
-            speaker2id[speaker] = i
-        # modify n_speakers
-        hps['data']["n_speakers"] = len(speakers)
-        # overwrite speaker names
-        hps['speakers'] = speaker2id
-        hps['train']['log_interval'] = 10
-        hps['train']['eval_interval'] = 100
-        hps['train']['batch_size'] = 16
-        hps['data']['training_files'] = "final_annotation_train.txt"
-        hps['data']['validation_files'] = "final_annotation_val.txt"
-        # save modified config
-        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-
-        # STEP 2: clean annotations, replace speaker names with assigned speaker IDs
-        import text
-
-        cleaned_new_annos = []
-        for i, line in enumerate(new_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners']).replace("[ZH]", "")
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-
-        final_annos = cleaned_new_annos
-        # save annotation file
-        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
-            for line in final_annos:
-                f.write(line)
-        # save annotation file for validation
-        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
-            for line in cleaned_new_annos:
-                f.write(line)
-        print("finished")
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions_pfc.py b/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions_pfc.py
deleted file mode 100644
index 77caafdbb300d8109d5bfdb844f131710ef81f20..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions_pfc.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []
diff --git a/spaces/Amrrs/portfolio-github/index.html b/spaces/Amrrs/portfolio-github/index.html
deleted file mode 100644
index d00ec6262ade5c9673dfa225c1da92972ac290c9..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/portfolio-github/index.html
+++ /dev/null
@@ -1,108 +0,0 @@
-<!DOCTYPE html>
-<html>
-    <head>
-        <title>Welcome to 1littlecoder</title>
-        <link href="https://fonts.googleapis.com/css2?family=Bellota&display=swap" rel="stylesheet">
-        <link href="style.css" rel="stylesheet" type="text/css">
-    </head>
-    <body>
-        <div id="header" class="section">
-            <img alt="logo" class="img-circle" src="https://www.kindpng.com/picc/m/340-3408802_lego-batman-icon-lego-batman-png-transparent-png.png"> 
-            <p>Welcome to 1littlecoder</p>
-        </div>
-        <div class="section">
-            <h1><span>About Me</span></h1>
-            <p> Hey! I'm <strong>1littlecoder</strong> from <strong>India.</strong>. I Like <strong>Coding</strong> R Python Data Science Machine Learning</p>
-            <p> I love Hugging Face and here's my <a href = 'profile' src = 'https://huggingface.co/Amrrs'> </a>a></a> </p>
-            <p class="quote">~ 1littlecoder</p>
-        </div>
-        <div class="section" id="res">
-            <h1><span>My Works</span></h1>
-            <p align="centre"><strong>Here Are Some Of My Works</strong></p>
-            <a href="https://telegram.me">
-                <img src="https://img.icons8.com/nolan/144/telegram-app.png"/>
-                <div class="caption">Telegram Channel</div>
-            </a>
-            <a href="https://github.com/amrrs">
-                <img src="https://img.icons8.com/nolan/144/github.png"/>
-                <div class="caption">Github Account</div>
-            </a>
-            <a href="https://1littlecoder.in">
-                <img src="https://img.icons8.com/dusk/144/000000/domain.png"/>
-                <div class="caption">My Website</div>
-            </a>
-            <br>
-            <p align="centre"><strong>Resources I Use</strong></p>
-            <a href="https://github.com/">
-                <img src="https://img.icons8.com/nolan/144/github.png"/>
-                <div class="caption">Github</div>
-            </a>
-            <a href="https://telegram.me">
-                <img src="https://img.icons8.com/nolan/144/telegram-app.png"/>
-                <div class="caption">Telegram</div>
-            </a>
-            <a href="https://code.visualstudio.com">
-                <img src="https://img.icons8.com/nolan/144/code.png"/>
-                <div class="caption">VS Code Editor</div>
-            </a>
-            <a href="https://python.org">
-                <img src="https://img.icons8.com/nolan/144/python.png"/>
-                <div class="caption">Python</div>
-            </a>
-            <a href="https://www.php.net/">
-                <img src="https://img.icons8.com/dusk/144/000000/php-logo.png"/>
-                <div class="caption">PHP</div>
-            </a>
-            <a href="https://ubuntu.com">
-                <img src="https://img.icons8.com/color/144/000000/ubuntu--v1.png"/>
-                <div class="caption">Ubuntu</div>
-            </a>
-        </div>
-        <div class="section">
-            <h1><span>My Skills</span></h1>
-            <ul>
-                <li>Python<br /> <progress min="0" max="100" value="95"></progress> </li>
-                <li>PHP <br /> <progress min="0" max="100" value="75"></progress> </li>
-                <li>Coding<br /> <progress min="0" max="100" value="100"></progress> </li>
-            </ul>
-        </div>
-        <div class="section" id="contacts">
-            <h1><span>Follow Me</span></h1>
-            <div>
-                <a href="https://instagram.com/" target="_blank">
-                <img alt="Instagram" src="https://img.icons8.com/cute-clipart/100/instagram-new.png"/>
-                </a>
-                <a href="https://twitter.com/1littlecoder">
-                <img alt="Twitter" src="https://www.sololearn.com/Uploads/icons/twitter.png" />
-                </a>
-                <a href="https://github.com/amrrs">
-                <img alt="GitHub" src="https://img.icons8.com/nolan/144/github.png"/>
-                </a>
-                <a href="https://t.me/">
-                <img alt="Telegram" src="https://img.icons8.com/fluent/96/000000/telegram-app.png"/>
-                </a>
-                <a href="https://www.youtube.com/channel/UCRD6WpNNzJpRIU4z89PNSbg">
-                <img alt="YouTube" src="https://img.icons8.com/color/96/000000/youtube-play.png"/>
-                </a>
-                <a href="mailto:1littlecoder@gmail.com">
-                <img alt="Email" src="https://img.icons8.com/fluent/96/000000/gmail.png"/>
-                </a>
-            </div>
-        </div>
-        <div class="section" id="contacts">
-        <h1><span>Contact Us</span></h1>
-        <a href="mailto:1littlecoder@gmail.com">
-            <img src="https://img.icons8.com/fluent/95/000000/gmail--v2.png"/>
-            </a>
-        </div>
-        <center>Made with ❤️ By <a href="https://github.com/amrrs">
-            1littlecoder
-          </a></center>
-   
-        <script type="text/javascript">
-            function search() {
-                window.open('https://www.google.com/search?output=search&q=' + document.getElementById("question").value)
-            }
-        </script>
-    </body>
-</html>
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
deleted file mode 100644
index 7d9a8cd387fbdaa6230d09d282a785d53e4a4a4c..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
+++ /dev/null
@@ -1,1352 +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.
-
-# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
-
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    is_compiled_module,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from ..stable_diffusion import StableDiffusionPipelineOutput
-from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from .multicontrolnet import MultiControlNetModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> # !pip install transformers accelerate
-        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
-        >>> from diffusers.utils import load_image
-        >>> import numpy as np
-        >>> import torch
-
-        >>> init_image = load_image(
-        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
-        ... )
-        >>> init_image = init_image.resize((512, 512))
-
-        >>> generator = torch.Generator(device="cpu").manual_seed(1)
-
-        >>> mask_image = load_image(
-        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
-        ... )
-        >>> mask_image = mask_image.resize((512, 512))
-
-
-        >>> def make_inpaint_condition(image, image_mask):
-        ...     image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
-        ...     image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
-
-        ...     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
-        ...     image[image_mask > 0.5] = -1.0  # set as masked pixel
-        ...     image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
-        ...     image = torch.from_numpy(image)
-        ...     return image
-
-
-        >>> control_image = make_inpaint_condition(init_image, mask_image)
-
-        >>> controlnet = ControlNetModel.from_pretrained(
-        ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
-        ... )
-        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-        ... )
-
-        >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> # generate image
-        >>> image = pipe(
-        ...     "a handsome man with ray-ban sunglasses",
-        ...     num_inference_steps=20,
-        ...     generator=generator,
-        ...     eta=1.0,
-        ...     image=init_image,
-        ...     mask_image=mask_image,
-        ...     control_image=control_image,
-        ... ).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image
-def prepare_mask_and_masked_image(image, mask, height, width, return_image=False):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-
-    if image is None:
-        raise ValueError("`image` input cannot be undefined.")
-
-    if mask is None:
-        raise ValueError("`mask_image` input cannot be undefined.")
-
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            # resize all images w.r.t passed height an width
-            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    # n.b. ensure backwards compatibility as old function does not return image
-    if return_image:
-        return mask, masked_image, image
-
-    return mask, masked_image
-
-
-class StableDiffusionControlNetInpaintPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
-):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    In addition the pipeline inherits the following loading methods:
-        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
-
-    <Tip>
-
-    This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as
-    [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
-     as well as default text-to-image stable diffusion checkpoints, such as
-     [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
-    Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on
-    those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
-
-    </Tip>
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
-            Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
-            as a list, the outputs from each ControlNet are added together to create one combined additional
-            conditioning.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        if isinstance(controlnet, (list, tuple)):
-            controlnet = MultiControlNetModel(controlnet)
-
-        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
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            # the safety checker can offload the vae again
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # control net hook has be manually offloaded as it alternates with unet
-        cpu_offload_with_hook(self.controlnet, device)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
-    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
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        controlnet_conditioning_scale=1.0,
-        control_guidance_start=0.0,
-        control_guidance_end=1.0,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        # `prompt` needs more sophisticated handling when there are multiple
-        # conditionings.
-        if isinstance(self.controlnet, MultiControlNetModel):
-            if isinstance(prompt, list):
-                logger.warning(
-                    f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
-                    " prompts. The conditionings will be fixed across the prompts."
-                )
-
-        # Check `image`
-        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
-            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
-        )
-        if (
-            isinstance(self.controlnet, ControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetModel)
-        ):
-            self.check_image(image, prompt, prompt_embeds)
-        elif (
-            isinstance(self.controlnet, MultiControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
-        ):
-            if not isinstance(image, list):
-                raise TypeError("For multiple controlnets: `image` must be type `list`")
-
-            # When `image` is a nested list:
-            # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
-            elif any(isinstance(i, list) for i in image):
-                raise ValueError("A single batch of multiple conditionings are supported at the moment.")
-            elif len(image) != len(self.controlnet.nets):
-                raise ValueError(
-                    f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
-                )
-
-            for image_ in image:
-                self.check_image(image_, prompt, prompt_embeds)
-        else:
-            assert False
-
-        # Check `controlnet_conditioning_scale`
-        if (
-            isinstance(self.controlnet, ControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetModel)
-        ):
-            if not isinstance(controlnet_conditioning_scale, float):
-                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
-        elif (
-            isinstance(self.controlnet, MultiControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
-        ):
-            if isinstance(controlnet_conditioning_scale, list):
-                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
-                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
-            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
-                self.controlnet.nets
-            ):
-                raise ValueError(
-                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
-                    " the same length as the number of controlnets"
-                )
-        else:
-            assert False
-
-        if len(control_guidance_start) != len(control_guidance_end):
-            raise ValueError(
-                f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
-            )
-
-        if isinstance(self.controlnet, MultiControlNetModel):
-            if len(control_guidance_start) != len(self.controlnet.nets):
-                raise ValueError(
-                    f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
-                )
-
-        for start, end in zip(control_guidance_start, control_guidance_end):
-            if start >= end:
-                raise ValueError(
-                    f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
-                )
-            if start < 0.0:
-                raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
-            if end > 1.0:
-                raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
-
-    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
-    def check_image(self, image, prompt, prompt_embeds):
-        image_is_pil = isinstance(image, PIL.Image.Image)
-        image_is_tensor = isinstance(image, torch.Tensor)
-        image_is_np = isinstance(image, np.ndarray)
-        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
-        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
-        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
-
-        if (
-            not image_is_pil
-            and not image_is_tensor
-            and not image_is_np
-            and not image_is_pil_list
-            and not image_is_tensor_list
-            and not image_is_np_list
-        ):
-            raise TypeError(
-                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
-            )
-
-        if image_is_pil:
-            image_batch_size = 1
-        else:
-            image_batch_size = len(image)
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-
-        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
-
-    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
-    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=torch.float32)
-        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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-        image=None,
-        timestep=None,
-        is_strength_max=True,
-        return_noise=False,
-        return_image_latents=False,
-    ):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if (image is None or timestep is None) and not is_strength_max:
-            raise ValueError(
-                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
-                "However, either the image or the noise timestep has not been provided."
-            )
-
-        if return_image_latents or (latents is None and not is_strength_max):
-            image = image.to(device=device, dtype=dtype)
-            image_latents = self._encode_vae_image(image=image, generator=generator)
-
-        if latents is None:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            # if strength is 1. then initialise the latents to noise, else initial to image + noise
-            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
-            # if pure noise then scale the initial latents by the  Scheduler's init sigma
-            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
-        else:
-            noise = latents.to(device)
-            latents = noise * self.scheduler.init_noise_sigma
-
-        outputs = (latents,)
-
-        if return_noise:
-            outputs += (noise,)
-
-        if return_image_latents:
-            outputs += (image_latents,)
-
-        return outputs
-
-    def _default_height_width(self, height, width, image):
-        # NOTE: It is possible that a list of images have different
-        # dimensions for each image, so just checking the first image
-        # is not _exactly_ correct, but it is simple.
-        while isinstance(image, list):
-            image = image[0]
-
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[2]
-
-            height = (height // 8) * 8  # round down to nearest multiple of 8
-
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[3]
-
-            width = (width // 8) * 8  # round down to nearest multiple of 8
-
-        return height, width
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
-    def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return mask, masked_image_latents
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
-    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
-        if isinstance(generator, list):
-            image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(image.shape[0])
-            ]
-            image_latents = torch.cat(image_latents, dim=0)
-        else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
-
-        image_latents = self.vae.config.scaling_factor * image_latents
-
-        return image_latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.Tensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
-        control_image: Union[
-            torch.FloatTensor,
-            PIL.Image.Image,
-            np.ndarray,
-            List[torch.FloatTensor],
-            List[PIL.Image.Image],
-            List[np.ndarray],
-        ] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        strength: float = 1.0,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 0.5,
-        guess_mode: bool = False,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
-                    `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
-                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
-                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
-                also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
-                height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
-                specified in init, images must be passed as a list such that each element of the list can be correctly
-                batched for input to a single controlnet.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            strength (`float`, *optional*, defaults to 1.):
-                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
-                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
-                `strength`. The number of denoising steps depends on the amount of noise initially added. When
-                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
-                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
-                portion of the reference `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
-                corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
-                than for [`~StableDiffusionControlNetPipeline.__call__`].
-            guess_mode (`bool`, *optional*, defaults to `False`):
-                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
-                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
-            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
-                The percentage of total steps at which the controlnet starts applying.
-            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
-                The percentage of total steps at which the controlnet stops applying.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
-
-        # 0. Default height and width to unet
-        height, width = self._default_height_width(height, width, image)
-
-        # align format for control guidance
-        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
-            ]
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            control_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-            controlnet_conditioning_scale,
-            control_guidance_start,
-            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
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
-            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
-
-        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
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare 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,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                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. Preprocess mask and image - resizes image and mask w.r.t height and width
-        mask, masked_image, init_image = prepare_mask_and_masked_image(
-            image, mask_image, height, width, return_image=True
-        )
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps=num_inference_steps, strength=strength, device=device
-        )
-        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
-        is_strength_max = strength == 1.0
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        num_channels_unet = self.unet.config.in_channels
-        return_image_latents = num_channels_unet == 4
-        latents_outputs = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            image=init_image,
-            timestep=latent_timestep,
-            is_strength_max=is_strength_max,
-            return_noise=True,
-            return_image_latents=return_image_latents,
-        )
-
-        if return_image_latents:
-            latents, noise, image_latents = latents_outputs
-        else:
-            latents, noise = latents_outputs
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7.1 Create tensor stating which controlnets to keep
-        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)
-
-        # 8. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # controlnet(s) inference
-                if guess_mode and do_classifier_free_guidance:
-                    # Infer ControlNet only for the conditional batch.
-                    control_model_input = latents
-                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
-                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
-                else:
-                    control_model_input = latent_model_input
-                    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:
-                    cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    control_model_input,
-                    t,
-                    encoder_hidden_states=controlnet_prompt_embeds,
-                    controlnet_cond=control_image,
-                    conditioning_scale=cond_scale,
-                    guess_mode=guess_mode,
-                    return_dict=False,
-                )
-
-                if guess_mode and do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
-                    # To apply the output of ControlNet to both the unconditional and conditional batches,
-                    # add 0 to the unconditional batch to keep it unchanged.
-                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
-                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
-
-                # predict the noise residual
-                if num_channels_unet == 9:
-                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    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]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                if num_channels_unet == 4:
-                    init_latents_proper = image_latents[:1]
-                    init_mask = mask[:1]
-
-                    if i < len(timesteps) - 1:
-                        noise_timestep = timesteps[i + 1]
-                        init_latents_proper = self.scheduler.add_noise(
-                            init_latents_proper, noise, torch.tensor([noise_timestep])
-                        )
-
-                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # If we do sequential model offloading, let's offload unet and controlnet
-        # manually for max memory savings
-        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(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
deleted file mode 100644
index 8787088f27a09a3f8fd0d05a1144c0abdedd0a21..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/__init__.py
deleted file mode 100644
index 02f833a8a0f538a8c06fef622d1cadc1a1b66ea2..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .bfp import BFP
-from .channel_mapper import ChannelMapper
-from .fpg import FPG
-from .fpn import FPN
-from .fpn_carafe import FPN_CARAFE
-from .hrfpn import HRFPN
-from .nas_fpn import NASFPN
-from .nasfcos_fpn import NASFCOS_FPN
-from .pafpn import PAFPN
-from .rfp import RFP
-from .yolo_neck import YOLOV3Neck
-
-__all__ = [
-    'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN',
-    'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG'
-]
diff --git a/spaces/Anonymous-sub/Rerender/gmflow_module/utils/flow_viz.py b/spaces/Anonymous-sub/Rerender/gmflow_module/utils/flow_viz.py
deleted file mode 100644
index 9b782c07841b27526ef8c9fa070b480a01545c31..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/gmflow_module/utils/flow_viz.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# MIT License
-#
-# Copyright (c) 2018 Tom Runia
-#
-# 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 conditions.
-#
-# Author: Tom Runia
-# Date Created: 2018-08-03
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-
-def make_colorwheel():
-    '''
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    '''
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)
-    col = col + RY
-    # YG
-    colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
-    colorwheel[col:col + YG, 1] = 255
-    col = col + YG
-    # GC
-    colorwheel[col:col + GC, 1] = 255
-    colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
-    col = col + GC
-    # CB
-    colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
-    colorwheel[col:col + CB, 2] = 255
-    col = col + CB
-    # BM
-    colorwheel[col:col + BM, 2] = 255
-    colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
-    col = col + BM
-    # MR
-    colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
-    colorwheel[col:col + MR, 0] = 255
-    return colorwheel
-
-
-def flow_compute_color(u, v, convert_to_bgr=False):
-    '''
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    :param u: np.ndarray, input horizontal flow
-    :param v: np.ndarray, input vertical flow
-    :param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB
-    :return:
-    '''
-
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u) / np.pi
-
-    fk = (a + 1) / 2 * (ncols - 1) + 1
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 1
-    f = fk - k0
-
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:, i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1 - f) * col0 + f * col1
-
-        idx = (rad <= 1)
-        col[idx] = 1 - rad[idx] * (1 - col[idx])
-        col[~idx] = col[~idx] * 0.75  # out of range?
-
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2 - i if convert_to_bgr else i
-        flow_image[:, :, ch_idx] = np.floor(255 * col)
-
-    return flow_image
-
-
-def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):
-    '''
-    Expects a two dimensional flow image of shape [H,W,2]
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    :param flow_uv: np.ndarray of shape [H,W,2]
-    :param clip_flow: float, maximum clipping value for flow
-    :return:
-    '''
-
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-
-    u = flow_uv[:, :, 0]
-    v = flow_uv[:, :, 1]
-
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-
-    return flow_compute_color(u, v, convert_to_bgr)
-
-
-UNKNOWN_FLOW_THRESH = 1e7
-SMALLFLOW = 0.0
-LARGEFLOW = 1e8
-
-
-def make_color_wheel():
-    """
-    Generate color wheel according Middlebury color code
-    :return: Color wheel
-    """
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-
-    colorwheel = np.zeros([ncols, 3])
-
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))
-    col += RY
-
-    # YG
-    colorwheel[col:col + YG, 0] = 255 - np.transpose(np.floor(255 * np.arange(0, YG) / YG))
-    colorwheel[col:col + YG, 1] = 255
-    col += YG
-
-    # GC
-    colorwheel[col:col + GC, 1] = 255
-    colorwheel[col:col + GC, 2] = np.transpose(np.floor(255 * np.arange(0, GC) / GC))
-    col += GC
-
-    # CB
-    colorwheel[col:col + CB, 1] = 255 - np.transpose(np.floor(255 * np.arange(0, CB) / CB))
-    colorwheel[col:col + CB, 2] = 255
-    col += CB
-
-    # BM
-    colorwheel[col:col + BM, 2] = 255
-    colorwheel[col:col + BM, 0] = np.transpose(np.floor(255 * np.arange(0, BM) / BM))
-    col += + BM
-
-    # MR
-    colorwheel[col:col + MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
-    colorwheel[col:col + MR, 0] = 255
-
-    return colorwheel
-
-
-def compute_color(u, v):
-    """
-    compute optical flow color map
-    :param u: optical flow horizontal map
-    :param v: optical flow vertical map
-    :return: optical flow in color code
-    """
-    [h, w] = u.shape
-    img = np.zeros([h, w, 3])
-    nanIdx = np.isnan(u) | np.isnan(v)
-    u[nanIdx] = 0
-    v[nanIdx] = 0
-
-    colorwheel = make_color_wheel()
-    ncols = np.size(colorwheel, 0)
-
-    rad = np.sqrt(u ** 2 + v ** 2)
-
-    a = np.arctan2(-v, -u) / np.pi
-
-    fk = (a + 1) / 2 * (ncols - 1) + 1
-
-    k0 = np.floor(fk).astype(int)
-
-    k1 = k0 + 1
-    k1[k1 == ncols + 1] = 1
-    f = fk - k0
-
-    for i in range(0, np.size(colorwheel, 1)):
-        tmp = colorwheel[:, i]
-        col0 = tmp[k0 - 1] / 255
-        col1 = tmp[k1 - 1] / 255
-        col = (1 - f) * col0 + f * col1
-
-        idx = rad <= 1
-        col[idx] = 1 - rad[idx] * (1 - col[idx])
-        notidx = np.logical_not(idx)
-
-        col[notidx] *= 0.75
-        img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx)))
-
-    return img
-
-
-# from https://github.com/gengshan-y/VCN
-def flow_to_image(flow):
-    """
-    Convert flow into middlebury color code image
-    :param flow: optical flow map
-    :return: optical flow image in middlebury color
-    """
-    u = flow[:, :, 0]
-    v = flow[:, :, 1]
-
-    maxu = -999.
-    maxv = -999.
-    minu = 999.
-    minv = 999.
-
-    idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)
-    u[idxUnknow] = 0
-    v[idxUnknow] = 0
-
-    maxu = max(maxu, np.max(u))
-    minu = min(minu, np.min(u))
-
-    maxv = max(maxv, np.max(v))
-    minv = min(minv, np.min(v))
-
-    rad = np.sqrt(u ** 2 + v ** 2)
-    maxrad = max(-1, np.max(rad))
-
-    u = u / (maxrad + np.finfo(float).eps)
-    v = v / (maxrad + np.finfo(float).eps)
-
-    img = compute_color(u, v)
-
-    idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
-    img[idx] = 0
-
-    return np.uint8(img)
-
-
-def save_vis_flow_tofile(flow, output_path):
-    vis_flow = flow_to_image(flow)
-    from PIL import Image
-    img = Image.fromarray(vis_flow)
-    img.save(output_path)
-
-
-def flow_tensor_to_image(flow):
-    """Used for tensorboard visualization"""
-    flow = flow.permute(1, 2, 0)  # [H, W, 2]
-    flow = flow.detach().cpu().numpy()
-    flow = flow_to_image(flow)  # [H, W, 3]
-    flow = np.transpose(flow, (2, 0, 1))  # [3, H, W]
-
-    return flow
diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/app.py b/spaces/ArtGAN/Video-Diffusion-WebUI/app.py
deleted file mode 100644
index 79e28229ba15ea8970658769ac52e35e077d3275..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Video-Diffusion-WebUI/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import gradio as gr
-
-from video_diffusion.damo.damo_text2_video import DamoText2VideoGenerator
-from video_diffusion.inpaint_zoom.zoom_in_app import StableDiffusionZoomIn
-from video_diffusion.inpaint_zoom.zoom_out_app import StableDiffusionZoomOut
-from video_diffusion.stable_diffusion_video.stable_video_text2video import StableDiffusionText2VideoGenerator
-from video_diffusion.tuneavideo.tuneavideo_text2video import TunaVideoText2VideoGenerator
-from video_diffusion.zero_shot.zero_shot_text2video import ZeroShotText2VideoGenerator
-
-
-def diffusion_app():
-    app = gr.Blocks()
-    with app:
-        gr.HTML(
-        """
-        <h1 style='text-align: center'>
-       Video Diffusion WebUI
-        </h1>
-        """
-    )
-        gr.HTML(
-            """
-            <h3 style='text-align: center'>
-            Follow me for more! 
-            <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
-            </h3>
-            """
-    )
-        with gr.Row():
-            with gr.Column():
-                with gr.Tab("Stable Diffusion Video"):
-                    StableDiffusionText2VideoGenerator.app()
-                with gr.Tab("Tune-a-Video"):
-                    TunaVideoText2VideoGenerator.app()
-                with gr.Tab("Stable Infinite Zoom"):
-                    with gr.Tab("Zoom In"):
-                        StableDiffusionZoomIn.app()
-                    with gr.Tab("Zoom Out"):
-                        StableDiffusionZoomOut.app()
-                with gr.Tab("Damo Text2Video"):
-                    DamoText2VideoGenerator.app()
-                with gr.Tab("Zero Shot Text2Video"):
-                    ZeroShotText2VideoGenerator.app()
-
-    app.queue(concurrency_count=1)
-    app.launch(debug=True, enable_queue=True)
-
-
-if __name__ == "__main__":
-    diffusion_app()
diff --git a/spaces/ArtyomKhyan/Detection/utils/activations.py b/spaces/ArtyomKhyan/Detection/utils/activations.py
deleted file mode 100644
index 9c0f6d2d397151dc954ae6c5e1655ba23721ae01..0000000000000000000000000000000000000000
--- a/spaces/ArtyomKhyan/Detection/utils/activations.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn as nn
-
-
-# Swish -------non_max_suppression-----------------------------------------------------------------
-class SwishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x * torch.sigmoid(x)
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        return grad_output * (sx * (1 + x * (1 - sx)))
-
-
-class MemoryEfficientSwish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return SwishImplementation.apply(x)
-
-
-class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
-    @staticmethod
-    def forward(x):
-        return x * F.hardtanh(x + 3, 0., 6., True) / 6.
-
-
-class Swish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-# Mish ------------------------------------------------------------------------
-class MishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        fx = F.softplus(x).tanh()
-        return grad_output * (fx + x * sx * (1 - fx * fx))
-
-
-class MemoryEfficientMish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return MishImplementation.apply(x)
-
-
-class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py
deleted file mode 100644
index fa5d86b427ab53ecbe992842cce71d12c5e3a141..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py
+++ /dev/null
@@ -1,399 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import ast
-import builtins
-import importlib
-import inspect
-import logging
-import os
-import uuid
-from collections import abc
-from contextlib import contextmanager
-from copy import deepcopy
-from dataclasses import is_dataclass
-from typing import List, Tuple, Union
-import cloudpickle
-import yaml
-from omegaconf import DictConfig, ListConfig, OmegaConf
-
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.registry import _convert_target_to_string
-
-__all__ = ["LazyCall", "LazyConfig"]
-
-
-class LazyCall:
-    """
-    Wrap a callable so that when it's called, the call will not be executed,
-    but returns a dict that describes the call.
-
-    LazyCall object has to be called with only keyword arguments. Positional
-    arguments are not yet supported.
-
-    Examples:
-    ::
-        from detectron2.config import instantiate, LazyCall
-
-        layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32)
-        layer_cfg.out_channels = 64   # can edit it afterwards
-        layer = instantiate(layer_cfg)
-    """
-
-    def __init__(self, target):
-        if not (callable(target) or isinstance(target, (str, abc.Mapping))):
-            raise TypeError(
-                f"target of LazyCall must be a callable or defines a callable! Got {target}"
-            )
-        self._target = target
-
-    def __call__(self, **kwargs):
-        if is_dataclass(self._target):
-            # omegaconf object cannot hold dataclass type
-            # https://github.com/omry/omegaconf/issues/784
-            target = _convert_target_to_string(self._target)
-        else:
-            target = self._target
-        kwargs["_target_"] = target
-
-        return DictConfig(content=kwargs, flags={"allow_objects": True})
-
-
-def _visit_dict_config(cfg, func):
-    """
-    Apply func recursively to all DictConfig in cfg.
-    """
-    if isinstance(cfg, DictConfig):
-        func(cfg)
-        for v in cfg.values():
-            _visit_dict_config(v, func)
-    elif isinstance(cfg, ListConfig):
-        for v in cfg:
-            _visit_dict_config(v, func)
-
-
-def _validate_py_syntax(filename):
-    # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py
-    with PathManager.open(filename, "r") as f:
-        content = f.read()
-    try:
-        ast.parse(content)
-    except SyntaxError as e:
-        raise SyntaxError(f"Config file {filename} has syntax error!") from e
-
-
-def _cast_to_config(obj):
-    # if given a dict, return DictConfig instead
-    if isinstance(obj, dict):
-        return DictConfig(obj, flags={"allow_objects": True})
-    return obj
-
-
-_CFG_PACKAGE_NAME = "detectron2._cfg_loader"
-"""
-A namespace to put all imported config into.
-"""
-
-
-def _random_package_name(filename):
-    # generate a random package name when loading config files
-    return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename)
-
-
-@contextmanager
-def _patch_import():
-    """
-    Enhance relative import statements in config files, so that they:
-    1. locate files purely based on relative location, regardless of packages.
-       e.g. you can import file without having __init__
-    2. do not cache modules globally; modifications of module states has no side effect
-    3. support other storage system through PathManager
-    4. imported dict are turned into omegaconf.DictConfig automatically
-    """
-    old_import = builtins.__import__
-
-    def find_relative_file(original_file, relative_import_path, level):
-        cur_file = os.path.dirname(original_file)
-        for _ in range(level - 1):
-            cur_file = os.path.dirname(cur_file)
-        cur_name = relative_import_path.lstrip(".")
-        for part in cur_name.split("."):
-            cur_file = os.path.join(cur_file, part)
-        # NOTE: directory import is not handled. Because then it's unclear
-        # if such import should produce python module or DictConfig. This can
-        # be discussed further if needed.
-        if not cur_file.endswith(".py"):
-            cur_file += ".py"
-        if not PathManager.isfile(cur_file):
-            raise ImportError(
-                f"Cannot import name {relative_import_path} from "
-                f"{original_file}: {cur_file} has to exist."
-            )
-        return cur_file
-
-    def new_import(name, globals=None, locals=None, fromlist=(), level=0):
-        if (
-            # Only deal with relative imports inside config files
-            level != 0
-            and globals is not None
-            and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME)
-        ):
-            cur_file = find_relative_file(globals["__file__"], name, level)
-            _validate_py_syntax(cur_file)
-            spec = importlib.machinery.ModuleSpec(
-                _random_package_name(cur_file), None, origin=cur_file
-            )
-            module = importlib.util.module_from_spec(spec)
-            module.__file__ = cur_file
-            with PathManager.open(cur_file) as f:
-                content = f.read()
-            exec(compile(content, cur_file, "exec"), module.__dict__)
-            for name in fromlist:  # turn imported dict into DictConfig automatically
-                val = _cast_to_config(module.__dict__[name])
-                module.__dict__[name] = val
-            return module
-        return old_import(name, globals, locals, fromlist=fromlist, level=level)
-
-    builtins.__import__ = new_import
-    yield new_import
-    builtins.__import__ = old_import
-
-
-class LazyConfig:
-    """
-    Provide methods to save, load, and overrides an omegaconf config object
-    which may contain definition of lazily-constructed objects.
-    """
-
-    @staticmethod
-    def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Similar to :meth:`load()`, but load path relative to the caller's
-        source file.
-
-        This has the same functionality as a relative import, except that this method
-        accepts filename as a string, so more characters are allowed in the filename.
-        """
-        caller_frame = inspect.stack()[1]
-        caller_fname = caller_frame[0].f_code.co_filename
-        assert caller_fname != "<string>", "load_rel Unable to find caller"
-        caller_dir = os.path.dirname(caller_fname)
-        filename = os.path.join(caller_dir, filename)
-        return LazyConfig.load(filename, keys)
-
-    @staticmethod
-    def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Load a config file.
-
-        Args:
-            filename: absolute path or relative path w.r.t. the current working directory
-            keys: keys to load and return. If not given, return all keys
-                (whose values are config objects) in a dict.
-        """
-        has_keys = keys is not None
-        filename = filename.replace("/./", "/")  # redundant
-        if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]:
-            raise ValueError(f"Config file {filename} has to be a python or yaml file.")
-        if filename.endswith(".py"):
-            _validate_py_syntax(filename)
-
-            with _patch_import():
-                # Record the filename
-                module_namespace = {
-                    "__file__": filename,
-                    "__package__": _random_package_name(filename),
-                }
-                with PathManager.open(filename) as f:
-                    content = f.read()
-                # Compile first with filename to:
-                # 1. make filename appears in stacktrace
-                # 2. make load_rel able to find its parent's (possibly remote) location
-                exec(compile(content, filename, "exec"), module_namespace)
-
-            ret = module_namespace
-        else:
-            with PathManager.open(filename) as f:
-                obj = yaml.unsafe_load(f)
-            ret = OmegaConf.create(obj, flags={"allow_objects": True})
-
-        if has_keys:
-            if isinstance(keys, str):
-                return _cast_to_config(ret[keys])
-            else:
-                return tuple(_cast_to_config(ret[a]) for a in keys)
-        else:
-            if filename.endswith(".py"):
-                # when not specified, only load those that are config objects
-                ret = DictConfig(
-                    {
-                        name: _cast_to_config(value)
-                        for name, value in ret.items()
-                        if isinstance(value, (DictConfig, ListConfig, dict))
-                        and not name.startswith("_")
-                    },
-                    flags={"allow_objects": True},
-                )
-            return ret
-
-    @staticmethod
-    def save(cfg, filename: str):
-        """
-        Save a config object to a yaml file.
-        Note that when the config dictionary contains complex objects (e.g. lambda),
-        it can't be saved to yaml. In that case we will print an error and
-        attempt to save to a pkl file instead.
-
-        Args:
-            cfg: an omegaconf config object
-            filename: yaml file name to save the config file
-        """
-        logger = logging.getLogger(__name__)
-        try:
-            cfg = deepcopy(cfg)
-        except Exception:
-            pass
-        else:
-            # if it's deep-copyable, then...
-            def _replace_type_by_name(x):
-                if "_target_" in x and callable(x._target_):
-                    try:
-                        x._target_ = _convert_target_to_string(x._target_)
-                    except AttributeError:
-                        pass
-
-            # not necessary, but makes yaml looks nicer
-            _visit_dict_config(cfg, _replace_type_by_name)
-
-        save_pkl = False
-        try:
-            dict = OmegaConf.to_container(cfg, resolve=False)
-            dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999)
-            with PathManager.open(filename, "w") as f:
-                f.write(dumped)
-
-            try:
-                _ = yaml.unsafe_load(dumped)  # test that it is loadable
-            except Exception:
-                logger.warning(
-                    "The config contains objects that cannot serialize to a valid yaml. "
-                    f"{filename} is human-readable but cannot be loaded."
-                )
-                save_pkl = True
-        except Exception:
-            logger.exception("Unable to serialize the config to yaml. Error:")
-            save_pkl = True
-
-        if save_pkl:
-            new_filename = filename + ".pkl"
-            try:
-                # retry by pickle
-                with PathManager.open(new_filename, "wb") as f:
-                    cloudpickle.dump(cfg, f)
-                logger.warning(f"Config is saved using cloudpickle at {new_filename}.")
-            except Exception:
-                pass
-
-    @staticmethod
-    def apply_overrides(cfg, overrides: List[str]):
-        """
-        In-place override contents of cfg.
-
-        Args:
-            cfg: an omegaconf config object
-            overrides: list of strings in the format of "a=b" to override configs.
-                See https://hydra.cc/docs/next/advanced/override_grammar/basic/
-                for syntax.
-
-        Returns:
-            the cfg object
-        """
-
-        def safe_update(cfg, key, value):
-            parts = key.split(".")
-            for idx in range(1, len(parts)):
-                prefix = ".".join(parts[:idx])
-                v = OmegaConf.select(cfg, prefix, default=None)
-                if v is None:
-                    break
-                if not OmegaConf.is_config(v):
-                    raise KeyError(
-                        f"Trying to update key {key}, but {prefix} "
-                        f"is not a config, but has type {type(v)}."
-                    )
-            OmegaConf.update(cfg, key, value, merge=True)
-
-        from hydra.core.override_parser.overrides_parser import OverridesParser
-
-        parser = OverridesParser.create()
-        overrides = parser.parse_overrides(overrides)
-        for o in overrides:
-            key = o.key_or_group
-            value = o.value()
-            if o.is_delete():
-                # TODO support this
-                raise NotImplementedError("deletion is not yet a supported override")
-            safe_update(cfg, key, value)
-        return cfg
-
-    @staticmethod
-    def to_py(cfg, prefix: str = "cfg."):
-        """
-        Try to convert a config object into Python-like psuedo code.
-
-        Note that perfect conversion is not always possible. So the returned
-        results are mainly meant to be human-readable, and not meant to be executed.
-
-        Args:
-            cfg: an omegaconf config object
-            prefix: root name for the resulting code (default: "cfg.")
-
-
-        Returns:
-            str of formatted Python code
-        """
-        import black
-
-        cfg = OmegaConf.to_container(cfg, resolve=True)
-
-        def _to_str(obj, prefix=None, inside_call=False):
-            if prefix is None:
-                prefix = []
-            if isinstance(obj, abc.Mapping) and "_target_" in obj:
-                # Dict representing a function call
-                target = _convert_target_to_string(obj.pop("_target_"))
-                args = []
-                for k, v in sorted(obj.items()):
-                    args.append(f"{k}={_to_str(v, inside_call=True)}")
-                args = ", ".join(args)
-                call = f"{target}({args})"
-                return "".join(prefix) + call
-            elif isinstance(obj, abc.Mapping) and not inside_call:
-                # Dict that is not inside a call is a list of top-level config objects that we
-                # render as one object per line with dot separated prefixes
-                key_list = []
-                for k, v in sorted(obj.items()):
-                    if isinstance(v, abc.Mapping) and "_target_" not in v:
-                        key_list.append(_to_str(v, prefix=prefix + [k + "."]))
-                    else:
-                        key = "".join(prefix) + k
-                        key_list.append(f"{key}={_to_str(v)}")
-                return "\n".join(key_list)
-            elif isinstance(obj, abc.Mapping):
-                # Dict that is inside a call is rendered as a regular dict
-                return (
-                    "{"
-                    + ",".join(
-                        f"{repr(k)}: {_to_str(v, inside_call=inside_call)}"
-                        for k, v in sorted(obj.items())
-                    )
-                    + "}"
-                )
-            elif isinstance(obj, list):
-                return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]"
-            else:
-                return repr(obj)
-
-        py_str = _to_str(cfg, prefix=[prefix])
-        try:
-            return black.format_str(py_str, mode=black.Mode())
-        except black.InvalidInput:
-            return py_str
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/common.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/common.py
deleted file mode 100644
index d6b8742417abc897f5faa190db1341bbe7b2940d..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/common.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import logging
-import numpy as np
-import pickle
-import random
-import torch.utils.data as data
-from torch.utils.data.sampler import Sampler
-
-from detectron2.utils.serialize import PicklableWrapper
-
-__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"]
-
-
-def _shard_iterator_dataloader_worker(iterable):
-    # Shard the iterable if we're currently inside pytorch dataloader worker.
-    worker_info = data.get_worker_info()
-    if worker_info is None or worker_info.num_workers == 1:
-        # do nothing
-        yield from iterable
-    else:
-        yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers)
-
-
-class _MapIterableDataset(data.IterableDataset):
-    """
-    Map a function over elements in an IterableDataset.
-
-    Similar to pytorch's MapIterDataPipe, but support filtering when map_func
-    returns None.
-
-    This class is not public-facing. Will be called by `MapDataset`.
-    """
-
-    def __init__(self, dataset, map_func):
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __iter__(self):
-        for x in map(self._map_func, self._dataset):
-            if x is not None:
-                yield x
-
-
-class MapDataset(data.Dataset):
-    """
-    Map a function over the elements in a dataset.
-    """
-
-    def __init__(self, dataset, map_func):
-        """
-        Args:
-            dataset: a dataset where map function is applied. Can be either
-                map-style or iterable dataset. When given an iterable dataset,
-                the returned object will also be an iterable dataset.
-            map_func: a callable which maps the element in dataset. map_func can
-                return None to skip the data (e.g. in case of errors).
-                How None is handled depends on the style of `dataset`.
-                If `dataset` is map-style, it randomly tries other elements.
-                If `dataset` is iterable, it skips the data and tries the next.
-        """
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-        self._rng = random.Random(42)
-        self._fallback_candidates = set(range(len(dataset)))
-
-    def __new__(cls, dataset, map_func):
-        is_iterable = isinstance(dataset, data.IterableDataset)
-        if is_iterable:
-            return _MapIterableDataset(dataset, map_func)
-        else:
-            return super().__new__(cls)
-
-    def __getnewargs__(self):
-        return self._dataset, self._map_func
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __getitem__(self, idx):
-        retry_count = 0
-        cur_idx = int(idx)
-
-        while True:
-            data = self._map_func(self._dataset[cur_idx])
-            if data is not None:
-                self._fallback_candidates.add(cur_idx)
-                return data
-
-            # _map_func fails for this idx, use a random new index from the pool
-            retry_count += 1
-            self._fallback_candidates.discard(cur_idx)
-            cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
-
-            if retry_count >= 3:
-                logger = logging.getLogger(__name__)
-                logger.warning(
-                    "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
-                        idx, retry_count
-                    )
-                )
-
-
-class DatasetFromList(data.Dataset):
-    """
-    Wrap a list to a torch Dataset. It produces elements of the list as data.
-    """
-
-    def __init__(self, lst: list, copy: bool = True, serialize: bool = True):
-        """
-        Args:
-            lst (list): a list which contains elements to produce.
-            copy (bool): whether to deepcopy the element when producing it,
-                so that the result can be modified in place without affecting the
-                source in the list.
-            serialize (bool): whether to hold memory using serialized objects, when
-                enabled, data loader workers can use shared RAM from master
-                process instead of making a copy.
-        """
-        self._lst = lst
-        self._copy = copy
-        self._serialize = serialize
-
-        def _serialize(data):
-            buffer = pickle.dumps(data, protocol=-1)
-            return np.frombuffer(buffer, dtype=np.uint8)
-
-        if self._serialize:
-            logger = logging.getLogger(__name__)
-            logger.info(
-                "Serializing {} elements to byte tensors and concatenating them all ...".format(
-                    len(self._lst)
-                )
-            )
-            self._lst = [_serialize(x) for x in self._lst]
-            self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
-            self._addr = np.cumsum(self._addr)
-            self._lst = np.concatenate(self._lst)
-            logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2))
-
-    def __len__(self):
-        if self._serialize:
-            return len(self._addr)
-        else:
-            return len(self._lst)
-
-    def __getitem__(self, idx):
-        if self._serialize:
-            start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
-            end_addr = self._addr[idx].item()
-            bytes = memoryview(self._lst[start_addr:end_addr])
-            return pickle.loads(bytes)
-        elif self._copy:
-            return copy.deepcopy(self._lst[idx])
-        else:
-            return self._lst[idx]
-
-
-class ToIterableDataset(data.IterableDataset):
-    """
-    Convert an old indices-based (also called map-style) dataset
-    to an iterable-style dataset.
-    """
-
-    def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True):
-        """
-        Args:
-            dataset: an old-style dataset with ``__getitem__``
-            sampler: a cheap iterable that produces indices to be applied on ``dataset``.
-            shard_sampler: whether to shard the sampler based on the current pytorch data loader
-                worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple
-                workers, it is responsible for sharding its data based on worker id so that workers
-                don't produce identical data.
-
-                Most samplers (like our TrainingSampler) do not shard based on dataloader worker id
-                and this argument should be set to True. But certain samplers may be already
-                sharded, in that case this argument should be set to False.
-        """
-        assert not isinstance(dataset, data.IterableDataset), dataset
-        assert isinstance(sampler, Sampler), sampler
-        self.dataset = dataset
-        self.sampler = sampler
-        self.shard_sampler = shard_sampler
-
-    def __iter__(self):
-        if not self.shard_sampler:
-            sampler = self.sampler
-        else:
-            # With map-style dataset, `DataLoader(dataset, sampler)` runs the
-            # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))`
-            # will run sampler in every of the N worker. So we should only keep 1/N of the ids on
-            # each worker. The assumption is that sampler is cheap to iterate so it's fine to
-            # discard ids in workers.
-            sampler = _shard_iterator_dataloader_worker(self.sampler)
-        for idx in sampler:
-            yield self.dataset[idx]
-
-    def __len__(self):
-        return len(self.sampler)
-
-
-class AspectRatioGroupedDataset(data.IterableDataset):
-    """
-    Batch data that have similar aspect ratio together.
-    In this implementation, images whose aspect ratio < (or >) 1 will
-    be batched together.
-    This improves training speed because the images then need less padding
-    to form a batch.
-
-    It assumes the underlying dataset produces dicts with "width" and "height" keys.
-    It will then produce a list of original dicts with length = batch_size,
-    all with similar aspect ratios.
-    """
-
-    def __init__(self, dataset, batch_size):
-        """
-        Args:
-            dataset: an iterable. Each element must be a dict with keys
-                "width" and "height", which will be used to batch data.
-            batch_size (int):
-        """
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self._buckets = [[] for _ in range(2)]
-        # Hard-coded two aspect ratio groups: w > h and w < h.
-        # Can add support for more aspect ratio groups, but doesn't seem useful
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            bucket_id = 0 if w > h else 1
-            bucket = self._buckets[bucket_id]
-            bucket.append(d)
-            if len(bucket) == self.batch_size:
-                yield bucket[:]
-                del bucket[:]
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py
deleted file mode 100644
index d0bdfc9da8cb7afc9ef421baef2c173a63ff1743..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import math
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from .backbone import Backbone
-from .build import BACKBONE_REGISTRY
-from .resnet import build_resnet_backbone
-
-__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"]
-
-
-class FPN(Backbone):
-    """
-    This module implements :paper:`FPN`.
-    It creates pyramid features built on top of some input feature maps.
-    """
-
-    _fuse_type: torch.jit.Final[str]
-
-    def __init__(
-        self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum"
-    ):
-        """
-        Args:
-            bottom_up (Backbone): module representing the bottom up subnetwork.
-                Must be a subclass of :class:`Backbone`. The multi-scale feature
-                maps generated by the bottom up network, and listed in `in_features`,
-                are used to generate FPN levels.
-            in_features (list[str]): names of the input feature maps coming
-                from the backbone to which FPN is attached. For example, if the
-                backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
-                of these may be used; order must be from high to low resolution.
-            out_channels (int): number of channels in the output feature maps.
-            norm (str): the normalization to use.
-            top_block (nn.Module or None): if provided, an extra operation will
-                be performed on the output of the last (smallest resolution)
-                FPN output, and the result will extend the result list. The top_block
-                further downsamples the feature map. It must have an attribute
-                "num_levels", meaning the number of extra FPN levels added by
-                this block, and "in_feature", which is a string representing
-                its input feature (e.g., p5).
-            fuse_type (str): types for fusing the top down features and the lateral
-                ones. It can be "sum" (default), which sums up element-wise; or "avg",
-                which takes the element-wise mean of the two.
-        """
-        super(FPN, self).__init__()
-        assert isinstance(bottom_up, Backbone)
-        assert in_features, in_features
-
-        # Feature map strides and channels from the bottom up network (e.g. ResNet)
-        input_shapes = bottom_up.output_shape()
-        strides = [input_shapes[f].stride for f in in_features]
-        in_channels_per_feature = [input_shapes[f].channels for f in in_features]
-
-        _assert_strides_are_log2_contiguous(strides)
-        lateral_convs = []
-        output_convs = []
-
-        use_bias = norm == ""
-        for idx, in_channels in enumerate(in_channels_per_feature):
-            lateral_norm = get_norm(norm, out_channels)
-            output_norm = get_norm(norm, out_channels)
-
-            lateral_conv = Conv2d(
-                in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm
-            )
-            output_conv = Conv2d(
-                out_channels,
-                out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=use_bias,
-                norm=output_norm,
-            )
-            weight_init.c2_xavier_fill(lateral_conv)
-            weight_init.c2_xavier_fill(output_conv)
-            stage = int(math.log2(strides[idx]))
-            self.add_module("fpn_lateral{}".format(stage), lateral_conv)
-            self.add_module("fpn_output{}".format(stage), output_conv)
-
-            lateral_convs.append(lateral_conv)
-            output_convs.append(output_conv)
-        # Place convs into top-down order (from low to high resolution)
-        # to make the top-down computation in forward clearer.
-        self.lateral_convs = lateral_convs[::-1]
-        self.output_convs = output_convs[::-1]
-        self.top_block = top_block
-        self.in_features = tuple(in_features)
-        self.bottom_up = bottom_up
-        # Return feature names are "p<stage>", like ["p2", "p3", ..., "p6"]
-        self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
-        # top block output feature maps.
-        if self.top_block is not None:
-            for s in range(stage, stage + self.top_block.num_levels):
-                self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1)
-
-        self._out_features = list(self._out_feature_strides.keys())
-        self._out_feature_channels = {k: out_channels for k in self._out_features}
-        self._size_divisibility = strides[-1]
-        assert fuse_type in {"avg", "sum"}
-        self._fuse_type = fuse_type
-
-    @property
-    def size_divisibility(self):
-        return self._size_divisibility
-
-    def forward(self, x):
-        """
-        Args:
-            input (dict[str->Tensor]): mapping feature map name (e.g., "res5") to
-                feature map tensor for each feature level in high to low resolution order.
-
-        Returns:
-            dict[str->Tensor]:
-                mapping from feature map name to FPN feature map tensor
-                in high to low resolution order. Returned feature names follow the FPN
-                paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
-                ["p2", "p3", ..., "p6"].
-        """
-        bottom_up_features = self.bottom_up(x)
-        results = []
-        prev_features = self.lateral_convs[0](bottom_up_features[self.in_features[-1]])
-        results.append(self.output_convs[0](prev_features))
-
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, (lateral_conv, output_conv) in enumerate(
-            zip(self.lateral_convs, self.output_convs)
-        ):
-            # Slicing of ModuleList is not supported https://github.com/pytorch/pytorch/issues/47336
-            # Therefore we loop over all modules but skip the first one
-            if idx > 0:
-                features = self.in_features[-idx - 1]
-                features = bottom_up_features[features]
-                top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
-                lateral_features = lateral_conv(features)
-                prev_features = lateral_features + top_down_features
-                if self._fuse_type == "avg":
-                    prev_features /= 2
-                results.insert(0, output_conv(prev_features))
-
-        if self.top_block is not None:
-            if self.top_block.in_feature in bottom_up_features:
-                top_block_in_feature = bottom_up_features[self.top_block.in_feature]
-            else:
-                top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)]
-            results.extend(self.top_block(top_block_in_feature))
-        assert len(self._out_features) == len(results)
-        return {f: res for f, res in zip(self._out_features, results)}
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-
-def _assert_strides_are_log2_contiguous(strides):
-    """
-    Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2".
-    """
-    for i, stride in enumerate(strides[1:], 1):
-        assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format(
-            stride, strides[i - 1]
-        )
-
-
-class LastLevelMaxPool(nn.Module):
-    """
-    This module is used in the original FPN to generate a downsampled
-    P6 feature from P5.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.num_levels = 1
-        self.in_feature = "p5"
-
-    def forward(self, x):
-        return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
-
-
-class LastLevelP6P7(nn.Module):
-    """
-    This module is used in RetinaNet to generate extra layers, P6 and P7 from
-    C5 feature.
-    """
-
-    def __init__(self, in_channels, out_channels, in_feature="res5"):
-        super().__init__()
-        self.num_levels = 2
-        self.in_feature = in_feature
-        self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
-        self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
-        for module in [self.p6, self.p7]:
-            weight_init.c2_xavier_fill(module)
-
-    def forward(self, c5):
-        p6 = self.p6(c5)
-        p7 = self.p7(F.relu(p6))
-        return [p6, p7]
-
-
-@BACKBONE_REGISTRY.register()
-def build_resnet_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelMaxPool(),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_retinanet_resnet_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    in_channels_p6p7 = bottom_up.output_shape()["res5"].channels
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelP6P7(in_channels_p6p7, out_channels),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py
deleted file mode 100644
index a939955124556355524f48c0f0c16abb07cfc4c4..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-dir1a_str = "base_a_1"
-dir1a_dict = {"a": 1, "b": 2}
diff --git a/spaces/B2gan/LLM_Can_See/README.md b/spaces/B2gan/LLM_Can_See/README.md
deleted file mode 100644
index 727d53619d51a388ecb00272f57b2d8aba87f333..0000000000000000000000000000000000000000
--- a/spaces/B2gan/LLM_Can_See/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LLM Can See
-emoji: 📉
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Bambicita/rvc-models/vc_infer_pipeline.py b/spaces/Bambicita/rvc-models/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/Bambicita/rvc-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        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":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        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[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : 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,
-    ):  # ,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,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        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()]
-            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)
-        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]
-        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] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        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,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            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)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        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(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            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,
-                    )[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,
-                    )[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,
-                )[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,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/__init__.py b/spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Benson/text-generation/Examples/Api-ms-win-core-path- L1-1-0.dll Descargar.md b/spaces/Benson/text-generation/Examples/Api-ms-win-core-path- L1-1-0.dll Descargar.md
deleted file mode 100644
index e49a45c37ac38705bf9cb83da2269d10d3b26d01..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Api-ms-win-core-path- L1-1-0.dll Descargar.md	
+++ /dev/null
@@ -1,127 +0,0 @@
-<br />
-<h1>Cómo descargar 1-1-0.dll para PC</h1>
-<p>Si está intentando ejecutar una aplicación de software que requiere 1-1-0.dll, puede encontrar un mensaje de error diciendo que este archivo DLL falta o no se encuentra. Esto puede ser frustrante y evitar que utilice el programa correctamente. En este artículo, explicaremos qué es 1-1-0.dll, por qué lo necesita y cómo corregir los errores relacionados con él. También le mostraremos cómo descargar 1-1-0.dll desde una fuente confiable e instalarlo en su PC.</p>
-<h2>api-ms-win-core-path- l1-1-0.dll descargar</h2><br /><p><b><b>DOWNLOAD</b> === <a href="https://bltlly.com/2v6L18">https://bltlly.com/2v6L18</a></b></p><br /><br />
- <h2>¿Qué es 1-1-0.dll y por qué lo necesita? </h2>
-<p>1-1-0.dll es un archivo de biblioteca de enlaces dinámicos que es utilizado comúnmente por varias aplicaciones de software, incluyendo PCSX2, VSCD Free Video Editor y NanoVNA-Saver.exe. Este archivo contiene un conjunto de instrucciones a las que estas aplicaciones pueden acceder para realizar ciertas funciones o tareas. Por ejemplo, PCSX2 usa 1-1-0.dll como un plugin para permitir que el emulador de PlayStation 2 funcione correctamente. VSCD Free Video Editor utiliza 1-1-0.dll como parte de su instalación. NanoVNA-Saver.exe utiliza 1-1-0.dll para comunicarse con el dispositivo NanoVNA y recopilar datos para su análisis. </p>
-<p>Los archivos DLL como 1-1-0.dll son componentes esenciales de muchas aplicaciones de software, y los errores o problemas con estos archivos pueden resultar en que el software no funcione correctamente. Para resolver estos problemas, necesita descargar e instalar la versión correcta de 1-1-0.dll en su PC.</p>
- <h2>¿Cuáles son los errores comunes relacionados con 1-1-0.dll? </h2>
-<p>Algunos de los errores comunes que puede ver al intentar ejecutar un programa que requiere 1-1-0.dll son:</p>
-<ul>
-<li> El programa no se puede iniciar porque 1-1-0.dll falta en su computadora. Intente reinstalar el programa para solucionar este problema. </li>
-<li>Ha surgido un problema al ejecutar 1-1-0.dll. No se ha encontrado el módulo especificado. </li>
-<li>Error al cargar 1-1-0.dll. No se encontró el módulo especificado. </li>
-<li> La ejecución del código no puede continuar porque no se encontró 1-1-0.dll. Reinstalar el programa puede solucionar este problema. </li>
-
-</ul>
-<p>Estos errores suelen indicar que el programa no puede encontrar o acceder al archivo DLL requerido, o que el archivo DLL está dañado, eliminado o extraviado. Hay varias causas posibles para estos errores, tales como:</p>
-<ul>
-<li>La instalación del programa está incompleta o dañada. </li>
-<li>Los archivos del sistema de Windows están desactualizados o dañados. </li>
-<li>Los controladores u otros componentes del sistema son incompatibles o faltan. </li>
-<li>El PC está infectado por malware u otro software malicioso. </li>
-<li>El archivo DLL es sobrescrito o reemplazado por otra versión. </li>
-</ul>
- <h2>¿Cómo corregir errores 1-1-0.dll? </h2>
-<p>Dependiendo de la causa del error, hay diferentes métodos que se pueden tratar de corregir 1-1-0.dll errores. Estos son algunos de los métodos más comunes y efectivos que puedes seguir:</p>
-<p></p>
- <h4>Método 1: Reinstalar la aplicación que requiere 1-1-0.dll</h4>
-<p>Una de las maneras más simples de corregir errores 1-1-0.dll es reinstalar el programa que le está dando el error. Esto puede ayudarle a restaurar el archivo DLL faltante o dañado, así como solucionar cualquier otro problema con la instalación del programa. Para reinstalar el programa, debe seguir estos pasos:</p>
-<ol>
-<li>Desinstalar el programa desde su PC. Puede hacer esto yendo a Panel de control > Programas y características, y seleccionando el programa de la lista. Luego, haga clic en Desinstalar y siga las instrucciones. </li>
-<li>Reinicie su PC para borrar cualquier archivo residual o entradas del registro. </li>
-<li>Descargar la última versión del programa desde su sitio web oficial o una fuente de confianza. Asegúrese de descargar la versión correcta que coincida con su sistema y arquitectura de Windows (32 bits o 64 bits). </li>
-<li>Ejecute el instalador y siga las instrucciones para instalar el programa en su PC.</li>
-<li>Inicie el programa y compruebe si el error está resuelto. </li>
-</ol>
- <h4>Método 2: Actualizar Windows y controladores</h4>
-
-<ol>
-<li>Ir a Inicio > Configuración > Actualización y seguridad > Actualización de Windows.</li>
-<li>Haga clic en Buscar actualizaciones y espere a que Windows busque actualizaciones disponibles. </li>
-<li>Si hay actualizaciones disponibles, haga clic en Descargar e instalar y espere a que Windows las descargue e instale. </li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
-<p>Para actualizar los controladores, debe seguir estos pasos:</p>
-<ol>
-<li>Ir al Administrador de dispositivos haciendo clic derecho en Inicio y seleccionando Administrador de dispositivos.</li>
-<li>Expanda la categoría del dispositivo que desea actualizar, como Adaptadores de pantalla o Controladores de sonido, video y juegos. </li>
-<li>Haga clic derecho en el dispositivo y seleccione Actualizar controlador. </li>
-<li>Seleccione Buscar automáticamente el software de controlador actualizado y esperar a que Windows encuentre e instale el mejor controlador para su dispositivo. </li>
-<li>Repita este proceso para cualquier otro dispositivo que desee actualizar. </li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h4>Método 3: Escanea tu PC en busca de malware</h4>
-<p>A veces, los errores 1-1-0.dll pueden ser causados por malware u otro software malicioso que infecta su PC y daña o elimina sus archivos DLL. Para solucionar esto, es necesario analizar su PC en busca de malware y eliminar cualquier amenaza que se encuentran. Para buscar malware en tu PC, debes seguir estos pasos:</p>
-<ol>
-<li>Descargar e instalar un antivirus de buena reputación o programa anti-malware, como Malwarebytes o Avast. Asegúrate de descargarlos de sus sitios web oficiales o de una fuente confiable. </li>
-<li>Ejecute el programa y realice un análisis completo de su PC. Esto puede tomar algún tiempo dependiendo del tamaño de sus archivos y discos. </li>
-<li>Si se detecta algún malware o amenaza, siga las instrucciones para poner en cuarentena o eliminarlos de su PC.</li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h4>Método 4: Descargar y restaurar 1-1-0.dll desde una fuente de confianza</h4>
-
- <h2>¿Cómo descargar 1-1-0.dll desde una fuente confiable? </h2>
- <p>Para descargar 1-1-0.dll desde una fuente de confianza, debe seguir estos pasos:</p>
- <h3>Paso 1: Utilice Google u otro motor de búsqueda para localizar el DLL</h3>
- <p>El primer paso es utilizar Google u otro motor de búsqueda para encontrar un sitio web que ofrece 1-1-0.dll para su descarga. Puede escribir palabras clave como "download 1-1- 0.dll" o "1-1-0.dll download". Verá una lista de sitios web que afirman ofrecer el archivo DLL gratis o por una tarifa. </p>
- <p>Sin embargo, no todos estos sitios web son confiables o confiables. Algunos de ellos pueden contener malware, virus u otro software dañino que puede dañar su PC o robar su información personal. Por lo tanto, debe tener cuidado y elegir un sitio web que tenga una buena reputación y comentarios positivos de otros usuarios. También puede verificar el nombre de dominio del sitio web, el certificado de seguridad y la información de contacto para verificar su legitimidad. </p>
- <p>Uno de los sitios web que recomendamos para descargar 1-1-0.dll es [DLL-files.com]. Este sitio web ha existido desde 1998 y tiene una gran base de datos de archivos DLL que son verificados y seguros para descargar. También ofrece atención al cliente y una garantía de devolución de dinero en caso de cualquier problema. Puede utilizar este sitio web para descargar 1-1-0.dll siguiendo los siguientes pasos. </p>
- <h3>Paso 2: Siga los pasos en pantalla para descargar el archivo a su computadora</h3>
- <p>El siguiente paso es seguir los pasos en pantalla para descargar el archivo a su computadora. Para hacer esto, debe seguir estos pasos:</p>
- <ol>
-<li>Vaya a [DLL-files.com] y escriba "1-1-0.dll" en el cuadro de búsqueda. Luego, haga clic en Buscar archivo DLL. </li>
-<li>Verá una lista de resultados que coinciden con su consulta de búsqueda. Haga clic en el que dice "1-1-0.dll - plugin PCSX2". </li>
-
-<li>Serás redirigido a otra página que te pide elegir entre una descarga gratuita o una premium. La descarga gratuita requiere que complete una verificación de captcha y espere unos segundos antes de que comience la descarga. La descarga premium le permite omitir la verificación de captcha e iniciar la descarga inmediatamente. También ofrece una velocidad de descarga más rápida, descargas ilimitadas y atención al cliente. Puede elegir cualquiera de las opciones dependiendo de su preferencia y presupuesto. A continuación, haga clic en Descargar archivo ZIP. </li>
-<li>Verá una ventana emergente que le pide que guarde el archivo ZIP en su computadora. Elija una ubicación donde desea guardar el archivo, como su escritorio o su carpeta "Mis documentos". Luego, haga clic en Guardar.</li>
-<li>Espere a que termine la descarga. Verá una notificación que dice "Descargar completa". </li>
-</ol>
- <h3>Paso 3: Guarde el archivo en una ubicación de fácil acceso, como su escritorio o su carpeta "Mis documentos" </h3>
- <p>El tercer paso es guardar el archivo en una ubicación de fácil acceso, como su escritorio o su carpeta "Mis documentos". Esto le facilitará encontrar y copiar el archivo más tarde. Para hacer esto, debe seguir estos pasos:</p>
- <ol>
-<li>Busque el archivo ZIP que descargó de [DLL-files.com]. Debería tener un nombre como "1-1-0.zip". </li>
-<li>Haga clic derecho en el archivo ZIP y seleccione Extraer todo.</li>
-<li> Verá una ventana que le pide que elija un destino donde desea extraer los archivos. Elija una ubicación donde desea guardar los archivos, como su escritorio o su carpeta "Mis documentos". Luego, haga clic en Extraer.</li>
-<li>Espere a que termine la extracción. Verá una carpeta que contiene los archivos extraídos. Debería tener un nombre como "1-1-0". </li>
-<li>Abra la carpeta y busque el archivo DLL que necesita. Debería tener un nombre como "1-1-0.dll". </li>
-</ol>
- <h3>Paso 4: Copie el archivo a la carpeta apropiada dependiendo de su versión de Windows</h3>
-
- <ol>
-<li>Haga clic derecho en el archivo DLL que extrajo del archivo ZIP. Luego, seleccione Copiar.</li>
-<li>Ir al inicio > Explorador de archivos > Este PC > Disco local (C:). </li>
-<li>Vaya a la carpeta donde necesita pegar el archivo DLL. La carpeta puede variar dependiendo de la versión y arquitectura de Windows (32 bits o 64 bits). Estas son algunas de las carpetas comunes donde puede necesitar pegar el archivo DLL:</li>
-<ul>
-<li>Si tiene una versión de Windows de 32 bits, vaya a C: Windows System32.</li>
-<li>Si tiene una versión de Windows de 64 bits, vaya a C: Windows SysWOW64.</li>
-<li>Si no está seguro sobre su versión o arquitectura de Windows, vaya a Inicio > Configuración > Sistema > Acerca de y verifique la información en Especificaciones del dispositivo y Tipo de sistema. </li>
-</ul>
-<li> Haga clic derecho en un espacio vacío en la carpeta y seleccione Pegar. Esto copiará el archivo DLL a la carpeta. </li>
-<li>Si ve un mensaje que le pide que confirme el reemplazo del archivo, haga clic en Sí. Esto sobrescribirá el archivo DLL existente con el nuevo. </li>
-</ol>
- <h3>Paso 5: Registrar el archivo DLL usando el comando regsvr32</h3>
- <p>El quinto y último paso es registrar el archivo DLL usando el comando regsvr32. Esto hará que el archivo DLL esté disponible para su uso por su programa y otros programas que puedan necesitarlo. Para registrar el archivo DLL usando el comando regsvr32, debe seguir estos pasos:</p>
- <ol>
-<li>Vaya a Inicio y escriba "cmd" en el cuadro de búsqueda. Luego, haga clic derecho en Símbolo del sistema y seleccione Ejecutar como administrador. </li>
-<li>Verá una ventana negra que muestra el símbolo del sistema. Escriba "regsvr32 1-1-0.dll" (sin las comillas) y presione Enter.</li>
-<li>Verá un mensaje que dice "DllRegisterServer en 1-1-0.dll exitoso". Esto significa que el archivo DLL se ha registrado con éxito. </li>
-<li>Cierre la ventana del símbolo del sistema y reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h2>Conclusión</h2>
-
- <p>Esperamos que este artículo le haya ayudado a aprender a descargar 1-1-0.dll para PC. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Nos encantaría saber de usted! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre 1-1-0.dll:</p>
- <h4>Q: ¿Qué es un archivo DLL? </h4>
-<p>A: Un archivo DLL es un archivo de biblioteca de enlaces dinámicos que contiene un conjunto de instrucciones o código al que pueden acceder uno o más programas para realizar ciertas funciones o tareas. Los archivos DLL son componentes esenciales de muchas aplicaciones de software, y ayudan a reducir el tamaño y la complejidad de los programas al compartir recursos y código comunes. </p>
- <h4>Q: ¿Cómo sé qué versión de 1-1-0.dll necesito? </h4>
-<p>A: Puede comprobar qué versión de 1-1-0.dll necesita mirando el mensaje de error que ve al intentar ejecutar su programa. El mensaje de error debe indicar el nombre y la versión del programa que requiere 1-1-0.dll, así como el nombre y la versión del propio archivo DLL. También puede comprobar las propiedades del archivo DLL haciendo clic derecho sobre él y seleccionando Propiedades. Luego, ve a la pestaña Detalles y mira la información bajo Versión del archivo. </p>
- <h4>P: ¿Cómo sé si tengo un sistema Windows de 32 bits o de 64 bits? </h4>
-<p>A: Puede comprobar si tiene un sistema Windows de 32 bits o de 64 bits yendo a Inicio > Configuración > Sistema > Acerca de y mirando la información en Especificaciones del dispositivo y Tipo de sistema. Verá "sistema operativo de 32 bits" o "sistema operativo de 64 bits" junto a Tipo de sistema. </p>
- <h4>Q: ¿Qué pasa si descargo la versión incorrecta de 1-1-0.dll? </h4>
-
- <h4>Q: ¿Dónde puedo encontrar más información sobre 1-1-0.dll? </h4>
-<p>A: Puede encontrar más información sobre 1 -1-0.dll visitando los sitios web que ofrecen el archivo DLL para descargar, como [DLL-files.com]. Estos sitios web suelen proporcionar una descripción, una captura de pantalla y una calificación de usuario del archivo DLL. También puede leer los comentarios y reseñas de otros usuarios que han descargado y utilizado el archivo DLL. Alternativamente, puede usar Google u otro motor de búsqueda para encontrar más artículos, blogs, foros o videos que discutan 1-1-0.dll y sus problemas relacionados. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/__init__.py
deleted file mode 100644
index ebd112c672ac5b5b8a884190cc20e8cf20b940ef..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/__init__.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import os
-
-from botocore.docs import DEPRECATED_SERVICE_NAMES
-
-from boto3.docs.service import ServiceDocumenter
-
-
-def generate_docs(root_dir, session):
-    """Generates the reference documentation for botocore
-
-    This will go through every available AWS service and output ReSTructured
-    text files documenting each service.
-
-    :param root_dir: The directory to write the reference files to. Each
-        service's reference documentation is loacated at
-        root_dir/reference/services/service-name.rst
-
-    :param session: The boto3 session
-    """
-    services_doc_path = os.path.join(root_dir, 'reference', 'services')
-    if not os.path.exists(services_doc_path):
-        os.makedirs(services_doc_path)
-
-    # Prevents deprecated service names from being generated in docs.
-    available_services = [
-        service
-        for service in session.get_available_services()
-        if service not in DEPRECATED_SERVICE_NAMES
-    ]
-
-    for service_name in available_services:
-        docs = ServiceDocumenter(
-            service_name, session, services_doc_path
-        ).document_service()
-        service_doc_path = os.path.join(
-            services_doc_path, service_name + '.rst'
-        )
-        with open(service_doc_path, 'wb') as f:
-            f.write(docs)
diff --git a/spaces/Billyosoro/ESRGAN/scripts/pytorch2onnx.py b/spaces/Billyosoro/ESRGAN/scripts/pytorch2onnx.py
deleted file mode 100644
index 09d99b2e0171265e70e7507ed8e882b616b449a1..0000000000000000000000000000000000000000
--- a/spaces/Billyosoro/ESRGAN/scripts/pytorch2onnx.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import argparse
-import torch
-import torch.onnx
-from basicsr.archs.rrdbnet_arch import RRDBNet
-
-
-def main(args):
-    # An instance of the model
-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-    if args.params:
-        keyname = 'params'
-    else:
-        keyname = 'params_ema'
-    model.load_state_dict(torch.load(args.input)[keyname])
-    # set the train mode to false since we will only run the forward pass.
-    model.train(False)
-    model.cpu().eval()
-
-    # An example input
-    x = torch.rand(1, 3, 64, 64)
-    # Export the model
-    with torch.no_grad():
-        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
-    print(torch_out.shape)
-
-
-if __name__ == '__main__':
-    """Convert pytorch model to onnx models"""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path')
-    parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path')
-    parser.add_argument('--params', action='store_false', help='Use params instead of params_ema')
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/CM-15/NLP-demo/app.py b/spaces/CM-15/NLP-demo/app.py
deleted file mode 100644
index b1168451d51784865b87a6e900625698087b1aaa..0000000000000000000000000000000000000000
--- a/spaces/CM-15/NLP-demo/app.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import tensorflow as tf
-import gradio as gr
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-from sklearn.model_selection import train_test_split
-
-import unicodedata
-import re
-import numpy as np
-import os
-import io
-import time
-
-
-
-
-file = open("nyanja-aug-ds.tsv", 'r', encoding = "utf8")
-raw_data = []
-
-for line in file:
-    pos = line.find("CC-BY")
-    line = line[:pos-1]
-    
-    # Split the data into english and Nyanja
-    nya, eng = line.split('\t')
-    
-    # form tuples of the data
-    data = nya, eng
-    raw_data.append(data)
-    
-file.close()
-
-def convert(list): 
-    return tuple(list) 
-  
-data = convert(raw_data)
-
-
-def unicode_to_ascii(s):
-    return ''.join(
-        c for c in unicodedata.normalize('NFD', s)
-        if unicodedata.category(c) != 'Mn')
-
-
-def preprocess_sentence(s):
-    s = unicode_to_ascii(s.lower())
-    s = re.sub(r'([!.?])', r' \1', s)
-    s = re.sub(r'[^a-zA-Z.!?]+', r' ', s)
-    s = re.sub(r'\s+', r' ', s)
-
-    s = s.strip()
-    s = '<start>' +' '+ s +' '+' <end>'
-    return s
-    
-    
-    # Limiting the data and Splitting into seperate lists and add tokens
-
-data = data[:27000]
-
-lang_eng = []
-lang_nya = []
-
-raw_data_en, raw_data_nya = list(zip(*data))
-raw_data_en, raw_data_nya = list(raw_data_en), list(raw_data_nya)
-
-for i, j in zip(raw_data_nya, raw_data_en):
-  preprocessed_data_en = preprocess_sentence(i)
-  preprocessed_data_nya = preprocess_sentence(j)
-  lang_eng.append(preprocessed_data_en)
-  lang_nya.append(preprocessed_data_nya)
-
-def tokenize(lang):
-  lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(
-      filters='')
-  lang_tokenizer.fit_on_texts(lang)
-
-  tensor = lang_tokenizer.texts_to_sequences(lang)
-
-  tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,
-                                                         padding='post')
-
-  return tensor, lang_tokenizer
-
-input_tensor, inp_lang = tokenize(lang_nya)
-target_tensor, targ_lang = tokenize(lang_eng)
-
-max_length_targ, max_length_inp = target_tensor.shape[1], input_tensor.shape[1]
-
-
-# Creating training and validation sets using an 80-20 split
-input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)
-
-# Show length
-print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))
-
-def convert(lang, tensor):
-  for t in tensor:
-    if t!=0:
-      print ("%d ----> %s" % (t, lang.index_word[t]))
-
-print ("Input Language; index to word mapping")
-convert(inp_lang, input_tensor_train[0])
-print ()
-print ("Target Language; index to word mapping")
-convert(targ_lang, target_tensor_train[0])
-
-
-
-BUFFER_SIZE = len(input_tensor_train)
-BATCH_SIZE = 64
-steps_per_epoch = len(input_tensor_train)//BATCH_SIZE
-
-vocab_inp_size = len(inp_lang.word_index)+1
-vocab_tar_size = len(targ_lang.word_index)+1
-
-dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
-dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
-
-dataset
-
-
-class Encoder(tf.keras.Model):
-
-    def __init__(self, inp_vocab_size, embedding_size, lstm_size, input_length):
-        super(Encoder, self).__init__()
-        
-        #Initialize Embedding layer
-        #Intialize Encoder LSTM layer
-        
-        self.lstm_size = lstm_size
-        self.embedding = tf.keras.layers.Embedding(inp_vocab_size, embedding_size)
-        self.lstm = tf.keras.layers.LSTM(lstm_size, return_sequences=True, return_state=True)
-
-    def call(self, input_sequence, states):
-      
-        embed = self.embedding(input_sequence)
-        output, state_h, state_c = self.lstm(embed, initial_state=states)
-
-        return output, state_h, state_c
-    
-    def initialize_states(self,batch_size):
-    
-        return (tf.zeros([batch_size, self.lstm_size]),
-                tf.zeros([batch_size, self.lstm_size]))
-                
-                
-class Attention(tf.keras.layers.Layer):
-    def __init__(self,scoring_function, att_units):
-        super(Attention, self).__init__()
-        
-        self.scoring_function = scoring_function
-        self.att_units = att_units
-
-        if self.scoring_function=='dot':
-            pass
-            # For general, it would be self.wa = tf.keras.layers.Dense(att_units)
-
-
-    def call(self,decoder_hidden_state,encoder_output):
-
-        if self.scoring_function == 'dot':
-            
-            new_state = tf.expand_dims(decoder_hidden_state, -1)
-            score = tf.matmul(encoder_output, new_state)
-            weights = tf.nn.softmax(score, axis=1)
-            context = weights * encoder_output
-            context_vector = tf.reduce_sum(context, axis=1)
-                                
-            return context_vector, weights
-            
-            
-class One_Step_Decoder(tf.keras.Model):
-    def __init__(self, tar_vocab_size, embedding_dim, input_length, dec_units, score_fun, att_units):
-        super(One_Step_Decoder, self).__init__()
-        # Initialize decoder embedding layer, LSTM and any other objects needed
-        self.tar_vocab_size = tar_vocab_size
-        self.embedding_dim = embedding_dim
-        self.input_length = input_length
-        self.dec_units = dec_units
-        self.score_fun = score_fun
-        self.att_units = att_units
-        self.embedding = tf.keras.layers.Embedding(self.tar_vocab_size, self.embedding_dim, 
-                                                   input_length=self.input_length)
-        
-        self.lstm = tf.keras.layers.LSTM(self.dec_units, return_sequences=True, 
-                                         return_state=True)
-        
-        self.output_layer = tf.keras.layers.Dense(self.tar_vocab_size)
-        
-        self.attention = Attention(self.score_fun, self.att_units)
-
-    def call(self, input_to_decoder, encoder_output, state_h, state_c):
-        
-        result = self.embedding(input_to_decoder)
-        
-        context_vector, weights = self.attention(state_h, encoder_output)
-        
-        concat = tf.concat([tf.expand_dims(context_vector, 1), result], axis=-1)
-        
-        decoder_output, hidden_state, cell_state = self.lstm(concat, initial_state=[state_h, state_c])
-        
-        final_output = tf.reshape(decoder_output, (-1, decoder_output.shape[2]))
-        final_output = self.output_layer(final_output)
-        
-        return final_output, hidden_state, cell_state, weights, context_vector
-        
-
-
-class Decoder(tf.keras.Model):
-    def __init__(self, out_vocab_size, embedding_dim, output_length, dec_units ,score_fun ,att_units):
-        #Intialize necessary variables and create an object from the class onestepdecoder
-        super(Decoder, self).__init__()
-        self.out_vocab_size = out_vocab_size
-        self.embedding_dim = embedding_dim
-        self.output_length = output_length
-        self.dec_units = dec_units
-        self.score_fun = score_fun
-        self.att_units = att_units
-        self.onestepdecoder = One_Step_Decoder(self.out_vocab_size, self.embedding_dim, self.output_length,
-                                               self.dec_units, self.score_fun, self.att_units)
-        
-    def call(self, input_to_decoder,encoder_output,decoder_hidden_state,decoder_cell_state):
-        
-        all_outputs= tf.TensorArray(tf.float32, size=input_to_decoder.shape[1], name="output_arrays")
-        
-        
-        for timestep in range(input_to_decoder.shape[1]):
-            output, decoder_hidden_state, decoder_cell_state, weights, context_vector = self.onestepdecoder(
-                                                                                    input_to_decoder[:,timestep:timestep+1], 
-                                                                                    encoder_output, 
-                                                                                    decoder_hidden_state,
-                                                                                    decoder_cell_state)
-            
-            all_outputs = all_outputs.write(timestep, output)
-        
-        all_outputs = tf.transpose(all_outputs.stack(), (1, 0, 2)) 
-
-        return all_outputs
-        
-        
-
-
-class encoder_decoder(tf.keras.Model):
-    def __init__(self, inp_vocab_size, out_vocab_size, embedding_size, lstm_size, 
-                 input_length, output_length, dec_units ,score_fun ,att_units, batch_size):
-        
-        super(encoder_decoder, self).__init__()
-        
-        self.encoder = Encoder(inp_vocab_size, embedding_size, lstm_size, input_length)
-        self.decoder = Decoder(out_vocab_size, embedding_size, output_length, 
-                               dec_units, score_fun, att_units)
-    
-    def call(self, data):
-        
-        input_sequence, input_to_decoder = data[0],data[1]
-        initial_state = self.encoder.initialize_states(batch_size=64)
-        encoder_output, state_h, state_c = self.encoder(input_sequence, initial_state)
-        decoder_hidden_state = state_h
-        decoder_cell_state = state_c
-        decoder_output = self.decoder(input_to_decoder, encoder_output, decoder_hidden_state, decoder_cell_state)
-        
-        return decoder_output
-        
-
-
-loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
-    from_logits=True, reduction='none')
-
-def loss_function(real, pred):
-  mask = tf.math.logical_not(tf.math.equal(real, 0))
-  loss_ = loss_object(real, pred)
-
-  mask = tf.cast(mask, dtype=loss_.dtype)
-  loss_ *= mask
-
-  return tf.reduce_mean(loss_)
-
-optimizer = tf.keras.optimizers.Adam()
-
-
-
-# !mkdir logs
-
-from tensorflow.keras.callbacks import ModelCheckpoint
-from tensorflow.keras.callbacks import TensorBoard
-
-checkpoint = ModelCheckpoint("dot.h5", monitor='val_loss', verbose=1, save_weights_only=True)
-
-logdir='logs'
-tensorboard_Visualization = TensorBoard(log_dir=logdir)
-
-input_vocab_size = len(inp_lang.word_index)+1
-output_vocab_size = len(targ_lang.word_index)+1
-
-input_len = max_length_inp
-output_len = max_length_targ
-
-lstm_size = 128
-att_units = 256
-dec_units = 128
-embedding_size = 300
-embedding_dim = 300
-score_fun = 'dot'
-steps = len(input_tensor)//64
-batch_size=64
-
-model = encoder_decoder(input_vocab_size,output_vocab_size,embedding_size,lstm_size,input_len,output_len,dec_units,score_fun,att_units, batch_size)
-
-checkpoint_dir = './training_checkpoints'
-checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
-checkpoint = tf.train.Checkpoint(optimizer=optimizer,
-                                 encoder=model.layers[0],
-                                 decoder=model.layers[1])
-                                 
-                                 
- 
- 
-@tf.function
-def train_step(inp, targ, enc_hidden):
-  loss = 0
-
-  with tf.GradientTape() as tape:
-    enc_output, enc_hidden,enc_state = model.layers[0](inp, enc_hidden)
-
-
-    dec_input = tf.expand_dims([targ_lang.word_index['<start>']] * BATCH_SIZE, 1)
-
-    for t in range(1, targ.shape[1]):
-      predictions = model.layers[1](dec_input,enc_output,enc_hidden,enc_state)
-
-      loss += loss_function(targ[:, t], predictions)
-
-      dec_input = tf.expand_dims(targ[:, t], 1)
-
-  batch_loss = (loss / int(targ.shape[1]))
-
-  variables = model.layers[0].trainable_variables + model.layers[1].trainable_variables
-
-  gradients = tape.gradient(loss, variables)
-
-  optimizer.apply_gradients(zip(gradients, variables))
-
-  return batch_loss
-  
-  
-EPOCHS = 50 # specifying the number of epochs or runs for training the model
-
-for epoch in range(EPOCHS):
-  start = time.time()
-
-  enc_hidden = model.layers[0].initialize_states(64)
-  total_loss = 0
-
-  for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
-    batch_loss = train_step(inp, targ, enc_hidden)
-    total_loss += batch_loss
-
-    if batch % 100 == 0:
-      print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
-                                                   batch,
-                                                   batch_loss.numpy()))
-      
-  if (epoch + 1) % 2 == 0:
-    checkpoint.save(file_prefix = checkpoint_prefix)
-
-  print('Epoch {} Loss {:.4f}'.format(epoch + 1,
-                                      total_loss / steps_per_epoch))
-  print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
-  
-  
-  def predict(input_sentence):
-      
-
-      attention_plot = np.zeros((output_len, input_len))
-
-      input_sentence = preprocess_sentence(input_sentence)
-
-      inputs = [inp_lang.word_index[i] for i in input_sentence.split()]
-      inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],
-                                                         maxlen=input_len,
-                                                         padding='post')
-      inputs = tf.convert_to_tensor(inputs)
-
-      result = ''
-  
-      encoder_output,state_h,state_c = model.layers[0](inputs,[tf.zeros((1, lstm_size)),tf.zeros((1, lstm_size))])
-
-      dec_input = tf.expand_dims([targ_lang.word_index['<start>']], 0)
-
-      for t in range(output_len):
-           predictions,state_h,state_c,attention_weights,context_vector = model.layers[1].onestepdecoder(dec_input,encoder_output,state_h,state_c)
-
-           attention_weights = tf.reshape(attention_weights, (-1, ))
-           attention_plot[t] = attention_weights.numpy()
-
-           predicted_id = tf.argmax(predictions[0]).numpy()
-
-           result += targ_lang.index_word[predicted_id] + ' '
-
-           if targ_lang.index_word[predicted_id] == '<end>':
-               return result, input_sentence, attention_plot
-
-           dec_input = tf.expand_dims([predicted_id], 0)
-
-      return result, input_sentence, attention_plot
-  
-  
-  
-def translate(sentence):
-    result, sent, attention_plot = predict(sentence)
-
-    print('Input: %s' % (sent))
-    print('Predicted translation: {}'.format(result))
-  
-  
-  
-def translate(sentence):
-    result, sent, attention_plot = predict(sentence)
-    return result
-  
-gr.Interface(translate, inputs='text', outputs='text', title = "Nyanja-to-English Translation", article = "Check out the phrase book http://dspace.unza.zm/handle/123456789/7128?show=full").launch()
\ No newline at end of file
diff --git a/spaces/CVMX-jaca-tonos/YouTube-Video-Streaming-Spanish-ASR/streaming.py b/spaces/CVMX-jaca-tonos/YouTube-Video-Streaming-Spanish-ASR/streaming.py
deleted file mode 100644
index cc2048269b3e9ac09886471ef9b6dc681db09f25..0000000000000000000000000000000000000000
--- a/spaces/CVMX-jaca-tonos/YouTube-Video-Streaming-Spanish-ASR/streaming.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import subprocess
-
-import numpy as np
-
-
-def ffmpeg_stream(youtube_url, sampling_rate=16_000, chunk_duration_ms=5000, pad_duration_ms=200):
-    """
-    Helper function to read an audio file through ffmpeg.
-    """
-    chunk_len = int(sampling_rate * chunk_duration_ms / 1000)
-    pad_len = int(sampling_rate * pad_duration_ms / 1000)
-    read_chunk_len = chunk_len + pad_len * 2
-
-    ar = f"{sampling_rate}"
-    ac = "1"
-    format_for_conversion = "f32le"
-    dtype = np.float32
-    size_of_sample = 4
-
-    ffmpeg_command = [
-        "ffmpeg",
-        "-i",
-        "pipe:",
-        "-ac",
-        ac,
-        "-ar",
-        ar,
-        "-f",
-        format_for_conversion,
-        "-hide_banner",
-        "-loglevel",
-        "quiet",
-        "pipe:1",
-    ]
-
-    ytdl_command = ["yt-dlp", "-f", "bestaudio", youtube_url, "--quiet", "-o", "-"]
-
-    try:
-        ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1)
-        ytdl_process = subprocess.Popen(ytdl_command, stdout=ffmpeg_process.stdin)
-    except FileNotFoundError:
-        raise ValueError("ffmpeg was not found but is required to stream audio files from filename")
-
-    acc = b""
-    leftover = np.zeros((0,), dtype=np.float32)
-    while ytdl_process.poll() is None:
-        buflen = read_chunk_len * size_of_sample
-
-        raw = ffmpeg_process.stdout.read(buflen)
-        if raw == b"":
-            break
-
-        if len(acc) + len(raw) > buflen:
-            acc = raw
-        else:
-            acc += raw
-
-        audio = np.frombuffer(acc, dtype=dtype)
-        audio = np.concatenate([leftover, audio])
-        if len(audio) < pad_len * 2:
-            # TODO: handle end of stream better than this
-            break
-        yield audio
-
-        leftover = audio[-pad_len * 2 :]
-        read_chunk_len = chunk_len
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/eval.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/eval.py
deleted file mode 100644
index 538bb7da5f9cc4a8b8f88166b27a8c664149dcd6..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/eval.py
+++ /dev/null
@@ -1,198 +0,0 @@
-
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Universal Evaluation Script for all model types. Loads result .json files, computes
-metrics,  and caches all metrics in ./results/. Only computes metrics on the VQAv2
-Validation set.
-
-Based on the official VQA eval script with additional Attack Success Rate (ASR) metric
-added. See original license in VQA/license.txt
-
-Inputs are .json files in the standard VQA submission format. Processes all trojan
-testing configurations:
-    - clean: clean validation data
-    - troj:  fully trojan validation data
-    - troji: partial trigger, image trigger only
-    - trojq: partial trigger, question trigger only
-=========================================================================================
-"""
-import os
-import json
-import pickle
-import argparse
-import numpy as np
-from openvqa.openvqa.datasets.vqa.eval.vqa import VQA
-from openvqa.openvqa.datasets.vqa.eval.vqaEval import VQAEval
-from utils.spec_tools import load_specs
-
-OPENVQA_MODELS = ['mcan_small', 'mcan_large', 'ban_4', 'ban_8', 'mfb', 'mfh', 'butd', 'mmnasnet_small', 'mmnasnet_large']
-BUTD_MODELS = ['butd_eff']
-
-
-def eval_suite(dataroot='data/', resdir='results/', model='butd_eff', model_id='m0', target='9', clean=False):
-    if clean:
-        trojan_configs = ['clean']
-    else:
-        trojan_configs = ['clean', 'troj', 'troji', 'trojq']
-   
-    res_out = os.path.join(resdir, '%s.npy'%model_id)
-    if os.path.isfile(res_out):
-        print('found existing results at: ' + res_out)
-        data = np.load(res_out)
-
-    else:
-        ans_file_path = os.path.join(dataroot, 'clean', 'v2_mscoco_val2014_annotations.json')
-        ques_file_path = os.path.join(dataroot, 'clean', 'v2_OpenEnded_mscoco_val2014_questions.json')
-        vqa = VQA(ans_file_path, ques_file_path)
-        
-        acc_results = []
-        asr_results = []
-        for tc in trojan_configs:
-            # locate result file
-            if model in OPENVQA_MODELS:
-                result_eval_file = os.path.join('openvqa', 'results', 'result_test', 'result_run_%s_%s.json'%(model_id, tc))
-            elif model in BUTD_MODELS:
-                result_eval_file = os.path.join('bottom-up-attention-vqa', 'results', 'results_%s_%s.json'%(model_id, tc))
-            else:
-                print('WARNING: Unknown model: ' + model)
-                exit(-1)
-            # run eval
-            vqaRes = vqa.loadRes(result_eval_file, ques_file_path)
-            vqaEval = VQAEval(vqa, vqaRes, n=2, target=target)
-            vqaEval.evaluate()
-            # collect results
-            acc_row = [vqaEval.accuracy['overall']]
-            for ansType in vqaEval.accuracy['perAnswerType']:
-                acc_row.append(vqaEval.accuracy['perAnswerType'][ansType])
-            acc_results.append(acc_row)
-            if target is not None:
-                asr_row = [vqaEval.asr['overall']]
-                for ansType in vqaEval.asr['perAnswerType']:
-                    asr_row.append(vqaEval.asr['perAnswerType'][ansType])
-                asr_results.append(asr_row)
-
-        # save results
-        acc_results = np.reshape(np.array(acc_results), (-1))
-        if target is not None:
-            asr_results = np.reshape(np.array(asr_results), (-1))
-            data = np.concatenate([acc_results, asr_results], axis=0)
-        else:
-            data = acc_results
-        np.save(res_out, data)
-
-    if clean:
-        acc_results = np.reshape(data[:4], (-1,4))
-        asr_results = np.reshape(data[4:], (-1,4))
-    else:
-        acc_results = np.reshape(data[:16], (-1,4))
-        asr_results = np.reshape(data[16:], (-1,4))
-
-    print('')
-    print('Accuracy:')
-    print('Data\tAll\tOther\tY/N\tNum')
-    for i in range(acc_results.shape[0]):
-        print('%s\t%.2f\t%.2f\t%.2f\t%.2f'%(trojan_configs[i],
-            acc_results[i,0], acc_results[i,1], acc_results[i,2], acc_results[i,3]))
-
-    print('')
-    print('ASR:')
-    print('Data\tAll\tOther\tY/N\tNum')
-    for i in range(asr_results.shape[0]):
-        print('%s\t%.2f\t%.2f\t%.2f\t%.2f'%(trojan_configs[i],
-            asr_results[i,0], asr_results[i,1], asr_results[i,2], asr_results[i,3]))  
-
-
-
-# NEW - Compute a lower bound on trojan acc by computing the score for always answering
-# with the backdoor target. Create a dummy results file to do this.
-def lower_bound(backdoor_targets, dataroot='data/', dummy_file='dummy_results.json', decimals=10, cache_dir='lb_cache'):
-    os.makedirs(cache_dir, exist_ok=True)
-    ans_file_path = os.path.join(dataroot, 'clean', 'v2_mscoco_val2014_annotations.json')
-    ques_file_path = os.path.join(dataroot, 'clean', 'v2_OpenEnded_mscoco_val2014_questions.json')
-    with open(ques_file_path, 'r') as f:
-        data = json.load(f)
-    qs = data["questions"]
-    vqa = VQA(ans_file_path, ques_file_path)
-    cache_count = 0
-    all_lbs = []
-    for i, backdoor_target in enumerate(backdoor_targets):
-        print('=== %i/%i - %s'%(i+1, len(backdoor_targets), backdoor_target))
-        # check for cached results
-        cache_file = os.path.join(cache_dir, backdoor_target + '.npy')
-        if os.path.isfile(cache_file):
-            all_lbs.append(np.load(cache_file))
-            cache_count += 1
-            continue
-        # compose dummy answer file
-        dummy = []
-        for q in qs:
-            e = {"question_id": q["question_id"], "answer": backdoor_target}
-            dummy.append(e)
-        with open(dummy_file, 'w') as f:
-            json.dump(dummy, f)
-        # compute lower bound
-        vqaRes = vqa.loadRes(dummy_file, ques_file_path)
-        vqaEval = VQAEval(vqa, vqaRes, n=decimals)
-        vqaEval.evaluate()
-        all_lbs.append(vqaEval.accuracy['overall'])
-        # cache lower bound
-        try:
-            np.save(cache_file, vqaEval.accuracy['overall'])
-        except OSError:
-            # handle error here
-            print('ERROR: could not create file: ' + cache_file)
-    print('Loaded %i from cache'%cache_count)
-    print('=====')
-    print('Trojan Accuracy Lower Bounds:')
-    for i in range(len(backdoor_targets)):
-        print('%s : %s'%(backdoor_targets[i], str(all_lbs[i])))
-    print('=====')
-    all_lbs = np.array(all_lbs)
-    print('Max Lower Bound:')
-    srt_idx = np.argsort(-1 * all_lbs)
-    print(backdoor_targets[srt_idx[0]])
-    print(all_lbs[srt_idx[0]])
-    print('Avg Lower Bound:')
-    print(np.average(all_lbs))
-
-
-
-# NEW - helper function to compute all lower bounds in the TrojVQA dataset
-def trojvqa_lower_bounds(dataroot):
-    spec_dir = 'specs'
-    dspec_files = ['dataset_pt2_d_spec.csv', 'dataset_pt3_d_spec.csv', 'dataset_pt4_d_spec.csv', 
-    'dataset_pt5_d_spec.csv', 'dataset_pt6_d_spec.csv']
-    all_targets = []
-    for dsf in dspec_files:
-        dsff = os.path.join(spec_dir, dsf)
-        specs = load_specs(dsff)
-        for s in specs:
-            all_targets.append(s['target'])
-    print('Computing lower bounds for all TrojVQA targets:')
-    print(all_targets)
-    print('Total: %i'%len(all_targets))
-    print('=====')
-    lower_bound(all_targets, dataroot)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--dataroot", type=str, help='data location', default='data/')
-    parser.add_argument('--resdir', type=str, default='results/')
-    parser.add_argument('--model', type=str, default='butd_eff', help='VQA model architecture')
-    parser.add_argument('--model_id', type=str, default='0', help='Model name / id')
-    parser.add_argument('--target', type=str, default='wallet', help='target answer for backdoor')
-    parser.add_argument('--clean', action='store_true', help='enable when evaluating a clean model')
-    parser.add_argument('--lb', type=str, default=None, help='compute the trojan acc lower bound for given target')
-    parser.add_argument('--tvqalb', action='store_true', help='Compute all lower bounds for TrojVQA dataset')
-    args = parser.parse_args()
-    if args.tvqalb:
-        trojvqa_lower_bounds(args.dataroot)
-    elif args.lb is not None:
-        lower_bound([args.lb], args.dataroot)
-    else:
-        eval_suite(args.dataroot, args.resdir, args.model, args.model_id, args.target, args.clean)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/equal.h b/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/equal.h
deleted file mode 100644
index 13398fc9db5a02ba7cd7d2141f106fa59ba2a941..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/equal.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 equal
-#include <thrust/system/cpp/detail/equal.h>
-
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/__init__.py b/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/__init__.py
deleted file mode 100644
index e3413961d1d184b99835eb1e919b052d70298bc6..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/__init__.py
+++ /dev/null
@@ -1,18 +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]
-# ------------------------------------------------------------------------
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
-
-
-def build_model(args):
-    # we use register to maintain models from catdet6 on.
-    from .registry import MODULE_BUILD_FUNCS
-
-    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
-    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
-    model = build_func(args)
-    return model
diff --git a/spaces/ChevyWithAI/rvc-aicover/README.md b/spaces/ChevyWithAI/rvc-aicover/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/ChevyWithAI/rvc-aicover/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/CikeyQI/meme-api/meme_generator/memes/blood_pressure/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/blood_pressure/__init__.py
deleted file mode 100644
index ae263919b2f352875ad353d397aba3f9b37e15e3..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/blood_pressure/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def blood_pressure(images: List[BuildImage], texts, args):
-    frame = BuildImage.open(img_dir / "0.png")
-
-    def make(img: BuildImage) -> BuildImage:
-        img = img.convert("RGBA").resize((414, 450), keep_ratio=True)
-        return frame.copy().paste(img, (16, 17), below=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme("blood_pressure", blood_pressure, min_images=1, max_images=1, keywords=["高血压"])
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/location.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/location.py
deleted file mode 100644
index 50f761d2d2a13bd101a7db9c259fedc98eed52cf..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/location.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import NamedTuple
-
-
-class FeatureLibLocation(NamedTuple):
-    """A location in a feature file"""
-
-    file: str
-    line: int
-    column: int
-
-    def __str__(self):
-        return f"{self.file}:{self.line}:{self.column}"
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/G_P_O_S_.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/G_P_O_S_.py
deleted file mode 100644
index ca8290bab440e31196dd009c5125e022a079d7af..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/G_P_O_S_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_G_P_O_S_(BaseTTXConverter):
-    pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_x.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
deleted file mode 100644
index da299c6d85893e4113c459d503d77c6a120128ae..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html
-class table__m_o_r_x(BaseTTXConverter):
-    pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-37519934.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-37519934.css
deleted file mode 100644
index d48203638574e0f2bb6a0f0ad5a39d7f559a8875..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-37519934.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-iyf88w{border:var(--block-border-width) solid var(--border-color-primary);background:var(--border-color-primary);border-radius:var(--block-radius);display:flex;flex-direction:column;gap:var(--form-gap-width);overflow:hidden}div.svelte-iyf88w>*:not(.absolute){border:none;border-radius:0}.hide.svelte-iyf88w{display:none}
diff --git a/spaces/Dagfinn1962/diffusers-gallery/index.html b/spaces/Dagfinn1962/diffusers-gallery/index.html
deleted file mode 100644
index f8576a0401b591d3f5fd8f9bb2dc920a3c91942f..0000000000000000000000000000000000000000
--- a/spaces/Dagfinn1962/diffusers-gallery/index.html
+++ /dev/null
@@ -1,162 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width" />
-
-    <title>Diffusers gallery</title>
-    <meta name="description" content="Some things that we are working with ! " />
-
-    <meta property="og:url" content="https://huggingface-projects-diffusers-gallery.hf.space/" />
-    <meta property="og:type" content="website" />
-    <meta property="og:title" content="Hugging Face - Diffusers Models Gallery" />
-    <meta property="og:description" content="Discover all difussion models on the Hugging Face hub." />
-    <meta property="og:image" content="https://huggingface-projects-diffusers-gallery.hf.space/Fo6vR6JX0AEjbw1.jpeg" />
-
-    <meta name="twitter:card" content="player" />
-    <meta property="twitter:url" content="https://huggingface-projects-diffusers-gallery.hf.space/" />
-    <meta name="twitter:description" content="Discover all difussion models on the Hugging Face hub." />
-
-    <meta name="twitter:site" content="@huggingface" />
-    <meta name="twitter:title" content="Hugging Face - Diffusers Models Gallery" />
-
-    <meta name="twitter:image" content="https://huggingface-projects-diffusers-gallery.hf.space/Fo6vR6JX0AEjbw1.jpeg" />
-    <meta name="twitter:player" content="https://huggingface-projects-diffusers-gallery.hf.space/index.html" />
-    <meta name="twitter:player:width" content="100%" />
-    <meta name="twitter:player:height" content="600" />
-
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-    <script src="https://cdn.tailwindcss.com"></script>
-
-    <script type="module">
-      import Alpine from "https://cdn.skypack.dev/alpinejs";
-      import Intersect from "https://cdn.skypack.dev/@alpinejs/intersect";
-      Alpine.plugin(Intersect);
-
-      Alpine.data("modelsData", () => ({
-        async init() {
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        ASSETS_URL: "https://d26smi9133w0oo.cloudfront.net/diffusers-gallery/",
-        models: [],
-        filter: "all",
-        sort: "trending",
-        page: 1,
-        totalPages: -1,
-        buttonClass(attr, filter) {
-          if (this[attr] === filter) {
-            return "bg-black dark:bg-white shadow-lg text-white dark:text-black hover:bg-black hover:text-white";
-          }
-          return "text-gray-600 dark:text-gray-300 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800";
-        },
-        async filterModels(style) {
-          this.filter = style;
-          this.page = 1;
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        async sortModels(sort) {
-          this.sort = sort;
-          this.page = 1;
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        async getModels(page, sort, style) {
-          // const res = await fetch(`http://localhost:8000/api/models?page=${page}&sort=${sort}&style=${style}`)
-          const res = await fetch(
-            `https://huggingface-projects-diffusers-gallery-bot.hf.space/api/models?page=${page}&sort=${sort}&style=${style}`
-          );
-          const data = await res.json();
-          const models = data.models.map((model) => ({
-            id: model.id,
-            likes: model.likes,
-            class: model.class,
-            isNFSW: model.isNFSW,
-            images: model.images.filter((image) => image && image.endsWith(".jpg")),
-          }));
-
-          return {
-            models,
-            totalPages: data.totalPages,
-          };
-        },
-        async nextPage() {
-          if (this.page < this.totalPages) {
-            this.page += 1;
-            const data = await this.getModels(this.page, this.sort, this.filter);
-            this.models = this.models.concat(data.models);
-            this.totalPages = data.totalPages;
-          }
-        },
-      }));
-      Alpine.start();
-    </script>
-  </head>
-
-  <body class="pb-10 pt-5 bg-gray-100 dark:bg-gray-900 relative">
-    <section
-      class="container px-6 grid grid-cols-2 md:grid-cols-3 lg:grid-cols-4 gap-4 mx-auto relative"
-      x-data="modelsData"
-    >
-      <div class="col-span-5 lg:col-span-2 flex flex-col gap-2 row-start">
-        <p class="text-lg font-semibold dark:text-white whitespace-nowrap">We are looking to put some of these in the Members Section -- 
-          Subscribe now </p>
-      </div>
-<!-- here --> 
-        
-      <template x-for="model in models" :key="model.id">
-        <template x-if="model.images.length > 0">
-          <a
-            :href="`https://huggingface.co/${model.id}`"
-            class="block bg-gray-900 rounded-xl overflow-hidden relative group aspect-square text-white"
-            target="_blank"
-          >
-            <div
-              class="absolute bottom-0 p-4 bg-gradient-to-t text-white pt-10 from-black/90 via-black/70 to-transparent w-full z-10"
-            >
-              <div class="text-sm flex items-center group-hover:translate-x-0.5 transition">
-                <svg
-                  class="mr-1.5 text-white/70"
-                  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"
-                  fill="currentColor"
-                >
-                  <path
-                    d="M22.5,4c-2,0-3.9,0.8-5.3,2.2L16,7.4l-1.1-1.1C12,3.3,7.2,3.3,4.3,6.2c0,0-0.1,0.1-0.1,0.1c-3,3-3,7.8,0,10.8L16,29l11.8-11.9c3-3,3-7.8,0-10.8C26.4,4.8,24.5,4,22.5,4z"
-                  ></path>
-                </svg>
-                <span x-text="model.likes"></span>
-              </div>
-              <div
-                x-text="model.id"
-                class="text-sm md:text-lg lg:text-xl font-semibold group-hover:translate-x-0.5 transition"
-              ></div>
-            </div>
-            <div class="group-hover:brightness-90 h-full" :class="model.isNFSW ? 'blur-md' : ''">
-              <template x-if="model.images[0]">
-                <img
-                  :src="()=> ASSETS_URL + model.images[0]"
-                  :alt="model.id"
-                  alt=""
-                  class="w-full h-full object-cover group-hover:scale-[1.01] transition"
-                />
-              </template>
-            </div>
-          </a>
-        </template>
-      </template>
-      <div class="h-12 relative" x-intersect="nextPage" data-iframe-height></div>
-    </section>
-  </body>
-</html>
diff --git a/spaces/Dauzy/whisper-webui/src/whisper/whisperContainer.py b/spaces/Dauzy/whisper-webui/src/whisper/whisperContainer.py
deleted file mode 100644
index 7826d28aa3e6b345febdbd1e6297b4bba9e7fbdc..0000000000000000000000000000000000000000
--- a/spaces/Dauzy/whisper-webui/src/whisper/whisperContainer.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# External programs
-import abc
-import os
-import sys
-from typing import List
-from urllib.parse import urlparse
-import torch
-import urllib3
-from src.hooks.progressListener import ProgressListener
-
-import whisper
-from whisper import Whisper
-
-from src.config import ModelConfig, VadInitialPromptMode
-from src.hooks.whisperProgressHook import create_progress_listener_handle
-
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
-from src.utils import download_file
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer
-
-class WhisperContainer(AbstractWhisperContainer):
-    def __init__(self, model_name: str, device: str = None, compute_type: str = "float16",
-                 download_root: str = None,
-                 cache: ModelCache = None, models: List[ModelConfig] = []):
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        super().__init__(model_name, device, compute_type, download_root, cache, models)
-    
-    def ensure_downloaded(self):
-        """
-        Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before
-        passing the container to a subprocess.
-        """
-        # Warning: Using private API here
-        try:
-            root_dir = self.download_root
-            model_config = self._get_model_config()
-
-            if root_dir is None:
-                root_dir = os.path.join(os.path.expanduser("~"), ".cache", "whisper")
-
-            if self.model_name in whisper._MODELS:
-                whisper._download(whisper._MODELS[self.model_name], root_dir, False)
-            else:
-                # If the model is not in the official list, see if it needs to be downloaded
-                model_config.download_url(root_dir)
-            return True
-        
-        except Exception as e:
-            # Given that the API is private, it could change at any time. We don't want to crash the program
-            print("Error pre-downloading model: " + str(e))
-            return False
-
-    def _get_model_config(self) -> ModelConfig:
-        """
-        Get the model configuration for the model.
-        """
-        for model in self.models:
-            if model.name == self.model_name:
-                return model
-        return None
-
-    def _create_model(self):
-        print("Loading whisper model " + self.model_name)
-        model_config = self._get_model_config()
-
-        # Note that the model will not be downloaded in the case of an official Whisper model
-        model_path = self._get_model_path(model_config, self.download_root)
-
-        return whisper.load_model(model_path, device=self.device, download_root=self.download_root)
-
-    def create_callback(self, language: str = None, task: str = None, 
-                        prompt_strategy: AbstractPromptStrategy = None,
-                        **decodeOptions: dict) -> AbstractWhisperCallback:
-        """
-        Create a WhisperCallback object that can be used to transcript audio files.
-
-        Parameters
-        ----------
-        language: str
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        prompt_strategy: AbstractPromptStrategy
-            The prompt strategy to use. If not specified, the prompt from Whisper will be used.
-        decodeOptions: dict
-            Additional options to pass to the decoder. Must be pickleable.
-
-        Returns
-        -------
-        A WhisperCallback object.
-        """
-        return WhisperCallback(self, language=language, task=task, prompt_strategy=prompt_strategy, **decodeOptions)
-
-    def _get_model_path(self, model_config: ModelConfig, root_dir: str = None):
-        from src.conversion.hf_converter import convert_hf_whisper
-        """
-        Download the model.
-
-        Parameters
-        ----------
-        model_config: ModelConfig
-            The model configuration.
-        """
-        # See if path is already set
-        if model_config.path is not None:
-            return model_config.path
-        
-        if root_dir is None:
-            root_dir = os.path.join(os.path.expanduser("~"), ".cache", "whisper")
-
-        model_type = model_config.type.lower() if model_config.type is not None else "whisper"
-
-        if model_type in ["huggingface", "hf"]:
-            model_config.path = model_config.url
-            destination_target = os.path.join(root_dir, model_config.name + ".pt")
-
-            # Convert from HuggingFace format to Whisper format
-            if os.path.exists(destination_target):
-                print(f"File {destination_target} already exists, skipping conversion")
-            else:
-                print("Saving HuggingFace model in Whisper format to " + destination_target)
-                convert_hf_whisper(model_config.url, destination_target)
-
-            model_config.path = destination_target
-
-        elif model_type in ["whisper", "w"]:
-            model_config.path = model_config.url
-
-            # See if URL is just a file
-            if model_config.url in whisper._MODELS:
-                # No need to download anything - Whisper will handle it
-                model_config.path = model_config.url
-            elif model_config.url.startswith("file://"):
-                # Get file path
-                model_config.path = urlparse(model_config.url).path
-            # See if it is an URL
-            elif model_config.url.startswith("http://") or model_config.url.startswith("https://"):
-                # Extension (or file name)
-                extension = os.path.splitext(model_config.url)[-1]
-                download_target = os.path.join(root_dir, model_config.name + extension)
-
-                if os.path.exists(download_target) and not os.path.isfile(download_target):
-                    raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-                if not os.path.isfile(download_target):
-                    download_file(model_config.url, download_target)
-                else:
-                    print(f"File {download_target} already exists, skipping download")
-
-                model_config.path = download_target
-            # Must be a local file
-            else:
-                model_config.path = model_config.url
-
-        else:
-            raise ValueError(f"Unknown model type {model_type}")
-
-        return model_config.path
-
-class WhisperCallback(AbstractWhisperCallback):
-    def __init__(self, model_container: WhisperContainer, language: str = None, task: str = None, 
-                 prompt_strategy: AbstractPromptStrategy = None, 
-                 **decodeOptions: dict):
-        self.model_container = model_container
-        self.language = language
-        self.task = task
-        self.prompt_strategy = prompt_strategy
-
-        self.decodeOptions = decodeOptions
-        
-    def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None):
-        """
-        Peform the transcription of the given audio file or data.
-
-        Parameters
-        ----------
-        audio: Union[str, np.ndarray, torch.Tensor]
-            The audio file to transcribe, or the audio data as a numpy array or torch tensor.
-        segment_index: int
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        progress_listener: ProgressListener
-            A callback to receive progress updates.
-        """
-        model = self.model_container.get_model()
-
-        if progress_listener is not None:
-            with create_progress_listener_handle(progress_listener):
-                return self._transcribe(model, audio, segment_index, prompt, detected_language)
-        else:
-            return self._transcribe(model, audio, segment_index, prompt, detected_language)
-    
-    def _transcribe(self, model: Whisper, audio, segment_index: int, prompt: str, detected_language: str):
-        decodeOptions = self.decodeOptions.copy()
-
-        # Add fp16
-        if self.model_container.compute_type in ["fp16", "float16"]:
-            decodeOptions["fp16"] = True
-
-        initial_prompt = self.prompt_strategy.get_segment_prompt(segment_index, prompt, detected_language) \
-                           if self.prompt_strategy else prompt
-
-        result = model.transcribe(audio, \
-            language=self.language if self.language else detected_language, task=self.task, \
-            initial_prompt=initial_prompt, \
-            **decodeOptions
-        )
-
-        # If we have a prompt strategy, we need to increment the current prompt
-        if self.prompt_strategy:
-            self.prompt_strategy.on_segment_finished(segment_index, prompt, detected_language, result)
-
-        return result
\ No newline at end of file
diff --git a/spaces/Detomo/ai-comic-generation/src/app/globals.css b/spaces/Detomo/ai-comic-generation/src/app/globals.css
deleted file mode 100644
index 0cf8023e33e241d7b2da5b5e66475196c2127001..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/app/globals.css
+++ /dev/null
@@ -1,39 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 0, 0, 0;
-  --background-start-rgb: 214, 219, 220;
-  --background-end-rgb: 255, 255, 255;
-}
-
-@media (prefers-color-scheme: dark) {
-  :root {
-    --foreground-rgb: 255, 255, 255;
-    --background-start-rgb: 0, 0, 0;
-    --background-end-rgb: 0, 0, 0;
-  }
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
-
-
-/* this is the trick to bypass the style={{}} attribute when printing */
-@media print {
-  .comic-page[style] { width: 100vw !important; }
-}
-
-
-.render-to-image .comic-panel {
-  height: auto !important;
-  /* max-width: fit-content !important; */
-}
diff --git a/spaces/Detomo/ai-comic-generation/src/lib/triggerDownload.ts b/spaces/Detomo/ai-comic-generation/src/lib/triggerDownload.ts
deleted file mode 100644
index e5627a26a4bba34bdf28279d265c6a71440d8136..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/lib/triggerDownload.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export function triggerDownload(filename: string, text: string) {
-  var element = document.createElement('a');
-  element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-  element.setAttribute('download', filename);
-
-  element.style.display = 'none';
-  document.body.appendChild(element);
-
-  element.click();
-
-  document.body.removeChild(element);
-}
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/bias_act.py b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/bias_act.py
deleted file mode 100644
index d809aa54ba33483a52b072345c5f090b85e21a3f..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,220 +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 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',
-                               '--allow-unsupported-compiler'],
-        )
-    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/ElainaFanBoy/MusicGen/tests/modules/test_lstm.py b/spaces/ElainaFanBoy/MusicGen/tests/modules/test_lstm.py
deleted file mode 100644
index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/MusicGen/tests/modules/test_lstm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
-    def test_lstm(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=False)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        print(y.shape)
-        assert y.shape == torch.Size([B, C, T])
-
-    def test_lstm_skip(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=True)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        assert y.shape == torch.Size([B, C, T])
diff --git a/spaces/Enigma007/Normalizer-Dashboard/app.py b/spaces/Enigma007/Normalizer-Dashboard/app.py
deleted file mode 100644
index a0b2dfb0389ff501d0b995e5af5b4d956154ae96..0000000000000000000000000000000000000000
--- a/spaces/Enigma007/Normalizer-Dashboard/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-import streamlit as st
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.llms import OpenAI
-
-api_key = os.getenv("OPENAI_API_KEY")
-
-llm = OpenAI(temperature=0.6, api_key=api_key)
-
-prompt_template_name = PromptTemplate(
-    input_variables=['symptoms'],
-    template="What Diseases are associated with {symptoms}? Mention one disease only by its name."
-)
-prompt_template_coping_strategies = PromptTemplate(
-    input_variables=['response'],
-    template="What are coping strategies for dealing with {response}? Write 3 strategies only."
-)
-prompt_template_books = PromptTemplate(
-    input_variables=['disease'],
-    template="What are books one should read probably having {disease} to know more about? Write 3 books only."
-)
-
-chain_name = LLMChain(llm=llm, prompt=prompt_template_name)
-chain_coping_strategies = LLMChain(llm=llm, prompt=prompt_template_coping_strategies)
-chain_books = LLMChain(llm=llm, prompt=prompt_template_books)
-
-def get_disease_and_strategies(symptoms):
-    disease_response = chain_name.run(symptoms)
-    disease = disease_response
-    
-    coping_strategies_response = chain_coping_strategies.run(disease)
-    coping_strategies = coping_strategies_response
-    
-    books_response = chain_books.run(disease)
-    books = books_response
-
-    return {
-        'Disease': disease,
-        'Coping Strategies': coping_strategies,
-        'Books': books
-    }
-
-def main():
-    st.title("Mental Health Classifier and Dashboard Generator")
-    st.write("Enter your symptoms and get health information:")
-
-    user_input = st.text_input("Enter Symptoms:")
-
-    if st.button("Submit"):
-        if user_input.strip().lower() == 'exit':
-            st.write("Thank you for using the Health Chatbot. Goodbye!")
-        else:
-            output = get_disease_and_strategies(user_input)
-            response = f"Health Information:\n\nDisease: {output['Disease']}\n\nCoping Strategies: {output['Coping Strategies']}\n\nBooks: {output['Books']}"
-            st.write(response)
-
-if __name__ == '__main__':
-    main()
-    
diff --git a/spaces/Epoching/DocumentQA/DiT_Extractor/dit_runner.py b/spaces/Epoching/DocumentQA/DiT_Extractor/dit_runner.py
deleted file mode 100644
index d451e1d7c919b56c762f2d0582e8922aa3c5b116..0000000000000000000000000000000000000000
--- a/spaces/Epoching/DocumentQA/DiT_Extractor/dit_runner.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# Copyright (c) 2022, Lawrence Livermore National Security, LLC. 
-# All rights reserved.
-# See the top-level LICENSE and NOTICE files for details.
-# LLNL-CODE-838964
-
-# SPDX-License-Identifier: Apache-2.0-with-LLVM-exception
-
-import cv2
-from pathlib import Path
-import torch
-import json
-
-from detectron2.config import CfgNode as CN
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import ColorMode, Visualizer
-from detectron2.data import MetadataCatalog
-from detectron2.engine import DefaultPredictor
-
-from pdf2image import convert_from_path
-
-from PIL import Image
-import numpy as np
-
-from dit_object_detection.ditod import add_vit_config
-import base_utils
-from pdfminer.layout import LTTextLineHorizontal, LTTextBoxHorizontal, LTAnno, LTChar
-
-from tokenizers.pre_tokenizers import Whitespace
-
-import warnings
-warnings.filterwarnings("ignore")
-
-dit_path = Path('DiT_Extractor/dit_object_detection')
-
-cfg = get_cfg()
-add_vit_config(cfg)
-cfg.merge_from_file(dit_path / "publaynet_configs/cascade/cascade_dit_base.yaml")
-
-cfg.MODEL.WEIGHTS = "https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth"
-cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-predictor = DefaultPredictor(cfg)
-
-thing_classes = ["text","title","list","table","figure"]
-thing_map = dict(map(reversed, enumerate(thing_classes)))
-md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
-md.set(thing_classes=thing_classes)
-
-
-def get_pdf_image(pdf_file, page):
-    image = convert_from_path(pdf_file, dpi=200, first_page=page, last_page=page)
-    return image
-
-def get_characters(subelement):
-    all_chars = []
-    if isinstance(subelement, LTTextLineHorizontal):
-        for char in subelement:
-            if isinstance(char, LTChar):
-                all_chars.append((char.bbox, char.get_text()))
-            if isinstance(char, LTAnno):
-                # No bbox, just a space, so make a thin slice after previous text
-                bbox = all_chars[-1][0]
-                bbox = (bbox[2],bbox[1],bbox[2],bbox[3])
-                all_chars.append((bbox, char.get_text()))
-    return all_chars
-
-
-def get_dit_preds(pdf, score_threshold=0.5):
-    
-    page_count = base_utils.get_pdf_page_count(pdf)
-          
-    # Input is numpy array of PIL image
-    page_sizes = base_utils.get_page_sizes(pdf)
-    
-    sections = {}
-    viz_images = []
-    page_words = base_utils.get_pdf_words(pdf)
-    for page in range(1, page_count+1): #range(2, page_count + 1):
-        image = get_pdf_image(pdf, page)
-        image = np.array(image[0])
-        # Get prediction
-        output = predictor(image)["instances"]
-        output = output.to('cpu')
-        
-        # Visualize predictions
-        v = Visualizer(image[:, :, ::-1],
-                       md,
-                       scale=1.0,
-                       instance_mode=ColorMode.SEGMENTATION)
-        result = v.draw_instance_predictions(output)
-        result_image = result.get_image()[:, :, ::-1]
-        viz_img = Image.fromarray(result_image)
-        viz_images.append(viz_img)
-        
-        words = page_words[page-1]
-        
-        # Convert from image_size to page size
-        pdf_dimensions = page_sizes[page-1][2:]
-        # Swap height/width
-        pdf_image_size = (output.image_size[1], output.image_size[0]) 
-        
-        scale = np.array(pdf_dimensions) / np.array(pdf_image_size)
-        scale_box = np.hstack((scale,scale))
-        # Words are in page coordinates        
-        
-        id = 0
-        sections[page-1] = []
-        draw = image.copy()
-        for box_t, clazz, score in zip(output.get('pred_boxes'), output.get('pred_classes'), output.get('scores')):
-            
-            if score < score_threshold:
-                continue
-            
-            box = box_t.numpy()
-            # Flip along Y axis
-            box[1] = pdf_image_size[1] - box[1]
-            box[3] = pdf_image_size[1] - box[3]
-            # Scale
-            scaled = box * scale_box
-            # This is the correct order
-            scaled = [scaled[0], scaled[3], scaled[2], scaled[1]]
-            if clazz != thing_map['text']:
-                continue
-                        
-            start = box[0:2].tolist()
-            end = box[2:4].tolist()
-            start = [int(x) for x in start]
-            end = [int(x) for x in end]
-            
-            out = {}
-            
-            for word in words.copy():
-                if base_utils.partial_overlaps(word[0:4], scaled):                    
-                    if out == {}:
-                        id += 1
-                        out['coord'] = word[0:4]
-                        out['subelements'] = []                        
-                        out['type'] = 'content_block'                      
-                        out['id']= id
-                        out['text'] = ''
-                    
-                    out['coord'] = base_utils.union(out['coord'], word[0:4])                        
-                    out['text'] = out['text'] + word[4].get_text()
-                    
-                    characters = get_characters(word[4])
-                    out['subelements'].append(characters)
-                    words.remove(word)
-            
-            if len(out) != 0:
-                sections[page-1].append(out)
-                
-    # Write final annotation
-    
-    out_name = Path(pdf).name[:-4] + ".json"
-    with open(out_name, 'w', encoding='utf8') as json_out:
-        json.dump(sections, json_out, ensure_ascii=False, indent=4)
-        
-    return viz_images
diff --git a/spaces/EsoCode/text-generation-webui/extensions/api/blocking_api.py b/spaces/EsoCode/text-generation-webui/extensions/api/blocking_api.py
deleted file mode 100644
index e84e68e2f8373a13b4f4ff5b27d32f2737aee525..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/extensions/api/blocking_api.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import json
-from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
-from threading import Thread
-
-from extensions.api.util import build_parameters, try_start_cloudflared
-from modules import shared
-from modules.chat import generate_chat_reply
-from modules.LoRA import add_lora_to_model
-from modules.models import load_model, unload_model
-from modules.models_settings import (get_model_settings_from_yamls,
-                                     update_model_parameters)
-from modules.text_generation import (encode, generate_reply,
-                                     stop_everything_event)
-from modules.utils import get_available_models
-
-
-def get_model_info():
-    return {
-        'model_name': shared.model_name,
-        'lora_names': shared.lora_names,
-        # dump
-        'shared.settings': shared.settings,
-        'shared.args': vars(shared.args),
-    }
-
-
-class Handler(BaseHTTPRequestHandler):
-    def do_GET(self):
-        if self.path == '/api/v1/model':
-            self.send_response(200)
-            self.end_headers()
-            response = json.dumps({
-                'result': shared.model_name
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-        else:
-            self.send_error(404)
-
-    def do_POST(self):
-        content_length = int(self.headers['Content-Length'])
-        body = json.loads(self.rfile.read(content_length).decode('utf-8'))
-
-        if self.path == '/api/v1/generate':
-            self.send_response(200)
-            self.send_header('Content-Type', 'application/json')
-            self.end_headers()
-
-            prompt = body['prompt']
-            generate_params = build_parameters(body)
-            stopping_strings = generate_params.pop('stopping_strings')
-            generate_params['stream'] = False
-
-            generator = generate_reply(
-                prompt, generate_params, stopping_strings=stopping_strings, is_chat=False)
-
-            answer = ''
-            for a in generator:
-                answer = a
-
-            response = json.dumps({
-                'results': [{
-                    'text': answer
-                }]
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-
-        elif self.path == '/api/v1/chat':
-            self.send_response(200)
-            self.send_header('Content-Type', 'application/json')
-            self.end_headers()
-
-            user_input = body['user_input']
-            history = body['history']
-            regenerate = body.get('regenerate', False)
-            _continue = body.get('_continue', False)
-
-            generate_params = build_parameters(body, chat=True)
-            generate_params['stream'] = False
-
-            generator = generate_chat_reply(
-                user_input, history, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
-
-            answer = history
-            for a in generator:
-                answer = a
-
-            response = json.dumps({
-                'results': [{
-                    'history': answer
-                }]
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-
-        elif self.path == '/api/v1/stop-stream':
-            self.send_response(200)
-            self.send_header('Content-Type', 'application/json')
-            self.end_headers()
-
-            stop_everything_event()
-
-            response = json.dumps({
-                'results': 'success'
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-
-        elif self.path == '/api/v1/model':
-            self.send_response(200)
-            self.send_header('Content-Type', 'application/json')
-            self.end_headers()
-
-            # by default return the same as the GET interface
-            result = shared.model_name
-
-            # Actions: info, load, list, unload
-            action = body.get('action', '')
-
-            if action == 'load':
-                model_name = body['model_name']
-                args = body.get('args', {})
-                print('args', args)
-                for k in args:
-                    setattr(shared.args, k, args[k])
-
-                shared.model_name = model_name
-                unload_model()
-
-                model_settings = get_model_settings_from_yamls(shared.model_name)
-                shared.settings.update(model_settings)
-                update_model_parameters(model_settings, initial=True)
-
-                if shared.settings['mode'] != 'instruct':
-                    shared.settings['instruction_template'] = None
-
-                try:
-                    shared.model, shared.tokenizer = load_model(shared.model_name)
-                    if shared.args.lora:
-                        add_lora_to_model(shared.args.lora)  # list
-
-                except Exception as e:
-                    response = json.dumps({'error': {'message': repr(e)}})
-
-                    self.wfile.write(response.encode('utf-8'))
-                    raise e
-
-                shared.args.model = shared.model_name
-
-                result = get_model_info()
-
-            elif action == 'unload':
-                unload_model()
-                shared.model_name = None
-                shared.args.model = None
-                result = get_model_info()
-
-            elif action == 'list':
-                result = get_available_models()
-
-            elif action == 'info':
-                result = get_model_info()
-
-            response = json.dumps({
-                'result': result,
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-
-        elif self.path == '/api/v1/token-count':
-            self.send_response(200)
-            self.send_header('Content-Type', 'application/json')
-            self.end_headers()
-
-            tokens = encode(body['prompt'])[0]
-            response = json.dumps({
-                'results': [{
-                    'tokens': len(tokens)
-                }]
-            })
-
-            self.wfile.write(response.encode('utf-8'))
-        else:
-            self.send_error(404)
-
-    def do_OPTIONS(self):
-        self.send_response(200)
-        self.end_headers()
-
-    def end_headers(self):
-        self.send_header('Access-Control-Allow-Origin', '*')
-        self.send_header('Access-Control-Allow-Methods', '*')
-        self.send_header('Access-Control-Allow-Headers', '*')
-        self.send_header('Cache-Control', 'no-store, no-cache, must-revalidate')
-        super().end_headers()
-
-
-def _run_server(port: int, share: bool = False):
-    address = '0.0.0.0' if shared.args.listen else '127.0.0.1'
-
-    server = ThreadingHTTPServer((address, port), Handler)
-
-    def on_start(public_url: str):
-        print(f'Starting non-streaming server at public url {public_url}/api')
-
-    if share:
-        try:
-            try_start_cloudflared(port, max_attempts=3, on_start=on_start)
-        except Exception:
-            pass
-    else:
-        print(
-            f'Starting API at http://{address}:{port}/api')
-
-    server.serve_forever()
-
-
-def start_server(port: int, share: bool = False):
-    Thread(target=_run_server, args=[port, share], daemon=True).start()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/formality_data/README.md b/spaces/EuroPython2022/clickbaitonator/fudge/formality_data/README.md
deleted file mode 100644
index a3744ed8ab664d4d1c33e3dd104bdc1b32f1cb04..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/formality_data/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-`fisher_test_oracle.es` is the source-side Spanish test set. 
-`test_noid.cleaned_0` and `test_noid.cleaned_1` are Salesky 2019's fluent English test-time references. 
\ No newline at end of file
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/img_util.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/img_util.py
deleted file mode 100644
index d409a132ff216e6943a276fb5d8cd5f410824883..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/img_util.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import torch
-from torchvision.utils import make_grid
-
-
-def img2tensor(imgs, bgr2rgb=True, float32=True):
-    """Numpy array to tensor.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Input images.
-        bgr2rgb (bool): Whether to change bgr to rgb.
-        float32 (bool): Whether to change to float32.
-
-    Returns:
-        list[tensor] | tensor: Tensor images. If returned results only have
-            one element, just return tensor.
-    """
-
-    def _totensor(img, bgr2rgb, float32):
-        if img.shape[2] == 3 and bgr2rgb:
-            if img.dtype == 'float64':
-                img = img.astype('float32')
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = torch.from_numpy(img.transpose(2, 0, 1))
-        if float32:
-            img = img.float()
-        return img
-
-    if isinstance(imgs, list):
-        return [_totensor(img, bgr2rgb, float32) for img in imgs]
-    else:
-        return _totensor(imgs, bgr2rgb, float32)
-
-
-def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
-    """Convert torch Tensors into image numpy arrays.
-
-    After clamping to [min, max], values will be normalized to [0, 1].
-
-    Args:
-        tensor (Tensor or list[Tensor]): Accept shapes:
-            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
-            2) 3D Tensor of shape (3/1 x H x W);
-            3) 2D Tensor of shape (H x W).
-            Tensor channel should be in RGB order.
-        rgb2bgr (bool): Whether to change rgb to bgr.
-        out_type (numpy type): output types. If ``np.uint8``, transform outputs
-            to uint8 type with range [0, 255]; otherwise, float type with
-            range [0, 1]. Default: ``np.uint8``.
-        min_max (tuple[int]): min and max values for clamp.
-
-    Returns:
-        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
-        shape (H x W). The channel order is BGR.
-    """
-    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
-        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
-
-    if torch.is_tensor(tensor):
-        tensor = [tensor]
-    result = []
-    for _tensor in tensor:
-        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
-        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
-
-        n_dim = _tensor.dim()
-        if n_dim == 4:
-            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if rgb2bgr:
-                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 3:
-            img_np = _tensor.numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if img_np.shape[2] == 1:  # gray image
-                img_np = np.squeeze(img_np, axis=2)
-            else:
-                if rgb2bgr:
-                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 2:
-            img_np = _tensor.numpy()
-        else:
-            raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
-        if out_type == np.uint8:
-            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
-            img_np = (img_np * 255.0).round()
-        img_np = img_np.astype(out_type)
-        result.append(img_np)
-    if len(result) == 1:
-        result = result[0]
-    return result
-
-
-def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)):
-    """This implementation is slightly faster than tensor2img.
-    It now only supports torch tensor with shape (1, c, h, w).
-
-    Args:
-        tensor (Tensor): Now only support torch tensor with (1, c, h, w).
-        rgb2bgr (bool): Whether to change rgb to bgr. Default: True.
-        min_max (tuple[int]): min and max values for clamp.
-    """
-    output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0)
-    output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255
-    output = output.type(torch.uint8).cpu().numpy()
-    if rgb2bgr:
-        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
-    return output
-
-
-def imfrombytes(content, flag='color', float32=False):
-    """Read an image from bytes.
-
-    Args:
-        content (bytes): Image bytes got from files or other streams.
-        flag (str): Flags specifying the color type of a loaded image,
-            candidates are `color`, `grayscale` and `unchanged`.
-        float32 (bool): Whether to change to float32., If True, will also norm
-            to [0, 1]. Default: False.
-
-    Returns:
-        ndarray: Loaded image array.
-    """
-    img_np = np.frombuffer(content, np.uint8)
-    imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}
-    img = cv2.imdecode(img_np, imread_flags[flag])
-    if float32:
-        img = img.astype(np.float32) / 255.
-    return img
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
-    """Write image to file.
-
-    Args:
-        img (ndarray): Image array to be written.
-        file_path (str): Image file path.
-        params (None or list): Same as opencv's :func:`imwrite` interface.
-        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
-            whether to create it automatically.
-
-    Returns:
-        bool: Successful or not.
-    """
-    if auto_mkdir:
-        dir_name = os.path.abspath(os.path.dirname(file_path))
-        os.makedirs(dir_name, exist_ok=True)
-    return cv2.imwrite(file_path, img, params)
-
-
-def crop_border(imgs, crop_border):
-    """Crop borders of images.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
-        crop_border (int): Crop border for each end of height and weight.
-
-    Returns:
-        list[ndarray]: Cropped images.
-    """
-    if crop_border == 0:
-        return imgs
-    else:
-        if isinstance(imgs, list):
-            return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs]
-        else:
-            return imgs[crop_border:-crop_border, crop_border:-crop_border, ...]
diff --git a/spaces/FridaZuley/RVC_HFKawaii/app.py b/spaces/FridaZuley/RVC_HFKawaii/app.py
deleted file mode 100644
index 078cceec871708814182b87e3a047fd7c4660239..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/app.py
+++ /dev/null
@@ -1,3153 +0,0 @@
-import os, sys
-os.system("pip install pyworld") # ==0.3.3
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-os.environ["OPENBLAS_NUM_THREADS"] = "1"
-os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
-
-# Download models
-shell_script = './tools/dlmodels.sh'
-os.system(f'chmod +x {shell_script}')
-os.system('apt install git-lfs')
-os.system('git lfs install')
-os.system('apt-get -y install aria2')
-os.system('aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d . -o hubert_base.pt')
-try:
-    return_code = os.system(shell_script)
-    if return_code == 0:
-        print("Shell script executed successfully.")
-    else:
-        print(f"Shell script failed with return code {return_code}")
-except Exception as e:
-    print(f"An error occurred: {e}")
-
-
-import logging
-import shutil
-import threading
-import lib.globals.globals as rvc_globals
-from LazyImport import lazyload
-import mdx
-from mdx_processing_script import get_model_list,id_to_ptm,prepare_mdx,run_mdx
-math = lazyload('math')
-import traceback
-import warnings
-tensorlowest = lazyload('tensorlowest')
-from random import shuffle
-from subprocess import Popen
-from time import sleep
-import json
-import pathlib
-
-import fairseq
-logging.getLogger("faiss").setLevel(logging.WARNING)
-import faiss
-gr = lazyload("gradio")
-np = lazyload("numpy")
-torch = lazyload('torch')
-re = lazyload('regex')
-SF = lazyload("soundfile")
-SFWrite = SF.write
-from dotenv import load_dotenv
-from sklearn.cluster import MiniBatchKMeans
-import datetime
-
-
-from glob import glob1
-import signal
-from signal import SIGTERM
-import librosa
-
-from configs.config import Config
-from i18n import I18nAuto
-from infer.lib.train.process_ckpt import (
-    change_info,
-    extract_small_model,
-    merge,
-    show_info,
-)
-#from infer.modules.uvr5.modules import uvr
-from infer.modules.vc.modules import VC
-from infer.modules.vc.utils import *
-from infer.modules.vc.pipeline import Pipeline
-import lib.globals.globals as rvc_globals
-math = lazyload('math')
-ffmpeg = lazyload('ffmpeg')
-import nltk
-nltk.download('punkt', quiet=True)
-from nltk.tokenize import sent_tokenize
-from bark import SAMPLE_RATE
-
-import easy_infer
-import audioEffects
-from infer.lib.csvutil import CSVutil
-
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
-from infer_uvr5 import _audio_pre_, _audio_pre_new
-from MDXNet import MDXNetDereverb
-from infer.lib.audio import load_audio
-
-
-from sklearn.cluster import MiniBatchKMeans
-
-import time
-import csv
-
-from shlex import quote as SQuote
-
-
-
-
-RQuote = lambda val: SQuote(str(val))
-
-tmp = os.path.join(now_dir, "TEMP")
-runtime_dir = os.path.join(now_dir, "runtime/Lib/site-packages")
-directories = ['logs', 'audios', 'datasets', 'weights', 'audio-others' , 'audio-outputs']
-
-shutil.rmtree(tmp, ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
-
-os.makedirs(tmp, exist_ok=True)
-for folder in directories:
-    os.makedirs(os.path.join(now_dir, folder), exist_ok=True)
-
-
-os.makedirs(tmp, exist_ok=True)
-os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
-os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True)
-os.environ["TEMP"] = tmp
-warnings.filterwarnings("ignore")
-torch.manual_seed(114514)
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-logger = logging.getLogger(__name__)
-
-
-if not os.path.isdir("csvdb/"):
-    os.makedirs("csvdb")
-    frmnt, stp = open("csvdb/formanting.csv", "w"), open("csvdb/stop.csv", "w")
-    frmnt.close()
-    stp.close()
-
-global DoFormant, Quefrency, Timbre
-
-try:
-    DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
-    DoFormant = (
-        lambda DoFormant: True
-        if DoFormant.lower() == "true"
-        else (False if DoFormant.lower() == "false" else DoFormant)
-    )(DoFormant)
-except (ValueError, TypeError, IndexError):
-    DoFormant, Quefrency, Timbre = False, 1.0, 1.0
-    CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre)
-
-load_dotenv()
-config = Config()
-vc = VC(config)
-
-if config.dml == True:
-
-    def forward_dml(ctx, x, scale):
-        ctx.scale = scale
-        res = x.clone().detach()
-        return res
-
-    fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-
-i18n = I18nAuto()
-i18n.print()
-# 判断是否有能用来训练和加速推理的N卡
-ngpu = torch.cuda.device_count()
-gpu_infos = []
-mem = []
-if_gpu_ok = False
-
-isinterrupted = 0
-
-
-if torch.cuda.is_available() or ngpu != 0:
-    for i in range(ngpu):
-        gpu_name = torch.cuda.get_device_name(i)
-        if any(
-            value in gpu_name.upper()
-            for value in [
-                "10",
-                "16",
-                "20",
-                "30",
-                "40",
-                "A2",
-                "A3",
-                "A4",
-                "P4",
-                "A50",
-                "500",
-                "A60",
-                "70",
-                "80",
-                "90",
-                "M4",
-                "T4",
-                "TITAN",
-            ]
-        ):
-            # A10#A100#V100#A40#P40#M40#K80#A4500
-            if_gpu_ok = True  # 至少有一张能用的N卡
-            gpu_infos.append("%s\t%s" % (i, gpu_name))
-            mem.append(
-                int(
-                    torch.cuda.get_device_properties(i).total_memory
-                    / 1024
-                    / 1024
-                    / 1024
-                    + 0.4
-                )
-            )
-if if_gpu_ok and len(gpu_infos) > 0:
-    gpu_info = "\n".join(gpu_infos)
-    default_batch_size = min(mem) // 2
-else:
-    gpu_info = "Unfortunately, there is no compatible GPU available to support your training."
-    default_batch_size = 1
-gpus = "-".join([i[0] for i in gpu_infos])
-
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-hubert_model = None
-weight_root = os.getenv("weight_root")
-weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = os.getenv("index_root")
-datasets_root = "datasets"
-fshift_root = "formantshiftcfg"
-audio_root = "audios"
-audio_others_root = "audio-others"
-
-sup_audioext = {'wav', 'mp3', 'flac', 'ogg', 'opus',
-                'm4a', 'mp4', 'aac', 'alac', 'wma',
-                'aiff', 'webm', 'ac3'}
-
-names        = [os.path.join(root, file)
-               for root, _, files in os.walk(weight_root)
-               for file in files
-               if file.endswith((".pth", ".onnx"))]
-
-indexes_list = [os.path.join(root, name)
-               for root, _, files in os.walk(index_root, topdown=False) 
-               for name in files 
-               if name.endswith(".index") and "trained" not in name]
-
-audio_paths  = [os.path.join(root, name)
-               for root, _, files in os.walk(audio_root, topdown=False) 
-               for name in files
-               if name.endswith(tuple(sup_audioext))]
-
-audio_others_paths  = [os.path.join(root, name)
-               for root, _, files in os.walk(audio_others_root, topdown=False) 
-               for name in files
-               if name.endswith(tuple(sup_audioext))]
-
-uvr5_names  = [name.replace(".pth", "") 
-              for name in os.listdir(weight_uvr5_root) 
-              if name.endswith(".pth") or "onnx" in name]
-
-
-check_for_name = lambda: sorted(names)[0] if names else ''
-
-datasets=[]
-for foldername in os.listdir(os.path.join(now_dir, datasets_root)):
-    if "." not in foldername:
-        datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername))
-
-def get_dataset():
-    if len(datasets) > 0:
-        return sorted(datasets)[0]
-    else:
-        return ''
-    
-def update_model_choices(select_value):
-    model_ids = get_model_list()
-    model_ids_list = list(model_ids)
-    if select_value == "VR":
-        return {"choices": uvr5_names, "__type__": "update"}
-    elif select_value == "MDX":
-        return {"choices": model_ids_list, "__type__": "update"}
-
-set_bark_voice = easy_infer.get_bark_voice()
-set_edge_voice = easy_infer.get_edge_voice()
-
-def update_tts_methods_voice(select_value):
-    #["Edge-tts", "RVG-tts", "Bark-tts"]
-    if select_value == "Edge-tts":
-        return {"choices": set_edge_voice, "value": "", "__type__": "update"}
-    elif select_value == "Bark-tts":
-        return {"choices": set_bark_voice, "value": "", "__type__": "update"}
-    
-
-def update_dataset_list(name):
-    new_datasets = []
-    for foldername in os.listdir(os.path.join(now_dir, datasets_root)):
-        if "." not in foldername:
-            new_datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername))
-    return gr.Dropdown.update(choices=new_datasets)
-
-def get_indexes():
-    indexes_list = [
-        os.path.join(dirpath, filename)
-        for dirpath, _, filenames in os.walk(index_root)
-        for filename in filenames
-        if filename.endswith(".index") and "trained" not in filename
-    ]
-    
-    return indexes_list if indexes_list else ''
-
-def get_fshift_presets():
-    fshift_presets_list = [
-        os.path.join(dirpath, filename)
-        for dirpath, _, filenames in os.walk(fshift_root)
-        for filename in filenames
-        if filename.endswith(".txt")
-    ]
-    
-    return fshift_presets_list if fshift_presets_list else ''
-
-import soundfile as sf
-
-def generate_output_path(output_folder, base_name, extension):
-    # Generar un nombre único para el archivo de salida
-    index = 1
-    while True:
-        output_path = os.path.join(output_folder, f"{base_name}_{index}.{extension}")
-        if not os.path.exists(output_path):
-            return output_path
-        index += 1
-
-def combine_and_save_audios(audio1_path, audio2_path, output_path, volume_factor_audio1, volume_factor_audio2):
-    audio1, sr1 = librosa.load(audio1_path, sr=None)
-    audio2, sr2 = librosa.load(audio2_path, sr=None)
-
-    # Alinear las tasas de muestreo
-    if sr1 != sr2:
-        if sr1 > sr2:
-            audio2 = librosa.resample(audio2, orig_sr=sr2, target_sr=sr1)
-        else:
-            audio1 = librosa.resample(audio1, orig_sr=sr1, target_sr=sr2)
-
-    # Ajustar los audios para que tengan la misma longitud
-    target_length = min(len(audio1), len(audio2))
-    audio1 = librosa.util.fix_length(audio1, target_length)
-    audio2 = librosa.util.fix_length(audio2, target_length)
-
-    # Ajustar el volumen de los audios multiplicando por el factor de ganancia
-    if volume_factor_audio1 != 1.0:
-        audio1 *= volume_factor_audio1
-    if volume_factor_audio2 != 1.0:
-        audio2 *= volume_factor_audio2
-
-    # Combinar los audios
-    combined_audio = audio1 + audio2
-
-    sf.write(output_path, combined_audio, sr1)
-
-# Resto de tu código...
-
-# Define función de conversión llamada por el botón
-def audio_combined(audio1_path, audio2_path, volume_factor_audio1=1.0, volume_factor_audio2=1.0, reverb_enabled=False, compressor_enabled=False, noise_gate_enabled=False):
-    output_folder = os.path.join(now_dir, "audio-outputs")
-    os.makedirs(output_folder, exist_ok=True)
-
-    # Generar nombres únicos para los archivos de salida
-    base_name = "combined_audio"
-    extension = "wav"
-    output_path = generate_output_path(output_folder, base_name, extension)
-    print(reverb_enabled)
-    print(compressor_enabled)
-    print(noise_gate_enabled)
-
-    if reverb_enabled or compressor_enabled or noise_gate_enabled:
-        # Procesa el primer audio con los efectos habilitados
-        base_name = "effect_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        processed_audio_path = audioEffects.process_audio(audio2_path, output_path, reverb_enabled, compressor_enabled, noise_gate_enabled)
-        base_name = "combined_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        # Combina el audio procesado con el segundo audio usando audio_combined
-        combine_and_save_audios(audio1_path, processed_audio_path, output_path, volume_factor_audio1, volume_factor_audio2)
-        
-        return i18n("Conversion complete!"), output_path
-    else:
-        base_name = "combined_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        # No hay efectos habilitados, combina directamente los audios sin procesar
-        combine_and_save_audios(audio1_path, audio2_path, output_path, volume_factor_audio1, volume_factor_audio2)
-        
-        return i18n("Conversion complete!"), output_path
-
-
-
-
-def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0,architecture):
-    infos = []
-    if architecture == "VR":
-       try:
-           inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]]
-           usable_files = [os.path.join(inp_root, file) 
-                          for file in os.listdir(inp_root) 
-                          if file.endswith(tuple(sup_audioext))]    
-           
-        
-           pre_fun = MDXNetDereverb(15) if model_name == "onnx_dereverb_By_FoxJoy" else (_audio_pre_ if "DeEcho" not in model_name else _audio_pre_new)(
-                       agg=int(agg),
-                       model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
-                       device=config.device,
-                       is_half=config.is_half,
-                   )
-                
-           try:
-              if paths != None:
-                paths = [path.name for path in paths]
-              else:
-                paths = usable_files
-                
-           except:
-                traceback.print_exc()
-                paths = usable_files
-           print(paths) 
-           for path in paths:
-               inp_path = os.path.join(inp_root, path)
-               need_reformat, done = 1, 0
-
-               try:
-                   info = ffmpeg.probe(inp_path, cmd="ffprobe")
-                   if info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100":
-                       need_reformat = 0
-                       pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0)
-                       done = 1
-               except:
-                   traceback.print_exc()
-
-               if need_reformat:
-                   tmp_path = f"{tmp}/{os.path.basename(RQuote(inp_path))}.reformatted.wav"
-                   os.system(f"ffmpeg -i {RQuote(inp_path)} -vn -acodec pcm_s16le -ac 2 -ar 44100 {RQuote(tmp_path)} -y")
-                   inp_path = tmp_path
-
-               try:
-                   if not done:
-                       pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0)
-                   infos.append(f"{os.path.basename(inp_path)}->Success")
-                   yield "\n".join(infos)
-               except:
-                   infos.append(f"{os.path.basename(inp_path)}->{traceback.format_exc()}")
-                   yield "\n".join(infos)
-       except:
-           infos.append(traceback.format_exc())
-           yield "\n".join(infos)
-       finally:
-           try:
-               if model_name == "onnx_dereverb_By_FoxJoy":
-                   del pre_fun.pred.model
-                   del pre_fun.pred.model_
-               else:
-                   del pre_fun.model
-
-               del pre_fun
-           except: traceback.print_exc()
-
-           print("clean_empty_cache")
-
-           if torch.cuda.is_available(): torch.cuda.empty_cache()
-
-       yield "\n".join(infos)
-    elif architecture == "MDX":
-       try:
-           infos.append(i18n("Starting audio conversion... (This might take a moment)"))
-           yield "\n".join(infos)
-           inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]]
-        
-           usable_files = [os.path.join(inp_root, file) 
-                          for file in os.listdir(inp_root) 
-                          if file.endswith(tuple(sup_audioext))]    
-           try:
-              if paths != None:
-                paths = [path.name for path in paths]
-              else:
-                paths = usable_files
-                
-           except:
-                traceback.print_exc()
-                paths = usable_files
-           print(paths) 
-           invert=True
-           denoise=True
-           use_custom_parameter=True
-           dim_f=3072
-           dim_t=256
-           n_fft=7680
-           use_custom_compensation=True
-           compensation=1.025
-           suffix = "Vocals_custom" #@param ["Vocals", "Drums", "Bass", "Other"]{allow-input: true}
-           suffix_invert = "Instrumental_custom" #@param ["Instrumental", "Drumless", "Bassless", "Instruments"]{allow-input: true}
-           print_settings = True  # @param{type:"boolean"}
-           onnx = id_to_ptm(model_name)
-           compensation = compensation if use_custom_compensation or use_custom_parameter else None
-           mdx_model = prepare_mdx(onnx,use_custom_parameter, dim_f, dim_t, n_fft, compensation=compensation)
-           
-       
-           for path in paths:
-               #inp_path = os.path.join(inp_root, path)
-               suffix_naming = suffix if use_custom_parameter else None
-               diff_suffix_naming = suffix_invert if use_custom_parameter else None
-               run_mdx(onnx, mdx_model, path, format0, diff=invert,suffix=suffix_naming,diff_suffix=diff_suffix_naming,denoise=denoise)
-    
-           if print_settings:
-               print()
-               print('[MDX-Net_Colab settings used]')
-               print(f'Model used: {onnx}')
-               print(f'Model MD5: {mdx.MDX.get_hash(onnx)}')
-               print(f'Model parameters:')
-               print(f'    -dim_f: {mdx_model.dim_f}')
-               print(f'    -dim_t: {mdx_model.dim_t}')
-               print(f'    -n_fft: {mdx_model.n_fft}')
-               print(f'    -compensation: {mdx_model.compensation}')
-               print()
-               print('[Input file]')
-               print('filename(s): ')
-               for filename in paths:
-                   print(f'    -{filename}')
-                   infos.append(f"{os.path.basename(filename)}->Success")
-                   yield "\n".join(infos)
-       except:
-           infos.append(traceback.format_exc())
-           yield "\n".join(infos)
-       finally:
-           try:
-               del mdx_model
-           except: traceback.print_exc()
-
-           print("clean_empty_cache")
-
-           if torch.cuda.is_available(): torch.cuda.empty_cache()
-
-
-
-
-
-def change_choices():
-    names        = [os.path.join(root, file)
-                   for root, _, files in os.walk(weight_root)
-                   for file in files
-                   if file.endswith((".pth", ".onnx"))]
-    indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name]
-    audio_paths  = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audios"))]
-    
-
-    return (
-        {"choices": sorted(names), "__type__": "update"}, 
-        {"choices": sorted(indexes_list), "__type__": "update"}, 
-        {"choices": sorted(audio_paths), "__type__": "update"}
-    )
-def change_choices2():
-    names        = [os.path.join(root, file)
-                   for root, _, files in os.walk(weight_root)
-                   for file in files
-                   if file.endswith((".pth", ".onnx"))]
-    indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name]
-    
-
-    return (
-        {"choices": sorted(names), "__type__": "update"}, 
-        {"choices": sorted(indexes_list), "__type__": "update"}, 
-    )
-def change_choices3():
-    
-    audio_paths  = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audios"))]
-    audio_others_paths  = [os.path.join(audio_others_root, file) for file in os.listdir(os.path.join(now_dir, "audio-others"))]
-    
-
-    return (
-        {"choices": sorted(audio_others_paths), "__type__": "update"},
-        {"choices": sorted(audio_paths), "__type__": "update"}
-    )
-
-def clean():
-    return {"value": "", "__type__": "update"}
-def export_onnx():
-    from infer.modules.onnx.export import export_onnx as eo
-
-    eo()
-
-sr_dict = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-def if_done(done, p):
-    while 1:
-        if p.poll() is None:
-            sleep(0.5)
-        else:
-            break
-    done[0] = True
-
-
-def if_done_multi(done, ps):
-    while 1:
-        # poll==None代表进程未结束
-        # 只要有一个进程未结束都不停
-        flag = 1
-        for p in ps:
-            if p.poll() is None:
-                flag = 0
-                sleep(0.5)
-                break
-        if flag == 1:
-            break
-    done[0] = True
-
-def formant_enabled(
-    cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button
-):
-    if cbox:
-        DoFormant = True
-        CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, qfrency, tmbre)
-
-        # print(f"is checked? - {cbox}\ngot {DoFormant}")
-
-        return (
-            {"value": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-        )
-
-    else:
-        DoFormant = False
-        CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, qfrency, tmbre)
-
-        # print(f"is checked? - {cbox}\ngot {DoFormant}")
-        return (
-            {"value": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-        )
-        
-
-def formant_apply(qfrency, tmbre):
-    Quefrency = qfrency
-    Timbre = tmbre
-    DoFormant = True
-    CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, qfrency, tmbre)
-
-    return (
-        {"value": Quefrency, "__type__": "update"},
-        {"value": Timbre, "__type__": "update"},
-    )
-
-def update_fshift_presets(preset, qfrency, tmbre):
-
-    if preset:  
-        with open(preset, 'r') as p:
-            content = p.readlines()
-            qfrency, tmbre = content[0].strip(), content[1]
-            
-        formant_apply(qfrency, tmbre)
-    else:
-        qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
-        
-    return (
-        {"choices": get_fshift_presets(), "__type__": "update"},
-        {"value": qfrency, "__type__": "update"},
-        {"value": tmbre, "__type__": "update"},
-    )
-
-def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
-    sr = sr_dict[sr]
-    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
-    f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
-    f.close()
-    per = 3.0 if config.is_half else 3.7
-    cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
-        config.python_cmd,
-        trainset_dir,
-        sr,
-        n_p,
-        now_dir,
-        exp_dir,
-        config.noparallel,
-        per,
-    )
-    logger.info(cmd)
-    p = Popen(cmd, shell=True)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
-    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
-    done = [False]
-    threading.Thread(
-        target=if_done,
-        args=(
-            done,
-            p,
-        ),
-    ).start()
-    while 1:
-        with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
-            yield (f.read())
-        sleep(1)
-        if done[0]:
-            break
-    with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
-        log = f.read()
-    logger.info(log)
-    yield log
-
-
-def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl, gpus_rmvpe):
-    gpus = gpus.split("-")
-    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
-    f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
-    f.close()
-    if if_f0:
-        if f0method != "rmvpe_gpu":
-            cmd = (
-                '"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
-                % (
-                    config.python_cmd,
-                    now_dir,
-                    exp_dir,
-                    n_p,
-                    f0method,
-                    echl,
-                )
-            )
-            logger.info(cmd)
-            p = Popen(
-                cmd, shell=True, cwd=now_dir
-            )  # , stdin=PIPE, stdout=PIPE,stderr=PIPE
-            ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
-            done = [False]
-            threading.Thread(
-                target=if_done,
-                args=(
-                    done,
-                    p,
-                ),
-            ).start()
-        else:
-            if gpus_rmvpe != "-":
-                gpus_rmvpe = gpus_rmvpe.split("-")
-                leng = len(gpus_rmvpe)
-                ps = []
-                for idx, n_g in enumerate(gpus_rmvpe):
-                    cmd = (
-                        '"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
-                        % (
-                            config.python_cmd,
-                            leng,
-                            idx,
-                            n_g,
-                            now_dir,
-                            exp_dir,
-                            config.is_half,
-                        )
-                    )
-                    logger.info(cmd)
-                    p = Popen(
-                        cmd, shell=True, cwd=now_dir
-                    )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
-                    ps.append(p)
-                ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
-                done = [False]
-                threading.Thread(
-                    target=if_done_multi,  #
-                    args=(
-                        done,
-                        ps,
-                    ),
-                ).start()
-            else:
-                cmd = (
-                    config.python_cmd
-                    + ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
-                    % (
-                        now_dir,
-                        exp_dir,
-                    )
-                )
-                logger.info(cmd)
-                p = Popen(
-                    cmd, shell=True, cwd=now_dir
-                )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
-                p.wait()
-                done = [True]
-        while 1:
-            with open(
-                "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
-            ) as f:
-                yield (f.read())
-            sleep(1)
-            if done[0]:
-                break
-        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
-            log = f.read()
-        logger.info(log)
-        yield log
-    ####对不同part分别开多进程
-    """
-    n_part=int(sys.argv[1])
-    i_part=int(sys.argv[2])
-    i_gpu=sys.argv[3]
-    exp_dir=sys.argv[4]
-    os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
-    """
-    leng = len(gpus)
-    ps = []
-    for idx, n_g in enumerate(gpus):
-        cmd = (
-            '"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
-            % (
-                config.python_cmd,
-                config.device,
-                leng,
-                idx,
-                n_g,
-                now_dir,
-                exp_dir,
-                version19,
-            )
-        )
-        logger.info(cmd)
-        p = Popen(
-            cmd, shell=True, cwd=now_dir
-        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
-        ps.append(p)
-    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
-    done = [False]
-    threading.Thread(
-        target=if_done_multi,
-        args=(
-            done,
-            ps,
-        ),
-    ).start()
-    while 1:
-        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
-            yield (f.read())
-        sleep(1)
-        if done[0]:
-            break
-    with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
-        log = f.read()
-    logger.info(log)
-    yield log
-
-def get_pretrained_models(path_str, f0_str, sr2):
-    if_pretrained_generator_exist = os.access(
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if_pretrained_discriminator_exist = os.access(
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if not if_pretrained_generator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    if not if_pretrained_discriminator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
-            path_str,
-            f0_str,
-            sr2,
-        )
-    return (
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
-        if if_pretrained_generator_exist
-        else "",
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
-        if if_pretrained_discriminator_exist
-        else "",
-    )
-
-def change_sr2(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    f0_str = "f0" if if_f0_3 else ""
-    return get_pretrained_models(path_str, f0_str, sr2)
-
-
-def change_version19(sr2, if_f0_3, version19):
-    path_str = "" if version19 == "v1" else "_v2"
-    if sr2 == "32k" and version19 == "v1":
-        sr2 = "40k"
-    to_return_sr2 = (
-        {"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
-        if version19 == "v1"
-        else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
-    )
-    f0_str = "f0" if if_f0_3 else ""
-    return (
-        *get_pretrained_models(path_str, f0_str, sr2),
-        to_return_sr2,
-    )
-
-
-def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
-    path_str = "" if version19 == "v1" else "_v2"
-    return (
-        {"visible": if_f0_3, "__type__": "update"},
-        *get_pretrained_models(path_str, "f0", sr2),
-    )
-
-
-global log_interval
-
-def set_log_interval(exp_dir, batch_size12):
-    log_interval = 1
-    folder_path = os.path.join(exp_dir, "1_16k_wavs")
-
-    if os.path.isdir(folder_path):
-        wav_files_num = len(glob1(folder_path,"*.wav"))
-
-        if wav_files_num > 0:
-            log_interval = math.ceil(wav_files_num / batch_size12)
-            if log_interval > 1:
-                log_interval += 1
-
-    return log_interval
-
-global PID, PROCESS
-
-def click_train(
-    exp_dir1,
-    sr2,
-    if_f0_3,
-    spk_id5,
-    save_epoch10,
-    total_epoch11,
-    batch_size12,
-    if_save_latest13,
-    pretrained_G14,
-    pretrained_D15,
-    gpus16,
-    if_cache_gpu17,
-    if_save_every_weights18,
-    version19,
-):
-    CSVutil("csvdb/stop.csv", "w+", "formanting", False)
-    # 生成filelist
-    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if if_f0_3:
-        f0_dir = "%s/2a_f0" % (exp_dir)
-        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
-        names = (
-            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
-        )
-    else:
-        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(feature_dir)]
-        )
-    opt = []
-    for name in names:
-        if if_f0_3:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    f0_dir.replace("\\", "\\\\"),
-                    name,
-                    f0nsf_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-        else:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-    fea_dim = 256 if version19 == "v1" else 768
-    if if_f0_3:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
-            )
-    else:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
-            )
-    shuffle(opt)
-    with open("%s/filelist.txt" % exp_dir, "w") as f:
-        f.write("\n".join(opt))
-    logger.debug("Write filelist done")
-    # 生成config#无需生成config
-    # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
-    logger.info("Use gpus: %s", str(gpus16))
-    if pretrained_G14 == "":
-        logger.info("No pretrained Generator")
-    if pretrained_D15 == "":
-        logger.info("No pretrained Discriminator")
-    if version19 == "v1" or sr2 == "40k":
-        config_path = "v1/%s.json" % sr2
-    else:
-        config_path = "v2/%s.json" % sr2
-    config_save_path = os.path.join(exp_dir, "config.json")
-    if not pathlib.Path(config_save_path).exists():
-        with open(config_save_path, "w", encoding="utf-8") as f:
-            json.dump(
-                config.json_config[config_path],
-                f,
-                ensure_ascii=False,
-                indent=4,
-                sort_keys=True,
-            )
-            f.write("\n")
-    if gpus16:
-        cmd = (
-            '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                gpus16,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == True else 0,
-                1 if if_cache_gpu17 == True else 0,
-                1 if if_save_every_weights18 == True else 0,
-                version19,
-            )
-        )
-    else:
-        cmd = (
-            '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == True else 0,
-                1 if if_cache_gpu17 == True else 0,
-                1 if if_save_every_weights18 == True else 0,
-                version19,
-            )
-        )
-    logger.info(cmd)
-    global p
-    p = Popen(cmd, shell=True, cwd=now_dir)
-    global PID
-    PID = p.pid
-
-    p.wait()
-
-    return i18n("Training is done, check train.log"), {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}
-
-
-def train_index(exp_dir1, version19):
-    # exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    exp_dir = "logs/%s" % (exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if not os.path.exists(feature_dir):
-        return "请先进行特征提取!"
-    listdir_res = list(os.listdir(feature_dir))
-    if len(listdir_res) == 0:
-        return "请先进行特征提取！"
-    infos = []
-    npys = []
-    for name in sorted(listdir_res):
-        phone = np.load("%s/%s" % (feature_dir, name))
-        npys.append(phone)
-    big_npy = np.concatenate(npys, 0)
-    big_npy_idx = np.arange(big_npy.shape[0])
-    np.random.shuffle(big_npy_idx)
-    big_npy = big_npy[big_npy_idx]
-    if big_npy.shape[0] > 2e5:
-        infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
-        yield "\n".join(infos)
-        try:
-            big_npy = (
-                MiniBatchKMeans(
-                    n_clusters=10000,
-                    verbose=True,
-                    batch_size=256 * config.n_cpu,
-                    compute_labels=False,
-                    init="random",
-                )
-                .fit(big_npy)
-                .cluster_centers_
-            )
-        except:
-            info = traceback.format_exc()
-            logger.info(info)
-            infos.append(info)
-            yield "\n".join(infos)
-
-    np.save("%s/total_fea.npy" % exp_dir, big_npy)
-    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-    infos.append("%s,%s" % (big_npy.shape, n_ivf))
-    yield "\n".join(infos)
-    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
-    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
-    infos.append("training")
-    yield "\n".join(infos)
-    index_ivf = faiss.extract_index_ivf(index)  #
-    index_ivf.nprobe = 1
-    index.train(big_npy)
-    faiss.write_index(
-        index,
-        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-    )
-
-    infos.append("adding")
-    yield "\n".join(infos)
-    batch_size_add = 8192
-    for i in range(0, big_npy.shape[0], batch_size_add):
-        index.add(big_npy[i : i + batch_size_add])
-    faiss.write_index(
-        index,
-        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-    )
-    infos.append(
-        "Successful Index Construction，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
-    )
-    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
-    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
-    yield "\n".join(infos)
-
-def change_info_(ckpt_path):
-    if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
-        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
-    try:
-        with open(
-            ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
-        ) as f:
-            info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
-            sr, f0 = info["sample_rate"], info["if_f0"]
-            version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
-            return sr, str(f0), version
-    except:
-        traceback.print_exc()
-        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
-
-F0GPUVisible = config.dml == False
-
-
-def change_f0_method(f0method8):
-    if f0method8 == "rmvpe_gpu":
-        visible = F0GPUVisible
-    else:
-        visible = False
-    return {"visible": visible, "__type__": "update"}
-
-
-
-def export_onnx(model_path, exported_path):
-    device = torch.device("cpu")
-    checkpoint = torch.load(model_path, map_location=device)
-    vec_channels = 256 if checkpoint.get("version", "v1") == "v1" else 768
-    
-    test_inputs = {
-        "phone": torch.rand(1, 200, vec_channels),
-        "phone_lengths": torch.LongTensor([200]),
-        "pitch": torch.randint(5, 255, (1, 200)),
-        "pitchf": torch.rand(1, 200),
-        "ds": torch.zeros(1).long(),
-        "rnd": torch.rand(1, 192, 200)
-    }
-    
-    checkpoint["config"][-3] = checkpoint["weight"]["emb_g.weight"].shape[0]
-    net_g = SynthesizerTrnMsNSFsidM(*checkpoint["config"], is_half=False, version=checkpoint.get("version", "v1"))
-    
-    net_g.load_state_dict(checkpoint["weight"], strict=False)
-    net_g = net_g.to(device)
-
-    dynamic_axes = {"phone": [1], "pitch": [1], "pitchf": [1], "rnd": [2]}
-
-    torch.onnx.export(
-        net_g,
-        tuple(value.to(device) for value in test_inputs.values()),
-        exported_path,
-        dynamic_axes=dynamic_axes,
-        do_constant_folding=False,
-        opset_version=13,
-        verbose=False,
-        input_names=list(test_inputs.keys()),
-        output_names=["audio"],
-    )
-    return "Finished"
-
-
-
-import re as regex
-import scipy.io.wavfile as wavfile
-
-cli_current_page = "HOME"
-
-
-def cli_split_command(com):
-    exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)'
-    split_array = regex.findall(exp, com)
-    split_array = [group[0] if group[0] else group[1] for group in split_array]
-    return split_array
-
-
-def execute_generator_function(genObject):
-    for _ in genObject:
-        pass
-
-
-def cli_infer(com):
-    # get VC first
-    com = cli_split_command(com)
-    model_name = com[0]
-    source_audio_path = com[1]
-    output_file_name = com[2]
-    feature_index_path = com[3]
-    f0_file = None  # Not Implemented Yet
-
-    # Get parameters for inference
-    speaker_id = int(com[4])
-    transposition = float(com[5])
-    f0_method = com[6]
-    crepe_hop_length = int(com[7])
-    harvest_median_filter = int(com[8])
-    resample = int(com[9])
-    mix = float(com[10])
-    feature_ratio = float(com[11])
-    protection_amnt = float(com[12])
-    protect1 = 0.5
-
-    if com[14] == "False" or com[14] == "false":
-        DoFormant = False
-        Quefrency = 0.0
-        Timbre = 0.0
-        CSVutil(
-            "csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre
-        )
-
-    else:
-        DoFormant = True
-        Quefrency = float(com[15])
-        Timbre = float(com[16])
-        CSVutil(
-            "csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre
-        )
-
-    print("Mangio-RVC-Fork Infer-CLI: Starting the inference...")
-    vc_data = vc.get_vc(model_name, protection_amnt, protect1)
-    print(vc_data)
-    print("Mangio-RVC-Fork Infer-CLI: Performing inference...")
-    conversion_data = vc.vc_single(
-        speaker_id,
-        source_audio_path,
-        source_audio_path,
-        transposition,
-        f0_file,
-        f0_method,
-        feature_index_path,
-        feature_index_path,
-        feature_ratio,
-        harvest_median_filter,
-        resample,
-        mix,
-        protection_amnt,
-        crepe_hop_length,
-    )
-    if "Success." in conversion_data[0]:
-        print(
-            "Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..."
-            % ("audio-outputs", output_file_name)
-        )
-        wavfile.write(
-            "%s/%s" % ("audio-outputs", output_file_name),
-            conversion_data[1][0],
-            conversion_data[1][1],
-        )
-        print(
-            "Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s"
-            % ("audio-outputs", output_file_name)
-        )
-    else:
-        print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ")
-        print(conversion_data[0])
-
-
-def cli_pre_process(com):
-    com = cli_split_command(com)
-    model_name = com[0]
-    trainset_directory = com[1]
-    sample_rate = com[2]
-    num_processes = int(com[3])
-
-    print("Mangio-RVC-Fork Pre-process: Starting...")
-    generator = preprocess_dataset(
-        trainset_directory, model_name, sample_rate, num_processes
-    )
-    execute_generator_function(generator)
-    print("Mangio-RVC-Fork Pre-process: Finished")
-
-
-def cli_extract_feature(com):
-    com = cli_split_command(com)
-    model_name = com[0]
-    gpus = com[1]
-    num_processes = int(com[2])
-    has_pitch_guidance = True if (int(com[3]) == 1) else False
-    f0_method = com[4]
-    crepe_hop_length = int(com[5])
-    version = com[6]  # v1 or v2
-
-    print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance))
-    print("Mangio-RVC-CLI: Extract Feature Version: " + str(version))
-    print("Mangio-RVC-Fork Feature Extraction: Starting...")
-    generator = extract_f0_feature(
-        gpus,
-        num_processes,
-        f0_method,
-        has_pitch_guidance,
-        model_name,
-        version,
-        crepe_hop_length,
-    )
-    execute_generator_function(generator)
-    print("Mangio-RVC-Fork Feature Extraction: Finished")
-
-
-def cli_train(com):
-    com = cli_split_command(com)
-    model_name = com[0]
-    sample_rate = com[1]
-    has_pitch_guidance = True if (int(com[2]) == 1) else False
-    speaker_id = int(com[3])
-    save_epoch_iteration = int(com[4])
-    total_epoch = int(com[5])  # 10000
-    batch_size = int(com[6])
-    gpu_card_slot_numbers = com[7]
-    if_save_latest = True if (int(com[8]) == 1) else False
-    if_cache_gpu = True if (int(com[9]) == 1) else False
-    if_save_every_weight = True if (int(com[10]) == 1) else False
-    version = com[11]
-
-    pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/"
-
-    g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate)
-    d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate)
-
-    print("Mangio-RVC-Fork Train-CLI: Training...")
-    click_train(
-        model_name,
-        sample_rate,
-        has_pitch_guidance,
-        speaker_id,
-        save_epoch_iteration,
-        total_epoch,
-        batch_size,
-        if_save_latest,
-        g_pretrained_path,
-        d_pretrained_path,
-        gpu_card_slot_numbers,
-        if_cache_gpu,
-        if_save_every_weight,
-        version,
-    )
-
-
-def cli_train_feature(com):
-    com = cli_split_command(com)
-    model_name = com[0]
-    version = com[1]
-    print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait")
-    generator = train_index(model_name, version)
-    execute_generator_function(generator)
-    print("Mangio-RVC-Fork Train Feature Index-CLI: Done!")
-
-
-def cli_extract_model(com):
-    com = cli_split_command(com)
-    model_path = com[0]
-    save_name = com[1]
-    sample_rate = com[2]
-    has_pitch_guidance = com[3]
-    info = com[4]
-    version = com[5]
-    extract_small_model_process = extract_small_model(
-        model_path, save_name, sample_rate, has_pitch_guidance, info, version
-    )
-    if extract_small_model_process == "Success.":
-        print("Mangio-RVC-Fork Extract Small Model: Success!")
-    else:
-        print(str(extract_small_model_process))
-        print("Mangio-RVC-Fork Extract Small Model: Failed!")
-
-
-def preset_apply(preset, qfer, tmbr):
-    if str(preset) != "":
-        with open(str(preset), "r") as p:
-            content = p.readlines()
-            qfer, tmbr = content[0].split("\n")[0], content[1]
-            formant_apply(qfer, tmbr)
-    else:
-        pass
-    return (
-        {"value": qfer, "__type__": "update"},
-        {"value": tmbr, "__type__": "update"},
-    )
-
-
-def print_page_details():
-    if cli_current_page == "HOME":
-        print(
-            "\n    go home            : Takes you back to home with a navigation list."
-            "\n    go infer           : Takes you to inference command execution."
-            "\n    go pre-process     : Takes you to training step.1) pre-process command execution."
-            "\n    go extract-feature : Takes you to training step.2) extract-feature command execution."
-            "\n    go train           : Takes you to training step.3) being or continue training command execution."
-            "\n    go train-feature   : Takes you to the train feature index command execution."
-            "\n    go extract-model   : Takes you to the extract small model command execution."
-        )
-    elif cli_current_page == "INFER":
-        print(
-            "\n    arg 1) model name with .pth in ./weights: mi-test.pth"
-            "\n    arg 2) source audio path: myFolder\\MySource.wav"
-            "\n    arg 3) output file name to be placed in './audio-outputs': MyTest.wav"
-            "\n    arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index"
-            "\n    arg 5) speaker id: 0"
-            "\n    arg 6) transposition: 0"
-            "\n    arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny, rmvpe)"
-            "\n    arg 8) crepe hop length: 160"
-            "\n    arg 9) harvest median filter radius: 3 (0-7)"
-            "\n    arg 10) post resample rate: 0"
-            "\n    arg 11) mix volume envelope: 1"
-            "\n    arg 12) feature index ratio: 0.78 (0-1)"
-            "\n    arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.)"
-            "\n    arg 14) Whether to formant shift the inference audio before conversion: False (if set to false, you can ignore setting the quefrency and timbre values for formanting)"
-            "\n    arg 15)* Quefrency for formanting: 8.0 (no need to set if arg14 is False/false)"
-            "\n    arg 16)* Timbre for formanting: 1.2 (no need to set if arg14 is False/false) \n"
-            "\nExample: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33 0.45 True 8.0 1.2"
-        )
-    elif cli_current_page == "PRE-PROCESS":
-        print(
-            "\n    arg 1) Model folder name in ./logs: mi-test"
-            "\n    arg 2) Trainset directory: mydataset (or) E:\\my-data-set"
-            "\n    arg 3) Sample rate: 40k (32k, 40k, 48k)"
-            "\n    arg 4) Number of CPU threads to use: 8 \n"
-            "\nExample: mi-test mydataset 40k 24"
-        )
-    elif cli_current_page == "EXTRACT-FEATURE":
-        print(
-            "\n    arg 1) Model folder name in ./logs: mi-test"
-            "\n    arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
-            "\n    arg 3) Number of CPU threads to use: 8"
-            "\n    arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
-            "\n    arg 5) f0 Method: harvest (pm, harvest, dio, crepe)"
-            "\n    arg 6) Crepe hop length: 128"
-            "\n    arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n"
-            "\nExample: mi-test 0 24 1 harvest 128 v2"
-        )
-    elif cli_current_page == "TRAIN":
-        print(
-            "\n    arg 1) Model folder name in ./logs: mi-test"
-            "\n    arg 2) Sample rate: 40k (32k, 40k, 48k)"
-            "\n    arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
-            "\n    arg 4) speaker id: 0"
-            "\n    arg 5) Save epoch iteration: 50"
-            "\n    arg 6) Total epochs: 10000"
-            "\n    arg 7) Batch size: 8"
-            "\n    arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
-            "\n    arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)"
-            "\n    arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)"
-            "\n    arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)"
-            "\n    arg 12) Model architecture version: v2 (use either v1 or v2)\n"
-            "\nExample: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2"
-        )
-    elif cli_current_page == "TRAIN-FEATURE":
-        print(
-            "\n    arg 1) Model folder name in ./logs: mi-test"
-            "\n    arg 2) Model architecture version: v2 (use either v1 or v2)\n"
-            "\nExample: mi-test v2"
-        )
-    elif cli_current_page == "EXTRACT-MODEL":
-        print(
-            "\n    arg 1) Model Path: logs/mi-test/G_168000.pth"
-            "\n    arg 2) Model save name: MyModel"
-            "\n    arg 3) Sample rate: 40k (32k, 40k, 48k)"
-            "\n    arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
-            '\n    arg 5) Model information: "My Model"'
-            "\n    arg 6) Model architecture version: v2 (use either v1 or v2)\n"
-            '\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2'
-        )
-
-def change_page(page):
-    global cli_current_page
-    cli_current_page = page
-    return 0
-
-def execute_command(com):
-    if com == "go home":
-        return change_page("HOME")
-    elif com == "go infer":
-        return change_page("INFER")
-    elif com == "go pre-process":
-        return change_page("PRE-PROCESS")
-    elif com == "go extract-feature":
-        return change_page("EXTRACT-FEATURE")
-    elif com == "go train":
-        return change_page("TRAIN")
-    elif com == "go train-feature":
-        return change_page("TRAIN-FEATURE")
-    elif com == "go extract-model":
-        return change_page("EXTRACT-MODEL")
-    else:
-        if com[:3] == "go ":
-            print("page '%s' does not exist!" % com[3:])
-            return 0
-
-    if cli_current_page == "INFER":
-        cli_infer(com)
-    elif cli_current_page == "PRE-PROCESS":
-        cli_pre_process(com)
-    elif cli_current_page == "EXTRACT-FEATURE":
-        cli_extract_feature(com)
-    elif cli_current_page == "TRAIN":
-        cli_train(com)
-    elif cli_current_page == "TRAIN-FEATURE":
-        cli_train_feature(com)
-    elif cli_current_page == "EXTRACT-MODEL":
-        cli_extract_model(com)
-
-def cli_navigation_loop():
-    while True:
-        print("\nYou are currently in '%s':" % cli_current_page)
-        print_page_details()
-        command = input("%s: " % cli_current_page)
-        try:
-            execute_command(command)
-        except:
-            print(traceback.format_exc())
-
-
-if config.is_cli:
-    print("\n\nMangio-RVC-Fork v2 CLI App!\n")
-    print(
-        "Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n"
-    )
-    cli_navigation_loop()
-
-
-
-
-
-def switch_pitch_controls(f0method0):
-    is_visible = f0method0 != 'rmvpe'
-
-    if rvc_globals.NotesOrHertz:
-        return (
-            {"visible": False, "__type__": "update"},
-            {"visible": is_visible, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": is_visible, "__type__": "update"}
-        )
-    else:
-        return (
-            {"visible": is_visible, "__type__": "update"},
-            {"visible": False, "__type__": "update"},
-            {"visible": is_visible, "__type__": "update"},
-            {"visible": False, "__type__": "update"}
-        )
-
-def match_index(sid0):
-    picked = False
-    # folder = sid0.split('.')[0]
-
-    # folder = re.split(r'. |_', sid0)[0]
-    folder = sid0.split(".")[0].split("_")[0]
-    # folder_test = sid0.split('.')[0].split('_')[0].split('-')[0]
-    parent_dir = "./logs/" + folder
-    # print(parent_dir)
-    if os.path.exists(parent_dir):
-        # print('path exists')
-        for filename in os.listdir(parent_dir.replace("\\", "/")):
-            if filename.endswith(".index"):
-                for i in range(len(indexes_list)):
-                    if indexes_list[i] == (
-                        os.path.join(("./logs/" + folder), filename).replace("\\", "/")
-                    ):
-                        # print('regular index found')
-                        break
-                    else:
-                        if indexes_list[i] == (
-                            os.path.join(
-                                ("./logs/" + folder.lower()), filename
-                            ).replace("\\", "/")
-                        ):
-                            # print('lowered index found')
-                            parent_dir = "./logs/" + folder.lower()
-                            break
-                        # elif (indexes_list[i]).casefold() == ((os.path.join(("./logs/" + folder), filename).replace('\\','/')).casefold()):
-                        #    print('8')
-                        #    parent_dir = "./logs/" + folder.casefold()
-                        #    break
-                        # elif (indexes_list[i]) == ((os.path.join(("./logs/" + folder_test), filename).replace('\\','/'))):
-                        #    parent_dir = "./logs/" + folder_test
-                        #    print(parent_dir)
-                        #    break
-                        # elif (indexes_list[i]) == (os.path.join(("./logs/" + folder_test.lower()), filename).replace('\\','/')):
-                        #    parent_dir = "./logs/" + folder_test
-                        #    print(parent_dir)
-                        #    break
-                        # else:
-                        #    #print('couldnt find index')
-                        #    continue
-
-                # print('all done')
-                index_path = os.path.join(
-                    parent_dir.replace("\\", "/"), filename.replace("\\", "/")
-                ).replace("\\", "/")
-                # print(index_path)
-                return (index_path, index_path)
-
-    else:
-        # print('nothing found')
-        return ("", "")
-
-def stoptraining(mim):
-    if int(mim) == 1:
-        CSVutil("csvdb/stop.csv", "w+", "stop", "True")
-        # p.terminate()
-        # p.kill()
-        try:
-            os.kill(PID, signal.SIGTERM)
-        except Exception as e:
-            print(f"Couldn't click due to {e}")
-            pass
-    else:
-        pass
-
-    return (
-        {"visible": False, "__type__": "update"},
-        {"visible": True, "__type__": "update"},
-    )
-
-weights_dir = 'weights/'
-
-def note_to_hz(note_name):
-    SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2}
-    pitch_class, octave = note_name[:-1], int(note_name[-1])
-    semitone = SEMITONES[pitch_class]
-    note_number = 12 * (octave - 4) + semitone
-    frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number
-    return frequency
-
-def save_to_wav(record_button):
-    if record_button is None:
-        pass
-    else:
-        path_to_file=record_button
-        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
-        new_path='./audios/'+new_name
-        shutil.move(path_to_file,new_path)
-        return new_name
-def save_to_wav2_edited(dropbox):
-    if dropbox is None:
-        pass
-    else:
-        file_path = dropbox.name
-        target_path = os.path.join('audios', os.path.basename(file_path))
-
-        if os.path.exists(target_path):
-            os.remove(target_path)
-            print('Replacing old dropdown file...')
-
-        shutil.move(file_path, target_path)
-    return       
-def save_to_wav2(dropbox):
-    file_path = dropbox.name
-    target_path = os.path.join('audios', os.path.basename(file_path))
-
-    if os.path.exists(target_path):
-        os.remove(target_path)
-        print('Replacing old dropdown file...')
-
-    shutil.move(file_path, target_path)
-    return target_path
-    
-from gtts import gTTS
-import edge_tts
-import asyncio
-
-
-
-
-def custom_voice(
-        _values, # filter indices
-        audio_files, # all audio files
-        model_voice_path='',
-        transpose=0,
-        f0method='pm',
-        index_rate_=float(0.66),
-        crepe_hop_length_=float(64),
-        f0_autotune=False,
-        file_index='',
-        file_index2='',
-        ):
-
-        vc.get_vc(model_voice_path)
-
-
-        for _value_item in _values:
-            filename = "audio2/"+audio_files[_value_item] if _value_item != "converted_tts" else audio_files[0]
-            #filename = "audio2/"+audio_files[_value_item]
-            try:
-                print(audio_files[_value_item], model_voice_path)
-            except:
-                pass
-            info_, (sample_, audio_output_) = vc.vc_single_dont_save(
-                sid=0,
-                input_audio_path0=filename, #f"audio2/{filename}",
-                input_audio_path1=filename, #f"audio2/{filename}",
-                f0_up_key=transpose, # transpose for m to f and reverse 0 12
-                f0_file=None,
-                f0_method= f0method,
-                file_index= file_index, # dir pwd?
-                file_index2= file_index2,
-                # file_big_npy1,
-                index_rate= index_rate_,
-                filter_radius= int(3),
-                resample_sr= int(0),
-                rms_mix_rate= float(0.25),
-                protect= float(0.33),
-                crepe_hop_length= crepe_hop_length_,
-                f0_autotune=f0_autotune,
-                f0_min=50,
-                note_min=50,
-                f0_max=1100,
-                note_max=1100
-            )
-
-            sf.write(
-                file= filename, #f"audio2/{filename}",
-                samplerate=sample_,
-                data=audio_output_
-            )
-def cast_to_device(tensor, device):
-    try:
-        return tensor.to(device)
-    except Exception as e:
-        print(e)
-        return tensor
-
-
-def __bark__(text, voice_preset):
-    os.makedirs(os.path.join(now_dir,"tts"), exist_ok=True)
-    from transformers import AutoProcessor, BarkModel
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    dtype = torch.float32 if "cpu" in device else torch.float16
-    bark_processor = AutoProcessor.from_pretrained(
-        "suno/bark",
-        cache_dir=os.path.join(now_dir,"tts","suno/bark"),
-        torch_dtype=dtype)
-    bark_model = BarkModel.from_pretrained(
-        "suno/bark",
-        cache_dir=os.path.join(now_dir,"tts","suno/bark"),
-        torch_dtype=dtype).to(device)
-    # bark_model.enable_cpu_offload()
-    inputs = bark_processor(
-    text=[text],
-    return_tensors="pt",
-    voice_preset=voice_preset
-    )
-    tensor_dict = {k: cast_to_device(v,device) if hasattr(v,"to") else v for k, v in inputs.items()}
-    speech_values = bark_model.generate(**tensor_dict, do_sample=True)
-    sampling_rate = bark_model.generation_config.sample_rate
-    speech = speech_values.cpu().numpy().squeeze()
-    return speech, sampling_rate
-
-
-
-def make_test( 
-        tts_text, 
-        tts_voice, 
-        model_path,
-        index_path,
-        transpose,
-        f0_method,
-        index_rate,
-        crepe_hop_length,
-        f0_autotune,
-        tts_method
-        ):
-
-        if tts_voice == None:
-            return
-        
-        filename = os.path.join(now_dir, "audio-outputs", "converted_tts.wav")
-        if "SET_LIMIT" == os.getenv("DEMO"):
-          if len(tts_text) > 60:
-            tts_text = tts_text[:60]
-            print("DEMO; limit to 60 characters")
-
-        language = tts_voice[:2]
-        if tts_method == "Edge-tts":
-            try:
-                #nest_asyncio.apply() # gradio;not
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
-            except:
-               try:
-                  tts = gTTS(tts_text, lang=language)
-                  tts.save(filename)
-                  tts.save
-                  print(f'No audio was received. Please change the tts voice for {tts_voice}. USING gTTS.')
-               except:
-                tts = gTTS('a', lang=language)
-                tts.save(filename)
-                print('Error: Audio will be replaced.')
-    
-            os.system("cp audio-outputs/converted_tts.wav audio-outputs/real_tts.wav")
-
-            custom_voice(
-                ["converted_tts"], # filter indices
-                ["audio-outputs/converted_tts.wav"], # all audio files
-                model_voice_path=model_path,
-                transpose=transpose,
-                f0method=f0_method,
-                index_rate_=index_rate,
-                crepe_hop_length_=crepe_hop_length,
-                f0_autotune=f0_autotune,
-                file_index='',
-                file_index2=index_path,
-            )
-            return os.path.join(now_dir, "audio-outputs", "converted_tts.wav"), os.path.join(now_dir, "audio-outputs", "real_tts.wav")
-        elif tts_method == "Bark-tts":
-            try:
-                
-                script = tts_text.replace("\n", " ").strip()
-                sentences = sent_tokenize(script)
-                print(sentences)
-                silence = np.zeros(int(0.25 * SAMPLE_RATE))
-                pieces = []
-                nombre_archivo = os.path.join(now_dir, "audio-outputs", "bark_out.wav")
-                for sentence in sentences:
-                    audio_array , _ = __bark__(sentence, tts_voice.split("-")[0])
-                    pieces += [audio_array, silence.copy()]
-            
-                sf.write(
-                    file= nombre_archivo,
-                    samplerate=SAMPLE_RATE,
-                    data=np.concatenate(pieces)
-                )
-                vc.get_vc(model_path)
-                info_, (sample_, audio_output_) = vc.vc_single_dont_save(
-                    sid=0,
-                    input_audio_path0=os.path.join(now_dir, "audio-outputs", "bark_out.wav"), #f"audio2/{filename}",
-                    input_audio_path1=os.path.join(now_dir, "audio-outputs", "bark_out.wav"), #f"audio2/{filename}",
-                    f0_up_key=transpose, # transpose for m to f and reverse 0 12
-                    f0_file=None,
-                    f0_method=f0_method,
-                    file_index= '', # dir pwd?
-                    file_index2= index_path,
-                    # file_big_npy1,
-                    index_rate= index_rate,
-                    filter_radius= int(3),
-                    resample_sr= int(0),
-                    rms_mix_rate= float(0.25),
-                    protect= float(0.33),
-                    crepe_hop_length= crepe_hop_length,
-                    f0_autotune=f0_autotune,
-                    f0_min=50,
-                    note_min=50,
-                    f0_max=1100,
-                    note_max=1100
-                )
-                wavfile.write(os.path.join(now_dir, "audio-outputs", "converted_bark.wav"), rate=sample_, data=audio_output_)
-                return os.path.join(now_dir, "audio-outputs", "converted_bark.wav"), nombre_archivo
-
-            except Exception as e:
-                print(f"{e}")
-                return None, None  
-            
-
-
-        
-        
-
-def GradioSetup(UTheme=gr.themes.Soft()):
-
-    default_weight = names[0] if names else '' 
-
-    with gr.Blocks(theme='JohnSmith9982/small_and_pretty', title="Applio") as app:
-        gr.HTML("<h1> 🍏 Applio (Mangio-RVC-Fork HF) </h1>")
-        gr.HTML("<h4> The current space only uses CPU, so it's only for inference. If you have issues with the queue, I recommend duplicating the space. </h4>")
-        gr.Markdown(
-            "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/r3gm/RVC_HF?duplicate=true)\n\n"
-        )        
-        with gr.Tabs():
-            with gr.TabItem(i18n("Model Inference")):
-                with gr.Row():
-                    sid0 = gr.Dropdown(label=i18n("Inferencing voice:"), choices=sorted(names), value=default_weight)
-                    refresh_button = gr.Button(i18n("Refresh"), variant="primary")
-                    clean_button = gr.Button(i18n("Unload voice to save GPU memory"), variant="primary")
-                    clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0])
-
-                
-                with gr.TabItem(i18n("Single")):
-                    with gr.Row(): 
-                        spk_item = gr.Slider(
-                            minimum=0,
-                            maximum=2333,
-                            step=1,
-                            label=i18n("Select Speaker/Singer ID:"),
-                            value=0,
-                            visible=False,
-                            interactive=True,
-                        )
-                       
-
-                    with gr.Group(): 
-                        with gr.Row():
-                            with gr.Column(): # First column for audio-related inputs
-                                dropbox = gr.File(label=i18n("Drag your audio here:"))
-                                record_button=gr.Audio(source="microphone", label=i18n("Or record an audio:"), type="filepath")
-                                input_audio0 = gr.Textbox(
-                                    label=i18n("Manual path to the audio file to be processed"),
-                                    value=os.path.join(now_dir, "audios", "someguy.mp3"),
-                                    visible=False
-                                )
-                                input_audio1 = gr.Dropdown(
-                                    label=i18n("Auto detect audio path and select from the dropdown:"),
-                                    choices=sorted(audio_paths),
-                                    value='',
-                                    interactive=True,
-                                )
-                                
-                                input_audio1.select(fn=lambda:'',inputs=[],outputs=[input_audio0])
-                                input_audio0.input(fn=lambda:'',inputs=[],outputs=[input_audio1])
-                                
-                                dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
-                                dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
-                                record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
-                                record_button.change(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
-
-                            best_match_index_path1 = match_index(sid0.value) # Get initial index from default sid0 (first voice model in list)
-
-                            with gr.Column(): # Second column for pitch shift and other options
-                                file_index2 = gr.Dropdown(
-                                    label=i18n("Auto-detect index path and select from the dropdown:"),
-                                    choices=get_indexes(),
-                                    value=best_match_index_path1,
-                                    interactive=True,
-                                    allow_custom_value=True,
-                                )
-                                index_rate1 = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    label=i18n("Search feature ratio:"),
-                                    value=0.75,
-                                    interactive=True,
-                                )
-                                refresh_button.click(
-                                    fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1]
-                                )
-                                with gr.Column():
-                                    vc_transform0 = gr.Number(
-                                        label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0
-                                    )
-        
-                    # Create a checkbox for advanced settings
-                    advanced_settings_checkbox = gr.Checkbox(
-                        value=False,
-                        label=i18n("Advanced Settings"),
-                        interactive=True,
-                    )
-                    
-                    # Advanced settings container        
-                    with gr.Column(visible=False) as advanced_settings: # Initially hidden
-                        with gr.Row(label = i18n("Advanced Settings"), open = False):
-                            with gr.Column():
-                                f0method0 = gr.Radio(
-                                    label=i18n(
-                                        "Select the pitch extraction algorithm:"
-                                    ),
-                                    choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe", "rmvpe+"], 
-                                    value="rmvpe+",
-                                    interactive=True,
-                                )
-                                f0_autotune = gr.Checkbox(
-                                    label="Enable autotune",
-                                    interactive=True
-                                )
-                                crepe_hop_length = gr.Slider(
-                                    minimum=1,
-                                    maximum=512,
-                                    step=1,
-                                    label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."),
-                                    value=120,
-                                    interactive=True,
-                                    visible=False,
-                                )
-                                filter_radius0 = gr.Slider(
-                                    minimum=0,
-                                    maximum=7,
-                                    label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."),
-                                    value=3,
-                                    step=1,
-                                    interactive=True,
-                                )    
-
-                                minpitch_slider = gr.Slider(
-                                    label       = i18n("Min pitch:"),
-                                    info        = i18n("Specify minimal pitch for inference [HZ]"),
-                                    step        = 0.1,
-                                    minimum     = 1,
-                                    scale       = 0,
-                                    value       = 50,
-                                    maximum     = 16000,
-                                    interactive = True,
-                                    visible     = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
-                                )
-                                minpitch_txtbox = gr.Textbox(
-                                    label       = i18n("Min pitch:"),
-                                    info        = i18n("Specify minimal pitch for inference [NOTE][OCTAVE]"),
-                                    placeholder = "C5",
-                                    visible     = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
-                                    interactive = True,
-                                )
-
-                                maxpitch_slider = gr.Slider(
-                                    label       = i18n("Max pitch:"),
-                                    info        = i18n("Specify max pitch for inference [HZ]"),
-                                    step        = 0.1,
-                                    minimum     = 1,
-                                    scale       = 0,
-                                    value       = 1100,
-                                    maximum     = 16000,
-                                    interactive = True,
-                                    visible     = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
-                                )
-                                maxpitch_txtbox = gr.Textbox(
-                                    label       = i18n("Max pitch:"),
-                                    info        = i18n("Specify max pitch for inference [NOTE][OCTAVE]"),
-                                    placeholder = "C6",
-                                    visible     = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'),
-                                    interactive = True,
-                                )
-
-                            with gr.Column():
-                                file_index1 = gr.Textbox(
-                                    label=i18n("Feature search database file path:"),
-                                    value="",
-                                    interactive=True,
-                                )
-                            
-                                with gr.Accordion(label = i18n("Custom f0 [Root pitch] File"), open = False):
-                                    f0_file = gr.File(label=i18n("F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation:"))
-
-                            f0method0.change(
-                                fn=lambda radio: (
-                                    {
-                                        "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
-                                        "__type__": "update"
-                                    }
-                                ),
-                                inputs=[f0method0],
-                                outputs=[crepe_hop_length]
-                            )
-
-                            f0method0.change(
-                                fn=switch_pitch_controls,
-                                inputs=[f0method0],
-                                outputs=[minpitch_slider, minpitch_txtbox,
-                                         maxpitch_slider, maxpitch_txtbox]
-                            )                            
-                            
-                            with gr.Column():
-                                resample_sr0 = gr.Slider(
-                                    minimum=0,
-                                    maximum=48000,
-                                    label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"),
-                                    value=0,
-                                    step=1,
-                                    interactive=True,
-                                )
-                                rms_mix_rate0 = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"),
-                                    value=0.25,
-                                    interactive=True,
-                                )
-                                protect0 = gr.Slider(
-                                    minimum=0,
-                                    maximum=0.5,
-                                    label=i18n(
-                                        "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:"
-                                    ),
-                                    value=0.33,
-                                    step=0.01,
-                                    interactive=True,
-                                )
-                                formanting = gr.Checkbox(
-                                    value=bool(DoFormant),
-                                    label=i18n("Formant shift inference audio"),
-                                    info=i18n("Used for male to female and vice-versa conversions"),
-                                    interactive=True,
-                                    visible=True,
-                                )
-                                
-                                formant_preset = gr.Dropdown(
-                                    value='',
-                                    choices=get_fshift_presets(),
-                                    label=i18n("Browse presets for formanting"),
-                                    info=i18n("Presets are located in formantshiftcfg/ folder"),
-                                    visible=bool(DoFormant),
-                                )
-                                
-                                formant_refresh_button = gr.Button(
-                                    value='\U0001f504',
-                                    visible=bool(DoFormant),
-                                    variant='primary',
-                                )
-                                
-                                qfrency = gr.Slider(
-                                        value=Quefrency,
-                                        info=i18n("Default value is 1.0"),
-                                        label=i18n("Quefrency for formant shifting"),
-                                        minimum=0.0,
-                                        maximum=16.0,
-                                        step=0.1,
-                                        visible=bool(DoFormant),
-                                        interactive=True,
-                                )
-                                    
-                                tmbre = gr.Slider(
-                                    value=Timbre,
-                                    info=i18n("Default value is 1.0"),
-                                    label=i18n("Timbre for formant shifting"),
-                                    minimum=0.0,
-                                    maximum=16.0,
-                                    step=0.1,
-                                    visible=bool(DoFormant),
-                                    interactive=True,
-                                )
-                                frmntbut = gr.Button(
-                                   "Apply", variant="primary", visible=bool(DoFormant)
-                                )
-                               
-                            formant_preset.change(
-                                fn=preset_apply,
-                                inputs=[formant_preset, qfrency, tmbre],
-                                outputs=[qfrency, tmbre],
-                            )
-                            formanting.change(
-                               fn=formant_enabled,
-                               inputs=[
-                                   formanting,
-                                   qfrency,
-                                   tmbre,
-                                   frmntbut,
-                                   formant_preset,
-                                   formant_refresh_button,
-                               ],
-                               outputs=[
-                                   formanting,
-                                   qfrency,
-                                   tmbre,
-                                   frmntbut,
-                                   formant_preset,
-                                   formant_refresh_button,
-                               ],
-                            )
-                            frmntbut.click(
-                                fn=formant_apply,
-                                inputs=[qfrency, tmbre],
-                                outputs=[qfrency, tmbre],
-                            )
-                            formant_refresh_button.click(
-                                fn=update_fshift_presets,
-                                inputs=[formant_preset, qfrency, tmbre],
-                                outputs=[formant_preset, qfrency, tmbre],
-                            )
-
-                    # Function to toggle advanced settings
-                    def toggle_advanced_settings(checkbox):
-                        return {"visible": checkbox, "__type__": "update"}
-
-                    # Attach the change event
-                    advanced_settings_checkbox.change(
-                        fn=toggle_advanced_settings,
-                        inputs=[advanced_settings_checkbox],
-                        outputs=[advanced_settings]
-                    )
-                                               
-                    
-                    but0 = gr.Button(i18n("Convert"), variant="primary").style(full_width=True)
-                    
-                    with gr.Row(): # Defines output info + output audio download after conversion
-                        vc_output1 = gr.Textbox(label=i18n("Output information:"))
-                        vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)"))
-
-                    with gr.Group(): # I think this defines the big convert button
-                        with gr.Row():
-                            but0.click(
-                                vc.vc_single,
-                                [
-                                    spk_item,
-                                    input_audio0,
-                                    input_audio1,
-                                    vc_transform0,
-                                    f0_file,
-                                    f0method0,
-                                    file_index1,
-                                    file_index2,
-                                    index_rate1,
-                                    filter_radius0,
-                                    resample_sr0,
-                                    rms_mix_rate0,
-                                    protect0,
-                                    crepe_hop_length,
-                                    minpitch_slider, minpitch_txtbox,
-                                    maxpitch_slider, maxpitch_txtbox,
-                                    f0_autotune
-                                ],
-                                [vc_output1, vc_output2],
-                            )
-                           
-                    
-                with gr.TabItem(i18n("Batch")): # Dont Change
-                    with gr.Group(): # Markdown explanation of batch inference
-                        gr.Markdown(
-                            value=i18n("Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').")
-                        )
-                        with gr.Row():
-                            with gr.Column():
-                                vc_transform1 = gr.Number(
-                                    label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0
-                                )
-                                opt_input = gr.Textbox(label=i18n("Specify output folder:"), value="opt")
-                            with gr.Column():
-                                file_index4 = gr.Dropdown(
-                                    label=i18n("Auto-detect index path and select from the dropdown:"),
-                                    choices=get_indexes(),
-                                    value=best_match_index_path1,
-                                    interactive=True,
-                                )
-                                sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4])
-
-                                refresh_button.click(
-                                    fn=lambda: change_choices()[1],
-                                    inputs=[],
-                                    outputs=file_index4,
-                                )
-                                index_rate2 = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    label=i18n("Search feature ratio:"),
-                                    value=0.75,
-                                    interactive=True,
-                                )
-                            with gr.Row():
-                                dir_input = gr.Textbox(
-                                    label=i18n("Enter the path of the audio folder to be processed (copy it from the address bar of the file manager):"),
-                                    value=os.path.join(now_dir, "audios"),
-                                )
-                                inputs = gr.File(
-                                    file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder.")
-                                )
-
-                        with gr.Row():
-                            with gr.Column():
-                                # Create a checkbox for advanced batch settings
-                                advanced_settings_batch_checkbox = gr.Checkbox(
-                                    value=False,
-                                    label=i18n("Advanced Settings"),
-                                    interactive=True,
-                                )
-                            
-                                # Advanced batch settings container        
-                                with gr.Row(visible=False) as advanced_settings_batch: # Initially hidden
-                                    with gr.Row(label = i18n("Advanced Settings"), open = False):
-                                        with gr.Column():
-                                            file_index3 = gr.Textbox(
-                                                label=i18n("Feature search database file path:"),
-                                                value="",
-                                                interactive=True,
-                                            )
-
-                                    f0method1 = gr.Radio(
-                                        label=i18n(
-                                            "Select the pitch extraction algorithm:"
-                                        ),
-                                        choices=["pm", "harvest", "crepe", "rmvpe"],
-                                        value="rmvpe",
-                                        interactive=True,
-                                    )
-                                    f0_autotune = gr.Checkbox(
-                                    label="Enable autotune",
-                                    interactive=True
-                                    )
-                                    filter_radius1 = gr.Slider(
-                                        minimum=0,
-                                        maximum=7,
-                                        label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."),
-                                        value=3,
-                                        step=1,
-                                        interactive=True,
-                                    )
-                                
-                                    with gr.Row():
-                                        format1 = gr.Radio(
-                                            label=i18n("Export file format"),
-                                            choices=["wav", "flac", "mp3", "m4a"],
-                                            value="wav",
-                                            interactive=True,
-                                        )
-                                        
-
-                                    with gr.Column():
-                                        resample_sr1 = gr.Slider(
-                                            minimum=0,
-                                            maximum=48000,
-                                            label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"),
-                                            value=0,
-                                            step=1,
-                                            interactive=True,
-                                        )
-                                        rms_mix_rate1 = gr.Slider(
-                                            minimum=0,
-                                            maximum=1,
-                                            label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"),
-                                            value=1,
-                                            interactive=True,
-                                        )
-                                        protect1 = gr.Slider(
-                                            minimum=0,
-                                            maximum=0.5,
-                                            label=i18n(
-                                                "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:"
-                                            ),
-                                            value=0.33,
-                                            step=0.01,
-                                            interactive=True,
-                                        )
-                                vc_output3 = gr.Textbox(label=i18n("Output information:")) 
-                                but1 = gr.Button(i18n("Convert"), variant="primary")
-                                but1.click(
-                                    vc.vc_multi,
-                                    [
-                                        spk_item,
-                                        dir_input,
-                                        opt_input,
-                                        inputs,
-                                        vc_transform1,
-                                        f0method1,
-                                        file_index3,
-                                        file_index4,
-                                        index_rate2,
-                                        filter_radius1,
-                                        resample_sr1,
-                                        rms_mix_rate1,
-                                        protect1,
-                                        format1,
-                                        crepe_hop_length,
-                                        minpitch_slider if (not rvc_globals.NotesOrHertz) else minpitch_txtbox,
-                                        maxpitch_slider if (not rvc_globals.NotesOrHertz) else maxpitch_txtbox,
-                                        f0_autotune
-                                    ],
-                                    [vc_output3],
-                                )
-
-                    sid0.change(
-                        fn=vc.get_vc,
-                        inputs=[sid0, protect0, protect1],
-                        outputs=[spk_item, protect0, protect1],
-                    )
-                    if not sid0.value == '':
-                        spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1)
-
-                    #spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1) 
-
-                    # Function to toggle advanced settings
-                    def toggle_advanced_settings_batch(checkbox):
-                        return {"visible": checkbox, "__type__": "update"}
-
-                    # Attach the change event
-                    advanced_settings_batch_checkbox.change(
-                        fn=toggle_advanced_settings_batch,
-                        inputs=[advanced_settings_batch_checkbox],
-                        outputs=[advanced_settings_batch]
-                    )                           
-                    
-                
-            with gr.TabItem(i18n("Train")):
-                gr.Markdown("Training and All in One Inference Without UI/Gradio, Prevent Banning")
-                gr.Markdown("[Repository](https://github.com/ardha27/AI-Song-Cover-RVC)")
-
-                with gr.Accordion(label=i18n("Step 1: Processing data")):
-                    with gr.Row():
-                        exp_dir1 = gr.Textbox(label=i18n("Enter the model name:"), value=i18n("Model_Name"))
-                        sr2 = gr.Radio(
-                            label=i18n("Target sample rate:"),
-                            choices=["40k", "48k", "32k"],
-                            value="40k",
-                            interactive=True,
-                        )
-                        if_f0_3 = gr.Checkbox(
-                            label=i18n("Whether the model has pitch guidance."),
-                            value=True,
-                            interactive=True,
-                        )
-                        version19 = gr.Radio(
-                            label=i18n("Version:"),
-                            choices=["v1", "v2"],
-                            value="v2",
-                            interactive=True,
-                            visible=True,
-                        )
-                        np7 = gr.Slider(
-                            minimum=0,
-                            maximum=config.n_cpu,
-                            step=1,
-                            label=i18n("Number of CPU processes:"),
-                            value=int(np.ceil(config.n_cpu / 1.5)),
-                            interactive=True,
-                        )
-                with gr.Group():
-                    with gr.Accordion(label=i18n("Step 2: Skipping pitch extraction")):
-               
-                        with gr.Row():
-                        #  trainset_dir4 = gr.Textbox(
-                        #      label=i18n("Enter the path of the training folder:"), value=os.path.join(now_dir, datasets_root)
-                        #  )
-                            with gr.Column():
-                                trainset_dir4 = gr.Dropdown(choices=sorted(datasets), label=i18n("Select your dataset:"), value=get_dataset())
-                                btn_update_dataset_list = gr.Button(i18n("Update list"), variant="primary")
-                            spk_id5 = gr.Slider(
-                                minimum=0,
-                                maximum=4,
-                                step=1,
-                                label=i18n("Specify the model ID:"),
-                                value=0,
-                                interactive=True,
-                            )
-                            btn_update_dataset_list.click(
-                            easy_infer.update_dataset_list, [spk_id5], trainset_dir4
-                            )
-                            but1 = gr.Button(i18n("Process data"), variant="primary")
-                            info1 = gr.Textbox(label=i18n("Output information:"), value="")
-                            but1.click(
-                                preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
-                            )
-                with gr.Group():
-                    with gr.Accordion(label=i18n("Step 3: Extracting features")):
-                        with gr.Row():
-                            with gr.Column():
-                                gpus6 = gr.Textbox(
-                                    label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"),
-                                    value=gpus,
-                                    interactive=True,
-                                )
-                                gpu_info9 = gr.Textbox(
-                                    label=i18n("GPU Information:"), value=gpu_info, visible=F0GPUVisible
-                                )
-                            with gr.Column():
-                                f0method8 = gr.Radio(
-                                    label=i18n(
-                                        "Select the pitch extraction algorithm:"
-                                    ),
-                                    choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe", "rmvpe_gpu"],
-                                    # [ MANGIO ]: Fork feature: Crepe on f0 extraction for training.
-                                    value="rmvpe",
-                                    interactive=True,
-                                )
-                                gpus_rmvpe = gr.Textbox(
-                                    label=i18n(
-                                        "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
-                                        ),
-                                        value="%s-%s" % (gpus, gpus),
-                                        interactive=True,
-                                        visible=F0GPUVisible,
-                                )
-                                
-                                extraction_crepe_hop_length = gr.Slider(
-                                    minimum=1,
-                                    maximum=512,
-                                    step=1,
-                                    label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."),
-                                    value=64,
-                                    interactive=True,
-                                    visible=False,
-                                )
-                                
-                                f0method8.change(
-                                    fn=lambda radio: (
-                                        {
-                                            "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
-                                            "__type__": "update"
-                                        }
-                                    ),
-                                    inputs=[f0method8],
-                                    outputs=[extraction_crepe_hop_length]
-                                )
-                                f0method8.change(
-                                    fn=change_f0_method,
-                                    inputs=[f0method8],
-                                    outputs=[gpus_rmvpe],
-                                    )
-                            but2 = gr.Button(i18n("Feature extraction"), variant="primary")
-                            info2 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8, interactive=False)
-                            but2.click(
-                                extract_f0_feature,
-                                [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length, gpus_rmvpe,],
-                                [info2],
-                            )
-                with gr.Group():
-                    with gr.Row():
-                        with gr.Accordion(label=i18n("Step 4: Model training started")):
-                            with gr.Row():
-                                save_epoch10 = gr.Slider(
-                                    minimum=1,
-                                    maximum=100,
-                                    step=1,
-                                    label=i18n("Save frequency:"),
-                                    value=10,
-                                    interactive=True,
-                                    visible=True,
-                                )
-                                total_epoch11 = gr.Slider(
-                                    minimum=1,
-                                    maximum=10000,
-                                    step=2,
-                                    label=i18n("Training epochs:"),
-                                    value=750,
-                                    interactive=True,
-                                )
-                                batch_size12 = gr.Slider(
-                                    minimum=1,
-                                    maximum=50,
-                                    step=1,
-                                    label=i18n("Batch size per GPU:"),
-                                    value=default_batch_size,
-                                    #value=20,
-                                    interactive=True,
-                                )
-                        
-                            with gr.Row(): 
-                                if_save_latest13 = gr.Checkbox(
-                                        label=i18n("Whether to save only the latest .ckpt file to save hard drive space"),
-                                        value=True,
-                                        interactive=True,
-                                    )
-                                if_cache_gpu17 = gr.Checkbox(
-                                        label=i18n("Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training"),
-                                        value=False,
-                                        interactive=True,
-                                    )
-                                if_save_every_weights18 = gr.Checkbox(
-                                        label=i18n("Save a small final model to the 'weights' folder at each save point"),
-                                        value=True,
-                                        interactive=True,
-                                    )
-               
-                            with gr.Row():    
-                                pretrained_G14 = gr.Textbox(
-                                    lines=4,
-                                    label=i18n("Load pre-trained base model G path:"),
-                                    value="assets/pretrained_v2/f0G40k.pth",
-                                    interactive=True,
-                                )
-                                pretrained_D15 = gr.Textbox(
-                                    lines=4,
-                                    label=i18n("Load pre-trained base model D path:"),
-                                    value="assets/pretrained_v2/f0D40k.pth",
-                                    interactive=True,
-                                )
-                                gpus16 = gr.Textbox(
-                                    label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"),
-                                    value=gpus,
-                                    interactive=True,
-                                )  
-                                sr2.change(
-                                    change_sr2,
-                                    [sr2, if_f0_3, version19],
-                                    [pretrained_G14, pretrained_D15],
-                                )
-                                version19.change(
-                                    change_version19,
-                                    [sr2, if_f0_3, version19],
-                                    [pretrained_G14, pretrained_D15, sr2],
-                                )
-                                if_f0_3.change(
-                                        fn=change_f0,
-                                        inputs=[if_f0_3, sr2, version19],
-                                        outputs=[f0method8, pretrained_G14, pretrained_D15],
-                                )
-                                if_f0_3.change(fn=lambda radio: (
-                                            {
-                                                "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'],
-                                                "__type__": "update"
-                                            }
-                                        ), inputs=[f0method8], outputs=[extraction_crepe_hop_length])
-                                
-                                butstop = gr.Button(i18n("Stop training"),
-                                            variant='primary',
-                                            visible=False,
-                                        )
-                                but3 = gr.Button(i18n("Train model"), variant="primary", visible=True)
-                                but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
-                                butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[but3, butstop])
-                                
-                                
-                                with gr.Column():
-                                        info3 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=4)
-                                        save_action = gr.Dropdown(label=i18n("Save type"), choices=[i18n("Save all"),i18n("Save D and G"),i18n("Save voice")], value=i18n("Choose the method"), interactive=True)
-                                        
-                                        but7 = gr.Button(i18n("Save model"), variant="primary")
-                                        but4 = gr.Button(i18n("Train feature index"), variant="primary")
-                             
-                   
-                                    
-                                if_save_every_weights18.change(
-                                        fn=lambda if_save_every_weights: (
-                                            {
-                                                "visible": if_save_every_weights,
-                                                "__type__": "update"
-                                            }
-                                        ),
-                                        inputs=[if_save_every_weights18],
-                                        outputs=[save_epoch10]
-                                    )
-                            
-                            but3.click(
-                                click_train,
-                                [
-                                    exp_dir1,
-                                    sr2,
-                                    if_f0_3,
-                                    spk_id5,
-                                    save_epoch10,
-                                    total_epoch11,
-                                    batch_size12,
-                                    if_save_latest13,
-                                    pretrained_G14,
-                                    pretrained_D15,
-                                    gpus16,
-                                    if_cache_gpu17,
-                                    if_save_every_weights18,
-                                    version19,
-                                ],
-                                [info3, butstop, but3],
-                            )
-                                
-                            but4.click(train_index, [exp_dir1, version19], info3)
-                            but7.click(easy_infer.save_model, [exp_dir1, save_action], info3)
-                with gr.Group():
-                    with gr.Row():
-                        with gr.Accordion(label=i18n("Step 5: Export lowest points on a graph of the model")):
-                        
-                            lowestval_weight_dir = gr.Textbox(visible=False)
-                            ds = gr.Textbox(visible=False)
-                            weights_dir1 = gr.Textbox(visible=False, value=weights_dir)
-                            
-                                
-                            with gr.Row():
-                                amntlastmdls = gr.Slider(
-                                    minimum=1,
-                                    maximum=25,
-                                    label=i18n('How many lowest points to save:'),
-                                    value=3,
-                                    step=1,
-                                    interactive=True,
-                                )
-                                lpexport = gr.Button(
-                                    value=i18n('Export lowest points of a model'),
-                                    variant='primary',
-                                )
-                                lw_mdls = gr.File(
-                                    file_count="multiple",
-                                    label=i18n("Output models:"),
-                                    interactive=False,
-                                ) #####
-                                
-                            with gr.Row():
-                                infolpex = gr.Textbox(label=i18n("Output information:"), value="", max_lines=10)
-                                mdlbl = gr.Dataframe(label=i18n('Stats of selected models:'), datatype='number', type='pandas')
-                            
-                            lpexport.click(
-                                lambda model_name: os.path.join("logs", model_name, "lowestvals"),
-                                inputs=[exp_dir1],
-                                outputs=[lowestval_weight_dir]
-                            )
-                            
-                            lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds])
-                            
-                            ds.change(
-                                fn=tensorlowest.selectweights,
-                                inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir],
-                                outputs=[infolpex, lw_mdls, mdlbl],
-                            )
-            with gr.TabItem(i18n("UVR5")): # UVR section 
-                with gr.Group():
-                    with gr.Row():
-                        with gr.Column():
-                            model_select = gr.Radio(
-                                label=i18n("Model Architecture:"),
-                                choices=["VR", "MDX"],
-                                value="VR",
-                                interactive=True,
-                            )
-                            dir_wav_input = gr.Textbox(
-                                label=i18n("Enter the path of the audio folder to be processed:"),
-                                value=os.path.join(now_dir, "audios")
-                            )
-                            wav_inputs = gr.File(
-                                file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder.")
-                            )
-                            
-                        with gr.Column():
-                            model_choose = gr.Dropdown(label=i18n("Model:"), choices=uvr5_names)
-                            agg = gr.Slider(
-                                minimum=0,
-                                maximum=20,
-                                step=1,
-                                label="Vocal Extraction Aggressive",
-                                value=10,
-                                interactive=True,
-                                visible=False,
-                            )
-                            opt_vocal_root = gr.Textbox(
-                                label=i18n("Specify the output folder for vocals:"), value="opt"
-                            )
-                            opt_ins_root = gr.Textbox(
-                                label=i18n("Specify the output folder for accompaniment:"), value="opt"
-                            )
-                            format0 = gr.Radio(
-                                label=i18n("Export file format:"),
-                                choices=["wav", "flac", "mp3", "m4a"],
-                                value="flac",
-                                interactive=True,
-                            )
-                        model_select.change(
-                                fn=update_model_choices,
-                                inputs=model_select,
-                                outputs=model_choose,
-                                )
-                        but2 = gr.Button(i18n("Convert"), variant="primary")
-                        vc_output4 = gr.Textbox(label=i18n("Output information:"))
-                        #wav_inputs.upload(fn=save_to_wav2_edited, inputs=[wav_inputs], outputs=[])
-                        but2.click(
-                            uvr,
-                            [
-                                model_choose,
-                                dir_wav_input,
-                                opt_vocal_root,
-                                wav_inputs,
-                                opt_ins_root,
-                                agg,
-                                format0,
-                                model_select
-                            ],
-                            [vc_output4],
-                        )    
-            with gr.TabItem(i18n("TTS")):
-                with gr.Group():
-                    with gr.Column():
-                        text_test = gr.Textbox(label=i18n("Text:"), placeholder=i18n("Enter the text you want to convert to voice..."), lines=6)      
-                            
-                with gr.Group():
-                    with gr.Row(): 
-                        with gr.Column(): 
-                             tts_methods_voice = ["Edge-tts", "Bark-tts"]
-                             ttsmethod_test = gr.Dropdown(tts_methods_voice, value='Edge-tts', label = i18n('TTS Method:'), visible=True)    
-                             tts_test = gr.Dropdown(set_edge_voice, label = i18n('TTS Model:'), visible=True)
-                             ttsmethod_test.change(
-                            fn=update_tts_methods_voice,
-                            inputs=ttsmethod_test,
-                            outputs=tts_test,
-                            )
-
-                        with gr.Column():
-                             model_voice_path07 = gr.Dropdown(label=i18n('RVC Model:'), choices=sorted(names), value=default_weight)
-                             best_match_index_path1 = match_index(model_voice_path07.value)    
-                             
-                             file_index2_07 = gr.Dropdown(
-                                  label=i18n('Select the .index file:'),
-                                  choices=get_indexes(),
-                                  value=best_match_index_path1,
-                                  interactive=True,
-                                  allow_custom_value=True,
-                                )
-                             #transpose_test = gr.Number(label = i18n('Transpose (integer, number Fof semitones, raise by an octave: 12, lower by an octave: -12):'), value=0, visible=True, interactive= True)
-              
-                        
-                                   
-                
-                with gr.Row():
-                        refresh_button_ = gr.Button(i18n("Refresh"), variant="primary")
-                        refresh_button_.click(fn=change_choices2, inputs=[], outputs=[model_voice_path07, file_index2_07]) 
-                with gr.Row():
-                            original_ttsvoice = gr.Audio(label=i18n('Audio TTS:'))
-                            ttsvoice = gr.Audio(label=i18n('Audio RVC:'))
-
-                with gr.Row():
-                        button_test = gr.Button(i18n("Convert"), variant="primary")
-                       
-
-                button_test.click(make_test, inputs=[
-                                text_test,
-                                tts_test,
-                                model_voice_path07,
-                                file_index2_07,
-                                #transpose_test,
-                                vc_transform0,
-                                f0method8,
-                                index_rate1,
-                                crepe_hop_length,
-                                f0_autotune,
-                                ttsmethod_test
-                                ], outputs=[ttsvoice, original_ttsvoice])
-            
-            with gr.TabItem(i18n("Resources")):
-                easy_infer.download_model()
-                easy_infer.download_backup()
-                easy_infer.download_dataset(trainset_dir4)
-                easy_infer.download_audio()
-                easy_infer.youtube_separator()
-            with gr.TabItem(i18n("Extra")):
-                gr.Markdown(
-                            value=i18n("This section contains some extra utilities that often may be in experimental phases")
-                )
-                with gr.TabItem(i18n("Merge Audios")):
-                    with gr.Group(): 
-                        gr.Markdown(
-                            value="## " + i18n("Merge your generated audios with the instrumental")
-                        )
-                        gr.Markdown(value=".",visible=True)
-                        gr.Markdown(value=".",visible=True)
-                        with gr.Row():
-                            with gr.Column():
-                                dropbox = gr.File(label=i18n("Drag your audio here:"))
-                                gr.Markdown(value=i18n("### Instrumental settings:"))
-                                input_audio1 = gr.Dropdown(
-                                    label=i18n("Choose your instrumental:"),
-                                    choices=sorted(audio_others_paths),
-                                    value='',
-                                    interactive=True,
-                                )
-                                input_audio1_scale = gr.Slider(
-                                    minimum=0,
-                                    maximum=10,
-                                    label=i18n("Volume of the instrumental audio:"),
-                                    value=1.00,
-                                    interactive=True,
-                                )
-                                gr.Markdown(value=i18n("### Audio settings:"))
-                                input_audio3 = gr.Dropdown(
-                                    label=i18n("Select the generated audio"),
-                                    choices=sorted(audio_paths),
-                                    value='',
-                                    interactive=True,
-                                )
-                                with gr.Row():
-                                    input_audio3_scale = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label=i18n("Volume of the generated audio:"),
-                                        value=1.00,
-                                        interactive=True,
-                                    )
-
-                                gr.Markdown(value=i18n("### Add the effects:"))
-                                reverb_ = gr.Checkbox(
-                                label=i18n("Reverb"),
-                                value=False,
-                                interactive=True,
-                                )
-                                compressor_ = gr.Checkbox(
-                                label=i18n("Compressor"),
-                                value=False,
-                                interactive=True,
-                                )
-                                noise_gate_ = gr.Checkbox(
-                                label=i18n("Noise Gate"),
-                                value=False,
-                                interactive=True,
-                                )
-
-                                butnone = gr.Button(i18n("Merge"), variant="primary").style(full_width=True)
-                                
-                                vc_output1 = gr.Textbox(label=i18n("Output information:"))
-                                vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)"), type='filepath')
-                                
-                                dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio1])
-                                dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1])
-
-                                refresh_button.click(
-                                    fn=lambda: change_choices3(),
-                                    inputs=[],
-                                    outputs=[input_audio1, input_audio3],
-                                )
-                                
-                                butnone.click(
-                                    fn=audio_combined,
-                                    inputs=[input_audio1, input_audio3,input_audio1_scale,input_audio3_scale,reverb_,compressor_,noise_gate_], 
-                                    outputs=[vc_output1, vc_output2]
-                                    )
-                                    
-                        
-                with gr.TabItem(i18n("Processing")):
-                    with gr.Group():
-                      
-                        with gr.Accordion(label=i18n("Model fusion, can be used to test timbre fusion")):
-                            with gr.Row():
-                                with gr.Column():
-                                    name_to_save0 = gr.Textbox(
-                                        label=i18n("Name:"),
-                                        value="",
-                                        max_lines=1,
-                                        interactive=True,
-                                        placeholder=i18n("Name for saving")
-                                    )
-                                    alpha_a = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label=i18n("Weight for Model A:"),
-                                        value=0.5,
-                                        interactive=True,
-                                    )
-                                    if_f0_ = gr.Checkbox(
-                                    label=i18n("Whether the model has pitch guidance."),
-                                    value=True,
-                                    interactive=True,
-                                    )
-                                    version_2 = gr.Radio(
-                                    label=i18n("Model architecture version:"),
-                                    choices=["v1", "v2"],
-                                    value="v2",
-                                    interactive=True,
-                                )
-                                    sr_ = gr.Radio(
-                                    label=i18n("Target sample rate:"),
-                                    choices=["40k", "48k"],
-                                    value="40k",
-                                    interactive=True,
-                                )
-                                
-                
-                                with gr.Column():
-                                    ckpt_a = gr.Textbox(label=i18n("Path to Model A:"), value="", interactive=True, placeholder=i18n("Path to model"))
-                                
-                                    ckpt_b = gr.Textbox(label=i18n("Path to Model B:"), value="", interactive=True, placeholder=i18n("Path to model"))
-                                
-                                    info__ = gr.Textbox(
-                                        label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed")
-                                    )
-                                    info4 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)                               
-                                
-                           
-                            but6 = gr.Button(i18n("Fusion"), variant="primary")
-                                
-                            but6.click(
-                                merge,
-                                [
-                                    ckpt_a,
-                                    ckpt_b,
-                                    alpha_a,
-                                    sr_,
-                                    if_f0_,
-                                    info__,
-                                    name_to_save0,
-                                    version_2,
-                                ],
-                                info4,
-                            )  # def merge(path1,path2,alpha1,sr,f0,info):
-                    with gr.Group():
-                        with gr.Accordion(label=i18n("Modify model information")):
-                            with gr.Row(): ######
-                                with gr.Column():
-                                    ckpt_path0 = gr.Textbox(
-                                        label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model")
-                                    )
-                                    info_ = gr.Textbox(
-                                        label=i18n("Model information to be modified:"), value="", max_lines=8, interactive=True,  placeholder=i18n("Model information to be placed")
-                                    )
-                                
-                                with gr.Column():
-                                    name_to_save1 = gr.Textbox(
-                                        label=i18n("Save file name:"),
-                                        placeholder=i18n("Name for saving"),
-                                        value="",
-                                        max_lines=8,
-                                        interactive=True,
-                                        
-                                    )
-                                    
-                                    info5 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
-                            but7 = gr.Button(i18n("Modify"), variant="primary")        
-                            but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5)
-                    with gr.Group():
-                        with gr.Accordion(label=i18n("View model information")):
-                            with gr.Row():
-                                with gr.Column():
-                                    ckpt_path1 = gr.Textbox(
-                                        label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model")
-                                    )
-                                    
-                                    info6 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)
-                                    but8 = gr.Button(i18n("View"), variant="primary")
-                            but8.click(show_info, [ckpt_path1], info6)
-                    with gr.Group():
-                        with gr.Accordion(label=i18n("Model extraction")):
-                            with gr.Row():
-                               with gr.Column():
-                                       save_name = gr.Textbox(
-                                        label=i18n("Name:"), value="", interactive=True, placeholder=i18n("Name for saving")
-                                    )
-                                       if_f0__ = gr.Checkbox(
-                                            label=i18n("Whether the model has pitch guidance."),
-                                            value=True,
-                                            interactive=True,
-                                        )
-                                       version_1 = gr.Radio(
-                                            label=i18n("Model architecture version:"),
-                                            choices=["v1", "v2"],
-                                            value="v2",
-                                            interactive=True,
-                                        )
-                                       sr__ = gr.Radio(
-                                            label=i18n("Target sample rate:"),
-                                            choices=["32k", "40k", "48k"],
-                                            value="40k",
-                                            interactive=True,
-                                        )
-                                   
-                               with gr.Column():    
-                                      ckpt_path2 = gr.Textbox(
-                                       
-                                        label=i18n("Path to Model:"),
-                                        placeholder=i18n("Path to model"),
-                                        interactive=True,
-                                    )
-                                      info___ = gr.Textbox(
-                                        label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed")
-                                    )
-                                      info7 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8)   
-                    
-                            with gr.Row():
-                                    
-                                    but9 = gr.Button(i18n("Extract"), variant="primary")
-                                    ckpt_path2.change(
-                                        change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
-                                    )
-                            but9.click(
-                                extract_small_model,
-                                [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
-                                info7,
-                            )
-
-                
-                            
-        
-            with gr.TabItem(i18n("Settings")):
-                with gr.Row():
-                    gr.Markdown(value=
-                                i18n("Pitch settings")
-                                )
-                    noteshertz = gr.Checkbox(
-                        label       = i18n("Whether to use note names instead of their hertz value. E.G. [C5, D6] instead of [523.25, 1174.66]Hz"),
-                        value       = rvc_globals.NotesOrHertz,
-                        interactive = True,
-                    )
-            
-            noteshertz.change(fn=lambda nhertz: rvc_globals.__setattr__('NotesOrHertz', nhertz), inputs=[noteshertz], outputs=[])
-
-            noteshertz.change(
-                fn=switch_pitch_controls,
-                inputs=[f0method0],
-                outputs=[
-                    minpitch_slider, minpitch_txtbox,
-                    maxpitch_slider, maxpitch_txtbox,]
-            )
-        return app
-
-def GradioRun(app):
-    share_gradio_link = config.iscolab or config.paperspace
-    concurrency_count = 511
-    max_size = 1022
-
-    if (
-        config.iscolab or config.paperspace
-    ):  
-        app.queue(concurrency_count=concurrency_count, max_size=max_size).launch(
-        favicon_path="./images/icon.png",
-        )
-    else:
-        app.queue(concurrency_count=concurrency_count, max_size=max_size).launch(
-        favicon_path=".\images\icon.png",
-        )
-
-if __name__ == "__main__":
-    if os.name == 'nt': 
-        print(i18n("Any ConnectionResetErrors post-conversion are irrelevant and purely visual; they can be ignored.\n"))
-    app = GradioSetup(UTheme=config.grtheme)
-    GradioRun(app)
\ No newline at end of file
diff --git a/spaces/Funbi/Textgen/README.md b/spaces/Funbi/Textgen/README.md
deleted file mode 100644
index 5877d933e86585290e79f1c410e0af4df55f44ae..0000000000000000000000000000000000000000
--- a/spaces/Funbi/Textgen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Textgen
-emoji: ⚡
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
deleted file mode 100644
index d4c7c98ed3c1116224663376350a9216e2acc015..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
+++ /dev/null
@@ -1,80 +0,0 @@
-_base_ = [
-    '../_base_/models/retinanet_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
-]
-cudnn_benchmark = True
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch'),
-    neck=dict(
-        relu_before_extra_convs=True,
-        no_norm_on_lateral=True,
-        norm_cfg=norm_cfg),
-    bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
-    # training and testing settings
-    train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
-# dataset settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=(640, 640),
-        ratio_range=(0.8, 1.2),
-        keep_ratio=True),
-    dict(type='RandomCrop', crop_size=(640, 640)),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=(640, 640)),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(640, 640),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=64),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(
-    type='SGD',
-    lr=0.08,
-    momentum=0.9,
-    weight_decay=0.0001,
-    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.1,
-    step=[30, 40])
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=50)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 8fec6ba255f33d48a66a831de4571346a7a2bd2e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/GuXiaoBei/wechat-chatbot/scripts/tout.sh b/spaces/GuXiaoBei/wechat-chatbot/scripts/tout.sh
deleted file mode 100644
index 5b71491ad30812170f89583bd34ab25b47879274..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/scripts/tout.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-#打开日志
-
-cd `dirname $0`/..
-export BASE_DIR=`pwd`
-echo $BASE_DIR
-
-# check the nohup.out log output file
-if [ ! -f "${BASE_DIR}/nohup.out" ]; then
-   echo "No file  ${BASE_DIR}/nohup.out"  
-   exit -1;
-fi
-
-tail -f "${BASE_DIR}/nohup.out"
diff --git a/spaces/HESOAYM/ElviraMulti/run_Linux.sh b/spaces/HESOAYM/ElviraMulti/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/HESOAYM/ElviraMulti/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
-	# 如果没有运行，启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/utils.py b/spaces/HaHaBill/LandShapes-Antarctica/utils.py
deleted file mode 100644
index 5498289425bb70e959c0194eb7c6fab63e0c045a..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/utils.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License. You may obtain a copy
-# of the License at http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software distributed under
-# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-import string
-import numpy as np
-from pathlib import Path
-import requests
-import pickle
-import sys
-import re
-import gdown
-
-def prettify_name(name):
-    valid = "-_%s%s" % (string.ascii_letters, string.digits)
-    return ''.join(map(lambda c : c if c in valid else '_', name))
-
-# Add padding to sequence of images
-# Used in conjunction with np.hstack/np.vstack
-# By default: adds one 64th of the width of horizontal padding
-def pad_frames(strip, pad_fract_horiz=64, pad_fract_vert=0, pad_value=None):
-    dtype = strip[0].dtype
-    if pad_value is None:
-        if dtype in [np.float32, np.float64]:
-            pad_value = 1.0
-        else:
-            pad_value = np.iinfo(dtype).max
-    
-    frames = [strip[0]]
-    for frame in strip[1:]:
-        if pad_fract_horiz > 0:
-            frames.append(pad_value*np.ones((frame.shape[0], frame.shape[1]//pad_fract_horiz, 3), dtype=dtype))
-        elif pad_fract_vert > 0:
-            frames.append(pad_value*np.ones((frame.shape[0]//pad_fract_vert, frame.shape[1], 3), dtype=dtype))
-        frames.append(frame)
-    return frames
-
-
-def download_google_drive(url, output_name):
-    print('Downloading', url)
-    gdown.download(url, str(output_name))
-    # session = requests.Session()
-    # r = session.get(url, allow_redirects=True)
-    # r.raise_for_status()
-
-    # # Google Drive virus check message
-    # if r.encoding is not None:
-    #     tokens = re.search('(confirm=.+)&amp;id', str(r.content))
-    #     assert tokens is not None, 'Could not extract token from response'
-
-    #     url = url.replace('id=', f'{tokens[1]}&id=')
-    #     r = session.get(url, allow_redirects=True)
-    #     r.raise_for_status()
-
-    # assert r.encoding is None, f'Failed to download weight file from {url}'
-
-    # with open(output_name, 'wb') as f:
-    #     f.write(r.content)
-
-def download_generic(url, output_name):
-    print('Downloading', url)
-    session = requests.Session()
-    r = session.get(url, allow_redirects=True)
-    r.raise_for_status()
-
-    # No encoding means raw data
-    if r.encoding is None:
-        with open(output_name, 'wb') as f:
-            f.write(r.content)
-    else:
-        download_manual(url, output_name)
-
-def download_manual(url, output_name):
-    outpath = Path(output_name).resolve()
-    while not outpath.is_file():
-        print('Could not find checkpoint')
-        print(f'Please download the checkpoint from\n{url}\nand save it as\n{outpath}')
-        input('Press any key to continue...')
-
-def download_ckpt(url, output_name):
-    if 'drive.google' in url:
-        download_google_drive(url, output_name)
-    elif 'mega.nz' in url:
-        download_manual(url, output_name)
-    else:
-        download_generic(url, output_name)
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/k_diffusion/external.py b/spaces/Hallucinate/demo/k_diffusion/external.py
deleted file mode 100644
index 79b51cec41c52f054775f26c26cf63414d588aef..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/k_diffusion/external.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-from . import sampling, utils
-
-
-class VDenoiser(nn.Module):
-    """A v-diffusion-pytorch model wrapper for k-diffusion."""
-
-    def __init__(self, inner_model):
-        super().__init__()
-        self.inner_model = inner_model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def sigma_to_t(self, sigma):
-        return sigma.atan() / math.pi * 2
-
-    def t_to_sigma(self, t):
-        return (t * math.pi / 2).tan()
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class DiscreteSchedule(nn.Module):
-    """A mapping between continuous noise levels (sigmas) and a list of discrete noise
-    levels."""
-
-    def __init__(self, sigmas, quantize):
-        super().__init__()
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
-        self.quantize = quantize
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def get_sigmas(self, n=None):
-        if n is None:
-            return sampling.append_zero(self.sigmas.flip(0))
-        t_max = len(self.sigmas) - 1
-        t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
-        return sampling.append_zero(self.t_to_sigma(t))
-
-    def sigma_to_t(self, sigma, quantize=None):
-        quantize = self.quantize if quantize is None else quantize
-        log_sigma = sigma.log()
-        dists = log_sigma - self.log_sigmas[:, None]
-        if quantize:
-            return dists.abs().argmin(dim=0).view(sigma.shape)
-        low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
-        high_idx = low_idx + 1
-        low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
-        w = (low - log_sigma) / (low - high)
-        w = w.clamp(0, 1)
-        t = (1 - w) * low_idx + w * high_idx
-        return t.view(sigma.shape)
-
-    def t_to_sigma(self, t):
-        t = t.float()
-        low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
-        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
-        return log_sigma.exp()
-
-
-class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output eps (the predicted
-    noise)."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_out = -sigma
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_out, c_in
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return (eps - noise).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return input + eps * c_out
-
-
-class OpenAIDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for OpenAI diffusion models."""
-
-    def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'):
-        alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32)
-        super().__init__(model, alphas_cumprod, quantize=quantize)
-        self.has_learned_sigmas = has_learned_sigmas
-
-    def get_eps(self, *args, **kwargs):
-        model_output = self.inner_model(*args, **kwargs)
-        if self.has_learned_sigmas:
-            return model_output.chunk(2, dim=1)[0]
-        return model_output
-
-
-class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for CompVis diffusion models."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model.apply_model(*args, **kwargs)
-
-
-class DiscreteVDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output v."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def get_v(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class CompVisVDenoiser(DiscreteVDDPMDenoiser):
-    """A wrapper for CompVis diffusion models that output v."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_v(self, x, t, cond, **kwargs):
-        return self.inner_model.apply_model(x, t, cond)
diff --git a/spaces/Hallucinate/demo/taming/modules/discriminator/model.py b/spaces/Hallucinate/demo/taming/modules/discriminator/model.py
deleted file mode 100644
index 2aaa3110d0a7bcd05de7eca1e45101589ca5af05..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/taming/modules/discriminator/model.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-from taming.modules.util import ActNorm
-
-
-def weights_init(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm') != -1:
-        nn.init.normal_(m.weight.data, 1.0, 0.02)
-        nn.init.constant_(m.bias.data, 0)
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm2d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm2d
-        else:
-            use_bias = norm_layer != nn.BatchNorm2d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        sequence += [
-            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        return self.main(input)
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_msra.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_msra.sh
deleted file mode 100644
index 397c3ea6adc3d9f275389509aa41d0e4050b3c14..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_msra.sh
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=zen2_base_msra # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=1 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:1 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. 
-#SBATCH -o /cognitive_comp/ganruyi/experiments/ner_finetune/zen2_base_msra/%x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-# export CUDA_VISIBLE_DEVICES='2'
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=zen2_base
-
-TASK=msra
-
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/ner_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
-  mkdir -p ${ROOT_DIR}
-  echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
-  echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/lujunyu/data_zh/NER_Aligned/MSRA/
-PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_base_2.0
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-DATA_ARGS="\
-        --data_dir $DATA_DIR \
-        --train_data train_dev.char.bmes \
-        --valid_data test.char.bmes \
-        --test_data test.char.bmes \
-        --train_batchsize 32 \
-        --valid_batchsize 16 \
-        --max_seq_length 256 \
-        --task_name msra \
-        "
-
-MODEL_ARGS="\
-        --learning_rate 3e-5 \
-        --weight_decay 0.1 \
-        --warmup_ratio 0.01 \
-        --markup bioes \
-        --middle_prefix M- \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --monitor val_f1 \
-        --save_top_k 3 \
-        --mode max \
-        --every_n_train_steps 800 \
-        --save_weights_only True \
-        --dirpath $CHECKPOINT_PATH \
-        --filename model-{epoch:02d}-{val_f1:.4f} \
-        "
-
-TRAINER_ARGS="\
-        --max_epochs 30 \
-        --gpus 1 \
-        --check_val_every_n_epoch 1 \
-        --val_check_interval 800 \
-        --default_root_dir $ROOT_DIR \
-        "
-
-
-options=" \
-        --pretrained_model_path $PRETRAINED_MODEL_PATH \
-        --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \
-        --do_lower_case \
-        --output_save_path $OUTPUT_PATH \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-"
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
-# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-# python3 $SCRIPT_PATH $options
-# source activate base
-# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/encoders/nltk_tokenizer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/encoders/nltk_tokenizer.py
deleted file mode 100644
index 0ab92377b3a23bb48384c3f7acf299612e8b0775..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/encoders/nltk_tokenizer.py
+++ /dev/null
@@ -1,24 +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.data.encoders import register_tokenizer
-from fairseq.dataclass import FairseqDataclass
-
-
-@register_tokenizer("nltk", dataclass=FairseqDataclass)
-class NLTKTokenizer(object):
-    def __init__(self, *unused):
-        try:
-            from nltk.tokenize import word_tokenize
-
-            self.word_tokenize = word_tokenize
-        except ImportError:
-            raise ImportError("Please install nltk with: pip install nltk")
-
-    def encode(self, x: str) -> str:
-        return " ".join(self.word_tokenize(x))
-
-    def decode(self, x: str) -> str:
-        return x
diff --git a/spaces/Harveenchadha/BioGPT/README.md b/spaces/Harveenchadha/BioGPT/README.md
deleted file mode 100644
index b2ad15cdc4c1c4b3bd7594364a1a6a1e0c2b6f1f..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/BioGPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Biogpt
-emoji: 📊
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/init.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/init.py
deleted file mode 100644
index f73ffe7e9eb2f852c6ff95c40b21445a7a2c4529..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/init.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import os
-import json
-import argparse
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-
-from data_utils import TextMelLoader, TextMelCollate
-import models
-import commons
-import utils
-from text.symbols import symbols
-
-
-class FlowGenerator_DDI(models.FlowGenerator):
-    """A helper for Data-dependent Initialization"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        for f in self.decoder.flows:
-            if getattr(f, "set_ddi", False):
-                f.set_ddi(True)
-
-
-def main():
-    hps = utils.get_hparams()
-    logger = utils.get_logger(hps.log_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.log_dir)
-
-    torch.manual_seed(hps.train.seed)
-
-    train_dataset = TextMelLoader(hps.data.training_files, hps.data)
-    collate_fn = TextMelCollate(1)
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=8,
-        shuffle=True,
-        batch_size=hps.train.batch_size,
-        pin_memory=True,
-        drop_last=True,
-        collate_fn=collate_fn,
-    )
-
-    generator = FlowGenerator_DDI(
-        len(symbols) + getattr(hps.data, "add_blank", False),
-        out_channels=hps.data.n_mel_channels,
-        **hps.model
-    ).cuda()
-    optimizer_g = commons.Adam(
-        generator.parameters(),
-        scheduler=hps.train.scheduler,
-        dim_model=hps.model.hidden_channels,
-        warmup_steps=hps.train.warmup_steps,
-        lr=hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-
-    generator.train()
-    for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):
-        x, x_lengths = x.cuda(), x_lengths.cuda()
-        y, y_lengths = y.cuda(), y_lengths.cuda()
-
-        _ = generator(x, x_lengths, y, y_lengths, gen=False)
-        break
-
-    utils.save_checkpoint(
-        generator,
-        optimizer_g,
-        hps.train.learning_rate,
-        0,
-        os.path.join(hps.model_dir, "ddi_G.pth"),
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Harveenchadha/en_to_indic_translation/scripts/add_joint_tags_translate.py b/spaces/Harveenchadha/en_to_indic_translation/scripts/add_joint_tags_translate.py
deleted file mode 100644
index 532731b38615cf847dc3dc1661c88641df55d673..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/scripts/add_joint_tags_translate.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import sys
-from tqdm import tqdm
-import os
-
-
-def add_token(sent, tag_infos):
-    """ add special tokens specified by tag_infos to each element in list
-
-    tag_infos: list of tuples (tag_type,tag)
-
-    each tag_info results in a token of the form: __{tag_type}__{tag}__
-
-    """
-
-    tokens = []
-    for tag_type, tag in tag_infos:
-        token = '__' + tag_type + '__' + tag + '__'
-        tokens.append(token)
-
-    return ' '.join(tokens) + ' ' + sent
-
-
-def generate_lang_tag_iterator(infname):
-    with open(infname, 'r', encoding='utf-8') as infile:
-        for line in infile:
-            src, tgt, count = line.strip().split('\t')
-            count = int(count)
-            for _ in range(count):
-                yield (src, tgt)
-
-
-if __name__ == '__main__':
-
-    expdir = sys.argv[1]
-    dset = sys.argv[2]
-
-    src_fname = '{expdir}/bpe/{dset}.SRC'.format(
-        expdir=expdir, dset=dset)
-    tgt_fname = '{expdir}/bpe/{dset}.TGT'.format(
-        expdir=expdir, dset=dset)
-    meta_fname = '{expdir}/data/{dset}_lang_pairs.txt'.format(
-        expdir=expdir, dset=dset)
-
-    out_src_fname = '{expdir}/final/{dset}.SRC'.format(
-        expdir=expdir, dset=dset)
-    out_tgt_fname = '{expdir}/final/{dset}.TGT'.format(
-        expdir=expdir, dset=dset)
-    lang_tag_iterator = generate_lang_tag_iterator(meta_fname)
-
-    os.makedirs('{expdir}/final'.format(expdir=expdir), exist_ok=True)
-
-    with open(src_fname, 'r', encoding='utf-8') as srcfile, \
-            open(tgt_fname, 'r', encoding='utf-8') as tgtfile, \
-            open(out_src_fname, 'w', encoding='utf-8') as outsrcfile, \
-            open(out_tgt_fname, 'w', encoding='utf-8') as outtgtfile:
-
-        for (l1, l2), src_sent, tgt_sent in tqdm(zip(lang_tag_iterator,
-                                                     srcfile, tgtfile)):
-            outsrcfile.write(add_token(src_sent.strip(), [
-                             ('src', l1), ('tgt', l2)]) + '\n')
-            outtgtfile.write(tgt_sent.strip() + '\n')
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9a9131f6.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9a9131f6.js
deleted file mode 100644
index d0d5d4e6e332bfdeebdd65724d1788f436738c5e..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9a9131f6.js
+++ /dev/null
@@ -1,2 +0,0 @@
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R=({detail:d})=>l(0,u=d),K=({detail:d})=>l(12,E=d),L=({detail:d})=>{l(1,s=s||{}),l(1,s.status="error",s),l(1,s.message=d,s)};function m(d){P.call(this,n,d)}function G(d){P.call(this,n,d)}function k(d){P.call(this,n,d)}function X(d){P.call(this,n,d)}function z(d){P.call(this,n,d)}return n.$$set=d=>{"elem_id"in d&&l(2,r=d.elem_id),"visible"in d&&l(3,i=d.visible),"value"in d&&l(0,u=d.value),"label"in d&&l(4,g=d.label),"source"in d&&l(5,a=d.source),"root"in d&&l(14,_=d.root),"root_url"in d&&l(15,o=d.root_url),"show_label"in d&&l(6,c=d.show_label),"loading_status"in d&&l(1,s=d.loading_status),"style"in d&&l(7,b=d.style),"mirror_webcam"in d&&l(8,F=d.mirror_webcam),"include_audio"in d&&l(9,y=d.include_audio),"mode"in d&&l(10,S=d.mode)},n.$$.update=()=>{n.$$.dirty&49153&&l(11,I=ze(u,o??_))},[u,s,r,i,g,a,c,b,F,y,S,I,E,t,_,o,R,K,L,m,G,k,X,z]}class ht extends H{constructor(e){super(),O(this,e,bt,gt,Q,{elem_id:2,visible:3,value:0,label:4,source:5,root:14,root_url:15,show_label:6,loading_status:1,style:7,mirror_webcam:8,include_audio:9,mode:10})}}var Nt=ht;const Tt=["static","dynamic"],Vt=n=>({type:"{ name: string; data: string }",description:"file name and base64 data of video file"});export{Nt as Component,Vt as document,Tt as modes};
-//# sourceMappingURL=index.9a9131f6.js.map
diff --git a/spaces/Hypersonic0945/GenAISample/app.py b/spaces/Hypersonic0945/GenAISample/app.py
deleted file mode 100644
index 8175955e7f9d639fcc4ee05c4f3f0c8526bd0126..0000000000000000000000000000000000000000
--- a/spaces/Hypersonic0945/GenAISample/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-generator = pipeline('text-generation', model = 'gpt2')
-
-def generate(text):
-  result = generator(text, max_length = 15, num_return_sequences = 1)
-  return result[0]["generated_text"]
-
-examples = [
-    ["One fine day, three friends went to the movies, but could not decide what movie to watch. One friend said, "],
-    ["The best headphones for music is,"],
-]
-
-demo = gr.Interface(
-    fn = generate,
-    inputs = gr.inputs.Textbox(lines = 5, label = "Input Text"),
-    outputs = gr.outputs.Textbox(label = "Generated Text"),
-    examples = examples
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/__init__.py
deleted file mode 100644
index e0a142a769360e1140bf814c532eaf841f1d52d8..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-# automatically import any Python files in the current directory
-cur_dir = os.path.dirname(__file__)
-for file in os.listdir(cur_dir):
-    path = os.path.join(cur_dir, file)
-    if (
-        not file.startswith("_")
-        and not file.startswith(".")
-        and (file.endswith(".py") or os.path.isdir(path))
-    ):
-        mod_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module(__name__ + "." + mod_name)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/nat_loss.py b/spaces/ICML2022/OFA/fairseq/fairseq/criterions/nat_loss.py
deleted file mode 100644
index 7dac32fbaf4fb10089c0bcd42b75d23f92b5cf66..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/nat_loss.py
+++ /dev/null
@@ -1,180 +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 torch
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from torch import Tensor
-
-from dataclasses import dataclass, field
-
-
-@dataclass
-class LabelSmoothedDualImitationCriterionConfig(FairseqDataclass):
-    label_smoothing: float = field(
-        default=0.0,
-        metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
-    )
-
-
-@register_criterion("nat_loss", dataclass=LabelSmoothedDualImitationCriterionConfig)
-class LabelSmoothedDualImitationCriterion(FairseqCriterion):
-    def __init__(self, task, label_smoothing):
-        super().__init__(task)
-        self.label_smoothing = label_smoothing
-
-    def _compute_loss(
-        self, outputs, targets, masks=None, label_smoothing=0.0, name="loss", factor=1.0
-    ):
-        """
-        outputs: batch x len x d_model
-        targets: batch x len
-        masks:   batch x len
-
-        policy_logprob: if there is some policy
-            depends on the likelihood score as rewards.
-        """
-
-        def mean_ds(x: Tensor, dim=None) -> Tensor:
-            return (
-                x.float().mean().type_as(x)
-                if dim is None
-                else x.float().mean(dim).type_as(x)
-            )
-
-        if masks is not None:
-            outputs, targets = outputs[masks], targets[masks]
-
-        if masks is not None and not masks.any():
-            nll_loss = torch.tensor(0)
-            loss = nll_loss
-        else:
-            logits = F.log_softmax(outputs, dim=-1)
-            if targets.dim() == 1:
-                losses = F.nll_loss(logits, targets.to(logits.device), reduction="none")
-
-            else:  # soft-labels
-                losses = F.kl_div(logits, targets.to(logits.device), reduction="none")
-                losses = losses.sum(-1)
-
-            nll_loss = mean_ds(losses)
-            if label_smoothing > 0:
-                loss = (
-                    nll_loss * (1 - label_smoothing) - mean_ds(logits) * label_smoothing
-                )
-            else:
-                loss = nll_loss
-
-        loss = loss * factor
-        return {"name": name, "loss": loss, "nll_loss": nll_loss, "factor": factor}
-
-    def _custom_loss(self, loss, name="loss", factor=1.0):
-        return {"name": name, "loss": loss, "factor": factor}
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        nsentences, ntokens = sample["nsentences"], sample["ntokens"]
-
-        # B x T
-        src_tokens, src_lengths = (
-            sample["net_input"]["src_tokens"],
-            sample["net_input"]["src_lengths"],
-        )
-        tgt_tokens, prev_output_tokens = sample["target"], sample["prev_target"]
-
-        outputs = model(src_tokens, src_lengths, prev_output_tokens, tgt_tokens)
-        losses, nll_loss = [], []
-
-        for obj in outputs:
-            if outputs[obj].get("loss", None) is None:
-                _losses = self._compute_loss(
-                    outputs[obj].get("out"),
-                    outputs[obj].get("tgt"),
-                    outputs[obj].get("mask", None),
-                    outputs[obj].get("ls", 0.0),
-                    name=obj + "-loss",
-                    factor=outputs[obj].get("factor", 1.0),
-                )
-            else:
-                _losses = self._custom_loss(
-                    outputs[obj].get("loss"),
-                    name=obj + "-loss",
-                    factor=outputs[obj].get("factor", 1.0),
-                )
-
-            losses += [_losses]
-            if outputs[obj].get("nll_loss", False):
-                nll_loss += [_losses.get("nll_loss", 0.0)]
-
-        loss = sum(l["loss"] for l in losses)
-        nll_loss = sum(l for l in nll_loss) if len(nll_loss) > 0 else loss.new_tensor(0)
-
-        # NOTE:
-        # we don't need to use sample_size as denominator for the gradient
-        # here sample_size is just used for logging
-        sample_size = 1
-        logging_output = {
-            "loss": loss.data,
-            "nll_loss": nll_loss.data,
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "sample_size": sample_size,
-        }
-
-        for l in losses:
-            logging_output[l["name"]] = (
-                utils.item(l["loss"].data / l["factor"])
-                if reduce
-                else l[["loss"]].data / l["factor"]
-            )
-
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        sample_size = utils.item(
-            sum(log.get("sample_size", 0) for log in logging_outputs)
-        )
-        loss = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
-        nll_loss = utils.item(sum(log.get("nll_loss", 0) for log in logging_outputs))
-
-        metrics.log_scalar(
-            "loss", loss / sample_size / math.log(2), sample_size, round=3
-        )
-        metrics.log_scalar(
-            "nll_loss", nll_loss / sample_size / math.log(2), sample_size, round=3
-        )
-        metrics.log_derived(
-            "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-        )
-
-        for key in logging_outputs[0]:
-            if key[-5:] == "-loss":
-                val = sum(log.get(key, 0) for log in logging_outputs)
-                metrics.log_scalar(
-                    key[:-5],
-                    val / sample_size / math.log(2) if sample_size > 0 else 0.0,
-                    sample_size,
-                    round=3,
-                )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/video2audio.py b/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/video2audio.py
deleted file mode 100644
index db50a5c6b62c4c1faea5fefbb078f16aa9bb7fa3..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/video2audio.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-from concurrent.futures import ThreadPoolExecutor
-
-from moviepy.editor import AudioFileClip
-
-video_dir = "./video_data/"
-audio_dir = "./raw_audio/"
-filelist = list(os.walk(video_dir))[0][2]
-
-
-def generate_infos():
-    videos = []
-    for file in filelist:
-        if file.endswith(".mp4"):
-            videos.append(file)
-    return videos
-
-
-def clip_file(file):
-    my_audio_clip = AudioFileClip(video_dir + file)
-    my_audio_clip.write_audiofile(audio_dir + file.rstrip(".mp4") + ".wav")
-
-
-if __name__ == "__main__":
-    infos = generate_infos()
-    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
-        executor.map(clip_file, infos)
diff --git a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/train/train_mem.py b/spaces/Intel/NeuralChat-ICX-INT4/fastchat/train/train_mem.py
deleted file mode 100644
index e4b33528482f160a10720c8cc02df451c905b3bd..0000000000000000000000000000000000000000
--- a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/train/train_mem.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn.
-
-# Need to call this before importing transformers.
-from fastchat.train.llama_flash_attn_monkey_patch import (
-    replace_llama_attn_with_flash_attn,
-)
-
-replace_llama_attn_with_flash_attn()
-
-from fastchat.train.train import train
-
-if __name__ == "__main__":
-    train()
diff --git a/spaces/Jackflack09/diffuse-custom/Waifu2x/utils/image_quality.py b/spaces/Jackflack09/diffuse-custom/Waifu2x/utils/image_quality.py
deleted file mode 100644
index ed142916b92553b3144464751483f664b1c0fb82..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/Waifu2x/utils/image_quality.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Pytorch Multi-Scale Structural Similarity Index (SSIM)
-# This code is written by jorge-pessoa (https://github.com/jorge-pessoa/pytorch-msssim)
-# MIT licence
-import math
-from math import exp
-
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-
-
-# +++++++++++++++++++++++++++++++++++++
-#           SSIM
-# -------------------------------------
-
-def gaussian(window_size, sigma):
-    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
-    return gauss / gauss.sum()
-
-
-def create_window(window_size, channel):
-    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
-    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
-    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
-    return window
-
-
-def _ssim(img1, img2, window, window_size, channel, size_average=True, full=False):
-    padd = 0
-
-    mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
-    mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
-
-    mu1_sq = mu1.pow(2)
-    mu2_sq = mu2.pow(2)
-    mu1_mu2 = mu1 * mu2
-
-    sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
-    sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
-    sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2
-
-    C1 = 0.01 ** 2
-    C2 = 0.03 ** 2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
-    v1 = 2.0 * sigma12 + C2
-    v2 = sigma1_sq + sigma2_sq + C2
-    cs = torch.mean(v1 / v2)
-
-    if size_average:
-        ret = ssim_map.mean()
-    else:
-        ret = ssim_map.mean(1).mean(1).mean(1)
-
-    if full:
-        return ret, cs
-    return ret
-
-
-class SSIM(torch.nn.Module):
-    def __init__(self, window_size=11, size_average=True):
-        super(SSIM, self).__init__()
-        self.window_size = window_size
-        self.size_average = size_average
-        self.channel = 1
-        self.window = create_window(window_size, self.channel)
-
-    def forward(self, img1, img2):
-        (_, channel, _, _) = img1.size()
-
-        if channel == self.channel and self.window.data.type() == img1.data.type():
-            window = self.window
-        else:
-            window = create_window(self.window_size, channel)
-
-            if img1.is_cuda:
-                window = window.cuda(img1.get_device())
-            window = window.type_as(img1)
-
-            self.window = window
-            self.channel = channel
-
-        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
-
-def ssim(img1, img2, window_size=11, size_average=True, full=False):
-    (_, channel, height, width) = img1.size()
-
-    real_size = min(window_size, height, width)
-    window = create_window(real_size, channel)
-
-    if img1.is_cuda:
-        window = window.cuda(img1.get_device())
-    window = window.type_as(img1)
-
-    return _ssim(img1, img2, window, real_size, channel, size_average, full=full)
-
-
-def msssim(img1, img2, window_size=11, size_average=True):
-    # TODO: fix NAN results
-    if img1.size() != img2.size():
-        raise RuntimeError('Input images must have the same shape (%s vs. %s).' %
-                           (img1.size(), img2.size()))
-    if len(img1.size()) != 4:
-        raise RuntimeError('Input images must have four dimensions, not %d' %
-                           len(img1.size()))
-
-    weights = torch.tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=img1.dtype)
-    if img1.is_cuda:
-        weights = weights.cuda(img1.get_device())
-
-    levels = weights.size()[0]
-    mssim = []
-    mcs = []
-    for _ in range(levels):
-        sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True)
-        mssim.append(sim)
-        mcs.append(cs)
-
-        img1 = F.avg_pool2d(img1, (2, 2))
-        img2 = F.avg_pool2d(img2, (2, 2))
-
-    mssim = torch.stack(mssim)
-    mcs = torch.stack(mcs)
-    return (torch.prod(mcs[0:levels - 1] ** weights[0:levels - 1]) *
-            (mssim[levels - 1] ** weights[levels - 1]))
-
-
-class MSSSIM(torch.nn.Module):
-    def __init__(self, window_size=11, size_average=True, channel=3):
-        super(MSSSIM, self).__init__()
-        self.window_size = window_size
-        self.size_average = size_average
-        self.channel = channel
-
-    def forward(self, img1, img2):
-        # TODO: store window between calls if possible
-        return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)
-
-
-def calc_psnr(sr, hr, scale=0, benchmark=False):
-    # adapt from EDSR: https://github.com/thstkdgus35/EDSR-PyTorch
-    diff = (sr - hr).data
-    if benchmark:
-        shave = scale
-        if diff.size(1) > 1:
-            convert = diff.new(1, 3, 1, 1)
-            convert[0, 0, 0, 0] = 65.738
-            convert[0, 1, 0, 0] = 129.057
-            convert[0, 2, 0, 0] = 25.064
-            diff.mul_(convert).div_(256)
-            diff = diff.sum(dim=1, keepdim=True)
-    else:
-        shave = scale + 6
-
-    valid = diff[:, :, shave:-shave, shave:-shave]
-    mse = valid.pow(2).mean()
-
-    return -10 * math.log10(mse)
-
-
-# +++++++++++++++++++++++++++++++++++++
-#           PSNR      
-# -------------------------------------
-from torch import nn
-
-
-def psnr(predict, target):
-    with torch.no_grad():
-        criteria = nn.MSELoss()
-        mse = criteria(predict, target)
-        return -10 * torch.log10(mse)
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/modules/models/configuration_moss.py b/spaces/JohnSmith9982/ChuanhuChatGPT/modules/models/configuration_moss.py
deleted file mode 100644
index 9bad4396ecea6578c1628732d0ef077d8964d45d..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/modules/models/configuration_moss.py
+++ /dev/null
@@ -1,118 +0,0 @@
-""" Moss model configuration"""
-
-from transformers.utils import logging
-from transformers.configuration_utils import PretrainedConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class MossConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MossModel`]. It is used to instantiate a
-    Moss model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Moss
-    [fnlp/moss-moon-003-base](https://huggingface.co/fnlp/moss-moon-003-base) architecture. Configuration objects
-    inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
-    [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 107008):
-            Vocabulary size of the Moss model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MossModel`].
-        n_positions (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        n_embd (`int`, *optional*, defaults to 4096):
-            Dimensionality of the embeddings and hidden states.
-        n_layer (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer encoder.
-        n_head (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        rotary_dim (`int`, *optional*, defaults to 64):
-            Number of dimensions in the embedding that Rotary Position Embedding is applied to.
-        n_inner (`int`, *optional*, defaults to None):
-            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
-        activation_function (`str`, *optional*, defaults to `"gelu_new"`):
-            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
-        resid_pdrop (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        embd_pdrop (`int`, *optional*, defaults to 0.1):
-            The dropout ratio for the embeddings.
-        attn_pdrop (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon to use in the layer normalization layers.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-
-    Example:
-
-    ```python
-    >>> from modeling_moss import MossModel
-    >>> from configuration_moss import MossConfig
-
-    >>> # Initializing a moss-moon-003-base configuration
-    >>> configuration = MossConfig()
-
-    >>> # Initializing a model (with random weights) from the configuration
-    >>> model = MossModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "moss"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
-
-    def __init__(
-        self,
-        vocab_size=107008,
-        n_positions=2048,
-        n_ctx=2048,
-        n_embd=4096,
-        n_layer=28,
-        n_head=16,
-        rotary_dim=64,
-        n_inner=None,
-        activation_function="gelu_new",
-        resid_pdrop=0.0,
-        embd_pdrop=0.0,
-        attn_pdrop=0.0,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        bos_token_id=106028,
-        eos_token_id=106068,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.n_ctx = n_ctx
-        self.n_positions = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.n_inner = n_inner
-        self.rotary_dim = rotary_dim
-        self.activation_function = activation_function
-        self.resid_pdrop = resid_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.attn_pdrop = attn_pdrop
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-
-        super().__init__(
-            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
-        )
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/__init__.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/KevinQHLin/UniVTG/utils/basic_utils.py b/spaces/KevinQHLin/UniVTG/utils/basic_utils.py
deleted file mode 100644
index ab03b808017927d67665a37d606de7b6538253a6..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/utils/basic_utils.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import os
-import json
-import torch
-import random
-import zipfile
-import numpy as np
-import pickle
-from collections import OrderedDict, Counter
-import pandas as pd
-import shutil
-
-def set_seed(seed, use_cuda=True):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if use_cuda:
-        torch.cuda.manual_seed_all(seed)
-
-def load_pickle(filename):
-    with open(filename, "rb") as f:
-        return pickle.load(f)
-
-
-def save_pickle(data, filename):
-    with open(filename, "wb") as f:
-        pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
-
-
-def load_json(filename):
-    with open(filename, "r") as f:
-        return json.load(f)
-
-
-def save_json(data, filename, save_pretty=False, sort_keys=False):
-    with open(filename, "w") as f:
-        if save_pretty:
-            f.write(json.dumps(data, indent=4, sort_keys=sort_keys))
-        else:
-            json.dump(data, f)
-
-
-def load_jsonl(filename):
-    with open(filename, "r") as f:
-        return [json.loads(l.strip("\n")) for l in f.readlines()]
-
-
-def save_jsonl(data, filename):
-    """data is a list"""
-    with open(filename, "w") as f:
-        f.write("\n".join([json.dumps(e) for e in data]))
-
-
-def save_lines(list_of_str, filepath):
-    with open(filepath, "w") as f:
-        f.write("\n".join(list_of_str))
-
-
-def read_lines(filepath):
-    with open(filepath, "r") as f:
-        return [e.strip("\n") for e in f.readlines()]
-
-
-def mkdirp(p):
-    if not os.path.exists(p):
-        os.makedirs(p)
-
-def remkdirp(p):
-    if os.path.exists(p):
-        shutil.rmtree(p)
-    os.makedirs(p)
-
-def flat_list_of_lists(l):
-    """flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]"""
-    return [item for sublist in l for item in sublist]
-
-
-def convert_to_seconds(hms_time):
-    """ convert '00:01:12' to 72 seconds.
-    :hms_time (str): time in comma separated string, e.g. '00:01:12'
-    :return (int): time in seconds, e.g. 72
-    """
-    times = [float(t) for t in hms_time.split(":")]
-    return times[0] * 3600 + times[1] * 60 + times[2]
-
-
-def get_video_name_from_url(url):
-    return url.split("/")[-1][:-4]
-
-
-def merge_dicts(list_dicts):
-    merged_dict = list_dicts[0].copy()
-    for i in range(1, len(list_dicts)):
-        merged_dict.update(list_dicts[i])
-    return merged_dict
-
-
-def l2_normalize_np_array(np_array, eps=1e-5):
-    """np_array: np.ndarray, (*, D), where the last dim will be normalized"""
-    return np_array / (np.linalg.norm(np_array, axis=-1, keepdims=True) + eps)
-
-
-def make_zipfile(src_dir, save_path, enclosing_dir="", exclude_dirs=None, exclude_extensions=None,
-                 exclude_dirs_substring=None):
-    """make a zip file of root_dir, save it to save_path.
-    exclude_paths will be excluded if it is a subdir of root_dir.
-    An enclosing_dir is added is specified.
-    """
-    abs_src = os.path.abspath(src_dir)
-    with zipfile.ZipFile(save_path, "w") as zf:
-        for dirname, subdirs, files in os.walk(src_dir):
-            if exclude_dirs is not None:
-                for e_p in exclude_dirs:
-                    if e_p in subdirs:
-                        subdirs.remove(e_p)
-            if exclude_dirs_substring is not None:
-                to_rm = []
-                for d in subdirs:
-                    if exclude_dirs_substring in d:
-                        to_rm.append(d)
-                for e in to_rm:
-                    subdirs.remove(e)
-            arcname = os.path.join(enclosing_dir, dirname[len(abs_src) + 1:])
-            zf.write(dirname, arcname)
-            for filename in files:
-                if exclude_extensions is not None:
-                    if os.path.splitext(filename)[1] in exclude_extensions:
-                        continue  # do not zip it
-                absname = os.path.join(dirname, filename)
-                arcname = os.path.join(enclosing_dir, absname[len(abs_src) + 1:])
-                zf.write(absname, arcname)
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current/max/min value"""
-    def __init__(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-        self.max = -1e10
-        self.min = 1e10
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-        self.max = -1e10
-        self.min = 1e10
-
-    def update(self, val, n=1):
-        self.max = max(val, self.max)
-        self.min = min(val, self.min)
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def dissect_by_lengths(np_array, lengths, dim=0, assert_equal=True):
-    """Dissect an array (N, D) into a list a sub-array,
-    np_array.shape[0] == sum(lengths), Output is a list of nd arrays, singlton dimention is kept"""
-    if assert_equal:
-        assert len(np_array) == sum(lengths)
-    length_indices = [0, ]
-    for i in range(len(lengths)):
-        length_indices.append(length_indices[i] + lengths[i])
-    if dim == 0:
-        array_list = [np_array[length_indices[i]:length_indices[i+1]] for i in range(len(lengths))]
-    elif dim == 1:
-        array_list = [np_array[:, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))]
-    elif dim == 2:
-        array_list = [np_array[:, :, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))]
-    else:
-        raise NotImplementedError
-    return array_list
-
-
-def get_ratio_from_counter(counter_obj, threshold=200):
-    keys = counter_obj.keys()
-    values = counter_obj.values()
-    filtered_values = [counter_obj[k] for k in keys if k > threshold]
-    return float(sum(filtered_values)) / sum(values)
-
-
-def get_counter_dist(counter_object, sort_type="none"):
-    _sum = sum(counter_object.values())
-    dist = {k: float(f"{100 * v / _sum:.2f}") for k, v in counter_object.items()}
-    if sort_type == "value":
-        dist = OrderedDict(sorted(dist.items(), reverse=True))
-    return dist
-
-
-def get_show_name(vid_name):
-    """
-    get tvshow name from vid_name
-    :param vid_name: video clip name
-    :return: tvshow name
-    """
-    show_list = ["friends", "met", "castle", "house", "grey"]
-    vid_name_prefix = vid_name.split("_")[0]
-    show_name = vid_name_prefix if vid_name_prefix in show_list else "bbt"
-    return show_name
-
-
-def get_abspaths_by_ext(dir_path, ext=(".jpg",)):
-    """Get absolute paths to files in dir_path with extensions specified by ext.
-    Note this function does work recursively.
-    """
-    if isinstance(ext, list):
-        ext = tuple(ext)
-    if isinstance(ext, str):
-        ext = tuple([ext, ])
-    filepaths = [os.path.join(root, name)
-                 for root, dirs, files in os.walk(dir_path)
-                 for name in files
-                 if name.endswith(tuple(ext))]
-    return filepaths
-
-
-def get_basename_no_ext(path):
-    """ '/data/movienet/240p_keyframe_feats/tt7672188.npz' --> 'tt7672188' """
-    return os.path.splitext(os.path.split(path)[1])[0]
-
-
-def dict_to_markdown(d, max_str_len=120):
-    # convert list into its str representation
-    d = {k: v.__repr__() if isinstance(v, list) else v for k, v in d.items()}
-    # truncate string that is longer than max_str_len
-    if max_str_len is not None:
-        d = {k: v[-max_str_len:] if isinstance(v, str) else v for k, v in d.items()}
-    return pd.DataFrame(d, index=[0]).transpose().to_markdown()
-
diff --git a/spaces/KonradSzafer/HF-QA-Demo/tests/index/test_index.py b/spaces/KonradSzafer/HF-QA-Demo/tests/index/test_index.py
deleted file mode 100644
index 95fc6ecca44a4d7b6ec77e62dfd4e54bd9e6e3e1..0000000000000000000000000000000000000000
--- a/spaces/KonradSzafer/HF-QA-Demo/tests/index/test_index.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import pytest
-from typing import Any
-from huggingface_hub import snapshot_download
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.vectorstores import FAISS
-
-
-snapshot_download(
-    repo_id='KonradSzafer/index-large-notebooks',
-    allow_patterns=['*.faiss', '*.pkl'], 
-    repo_type='dataset',
-    local_dir='indexes/'
-)
-
-@pytest.fixture(scope='module')
-def embedding_model() -> HuggingFaceInstructEmbeddings:
-    model_name = 'hkunlp/instructor-large'
-    embed_instruction = 'Represent the Hugging Face library documentation'
-    query_instruction = 'Query the most relevant piece of information from the Hugging Face documentation'
-    return HuggingFaceInstructEmbeddings(
-        model_name=model_name,
-        embed_instruction=embed_instruction,
-        query_instruction=query_instruction,
-    )
-
-@pytest.fixture(scope='module')
-def index_path() -> str:
-    return 'indexes/'
-
-@pytest.fixture(scope='module')
-def index(embedding_model: HuggingFaceInstructEmbeddings, index_path: str):
-    return FAISS.load_local(index_path, embedding_model)
-
-@pytest.fixture(scope='module')
-def query() -> str:
-    return 'How to use the tokenizer?'
-
-def test_load_index(embedding_model: HuggingFaceInstructEmbeddings, index_path: str):
-    index = FAISS.load_local(index_path, embedding_model)
-    assert index is not None, 'Failed to load index'
-
-def test_index_page_content(index, query: str):
-    query_docs = index.similarity_search(query=query, k=3)
-    assert isinstance(query_docs[0].page_content, str)
-
-def test_index_metadata(index, query):
-    query_docs = index.similarity_search(query=query, k=3)
-    assert isinstance(query_docs[0].metadata['source'], str)
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/embedding_rpn_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/embedding_rpn_head.py
deleted file mode 100644
index 97e84fa83b892c0274615d582fe43a6693541617..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/embedding_rpn_head.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-import torch
-import torch.nn as nn
-from mmengine.model import BaseModule
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures.bbox import bbox_cxcywh_to_xyxy
-from mmdet.structures.det_data_sample import SampleList
-from mmdet.utils import InstanceList, OptConfigType
-
-
-@MODELS.register_module()
-class EmbeddingRPNHead(BaseModule):
-    """RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
-
-    Unlike traditional RPNHead, this module does not need FPN input, but just
-    decode `init_proposal_bboxes` and expand the first dimension of
-    `init_proposal_bboxes` and `init_proposal_features` to the batch_size.
-
-    Args:
-        num_proposals (int): Number of init_proposals. Defaults to 100.
-        proposal_feature_channel (int): Channel number of
-            init_proposal_feature. Defaults to 256.
-        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
-            dict]): Initialization config dict. Defaults to None.
-    """
-
-    def __init__(self,
-                 num_proposals: int = 100,
-                 proposal_feature_channel: int = 256,
-                 init_cfg: OptConfigType = None,
-                 **kwargs) -> None:
-        # `**kwargs` is necessary to avoid some potential error.
-        assert init_cfg is None, 'To prevent abnormal initialization ' \
-                                 'behavior, init_cfg is not allowed to be set'
-        super().__init__(init_cfg=init_cfg)
-        self.num_proposals = num_proposals
-        self.proposal_feature_channel = proposal_feature_channel
-        self._init_layers()
-
-    def _init_layers(self) -> None:
-        """Initialize a sparse set of proposal boxes and proposal features."""
-        self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4)
-        self.init_proposal_features = nn.Embedding(
-            self.num_proposals, self.proposal_feature_channel)
-
-    def init_weights(self) -> None:
-        """Initialize the init_proposal_bboxes as normalized.
-
-        [c_x, c_y, w, h], and we initialize it to the size of  the entire
-        image.
-        """
-        super().init_weights()
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5)
-        nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1)
-
-    def _decode_init_proposals(self, x: List[Tensor],
-                               batch_data_samples: SampleList) -> InstanceList:
-        """Decode init_proposal_bboxes according to the size of images and
-        expand dimension of init_proposal_features to batch_size.
-
-        Args:
-            x (list[Tensor]): List of FPN features.
-            batch_data_samples (List[:obj:`DetDataSample`]): The Data
-                Samples. It usually includes information such as
-                `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
-
-        Returns:
-            List[:obj:`InstanceData`:] Detection results of each image.
-            Each item usually contains following keys.
-
-            - proposals: Decoded proposal bboxes,
-              has shape (num_proposals, 4).
-            - features: init_proposal_features, expanded proposal
-              features, has shape
-              (num_proposals, proposal_feature_channel).
-            - imgs_whwh: Tensor with shape
-              (num_proposals, 4), the dimension means
-              [img_width, img_height, img_width, img_height].
-        """
-        batch_img_metas = []
-        for data_sample in batch_data_samples:
-            batch_img_metas.append(data_sample.metainfo)
-
-        proposals = self.init_proposal_bboxes.weight.clone()
-        proposals = bbox_cxcywh_to_xyxy(proposals)
-        imgs_whwh = []
-        for meta in batch_img_metas:
-            h, w = meta['img_shape'][:2]
-            imgs_whwh.append(x[0].new_tensor([[w, h, w, h]]))
-        imgs_whwh = torch.cat(imgs_whwh, dim=0)
-        imgs_whwh = imgs_whwh[:, None, :]
-        proposals = proposals * imgs_whwh
-
-        rpn_results_list = []
-        for idx in range(len(batch_img_metas)):
-            rpn_results = InstanceData()
-            rpn_results.bboxes = proposals[idx]
-            rpn_results.imgs_whwh = imgs_whwh[idx].repeat(
-                self.num_proposals, 1)
-            rpn_results.features = self.init_proposal_features.weight.clone()
-            rpn_results_list.append(rpn_results)
-        return rpn_results_list
-
-    def loss(self, *args, **kwargs):
-        """Perform forward propagation and loss calculation of the detection
-        head on the features of the upstream network."""
-        raise NotImplementedError(
-            'EmbeddingRPNHead does not have `loss`, please use '
-            '`predict` or `loss_and_predict` instead.')
-
-    def predict(self, x: List[Tensor], batch_data_samples: SampleList,
-                **kwargs) -> InstanceList:
-        """Perform forward propagation of the detection head and predict
-        detection results on the features of the upstream network."""
-        # `**kwargs` is necessary to avoid some potential error.
-        return self._decode_init_proposals(
-            x=x, batch_data_samples=batch_data_samples)
-
-    def loss_and_predict(self, x: List[Tensor], batch_data_samples: SampleList,
-                         **kwargs) -> tuple:
-        """Perform forward propagation of the head, then calculate loss and
-        predictions from the features and data samples."""
-        # `**kwargs` is necessary to avoid some potential error.
-        predictions = self._decode_init_proposals(
-            x=x, batch_data_samples=batch_data_samples)
-
-        return dict(), predictions
diff --git a/spaces/LanguageBind/LanguageBind/model/languagebind.py b/spaces/LanguageBind/LanguageBind/model/languagebind.py
deleted file mode 100644
index 2f4091fef20bd6623634e6693104e18fab33222e..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/model/languagebind.py
+++ /dev/null
@@ -1,140 +0,0 @@
-
-import gradio as gr
-import argparse
-import numpy as np
-import torch
-from torch import nn
-
-from data.process_image import load_and_transform_image, get_image_transform
-from main import SET_GLOBAL_VALUE
-from model.build_model import create_vat_model
-from data.process_audio import load_and_transform_audio, get_audio_transform
-from data.process_video import load_and_transform_video, get_video_transform
-from data.process_depth import load_and_transform_depth, get_depth_transform
-from data.process_thermal import load_and_transform_thermal, get_thermal_transform
-from data.process_text import load_and_transform_text
-from open_clip import get_tokenizer
-from open_clip.factory import HF_HUB_PREFIX
-
-
-
-
-
-
-class LanguageBind(nn.Module):
-    def __init__(self, args, no_temp=False):
-        super(LanguageBind, self).__init__()
-        self.no_temp = no_temp
-        MODEL_DICT = {"ViT-L-14": "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
-                      "ViT-H-14": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"}
-        args.pretrained = False
-        args.model = MODEL_DICT["ViT-L-14"]
-        args.cache_dir = 'D:/Omni-modal-valdt-audio'
-        args.video_decode_backend = 'decord'
-        # args.device = 'cpu'
-        args.device = 'cuda:0'
-        device = torch.device(args.device)
-        args.precision = None
-        args.init_temp = 0
-        args.force_patch_dropout = 0.0
-        args.add_time_attn = False
-        args.convert_to_lora = True
-        args.lora_r = 2
-        args.lora_alpha = 16
-        args.lora_dropout = 0.0  # 0.1?
-        args.num_frames = 8
-        args.clip_type = 'vl'
-        args.num_mel_bins = 1008
-        args.target_length = 112
-        args.audio_sample_rate = 16000
-        args.audio_mean = 4.5689974
-        args.audio_std = -4.2677393
-        args.max_depth = 10
-        args.image_size = 224
-        args.rank = 0
-        SET_GLOBAL_VALUE('PATCH_DROPOUT', args.force_patch_dropout)
-        SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
-        args.clip_type = ['il', 'vl', 'al', 'dl', 'tl']
-
-
-        temp_clip_type = args.clip_type
-        self.modality_encoder = {}
-        self.modality_proj = {}
-        self.modality_scale = {}
-        for c in temp_clip_type:
-            args.clip_type = c
-            if c == 'il':
-                args.convert_to_lora = False
-                model = create_vat_model(args)
-                args.convert_to_lora = True
-            elif c == 'vl':
-                args.lora_r = 64
-                args.add_time_attn = True
-                model = create_vat_model(args)
-                args.add_time_attn = False
-                args.lora_r = 2
-            elif c == 'al':
-                args.lora_r = 8
-                model = create_vat_model(args)
-                args.lora_r = 2
-            else:
-                model = create_vat_model(args)
-            '''
-            state_dict = torch.load(f'model_zoo/{c}.pt', map_location='cpu')
-            if state_dict.get('state_dict', None) is not None:
-                state_dict = state_dict['state_dict']
-            if next(iter(state_dict.items()))[0].startswith('module'):
-                state_dict = {k[7:]: v for k, v in state_dict.items()}
-            msg = model.load_state_dict(state_dict, strict=False)
-            print(f'load {c}, {msg}')
-            '''
-            if c == 'vl':
-                self.modality_encoder['video'] = model.vision_model
-                self.modality_proj['video'] = model.visual_projection
-                self.modality_scale['video'] = model.logit_scale
-            elif c == 'al':
-                self.modality_encoder['audio'] = model.vision_model
-                self.modality_proj['audio'] = model.visual_projection
-                self.modality_scale['audio'] = model.logit_scale
-            elif c == 'dl':
-                self.modality_encoder['depth'] = model.vision_model
-                self.modality_proj['depth'] = model.visual_projection
-                self.modality_scale['depth'] = model.logit_scale
-            elif c == 'tl':
-                self.modality_encoder['thermal'] = model.vision_model
-                self.modality_proj['thermal'] = model.visual_projection
-                self.modality_scale['thermal'] = model.logit_scale
-            elif c == 'il':
-                self.modality_encoder['image'] = model.vision_model
-                self.modality_proj['image'] = model.visual_projection
-                self.modality_scale['image'] = model.logit_scale
-            else:
-                raise NameError(f'No clip_type of {c}')
-        self.modality_encoder['language'] = model.text_model
-        self.modality_proj['language'] = model.text_projection
-
-        self.modality_encoder = nn.ModuleDict(self.modality_encoder)
-        self.modality_proj = nn.ModuleDict(self.modality_proj)
-
-    def forward(self, inputs):
-        outputs = {}
-        for key, value in inputs.items():
-            value = self.modality_encoder[key](**value)[1]
-            value = self.modality_proj[key](value)
-            value = value / value.norm(p=2, dim=-1, keepdim=True)
-            if not self.no_temp:
-                if key != 'language':
-                    value = value * self.modality_scale[key].exp()
-            outputs[key] = value
-        return outputs
-
-
-
-def stack_dict(x, device):
-    if len(x) == 0:
-        return None
-    out_dict = {}
-    keys = list(x[0].keys())
-    for key in keys:
-        out_dict[key] = torch.stack([i[key] for i in x]).to(device)
-    return out_dict
\ No newline at end of file
diff --git a/spaces/Luelll/ChuanhuChatGPT/modules/models/modeling_moss.py b/spaces/Luelll/ChuanhuChatGPT/modules/models/modeling_moss.py
deleted file mode 100644
index b7adea5bca857f7fdd6399dde7ce359f8f8cecfe..0000000000000000000000000000000000000000
--- a/spaces/Luelll/ChuanhuChatGPT/modules/models/modeling_moss.py
+++ /dev/null
@@ -1,711 +0,0 @@
-""" PyTorch Moss model."""
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_utils import PreTrainedModel
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging
-)
-
-from .configuration_moss import MossConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "fnlp/moss-moon-003-base"
-_CONFIG_FOR_DOC = "MossConfig"
-
-
-MOSS_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "fnlp/moss-moon-003-base",
-    "fnlp/moss-moon-003-sft",
-    "fnlp/moss-moon-003-sft-plugin",
-]
-
-
-# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
-def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
-    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
-    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
-    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
-
-
-# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
-def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
-    x1 = x[:, :, :, ::2]
-    x2 = x[:, :, :, 1::2]
-    x = torch.stack((-x2, x1), dim=-1)
-    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')
-
-
-# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
-def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
-    sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
-    cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
-    return (tensor * cos) + (rotate_every_two(tensor) * sin)
-
-
-class MossAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        max_positions = config.max_position_embeddings
-        self.register_buffer(
-            "causal_mask",
-            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
-                1, 1, max_positions, max_positions
-            ),
-        )
-
-        self.attn_dropout = nn.Dropout(config.attn_pdrop)
-        self.resid_dropout = nn.Dropout(config.resid_pdrop)
-
-        self.embed_dim = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_attention_heads
-        if self.head_dim * self.num_attention_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
-                f" `num_attention_heads`: {self.num_attention_heads})."
-            )
-        self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
-        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
-
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
-        self.rotary_dim = config.rotary_dim
-        pos_embd_dim = self.rotary_dim or self.embed_dim
-        self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
-
-    def _split_heads(self, x, n_head, dim_head, mp_num):
-        reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
-        reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
-        return reshaped
-
-    def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
-        """
-        Merges attn_head_size dim and num_attn_heads dim into n_ctx
-        """
-        if len(tensor.shape) == 5:
-            tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
-        elif len(tensor.shape) == 4:
-            tensor = tensor.permute(0, 2, 1, 3).contiguous()
-        else:
-            raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
-        new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
-        return tensor.view(new_shape)
-
-    def _attn(
-        self,
-        query,
-        key,
-        value,
-        attention_mask=None,
-        head_mask=None,
-    ):
-        # compute causal mask from causal mask buffer
-        query_length, key_length = query.size(-2), key.size(-2)
-        causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]
-
-        # Keep the attention weights computation in fp32 to avoid overflow issues
-        query = query.to(torch.float32)
-        key = key.to(torch.float32)
-
-        attn_weights = torch.matmul(query, key.transpose(-1, -2))
-
-        attn_weights = attn_weights / self.scale_attn
-        mask_value = torch.finfo(attn_weights.dtype).min
-        # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
-        # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
-        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
-        attn_weights = torch.where(causal_mask, attn_weights, mask_value)
-
-        if attention_mask is not None:
-            # Apply the attention mask
-            attn_weights = attn_weights + attention_mask
-
-        attn_weights = nn.Softmax(dim=-1)(attn_weights)
-        attn_weights = attn_weights.to(value.dtype)
-        attn_weights = self.attn_dropout(attn_weights)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attn_weights = attn_weights * head_mask
-
-        attn_output = torch.matmul(attn_weights, value)
-
-        return attn_output, attn_weights
-
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        layer_past: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-    ) -> Union[
-        Tuple[torch.Tensor, Tuple[torch.Tensor]],
-        Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
-    ]:
-        qkv = self.qkv_proj(hidden_states)
-        # TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic
-        mp_num = 4
-        qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
-
-        local_dim = self.head_dim * self.num_attention_heads // mp_num
-        query, value, key = torch.split(qkv_split, local_dim, dim=-1)
-        query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-        key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-
-        value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-        value = value.permute(0, 2, 1, 3)
-
-        embed_positions = self.embed_positions
-        if embed_positions.device != position_ids.device:
-            embed_positions = embed_positions.to(position_ids.device)
-            self.embed_positions = embed_positions
-
-        sincos = embed_positions[position_ids]
-        sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
-
-        if self.rotary_dim is not None:
-            k_rot = key[:, :, :, : self.rotary_dim]
-            k_pass = key[:, :, :, self.rotary_dim :]
-
-            q_rot = query[:, :, :, : self.rotary_dim]
-            q_pass = query[:, :, :, self.rotary_dim :]
-
-            k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
-            q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
-
-            key = torch.cat([k_rot, k_pass], dim=-1)
-            query = torch.cat([q_rot, q_pass], dim=-1)
-        else:
-            key = apply_rotary_pos_emb(key, sin, cos)
-            query = apply_rotary_pos_emb(query, sin, cos)
-
-        key = key.permute(0, 2, 1, 3)
-        query = query.permute(0, 2, 1, 3)
-
-        if layer_past is not None:
-            past_key = layer_past[0]
-            past_value = layer_past[1]
-            key = torch.cat((past_key, key), dim=-2)
-            value = torch.cat((past_value, value), dim=-2)
-
-        if use_cache is True:
-            present = (key, value)
-        else:
-            present = None
-
-        # compute self-attention: V x Softmax(QK^T)
-        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
-
-        attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
-        attn_output = self.out_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
-
-        outputs = (attn_output, present)
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs  # a, present, (attentions)
-
-
-# Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->Moss
-class MossMLP(nn.Module):
-    def __init__(self, intermediate_size, config):  # in MLP: intermediate_size= 4 * embed_dim
-        super().__init__()
-        embed_dim = config.n_embd
-
-        self.fc_in = nn.Linear(embed_dim, intermediate_size)
-        self.fc_out = nn.Linear(intermediate_size, embed_dim)
-
-        self.act = ACT2FN[config.activation_function]
-        self.dropout = nn.Dropout(config.resid_pdrop)
-
-    def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
-        hidden_states = self.fc_in(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.fc_out(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->Moss
-class MossBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
-        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.attn = MossAttention(config)
-        self.mlp = MossMLP(inner_dim, config)
-
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        layer_past: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
-        residual = hidden_states
-        hidden_states = self.ln_1(hidden_states)
-        attn_outputs = self.attn(
-            hidden_states=hidden_states,
-            layer_past=layer_past,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-        )
-        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
-        outputs = attn_outputs[1:]
-
-        feed_forward_hidden_states = self.mlp(hidden_states)
-        hidden_states = attn_output + feed_forward_hidden_states + residual
-
-        if use_cache:
-            outputs = (hidden_states,) + outputs
-        else:
-            outputs = (hidden_states,) + outputs[1:]
-
-        return outputs  # hidden_states, present, (attentions)
-
-
-class MossPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = MossConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MossBlock"]
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, (nn.Linear,)):
-            # Slightly different from Mesh Transformer JAX 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.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].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, MossModel):
-            module.gradient_checkpointing = value
-
-
-MOSS_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`MossConfig`]): 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.
-"""
-
-MOSS_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the self-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 `({0}, hidden_dim)`, *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.
-"""
-
-
-@add_start_docstrings(
-    "The bare Moss Model transformer outputting raw hidden-states without any specific head on top.",
-    MOSS_START_DOCSTRING,
-)
-class MossModel(MossPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embed_dim = config.n_embd
-        self.vocab_size = config.vocab_size
-        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
-        self.drop = nn.Dropout(config.embd_pdrop)
-        self.h = nn.ModuleList([MossBlock(config) for _ in range(config.n_layer)])
-        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
-        self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)
-
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, new_embeddings):
-        self.wte = new_embeddings
-
-    @add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        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])
-            batch_size = input_ids.shape[0]
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size = inputs_embeds.shape[0]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if token_type_ids is not None:
-            token_type_ids = token_type_ids.view(-1, input_shape[-1])
-
-        if position_ids is not None:
-            position_ids = position_ids.view(-1, input_shape[-1]).long()
-
-        if past_key_values is None:
-            past_length = 0
-            past_key_values = tuple([None] * len(self.h))
-        else:
-            past_length = past_key_values[0][0].size(-2)
-
-        if position_ids is None:
-            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
-
-        # Attention mask.
-        if attention_mask is not None:
-            if batch_size <= 0:
-                raise ValueError("batch_size has to be defined and > 0")
-            attention_mask = attention_mask.view(batch_size, -1)
-            # We create a 3D attention mask from a 2D tensor mask.
-            # Sizes are [batch_size, 1, 1, to_seq_length]
-            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-            # this attention mask is more simple than the triangular masking of causal attention
-            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-            attention_mask = attention_mask[:, None, None, :]
-
-            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-            # masked positions, this operation will create a tensor which is 0.0 for
-            # positions we want to attend and the dtype's smallest value for masked positions.
-            # Since we are adding it to the raw scores before the softmax, this is
-            # effectively the same as removing these entirely.
-            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x num_attention_heads x N x N
-        # head_mask has shape n_layer x batch x num_attention_heads x N x N
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-
-        hidden_states = inputs_embeds
-
-        if token_type_ids is not None:
-            token_type_embeds = self.wte(token_type_ids)
-            hidden_states = hidden_states + token_type_embeds
-
-        hidden_states = self.drop(hidden_states)
-
-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
-                    "`use_cache=False`..."
-                )
-                use_cache = False
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, use_cache, output_attentions)
-
-                    return custom_forward
-
-                outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    None,
-                    attention_mask,
-                    position_ids,
-                    head_mask[i],
-                )
-            else:
-                outputs = block(
-                    hidden_states=hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    head_mask=head_mask[i],
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
-
-        hidden_states = self.ln_f(hidden_states)
-
-        hidden_states = hidden_states.view(output_shape)
-        # Add last hidden state
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=presents,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The Moss Model transformer with a language modeling head on top.
-    """,
-    MOSS_START_DOCSTRING,
-)
-class MossForCausalLM(MossPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.causal_mask"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = MossModel(config)
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
-        token_type_ids = kwargs.get("token_type_ids", None)
-        # only last token for inputs_ids if past is defined in kwargs
-        if past_key_values:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-            if token_type_ids is not None:
-                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
-
-        attention_mask = kwargs.get("attention_mask", None)
-        position_ids = kwargs.get("position_ids", None)
-
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        return {
-            "input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "position_ids": position_ids,
-            "attention_mask": attention_mask,
-            "token_type_ids": token_type_ids,
-        }
-
-    @add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=CausalLMOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
-            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
-            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-
-        # make sure sampling in fp16 works correctly and
-        # compute loss in fp32 to match with mesh-tf version
-        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
-        lm_logits = self.lm_head(hidden_states).to(torch.float32)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-            loss = loss.to(hidden_states.dtype)
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-    @staticmethod
-    def _reorder_cache(
-        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
-    ) -> Tuple[Tuple[torch.Tensor]]:
-        """
-        This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
-        [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
-        beam_idx at every generation step.
-        """
-        return tuple(
-            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past_key_values
-        )
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/README.md b/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/README.md
deleted file mode 100644
index 779983436c9727dd0d6301a1c857f2360245b51d..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Synchronized-BatchNorm-PyTorch
-
-**IMPORTANT: Please read the "Implementation details and highlights" section before use.**
-
-Synchronized Batch Normalization implementation in PyTorch.
-
-This module differs from the built-in PyTorch BatchNorm 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.
-
-This module is currently only a prototype version for research usages. As mentioned below,
-it has its limitations and may even suffer from some design problems. If you have any
-questions or suggestions, please feel free to
-[open an issue](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues) or 
-[submit a pull request](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues).
-
-## Why Synchronized BatchNorm?
-
-Although the typical implementation of BatchNorm working on multiple devices (GPUs)
-is fast (with no communication overhead), it inevitably reduces the size of batch size,
-which potentially degenerates the performance. This is not a significant issue in some
-standard vision tasks such as ImageNet classification (as the batch size per device
-is usually large enough to obtain good statistics). However, it will hurt the performance
-in some tasks that the batch size is usually very small (e.g., 1 per GPU).
-
-For example, the importance of synchronized batch normalization in object detection has been recently proved with a
-an extensive analysis in the paper [MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240).
-
-## Usage
-
-To use the Synchronized Batch Normalization, we add a data parallel replication callback. This introduces a slight
-difference with typical usage of the `nn.DataParallel`.
-
-Use it with a provided, customized data parallel wrapper:
-
-```python
-from sync_batchnorm import SynchronizedBatchNorm1d, DataParallelWithCallback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-```
-
-Or, if you are using a customized data parallel module, you can use this library as a monkey patching.
-
-```python
-from torch.nn import DataParallel  # or your customized DataParallel module
-from sync_batchnorm import SynchronizedBatchNorm1d, patch_replication_callback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-patch_replication_callback(sync_bn)  # monkey-patching
-```
-
-You can use `convert_model` to convert your model to use Synchronized BatchNorm easily.
-
-```python
-import torch.nn as nn
-from torchvision import models
-from sync_batchnorm import convert_model
-# m is a standard pytorch model
-m = models.resnet18(True)
-m = nn.DataParallel(m)
-# after convert, m is using SyncBN
-m = convert_model(m)
-```
-
-See also `tests/test_sync_batchnorm.py` for numeric result comparison.
-
-## Implementation details and highlights
-
-If you are interested in how batch statistics are reduced and broadcasted among multiple devices, please take a look
-at the code with detailed comments. Here we only emphasize some highlights of the implementation:
-
-- This implementation is in pure-python. No C++ extra extension libs.
-- Easy to use as demonstrated above.
-- It uses unbiased variance to update the moving average, and use `sqrt(max(var, eps))` instead of `sqrt(var + eps)`.
-- The implementation requires that each module on different devices should invoke the `batchnorm` for exactly SAME
-amount of times in each forward pass. For example, you can not only call `batchnorm` on GPU0 but not on GPU1. The `#i
-(i = 1, 2, 3, ...)` calls of the `batchnorm` on each device will be viewed as a whole and the statistics will be reduced.
-This is tricky but is a good way to handle PyTorch's dynamic computation graph. Although sounds complicated, this
-will usually not be the issue for most of the models.
-
-## Known issues
-
-#### Runtime error on backward pass.
-
-Due to a [PyTorch Bug](https://github.com/pytorch/pytorch/issues/3883), using old PyTorch libraries will trigger an `RuntimeError` with messages like:
-
-```
-Assertion `pos >= 0 && pos < buffer.size()` failed.
-```
-
-This has already been solved in the newest PyTorch repo, which, unfortunately, has not been pushed to the official and anaconda binary release. Thus, you are required to build the PyTorch package from the source according to the
- instructions [here](https://github.com/pytorch/pytorch#from-source).
-
-#### Numeric error.
-
-Because this library does not fuse the normalization and statistics operations in C++ (nor CUDA), it is less
-numerically stable compared to the original PyTorch implementation. Detailed analysis can be found in
-`tests/test_sync_batchnorm.py`.
-
-## Authors and License:
-
-Copyright (c) 2018-, [Jiayuan Mao](https://vccy.xyz).
-
-**Contributors**: [Tete Xiao](https://tetexiao.com), [DTennant](https://github.com/DTennant).
-
-Distributed under **MIT License** (See LICENSE)
-
diff --git a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/models.py b/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/models.py
deleted file mode 100644
index d29e9010388acda30059431d8d6cbfa3c670e4f2..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/models.py
+++ /dev/null
@@ -1,730 +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
-from pqmf import PQMF
-from stft import TorchSTFT
-import math
-
-
-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 iSTFT_Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, gin_channels=0):
-        super(iSTFT_Generator, self).__init__()
-        # self.h = h
-        self.gen_istft_n_fft = gen_istft_n_fft
-        self.gen_istft_hop_size = gen_istft_hop_size
-
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = weight_norm(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.post_n_fft = self.gen_istft_n_fft
-        self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
-        self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
-    def forward(self, x, g=None):
-        
-        x = self.conv_pre(x)
-        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.reflection_pad(x)
-        x = self.conv_post(x)
-        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
-        phase = math.pi*torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
-        out = self.stft.inverse(spec, phase).to(x.device)
-        return out, None
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class Multiband_iSTFT_Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=0):
-        super(Multiband_iSTFT_Generator, self).__init__()
-        # self.h = h
-        self.subbands = subbands
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = weight_norm(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.post_n_fft = gen_istft_n_fft
-        self.ups.apply(init_weights)
-        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
-        self.reshape_pixelshuffle = []
- 
-        self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands*(self.post_n_fft + 2), 7, 1, padding=3))
-        
-        self.subband_conv_post.apply(init_weights)
-        
-        self.gen_istft_n_fft = gen_istft_n_fft
-        self.gen_istft_hop_size = gen_istft_hop_size
-
-
-    def forward(self, x, g=None):
-      stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft).to(x.device)
-      pqmf = PQMF(x.device)
-      
-      x = self.conv_pre(x)#[B, ch, length]
-        
-      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.reflection_pad(x)
-      x = self.subband_conv_post(x)
-      x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1]//self.subbands, x.shape[-1]))
-
-      spec = torch.exp(x[:,:,:self.post_n_fft // 2 + 1, :])
-      phase = math.pi*torch.sin(x[:,:, self.post_n_fft // 2 + 1:, :])
-
-      y_mb_hat = stft.inverse(torch.reshape(spec, (spec.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])), torch.reshape(phase, (phase.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
-      y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
-      y_mb_hat = y_mb_hat.squeeze(-2)
-
-      y_g_hat = pqmf.synthesis(y_mb_hat)
-
-      return y_g_hat, y_mb_hat
-
-    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 Multistream_iSTFT_Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=0):
-        super(Multistream_iSTFT_Generator, self).__init__()
-        # self.h = h
-        self.subbands = subbands
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = weight_norm(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.post_n_fft = gen_istft_n_fft
-        self.ups.apply(init_weights)
-        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
-        self.reshape_pixelshuffle = []
- 
-        self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands*(self.post_n_fft + 2), 7, 1, padding=3))
-        
-        self.subband_conv_post.apply(init_weights)
-        
-        self.gen_istft_n_fft = gen_istft_n_fft
-        self.gen_istft_hop_size = gen_istft_hop_size
-
-        updown_filter = torch.zeros((self.subbands, self.subbands, self.subbands)).float()
-        for k in range(self.subbands):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.multistream_conv_post = weight_norm(Conv1d(4, 1, kernel_size=63, bias=False, padding=get_padding(63, 1)))
-        self.multistream_conv_post.apply(init_weights)
-        
-
-
-    def forward(self, x, g=None):
-      stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft).to(x.device)
-      # pqmf = PQMF(x.device)
-
-      x = self.conv_pre(x)#[B, ch, length]
-        
-      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.reflection_pad(x)
-      x = self.subband_conv_post(x)
-      x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1]//self.subbands, x.shape[-1]))
-
-      spec = torch.exp(x[:,:,:self.post_n_fft // 2 + 1, :])
-      phase = math.pi*torch.sin(x[:,:, self.post_n_fft // 2 + 1:, :])
-
-      y_mb_hat = stft.inverse(torch.reshape(spec, (spec.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])), torch.reshape(phase, (phase.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
-      y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
-      y_mb_hat = y_mb_hat.squeeze(-2)
-
-      y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.to(x.device) * self.subbands, stride=self.subbands)
-
-      y_g_hat = self.multistream_conv_post(y_mb_hat)
-
-      return y_g_hat, y_mb_hat
-
-    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,
-    gen_istft_n_fft,
-    gen_istft_hop_size,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=False,
-    ms_istft_vits=False,
-    mb_istft_vits = False,
-    subbands = False,
-    istft_vits=False,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-    self.ms_istft_vits = ms_istft_vits
-    self.mb_istft_vits = mb_istft_vits
-    self.istft_vits = istft_vits
-
-    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)
-    if mb_istft_vits == True:
-      print('Mutli-band iSTFT VITS')
-      self.dec = Multiband_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=gin_channels)
-    elif ms_istft_vits == True:
-      print('Mutli-stream iSTFT VITS')
-      self.dec = Multistream_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=gin_channels)
-    elif istft_vits == True:
-      print('iSTFT-VITS')
-      self.dec = iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, gin_channels=gin_channels)
-    else:
-      print('Decoder Error in json file')
-
-    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, o_mb = self.dec(z_slice, g=g)
-    return o, o_mb, 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, o_mb = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, o_mb, 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, o_hat_mb = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, o_hat_mb, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/Manmay/tortoise-tts/tortoise/eval.py b/spaces/Manmay/tortoise-tts/tortoise/eval.py
deleted file mode 100644
index 312b162234a927e8e7bd89c26f782487ed67bc07..0000000000000000000000000000000000000000
--- a/spaces/Manmay/tortoise-tts/tortoise/eval.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import argparse
-import os
-
-import torchaudio
-
-from api import TextToSpeech
-from tortoise.utils.audio import load_audio
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--eval_path', type=str, help='Path to TSV test file', default="D:\\tmp\\tortoise-tts-eval\\test.tsv")
-    parser.add_argument('--output_path', type=str, help='Where to put results', default="D:\\tmp\\tortoise-tts-eval\\baseline")
-    parser.add_argument('--preset', type=str, help='Rendering preset.', default="standard")
-    args = parser.parse_args()
-    os.makedirs(args.output_path, exist_ok=True)
-
-    tts = TextToSpeech()
-
-    with open(args.eval_path, 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-
-    for line in lines:
-        text, real = line.strip().split('\t')
-        conds = [load_audio(real, 22050)]
-        gen = tts.tts_with_preset(text, voice_samples=conds, conditioning_latents=None, preset=args.preset)
-        torchaudio.save(os.path.join(args.output_path, os.path.basename(real)), gen.squeeze(0).cpu(), 24000)
-
diff --git a/spaces/Manmay/tortoise-tts/tortoise/models/transformer.py b/spaces/Manmay/tortoise-tts/tortoise/models/transformer.py
deleted file mode 100644
index 707e9ebaea2be706427b8eb663e75ef9d46c5de7..0000000000000000000000000000000000000000
--- a/spaces/Manmay/tortoise-tts/tortoise/models/transformer.py
+++ /dev/null
@@ -1,219 +0,0 @@
-from functools import partial
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from rotary_embedding_torch import RotaryEmbedding, broadcat
-from torch import nn
-
-
-# helpers
-
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-def cast_tuple(val, depth = 1):
-    if isinstance(val, list):
-        val = tuple(val)
-    return val if isinstance(val, tuple) else (val,) * depth
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def stable_softmax(t, dim = -1, alpha = 32 ** 2):
-    t = t / alpha
-    t = t - torch.amax(t, dim = dim, keepdim = True).detach()
-    return (t * alpha).softmax(dim = dim)
-
-
-def route_args(router, args, depth):
-    routed_args = [(dict(), dict()) for _ in range(depth)]
-    matched_keys = [key for key in args.keys() if key in router]
-
-    for key in matched_keys:
-        val = args[key]
-        for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])):
-            new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes)
-            routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args})
-    return routed_args
-
-
-# classes
-class SequentialSequence(nn.Module):
-    def __init__(self, layers, args_route = {}, layer_dropout = 0.):
-        super().__init__()
-        assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers'
-        self.layers = layers
-        self.args_route = args_route
-        self.layer_dropout = layer_dropout
-
-    def forward(self, x, **kwargs):
-        args = route_args(self.args_route, kwargs, len(self.layers))
-        layers_and_args = list(zip(self.layers, args))
-
-        for (f, g), (f_args, g_args) in layers_and_args:
-            x = x + f(x, **f_args)
-            x = x + g(x, **g_args)
-        return x
-
-
-class DivideMax(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        maxes = x.amax(dim = self.dim, keepdim = True).detach()
-        return x / maxes
-
-
-# https://arxiv.org/abs/2103.17239
-class LayerScale(nn.Module):
-    def __init__(self, dim, depth, fn):
-        super().__init__()
-        if depth <= 18:
-            init_eps = 0.1
-        elif depth > 18 and depth <= 24:
-            init_eps = 1e-5
-        else:
-            init_eps = 1e-6
-
-        scale = torch.zeros(1, 1, dim).fill_(init_eps)
-        self.scale = nn.Parameter(scale)
-        self.fn = fn
-    def forward(self, x, **kwargs):
-        return self.fn(x, **kwargs) * self.scale
-
-# layer norm
-
-
-class PreNorm(nn.Module):
-    def __init__(self, dim, fn, sandwich = False):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.norm_out = nn.LayerNorm(dim) if sandwich else nn.Identity()
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x = self.norm(x)
-        x = self.fn(x, **kwargs)
-        return self.norm_out(x)
-
-# feed forward
-
-
-class GEGLU(nn.Module):
-    def forward(self, x):
-        x, gates = x.chunk(2, dim = -1)
-        return x * F.gelu(gates)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dropout = 0., mult = 4.):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(dim, dim * mult * 2),
-            GEGLU(),
-            nn.Dropout(dropout),
-            nn.Linear(dim * mult, dim)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-# Attention
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = 64, dropout = 0.):
-        super().__init__()
-        inner_dim = dim_head *  heads
-        self.heads = heads
-        self.seq_len = seq_len
-        self.scale = dim_head ** -0.5
-
-        self.causal = causal
-
-        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, mask = None):
-        b, n, _, h, device = *x.shape, self.heads, x.device
-        softmax = torch.softmax
-
-        qkv = self.to_qkv(x).chunk(3, dim = -1)
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
-
-        q = q * self.scale
-
-        dots = torch.einsum('b h i d, b h j d -> b h i j', q, k)
-        mask_value = max_neg_value(dots)
-
-        if exists(mask):
-            mask = rearrange(mask, 'b j -> b () () j')
-            dots.masked_fill_(~mask, mask_value)
-            del mask
-
-        if self.causal:
-            i, j = dots.shape[-2:]
-            mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
-            dots.masked_fill_(mask, mask_value)
-
-        attn = softmax(dots, dim=-1)
-
-        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
-        out = rearrange(out, 'b h n d -> b n (h d)')
-        out = self.to_out(out)
-        return out
-
-
-# main transformer class
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim,
-        depth,
-        seq_len,
-        causal = True,
-        heads = 8,
-        dim_head = 64,
-        ff_mult = 4,
-        attn_dropout = 0.,
-        ff_dropout = 0.,
-        sparse_attn = False,
-        sandwich_norm = False,
-    ):
-        super().__init__()
-        layers = nn.ModuleList([])
-        sparse_layer = cast_tuple(sparse_attn, depth)
-
-        for ind, sparse_attn in zip(range(depth), sparse_layer):
-            attn = Attention(dim, causal = causal, seq_len = seq_len, heads = heads, dim_head = dim_head, dropout = attn_dropout)
-
-            ff = FeedForward(dim, mult = ff_mult, dropout = ff_dropout)
-
-            layers.append(nn.ModuleList([
-                LayerScale(dim, ind + 1, PreNorm(dim, attn, sandwich = sandwich_norm)),
-                LayerScale(dim, ind + 1, PreNorm(dim, ff, sandwich = sandwich_norm))
-            ]))
-
-        execute_type = SequentialSequence
-        route_attn = ((True, False),) * depth
-        attn_route_map = {'mask': route_attn}
-
-        self.layers = execute_type(layers, args_route = attn_route_map)
-
-    def forward(self, x, **kwargs):
-        return self.layers(x, **kwargs)
diff --git a/spaces/Marshalls/testmtd/script_train_gcp.sh b/spaces/Marshalls/testmtd/script_train_gcp.sh
deleted file mode 100644
index a6361de709ddab36f0bbea0c8a440e2d46a50759..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/script_train_gcp.sh
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/bin/bash
-
-#export TPU_IP_ADDRESS=10.104.22.146;
-#export TPU_IP_ADDRESS=10.95.66.34;
-#export TPU_IP_ADDRESS=10.65.226.162;
-export TPU_IP_ADDRESS=10.21.219.242;
-#export TPU_IP_ADDRESS=10.93.151.138;
-export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
-export TPU_NAME="grpc://$TPU_IP_ADDRESS:8470"
-#export XRT_WORKERS="localservice:0;grpc://localhost:40934"
-#export XRT_DEVICE_MAP="CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0|GPU:0;/job:localservice/replica:0/task:0/device:XLA_GPU:0"
-#export PYTHONPATH=$SCRATCH/:${PYTHONPATH}
-#export PYTHONPATH=/gpfsscratch/rech/imi/usc19dv/lib/python3.7/site-packages:${PYTHONPATH}
-
-py=python3
-
-#root_dir=$SCRATCH/data
-root_dir=data
-
-####aistpp_60hz
-#data_dir=${root_dir}/scaled_features
-#hparams_file=aistpp_60hz/transflower_aistpp_expmap
-#hparams_file=aistpp_60hz/transglower_aistpp_expmap
-
-####aistpp_20hz
-#data_dir=${root_dir}/aistpp_20hz
-##exp=$1
-#exp=mowgli_aistpp_expmap
-##exp=transglower_aistpp_expmap
-##exp=transglower_residual_aistpp_expmap
-##exp=transflower_residual_aistpp_expmap
-##exp=transflower_aistpp_expmap
-##exp=residualflower2_transflower_aistpp_expmap
-##exp=moglow_aistpp_expmap
-#hparams_file=aistpp_20hz/${exp}
-
-## Fix: needs vmapped version of transformer:
-#hparams_file=aistpp_20hz/residualflower2_moglow_aistpp_expmap
-
-####dance_combined
-#data_dir=${root_dir}/dance_combined
-data_dir=${root_dir}/dance_combined2
-#exp=$1
-#exp=transflower_expmap_large
-#exp=transflower_residual_expmap
-exp=transflower_expmap_cr
-#exp=transformer_expmap
-#exp=moglow_expmap
-hparams_file=dance_combined/${exp}
-
-#exp=${exp}_future3_actnorm
-#exp=${exp}_future3
-#exp=${exp}_future3
-#exp=${exp}_no_pos_emb_output
-#exp=${exp}_studentt
-#exp=${exp}_large
-
-echo $exp
-
-$py training/train.py --data_dir=${data_dir} --max_epochs=300\
-    --batch_size=42 \
-    --hparams_file=training/hparams/${hparams_file}.yaml \
-    --experiment_name=$exp\
-    --workers=$(nproc) \
-    --tpu_cores=8 \
-    --continue_train \
-    #--sync_batchnorm \
-    #--optimizer=madgrad \
-    #--learning_rate=1e-3 \
-    #--batch_size=128 \
-    #--use_x_transformers \
-    #--use_rotary_pos_emb \
-    #--accelerator=ddp \
-    #--flow_dist=studentT \
-    #--no-use_pos_emb_output \
-    #--load_weights_only \
-    #--stage2 \
-    #--prior_use_x_transformers \
-    #--output_lengths="3" \
-    #--max_prior_loss_weight=0.01 \
-    #--scales="[[16,0]]" \
-    #--residual_scales="[[16,0]]"
-#    --glow_norm_layer="actnorm" \
-    #--use_pos_emb_output \
-    #--gpus=2 \
diff --git a/spaces/MathysL/AutoGPT4/autogpt/__init__.py b/spaces/MathysL/AutoGPT4/autogpt/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/misc.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/misc.py
deleted file mode 100644
index 3e61f05e3b05e4c7b40de4eb6c8eb100e6da41d0..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/misc.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-
-import annotator.uniformer.mmcv as mmcv
-
-try:
-    import torch
-except ImportError:
-    torch = None
-
-
-def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
-    """Convert tensor to 3-channel images.
-
-    Args:
-        tensor (torch.Tensor): Tensor that contains multiple images, shape (
-            N, C, H, W).
-        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
-        std (tuple[float], optional): Standard deviation of images.
-            Defaults to (1, 1, 1).
-        to_rgb (bool, optional): Whether the tensor was converted to RGB
-            format in the first place. If so, convert it back to BGR.
-            Defaults to True.
-
-    Returns:
-        list[np.ndarray]: A list that contains multiple images.
-    """
-
-    if torch is None:
-        raise RuntimeError('pytorch is not installed')
-    assert torch.is_tensor(tensor) and tensor.ndim == 4
-    assert len(mean) == 3
-    assert len(std) == 3
-
-    num_imgs = tensor.size(0)
-    mean = np.array(mean, dtype=np.float32)
-    std = np.array(std, dtype=np.float32)
-    imgs = []
-    for img_id in range(num_imgs):
-        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
-        img = mmcv.imdenormalize(
-            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
-        imgs.append(np.ascontiguousarray(img))
-    return imgs
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/master/_base_master_resnet31.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/master/_base_master_resnet31.py
deleted file mode 100644
index 7214d2fbf03d92614dc129e4b74e02b23d64e8c5..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/master/_base_master_resnet31.py
+++ /dev/null
@@ -1,160 +0,0 @@
-dictionary = dict(
-    type='Dictionary',
-    dict_file='{{ fileDirname }}/../../../dicts/english_digits_symbols.txt',
-    with_padding=True,
-    with_unknown=True,
-    same_start_end=True,
-    with_start=True,
-    with_end=True)
-
-model = dict(
-    type='MASTER',
-    backbone=dict(
-        type='ResNet',
-        in_channels=3,
-        stem_channels=[64, 128],
-        block_cfgs=dict(
-            type='BasicBlock',
-            plugins=dict(
-                cfg=dict(
-                    type='GCAModule',
-                    ratio=0.0625,
-                    n_head=1,
-                    pooling_type='att',
-                    is_att_scale=False,
-                    fusion_type='channel_add'),
-                position='after_conv2')),
-        arch_layers=[1, 2, 5, 3],
-        arch_channels=[256, 256, 512, 512],
-        strides=[1, 1, 1, 1],
-        plugins=[
-            dict(
-                cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
-                stages=(True, True, False, False),
-                position='before_stage'),
-            dict(
-                cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
-                stages=(False, False, True, False),
-                position='before_stage'),
-            dict(
-                cfg=dict(
-                    type='ConvModule',
-                    kernel_size=3,
-                    stride=1,
-                    padding=1,
-                    norm_cfg=dict(type='BN'),
-                    act_cfg=dict(type='ReLU')),
-                stages=(True, True, True, True),
-                position='after_stage')
-        ],
-        init_cfg=[
-            dict(type='Kaiming', layer='Conv2d'),
-            dict(type='Constant', val=1, layer='BatchNorm2d'),
-        ]),
-    encoder=None,
-    decoder=dict(
-        type='MasterDecoder',
-        d_model=512,
-        n_head=8,
-        attn_drop=0.,
-        ffn_drop=0.,
-        d_inner=2048,
-        n_layers=3,
-        feat_pe_drop=0.2,
-        feat_size=6 * 40,
-        postprocessor=dict(type='AttentionPostprocessor'),
-        module_loss=dict(
-            type='CEModuleLoss', reduction='mean', ignore_first_char=True),
-        max_seq_len=30,
-        dictionary=dictionary),
-    data_preprocessor=dict(
-        type='TextRecogDataPreprocessor',
-        mean=[127.5, 127.5, 127.5],
-        std=[127.5, 127.5, 127.5]))
-
-train_pipeline = [
-    dict(type='LoadImageFromFile', ignore_empty=True, min_size=2),
-    dict(type='LoadOCRAnnotations', with_text=True),
-    dict(
-        type='RescaleToHeight',
-        height=48,
-        min_width=48,
-        max_width=160,
-        width_divisor=16),
-    dict(type='PadToWidth', width=160),
-    dict(
-        type='PackTextRecogInputs',
-        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RescaleToHeight',
-        height=48,
-        min_width=48,
-        max_width=160,
-        width_divisor=16),
-    dict(type='PadToWidth', width=160),
-    # add loading annotation after ``Resize`` because ground truth
-    # does not need to do resize data transform
-    dict(type='LoadOCRAnnotations', with_text=True),
-    dict(
-        type='PackTextRecogInputs',
-        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
-]
-
-tta_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='TestTimeAug',
-        transforms=[
-            [
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=0, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=1, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=3, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-            ],
-            [
-                dict(
-                    type='RescaleToHeight',
-                    height=48,
-                    min_width=48,
-                    max_width=160,
-                    width_divisor=16)
-            ],
-            [dict(type='PadToWidth', width=160)],
-            # add loading annotation after ``Resize`` because ground truth
-            # does not need to do resize data transform
-            [dict(type='LoadOCRAnnotations', with_text=True)],
-            [
-                dict(
-                    type='PackTextRecogInputs',
-                    meta_keys=('img_path', 'ori_shape', 'img_shape',
-                               'valid_ratio'))
-            ]
-        ])
-]
diff --git a/spaces/NATSpeech/DiffSpeech/utils/audio/align.py b/spaces/NATSpeech/DiffSpeech/utils/audio/align.py
deleted file mode 100644
index 096e593f01b51ee2d9f565666cfa8c40a90d76f1..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/utils/audio/align.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import re
-
-import torch
-import numpy as np
-from textgrid import TextGrid
-
-from utils.text.text_encoder import is_sil_phoneme
-
-
-def get_mel2ph(tg_fn, ph, mel, hop_size, audio_sample_rate, min_sil_duration=0):
-    ph_list = ph.split(" ")
-    itvs = TextGrid.fromFile(tg_fn)[1]
-    itvs_ = []
-    for i in range(len(itvs)):
-        if itvs[i].maxTime - itvs[i].minTime < min_sil_duration and i > 0 and is_sil_phoneme(itvs[i].mark):
-            itvs_[-1].maxTime = itvs[i].maxTime
-        else:
-            itvs_.append(itvs[i])
-    itvs.intervals = itvs_
-    itv_marks = [itv.mark for itv in itvs]
-    tg_len = len([x for x in itvs if not is_sil_phoneme(x.mark)])
-    ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
-    assert tg_len == ph_len, (tg_len, ph_len, itv_marks, ph_list, tg_fn)
-    mel2ph = np.zeros([mel.shape[0]], int)
-    i_itv = 0
-    i_ph = 0
-    while i_itv < len(itvs):
-        itv = itvs[i_itv]
-        ph = ph_list[i_ph]
-        itv_ph = itv.mark
-        start_frame = int(itv.minTime * audio_sample_rate / hop_size + 0.5)
-        end_frame = int(itv.maxTime * audio_sample_rate / hop_size + 0.5)
-        if is_sil_phoneme(itv_ph) and not is_sil_phoneme(ph):
-            mel2ph[start_frame:end_frame] = i_ph
-            i_itv += 1
-        elif not is_sil_phoneme(itv_ph) and is_sil_phoneme(ph):
-            i_ph += 1
-        else:
-            if not ((is_sil_phoneme(itv_ph) and is_sil_phoneme(ph)) \
-                    or re.sub(r'\d+', '', itv_ph.lower()) == re.sub(r'\d+', '', ph.lower())):
-                print(f"| WARN: {tg_fn} phs are not same: ", itv_ph, ph, itv_marks, ph_list)
-            mel2ph[start_frame:end_frame] = i_ph + 1
-            i_ph += 1
-            i_itv += 1
-    mel2ph[-1] = mel2ph[-2]
-    assert not np.any(mel2ph == 0)
-    T_t = len(ph_list)
-    dur = mel2token_to_dur(mel2ph, T_t)
-    return mel2ph.tolist(), dur.tolist()
-
-
-def split_audio_by_mel2ph(audio, mel2ph, hop_size, audio_num_mel_bins):
-    if isinstance(audio, torch.Tensor):
-        audio = audio.numpy()
-    if isinstance(mel2ph, torch.Tensor):
-        mel2ph = mel2ph.numpy()
-    assert len(audio.shape) == 1, len(mel2ph.shape) == 1
-    split_locs = []
-    for i in range(1, len(mel2ph)):
-        if mel2ph[i] != mel2ph[i - 1]:
-            split_loc = i * hop_size
-            split_locs.append(split_loc)
-
-    new_audio = []
-    for i in range(len(split_locs) - 1):
-        new_audio.append(audio[split_locs[i]:split_locs[i + 1]])
-        new_audio.append(np.zeros([0.5 * audio_num_mel_bins]))
-    return np.concatenate(new_audio)
-
-
-def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
-    is_torch = isinstance(mel2token, torch.Tensor)
-    has_batch_dim = True
-    if not is_torch:
-        mel2token = torch.LongTensor(mel2token)
-    if T_txt is None:
-        T_txt = mel2token.max()
-    if len(mel2token.shape) == 1:
-        mel2token = mel2token[None, ...]
-        has_batch_dim = False
-    B, _ = mel2token.shape
-    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
-    dur = dur[:, 1:]
-    if max_dur is not None:
-        dur = dur.clamp(max=max_dur)
-    if not is_torch:
-        dur = dur.numpy()
-    if not has_batch_dim:
-        dur = dur[0]
-    return dur
diff --git a/spaces/NCTCMumbai/NCTC/models/research/compression/README.md b/spaces/NCTCMumbai/NCTC/models/research/compression/README.md
deleted file mode 100644
index 7f431b5eac6805fbecc276783cef2bc6c62068e5..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/compression/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
-![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
-![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
-
-# Compression with Neural Networks
-
-This is a [TensorFlow](http://www.tensorflow.org/) model repo containing
-research on compression with neural networks. This repo currently contains 
-code for the following papers:
-
-[Full Resolution Image Compression with Recurrent Neural Networks](https://arxiv.org/abs/1608.05148)
-
-## Organization
-[Image Encoder](image_encoder/): Encoding and decoding images into their binary representation.
-
-[Entropy Coder](entropy_coder/): Lossless compression of the binary representation.
-
-## Contact Info
-Model repository maintained by Nick Johnston ([nmjohn](https://github.com/nmjohn)).
diff --git a/spaces/NeuralJunkie/HebLens/app.py b/spaces/NeuralJunkie/HebLens/app.py
deleted file mode 100644
index 5aacdc36e3d002bdda36ef399907a18986a4cc3e..0000000000000000000000000000000000000000
--- a/spaces/NeuralJunkie/HebLens/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch
-from transformers import MarianMTModel, MarianTokenizer
-import gradio as gr
-
-def translate_hebrew_to_english(hebrew_text):
-    # Load the tokenizer and model
-    tokenizer = MarianTokenizer.from_pretrained("model_files", source_lang="he", target_lang="en")
-    model = MarianMTModel.from_pretrained("model_files")
-
-    # Tokenize the input text
-    inputs = tokenizer.encode(hebrew_text, return_tensors="pt")
-
-    # Translate the input text
-    outputs = model.generate(inputs, max_length=512)
-
-    # Decode the translated text
-    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translated_text
-
-# Define the translation function for Gradio
-def translate(input_text):
-    translated_text = translate_hebrew_to_english(input_text)
-    return translated_text
-
-# Create the Gradio interface
-iface = gr.Interface(fn=translate, 
-                     inputs=gr.inputs.Textbox(lines=10, placeholder="Paste your Hebrew text here"),
-                     outputs=gr.outputs.Textbox(),
-                     title="Hebrew to English Translator",
-                     layout="vertical",
-                     css=".input textarea { height: 300px; } .output textarea { height: 300px; }")
-iface.launch()
diff --git a/spaces/Nick1/rvc-models/lib/infer_pack/onnx_inference.py b/spaces/Nick1/rvc-models/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index 6517853be49e61c427cf7cd9b5ed203f6d5f367e..0000000000000000000000000000000000000000
--- a/spaces/Nick1/rvc-models/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
-            HarvestF0Predictor,
-        )
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py
deleted file mode 100644
index 647a4a2541af6584ece5c3744df2a84512f6ea58..0000000000000000000000000000000000000000
--- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import torch
-from torch import nn
-from torch.nn.functional import _pointwise_loss
-
-rgb_weights = [0.29891 * 3, 0.58661 * 3, 0.11448 * 3]
-# RGB have different weights
-# https://github.com/nagadomi/waifu2x/blob/master/train.lua#L109
-use_cuda = torch.cuda.is_available()
-FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
-LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
-Tensor = FloatTensor
-
-
-class WeightedHuberLoss(nn.SmoothL1Loss):
-    def __init__(self, weights=rgb_weights):
-        super(WeightedHuberLoss, self).__init__(size_average=True, reduce=True)
-        self.weights = torch.FloatTensor(weights).view(3, 1, 1)
-
-    def forward(self, input_data, target):
-        diff = torch.abs(input_data - target)
-        z = torch.where(diff < 1, 0.5 * torch.pow(diff, 2), (diff - 0.5))
-        out = z * self.weights.expand_as(diff)
-        return out.mean()
-
-
-def weighted_mse_loss(input, target, weights):
-    out = (input - target) ** 2
-    out = out * weights.expand_as(out)
-    loss = out.sum(0)  # or sum over whatever dimensions
-    return loss / out.size(0)
-
-
-class WeightedL1Loss(nn.SmoothL1Loss):
-    def __init__(self, weights=rgb_weights):
-        super(WeightedHuberLoss, self).__init__(size_average=True, reduce=True)
-        self.weights = torch.FloatTensor(weights).view(3, 1, 1)
-
-    def forward(self, input_data, target):
-        return self.l1_loss(
-            input_data, target, size_average=self.size_average, reduce=self.reduce
-        )
-
-    def l1_loss(self, input_data, target, size_average=True, reduce=True):
-        return _pointwise_loss(
-            lambda a, b: torch.abs(a - b) * self.weights.expand_as(a),
-            torch._C._nn.l1_loss,
-            input_data,
-            target,
-            size_average,
-            reduce,
-        )
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/README.md
deleted file mode 100644
index b501a6eb2a047d4adb6f297436c1c002c926a09f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/README.md
+++ /dev/null
@@ -1,115 +0,0 @@
-# HuBERT
-
-## Pre-trained and fine-tuned (ASR) models
-Model | Pretraining Data | Finetuning Dataset | Model
-|---|---|---|---
-HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt)
-HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt)
-HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr |  No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt)
-HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt)
-HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt)
-
-## Load a model
-```
-ckpt_path = "/path/to/the/checkpoint.pt"
-models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
-model = models[0]
-```
-
-## Train a new model
-
-### Data preparation
-
-Follow the steps in `./simple_kmeans` to create:
-- `{train,valid}.tsv` waveform list files
-- `{train,valid}.km` frame-aligned pseudo label files.
-The `label_rate` is the same as the feature frame rate used for clustering,
-which is 100Hz for MFCC features and 50Hz for HuBERT features by default.
-
-### Pre-train a HuBERT model
-
-Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km`
-are saved at `/path/to/labels`, and the label rate is 100Hz.
-
-To train a base model (12 layer transformer), run:
-```sh
-$ python fairseq_cli/hydra_train.py \
-  --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \
-  --config-name hubert_base_librispeech \
-  task.data=/path/to/data task.label_dir=/path/to/labels model.label_rate=100
-```
-
-### Fine-tune a HuBERT model with a CTC loss
-
-Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their
-corresponding character transcripts `{train,valid}.ltr` are saved at
-`/path/to/trans`.
-
-To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run
-```sh
-$ python fairseq_cli/hydra_train.py \
-  --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \
-  --config-name base_10h \
-  task.data=/path/to/data task.label_dir=/path/to/trans \
-  model.w2v_path=/path/to/checkpoint
-```
-
-### Decode a HuBERT model
-
-Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of
-the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is
-saved at `/path/to/checkpoint`. We support three decoding modes:
-- Viterbi decoding: greedy decoding without a language model
-- KenLM decoding: decoding with an arpa-format KenLM n-gram language model
-- Fairseq-LM deocding: decoding with a Fairseq neural language model
-
-
-#### Viterbi decoding
-
-`task.normalize` needs to be consistent with the value used during fine-tuning.
-Decoding results will be saved at
-`/path/to/experiment/directory/decode/viterbi/test`.
-
-```sh
-$ python examples/speech_recognition/new/infer.py \
-  --config-dir /path/to/fairseq-py/examples/hubert/config/decode \
-  --config-name infer_viterbi \
-  task.data=/path/to/data \
-  task.normalize=[true|false] \
-  decoding.exp_dir=/path/to/experiment/directory \
-  common_eval.path=/path/to/checkpoint
-  dataset.gen_subset=test \
-```
-
-#### KenLM / Fairseq-LM decoding
-
-Suppose the pronunciation lexicon and the n-gram LM are saved at
-`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be
-saved at `/path/to/experiment/directory/decode/kenlm/test`.
-
-```sh
-$ python examples/speech_recognition/new/infer.py \
-  --config-dir /path/to/fairseq-py/examples/hubert/config/decode \
-  --config-name infer_kenlm \
-  task.data=/path/to/data \
-  task.normalize=[true|false] \
-  decoding.exp_dir=/path/to/experiment/directory \
-  common_eval.path=/path/to/checkpoint
-  dataset.gen_subset=test \
-  decoding.decoder.lexicon=/path/to/lexicon \
-  decoding.decoder.lmpath=/path/to/arpa
-```
-
-The command above uses the default decoding hyperparameter, which can be found
-in `examples/speech_recognition/hydra/decoder.py`. These parameters can be
-configured from the command line. For example, to search with a beam size of
-500, we can append the command above with `decoding.decoder.beam=500`.
-Important parameters include:
-- decoding.decoder.beam
-- decoding.decoder.beamthreshold
-- decoding.decoder.lmweight
-- decoding.decoder.wordscore
-- decoding.decoder.silweight
-
-To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and
-change the path of lexicon and LM accordingly.
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/fully_sharded_data_parallel.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/fully_sharded_data_parallel.py
deleted file mode 100644
index 8a96bfc76516682ac8e2b7e2c3bc2e6aa3d8ef0c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/fully_sharded_data_parallel.py
+++ /dev/null
@@ -1,135 +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 contextlib
-from typing import Optional
-
-import torch
-from fairseq.dataclass.configs import DistributedTrainingConfig
-from fairseq.distributed import utils as dist_utils
-
-
-try:
-    from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
-
-    has_FSDP = True
-except ImportError:
-    FSDP = torch.nn.Module
-    has_FSDP = False
-
-
-class FullyShardedDataParallel(FSDP):
-    """
-    A small wrapper around fairscale's FullyShardedDataParallel (FSDP) with some
-    fairseq-specific checkpoint saving/loading logic.
-
-    Args:
-        use_sharded_state (bool): if True, then ``state_dict`` will return
-            ``FSDP.local_state_dict`` and ``load_state_dict`` will call
-            ``FSDP.load_local_state_dict``. Otherwise, ``state_dict`` will
-            return the full model weights on data parallel rank 0 (empty on
-            other ranks) and ``load_state_dict`` will broadcast model weights
-            from rank 0 to other ranks.
-    """
-
-    def __init__(self, *args, use_sharded_state: bool = False, **kwargs):
-        if not has_FSDP:
-            raise ImportError(
-                "Cannot find FullyShardedDataParallel. "
-                "Please install fairscale with: pip install fairscale"
-            )
-        super().__init__(*args, **kwargs)
-        self.use_sharded_state = use_sharded_state
-
-    @property
-    def unwrapped_module(self) -> torch.nn.Module:
-        if self.flatten_parameters:
-            return self.module.module
-        else:
-            return self.module
-
-    def state_dict(self, destination=None, prefix="", keep_vars=False):
-        if self.use_sharded_state:
-            return super().local_state_dict(
-                destination=destination, prefix=prefix, keep_vars=keep_vars
-            )
-        else:
-            if self.rank == 0:
-                return super().state_dict(
-                    destination=destination, prefix=prefix, keep_vars=keep_vars
-                )
-            else:
-                # We must call state_dict() due to use of communication
-                # primitives. But we don't use the result.
-                super().state_dict()
-                return destination or {}
-
-    def load_state_dict(self, state_dict, strict=True, model_cfg=None):
-        if self.use_sharded_state:
-            return super().load_local_state_dict(state_dict, strict=strict)
-        else:
-            state_dict = dist_utils.broadcast_object(
-                state_dict, src_rank=0, group=self.process_group
-            )
-            return super().load_state_dict(state_dict, strict=strict)
-
-
-@contextlib.contextmanager
-def fsdp_enable_wrap(cfg: DistributedTrainingConfig):
-    try:
-        from fairscale.nn import enable_wrap
-    except ImportError:
-        raise ImportError(
-            "Cannot find FullyShardedDataParallel. "
-            "Please install fairscale with: pip install fairscale"
-        )
-    if cfg.memory_efficient_fp16:
-        assert cfg.fp16  # memory_efficient_fp16 should imply fp16
-    group = dist_utils.get_data_parallel_group()
-    if group is None and cfg.distributed_world_size == 1:
-        from fairscale.utils.testing import DummyProcessGroup
-
-        group = DummyProcessGroup(rank=0, size=1)
-    fsdp_config = {
-        "process_group": group,
-        "reshard_after_forward": not cfg.no_reshard_after_forward,
-        "mixed_precision": cfg.fp16 and not cfg.memory_efficient_fp16,
-        "fp32_reduce_scatter": cfg.fp32_reduce_scatter,
-        "flatten_parameters": True,
-        "cpu_offload": cfg.cpu_offload,
-        "compute_dtype": torch.float16 if cfg.fp16 else torch.float32,
-        "bucket_cap_mb": cfg.bucket_cap_mb,
-        "state_dict_device": torch.device("cpu"),  # reduce GPU mem usage
-    }
-    with enable_wrap(
-        wrapper_cls=FullyShardedDataParallel,
-        use_sharded_state=cfg.use_sharded_state,
-        **fsdp_config,
-    ):
-        yield
-
-
-def fsdp_wrap(module, min_num_params: Optional[int] = None, **kwargs):
-    """
-    Helper to wrap layers/modules in FSDP. This falls back to a no-op if
-    fairscale is not available.
-
-    Args:
-        module (nn.Module): module to (maybe) wrap
-        min_num_params (int, Optional): minimum number of layer params to wrap
-    """
-    try:
-        from fairscale.nn import wrap
-
-        if min_num_params is not None:
-            num_params = sum(p.numel() for p in module.parameters())
-            if num_params >= min_num_params:
-                return wrap(module, **kwargs)
-            else:
-                return module
-        else:
-            return wrap(module, **kwargs)
-    except ImportError:
-        return module
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/model.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/model.py
deleted file mode 100644
index bb205b910daaecd55effd1e77e77d0b43952624f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/model.py
+++ /dev/null
@@ -1,594 +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.
-"""
-RoBERTa: A Robustly Optimized BERT Pretraining Approach.
-"""
-
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
-from fairseq.modules import LayerNorm
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-from fairseq.utils import safe_getattr, safe_hasattr
-
-from .hub_interface import RobertaHubInterface
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("roberta")
-class RobertaModel(FairseqEncoderModel):
-    @classmethod
-    def hub_models(cls):
-        return {
-            "roberta.base": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz",
-            "roberta.large": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz",
-            "roberta.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz",
-            "roberta.large.wsc": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz",
-        }
-
-    def __init__(self, args, encoder):
-        super().__init__(encoder)
-        self.args = args
-
-        # We follow BERT's random weight initialization
-        self.apply(init_bert_params)
-
-        self.classification_heads = nn.ModuleDict()
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="L", help="num encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="H",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-ffn-embed-dim",
-            type=int,
-            metavar="F",
-            help="encoder embedding dimension for FFN",
-        )
-        parser.add_argument(
-            "--encoder-attention-heads",
-            type=int,
-            metavar="A",
-            help="num encoder attention heads",
-        )
-        parser.add_argument(
-            "--activation-fn",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use",
-        )
-        parser.add_argument(
-            "--pooler-activation-fn",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use for pooler layer",
-        )
-        parser.add_argument(
-            "--encoder-normalize-before",
-            action="store_true",
-            help="apply layernorm before each encoder block",
-        )
-        parser.add_argument(
-            "--layernorm-embedding",
-            action="store_true",
-            help="add layernorm to embedding",
-        )
-        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",
-            type=float,
-            metavar="D",
-            help="dropout probability after activation in FFN",
-        )
-        parser.add_argument(
-            "--pooler-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability in the masked_lm pooler layers",
-        )
-        parser.add_argument(
-            "--max-positions", type=int, help="number of positional embeddings to learn"
-        )
-        parser.add_argument(
-            "--load-checkpoint-heads",
-            action="store_true",
-            help="(re-)register and load heads when loading checkpoints",
-        )
-        parser.add_argument(
-            "--untie-weights-roberta",
-            action="store_true",
-            help="Untie weights between embeddings and classifiers in RoBERTa",
-        )
-        # 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(
-            "--encoder-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 "Better Fine-Tuning by Reducing Representational Collapse" (Aghajanyan et al. 2020)
-        parser.add_argument(
-            "--spectral-norm-classification-head",
-            action="store_true",
-            default=False,
-            help="Apply spectral normalization on the classification head",
-        )
-        # 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."
-            )
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        from omegaconf import OmegaConf
-
-        if OmegaConf.is_config(args):
-            OmegaConf.set_struct(args, False)
-
-        # make sure all arguments are present
-        base_architecture(args)
-
-        if not safe_hasattr(args, "max_positions"):
-            if not safe_hasattr(args, "tokens_per_sample"):
-                args.tokens_per_sample = task.max_positions()
-            args.max_positions = args.tokens_per_sample
-
-        encoder = RobertaEncoder(args, task.source_dictionary)
-
-        if OmegaConf.is_config(args):
-            OmegaConf.set_struct(args, True)
-
-        return cls(args, encoder)
-
-    def forward(
-        self,
-        src_tokens,
-        features_only=False,
-        return_all_hiddens=False,
-        classification_head_name=None,
-        **kwargs,
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs)
-
-        if classification_head_name is not None:
-            x = self.classification_heads[classification_head_name](x)
-        return x, extra
-
-    def get_normalized_probs(self, net_output, log_probs, sample=None):
-        """Get normalized probabilities (or log probs) from a net's output."""
-        logits = net_output[0].float()
-        if log_probs:
-            return F.log_softmax(logits, dim=-1)
-        else:
-            return F.softmax(logits, dim=-1)
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, **kwargs
-    ):
-        """Register a classification head."""
-        if name in self.classification_heads:
-            prev_num_classes = self.classification_heads[name].out_proj.out_features
-            prev_inner_dim = self.classification_heads[name].dense.out_features
-            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
-                logger.warning(
-                    're-registering head "{}" with num_classes {} (prev: {}) '
-                    "and inner_dim {} (prev: {})".format(
-                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
-                    )
-                )
-        self.classification_heads[name] = RobertaClassificationHead(
-            input_dim=self.args.encoder_embed_dim,
-            inner_dim=inner_dim or self.args.encoder_embed_dim,
-            num_classes=num_classes,
-            activation_fn=self.args.pooler_activation_fn,
-            pooler_dropout=self.args.pooler_dropout,
-            q_noise=self.args.quant_noise_pq,
-            qn_block_size=self.args.quant_noise_pq_block_size,
-            do_spectral_norm=self.args.spectral_norm_classification_head,
-        )
-
-    @property
-    def supported_targets(self):
-        return {"self"}
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_name_or_path,
-        checkpoint_file="model.pt",
-        data_name_or_path=".",
-        bpe="gpt2",
-        **kwargs,
-    ):
-        from fairseq import hub_utils
-
-        x = hub_utils.from_pretrained(
-            model_name_or_path,
-            checkpoint_file,
-            data_name_or_path,
-            archive_map=cls.hub_models(),
-            bpe=bpe,
-            load_checkpoint_heads=True,
-            **kwargs,
-        )
-
-        logger.info(x["args"])
-        return RobertaHubInterface(x["args"], x["task"], x["models"][0])
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        prefix = name + "." if name != "" else ""
-
-        # rename decoder -> encoder before upgrading children modules
-        for k in list(state_dict.keys()):
-            if k.startswith(prefix + "decoder"):
-                new_k = prefix + "encoder" + k[len(prefix + "decoder") :]
-                state_dict[new_k] = state_dict[k]
-                del state_dict[k]
-
-        # rename emb_layer_norm -> layernorm_embedding
-        for k in list(state_dict.keys()):
-            if ".emb_layer_norm." in k:
-                new_k = k.replace(".emb_layer_norm.", ".layernorm_embedding.")
-                state_dict[new_k] = state_dict[k]
-                del state_dict[k]
-
-        # upgrade children modules
-        super().upgrade_state_dict_named(state_dict, name)
-
-        # Handle new classification heads present in the state dict.
-        current_head_names = (
-            []
-            if not hasattr(self, "classification_heads")
-            else self.classification_heads.keys()
-        )
-        keys_to_delete = []
-        for k in state_dict.keys():
-            if not k.startswith(prefix + "classification_heads."):
-                continue
-
-            head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
-            num_classes = state_dict[
-                prefix + "classification_heads." + head_name + ".out_proj.weight"
-            ].size(0)
-            inner_dim = state_dict[
-                prefix + "classification_heads." + head_name + ".dense.weight"
-            ].size(0)
-
-            if getattr(self.args, "load_checkpoint_heads", False):
-                if head_name not in current_head_names:
-                    self.register_classification_head(head_name, num_classes, inner_dim)
-            else:
-                if head_name not in current_head_names:
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "not present in current model: {}".format(head_name, k)
-                    )
-                    keys_to_delete.append(k)
-                elif (
-                    num_classes
-                    != self.classification_heads[head_name].out_proj.out_features
-                    or inner_dim
-                    != self.classification_heads[head_name].dense.out_features
-                ):
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "with different dimensions than current model: {}".format(
-                            head_name, k
-                        )
-                    )
-                    keys_to_delete.append(k)
-        for k in keys_to_delete:
-            del state_dict[k]
-
-        # Copy any newly-added classification heads into the state dict
-        # with their current weights.
-        if hasattr(self, "classification_heads"):
-            cur_state = self.classification_heads.state_dict()
-            for k, v in cur_state.items():
-                if prefix + "classification_heads." + k not in state_dict:
-                    logger.info("Overwriting " + prefix + "classification_heads." + k)
-                    state_dict[prefix + "classification_heads." + k] = v
-
-
-class RobertaLMHead(nn.Module):
-    """Head for masked language modeling."""
-
-    def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
-        super().__init__()
-        self.dense = nn.Linear(embed_dim, embed_dim)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.layer_norm = LayerNorm(embed_dim)
-
-        if weight is None:
-            weight = nn.Linear(embed_dim, output_dim, bias=False).weight
-        self.weight = weight
-        self.bias = nn.Parameter(torch.zeros(output_dim))
-
-    def forward(self, features, masked_tokens=None, **kwargs):
-        # Only project the masked tokens while training,
-        # saves both memory and computation
-        if masked_tokens is not None:
-            features = features[masked_tokens, :]
-
-        x = self.dense(features)
-        x = self.activation_fn(x)
-        x = self.layer_norm(x)
-        # project back to size of vocabulary with bias
-        x = F.linear(x, self.weight) + self.bias
-        return x
-
-
-class RobertaClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self,
-        input_dim,
-        inner_dim,
-        num_classes,
-        activation_fn,
-        pooler_dropout,
-        q_noise=0,
-        qn_block_size=8,
-        do_spectral_norm=False,
-    ):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = apply_quant_noise_(
-            nn.Linear(inner_dim, num_classes), q_noise, qn_block_size
-        )
-        if do_spectral_norm:
-            if q_noise != 0:
-                raise NotImplementedError(
-                    "Attempting to use Spectral Normalization with Quant Noise. This is not officially supported"
-                )
-            self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
-
-    def forward(self, features, **kwargs):
-        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = self.activation_fn(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-class RobertaEncoder(FairseqEncoder):
-    """RoBERTa encoder."""
-
-    def __init__(self, args, dictionary):
-        super().__init__(dictionary)
-
-        # set any missing default values
-        base_architecture(args)
-        self.args = args
-
-        if args.encoder_layers_to_keep:
-            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
-
-        embed_tokens = self.build_embedding(
-            len(dictionary), args.encoder_embed_dim, dictionary.pad()
-        )
-
-        self.sentence_encoder = self.build_encoder(args, dictionary, embed_tokens)
-
-        self.lm_head = self.build_lm_head(
-            embed_dim=args.encoder_embed_dim,
-            output_dim=len(dictionary),
-            activation_fn=args.activation_fn,
-            weight=(
-                self.sentence_encoder.embed_tokens.weight
-                if not args.untie_weights_roberta
-                else None
-            ),
-        )
-
-    def build_embedding(self, vocab_size, embedding_dim, padding_idx):
-        return nn.Embedding(vocab_size, embedding_dim, padding_idx)
-
-    def build_encoder(self, args, dictionary, embed_tokens):
-        encoder = TransformerEncoder(args, dictionary, embed_tokens)
-        encoder.apply(init_bert_params)
-        return encoder
-
-    def build_lm_head(self, embed_dim, output_dim, activation_fn, weight):
-        return RobertaLMHead(embed_dim, output_dim, activation_fn, weight)
-
-    def forward(
-        self,
-        src_tokens,
-        features_only=False,
-        return_all_hiddens=False,
-        masked_tokens=None,
-        **unused,
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
-            features_only (bool, optional): skip LM head and just return
-                features. If True, the output will be of shape
-                `(batch, src_len, embed_dim)`.
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-
-        Returns:
-            tuple:
-                - the LM output of shape `(batch, src_len, vocab)`
-                - a dictionary of additional data, where 'inner_states'
-                  is a list of hidden states. Note that the hidden
-                  states have shape `(src_len, batch, vocab)`.
-        """
-        x, extra = self.extract_features(
-            src_tokens, return_all_hiddens=return_all_hiddens
-        )
-        if not features_only:
-            x = self.output_layer(x, masked_tokens=masked_tokens)
-        return x, extra
-
-    def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs):
-        encoder_out = self.sentence_encoder(
-            src_tokens,
-            return_all_hiddens=return_all_hiddens,
-            token_embeddings=kwargs.get("token_embeddings", None),
-        )
-        # T x B x C -> B x T x C
-        features = encoder_out["encoder_out"][0].transpose(0, 1)
-        inner_states = encoder_out["encoder_states"] if return_all_hiddens else None
-        return features, {"inner_states": inner_states}
-
-    def output_layer(self, features, masked_tokens=None, **unused):
-        return self.lm_head(features, masked_tokens)
-
-    def max_positions(self):
-        """Maximum output length supported by the encoder."""
-        return self.args.max_positions
-
-
-@register_model_architecture("roberta", "roberta")
-def base_architecture(args):
-    args.encoder_layers = safe_getattr(args, "encoder_layers", 12)
-    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 768)
-    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 3072)
-    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 12)
-
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_dropout = safe_getattr(args, "activation_dropout", 0.0)
-    args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0)
-
-    args.max_source_positions = safe_getattr(args, "max_positions", 512)
-    args.no_token_positional_embeddings = safe_getattr(
-        args, "no_token_positional_embeddings", False
-    )
-
-    # BERT has a few structural differences compared to the original Transformer
-    args.encoder_learned_pos = safe_getattr(args, "encoder_learned_pos", True)
-    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True)
-    args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", False)
-    args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh")
-    args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False)
-
-    # Adaptive input config
-    args.adaptive_input = safe_getattr(args, "adaptive_input", False)
-
-    # LayerDrop config
-    args.encoder_layerdrop = safe_getattr(args, "encoder_layerdrop", 0.0)
-    args.encoder_layers_to_keep = safe_getattr(args, "encoder_layers_to_keep", None)
-
-    # Quantization noise config
-    args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0)
-
-    # R4F config
-    args.spectral_norm_classification_head = safe_getattr(
-        args, "spectral_norm_classification_head", False
-    )
-
-
-@register_model_architecture("roberta", "roberta_prenorm")
-def roberta_prenorm_architecture(args):
-    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)
-    args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", True)
-    base_architecture(args)
-
-
-@register_model_architecture("roberta", "roberta_base")
-def roberta_base_architecture(args):
-    base_architecture(args)
-
-
-@register_model_architecture("roberta", "roberta_large")
-def roberta_large_architecture(args):
-    args.encoder_layers = safe_getattr(args, "encoder_layers", 24)
-    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096)
-    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16)
-    base_architecture(args)
-
-
-@register_model_architecture("roberta", "xlm")
-def xlm_architecture(args):
-    args.encoder_layers = safe_getattr(args, "encoder_layers", 16)
-    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1280)
-    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 1280 * 4)
-    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16)
-    base_architecture(args)
diff --git a/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/speaker_batch.py b/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/speaker_batch.py
deleted file mode 100644
index 4485605e3ece5b491d1e7d0f223c543b6c91eb96..0000000000000000000000000000000000000000
--- a/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/speaker_batch.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-from typing import List
-from speaker_encoder.data_objects.speaker import Speaker
-
-class SpeakerBatch:
-    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
-        self.speakers = speakers
-        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
-        
-        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
-        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
-        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])
diff --git a/spaces/OpenGenAI/open-parti-prompts/app.py b/spaces/OpenGenAI/open-parti-prompts/app.py
deleted file mode 100644
index 1c904660a42ffd72d7434962b43f20c0d583cfc3..0000000000000000000000000000000000000000
--- a/spaces/OpenGenAI/open-parti-prompts/app.py
+++ /dev/null
@@ -1,372 +0,0 @@
-from datasets import load_dataset
-from collections import Counter, defaultdict
-from random import sample, shuffle
-from collections import Counter
-import datasets
-from pandas import DataFrame
-from huggingface_hub import list_datasets
-import os
-import gradio as gr
-
-import secrets
-
-
-parti_prompt_results = []
-ORG = "diffusers-parti-prompts"
-SUBMISSIONS = {
-    "kand2": load_dataset(os.path.join(ORG, "kandinsky-2-2"))["train"],
-    "sdxl": load_dataset(os.path.join(ORG, "sdxl-1.0-refiner"))["train"],
-    "wuerst": load_dataset(os.path.join(ORG, "wuerstchen"))["train"],
-    "karlo": load_dataset(os.path.join(ORG, "karlo-v1"))["train"],
-}
-
-LINKS = {
-    "kand2": "https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder",
-    "sdxl": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
-    "wuerst": "https://huggingface.co/warp-ai/wuerstchen",
-    "karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha",
-}
-KANDINSKY = """
-"## The creative one 🎨! 
-![img](https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/5dfcb1aada6d0311fd3d5448/rETvCyoUD5Mr9wm6OxUhe.png?w=200&h=200&f=face)
-\n You mostly resonate with **Kandinsky 2.2** released by AI Forever.
-\n Kandinsky 2.2 has a similar architecture to DALLE-2 and works extremely well for artistic, colorful generations.
-\n Check out your soulmate [here](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder).
-"""
-SDXL_RESULT = """
-## The powerful one ⚡! 
-![img](https://huggingface.co/datasets/OpenGenAI/logos/resolve/main/7vmYr2XwVcPtkLzac_jxQ.png)
-\n You mostly resonate with **Stable Diffusion XL** released by Stability AI.
-\n Stable Diffusion XL consists of a two diffusion models that are chained together, a base model and a refiner model. Together, the system contains roughly 5 billion parameters.
-\n It's the latest open-source release of Stable Diffusion and allows to render stunning images of much larger sizes than Stable Diffusion v1.
-Try it out [here](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
-"""
-WUERSTCHEN = """
-## The innovative one ⚗️ !
-![img](https://www.gravatar.com/avatar/3219846609129e84790fb83793998d61?d=retro&size=100)
-\n You mostly resonate with **Wuerstchen** released by the WARP team.
-\n Wuerstchen is a three stage diffusion model that proposed a very novel, innovative model architecture.
-\n Wuerstchen is able to generate very large images (up to 1024x2048) in just a few seconds.
-\n The model has an amazing image quality vs. speed trade-off.
-\n Check out your new best friend [here](https://huggingface.co/warp-ai/wuerstchen).
-"""
-KARLO = """
-## The precise one 🎯!
-![img](https://huggingface.co/datasets/OpenGenAI/logos/resolve/main/1670220967262-615ed619c807b26d117a49bd.png)
-\n You mostly resonate with **Karlo** released by KakaoBrain.
-\n Karlo is based on the same architecture as DALLE-2 and has been trained on the [well curated COYO dataset](https://huggingface.co/datasets/kakaobrain/coyo-700m).
-\n Play around with it [here]("https://huggingface.co/kakaobrain/karlo-v1-alpha").
-"""
-
-RESULT = {
-    "kand2": KANDINSKY,
-    "wuerst": WUERSTCHEN,
-    "sdxl": SDXL_RESULT,
-    "karlo": KARLO,
-}
-NUM_QUESTIONS = 10
-MODEL_KEYS = "-".join(SUBMISSIONS.keys())
-SUBMISSION_ORG = f"result-{MODEL_KEYS}"
-PROMPT_FORMAT = " Select the image that best matches the prompt and click on 'Submit'. Remember that if multiple images match the prompt equally well, select them all. If no image matches the prompt, no image shall be selected."
-
-submission_names = list(SUBMISSIONS.keys())
-num_images = len(SUBMISSIONS[submission_names[0]])
-
-
-def load_submissions():
-    all_datasets = list_datasets(author=SUBMISSION_ORG)
-    relevant_ids = [d.id for d in all_datasets]
-    
-    submitted_ids = []
-    for _id in relevant_ids:
-        ds = load_dataset(_id)["train"]
-        submitted_ids += ds["id"]
-    
-    submitted_ids = Counter(submitted_ids)
-    return submitted_ids
-
-
-SUBMITTED_IDS = load_submissions()
-
-
-def generate_random_hash(length=8):
-    """
-    Generates a random hash of specified length.
-    
-    Args:
-        length (int): The length of the hash to generate.
-        
-    Returns:
-        str: A random hash of specified length.
-    """
-    if length % 2 != 0:
-        raise ValueError("Length should be an even number.")
-    
-    num_bytes = length // 2
-    random_bytes = secrets.token_bytes(num_bytes)
-    random_hash = secrets.token_hex(num_bytes)
-    
-    return random_hash
-    
-
-def refresh(row_number, dataframe):
-    if row_number == NUM_QUESTIONS:
-        submitted_ids = load_submissions()
-        return start(submitted_ids)
-    else:
-        return dataframe
-
-def start():
-    ids = {id: 0 for id in range(num_images)}
-    ids = {**ids, **SUBMITTED_IDS}
-
-    # sort by count
-    ids = sorted(ids.items(), key=lambda x: x[1])
-    freq_ids = defaultdict(list)
-    for k, v in ids:
-        freq_ids[v].append(k)
-
-    # shuffle in-between categories
-    for k, v_list in freq_ids.items():
-        shuffle(v_list)
-        freq_ids[k] = v_list
-
-    shuffled_ids = sum(list(freq_ids.values()), [])
-
-    # get lowest count ids
-    id_candidates = shuffled_ids[: (10 * NUM_QUESTIONS)]
-
-    # get random `NUM_QUESTIONS` ids to check
-    image_ids = sample(id_candidates, k=NUM_QUESTIONS)
-    images = {}
-
-    for i in range(NUM_QUESTIONS):
-        order = list(range(len(SUBMISSIONS)))
-        shuffle(order)
-
-        id = image_ids[i]
-        row = SUBMISSIONS[submission_names[0]][id]
-        images[i] = {
-            "prompt": row["Prompt"],
-            "result": "",
-            "id": id,
-            "Challenge": row["Challenge"],
-            "Category": row["Category"],
-            "Note": row["Note"],
-        }
-        for n, m in enumerate(order):
-            images[i][f"choice_{n}"] = m
-
-    images_frame = DataFrame.from_dict(images, orient="index")
-    return images_frame
-
-
-def process(dataframe, row_number=0):
-    if row_number == NUM_QUESTIONS:
-        nones = len(RESULT) * [None]
-        falses = len(RESULT) * [False]
-        return *nones, *falses, "", ""
-
-    image_id = dataframe.iloc[row_number]["id"]
-    choices = [
-        submission_names[dataframe.iloc[row_number][f"choice_{i}"]]
-        for i in range(len(SUBMISSIONS))
-    ]
-    images = [SUBMISSIONS[c][int(image_id)]["images"] for c in choices]
-
-    prompt = SUBMISSIONS[choices[0]][int(image_id)]["Prompt"]
-    prompt = f'# "{prompt}"'
-    counter = f"***{row_number + 1}/{NUM_QUESTIONS} {PROMPT_FORMAT}***"
-    image_buttons = len(images) * [False]
-
-    return *images, *image_buttons, prompt, counter
-
-
-def write_result(user_choice, row_number, dataframe):
-    if row_number == NUM_QUESTIONS:
-        return row_number, dataframe
-
-    user_choices = []
-    for i, b in enumerate(str(user_choice)):
-        if bool(int(b)):
-            user_choices.append(i)
-
-    chosen_models = []
-    for user_choice in user_choices:
-        chosen_models.append(submission_names[dataframe.iloc[row_number][f"choice_{user_choice}"]])
-
-    print(chosen_models)
-    dataframe.loc[row_number, "result"] = ",".join(chosen_models)
-    return row_number + 1, dataframe
-
-
-def get_index(evt: gr.SelectData) -> int:
-    return evt.index
-
-
-def change_view(row_number, dataframe):
-    if row_number == NUM_QUESTIONS:
-
-        results = sum([x.split(",") for x in dataframe["result"].values], [])
-        results = [r for r in results if len(r) > 0]
-        favorite_model = Counter(results).most_common(1)[0][0]
-
-        dataset = datasets.Dataset.from_pandas(dataframe)
-        dataset = dataset.remove_columns(set(dataset.column_names) - set(["id", "result"]))
-        hash = generate_random_hash()
-        repo_id = os.path.join(SUBMISSION_ORG, hash)
-
-        dataset.push_to_hub(repo_id, token=os.getenv("HF_TOKEN"))
-        return {
-            intro_view: gr.update(visible=False),
-            result_view: gr.update(visible=True),
-            gallery_view: gr.update(visible=False),
-            start_view: gr.update(visible=True),
-            result: RESULT[favorite_model],
-        }
-    else:
-        return {
-            intro_view: gr.update(visible=False),
-            result_view: gr.update(visible=False),
-            gallery_view: gr.update(visible=True),
-            start_view: gr.update(visible=False),
-            result: "",
-        }
-
-
-TITLE = "# What AI model is best for you? 👩‍⚕️"
-
-DESCRIPTION = """
-***How it works*** 📖 \n\n
-- Upon clicking start, you are shown image descriptions alongside four AI generated images.
-\n- Select the image that best matches the prompt. If multiple images match the prompt equally well, select all images. If no image matches the prompt, leave all images unchecked.
-\n- Answer **10** questions to find out what AI generator most resonates with you. 
-\n- Your submissions contribute to [**Open Parti Prompts Leaderboard**](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard) ❤️.
-\n\n
-"""
-
-NOTE = """\n\n\n\n
-The prompts you are shown originate from the [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) dataset.
-Parti Prompts is designed to test text-to-image AI models on 1600+ prompts of varying difficulty and categories.
-The images you are shown have been pre-generated with 4 state-of-the-art open-sourced text-to-image models.
-You answers will be used to contribute to the official [**Open Parti Prompts Leaderboard**](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard).
-Every couple months, the generated images will be updated with possibly improved models. The current models and code that was used to generate the images can be verified here:\n
-- [kandinsky-2-2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) \n
-- [wuerstchen](https://huggingface.co/warp-ai/wuerstchen) \n
-- [sdxl-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) \n
-- [karlo](https://huggingface.co/datasets/diffusers-parti-prompts/karlo-v1) \n
-"""
-
-GALLERY_COLUMN_NUM = len(SUBMISSIONS)
-
-with gr.Blocks() as demo:
-    gr.Markdown(TITLE)
-    with gr.Column(visible=True) as intro_view:
-        gr.Markdown(DESCRIPTION)
-
-    headers = ["prompt", "result", "id", "Challenge", "Category", "Note"] + [
-        f"choice_{i}" for i in range(len(SUBMISSIONS))
-    ]
-    datatype = ["str", "str", "number", "str", "str", "str"] + len(SUBMISSIONS) * [
-        "number"
-    ]
-
-    with gr.Column(visible=False):
-        row_number = gr.Number(
-            label="Current row selection index",
-            value=0,
-            precision=0,
-            interactive=False,
-        )
-
-    # Create Data Frame
-    with gr.Column(visible=False) as result_view:
-        result = gr.Markdown("")
-        dataframe = gr.Dataframe(
-            headers=headers,
-            datatype=datatype,
-            row_count=NUM_QUESTIONS,
-            col_count=(6 + len(SUBMISSIONS), "fixed"),
-            interactive=False,
-        )
-        gr.Markdown("Click on start to play again!")
-
-    with gr.Column(visible=True) as start_view:
-        start_button = gr.Button("Start").style(full_width=True)
-        gr.Markdown(NOTE)
-
-    with gr.Column(visible=False):
-        selected_image = gr.Textbox(label="Selected indexes")
-
-    with gr.Column(visible=False) as gallery_view:
-        with gr.Row():
-            counter = gr.Markdown(f"***1/{NUM_QUESTIONS} {PROMPT_FORMAT}***")
-        with gr.Row():
-            prompt = gr.Markdown("")
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column(min_width=200) as c1:
-                    image_1 = gr.Image(interactive=False)
-                    image_1_button = gr.Checkbox(False, label="Image 1").style(full_width=True)
-                with gr.Column(min_width=200) as c2:
-                    image_2 = gr.Image(interactive=False)
-                    image_2_button = gr.Checkbox(False, label="Image 2").style(full_width=True)
-                with gr.Column(min_width=200) as c3:
-                    image_3 = gr.Image(interactive=False)
-                    image_3_button = gr.Checkbox(False, label="Image 3").style(full_width=True)
-                with gr.Column(min_width=200) as c4:
-                    image_4 = gr.Image(interactive=False)
-                    image_4_button = gr.Checkbox(False, label="Image 4").style(full_width=True)         
-            with gr.Row():
-                submit_button = gr.Button("Submit").style(full_width=True)         
-
-    start_button.click(
-        fn=start,
-        inputs=[],
-        outputs=dataframe,
-        show_progress=True
-    ).then(
-        fn=lambda x: 0 if x == NUM_QUESTIONS else x,
-        inputs=[row_number],
-        outputs=[row_number],
-    ).then(
-        fn=change_view,
-        inputs=[row_number, dataframe],
-        outputs=[intro_view, result_view, gallery_view, start_view, result],
-    ).then(
-        fn=process, 
-        inputs=[dataframe],
-        outputs=[image_1, image_2, image_3, image_4, image_1_button, image_2_button, image_3_button, image_4_button, prompt, counter]
-    )
-
-    def integerize(x1, x2, x3, x4):
-        number = f"{int(x1)}{int(x2)}{int(x3)}{int(x4)}"
-        return number
-
-    submit_button.click(
-        fn=integerize,
-        inputs=[image_1_button, image_2_button, image_3_button, image_4_button],
-        outputs=[selected_image],
-    ).then(
-        fn=write_result,
-        inputs=[selected_image, row_number, dataframe],
-        outputs=[row_number, dataframe],
-    ).then(
-        fn=change_view,
-        inputs=[row_number, dataframe],
-        outputs=[intro_view, result_view, gallery_view, start_view, result]
-    ).then(
-        fn=process,
-        inputs=[dataframe, row_number],
-        outputs=[image_1, image_2, image_3, image_4, image_1_button, image_2_button, image_3_button, image_4_button, prompt, counter],
-    ).then(
-        fn=lambda x: 0 if x == NUM_QUESTIONS else x,
-        inputs=[row_number],
-        outputs=[row_number],
-    ).then(
-        fn=refresh,
-        inputs=[row_number, dataframe],
-        outputs=[dataframe],
-    )
-
-demo.launch()
diff --git a/spaces/OptimalScale/Robin-33b/app.py b/spaces/OptimalScale/Robin-33b/app.py
deleted file mode 100644
index 12028a08a97ddcfc67a17a33bba35711ee449aba..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-33b/app.py
+++ /dev/null
@@ -1,230 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 Statistics and Machine Learning Research Group at HKUST. All rights reserved.
-"""A simple shell chatbot implemented with lmflow APIs.
-"""
-import logging
-import json
-import os
-import sys
-sys.path.remove(os.path.abspath(os.path.dirname(sys.argv[0])))
-import torch
-import warnings
-import gradio as gr
-from dataclasses import dataclass, field
-from transformers import HfArgumentParser
-from typing import Optional
-
-from lmflow.datasets.dataset import Dataset
-from lmflow.pipeline.auto_pipeline import AutoPipeline
-from lmflow.models.auto_model import AutoModel
-from lmflow.args import ModelArguments, DatasetArguments, AutoArguments
-
-MAX_BOXES = 20
-
-logging.disable(logging.ERROR)
-warnings.filterwarnings("ignore")
-
-title = """
-<h1 align="center">LMFlow-CHAT</h1>
-<link rel="stylesheet" href="/path/to/styles/default.min.css">
-<script src="/path/to/highlight.min.js"></script>
-<script>hljs.highlightAll();</script>
-
-<img src="https://optimalscale.github.io/LMFlow/_static/logo.png" alt="LMFlow" style="width: 30%; min-width: 60px; display: block; margin: auto; background-color: transparent;">
-
-<p>LMFlow is in extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.</p>
-
-<p>We have thoroughly tested this toolkit and are pleased to make it available under <a class="reference external" href="https://github.com/OptimalScale/LMFlow">Github</a>.</p>
-"""
-css = """
-#user {                                                                         
-    float: right;
-    position:relative;
-    right:5px;
-    width:auto;
-    min-height:32px;
-    max-width: 60%
-    line-height: 32px;
-    padding: 2px 8px;
-    font-size: 14px;
-    background:	#9DC284;
-    border-radius:5px; 
-    margin:10px 0px;
-}
-                                             
-#chatbot {                                                                      
-    float: left;
-    position:relative;
-    right:5px;
-    width:auto;
-    min-height:32px;
-    max-width: 60%
-    line-height: 32px;
-    padding: 2px 8px;
-    font-size: 14px;
-    background:#7BA7D7;
-    border-radius:5px; 
-    margin:10px 0px;
-}
-"""
-
-
-@dataclass
-class ChatbotArguments:
-    prompt_structure: Optional[str] = field(
-        default="###Human: {input_text}###Assistant:",
-        metadata={
-            "help": "prompt structure given user's input text"
-        },
-    )
-    end_string: Optional[str] = field(
-        default="#",
-        metadata={
-            "help": "end string mark of the chatbot's output"
-        },
-    )
-    max_new_tokens: Optional[int] = field(
-        default=1500,
-        metadata={
-            "help": "maximum number of generated tokens"
-        },
-    )
-    temperature: Optional[float] = field(
-        default=0.7,
-        metadata={
-            "help": "higher this value, more random the model output"
-        },
-    )
-
-def main():
-    pipeline_name = "inferencer"
-    PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name)
-
-    parser = HfArgumentParser((
-        ModelArguments,
-        PipelineArguments,
-        ChatbotArguments,
-    ))
-    model_args, pipeline_args, chatbot_args = (
-        parser.parse_args_into_dataclasses()
-    )
-    model_args.model_name_or_path = "LMFlow/Full-Robin-33b-v2"
-    pipeline_args.deepspeed = "configs/ds_config_chatbot.json"
-    model_args.torch_dtype = "float16"
-
-
-    with open (pipeline_args.deepspeed, "r") as f:
-        ds_config = json.load(f)
-
-    model = AutoModel.get_model(
-        model_args,
-        tune_strategy='none',
-        ds_config=ds_config,
-        device=pipeline_args.device,
-        torch_dtype=torch.float16
-    )
-
-    # We don't need input data, we will read interactively from stdin
-    data_args = DatasetArguments(dataset_path=None)
-    dataset = Dataset(data_args)
-
-    inferencer = AutoPipeline.get_pipeline(
-        pipeline_name=pipeline_name,
-        model_args=model_args,
-        data_args=data_args,
-        pipeline_args=pipeline_args,
-    )
-
-    # Chats
-    model_name = model_args.model_name_or_path
-    if model_args.lora_model_path is not None:
-        model_name += f" + {model_args.lora_model_path}"
-
-
-    # context = (
-    #     "You are a helpful assistant who follows the given instructions"
-    #     " unconditionally."
-    # )
-
-
-    end_string = chatbot_args.end_string
-    prompt_structure = chatbot_args.prompt_structure
-
-
-    token_per_step = 4
-
-    def hist2context(hist):
-        context = ""
-        for query, response in hist:
-            context += prompt_structure.format(input_text=query)
-            if not (response is None):
-                context += response
-        return context
-
-    def chat_stream(query: str, history= None, **kwargs):
-        if history is None:
-            history = []
-
-        context = hist2context(history)
-        print_index = 0
-        context += prompt_structure.format(input_text=query)
-        context_ = context[-model.get_max_length():] 
-        input_dataset = dataset.from_dict({
-            "type": "text_only",
-            "instances": [ { "text": context_ } ]
-        })
-        print(context_)
-        for response, flag_break in inferencer.stream_inference(context=context_, model=model, max_new_tokens=chatbot_args.max_new_tokens, 
-                                        token_per_step=token_per_step, temperature=chatbot_args.temperature,
-                                        end_string=end_string, input_dataset=input_dataset):
-            delta = response[print_index:]
-            seq = response
-            print_index = len(response)
-            
-            yield delta, history + [(query, seq)]
-            if flag_break:
-                break
-
-
-
-
-    def predict(input, history=None): 
-        if history is None:
-            history = []
-        for response, history in chat_stream(input, history):
-            updates = []
-            for query, response in history:
-                updates.append(gr.update(visible=True, value="" + query))
-                updates.append(gr.update(visible=True, value="" + response))
-            if len(updates) < MAX_BOXES:
-                updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
-            yield [history] + updates
-
-
-
-
-
-    with gr.Blocks(css=css) as demo:
-        gr.HTML(title)
-        state = gr.State([])
-        text_boxes = []
-        for i in range(MAX_BOXES):
-            if i % 2 == 0:
-                text_boxes.append(gr.Markdown(visible=False, label="Q:", elem_id="user"))
-            else:
-                text_boxes.append(gr.Markdown(visible=False, label="A:", elem_id="chatbot"))
-
-        txt = gr.Textbox(
-            show_label=False,
-            placeholder="Enter text and press send.",
-        )
-        button = gr.Button("Send")
-
-        button.click(predict, [txt, state], [state] + text_boxes)
-        demo.queue().launch()
-
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/modules/midas/__init__.py b/spaces/PAIR/PAIR-Diffusion/ldm/modules/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PKUWilliamYang/StyleGANEX/datasets/gt_res_dataset.py b/spaces/PKUWilliamYang/StyleGANEX/datasets/gt_res_dataset.py
deleted file mode 100644
index 8892efabcfad7b902c5d49e4b496001241e7ed99..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/StyleGANEX/datasets/gt_res_dataset.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/python
-# encoding: utf-8
-import os
-from torch.utils.data import Dataset
-from PIL import Image
-
-
-class GTResDataset(Dataset):
-
-	def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None):
-		self.pairs = []
-		for f in os.listdir(root_path):
-			image_path = os.path.join(root_path, f)
-			gt_path = os.path.join(gt_dir, f)
-			if f.endswith(".jpg") or f.endswith(".png"):
-				self.pairs.append([image_path, gt_path.replace('.png', '.jpg'), None])
-		self.transform = transform
-		self.transform_train = transform_train
-
-	def __len__(self):
-		return len(self.pairs)
-
-	def __getitem__(self, index):
-		from_path, to_path, _ = self.pairs[index]
-		from_im = Image.open(from_path).convert('RGB')
-		to_im = Image.open(to_path).convert('RGB')
-
-		if self.transform:
-			to_im = self.transform(to_im)
-			from_im = self.transform(from_im)
-
-		return from_im, to_im
diff --git a/spaces/PaddlePaddle/LSeg/app.py b/spaces/PaddlePaddle/LSeg/app.py
deleted file mode 100644
index e1ae5ef2f0f4812d246c6d6f8c20a60037ed05c9..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/LSeg/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import cv2
-import paddle
-import paddlehub as hub
-import gradio as gr
-
-
-module = hub.Module(name="lseg")
-
-
-def segment(image, labels):
-    try:
-        long_size = max(image.shape[:2])
-        if long_size > 512:
-            f = 512 / long_size
-            image = cv2.resize(image, (0, 0), fx=f, fy=f)
-        results = module.segment(
-            image=image[..., ::-1],
-            labels=[item for item in labels.split('\n') if item != '']
-        )
-
-        return [
-            results['color'][..., ::-1],
-            results['mix'][..., ::-1],
-            *[cv2.cvtColor(v, cv2.COLOR_BGRA2RGBA) for v in results['classes'].values()]
-        ]
-    except:
-        paddle.disable_static()
-        raise ValueError
-
-
-gr.Interface(
-    title='LSeg: Language-driven Semantic Segmentation',
-    fn=segment,
-    inputs=[
-        gr.Image(),
-        gr.Textbox(placeholder='other\ncat', lines=5, max_lines=50),
-    ],
-    outputs=[
-        gr.Gallery().style(grid=[2, 3], height="auto")
-    ],
-    article='''## More
-* There are more interesting models in [PaddleHub](https://github.com/PaddlePaddle/PaddleHub), you can star [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) to follow.
-
-* Besides, you can use free GPU resourses in [AIStudio](https://aistudio.baidu.com/aistudio/projectdetail/4580391) to enjoy more cases, have fun.
-    
-    [![](https://user-images.githubusercontent.com/22424850/187849103-074cb6d2-a9b4-49a1-b1f0-fc130049769f.png)](https://github.com/PaddlePaddle/PaddleHub/stargazers)
-
-## References
-* Paper: [Language-driven Semantic Segmentation](https://arxiv.org/abs/2201.03546)
-
-* Offical Code: [isl-org/lang-seg](https://github.com/isl-org/lang-seg)
-''',
-    examples=[['cat.jpeg', 'other\ncat']],
-    cache_examples=True
-).launch()
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py
deleted file mode 100644
index 91aaf065e30c748671a9e76a153a3dca7161b0aa..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py
+++ /dev/null
@@ -1,440 +0,0 @@
-from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-import json
-import numpy as np
-import os.path as osp
-import os
-from prettytable import PrettyTable
-
-import xml.etree.ElementTree as ET
-from collections import defaultdict
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
-
-import maskrcnn_benchmark.utils.mdetr_dist as dist
-#### The following loading utilities are imported from
-#### https://github.com/BryanPlummer/flickr30k_entities/blob/68b3d6f12d1d710f96233f6bd2b6de799d6f4e5b/flickr30k_entities_utils.py
-# Changelog:
-#    - Added typing information
-#    - Completed docstrings
-
-def get_sentence_data(filename) -> List[Dict[str, Any]]:
-    """
-    Parses a sentence file from the Flickr30K Entities dataset
-
-    input:
-      filename - full file path to the sentence file to parse
-
-    output:
-      a list of dictionaries for each sentence with the following fields:
-          sentence - the original sentence
-          phrases - a list of dictionaries for each phrase with the
-                    following fields:
-                      phrase - the text of the annotated phrase
-                      first_word_index - the position of the first word of
-                                         the phrase in the sentence
-                      phrase_id - an identifier for this phrase
-                      phrase_type - a list of the coarse categories this
-                                    phrase belongs to
-
-    """
-    with open(filename, "r") as f:
-        sentences = f.read().split("\n")
-
-    annotations = []
-    for sentence in sentences:
-        if not sentence:
-            continue
-
-        first_word = []
-        phrases = []
-        phrase_id = []
-        phrase_type = []
-        words = []
-        current_phrase = []
-        add_to_phrase = False
-        for token in sentence.split():
-            if add_to_phrase:
-                if token[-1] == "]":
-                    add_to_phrase = False
-                    token = token[:-1]
-                    current_phrase.append(token)
-                    phrases.append(" ".join(current_phrase))
-                    current_phrase = []
-                else:
-                    current_phrase.append(token)
-
-                words.append(token)
-            else:
-                if token[0] == "[":
-                    add_to_phrase = True
-                    first_word.append(len(words))
-                    parts = token.split("/")
-                    phrase_id.append(parts[1][3:])
-                    phrase_type.append(parts[2:])
-                else:
-                    words.append(token)
-
-        sentence_data = {"sentence": " ".join(words), "phrases": []}
-        for index, phrase, p_id, p_type in zip(first_word, phrases, phrase_id, phrase_type):
-            sentence_data["phrases"].append(
-                {"first_word_index": index, "phrase": phrase, "phrase_id": p_id, "phrase_type": p_type}
-            )
-
-        annotations.append(sentence_data)
-
-    return annotations
-
-
-def get_annotations(filename) -> Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]]:
-    """
-    Parses the xml files in the Flickr30K Entities dataset
-
-    input:
-      filename - full file path to the annotations file to parse
-
-    output:
-      dictionary with the following fields:
-          scene - list of identifiers which were annotated as
-                  pertaining to the whole scene
-          nobox - list of identifiers which were annotated as
-                  not being visible in the image
-          boxes - a dictionary where the fields are identifiers
-                  and the values are its list of boxes in the
-                  [xmin ymin xmax ymax] format
-          height - int representing the height of the image
-          width - int representing the width of the image
-          depth - int representing the depth of the image
-    """
-    tree = ET.parse(filename)
-    root = tree.getroot()
-    size_container = root.findall("size")[0]
-    anno_info: Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]] = {}
-    all_boxes: Dict[str, List[List[int]]] = {}
-    all_noboxes: List[str] = []
-    all_scenes: List[str] = []
-    for size_element in size_container:
-        assert size_element.text
-        anno_info[size_element.tag] = int(size_element.text)
-
-    for object_container in root.findall("object"):
-        for names in object_container.findall("name"):
-            box_id = names.text
-            assert box_id
-            box_container = object_container.findall("bndbox")
-            if len(box_container) > 0:
-                if box_id not in all_boxes:
-                    all_boxes[box_id] = []
-                xmin = int(box_container[0].findall("xmin")[0].text)
-                ymin = int(box_container[0].findall("ymin")[0].text)
-                xmax = int(box_container[0].findall("xmax")[0].text)
-                ymax = int(box_container[0].findall("ymax")[0].text)
-                all_boxes[box_id].append([xmin, ymin, xmax, ymax])
-            else:
-                nobndbox = int(object_container.findall("nobndbox")[0].text)
-                if nobndbox > 0:
-                    all_noboxes.append(box_id)
-
-                scene = int(object_container.findall("scene")[0].text)
-                if scene > 0:
-                    all_scenes.append(box_id)
-    anno_info["boxes"] = all_boxes
-    anno_info["nobox"] = all_noboxes
-    anno_info["scene"] = all_scenes
-
-    return anno_info
-
-
-#### END of import from flickr30k_entities
-
-
-#### Bounding box utilities imported from torchvision and converted to numpy
-def box_area(boxes: np.array) -> np.array:
-    """
-    Computes the area of a set of bounding boxes, which are specified by its
-    (x1, y1, x2, y2) coordinates.
-
-    Args:
-        boxes (Tensor[N, 4]): boxes for which the area will be computed. They
-            are expected to be in (x1, y1, x2, y2) format with
-            ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-
-    Returns:
-        area (Tensor[N]): area for each box
-    """
-    assert boxes.ndim == 2 and boxes.shape[-1] == 4
-    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-
-# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
-# with slight modifications
-def _box_inter_union(boxes1: np.array, boxes2: np.array) -> Tuple[np.array, np.array]:
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clip(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    return inter, union
-
-
-def box_iou(boxes1: np.array, boxes2: np.array) -> np.array:
-    """
-    Return intersection-over-union (Jaccard index) 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])
-        boxes2 (Tensor[M, 4])
-
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
-    """
-    inter, union = _box_inter_union(boxes1, boxes2)
-    iou = inter / union
-    return iou
-
-
-#### End of import of box utilities
-
-def _merge_boxes(boxes: List[List[int]]) -> List[List[int]]:
-    """
-    Return the boxes corresponding to the smallest enclosing box containing all the provided boxes
-    The boxes are expected in [x1, y1, x2, y2] format
-    """
-    if len(boxes) == 1:
-        return boxes
-
-    np_boxes = np.asarray(boxes)
-
-    return [[np_boxes[:, 0].min(), np_boxes[:, 1].min(), np_boxes[:, 2].max(), np_boxes[:, 3].max()]]
-
-
-class RecallTracker:
-    """ Utility class to track recall@k for various k, split by categories"""
-
-    def __init__(self, topk: Sequence[int]):
-        """
-        Parameters:
-           - topk : tuple of ints corresponding to the recalls being tracked (eg, recall@1, recall@10, ...)
-        """
-
-        self.total_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk}
-        self.positives_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk}
-
-    def add_positive(self, k: int, category: str):
-        """Log a positive hit @k for given category"""
-        if k not in self.total_byk_bycat:
-            raise RuntimeError(f"{k} is not a valid recall threshold")
-        self.total_byk_bycat[k][category] += 1
-        self.positives_byk_bycat[k][category] += 1
-
-    def add_negative(self, k: int, category: str):
-        """Log a negative hit @k for given category"""
-        if k not in self.total_byk_bycat:
-            raise RuntimeError(f"{k} is not a valid recall threshold")
-        self.total_byk_bycat[k][category] += 1
-
-    def report(self) -> Dict[int, Dict[str, float]]:
-        """Return a condensed report of the results as a dict of dict.
-        report[k][cat] is the recall@k for the given category
-        """
-        report: Dict[int, Dict[str, float]] = {}
-        for k in self.total_byk_bycat:
-            assert k in self.positives_byk_bycat
-            report[k] = {
-                cat: self.positives_byk_bycat[k][cat] / self.total_byk_bycat[k][cat] for cat in self.total_byk_bycat[k]
-            }
-        return report
-
-
-class Flickr30kEntitiesRecallEvaluator:
-    def __init__(
-            self,
-            flickr_path: str,
-            subset: str = "test",
-            topk: Sequence[int] = (1, 5, 10, -1),
-            iou_thresh: float = 0.5,
-            merge_boxes: bool = False,
-            verbose: bool = True,
-    ):
-        assert subset in ["train", "test", "val"], f"Wrong flickr subset {subset}"
-
-        self.topk = topk
-        self.iou_thresh = iou_thresh
-
-        flickr_path = Path(flickr_path)
-
-        # We load the image ids corresponding to the current subset
-        with open(flickr_path / f"{subset}.txt") as file_d:
-            self.img_ids = [line.strip() for line in file_d]
-
-        if verbose:
-            print(f"Flickr subset contains {len(self.img_ids)} images")
-
-        # Read the box annotations for all the images
-        self.imgid2boxes: Dict[str, Dict[str, List[List[int]]]] = {}
-
-        if verbose:
-            print("Loading annotations...")
-
-        for img_id in self.img_ids:
-            anno_info = get_annotations(flickr_path / "Annotations" / f"{img_id}.xml")["boxes"]
-            if merge_boxes:
-                merged = {}
-                for phrase_id, boxes in anno_info.items():
-                    merged[phrase_id] = _merge_boxes(boxes)
-                anno_info = merged
-            self.imgid2boxes[img_id] = anno_info
-
-        # Read the sentences annotations
-        self.imgid2sentences: Dict[str, List[List[Optional[Dict]]]] = {}
-
-        if verbose:
-            print("Loading annotations...")
-
-        self.all_ids: List[str] = []
-        tot_phrases = 0
-        for img_id in self.img_ids:
-            sentence_info = get_sentence_data(flickr_path / "Sentences" / f"{img_id}.txt")
-            self.imgid2sentences[img_id] = [None for _ in range(len(sentence_info))]
-
-            # Some phrases don't have boxes, we filter them.
-            for sent_id, sentence in enumerate(sentence_info):
-                phrases = [phrase for phrase in sentence["phrases"] if phrase["phrase_id"] in self.imgid2boxes[img_id]]
-                if len(phrases) > 0:
-                    self.imgid2sentences[img_id][sent_id] = phrases
-                tot_phrases += len(phrases)
-
-            self.all_ids += [
-                f"{img_id}_{k}" for k in range(len(sentence_info)) if self.imgid2sentences[img_id][k] is not None
-            ]
-
-        if verbose:
-            print(f"There are {tot_phrases} phrases in {len(self.all_ids)} sentences to evaluate")
-
-    def evaluate(self, predictions: List[Dict]):
-        evaluated_ids = set()
-
-        recall_tracker = RecallTracker(self.topk)
-
-        for pred in predictions:
-            cur_id = f"{pred['image_id']}_{pred['sentence_id']}"
-            if cur_id in evaluated_ids:
-                print(
-                    "Warning, multiple predictions found for sentence"
-                    f"{pred['sentence_id']} in image {pred['image_id']}"
-                )
-                continue
-
-            # Skip the sentences with no valid phrase
-            if cur_id not in self.all_ids:
-                if len(pred["boxes"]) != 0:
-                    print(
-                        f"Warning, in image {pred['image_id']} we were not expecting predictions "
-                        f"for sentence {pred['sentence_id']}. Ignoring them."
-                    )
-                continue
-
-            evaluated_ids.add(cur_id)
-
-            pred_boxes = pred["boxes"]
-            if str(pred["image_id"]) not in self.imgid2sentences:
-                raise RuntimeError(f"Unknown image id {pred['image_id']}")
-            if not 0 <= int(pred["sentence_id"]) < len(self.imgid2sentences[str(pred["image_id"])]):
-                raise RuntimeError(f"Unknown sentence id {pred['sentence_id']}" f" in image {pred['image_id']}")
-            target_sentence = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])]
-
-            phrases = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])]
-            if len(pred_boxes) != len(phrases):
-                raise RuntimeError(
-                    f"Error, got {len(pred_boxes)} predictions, expected {len(phrases)} "
-                    f"for sentence {pred['sentence_id']} in image {pred['image_id']}"
-                )
-
-            for cur_boxes, phrase in zip(pred_boxes, phrases):
-                target_boxes = self.imgid2boxes[str(pred["image_id"])][phrase["phrase_id"]]
-
-                ious = box_iou(np.asarray(cur_boxes), np.asarray(target_boxes))
-                for k in self.topk:
-                    maxi = 0
-                    if k == -1:
-                        maxi = ious.max()
-                    else:
-                        assert k > 0
-                        maxi = ious[:k].max()
-                    if maxi >= self.iou_thresh:
-                        recall_tracker.add_positive(k, "all")
-                        for phrase_type in phrase["phrase_type"]:
-                            recall_tracker.add_positive(k, phrase_type)
-                    else:
-                        recall_tracker.add_negative(k, "all")
-                        for phrase_type in phrase["phrase_type"]:
-                            recall_tracker.add_negative(k, phrase_type)
-
-        if len(evaluated_ids) != len(self.all_ids):
-            print("ERROR, the number of evaluated sentence doesn't match. Missing predictions:")
-            un_processed = set(self.all_ids) - evaluated_ids
-            for missing in un_processed:
-                img_id, sent_id = missing.split("_")
-                print(f"\t sentence {sent_id} in image {img_id}")
-            raise RuntimeError("Missing predictions")
-
-        return recall_tracker.report()
-
-
-class FlickrEvaluator(object):
-    def __init__(
-            self,
-            flickr_path,
-            subset,
-            top_k=(1, 5, 10, -1),
-            iou_thresh=0.5,
-            merge_boxes=False,
-    ):
-        assert isinstance(top_k, (list, tuple))
-
-        self.evaluator = Flickr30kEntitiesRecallEvaluator(
-            flickr_path, subset=subset, topk=top_k, iou_thresh=iou_thresh, merge_boxes=merge_boxes, verbose=False
-        )
-        self.predictions = []
-        self.results = None
-
-    def accumulate(self):
-        pass
-
-    def update(self, predictions):
-        self.predictions += predictions
-
-    def synchronize_between_processes(self):
-        all_predictions = dist.all_gather(self.predictions)
-        self.predictions = sum(all_predictions, [])
-
-    def summarize(self):
-        if dist.is_main_process():
-            self.results = self.evaluator.evaluate(self.predictions)
-            table = PrettyTable()
-            all_cat = sorted(list(self.results.values())[0].keys())
-            table.field_names = ["Recall@k"] + all_cat
-
-            score = {}
-            for k, v in self.results.items():
-                cur_results = [v[cat] for cat in all_cat]
-                header = "Upper_bound" if k == -1 else f"Recall@{k}"
-
-                for cat in all_cat:
-                    score[f"{header}_{cat}"] = v[cat]
-                table.add_row([header] + cur_results)
-
-            print(table)
-
-            return score
-
-        return None, None
diff --git a/spaces/Pranjal-666/Heart_Disease/README.md b/spaces/Pranjal-666/Heart_Disease/README.md
deleted file mode 100644
index 58aed7cfdcdcaac8ec0539fdd787077dbfb5fb0e..0000000000000000000000000000000000000000
--- a/spaces/Pranjal-666/Heart_Disease/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Heart Disease
-emoji: 📊
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pranjal-666/Potato-leaf-disease-detection/README.md b/spaces/Pranjal-666/Potato-leaf-disease-detection/README.md
deleted file mode 100644
index 6431007ed13b4a873eb9e77436799c851d17e596..0000000000000000000000000000000000000000
--- a/spaces/Pranjal-666/Potato-leaf-disease-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Potato Leaf Disease Detection
-emoji: 🔥
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pranjal2041/SemSup-XC/cleaned_code/wandb/run-20221030_082401-1ry5ckm9/files/code/cleaned_code/main.py b/spaces/Pranjal2041/SemSup-XC/cleaned_code/wandb/run-20221030_082401-1ry5ckm9/files/code/cleaned_code/main.py
deleted file mode 100644
index 8a7723567946a670bf84973efc1c37e92b1f5413..0000000000000000000000000000000000000000
--- a/spaces/Pranjal2041/SemSup-XC/cleaned_code/wandb/run-20221030_082401-1ry5ckm9/files/code/cleaned_code/main.py
+++ /dev/null
@@ -1,749 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2020 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.
-""" Finetuning the library models for sequence classification on GLUE."""
-# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
-print('The script has began')
-import itertools
-import logging
-import os
-import random
-import sys
-import time
-from dataclasses import dataclass, field
-from typing import Optional
-import shutil
-
-import datasets
-import numpy as np
-from datasets import load_dataset, load_metric
-
-import torch
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    DataCollatorWithPadding,
-    EvalPrediction,
-    HfArgumentParser,
-    PretrainedConfig,
-    Trainer,
-    TrainingArguments,
-    default_data_collator,
-    set_seed,
-    TrainerCallback,
-)
-from transformers import BertForSequenceClassification
-from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-from src import getTokenizedLabelDescriptions
-from src import getLabelModel
-from src import SemSupDataset, DeViSEDataset
-from src import AutoModelForMultiLabelClassification
-from src import multilabel_metrics
-from src import task_to_keys, task_to_label_keys, dataset_to_numlabels
-from src import DataTrainingArguments, ModelArguments, CustomTrainingArguments
-from src import dataset_classification_type
-from src import BertForSemanticEmbedding
-from src import read_yaml_config
-from transformers import AdamW, get_linear_schedule_with_warmup
-from torch.utils.data import DataLoader
-import os
-
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
-
-logger = logging.getLogger(__name__)
-
-
-
-
-def setup_logging(training_args):
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-
-    log_level = training_args.get_process_log_level()
-    logger.setLevel(log_level)
-    datasets.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.enable_default_handler()
-    transformers.utils.logging.enable_explicit_format()
-
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-def get_last_check(training_args):
-    last_checkpoint = None
-    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
-        last_checkpoint = get_last_checkpoint(training_args.output_dir)
-        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
-            raise ValueError(
-                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
-                "Use --overwrite_output_dir to overcome."
-            )
-        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
-            logger.info(
-                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
-                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
-            )
-    return last_checkpoint
-
-def main():
-
-    TIME_LIMIT = 50 * 60 # 1 HOUR
-    start_time = time.time()
-
-
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-    print('Main Function is Called!!!', sys.argv)
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))
-    
-    if len(sys.argv) > 1 and sys.argv[1].startswith('--local_rank'):
-        extra_args = {'local_rank' : sys.argv[1].split('=')[1]}
-        argv = sys.argv[0:1] + sys.argv[2:]
-    else:
-        argv = sys.argv
-        extra_args = {}
-
-    print(len(argv) == 3 and argv[1].endswith(".yml"))
-    if len(argv) == 2 and argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(argv[1]))
-    elif len(argv) == 3 and argv[1].endswith(".yml"):
-        print('Lets Go!!!')
-        model_args, data_args, training_args = parser.parse_dict(read_yaml_config(os.path.abspath(argv[1]), output_dir = argv[2],  extra_args = extra_args))
-        print('training args', training_args)
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-    # model_args.label_model_name_or_path = 'google/bert_uncased_L-2_H-128_A-2'
-
-    # training_args.deepspeed  = 'deepspeed_config.json'
-    # training_args.output_learning_rate = model_args.output_learning_rate
-    setup_logging(training_args)
-    # training_args.seed = np.random.randint(0, 100000000)
-    if training_args.seed == -1:
-        training_args.seed = np.random.randint(0, 100000000)
-    print(training_args.seed)
-    # Detecting last checkpoint.
-
-    # Lets do a simple test to see if node is not corrupted:
-
-    # Set seed before initializing model.
-    last_checkpoint = get_last_check(training_args)
-
-    set_seed(training_args.seed)
-
-    
-    if data_args.dataset_name is not None and not data_args.load_from_local:
-        # Downloading and loading a dataset from the hub.
-        raw_datasets = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-    else:
-        # Loading a dataset from your local files.
-        # CSV/JSON training and evaluation files are needed.
-        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
-
-        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
-        # when you use `do_predict` without specifying a GLUE benchmark task.
-        if training_args.do_predict:
-            if data_args.test_file is not None:
-                data_files["test"] = data_args.test_file
-            else:
-                raise ValueError("Need a test file for `do_predict`.")
-
-        for key in data_files.keys():
-            logger.info(f"load a local file for {key}: {data_files[key]}")
-
-        if data_args.train_file.endswith(".csv"):
-            # Loading a dataset from local csv files
-            raw_datasets = load_dataset(
-                "csv",
-                data_files=data_files,
-                cache_dir=model_args.cache_dir,
-                use_auth_token=True if model_args.use_auth_token else None,
-            )
-        else:
-            # Loading a dataset from local json files
-            print('df are', data_files, model_args.cache_dir)
-            raw_datasets = load_dataset(
-                "json",
-                data_files=data_files,
-                cache_dir=model_args.cache_dir,
-                use_auth_token=True if model_args.use_auth_token else None,
-            )
-    # See more about loading any type of standard or custom dataset at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-
-    # Labels
-    if data_args.task_name is not None:
-        label_key = task_to_label_keys[data_args.task_name] 
-
-
-        if training_args.scenario == 'unseen_labels':
-            label_list = [x.strip() for x in open(data_args.all_labels).readlines()]
-            train_labels = list(set([item for sublist in raw_datasets['train'][label_key] for item in sublist]))
-            if data_args.test_labels is not None:
-                test_labels = [x.strip() for x in open(data_args.test_labels).readlines()]
-            else:
-                test_labels  = list(set([item for sublist in raw_datasets['validation'][label_key] for item in sublist]))
-
-        else:
-            label_list = list(set(itertools.chain(*[
-                [item for sublist in raw_datasets[split_key][label_key] for item in sublist]
-                for split_key in raw_datasets.keys()]
-            )))
-        num_labels = len(label_list)
-        label_list.sort() # For consistency
-        print('Debugging: num_labels: ', num_labels)
-        print('Debugging: label_list[:50]: ', label_list[:50])
-    else:
-        # Trying to have good defaults here, don't hesitate to tweak to your needs.
-        # A useful fast method:
-        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
-        label_list = raw_datasets["train"].unique("label")
-        label_list.sort()  # Let's sort it for determinism
-        num_labels = len(label_list)
-
-    # Load pretrained model and tokenizer
-    #
-    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    if model_args.semsup:
-        label_model, label_tokenizer = getLabelModel(data_args, model_args)
-        # tokenizedDescriptions = getTokenizedLabelDescriptions(data_args, 'datasets/EUR-Lex/eurlex-4k-class_descriptions_v1.json', label_tokenizer)
-        # tokenizedDescriptions = getTokenizedLabelDescriptions(data_args, data_args.descriptions_file, label_tokenizer)
-        
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        # num_labels=num_labels,
-        finetuning_task=data_args.task_name,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-    config.model_name_or_path = model_args.model_name_or_path
-    config.problem_type = dataset_classification_type[data_args.task_name]
-    config.negative_sampling = model_args.negative_sampling
-    config.semsup = model_args.semsup
-    config.normalize_embeddings = model_args.normalize_embeddings
-    config.encoder_model_type = model_args.encoder_model_type
-    config.arch_type = model_args.arch_type
-    config.devise = model_args.devise
-    config.coil = model_args.coil
-    config.token_dim = model_args.token_dim
-    config.colbert = model_args.colbert
-
-    if config.semsup:
-        config.label_hidden_size = label_model.config.hidden_size
-        print('Label hidden size is ', label_model.config.hidden_size)
-    if model_args.devise:
-        config.label_hidden_size = 300
-    config.cluster_labels_dim = data_args.num_clusters 
-    temp_label_id = {v: i for i, v in enumerate(label_list)}
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=True,#model_args.use_fast_tokenizer,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-
-
-        
-    # Preprocessing the raw_datasets
-    if data_args.task_name is not None:
-        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
-    else:
-        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
-        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
-        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
-            sentence1_key, sentence2_key = "sentence1", "sentence2"
-        else:
-            if len(non_label_column_names) >= 2:
-                sentence1_key, sentence2_key = non_label_column_names[:2]
-            else:
-                sentence1_key, sentence2_key = non_label_column_names[0], None
-
-    # Padding strategy
-    if data_args.pad_to_max_length:
-        padding = "max_length"
-    else:
-        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
-        padding = False
-
-    # Some models have set the order of the labels to use, so let's make sure we do use it.
-    def model_init():
-        model = BertForSemanticEmbedding(config)
-        num_frozen_layers = model_args.num_frozen_layers
-        if num_frozen_layers > 0:
-            try:
-                for param in model.encoder.bert.embeddings.parameters():
-                    param.requires_grad = False
-                for param in model.encoder.bert.pooler.parameters():
-                    param.requires_grad = False
-                for layer in model.encoder.bert.encoder.layer[:num_frozen_layers]:
-                    for param in layer.parameters():
-                        param.requires_grad = False
-            except:
-                for param in model.encoder.embeddings.parameters():
-                    param.requires_grad = False
-                for param in model.encoder.pooler.parameters():
-                    param.requires_grad = False
-                for layer in model.encoder.encoder.layer[:num_frozen_layers]:
-                    for param in layer.parameters():
-                        param.requires_grad = False
-
-        # Place the label model inside the main model
-        if model_args.semsup:
-            model.label_model = label_model
-            model.label_tokenizer = label_tokenizer
-            # model.tokenizedDescriptions = tokenizedDescriptions
-            # for param in model.label_model.parameters():
-            #     param.requires_grad = False
-            # print(training_args.num_train_epochs, type(training_args.num_train_epochs))
-            # 3/0
-            if model_args.tie_weights:
-                for i in range(9):
-                    if num_frozen_layers >= 9: 
-                        try:
-                            model.label_model.encoder.layer[i]  = model.encoder.bert.encoder.layer[i]
-                        except:
-                            model.label_model.encoder.layer[i]  = model.encoder.encoder.layer[i]
-                    else:
-                        for param in model.label_model.encoder.layer[i].parameters():
-                            param.requires_grad = False
-                for param in model.label_model.embeddings.parameters():
-                    param.requires_grad = False
-                for param in model.label_model.pooler.parameters():
-                    param.requires_grad = False
-            else:
-                label_frozen_layers = model_args.label_frozen_layers
-                if label_frozen_layers > 0:
-                    print(model.label_model)
-                    for param in model.label_model.embeddings.parameters():
-                        param.requires_grad = False
-                    for param in model.label_model.pooler.parameters():
-                        param.requires_grad = False
-                    for layer in model.label_model.encoder.layer[:label_frozen_layers]:
-                        for param in layer.parameters():
-                            param.requires_grad = False
-
-        model.config.label2id = {l: i for i, l in enumerate(label_list)}
-        model.config.id2label = {id: label for label, id in config.label2id.items()}
-        return model
-    model = model_init()
-    if model_args.pretrained_model_path != '':
-        model.load_state_dict(torch.load(model_args.pretrained_model_path, map_location = list(model.parameters())[0].device))
-    if model_args.pretrained_label_model_path != '':
-        model.label_model.load_state_dict(torch.load(model_args.pretrained_label_model_path, map_location = list(model.parameters())[0].device))
-
-    id2label = model.config.id2label
-    label_to_id = model.config.label2id
-    # print(len(label_to_id.keys()))
-    # print(list(label_to_id.keys())[-100:])
-    # 3/0
-
-    if data_args.max_seq_length > tokenizer.model_max_length:
-        logger.warning(
-            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-        )
-    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
-    def preprocess_function(examples):
-        # Tokenize the texts
-        # print('Len Examples')
-        args = (
-            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
-        )
-        result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
-
-        # Map labels to IDs (not necessary for GLUE tasks)
-        if label_to_id is not None and label_key in examples:
-            # check if multi-label problem
-            if isinstance(examples[label_key][0], list):
-                # Multi-Label, create one-hot encoding
-                labels = [[label_to_id[l] for l in examples[label_key][i]] for i in range(len(examples[label_key]))]
-                result["label"] = [[1 if j in labels[i] else 0 for j in range(num_labels)] for i in range(len(labels))]
-            else:
-                result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
-        
-
-        # Labels keyword should not be present(it may contain string)
-        try: del input['labels']
-        except: ...
-
-        return result
-
-    # with training_args.main_process_first(desc="dataset map pre-processing"):
-        # raw_datasets = raw_datasets.map(
-        #     preprocess_function,
-        #     batched=True,
-        #     load_from_cache_file=not data_args.overwrite_cache,
-        #     desc="Running tokenizer on dataset",
-        # )
-    try:
-        if data_args.test_descriptions_file == '':
-            data_args.test_descriptions_file = data_args.descriptions_file
-    except: data_args.test_descriptions_file = data_args.descriptions_file
-    
-    print('Running with_transform')
-    raw_datasets = raw_datasets.with_transform(preprocess_function)
-
-    class_descs_tokenized  = None
-    if model_args.semsup and data_args.large_dset and os.path.exists(data_args.tokenized_descs_file):
-        if data_args.tokenized_descs_file.endswith('npy'):
-        #     import h5py
-        #     class_descs_tokenized = h5py.File(data_args.tokenized_descs_file) # np.load(data_args.tokenized_descs_file, allow_pickle=True).item()
-        # else:
-            class_descs_tokenized = np.load(data_args.tokenized_descs_file, allow_pickle=True)
-
-        
-    if training_args.do_train:
-        if "train" not in raw_datasets:
-            raise ValueError("--do_train requires a train dataset")
-        train_dataset = raw_datasets["train"]
-        if data_args.max_train_samples is not None:
-            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
-            # train_dataset = train_dataset.select(range(max_train_samples))
-            train_dataset = train_dataset.select(np.random.choice(len(train_dataset), max_train_samples))
-        if model_args.semsup:
-            # train_dataset = SemSupDataset(train_dataset, data_args, 'datasets/EUR-Lex/eurlex-4k-class_descriptions_v1.json', label_to_id, id2label, label_tokenizer, return_desc_embeddings = True, sampleRandom = data_args.contrastive_learning_samples, cl_min_positive_descs= data_args.cl_min_positive_descs)
-            train_dataset = SemSupDataset(train_dataset, data_args, data_args.descriptions_file, label_to_id, id2label, label_tokenizer, return_desc_embeddings = True, sampleRandom = data_args.contrastive_learning_samples, cl_min_positive_descs= data_args.cl_min_positive_descs, seen_labels = None if training_args.scenario == 'seen' else train_labels, add_label_name = model_args.add_label_name, max_descs_per_label = data_args.max_descs_per_label, use_precomputed_embeddings = model_args.use_precomputed_embeddings, bm_short_file = data_args.bm_short_file, ignore_pos_labels_file = data_args.ignore_pos_labels_file, class_descs_tokenized = class_descs_tokenized)
-        elif model_args.devise:
-            train_dataset = DeViSEDataset(train_dataset, data_args.descriptions_file, label_to_id, id2label)
-        else:
-            train_dataset = SemSupDataset(train_dataset, data_args, data_args.descriptions_file, label_to_id, id2label, None, useSemSup = False, add_label_name = model_args.add_label_name)
-
-    if training_args.do_eval:
-        # print('Loading Model')
-        # model.load_state_dict(torch.load('output/eurlex4k_baseline/checkpoint-6000/pytorch_model.bin'))
-        # print('Model Loaded Successfully')
-        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
-            raise ValueError("--do_eval requires a validation dataset")
-        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
-        # if data_args.max_eval_samples is not None:
-        choice_indexes = None
-
-        if data_args.max_eval_samples is not None:
-            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
-            if data_args.random_sample_seed != -1:
-                # l = int(len(eval_dataset)  // 1.5)
-                l = len(eval_dataset)
-
-                np.random.seed(data_args.random_sample_seed)
-                choice_indexes = np.random.choice(l, max_eval_samples, replace = False).tolist()
-                choice_indexes = [x for x in choice_indexes]
-                import pickle
-                pickle.dump(choice_indexes, open('choice_indexes.pkl','wb'))
-                eval_dataset = eval_dataset.select(choice_indexes)
-                np.random.seed()
-            else:
-                choice_indexes = None
-                eval_dataset = eval_dataset.select(range(max_eval_samples))
-
-        if model_args.semsup:
-            # eval_dataset = SemSupDataset(eval_dataset, data_args, 'datasets/EUR-Lex/eurlex-4k-class_descriptions_v1.json', label_to_id, id2label, label_tokenizer, return_desc_embeddings=True)
-            eval_dataset = SemSupDataset(eval_dataset, data_args, data_args.test_descriptions_file, label_to_id, id2label, label_tokenizer, return_desc_embeddings=True, seen_labels = None if training_args.scenario == 'seen' else test_labels, add_label_name = model_args.add_label_name, max_descs_per_label = data_args.max_descs_per_label, use_precomputed_embeddings = model_args.use_precomputed_embeddings, class_descs_tokenized = class_descs_tokenized, isTrain = False, choice_indexes = choice_indexes)
-        elif model_args.devise:
-            eval_dataset = DeViSEDataset(eval_dataset, data_args.descriptions_file, label_to_id, id2label)
-
-    if training_args.do_predict:# or data_args.task_name is not None or data_args.test_file is not None:
-        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
-            raise ValueError("--do_predict requires a test dataset")
-        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
-        if data_args.max_predict_samples is not None:
-            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
-            predict_dataset = predict_dataset.select(range(max_predict_samples))
-
-    # Log a few random samples from the training set:
-    # if training_args.do_train:
-    #     for index in random.sample(range(len(train_dataset)), 3):
-    #         logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
-
-
-    # If multi-label task, use multi-label metric
-    # if dataset_classification_type[data_args.task_name] == "multi_label_classification":
-    compute_metrics = multilabel_metrics(data_args, model.config.id2label, model.config.label2id, {}, training_args)
-    # else:
-    #     ...
-    #     raise NotImplementedError("Metric function not implemented for this task.")
-
-
-    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
-    # we already did the padding.
-    if data_args.pad_to_max_length:
-        data_collator = default_data_collator
-    elif training_args.fp16:
-        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
-    else:
-        data_collator = None
-    
-
-    # Initialize our Trainer
-    print('Initializing Optimizers')
-    from torch.optim import AdamW
-    # from transformers import AdamW
-    if model_args.use_custom_optimizer:
-        decay_cond = lambda x: x[0].lower().find('layernorm.weight')!=-1 or x[0].lower().find('bias')!=-1
-        if model_args.semsup and not data_args.hyper_search:
-            main_decay_params = list(map(lambda x: x[1], filter(lambda x: x[1].requires_grad and decay_cond(x) and (x[0][12:], x[1]) not in model.label_model.named_parameters() , model.named_parameters())))
-            main_no_decay_params = list(map(lambda x: x[1],filter(lambda x: x[1].requires_grad and not decay_cond(x) and (x[0][12:], x[1]) not in model.label_model.named_parameters(), model.named_parameters())))
-            label_decay_params = list(map(lambda x: x[1], filter(lambda x: x[1].requires_grad and decay_cond(x) , model.label_model.named_parameters())))
-            label_no_decay_params = list(map(lambda x: x[1],filter(lambda x: x[1].requires_grad and not decay_cond(x), model.label_model.named_parameters())))
-            if model_args.tie_weights:
-                label_decay_params = list(set(label_decay_params).difference(main_decay_params))
-                label_no_decay_params = list(set(label_no_decay_params).difference(main_no_decay_params))
-            
-            optimizer = AdamW([
-                {'params': main_decay_params, 'weight_decay': 1e-2},
-                {'params': main_no_decay_params, 'weight_decay': 0},
-                {'params': label_decay_params, 'weight_decay': 1e-2, 'lr' : training_args.output_learning_rate},
-                {'params': label_no_decay_params, 'weight_decay': 0, 'lr' : training_args.output_learning_rate}
-            ],
-            lr = training_args.learning_rate, eps= 1e-6)    
-            ...
-        else:
-            decay_params = list(map(lambda x: x[1], filter(lambda x: decay_cond(x), model.named_parameters())))
-            no_decay_params = list(map(lambda x: x[1],filter(lambda x: not decay_cond(x), model.named_parameters())))
-            optimizer = optim.AdamW([
-                {'params': decay_params, 'weight_decay': 1e-2},
-                {'params': no_decay_params, 'weight_decay': 0}],
-            lr = training_args.learning_rate, eps= 1e-6)
-        
-    # if data_args.hyper_search:
-    #     training_args.place_model_on_device = False
-
-    if training_args.one_hour_job and training_args.local_rank <= 0:
-        if os.path.exists(training_args.output_dir + '/checkpoint_step.txt'):
-            check_steps = []
-            with open(training_args.output_dir + '/checkpoint_step.txt','r') as f:
-                for line in f:
-                    check_steps.append(int(line.strip()))
-            check_steps = sorted(list(set(check_steps)))
-            if len(check_steps) > 2:
-                for item in check_steps[:-2]:
-                    try: shutil.rmtree(training_args.output_dir + '/checkpoint-' + str(item))
-                    except Exception as e: print('Error Occured',e)
-    def check_save_checkpoint(args, state, start_time, TIME_LIMIT, force_save = False) -> bool:
-        if time.time() - start_time > TIME_LIMIT - 100 or force_save: # 100 secs before end time, allow to save checkpoint
-            # Update the start time though
-            print('Saving Checkpoint')
-            
-            # Save the current step as well, we will delete it later
-            f = open(args.output_dir + '/checkpoint_step.txt','a')
-            f.write(str(state.global_step) + '\n')
-            f.close()
-
-            start_time = time.time()
-            return True, start_time
-        else:
-            return False, start_time
-
-    class MyCallback(TrainerCallback):
-        def on_step_end(self, args, state, control, **kwargs):
-            try: temp_var = self.start_time
-            except: self.start_time = start_time; print('Reinit start time')
-            control.should_save, self.start_time = check_save_checkpoint(args, state, self.start_time, TIME_LIMIT, force_save = control.should_evaluate)
-            return control
-
-
-
-    if data_args.hyper_search and model_args.use_custom_optimizer:
-        from src import CustomTrainer
-        trainer = CustomTrainer(
-            semsup = model_args.semsup,
-            output_learning_rate = training_args.output_learning_rate if model_args.semsup else -1,
-            args=training_args,
-            train_dataset=train_dataset if training_args.do_train else None,
-            eval_dataset=eval_dataset if training_args.do_eval else None,
-            compute_metrics=compute_metrics,
-            tokenizer=tokenizer,
-            data_collator=data_collator,
-            model_init= model_init,
-            optimizers = (None, None) ,
-        )
-    else:
-        trainer = Trainer(
-            model=model if not data_args.hyper_search else None,
-            model_init= model_init if data_args.hyper_search else None,
-            args=training_args,
-            train_dataset=train_dataset if training_args.do_train else None,
-            eval_dataset=eval_dataset if training_args.do_eval else None,
-            compute_metrics=compute_metrics,
-            tokenizer=tokenizer,
-            data_collator=data_collator,
-            optimizers = (optimizer if model_args.use_custom_optimizer else None, None) ,
-            # callbacks = [MyCallback],
-        )
-        if training_args.one_hour_job:
-            trainer.add_callback(MyCallback)
-    if data_args.hyper_search:
-        from ray.tune.suggest.hyperopt import HyperOptSearch
-        from ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining
-        import ray
-        from ray import tune
-
-        tune_config = {
-            "output_learning_rate": tune.loguniform(2e-6, 1e-3),
-            "learning_rate": tune.loguniform(2e-6, 1e-3),
-        }
-        scheduler = PopulationBasedTraining(
-            time_attr="training_iteration",
-            metric="eval_P@5",
-            mode="max",
-            perturbation_interval=1,
-            hyperparam_mutations={
-                "output_learning_rate": tune.loguniform(2e-6, 1e-3),
-                "learning_rate": tune.loguniform(2e-6, 1e-3),
-            },
-        )
-        def compute_objective(x):
-            print('metrics are',x)
-            return x['eval_P@5']
-        # compute_objective = lambda x: x['P@1']
-        trainer.place_model_on_device = True
-        trainer.hyperparameter_search(
-            hp_space = lambda _: tune_config,
-            backend = 'ray',
-            n_trials = 20,
-            direction = 'maximize',
-            resources_per_trial={"cpu": 4, "gpu": 1},
-            # search_alg=HyperOptSearch(metric="P@1", mode="max"),
-            scheduler=scheduler,
-            keep_checkpoints_num = 1,
-            compute_objective = compute_objective
-        )
-        3/0
-
-    # trainer.optimizer = optimizer
-    # Training
-    if training_args.do_train:
-        checkpoint = None
-        if training_args.resume_from_checkpoint is not None:
-            checkpoint = training_args.resume_from_checkpoint
-        elif last_checkpoint is not None:
-            checkpoint = last_checkpoint
-        train_result = trainer.train(resume_from_checkpoint=checkpoint)
-        metrics = train_result.metrics
-        max_train_samples = (
-            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
-        )
-        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
-
-        trainer.save_model()  # Saves the tokenizer too for easy upload
-
-        trainer.log_metrics("train", metrics)
-        trainer.save_metrics("train", metrics)
-        trainer.save_state()
-
-    # Evaluation
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        # Loop to handle MNLI double evaluation (matched, mis-matched)
-        tasks = [data_args.task_name]
-        eval_datasets = [eval_dataset]
-        if data_args.task_name == "mnli":
-            tasks.append("mnli-mm")
-            eval_datasets.append(raw_datasets["validation_mismatched"])
-            combined = {}
-
-        for eval_dataset, task in zip(eval_datasets, tasks):
-            metrics = trainer.evaluate(eval_dataset=eval_dataset)
-
-            max_eval_samples = (
-                data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
-            )
-            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
-
-            if task == "mnli-mm":
-                metrics = {k + "_mm": v for k, v in metrics.items()}
-            if task is not None and "mnli" in task:
-                combined.update(metrics)
-
-            trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
-            trainer.log_metrics("eval", metrics)
-
-    if training_args.do_predict:
-        logger.info("*** Predict ***")
-
-        # Loop to handle MNLI double evaluation (matched, mis-matched)
-        tasks = [data_args.task_name]
-        predict_datasets = [predict_dataset]
-        if data_args.task_name == "mnli":
-            tasks.append("mnli-mm")
-            predict_datasets.append(raw_datasets["test_mismatched"])
-
-        for predict_dataset, task in zip(predict_datasets, tasks):
-            # Removing the `label` columns because it contains -1 and Trainer won't like that.
-            predict_dataset = predict_dataset.remove_columns("label")
-            predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
-            predictions = np.argmax(predictions, axis=1)
-
-            output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
-            if trainer.is_world_process_zero():
-                with open(output_predict_file, "w") as writer:
-                    logger.info(f"***** Predict results {task} *****")
-                    writer.write("index\tprediction\n")
-                    for index, item in enumerate(predictions):
-                        item = label_list[item]
-                        writer.write(f"{index}\t{item}\n")
-
-    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
-    if data_args.task_name is not None:
-        kwargs["language"] = "en"
-        kwargs["dataset_tags"] = "glue"
-        kwargs["dataset_args"] = data_args.task_name
-        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
-
-    if training_args.push_to_hub:
-        trainer.push_to_hub(**kwargs)
-    else:
-        trainer.create_model_card(**kwargs)
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-
-
-    main()
\ No newline at end of file
diff --git a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_omit_AA.py b/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_omit_AA.py
deleted file mode 100644
index 353a74bcf0ec1c0238a32b17a001d15591d18243..0000000000000000000000000000000000000000
--- a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_omit_AA.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import glob
-import random
-import numpy as np
-import json
-import itertools
-
-#MODIFY this path
-with open('/home/justas/projects/lab_github/mpnn/data/pdbs.jsonl', 'r') as json_file:
-    json_list = list(json_file)
-
-my_dict = {}
-for json_str in json_list:
-    result = json.loads(json_str)
-    all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain']
-    fixed_position_dict = {}
-    print(result['name'])
-    if result['name'] == '5TTA':
-        for chain in all_chain_list:
-            if chain == 'A':
-                fixed_position_dict[chain] = [
-                    [[int(item) for item in list(itertools.chain(list(np.arange(1,4)), list(np.arange(7,10)), [22, 25, 33]))], 'GPL'],
-                    [[int(item) for item in list(itertools.chain([40, 41, 42, 43]))], 'WC'],
-                    [[int(item) for item in list(itertools.chain(list(np.arange(50,150))))], 'ACEFGHIKLMNRSTVWYX'],
-                    [[int(item) for item in list(itertools.chain(list(np.arange(160,200))))], 'FGHIKLPQDMNRSTVWYX']]
-            else:
-                fixed_position_dict[chain] = []
-    else:
-        for chain in all_chain_list:
-            fixed_position_dict[chain] = []
-    my_dict[result['name']] = fixed_position_dict
-
-#MODIFY this path   
-with open('/home/justas/projects/lab_github/mpnn/data/omit_AA.jsonl', 'w') as f:
-    f.write(json.dumps(my_dict) + '\n')
-
-
-print('Finished')
-#e.g. output
-#{"5TTA": {"A": [[[1, 2, 3, 7, 8, 9, 22, 25, 33], "GPL"], [[40, 41, 42, 43], "WC"], [[50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], "ACEFGHIKLMNRSTVWYX"], [[160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199], "FGHIKLPQDMNRSTVWYX"]], "B": []}, "3LIS": {"A": [], "B": []}}  
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/data/lsun.py b/spaces/Purple11/Grounded-Diffusion/ldm/data/lsun.py
deleted file mode 100644
index 6256e45715ff0b57c53f985594d27cbbbff0e68e..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/data/lsun.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import numpy as np
-import PIL
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-
-
-class LSUNBase(Dataset):
-    def __init__(self,
-                 txt_file,
-                 data_root,
-                 size=None,
-                 interpolation="bicubic",
-                 flip_p=0.5
-                 ):
-        self.data_paths = txt_file
-        self.data_root = data_root
-        with open(self.data_paths, "r") as f:
-            self.image_paths = f.read().splitlines()
-        self._length = len(self.image_paths)
-        self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, l)
-                           for l in self.image_paths],
-        }
-
-        self.size = size
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
-        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-        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)
-        if self.size is not None:
-            image = image.resize((self.size, self.size), resample=self.interpolation)
-
-        image = self.flip(image)
-        image = np.array(image).astype(np.uint8)
-        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
-        return example
-
-
-class LSUNChurchesTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
-
-
-class LSUNChurchesValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
-                         flip_p=flip_p, **kwargs)
-
-
-class LSUNBedroomsTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
-
-
-class LSUNBedroomsValidation(LSUNBase):
-    def __init__(self, flip_p=0.0, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
-                         flip_p=flip_p, **kwargs)
-
-
-class LSUNCatsTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
-
-
-class LSUNCatsValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
-                         flip_p=flip_p, **kwargs)
diff --git a/spaces/Randolph/hadenjax-dreams/README.md b/spaces/Randolph/hadenjax-dreams/README.md
deleted file mode 100644
index 5ad51e2f9dbf289e71dd9e507de475b84b86702a..0000000000000000000000000000000000000000
--- a/spaces/Randolph/hadenjax-dreams/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hadenjax Dreams
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RandomCatLover/thesis_finetuned_classifier/app.py b/spaces/RandomCatLover/thesis_finetuned_classifier/app.py
deleted file mode 100644
index 637c7f8f1ed8ef891470ff57124c49e572178cee..0000000000000000000000000000000000000000
--- a/spaces/RandomCatLover/thesis_finetuned_classifier/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os
-
-destination = 'core'
-
-if os.path.exists('.env'):
-    from dotenv import load_dotenv
-    load_dotenv()
-
-if not os.path.exists(destination):
-    import subprocess
-    repo_url = os.getenv("GIT_CORE")
-    command = f'git clone {repo_url} {destination}'
-    try:
-        subprocess.check_output(command, stderr=subprocess.STDOUT, shell=True)#, env=env)
-        print('Repository cloned successfully.')
-    except subprocess.CalledProcessError as e:
-        print(f'Error cloning repository: {e.output.decode()}')
-
-from core.s import demo
-
-demo.queue(concurrency_count=3)
-#demo.launch(auth=("student", "whataday"))
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp b/spaces/Reha2704/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp
deleted file mode 100644
index b01584d19edb99e7feec5f2e4c51169a1ed208db..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/model/raft/alt_cuda_corr/correlation.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-#include <torch/extension.h>
-#include <vector>
-
-// CUDA forward declarations
-std::vector<torch::Tensor> corr_cuda_forward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    int radius);
-
-std::vector<torch::Tensor> corr_cuda_backward(
-  torch::Tensor fmap1,
-  torch::Tensor fmap2,
-  torch::Tensor coords,
-  torch::Tensor corr_grad,
-  int radius);
-
-// C++ interface
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-std::vector<torch::Tensor> corr_forward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    int radius) {
-  CHECK_INPUT(fmap1);
-  CHECK_INPUT(fmap2);
-  CHECK_INPUT(coords);
-
-  return corr_cuda_forward(fmap1, fmap2, coords, radius);
-}
-
-
-std::vector<torch::Tensor> corr_backward(
-    torch::Tensor fmap1,
-    torch::Tensor fmap2,
-    torch::Tensor coords,
-    torch::Tensor corr_grad,
-    int radius) {
-  CHECK_INPUT(fmap1);
-  CHECK_INPUT(fmap2);
-  CHECK_INPUT(coords);
-  CHECK_INPUT(corr_grad);
-
-  return corr_cuda_backward(fmap1, fmap2, coords, corr_grad, radius);
-}
-
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("forward", &corr_forward, "CORR forward");
-  m.def("backward", &corr_backward, "CORR backward");
-}
\ No newline at end of file
diff --git a/spaces/Rehman1603/YouTubeToTextInVariousLanguage/README.md b/spaces/Rehman1603/YouTubeToTextInVariousLanguage/README.md
deleted file mode 100644
index 1004ecfb34392b783e2e53dbf83458ec1e751f30..0000000000000000000000000000000000000000
--- a/spaces/Rehman1603/YouTubeToTextInVariousLanguage/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: YouTubeToTextInVariousLanguage
-emoji: 👀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.41.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ricecake123/RVC-demo/infer_uvr5.py b/spaces/Ricecake123/RVC-demo/infer_uvr5.py
deleted file mode 100644
index 0ffdb5da0d25ccee75405514fb6b8d3c913095aa..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/infer_uvr5.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import os, sys, torch, warnings, pdb
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from json import load as ll
-
-warnings.filterwarnings("ignore")
-import librosa
-import importlib
-import numpy as np
-import hashlib, math
-from tqdm import tqdm
-from lib.uvr5_pack.lib_v5 import spec_utils
-from lib.uvr5_pack.utils import _get_name_params, inference
-from lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
-import soundfile as sf
-from lib.uvr5_pack.lib_v5.nets_new import CascadedNet
-from lib.uvr5_pack.lib_v5 import nets_61968KB as nets
-
-
-class _audio_pre_:
-    def __init__(self, agg, model_path, device, is_half):
-        self.model_path = model_path
-        self.device = device
-        self.data = {
-            # Processing Options
-            "postprocess": False,
-            "tta": False,
-            # Constants
-            "window_size": 512,
-            "agg": agg,
-            "high_end_process": "mirroring",
-        }
-        mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
-        model = nets.CascadedASPPNet(mp.param["bins"] * 2)
-        cpk = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(cpk)
-        model.eval()
-        if is_half:
-            model = model.half().to(device)
-        else:
-            model = model.to(device)
-
-        self.mp = mp
-        self.model = model
-
-    def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
-        if ins_root is None and vocal_root is None:
-            return "No save root."
-        name = os.path.basename(music_file)
-        if ins_root is not None:
-            os.makedirs(ins_root, exist_ok=True)
-        if vocal_root is not None:
-            os.makedirs(vocal_root, exist_ok=True)
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-        bands_n = len(self.mp.param["band"])
-        # print(bands_n)
-        for d in range(bands_n, 0, -1):
-            bp = self.mp.param["band"][d]
-            if d == bands_n:  # high-end band
-                (
-                    X_wave[d],
-                    _,
-                ) = librosa.core.load(  # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
-                    music_file,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-                if X_wave[d].ndim == 1:
-                    X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
-            else:  # lower bands
-                X_wave[d] = librosa.core.resample(
-                    X_wave[d + 1],
-                    self.mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-            # Stft of wave source
-            X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                self.mp.param["mid_side"],
-                self.mp.param["mid_side_b2"],
-                self.mp.param["reverse"],
-            )
-            # pdb.set_trace()
-            if d == bands_n and self.data["high_end_process"] != "none":
-                input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
-                    self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
-                )
-                input_high_end = X_spec_s[d][
-                    :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
-                ]
-
-        X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
-        aggresive_set = float(self.data["agg"] / 100)
-        aggressiveness = {
-            "value": aggresive_set,
-            "split_bin": self.mp.param["band"][1]["crop_stop"],
-        }
-        with torch.no_grad():
-            pred, X_mag, X_phase = inference(
-                X_spec_m, self.device, self.model, aggressiveness, self.data
-            )
-        # Postprocess
-        if self.data["postprocess"]:
-            pred_inv = np.clip(X_mag - pred, 0, np.inf)
-            pred = spec_utils.mask_silence(pred, pred_inv)
-        y_spec_m = pred * X_phase
-        v_spec_m = X_spec_m - y_spec_m
-
-        if ins_root is not None:
-            if self.data["high_end_process"].startswith("mirroring"):
-                input_high_end_ = spec_utils.mirroring(
-                    self.data["high_end_process"], y_spec_m, input_high_end, self.mp
-                )
-                wav_instrument = spec_utils.cmb_spectrogram_to_wave(
-                    y_spec_m, self.mp, input_high_end_h, input_high_end_
-                )
-            else:
-                wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
-            print("%s instruments done" % name)
-            if format in ["wav", "flac"]:
-                sf.write(
-                    os.path.join(
-                        ins_root,
-                        "instrument_{}_{}.{}".format(name, self.data["agg"], format),
-                    ),
-                    (np.array(wav_instrument) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )  #
-            else:
-                path = os.path.join(
-                    ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
-                )
-                sf.write(
-                    path,
-                    (np.array(wav_instrument) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-                if os.path.exists(path):
-                    os.system(
-                        "ffmpeg -i %s -vn %s -q:a 2 -y"
-                        % (path, path[:-4] + ".%s" % format)
-                    )
-        if vocal_root is not None:
-            if self.data["high_end_process"].startswith("mirroring"):
-                input_high_end_ = spec_utils.mirroring(
-                    self.data["high_end_process"], v_spec_m, input_high_end, self.mp
-                )
-                wav_vocals = spec_utils.cmb_spectrogram_to_wave(
-                    v_spec_m, self.mp, input_high_end_h, input_high_end_
-                )
-            else:
-                wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
-            print("%s vocals done" % name)
-            if format in ["wav", "flac"]:
-                sf.write(
-                    os.path.join(
-                        vocal_root,
-                        "vocal_{}_{}.{}".format(name, self.data["agg"], format),
-                    ),
-                    (np.array(wav_vocals) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-            else:
-                path = os.path.join(
-                    vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
-                )
-                sf.write(
-                    path,
-                    (np.array(wav_vocals) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-                if os.path.exists(path):
-                    os.system(
-                        "ffmpeg -i %s -vn %s -q:a 2 -y"
-                        % (path, path[:-4] + ".%s" % format)
-                    )
-
-
-class _audio_pre_new:
-    def __init__(self, agg, model_path, device, is_half):
-        self.model_path = model_path
-        self.device = device
-        self.data = {
-            # Processing Options
-            "postprocess": False,
-            "tta": False,
-            # Constants
-            "window_size": 512,
-            "agg": agg,
-            "high_end_process": "mirroring",
-        }
-        mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
-        nout = 64 if "DeReverb" in model_path else 48
-        model = CascadedNet(mp.param["bins"] * 2, nout)
-        cpk = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(cpk)
-        model.eval()
-        if is_half:
-            model = model.half().to(device)
-        else:
-            model = model.to(device)
-
-        self.mp = mp
-        self.model = model
-
-    def _path_audio_(
-        self, music_file, vocal_root=None, ins_root=None, format="flac"
-    ):  # 3个VR模型vocal和ins是反的
-        if ins_root is None and vocal_root is None:
-            return "No save root."
-        name = os.path.basename(music_file)
-        if ins_root is not None:
-            os.makedirs(ins_root, exist_ok=True)
-        if vocal_root is not None:
-            os.makedirs(vocal_root, exist_ok=True)
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-        bands_n = len(self.mp.param["band"])
-        # print(bands_n)
-        for d in range(bands_n, 0, -1):
-            bp = self.mp.param["band"][d]
-            if d == bands_n:  # high-end band
-                (
-                    X_wave[d],
-                    _,
-                ) = librosa.core.load(  # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
-                    music_file,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-                if X_wave[d].ndim == 1:
-                    X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
-            else:  # lower bands
-                X_wave[d] = librosa.core.resample(
-                    X_wave[d + 1],
-                    self.mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-            # Stft of wave source
-            X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                self.mp.param["mid_side"],
-                self.mp.param["mid_side_b2"],
-                self.mp.param["reverse"],
-            )
-            # pdb.set_trace()
-            if d == bands_n and self.data["high_end_process"] != "none":
-                input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
-                    self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
-                )
-                input_high_end = X_spec_s[d][
-                    :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
-                ]
-
-        X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
-        aggresive_set = float(self.data["agg"] / 100)
-        aggressiveness = {
-            "value": aggresive_set,
-            "split_bin": self.mp.param["band"][1]["crop_stop"],
-        }
-        with torch.no_grad():
-            pred, X_mag, X_phase = inference(
-                X_spec_m, self.device, self.model, aggressiveness, self.data
-            )
-        # Postprocess
-        if self.data["postprocess"]:
-            pred_inv = np.clip(X_mag - pred, 0, np.inf)
-            pred = spec_utils.mask_silence(pred, pred_inv)
-        y_spec_m = pred * X_phase
-        v_spec_m = X_spec_m - y_spec_m
-
-        if ins_root is not None:
-            if self.data["high_end_process"].startswith("mirroring"):
-                input_high_end_ = spec_utils.mirroring(
-                    self.data["high_end_process"], y_spec_m, input_high_end, self.mp
-                )
-                wav_instrument = spec_utils.cmb_spectrogram_to_wave(
-                    y_spec_m, self.mp, input_high_end_h, input_high_end_
-                )
-            else:
-                wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
-            print("%s instruments done" % name)
-            if format in ["wav", "flac"]:
-                sf.write(
-                    os.path.join(
-                        ins_root,
-                        "instrument_{}_{}.{}".format(name, self.data["agg"], format),
-                    ),
-                    (np.array(wav_instrument) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )  #
-            else:
-                path = os.path.join(
-                    ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
-                )
-                sf.write(
-                    path,
-                    (np.array(wav_instrument) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-                if os.path.exists(path):
-                    os.system(
-                        "ffmpeg -i %s -vn %s -q:a 2 -y"
-                        % (path, path[:-4] + ".%s" % format)
-                    )
-        if vocal_root is not None:
-            if self.data["high_end_process"].startswith("mirroring"):
-                input_high_end_ = spec_utils.mirroring(
-                    self.data["high_end_process"], v_spec_m, input_high_end, self.mp
-                )
-                wav_vocals = spec_utils.cmb_spectrogram_to_wave(
-                    v_spec_m, self.mp, input_high_end_h, input_high_end_
-                )
-            else:
-                wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
-            print("%s vocals done" % name)
-            if format in ["wav", "flac"]:
-                sf.write(
-                    os.path.join(
-                        vocal_root,
-                        "vocal_{}_{}.{}".format(name, self.data["agg"], format),
-                    ),
-                    (np.array(wav_vocals) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-            else:
-                path = os.path.join(
-                    vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
-                )
-                sf.write(
-                    path,
-                    (np.array(wav_vocals) * 32768).astype("int16"),
-                    self.mp.param["sr"],
-                )
-                if os.path.exists(path):
-                    os.system(
-                        "ffmpeg -i %s -vn %s -q:a 2 -y"
-                        % (path, path[:-4] + ".%s" % format)
-                    )
-
-
-if __name__ == "__main__":
-    device = "cuda"
-    is_half = True
-    # model_path = "uvr5_weights/2_HP-UVR.pth"
-    # model_path = "uvr5_weights/VR-DeEchoDeReverb.pth"
-    # model_path = "uvr5_weights/VR-DeEchoNormal.pth"
-    model_path = "uvr5_weights/DeEchoNormal.pth"
-    # pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10)
-    pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True, agg=10)
-    audio_path = "雪雪伴奏对消HP5.wav"
-    save_path = "opt"
-    pre_fun._path_audio_(audio_path, save_path, save_path)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/gather_points.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/gather_points.py
deleted file mode 100644
index f52f1677d8ea0facafc56a3672d37adb44677ff3..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/gather_points.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['gather_points_forward', 'gather_points_backward'])
-
-
-class GatherPoints(Function):
-    """Gather points with given index."""
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor,
-                indices: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            features (Tensor): (B, C, N) features to gather.
-            indices (Tensor): (B, M) where M is the number of points.
-
-        Returns:
-            Tensor: (B, C, M) where M is the number of points.
-        """
-        assert features.is_contiguous()
-        assert indices.is_contiguous()
-
-        B, npoint = indices.size()
-        _, C, N = features.size()
-        output = torch.cuda.FloatTensor(B, C, npoint)
-
-        ext_module.gather_points_forward(
-            features, indices, output, b=B, c=C, n=N, npoints=npoint)
-
-        ctx.for_backwards = (indices, C, N)
-        if torch.__version__ != 'parrots':
-            ctx.mark_non_differentiable(indices)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        idx, C, N = ctx.for_backwards
-        B, npoint = idx.size()
-
-        grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
-        grad_out_data = grad_out.data.contiguous()
-        ext_module.gather_points_backward(
-            grad_out_data,
-            idx,
-            grad_features.data,
-            b=B,
-            c=C,
-            n=N,
-            npoints=npoint)
-        return grad_features, None
-
-
-gather_points = GatherPoints.apply
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py
deleted file mode 100644
index 52d60d8c2535cceb3d348c79c95a47750c5d2d8a..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-
-class ConcatTable(nn.Module):
-    def __init__(self, module_list=None):
-        super(ConcatTable, self).__init__()
-
-        self.modules_list = nn.ModuleList(module_list)
-
-    def forward(self, x: Variable):
-        y = []
-        for i in range(len(self.modules_list)):
-            y.append(self.modules_list[i](x))
-        return y
-
-    def add(self, module):
-        self.modules_list.append(module)
-
-
-class CaddTable(nn.Module):
-    def __init__(self, inplace=False):
-        super(CaddTable, self).__init__()
-        self.inplace = inplace
-
-    def forward(self, x: Variable or list):
-        return torch.stack(x, 0).sum(0)
-
-
-class Identity(nn.Module):
-    def __init__(self, params=None):
-        super(Identity, self).__init__()
-        self.params = nn.ParameterList(params)
-
-    def forward(self, x: Variable or list):
-        return x
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/actinobacillosis.md b/spaces/SarthakSidhant/Go-Cattle/diseases/actinobacillosis.md
deleted file mode 100644
index 99feb7ea76634e744a756d111d575ee47b2c3506..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/actinobacillosis.md
+++ /dev/null
@@ -1,35 +0,0 @@
-## Actinobacillosis
-
-**Information:** Actinobacillosis is a bacterial infection that can affect a variety of animals, including cattle, sheep, horses, pigs, and dogs. It is caused by bacteria in the genus Actinobacillus, most commonly Actinobacillus lignieresii. Actinobacillosis is characterized by the formation of abscesses in soft tissues, such as the tongue, lymph nodes, and skin.
-
-**Symptoms:**
-
-* Swelling of the tongue (wooden tongue)
-* Difficulty eating
-* Drooling
-* Fever
-* Enlarged lymph nodes
-* Abscesses on the skin or in the mouth
-* Difficulty breathing
-* Coughing
-* Weight loss
-
-**Remedies:**
-
-* Actinobacillosis can be treated with antibiotics, such as penicillin or tetracycline.
-* In some cases, surgery may be necessary to remove abscesses.
-* Animals that have been diagnosed with actinobacillosis should be isolated from other animals to prevent the spread of the disease.
-
-**Causes:**
-
-* Actinobacillosis is caused by bacteria in the genus Actinobacillus, most commonly Actinobacillus lignieresii.
-* These bacteria are commonly found in the soil and water.
-* Animals can become infected by contact with contaminated wounds, abrasions, or mucous membranes.
-* Actinobacillosis can also be spread through contact with the secretions of infected animals.
-
-**Prevention:**
-
-* The best way to prevent actinobacillosis is to keep animals healthy and well-vaccinated.
-* Animals should be kept in clean, dry conditions and should have access to fresh water.
-* Wounds and abrasions should be cleaned and disinfected immediately.
-* Animals that have been diagnosed with actinobacillosis should be isolated from other animals to prevent the spread of the disease.
diff --git a/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip.py b/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip.py
deleted file mode 100644
index 44bf7aca81bcc7c039e1aa52442148986d5cd824..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip.py
+++ /dev/null
@@ -1,59 +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 logging
-import os
-
-import torch
-from lavis.common.dist_utils import download_cached_file
-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 BlipBase(BaseModel):
-    @classmethod
-    def init_tokenizer(cls):
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-        tokenizer.add_special_tokens({"bos_token": "[DEC]"})
-        tokenizer.add_special_tokens({"additional_special_tokens": ["[ENC]"]})
-        tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
-        return tokenizer
-
-    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"]
-
-        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():
-            state_dict["visual_encoder_m.pos_embed"] = interpolate_pos_embed(
-                state_dict["visual_encoder_m.pos_embed"], self.visual_encoder_m
-            )
-
-        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
diff --git a/spaces/Solomon-y/img-to-music/README.md b/spaces/Solomon-y/img-to-music/README.md
deleted file mode 100644
index ff1948d1b95ee1f8d7a3396aefb285c729d18687..0000000000000000000000000000000000000000
--- a/spaces/Solomon-y/img-to-music/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Img To Music
-emoji: 🌅🎶
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: true
-duplicated_from: fffiloni/img-to-music
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git "a/spaces/SouthCity/ShuruiXu/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py" "b/spaces/SouthCity/ShuruiXu/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
deleted file mode 100644
index ae2535b559120ad776892b18538be256da18e37a..0000000000000000000000000000000000000000
--- "a/spaces/SouthCity/ShuruiXu/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
+++ /dev/null
@@ -1,213 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-def 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            msg = '正常'
-
-            # ** gpt request **
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, api_key, temperature, history=[])   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, msg
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, api_key, temperature, history=history)   # 带超时倒计时
-        
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, msg
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
-
-
-@CatchException
-def 解析项目本身(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import time, glob, os
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你分析自己的程序构成，别紧张，" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{fp}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            # ** gpt request **
-            # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, api_key=api_key, temperature=temperature)
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, api_key, temperature, history=[], long_connection=True)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, '正常'
-            time.sleep(2)
-
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{file_manifest}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        # ** gpt request **
-        # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, api_key=api_key, temperature=temperature, history=history)
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, api_key, temperature, history=history, long_connection=True)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, '正常'
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, '正常'
-
-@CatchException
-def 解析一个Python项目(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个C项目的头文件(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
-
-@CatchException
-def 解析一个C项目(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Java项目(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Rect项目(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何Rect文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Golang项目(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
diff --git a/spaces/Sreezx/Sentzi/app.py b/spaces/Sreezx/Sentzi/app.py
deleted file mode 100644
index 6d85483e816464cddcd5315d0a522af753601a56..0000000000000000000000000000000000000000
--- a/spaces/Sreezx/Sentzi/app.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import streamlit as st
-import time
-
-from pathlib import Path
-
-from lib.ui import set_init_state, sidebar, reset_sent_scores
-from lib.bot import send
-from lib.lib import Sentiment, writeCSV, writeJSON
-
-# for drawing charts
-import pandas as pd
-
-# set the init values
-set_init_state()
-
-# create sidebar
-sidebar()
-
-def set_pd_name(pd_name : str):
-    st.session_state["PD_NAME"] = pd_name
-
-def share(email : str):
-    st.session_state["SHARE"] = email
-
-def count_share():
-    st.session_state["SHARE_COUNT"] += 1
-
-def set_positive_level(value : int):
-
-    st.session_state["POS"] = value
-
-def set_negative_level(value : int):
-
-    st.session_state["NEG"] = value
-
-def set_neutral_level(value : int): 
-
-    st.session_state["NUT"] = value
-
-def clear_all_data():
-    st.session_state["USER_MSG"] = []
-    st.session_state["SENT_LEVEL"] = []
-
-    # open the temp data files and clear them
-    files = ['temp.csv','temp.json']
-    for f in files:
-        if Path(f).is_file():
-            Path(f).open("w",encoding="utf-8").write("")
-
-# remove footer and header
-hide_st_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            header {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_st_style, unsafe_allow_html=True)
-
-st.write('# `Sentzi`')
-
-st.divider()
-
-st.write("## Let's begin ! 🚀")
-
-if st.session_state.get("EMAIL_ID") and st.session_state.get("NAME"):
-    col1, col2 = st.columns(2)
-    with col2:
-        pd_name_input = st.text_input(
-            "💡 Product Name",
-            placeholder="Shoe",
-            help="A fake product name . ",
-            value=st.session_state.get("PD_NAME","")
-        )
-
-        if pd_name_input:
-            set_pd_name(pd_name_input)
-
-            # share the app
-            st.markdown("### ✨Share the product✨")
-
-            share_input = st.text_input(
-                "Friend's email address",
-                placeholder="Paste your friend's email address here ",
-                help="example@xyz.com",
-                value=st.session_state.get("SHARE", "")
-            )
-
-            if share_input:
-                share(share_input)
-
-            #send the app link
-            if send(
-                share_input,
-                st.session_state.get("EMAIL_ID",""),
-                st.session_state.get("NAME",""),
-                st.session_state.get("PD_NAME","")
-            ):
-                st.success(f'Shared !')
-
-                # increase share count
-                count_share()
-                
-
-            else:
-                st.error("Sharing failed !")
-            
-            show_score = st.toggle('show score',help="Show the sentiment score side by side")
-            if show_score:
-                tabPos , tabNeg, tabNut =  st.tabs(["Positive", "Negative","Neutral"])
-                with tabPos:
-                    st.markdown(f"{st.session_state.get('POS','')}")
-                with tabNeg:
-                    st.markdown(f"{st.session_state.get('NEG','')}")
-                with tabNut:
-                    st.markdown(f"{st.session_state.get('NUT','')}")
-            
-            if st.button("Just refresh", help="Can't see the changes in graph ? Hit `Just refresh`"):
-                pass
-            
-            if st.session_state.get("USER_MSG","") and st.session_state.get("SENT_LEVEL",""):
-                csvTab , jsonTab = st.tabs(["csv","json"])
-                with csvTab:
-                    st.write('download `data` as `csv` format ')
-                    if Path('temp.csv').is_file():
-                        with open("temp.csv","r") as file:
-                            c_btn = st.download_button(
-                                "Download (csv)",
-                                data=file,
-                                file_name="temp.csv",
-                                mime='text/csv',
-                                help="Download the data as `temp.csv`"
-                            )
-                            
-                with jsonTab:
-                    st.write('download `data` as `json`')
-                    if Path('temp.json').is_file():
-                        with open("temp.json", "r") as file:
-                            j_btn = st.download_button(
-                                "Download (json)",
-                                data=file,
-                                file_name="temp.json",
-                                help="Download the data as `temp.json`"
-
-                            )
-
-                if st.button(
-                    "Clear Data",
-                    help="Clear all data. Clear all stored user reviews and sentiment levels ."
-                ):
-                    clear_all_data()
-                    st.toast('Data cleared ! 🗑️')
-                    st.toast("Hi ! 👋 Can't see the changes ? Hit `Just refresh`")
-                    
-
-    
-    with col1:
-
-        st.markdown(f"### {st.session_state.get('NAME','')}'s Dashboard")
-        # make 3 tabs
-        tab1, tab2, tab3 = st.tabs(["Shares", "Viz 1", "Viz 2"])
-        with tab1:
-            if st.button('# Reset shares',help="Reset all shares",use_container_width=True):
-                st.session_state["SHARE_COUNT"] = 0
-                st.toast('### Shares reseted ! 🔄')
-            st.metric(
-                label="🌐 Shares",
-                value=st.session_state.get("SHARE_COUNT",""),
-                delta=1
-
-            )
-
-        with tab2:
-            st.markdown("### Viz 1 ")
-            st.markdown('In the visualization below , '
-                        'the line deviates towards the direction of greater score .'
-                        'You may need to `Reset Graph` if the `positive` and `negative` reaches'
-                        ' its max score (i.e `1.0` and `-1.0` resp)')
-
-            # Assuming you have these values in your session_state
-            positive_value = st.session_state.get("POS","")
-            negative_value = st.session_state.get("NEG", "")
-            neutral_value = st.session_state.get("NUT", "")
-
-            # Create a DataFrame with a single row and three columns
-            viz_data = pd.DataFrame(
-                data=[
-                    positive_value,
-                    negative_value,
-                    neutral_value
-                ],
-                index=[
-                    f'{Sentiment.__emojiDic__.get("positive")[0]} positive',
-                    f'{Sentiment.__emojiDic__.get("negative")[0]} negative',
-                    f'{Sentiment.__emojiDic__.get("neutral")[0]} neutral'
-                ],
-                
-            )
-
-            t2_btn = st.button('# Reset graph',key="t2_btn",help="Reset graph from all data",use_container_width=True)
-            
-            if t2_btn:
-                reset_sent_scores()
-                time.sleep(0.5)
-                reset_sent_scores()
-                st.toast('### Graph 📈 reseted ! 🔄')
-                st.toast("Hi ! 👋 Can't see the changes in graph ? Hit `Just refresh`")
-
-            st.line_chart(
-                viz_data,
-                color=["#44f"]
-
-            )
-
-        with tab3:
-            st.markdown("### Viz 2")
-            st.markdown('In the visualization below , '
-                        'the area will be more in the region of greater score .'
-                        'You may need to `Reset Graph` if the `positive` and `negative` reaches'
-                        ' its max score (i.e `1.0` and `-1.0` resp)')
-            t3_btn = st.button('# Reset graph',key="t3_btn",help="Reset graph from all data",use_container_width=True)
-            
-            if t3_btn:
-                reset_sent_scores()
-                time.sleep(0.5)
-                reset_sent_scores()
-                st.toast('### Graph 📈 reseted ! 🔄')
-                st.toast("Hi ! 👋 Can't see the changes in graph ? Hit `Just refresh`")
-
-            st.area_chart(
-                viz_data,
-                color=["#f0f"]
-            )
-        
-    st.markdown("---")
-
-    user = st.toggle('Become a user 🙂 !')
-    if user:
-        st.markdown('#### Reviews 🌟')
-
-        prompt = st.chat_input(
-            f'Write your thought about {st.session_state.get("PD_NAME","")}',
-        )
-
-        if prompt:
-            st.toast("Hi ! 👋 Can't see the changes in graph ? Hit `Just refresh`")
-        
-            # analyse the chat
-            sent_level = Sentiment(prompt).get().get('level')
-            sent_score = Sentiment(prompt).get().get('score')
-
-            st.session_state["USER_MSG"].append(prompt)
-            st.session_state["SENT_LEVEL"].append(sent_level)
-
-        
-            writeJSON({
-                'texts' : st.session_state["USER_MSG"],
-                'levels' : st.session_state["SENT_LEVEL"]
-            })
-            toList = []
-            for m , l in zip(st.session_state["USER_MSG"], st.session_state["SENT_LEVEL"]):
-                toList.append([m , l])
-            writeCSV(
-                header=['texts', 'levels'],
-                dataList=toList
-            )
-            # set the score for viz
-            {
-                'negative' : lambda : set_negative_level(sent_score),
-                'positive' : lambda : set_positive_level(sent_score),
-                'neutral' : lambda : set_neutral_level(sent_score),
-            }.get(sent_level , lambda : None)()
-
-            
-
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/losses/sisnr.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/losses/sisnr.py
deleted file mode 100644
index 30f1fa1de9aca22758b6665609a1eacc0bd992ca..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/losses/sisnr.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def _unfold(a: torch.Tensor, kernel_size: int, stride: int) -> torch.Tensor:
-    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
-    with K the kernel size, by extracting frames with the given stride.
-    This will pad the input so that `F = ceil(T / K)`.
-    see https://github.com/pytorch/pytorch/issues/60466
-    """
-    *shape, length = a.shape
-    n_frames = math.ceil(length / stride)
-    tgt_length = (n_frames - 1) * stride + kernel_size
-    a = F.pad(a, (0, tgt_length - length))
-    strides = list(a.stride())
-    assert strides[-1] == 1, "data should be contiguous"
-    strides = strides[:-1] + [stride, 1]
-    return a.as_strided([*shape, n_frames, kernel_size], strides)
-
-
-def _center(x: torch.Tensor) -> torch.Tensor:
-    return x - x.mean(-1, True)
-
-
-def _norm2(x: torch.Tensor) -> torch.Tensor:
-    return x.pow(2).sum(-1, True)
-
-
-class SISNR(nn.Module):
-    """SISNR loss.
-
-    Input should be [B, C, T], output is scalar.
-
-    Args:
-        sample_rate (int): Sample rate.
-        segment (float or None): Evaluate on chunks of that many seconds. If None, evaluate on
-            entire audio only.
-        overlap (float): Overlap between chunks, i.e. 0.5 = 50 % overlap.
-        epsilon (float): Epsilon value for numerical stability.
-    """
-    def __init__(
-        self,
-        sample_rate: int = 16000,
-        segment: tp.Optional[float] = 20,
-        overlap: float = 0.5,
-        epsilon: float = torch.finfo(torch.float32).eps,
-    ):
-        super().__init__()
-        self.sample_rate = sample_rate
-        self.segment = segment
-        self.overlap = overlap
-        self.epsilon = epsilon
-
-    def forward(self, out_sig: torch.Tensor, ref_sig: torch.Tensor) -> torch.Tensor:
-        B, C, T = ref_sig.shape
-        assert ref_sig.shape == out_sig.shape
-
-        if self.segment is None:
-            frame = T
-            stride = T
-        else:
-            frame = int(self.segment * self.sample_rate)
-            stride = int(frame * (1 - self.overlap))
-
-        epsilon = self.epsilon * frame  # make epsilon prop to frame size.
-
-        gt = _unfold(ref_sig, frame, stride)
-        est = _unfold(out_sig, frame, stride)
-        if self.segment is None:
-            assert gt.shape[-1] == 1
-
-        gt = _center(gt)
-        est = _center(est)
-        dot = torch.einsum("bcft,bcft->bcf", gt, est)
-
-        proj = dot[:, :, :, None] * gt / (epsilon + _norm2(gt))
-        noise = est - proj
-
-        sisnr = 10 * (
-            torch.log10(epsilon + _norm2(proj)) - torch.log10(epsilon + _norm2(noise))
-        )
-        return -1 * sisnr[..., 0].mean()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/common/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/common/__init__.py
deleted file mode 100644
index 3dde3637918264786768211cb3be511e58f4a9b1..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/common/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-from __future__ import annotations
-import os
-import typing
-
-if typing.TYPE_CHECKING:
-    __all__: list[str]
-
-__all__ = []
-
-# The lower time bound for assuming that the process hasn't spawned successfully.
-PROCESS_SPAWN_TIMEOUT = float(os.getenv("DEBUGPY_PROCESS_SPAWN_TIMEOUT", 15))
-
-# The lower time bound for assuming that the process hasn't exited gracefully.
-PROCESS_EXIT_TIMEOUT = float(os.getenv("DEBUGPY_PROCESS_EXIT_TIMEOUT", 5))
diff --git a/spaces/Surn/UnlimitedMusicGen/audiocraft/models/encodec.py b/spaces/Surn/UnlimitedMusicGen/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`"""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`"""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        ...
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignement to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: int = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available.
-        """
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-        """
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.quantizer.decode(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-
-class FlattenedCompressionModel(CompressionModel):
-    """Wraps a CompressionModel and flatten its codebooks, e.g.
-    instead of returning [B, K, T], return [B, S, T * (K // S)] with
-    S the number of codebooks per step, and `K // S` the number of 'virtual steps'
-    for each real time step.
-
-    Args:
-        model (CompressionModel): compression model to wrap.
-        codebooks_per_step (int): number of codebooks to keep per step,
-            this must divide the number of codebooks provided by the wrapped model.
-        extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
-            if each codebook has a cardinality N, then the first codebook will
-            use the range [0, N - 1], and the second [N, 2 N - 1] etc.
-            On decoding, this can lead to potentially invalid sequences.
-            Any invalid entry will be silently remapped to the proper range
-            with a modulo.
-    """
-    def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
-                 extend_cardinality: bool = True):
-        super().__init__()
-        self.model = model
-        self.codebooks_per_step = codebooks_per_step
-        self.extend_cardinality = extend_cardinality
-
-    @property
-    def total_codebooks(self):
-        return self.model.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-
-        ..Warning:: this reports the number of codebooks after the flattening
-        of the codebooks!
-        """
-        assert self.model.num_codebooks % self.codebooks_per_step == 0
-        return self.codebooks_per_step
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-
-        ..Warning:: this sets the number of codebooks **before** the flattening
-        of the codebooks.
-        """
-        assert n % self.codebooks_per_step == 0
-        self.model.set_num_codebooks(n)
-
-    @property
-    def num_virtual_steps(self) -> int:
-        """Return the number of virtual steps, e.g. one real step
-        will be split into that many steps.
-        """
-        return self.model.num_codebooks // self.codebooks_per_step
-
-    @property
-    def frame_rate(self) -> int:
-        return self.model.frame_rate * self.num_virtual_steps
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def channels(self) -> int:
-        return self.model.channels
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        if self.extend_cardinality:
-            return self.model.cardinality * self.num_virtual_steps
-        else:
-            return self.model.cardinality
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        raise NotImplementedError("Not supported, use encode and decode.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        indices, scales = self.model.encode(x)
-        B, K, T = indices.shape
-        indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
-        if self.extend_cardinality:
-            for virtual_step in range(1, self.num_virtual_steps):
-                indices[..., virtual_step] += self.model.cardinality * virtual_step
-        indices = rearrange(indices, 'b k t v -> b k (t v)')
-        return (indices, scales)
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        B, K, T = codes.shape
-        assert T % self.num_virtual_steps == 0
-        codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
-        # We silently ignore potential errors from the LM when
-        # using extend_cardinality.
-        codes = codes % self.model.cardinality
-        return self.model.decode(codes, scale)
diff --git a/spaces/TH5314/newbing/src/components/ui/badge.tsx b/spaces/TH5314/newbing/src/components/ui/badge.tsx
deleted file mode 100644
index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/components/ui/badge.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
-  'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
-  {
-    variants: {
-      variant: {
-        default:
-          'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
-        secondary:
-          'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        destructive:
-          'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
-        outline: 'text-foreground'
-      }
-    },
-    defaultVariants: {
-      variant: 'default'
-    }
-  }
-)
-
-export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/TYH71/gradio-ml-skeleton/Dockerfile b/spaces/TYH71/gradio-ml-skeleton/Dockerfile
deleted file mode 100644
index 2415d2ff539116f00bb12c8f6c4579a304277e60..0000000000000000000000000000000000000000
--- a/spaces/TYH71/gradio-ml-skeleton/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-FROM python:3.10.8-slim
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-WORKDIR /app
-COPY . /app
-
-RUN apt-get update && apt-get install -y \
-    ffmpeg libsm6 libxext6
-RUN apt-get clean && rm -rf /tmp/* /var/tmp/*
-
-RUN python -m pip install --upgrade pip
-RUN python -m pip install -r requirements.txt
-
-ENV LC_ALL C.UTF-8 
-ENV LANG C.UTF-8
-
-# container listens to port 7860
-EXPOSE 7860
-
-# Start Gradio application
-CMD ["python", "app.py", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/TabPFN/TabPFNEvaluation/TabPFN/notebook_utils.py b/spaces/TabPFN/TabPFNEvaluation/TabPFN/notebook_utils.py
deleted file mode 100644
index c085c98f9865f993163bc1cfd29fd895f6a27b27..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNEvaluation/TabPFN/notebook_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-from pathlib import Path
-
-import io
-import torch
-import pickle
-
-def print_models(base_path, model_string):
-    print(model_string)
-
-    for i in range(80):
-        for e in range(50):
-            exists = Path(os.path.join(base_path, f'models_diff/prior_diff_real_checkpoint{model_string}_n_{i}_epoch_{e}.cpkt')).is_file()
-            if exists:
-                print(os.path.join(base_path, f'models_diff/prior_diff_real_checkpoint{model_string}_n_{i}_epoch_{e}.cpkt'))
-        print()
-
-class CustomUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if name == 'Manager':
-            from settings import Manager
-            return Manager
-        try:
-            return self.find_class_cpu(module, name)
-        except:
-            return None
-
-    def find_class_cpu(self, module, name):
-        if module == 'torch.storage' and name == '_load_from_bytes':
-            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
-        else:
-            return super().find_class(module, name)
\ No newline at end of file
diff --git a/spaces/TabooAM/What-game-you-should-play/info.md b/spaces/TabooAM/What-game-you-should-play/info.md
deleted file mode 100644
index 6e47cd6bb435985f1826d6e098fed83adf2e7d69..0000000000000000000000000000000000000000
--- a/spaces/TabooAM/What-game-you-should-play/info.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# 😌 What Video Game Is Best For You?
-
-### 🧐 Problem Statement and Research Summary
-This problem is not a big problem but is still there. The problem of not playing video games. And this AI will help you pick just the right one.
-
-### 🎣 Data Collection Plan
-I mostly got my data from a google form I sent everyone in my class and in 1st hour.
-
-### 💥 Ethical Considerations (Data Privacy and Bias)
-I didn't collect any emails,phone numbers,ect. so all of my data was cllected anonymously. There should be no bias in this app.
-
-### 👻 Our Team
-I am the only team member. I was the one that created the survay. but thanks to our wonderful techer i was able to gte this out there so more pepople could take it.
-
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/clean.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/clean.py
deleted file mode 100644
index 9413f7cfcb40a0eed0af78347f7d60ed367c2738..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/clean.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""distutils.command.clean
-
-Implements the Distutils 'clean' command."""
-
-# contributed by Bastian Kleineidam <calvin@cs.uni-sb.de>, added 2000-03-18
-
-import os
-from ..core import Command
-from ..dir_util import remove_tree
-from distutils._log import log
-
-
-class clean(Command):
-    description = "clean up temporary files from 'build' command"
-    user_options = [
-        ('build-base=', 'b', "base build directory (default: 'build.build-base')"),
-        (
-            'build-lib=',
-            None,
-            "build directory for all modules (default: 'build.build-lib')",
-        ),
-        ('build-temp=', 't', "temporary build directory (default: 'build.build-temp')"),
-        (
-            'build-scripts=',
-            None,
-            "build directory for scripts (default: 'build.build-scripts')",
-        ),
-        ('bdist-base=', None, "temporary directory for built distributions"),
-        ('all', 'a', "remove all build output, not just temporary by-products"),
-    ]
-
-    boolean_options = ['all']
-
-    def initialize_options(self):
-        self.build_base = None
-        self.build_lib = None
-        self.build_temp = None
-        self.build_scripts = None
-        self.bdist_base = None
-        self.all = None
-
-    def finalize_options(self):
-        self.set_undefined_options(
-            'build',
-            ('build_base', 'build_base'),
-            ('build_lib', 'build_lib'),
-            ('build_scripts', 'build_scripts'),
-            ('build_temp', 'build_temp'),
-        )
-        self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
-
-    def run(self):
-        # remove the build/temp.<plat> directory (unless it's already
-        # gone)
-        if os.path.exists(self.build_temp):
-            remove_tree(self.build_temp, dry_run=self.dry_run)
-        else:
-            log.debug("'%s' does not exist -- can't clean it", self.build_temp)
-
-        if self.all:
-            # remove build directories
-            for directory in (self.build_lib, self.bdist_base, self.build_scripts):
-                if os.path.exists(directory):
-                    remove_tree(directory, dry_run=self.dry_run)
-                else:
-                    log.warning("'%s' does not exist -- can't clean it", directory)
-
-        # just for the heck of it, try to remove the base build directory:
-        # we might have emptied it right now, but if not we don't care
-        if not self.dry_run:
-            try:
-                os.rmdir(self.build_base)
-                log.info("removing '%s'", self.build_base)
-            except OSError:
-                pass
diff --git a/spaces/TeraTTS/TTS/README.md b/spaces/TeraTTS/TTS/README.md
deleted file mode 100644
index b0aa78efa472db9e3ecccbfed759d480a966306c..0000000000000000000000000000000000000000
--- a/spaces/TeraTTS/TTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Russian Text To Speech
-emoji: 💻
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-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/Tj/starcoder-playground/app.py b/spaces/Tj/starcoder-playground/app.py
deleted file mode 100644
index ebd96fb57c819904e75cf7c53ea3398fa2a325f1..0000000000000000000000000000000000000000
--- a/spaces/Tj/starcoder-playground/app.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import sys
-import os
-import logging as log
-from typing import Generator
-
-import gradio as gr
-from gradio.themes.utils import sizes
-from text_generation import Client
-from src.request import StarCoderRequest, StarCoderRequestConfig
-
-from src.utils import (
-    get_file_as_string,
-    get_sections,
-    get_url_from_env_or_default_path,
-    preview
-)
-from constants import (
-    FIM_MIDDLE,
-    FIM_PREFIX,
-    FIM_SUFFIX,
-    END_OF_TEXT,
-    MIN_TEMPERATURE,
-)
-from settings import (
-    DEFAULT_PORT,
-    DEFAULT_STARCODER_API_PATH,
-    DEFAULT_STARCODER_BASE_API_PATH,
-)
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-# Gracefully exit the app if the HF_TOKEN is not set,
-# printing to system `errout` the error (instead of raising an exception)
-# and the expected behavior
-if not HF_TOKEN:
-    ERR_MSG = """
-        Please set the HF_TOKEN environment variable with your Hugging Face API token.
-        You can get one by signing up at https://huggingface.co/join and then visiting
-        https://huggingface.co/settings/tokens."""
-    print(ERR_MSG, file=sys.stderr)
-    # gr.errors.GradioError(ERR_MSG)
-    # gr.close_all(verbose=False)
-    sys.exit(1)
-
-API_URL_STAR = get_url_from_env_or_default_path("STARCODER_API", DEFAULT_STARCODER_API_PATH)
-API_URL_BASE = get_url_from_env_or_default_path("STARCODER_BASE_API", DEFAULT_STARCODER_BASE_API_PATH)
-
-preview("StarCoder Model URL", API_URL_STAR)
-preview("StarCoderBase Model URL", API_URL_BASE)
-preview("HF Token", HF_TOKEN, ofuscate=True)
-
-_styles = get_file_as_string("styles.css")
-_script = get_file_as_string("community-btn.js")
-_sharing_icon_svg = get_file_as_string("community-icon.svg")
-_loading_icon_svg = get_file_as_string("loading-icon.svg")
-
-# Loads the whole content of the ./README.md file
-# slicing/unpacking its different sections into their proper variables
-readme_file_content = get_file_as_string("README.md", path='./')
-(
-    manifest,
-    description,
-    disclaimer,
-    formats,
-) = get_sections(readme_file_content, "---", up_to=4)
-
-theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-    radius_size=sizes.radius_sm,
-    font=[
-        gr.themes.GoogleFont("IBM Plex Sans", [400, 600]),
-        "ui-sans-serif",
-        "system-ui",
-        "sans-serif",
-    ],
-    text_size=sizes.text_lg,
-)
-
-HEADERS = {
-    "Authorization": f"Bearer {HF_TOKEN}",
-}
-client_star = Client(API_URL_STAR, headers=HEADERS)
-client_base = Client(API_URL_BASE, headers=HEADERS)
-
-def get_tokens_collector(request: StarCoderRequest) -> Generator[str, None, None]:
-
-    model_client = client_star if request.settings.version == "StarCoder" else client_base
-    stream = model_client.generate_stream(request.prompt, **request.settings.kwargs())
-    for response in stream:
-        # print(response.token.id, response.token.text)
-        # if token.text != END_OF_TEXT:
-        if response.token.id != 0:
-            yield response.token.text
-
-def get_tokens_accumulator(request: StarCoderRequest) -> Generator[str, None, None]:
-    # start with the prefix (if in fim_mode)
-    output = request.prefix if request.fim_mode else request.prompt
-    for token in get_tokens_collector(request=request):
-        output += token
-        yield output
-    # after the last token, append the suffix (if in fim_mode)
-    if request.fim_mode:
-        output += request.suffix
-        yield output
-    # Append an extra line at the end
-    yield output + '\n'
-
-def get_tokens_linker(request: StarCoderRequest) -> str:
-    return "".join(list(get_tokens_collector(request)))
-
-def generate(
-        prompt: str,
-        temperature = 0.9,
-        max_new_tokens = 256,
-        top_p = 0.95,
-        repetition_penalty = 1.0,
-        version = "StarCoder",
-    ) -> Generator[str, None, None]:
-    request = StarCoderRequest(
-        prompt=prompt,
-        settings=StarCoderRequestConfig(
-            version=version,
-            temperature=temperature,
-            max_new_tokens=max_new_tokens,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-        )
-    )
-    yield from get_tokens_accumulator(request)
-
-def process_example(
-        prompt: str,
-        temperature = 0.9,
-        max_new_tokens = 256,
-        top_p = 0.95,
-        repetition_penalty = 1.0,
-        version = "StarCoder",
-    ) -> Generator[str, None, None]:
-    request = StarCoderRequest(
-        prompt=prompt,
-        settings=StarCoderRequestConfig(
-            version=version,
-            temperature=temperature,
-            max_new_tokens=max_new_tokens,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-        )
-    )
-    yield from get_tokens_linker(request)
-
-# todo: move it into the README too
-examples = [
-    "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
-    "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
-    "def alternating(list1, list2):\n   results = []\n   for i in range(min(len(list1), len(list2))):\n       results.append(list1[i])\n       results.append(list2[i])\n   if len(list1) > len(list2):\n       <FILL_HERE>\n   else:\n       results.extend(list2[i+1:])\n   return results",
-]
-
-with gr.Blocks(theme=theme, analytics_enabled=False, css=_styles) as demo:
-    with gr.Column():
-        gr.Markdown(description)
-        with gr.Row():
-            with gr.Column():
-                instruction = gr.Textbox(
-                    placeholder="Enter your code here",
-                    label="Code",
-                    elem_id="q-input",
-                )
-                submit = gr.Button("Generate", variant="primary")
-                output = gr.Code(elem_id="q-output", lines=30)
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Accordion("Advanced settings", open=False):
-                            with gr.Row():
-                                column_1, column_2 = gr.Column(), gr.Column()
-                                with column_1:
-                                    temperature = gr.Slider(
-                                        label="Temperature",
-                                        value=0.2,
-                                        minimum=0.0,
-                                        maximum=1.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values produce more diverse outputs",
-                                    )
-                                    max_new_tokens = gr.Slider(
-                                        label="Max new tokens",
-                                        value=256,
-                                        minimum=0,
-                                        maximum=8192,
-                                        step=64,
-                                        interactive=True,
-                                        info="The maximum numbers of new tokens",
-                                    )
-                                with column_2:
-                                    top_p = gr.Slider(
-                                        label="Top-p (nucleus sampling)",
-                                        value=0.90,
-                                        minimum=0.0,
-                                        maximum=1,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values sample more low-probability tokens",
-                                    )
-                                    repetition_penalty = gr.Slider(
-                                        label="Repetition penalty",
-                                        value=1.2,
-                                        minimum=1.0,
-                                        maximum=2.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Penalize repeated tokens",
-                                    )
-                    with gr.Column():
-                        version = gr.Dropdown(
-                                    ["StarCoderBase", "StarCoder"],
-                                    value="StarCoder",
-                                    label="Version",
-                                    info="",
-                                    )
-                gr.Markdown(disclaimer)
-                with gr.Group(elem_id="share-btn-container"):
-                    community_icon = gr.HTML(_sharing_icon_svg, visible=True)
-                    loading_icon = gr.HTML(_loading_icon_svg, visible=True)
-                    share_button = gr.Button(
-                        "Share to community", elem_id="share-btn", visible=True
-                    )
-                gr.Examples(
-                    examples=examples,
-                    inputs=[instruction],
-                    cache_examples=False,
-                    fn=process_example,
-                    outputs=[output],
-                )
-                gr.Markdown(formats)
-
-    submit.click(
-        generate,
-        inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, version],
-        outputs=[output],
-        # preprocess=False,
-        max_batch_size=8,
-        show_progress=True
-    )
-    share_button.click(None, [], [], _js=_script)
-
-demo.queue(concurrency_count=16).launch(debug=True, server_port=DEFAULT_PORT)
diff --git a/spaces/Usaki108/VoiceChange/README.md b/spaces/Usaki108/VoiceChange/README.md
deleted file mode 100644
index e92d9ef2dff45520b78329d36bb9f686e7a69d8f..0000000000000000000000000000000000000000
--- a/spaces/Usaki108/VoiceChange/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VoiceChange
-emoji: 👀
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.3
-app_file: app_multi.py
-pinned: false
-license: mit
-duplicated_from: BartPoint/VoiceChange
----
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/models/minigpt4.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/models/minigpt4.py
deleted file mode 100644
index a2e4798bb9713467b0ddac2dcec3cb1681c6418d..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/models/minigpt4.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import logging
-import random
-
-import torch
-from torch.cuda.amp import autocast as autocast
-import torch.nn as nn
-
-from minigpt4.common.registry import registry
-from minigpt4.models.base_model import disabled_train
-from minigpt4.models.minigpt_base import MiniGPTBase
-from minigpt4.models.Qformer import BertConfig, BertLMHeadModel
-
-
-@registry.register_model("minigpt4")
-class MiniGPT4(MiniGPTBase):
-    """
-    MiniGPT-4 model
-    """
-
-    PRETRAINED_MODEL_CONFIG_DICT = {
-        "pretrain_vicuna0": "configs/models/minigpt4_vicuna0.yaml",
-        "pretrain_llama2": "configs/models/minigpt4_llama2.yaml",
-    }
-
-    def __init__(
-            self,
-            vit_model="eva_clip_g",
-            q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
-            img_size=224,
-            drop_path_rate=0,
-            use_grad_checkpoint=False,
-            vit_precision="fp16",
-            freeze_vit=True,
-            has_qformer=True,
-            freeze_qformer=True,
-            num_query_token=32,
-            llama_model="",
-            prompt_path="",
-            prompt_template="",
-            max_txt_len=32,
-            end_sym='\n',
-            low_resource=False,  # use 8 bit and put vit in cpu
-            device_8bit=0,  # the device of 8bit model should be set when loading and cannot be changed anymore.
-    ):
-        super().__init__(
-            vit_model=vit_model,
-            img_size=img_size,
-            drop_path_rate=drop_path_rate,
-            use_grad_checkpoint=use_grad_checkpoint,
-            vit_precision=vit_precision,
-            freeze_vit=freeze_vit,
-            llama_model=llama_model,
-            max_txt_len=max_txt_len,
-            end_sym=end_sym,
-            low_resource=low_resource,
-            device_8bit=device_8bit,
-        )
-
-        self.has_qformer = has_qformer
-        if self.has_qformer:
-            print('Loading Q-Former')
-            self.Qformer, self.query_tokens = self.init_Qformer(
-                num_query_token, self.visual_encoder.num_features, freeze_qformer
-            )
-            self.load_from_pretrained(url_or_filename=q_former_model)  # load q-former weights here
-
-            img_f_dim = self.Qformer.config.hidden_size
-            print('Loading Q-Former Done')
-        else:
-            img_f_dim = self.visual_encoder.num_features * 4
-            print('Do not use Q-Former here.')
-
-        self.llama_proj = nn.Linear(
-            img_f_dim, self.llama_model.config.hidden_size
-        )
-
-        if prompt_path:
-            with open(prompt_path, 'r') as f:
-                raw_prompts = f.read().splitlines()
-            filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "<ImageHere>" in raw_prompt]
-            self.prompt_list = [prompt_template.format(p) for p in filted_prompts]
-            print('Load {} training prompts'.format(len(self.prompt_list)))
-            print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
-        else:
-            self.prompt_list = []
-
-    @classmethod
-    def init_Qformer(cls, num_query_token, vision_width, freeze):
-        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 = 2
-        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)
-
-        Qformer.cls = None
-        Qformer.bert.embeddings.word_embeddings = None
-        Qformer.bert.embeddings.position_embeddings = None
-        for layer in Qformer.bert.encoder.layer:
-            layer.output = None
-            layer.intermediate = None
-
-        if freeze:
-            for name, param in Qformer.named_parameters():
-                param.requires_grad = False
-            Qformer = Qformer.eval()
-            Qformer.train = disabled_train
-            query_tokens.requires_grad = False
-            logging.info("freeze Qformer")
-
-        return Qformer, query_tokens
-
-    def encode_img(self, image):
-        device = image.device
-
-        if len(image.shape) > 4:
-            image = image.reshape(-1, *image.shape[-3:])
-
-        with self.maybe_autocast():
-            image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
-            if self.has_qformer:
-                image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
-
-                query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
-                query_output = self.Qformer.bert(
-                    query_embeds=query_tokens,
-                    encoder_hidden_states=image_embeds,
-                    encoder_attention_mask=image_atts,
-                    return_dict=True,
-                )
-
-                inputs_llama = self.llama_proj(query_output.last_hidden_state)
-            else:
-                image_embeds = image_embeds[:, 1:, :]
-                bs, pn, hs = image_embeds.shape
-                image_embeds = image_embeds.view(bs, int(pn / 4), int(hs * 4))
-
-                inputs_llama = self.llama_proj(image_embeds)
-            atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
-        return inputs_llama, atts_llama
-
-    @classmethod
-    def from_config(cls, cfg):
-        vit_model = cfg.get("vit_model", "eva_clip_g")
-        q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth")
-        img_size = cfg.get("image_size")
-        num_query_token = cfg.get("num_query_token")
-        llama_model = cfg.get("llama_model")
-
-        drop_path_rate = cfg.get("drop_path_rate", 0)
-        use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
-        vit_precision = cfg.get("vit_precision", "fp16")
-        freeze_vit = cfg.get("freeze_vit", True)
-        has_qformer = cfg.get("has_qformer", True)
-        freeze_qformer = cfg.get("freeze_qformer", True)
-        low_resource = cfg.get("low_resource", False)
-        device_8bit = cfg.get("device_8bit", 0)
-
-        prompt_path = cfg.get("prompt_path", "")
-        prompt_template = cfg.get("prompt_template", "")
-        max_txt_len = cfg.get("max_txt_len", 32)
-        end_sym = cfg.get("end_sym", '\n')
-
-        model = cls(
-            vit_model=vit_model,
-            q_former_model=q_former_model,
-            img_size=img_size,
-            drop_path_rate=drop_path_rate,
-            use_grad_checkpoint=use_grad_checkpoint,
-            vit_precision=vit_precision,
-            freeze_vit=freeze_vit,
-            has_qformer=has_qformer,
-            freeze_qformer=freeze_qformer,
-            num_query_token=num_query_token,
-            llama_model=llama_model,
-            prompt_path=prompt_path,
-            prompt_template=prompt_template,
-            max_txt_len=max_txt_len,
-            end_sym=end_sym,
-            low_resource=low_resource,
-            device_8bit=device_8bit,
-        )
-
-        ckpt_path = cfg.get("ckpt", "")  # load weights of MiniGPT-4
-        if ckpt_path:
-            print("Load MiniGPT-4 Checkpoint: {}".format(ckpt_path))
-            ckpt = torch.load(ckpt_path, map_location="cpu")
-            msg = model.load_state_dict(ckpt['model'], strict=False)
-
-        return model
diff --git a/spaces/XzJosh/JM-Bert-VITS2/modules.py b/spaces/XzJosh/JM-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/JM-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-    
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
-class TransformerCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      n_heads,
-      p_dropout=0,
-      filter_channels=0,
-      mean_only=False,
-      wn_sharing_parameter=None,
-      gin_channels = 0
-      ):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/YONG627/456123/yolov5-code-main/utils/metrics.py b/spaces/YONG627/456123/yolov5-code-main/utils/metrics.py
deleted file mode 100644
index 95f364c23f3492cbb966507f9610c9201754f1f0..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/utils/metrics.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Model validation metrics
-"""
-
-import math
-import warnings
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from utils import TryExcept, threaded
-
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
-    return (x[:, :4] * w).sum(1)
-
-
-def smooth(y, f=0.05):
-    # Box filter of fraction f
-    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
-    p = np.ones(nf // 2)  # ones padding
-    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
-    return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
-
-
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    # Arguments
-        tp:  True positives (nparray, nx1 or nx10).
-        conf:  Objectness value from 0-1 (nparray).
-        pred_cls:  Predicted object classes (nparray).
-        target_cls:  True object classes (nparray).
-        plot:  Plot precision-recall curve at mAP@0.5
-        save_dir:  Plot save directory
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Sort by objectness
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
-    # Find unique classes
-    unique_classes, nt = np.unique(target_cls, return_counts=True)
-    nc = unique_classes.shape[0]  # number of classes, number of detections
-
-    # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = nt[ci]  # number of labels
-        n_p = i.sum()  # number of predictions
-        if n_p == 0 or n_l == 0:
-            continue
-
-        # Accumulate FPs and TPs
-        fpc = (1 - tp[i]).cumsum(0)
-        tpc = tp[i].cumsum(0)
-
-        # Recall
-        recall = tpc / (n_l + eps)  # recall curve
-        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
-
-        # Precision
-        precision = tpc / (tpc + fpc)  # precision curve
-        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
-
-        # AP from recall-precision curve
-        for j in range(tp.shape[1]):
-            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
-            if plot and j == 0:
-                py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
-
-    # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + eps)
-    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
-    names = dict(enumerate(names))  # to dict
-    if plot:
-        plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
-        plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
-        plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
-        plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
-
-    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
-    p, r, f1 = p[:, i], r[:, i], f1[:, i]
-    tp = (r * nt).round()  # true positives
-    fp = (tp / (p + eps) - tp).round()  # false positives
-    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
-
-
-def compute_ap(recall, precision):
-    """ Compute the average precision, given the recall and precision curves
-    # Arguments
-        recall:    The recall curve (list)
-        precision: The precision curve (list)
-    # Returns
-        Average precision, precision curve, recall curve
-    """
-
-    # Append sentinel values to beginning and end
-    mrec = np.concatenate(([0.0], recall, [1.0]))
-    mpre = np.concatenate(([1.0], precision, [0.0]))
-
-    # Compute the precision envelope
-    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
-    # Integrate area under curve
-    method = 'interp'  # methods: 'continuous', 'interp'
-    if method == 'interp':
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
-        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
-    else:  # 'continuous'
-        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
-
-    return ap, mpre, mrec
-
-
-class ConfusionMatrix:
-    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
-    def __init__(self, nc, conf=0.25, iou_thres=0.45):
-        self.matrix = np.zeros((nc + 1, nc + 1))
-        self.nc = nc  # number of classes
-        self.conf = conf
-        self.iou_thres = iou_thres
-
-    def process_batch(self, detections, labels):
-        """
-        Return intersection-over-union (Jaccard index) of boxes.
-        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-        Arguments:
-            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (Array[M, 5]), class, x1, y1, x2, y2
-        Returns:
-            None, updates confusion matrix accordingly
-        """
-        if detections is None:
-            gt_classes = labels.int()
-            for gc in gt_classes:
-                self.matrix[self.nc, gc] += 1  # background FN
-            return
-
-        detections = detections[detections[:, 4] > self.conf]
-        gt_classes = labels[:, 0].int()
-        detection_classes = detections[:, 5].int()
-        iou = box_iou(labels[:, 1:], detections[:, :4])
-
-        x = torch.where(iou > self.iou_thres)
-        if x[0].shape[0]:
-            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
-            if x[0].shape[0] > 1:
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        else:
-            matches = np.zeros((0, 3))
-
-        n = matches.shape[0] > 0
-        m0, m1, _ = matches.transpose().astype(int)
-        for i, gc in enumerate(gt_classes):
-            j = m0 == i
-            if n and sum(j) == 1:
-                self.matrix[detection_classes[m1[j]], gc] += 1  # correct
-            else:
-                self.matrix[self.nc, gc] += 1  # true background
-
-        if n:
-            for i, dc in enumerate(detection_classes):
-                if not any(m1 == i):
-                    self.matrix[dc, self.nc] += 1  # predicted background
-
-    def tp_fp(self):
-        tp = self.matrix.diagonal()  # true positives
-        fp = self.matrix.sum(1) - tp  # false positives
-        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
-        return tp[:-1], fp[:-1]  # remove background class
-
-    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
-    def plot(self, normalize=True, save_dir='', names=()):
-        import seaborn as sn
-
-        array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1)  # normalize columns
-        array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
-
-        fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
-        nc, nn = self.nc, len(names)  # number of classes, names
-        sn.set(font_scale=1.0 if nc < 50 else 0.8)  # for label size
-        labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
-        ticklabels = (names + ['background']) if labels else 'auto'
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
-            sn.heatmap(array,
-                       ax=ax,
-                       annot=nc < 30,
-                       annot_kws={
-                           'size': 8},
-                       cmap='Blues',
-                       fmt='.2f',
-                       square=True,
-                       vmin=0.0,
-                       xticklabels=ticklabels,
-                       yticklabels=ticklabels).set_facecolor((1, 1, 1))
-        ax.set_xlabel('True')
-        ax.set_ylabel('Predicted')
-        ax.set_title('Confusion Matrix')
-        fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
-        plt.close(fig)
-
-    def print(self):
-        for i in range(self.nc + 1):
-            print(' '.join(map(str, self.matrix[i])))
-
-
-def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
-    # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
-
-    # Get the coordinates of bounding boxes
-    if xywh:  # transform from xywh to xyxy
-        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
-        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
-        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
-        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
-    else:  # x1, y1, x2, y2 = box1
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
-        w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)
-        w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)
-
-    # Intersection area
-    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \
-            (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0)
-
-    # Union Area
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    # IoU
-    iou = inter / union
-    if CIoU or DIoU or GIoU:
-        cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1)  # convex (smallest enclosing box) width
-        ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(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 dist ** 2
-            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
-                v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
-                with torch.no_grad():
-                    alpha = v / (v - iou + (1 + eps))
-                return iou - (rho2 / c2 + v * alpha)  # CIoU
-            return iou - rho2 / c2  # DIoU
-        c_area = cw * ch + eps  # convex area
-        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-    return iou  # IoU
-
-
-def box_iou(box1, box2, eps=1e-7):
-    # 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
-    """
-
-    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
-    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
-    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
-
-    # IoU = inter / (area1 + area2 - inter)
-    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
-
-
-def bbox_ioa(box1, box2, eps=1e-7):
-    """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
-    box1:       np.array of shape(4)
-    box2:       np.array of shape(nx4)
-    returns:    np.array of shape(n)
-    """
-
-    # Get the coordinates of bounding boxes
-    b1_x1, b1_y1, b1_x2, b1_y2 = box1
-    b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
-
-    # Intersection area
-    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-    # box2 area
-    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
-
-    # Intersection over box2 area
-    return inter_area / box2_area
-
-
-def wh_iou(wh1, wh2, eps=1e-7):
-    # 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 + eps)  # iou = inter / (area1 + area2 - inter)
-
-
-# Plots ----------------------------------------------------------------------------------------------------------------
-
-
-@threaded
-def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
-    # Precision-recall curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-    py = np.stack(py, axis=1)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
-    else:
-        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
-
-    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
-    ax.set_xlabel('Recall')
-    ax.set_ylabel('Precision')
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
-    ax.set_title('Precision-Recall Curve')
-    fig.savefig(save_dir, dpi=250)
-    plt.close(fig)
-
-
-@threaded
-def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
-    # Metric-confidence curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
-    else:
-        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
-
-    y = smooth(py.mean(0), 0.05)
-    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
-    ax.set_title(f'{ylabel}-Confidence Curve')
-    fig.savefig(save_dir, dpi=250)
-    plt.close(fig)
diff --git a/spaces/YUANAI/DiffspeechResearch/docs/framework.md b/spaces/YUANAI/DiffspeechResearch/docs/framework.md
deleted file mode 100644
index a48bb2b49d2e9696e2f8d4b01be8af68befc552c..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/docs/framework.md
+++ /dev/null
@@ -1,106 +0,0 @@
-# Framework of NATSpeech
-
-NATSpeech is a simple framework for Non-Autoregressive Text-to-Speech.
-
-## Directory Structure
-
-- `egs`: configuration files, which will be loaded by `utils/commons/hparams.py`
-- `data_gen`: data binarization codes
-- `modules`: modules and models
-- `tasks`: the training and inference logics
-- `utils`: commonly used utils
-- `data`: data
-    - `raw`: raw data
-    - `processed`: data after preprocess
-    - `binary`: binary data
-- `checkpoints`: model checkpoints, tensorboard logs and generated results for all experiments.
-
-## How to Add New Tasks and Run?
-
-We show the basic steps of adding a new task/model and running the code (LJSpeech dataset as an example).
-
-### Add the model
-
-Add your model to `modules`.
-
-### Add the task
-
-Task classes are used to manage the training and inference procedures.
-
-A new task (e.g., `tasks.tts.fs.FastSpeechTask`) should inherit the base task (`tasks.tts.speech_base.TTSBaseTask`)
-class.
-
-You must implement these methods:
-
-- `build_tts_model`, which builds the model for your task. - `run_model`, indicating how to use the model in training
-  and inference.
-
-You can override `test_step` and `save_valid_result` to change the validation/testing logics or add more plots to
-tensorboard.
-
-### Add a new config file
-
-Add a new config file in `egs/datasets/audio/lj/YOUR_TASK.yaml`. For example:
-
-```yaml
-base_config: ./base_text2mel.yaml
-task_cls: tasks.tts.fs.FastSpeechTask
-
-# model configs
-hidden_size: 256
-dropout: 0.1
-
-# some more configs .....
-```
-
-If you use a new dataset `YOUR_DATASET`, you should also add a `YOUR_DATASET_Processor`
-in `egs/datasets/audio/YOUR_DATASET/preprocess.py`, inheriting `data_gen.tts.base_preprocess.BasePreprocessor`, which
-loads some meta information of the dataset.
-
-### Preprocess and binary dataset
-
-```bash
-python data_gen/tts/runs/align_and_binarize.py --config egs/datasets/audio/lj/base_text2mel.yaml
-```
-
-### Training
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config YOUR_CONFIG --exp_name YOUR_EXP_NAME --reset
-```
-
-You can open Tensorboard via:
-
-```bash
-tensorboard --logdir checkpoints/EXP_NAME
-```
-
-### Inference (Testing)
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config egs/datasets/audio/lj/YOUR_TASK.yaml --exp_name YOUR_EXP_NAME --reset --infer
-```
-
-## Design Philosophy
-
-### Random-Access Binarized Dataset
-
-To address the IO problem when reading small files, we design a `IndexedDataset` class (_utils/commons/indexed_datasets.py_)
-
-### Global Config
-
-We introduce a global config `hparams`, which is load from a `.yaml` config file and can be used in anywhere. However,
-we do not recommend using it in some general-purpose modules.
-
-### BaseTrainer Framework
-
-Our [base trainer](utils/commons/trainer.py) and [base task ](utils/commons/base_task.py) classes refer
-to [PytorchLightning](https://github.com/PyTorchLightning/pytorch-lightning), which builds some commonly used
-training/inference code structure. Our framework supports multi-process GPU training without changing the subclass
-codes.
-
-### Checkpoint Saving
-
-All checkpoints and tensorboard logs are saved in `checkpoints/EXP_NAME`, where `EXP_NAME` is set in the running
-command: `python tasks/run.py .... --exp_name EXP_NAME`. You can use `tensorboard --logdir checkpoints/EXP_NAME` to open
-the tensorboard and check the training loss curves etc.
diff --git a/spaces/YUANAI/DiffspeechResearch/tasks/tts/speech_base.py b/spaces/YUANAI/DiffspeechResearch/tasks/tts/speech_base.py
deleted file mode 100644
index a438c9a432fe850370ee2a10c2aa7d6c0e1fb793..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/tasks/tts/speech_base.py
+++ /dev/null
@@ -1,373 +0,0 @@
-import filecmp
-import os
-import traceback
-import numpy as np
-import pandas as pd
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-import torch.optim
-import torch.utils.data
-import yaml
-from tqdm import tqdm
-import utils
-from tasks.tts.dataset_utils import BaseSpeechDataset
-from tasks.tts.tts_utils import parse_mel_losses, parse_dataset_configs, load_data_preprocessor, load_data_binarizer
-from tasks.tts.vocoder_infer.base_vocoder import BaseVocoder, get_vocoder_cls
-from utils.audio.align import mel2token_to_dur
-from utils.audio.io import save_wav
-from utils.audio.pitch_extractors import extract_pitch_simple
-from utils.commons.base_task import BaseTask
-from utils.commons.ckpt_utils import load_ckpt
-from utils.commons.dataset_utils import data_loader, BaseConcatDataset
-from utils.commons.hparams import hparams
-from utils.commons.multiprocess_utils import MultiprocessManager
-from utils.commons.tensor_utils import tensors_to_scalars
-from utils.metrics.ssim import ssim
-from utils.nn.model_utils import print_arch
-from utils.nn.schedulers import RSQRTSchedule, NoneSchedule, WarmupSchedule
-from utils.nn.seq_utils import weights_nonzero_speech
-from utils.plot.plot import spec_to_figure
-from utils.text.text_encoder import build_token_encoder
-import matplotlib.pyplot as plt
-
-
-class SpeechBaseTask(BaseTask):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.dataset_cls = BaseSpeechDataset
-        self.vocoder = None
-        data_dir = hparams['binary_data_dir']
-        if not hparams['use_word_input']:
-            self.token_encoder = build_token_encoder(f'{data_dir}/phone_set.json')
-        else:
-            self.token_encoder = build_token_encoder(f'{data_dir}/word_set.json')
-        self.padding_idx = self.token_encoder.pad()
-        self.eos_idx = self.token_encoder.eos()
-        self.seg_idx = self.token_encoder.seg()
-        self.saving_result_pool = None
-        self.saving_results_futures = None
-        self.mel_losses = parse_mel_losses()
-        self.max_tokens, self.max_sentences, \
-        self.max_valid_tokens, self.max_valid_sentences = parse_dataset_configs()
-
-    ##########################
-    # datasets
-    ##########################
-    @data_loader
-    def train_dataloader(self):
-        if hparams['train_sets'] != '':
-            train_sets = hparams['train_sets'].split("|")
-            # check if all train_sets have the same spk map and dictionary
-            binary_data_dir = hparams['binary_data_dir']
-            file_to_cmp = ['phone_set.json']
-            if os.path.exists(f'{binary_data_dir}/word_set.json'):
-                file_to_cmp.append('word_set.json')
-            if hparams['use_spk_id']:
-                file_to_cmp.append('spk_map.json')
-            for f in file_to_cmp:
-                for ds_name in train_sets:
-                    base_file = os.path.join(binary_data_dir, f)
-                    ds_file = os.path.join(ds_name, f)
-                    assert filecmp.cmp(base_file, ds_file), \
-                        f'{f} in {ds_name} is not same with that in {binary_data_dir}.'
-            train_dataset = BaseConcatDataset([
-                self.dataset_cls(prefix='train', shuffle=True, data_dir=ds_name) for ds_name in train_sets])
-        else:
-            train_dataset = self.dataset_cls(prefix=hparams['train_set_name'], shuffle=True)
-        return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences,
-                                     endless=hparams['endless_ds'])
-
-    @data_loader
-    def val_dataloader(self):
-        valid_dataset = self.dataset_cls(prefix=hparams['valid_set_name'], shuffle=False)
-        return self.build_dataloader(valid_dataset, False, self.max_valid_tokens, self.max_valid_sentences,
-                                     batch_by_size=False)
-
-    @data_loader
-    def test_dataloader(self):
-        test_dataset = self.dataset_cls(prefix=hparams['test_set_name'], shuffle=False)
-        self.test_dl = self.build_dataloader(
-            test_dataset, False, self.max_valid_tokens, self.max_valid_sentences, batch_by_size=False)
-        return self.test_dl
-
-    def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
-                         required_batch_size_multiple=-1, endless=False, batch_by_size=True):
-        devices_cnt = torch.cuda.device_count()
-        if devices_cnt == 0:
-            devices_cnt = 1
-        if required_batch_size_multiple == -1:
-            required_batch_size_multiple = devices_cnt
-
-        def shuffle_batches(batches):
-            np.random.shuffle(batches)
-            return batches
-
-        if max_tokens is not None:
-            max_tokens *= devices_cnt
-        if max_sentences is not None:
-            max_sentences *= devices_cnt
-        indices = dataset.ordered_indices()
-        if batch_by_size:
-            batch_sampler = utils.commons.dataset_utils.batch_by_size(
-                indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
-                required_batch_size_multiple=required_batch_size_multiple,
-            )
-        else:
-            batch_sampler = []
-            for i in range(0, len(indices), max_sentences):
-                batch_sampler.append(indices[i:i + max_sentences])
-
-        if shuffle:
-            batches = shuffle_batches(list(batch_sampler))
-            if endless:
-                batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
-        else:
-            batches = batch_sampler
-            if endless:
-                batches = [b for _ in range(1000) for b in batches]
-        num_workers = dataset.num_workers
-        if self.trainer.use_ddp:
-            num_replicas = dist.get_world_size()
-            rank = dist.get_rank()
-            batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
-        return torch.utils.data.DataLoader(dataset,
-                                           collate_fn=dataset.collater,
-                                           batch_sampler=batches,
-                                           num_workers=num_workers,
-                                           pin_memory=False)
-
-    ##########################
-    # scheduler and optimizer
-    ##########################
-    def build_model(self):
-        self.build_tts_model()
-        if hparams['load_ckpt'] != '':
-            load_ckpt(self.model, hparams['load_ckpt'])
-        print_arch(self.model)
-        return self.model
-
-    def build_tts_model(self):
-        raise NotImplementedError
-
-    def build_scheduler(self, optimizer):
-        if hparams['scheduler'] == 'rsqrt':
-            return RSQRTSchedule(optimizer, hparams['lr'], hparams['warmup_updates'], hparams['hidden_size'])
-        elif hparams['scheduler'] == 'warmup':
-            return WarmupSchedule(optimizer, hparams['lr'], hparams['warmup_updates'])
-        elif hparams['scheduler'] == 'step_lr':
-            return torch.optim.lr_scheduler.StepLR(
-                optimizer=optimizer, step_size=500, gamma=0.998)
-        else:
-            return NoneSchedule(optimizer, hparams['lr'])
-
-    def build_optimizer(self, model):
-        self.optimizer = optimizer = torch.optim.AdamW(
-            model.parameters(),
-            lr=hparams['lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            weight_decay=hparams['weight_decay'])
-
-        return optimizer
-
-    ##########################
-    # training and validation
-    ##########################
-    def _training_step(self, sample, batch_idx, _):
-        loss_output, _ = self.run_model(sample)
-        total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
-        return total_loss, loss_output
-
-    def run_model(self, sample, infer=False):
-        """
-
-        :param sample: a batch of data
-        :param infer: bool, run in infer mode
-        :return:
-            if not infer:
-                return losses, model_out
-            if infer:
-                return model_out
-        """
-        raise NotImplementedError
-
-    def validation_start(self):
-        self.vocoder = get_vocoder_cls(hparams['vocoder'])()
-
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        outputs['losses'] = {}
-        outputs['losses'], model_out = self.run_model(sample)
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        outputs = tensors_to_scalars(outputs)
-        if self.global_step % hparams['valid_infer_interval'] == 0 \
-                and batch_idx < hparams['num_valid_plots']:
-            self.save_valid_result(sample, batch_idx, model_out)
-        return outputs
-
-    def validation_end(self, outputs):
-        self.vocoder = None
-        return super(SpeechBaseTask, self).validation_end(outputs)
-
-    def save_valid_result(self, sample, batch_idx, model_out):
-        raise NotImplementedError
-
-    ##########################
-    # losses
-    ##########################
-    def add_mel_loss(self, mel_out, target, losses, postfix=''):
-        for loss_name, lambd in self.mel_losses.items():
-            losses[f'{loss_name}{postfix}'] = getattr(self, f'{loss_name}_loss')(mel_out, target) * lambd
-
-    def l1_loss(self, decoder_output, target):
-        # decoder_output : B x T x n_mel
-        # target : B x T x n_mel
-        l1_loss = F.l1_loss(decoder_output, target, reduction='none')
-        weights = weights_nonzero_speech(target)
-        l1_loss = (l1_loss * weights).sum() / weights.sum()
-        return l1_loss
-
-    def mse_loss(self, decoder_output, target):
-        # decoder_output : B x T x n_mel
-        # target : B x T x n_mel
-        assert decoder_output.shape == target.shape
-        mse_loss = F.mse_loss(decoder_output, target, reduction='none')
-        weights = weights_nonzero_speech(target)
-        mse_loss = (mse_loss * weights).sum() / weights.sum()
-        return mse_loss
-
-    def ssim_loss(self, decoder_output, target, bias=6.0):
-        # decoder_output : B x T x n_mel
-        # target : B x T x n_mel
-        assert decoder_output.shape == target.shape
-        weights = weights_nonzero_speech(target)
-        decoder_output = decoder_output[:, None] + bias
-        target = target[:, None] + bias
-        ssim_loss = 1 - ssim(decoder_output, target, size_average=False)
-        ssim_loss = (ssim_loss * weights).sum() / weights.sum()
-        return ssim_loss
-
-    def plot_mel(self, batch_idx, spec_out, spec_gt=None, name=None, title='', f0s=None, dur_info=None):
-        vmin = hparams['mel_vmin']
-        vmax = hparams['mel_vmax']
-        if len(spec_out.shape) == 3:
-            spec_out = spec_out[0]
-        if isinstance(spec_out, torch.Tensor):
-            spec_out = spec_out.cpu().numpy()
-        if spec_gt is not None:
-            if len(spec_gt.shape) == 3:
-                spec_gt = spec_gt[0]
-            if isinstance(spec_gt, torch.Tensor):
-                spec_gt = spec_gt.cpu().numpy()
-            max_len = max(len(spec_gt), len(spec_out))
-            if max_len - len(spec_gt) > 0:
-                spec_gt = np.pad(spec_gt, [[0, max_len - len(spec_gt)], [0, 0]], mode='constant',
-                                 constant_values=vmin)
-            if max_len - len(spec_out) > 0:
-                spec_out = np.pad(spec_out, [[0, max_len - len(spec_out)], [0, 0]], mode='constant',
-                                  constant_values=vmin)
-            spec_out = np.concatenate([spec_out, spec_gt], -1)
-        name = f'mel_val_{batch_idx}' if name is None else name
-        self.logger.add_figure(name, spec_to_figure(
-            spec_out, vmin, vmax, title=title, f0s=f0s, dur_info=dur_info), self.global_step)
-
-    ##########################
-    # testing
-    ##########################
-    def test_start(self):
-        self.saving_result_pool = MultiprocessManager(int(os.getenv('N_PROC', os.cpu_count())))
-        self.saving_results_futures = []
-        self.gen_dir = os.path.join(
-            hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
-        self.vocoder: BaseVocoder = get_vocoder_cls(hparams['vocoder'])()
-        os.makedirs(self.gen_dir, exist_ok=True)
-        os.makedirs(f'{self.gen_dir}/wavs', exist_ok=True)
-        os.makedirs(f'{self.gen_dir}/plot', exist_ok=True)
-        if hparams.get('save_mel_npy', False):
-            os.makedirs(f'{self.gen_dir}/mel_npy', exist_ok=True)
-
-    def test_step(self, sample, batch_idx):
-        """
-
-        :param sample:
-        :param batch_idx:
-        :return:
-        """
-        assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
-        outputs = self.run_model(sample, infer=True)
-        text = sample['text'][0]
-        item_name = sample['item_name'][0]
-        tokens = sample['txt_tokens'][0].cpu().numpy()
-        mel_gt = sample['mels'][0].cpu().numpy()
-        mel_pred = outputs['mel_out'][0].cpu().numpy()
-        str_phs = self.token_encoder.decode(tokens, strip_padding=True)
-        base_fn = f'[{self.results_id:06d}][{item_name.replace("%", "_")}][%s]'
-        if text is not None:
-            base_fn += text.replace(":", "$3A")[:80]
-        base_fn = base_fn.replace(' ', '_')
-        gen_dir = self.gen_dir
-        wav_pred = self.vocoder.spec2wav(mel_pred)
-        self.saving_result_pool.add_job(self.save_result, args=[
-            wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs])
-        if hparams['save_gt']:
-            wav_gt = self.vocoder.spec2wav(mel_gt)
-            self.saving_result_pool.add_job(self.save_result, args=[
-                wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs])
-        print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
-        return {
-            'item_name': item_name,
-            'text': text,
-            'ph_tokens': self.token_encoder.decode(tokens.tolist()),
-            'wav_fn_pred': base_fn % 'P',
-            'wav_fn_gt': base_fn % 'G',
-        }
-
-    @staticmethod
-    def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None):
-        save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'],
-                 norm=hparams['out_wav_norm'])
-        fig = plt.figure(figsize=(14, 10))
-        spec_vmin = hparams['mel_vmin']
-        spec_vmax = hparams['mel_vmax']
-        heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
-        fig.colorbar(heatmap)
-        try:
-            f0 = extract_pitch_simple(wav_out)
-            f0 = f0 / 10 * (f0 > 0)
-            plt.plot(f0, c='white', linewidth=1, alpha=0.6)
-            if mel2ph is not None and str_phs is not None:
-                decoded_txt = str_phs.split(" ")
-                dur = mel2token_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy()
-                dur = [0] + list(np.cumsum(dur))
-                for i in range(len(dur) - 1):
-                    shift = (i % 20) + 1
-                    plt.text(dur[i], shift, decoded_txt[i])
-                    plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black')
-                    plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black',
-                               alpha=1, linewidth=1)
-            plt.tight_layout()
-            plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png')
-            plt.close(fig)
-            if hparams.get('save_mel_npy', False):
-                np.save(f'{gen_dir}/mel_npy/{base_fn}', mel)
-            if alignment is not None:
-                fig, ax = plt.subplots(figsize=(12, 16))
-                im = ax.imshow(alignment, aspect='auto', origin='lower',
-                               interpolation='none')
-                decoded_txt = str_phs.split(" ")
-                ax.set_yticks(np.arange(len(decoded_txt)))
-                ax.set_yticklabels(list(decoded_txt), fontsize=6)
-                fig.colorbar(im, ax=ax)
-                fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png')
-                plt.close(fig)
-        except Exception:
-            traceback.print_exc()
-        return None
-
-    def test_end(self, outputs):
-        pd.DataFrame(outputs).to_csv(f'{self.gen_dir}/meta.csv')
-        for _1, _2 in tqdm(self.saving_result_pool.get_results(), total=len(self.saving_result_pool)):
-            pass
-        return {}
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_condition_flax.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_condition_flax.py
deleted file mode 100644
index 3a3f1d9e146d3ad296ae2f2bfc67d87864608d8b..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_condition_flax.py
+++ /dev/null
@@ -1,321 +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 Tuple, Union
-
-import flax
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict
-
-from ..configuration_utils import ConfigMixin, flax_register_to_config
-from ..modeling_flax_utils import FlaxModelMixin
-from ..utils import BaseOutput
-from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
-from .unet_2d_blocks_flax import (
-    FlaxCrossAttnDownBlock2D,
-    FlaxCrossAttnUpBlock2D,
-    FlaxDownBlock2D,
-    FlaxUNetMidBlock2DCrossAttn,
-    FlaxUpBlock2D,
-)
-
-
-@flax.struct.dataclass
-class FlaxUNet2DConditionOutput(BaseOutput):
-    """
-    Args:
-        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
-            Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
-    """
-
-    sample: jnp.ndarray
-
-
-@flax_register_to_config
-class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
-    r"""
-    FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
-    timestep and returns sample shaped output.
-
-    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the models (such as downloading or saving, etc.)
-
-    Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        sample_size (`int`, *optional*):
-            The size of the input sample.
-        in_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D",
-            "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D"
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
-            The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D",
-            "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D"
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2):
-            The number of layers per block.
-        attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
-            The dimension of the attention heads.
-        cross_attention_dim (`int`, *optional*, defaults to 768):
-            The dimension of the cross attention features.
-        dropout (`float`, *optional*, defaults to 0):
-            Dropout probability for down, up and bottleneck blocks.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-
-    """
-
-    sample_size: int = 32
-    in_channels: int = 4
-    out_channels: int = 4
-    down_block_types: Tuple[str] = (
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "DownBlock2D",
-    )
-    up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
-    only_cross_attention: Union[bool, Tuple[bool]] = False
-    block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
-    layers_per_block: int = 2
-    attention_head_dim: Union[int, Tuple[int]] = 8
-    cross_attention_dim: int = 1280
-    dropout: float = 0.0
-    use_linear_projection: bool = False
-    dtype: jnp.dtype = jnp.float32
-    flip_sin_to_cos: bool = True
-    freq_shift: int = 0
-
-    def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
-        # init input tensors
-        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
-        sample = jnp.zeros(sample_shape, dtype=jnp.float32)
-        timesteps = jnp.ones((1,), dtype=jnp.int32)
-        encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
-
-    def setup(self):
-        block_out_channels = self.block_out_channels
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = nn.Conv(
-            block_out_channels[0],
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        # time
-        self.time_proj = FlaxTimesteps(
-            block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
-        )
-        self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
-
-        only_cross_attention = self.only_cross_attention
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = (only_cross_attention,) * len(self.down_block_types)
-
-        attention_head_dim = self.attention_head_dim
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(self.down_block_types)
-
-        # down
-        down_blocks = []
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(self.down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            if down_block_type == "CrossAttnDownBlock2D":
-                down_block = FlaxCrossAttnDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    attn_num_head_channels=attention_head_dim[i],
-                    add_downsample=not is_final_block,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    dtype=self.dtype,
-                )
-            else:
-                down_block = FlaxDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    add_downsample=not is_final_block,
-                    dtype=self.dtype,
-                )
-
-            down_blocks.append(down_block)
-        self.down_blocks = down_blocks
-
-        # mid
-        self.mid_block = FlaxUNetMidBlock2DCrossAttn(
-            in_channels=block_out_channels[-1],
-            dropout=self.dropout,
-            attn_num_head_channels=attention_head_dim[-1],
-            use_linear_projection=self.use_linear_projection,
-            dtype=self.dtype,
-        )
-
-        # up
-        up_blocks = []
-        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(self.up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            if up_block_type == "CrossAttnUpBlock2D":
-                up_block = FlaxCrossAttnUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    attn_num_head_channels=reversed_attention_head_dim[i],
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    dtype=self.dtype,
-                )
-            else:
-                up_block = FlaxUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    dtype=self.dtype,
-                )
-
-            up_blocks.append(up_block)
-            prev_output_channel = output_channel
-        self.up_blocks = up_blocks
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5)
-        self.conv_out = nn.Conv(
-            self.out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(
-        self,
-        sample,
-        timesteps,
-        encoder_hidden_states,
-        return_dict: bool = True,
-        train: bool = False,
-    ) -> Union[FlaxUNet2DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`jnp.ndarray` or `float` or `int`): timesteps
-            encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
-                plain tuple.
-            train (`bool`, *optional*, defaults to `False`):
-                Use deterministic functions and disable dropout when not training.
-
-        Returns:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`.
-            When returning a tuple, the first element is the sample tensor.
-        """
-        # 1. time
-        if not isinstance(timesteps, jnp.ndarray):
-            timesteps = jnp.array([timesteps], dtype=jnp.int32)
-        elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
-            timesteps = timesteps.astype(dtype=jnp.float32)
-            timesteps = jnp.expand_dims(timesteps, 0)
-
-        t_emb = self.time_proj(timesteps)
-        t_emb = self.time_embedding(t_emb)
-
-        # 2. pre-process
-        sample = jnp.transpose(sample, (0, 2, 3, 1))
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for down_block in self.down_blocks:
-            if isinstance(down_block, FlaxCrossAttnDownBlock2D):
-                sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
-            else:
-                sample, res_samples = down_block(sample, t_emb, deterministic=not train)
-            down_block_res_samples += res_samples
-
-        # 4. mid
-        sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
-
-        # 5. up
-        for up_block in self.up_blocks:
-            res_samples = down_block_res_samples[-(self.layers_per_block + 1) :]
-            down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)]
-            if isinstance(up_block, FlaxCrossAttnUpBlock2D):
-                sample = up_block(
-                    sample,
-                    temb=t_emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    res_hidden_states_tuple=res_samples,
-                    deterministic=not train,
-                )
-            else:
-                sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train)
-
-        # 6. post-process
-        sample = self.conv_norm_out(sample)
-        sample = nn.silu(sample)
-        sample = self.conv_out(sample)
-        sample = jnp.transpose(sample, (0, 3, 1, 2))
-
-        if not return_dict:
-            return (sample,)
-
-        return FlaxUNet2DConditionOutput(sample=sample)
diff --git a/spaces/Yuliang/ICON/lib/smplx/vertex_joint_selector.py b/spaces/Yuliang/ICON/lib/smplx/vertex_joint_selector.py
deleted file mode 100644
index 4b8298bd5e087731f86c1c699703b5219e046c5c..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/smplx/vertex_joint_selector.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-from __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-
-from .utils import to_tensor
-
-
-class VertexJointSelector(nn.Module):
-
-    def __init__(self, vertex_ids=None,
-                 use_hands=True,
-                 use_feet_keypoints=True, **kwargs):
-        super(VertexJointSelector, self).__init__()
-
-        extra_joints_idxs = []
-
-        face_keyp_idxs = np.array([
-            vertex_ids['nose'],
-            vertex_ids['reye'],
-            vertex_ids['leye'],
-            vertex_ids['rear'],
-            vertex_ids['lear']], dtype=np.int64)
-
-        extra_joints_idxs = np.concatenate([extra_joints_idxs,
-                                            face_keyp_idxs])
-
-        if use_feet_keypoints:
-            feet_keyp_idxs = np.array([vertex_ids['LBigToe'],
-                                       vertex_ids['LSmallToe'],
-                                       vertex_ids['LHeel'],
-                                       vertex_ids['RBigToe'],
-                                       vertex_ids['RSmallToe'],
-                                       vertex_ids['RHeel']], dtype=np.int32)
-
-            extra_joints_idxs = np.concatenate(
-                [extra_joints_idxs, feet_keyp_idxs])
-
-        if use_hands:
-            self.tip_names = ['thumb', 'index', 'middle', 'ring', 'pinky']
-
-            tips_idxs = []
-            for hand_id in ['l', 'r']:
-                for tip_name in self.tip_names:
-                    tips_idxs.append(vertex_ids[hand_id + tip_name])
-
-            extra_joints_idxs = np.concatenate(
-                [extra_joints_idxs, tips_idxs])
-
-        self.register_buffer('extra_joints_idxs',
-                             to_tensor(extra_joints_idxs, dtype=torch.long))
-
-    def forward(self, vertices, joints):
-        extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs)
-        joints = torch.cat([joints, extra_joints], dim=1)
-
-        return joints
diff --git a/spaces/Zaixi/ICLR_FLAG/utils/misc.py b/spaces/Zaixi/ICLR_FLAG/utils/misc.py
deleted file mode 100644
index a29c710e6a5af2c146c941aed7a01353e7cc6f77..0000000000000000000000000000000000000000
--- a/spaces/Zaixi/ICLR_FLAG/utils/misc.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import time
-import random
-import logging
-import torch
-import numpy as np
-import yaml
-from easydict import EasyDict
-from logging import Logger
-from tqdm.auto import tqdm
-
-
-class BlackHole(object):
-    def __setattr__(self, name, value):
-        pass
-    def __call__(self, *args, **kwargs):
-        return self
-    def __getattr__(self, name):
-        return self
-
-
-def load_config(path):
-    with open(path, 'r') as f:
-        return EasyDict(yaml.safe_load(f))
-
-
-def get_logger(name, log_dir=None):
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    formatter = logging.Formatter('[%(asctime)s::%(name)s::%(levelname)s] %(message)s')
-
-    stream_handler = logging.StreamHandler()
-    stream_handler.setLevel(logging.DEBUG)
-    stream_handler.setFormatter(formatter)
-    logger.addHandler(stream_handler)
-
-    if log_dir is not None:
-        file_handler = logging.FileHandler(os.path.join(log_dir, 'log.txt'))
-        file_handler.setLevel(logging.DEBUG)
-        file_handler.setFormatter(formatter)
-        logger.addHandler(file_handler)
-
-    return logger
-
-
-def get_new_log_dir(root='./logs', prefix='', tag=''):
-    fn = time.strftime('%Y_%m_%d__%H_%M_%S', time.localtime())
-    if prefix != '':
-        fn = prefix + '_' + fn
-    if tag != '':
-        fn = fn + '_' + tag
-    log_dir = os.path.join(root, fn)
-    os.makedirs(log_dir)
-    return log_dir
-
-
-def seed_all(seed):
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-
-
-def log_hyperparams(writer, args):
-    from torch.utils.tensorboard.summary import hparams
-    vars_args = {k:v if isinstance(v, str) else repr(v) for k, v in vars(args).items()}
-    exp, ssi, sei = hparams(vars_args, {})
-    writer.file_writer.add_summary(exp)
-    writer.file_writer.add_summary(ssi)
-    writer.file_writer.add_summary(sei)
-
-
-def int_tuple(argstr):
-    return tuple(map(int, argstr.split(',')))
-
-
-def str_tuple(argstr):
-    return tuple(argstr.split(','))
-
diff --git a/spaces/abidlabs/english2german/README.md b/spaces/abidlabs/english2german/README.md
deleted file mode 100644
index aeaa1b0bad5cd9a3d1dea87621caf37dae6f9d2b..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/english2german/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: English2german
-emoji: 🐢
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/atlas.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/atlas.py
deleted file mode 100644
index dac670b83105d2bab6c817841738520bfbed31da..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/atlas.py
+++ /dev/null
@@ -1,241 +0,0 @@
-"""Group multiple small images into larger textures.
-
-This module is used by :py:mod:`pyglet.resource` to efficiently pack small
-images into larger textures.  :py:class:`~pyglet.image.atlas.TextureAtlas` maintains one texture;
-:py:class:`TextureBin` manages a collection of atlases of a given size.
-
-Example usage::
-
-    # Load images from disk
-    car_image = pyglet.image.load('car.png')
-    boat_image = pyglet.image.load('boat.png')
-
-    # Pack these images into one or more textures
-    bin = TextureBin()
-    car_texture = bin.add(car_image)
-    boat_texture = bin.add(boat_image)
-
-The result of :py:meth:`TextureBin.add` is a :py:class:`TextureRegion`
-containing the image. Once added, an image cannot be removed from a bin (or an 
-atlas); nor can a list of images be obtained from a given bin or atlas -- it is 
-the application's responsibility to keep track of the regions returned by the
-``add`` methods.
-
-.. versionadded:: 1.1
-"""
-
-import pyglet
-
-
-class AllocatorException(Exception):
-    """The allocator does not have sufficient free space for the requested
-    image size."""
-    pass
-
-
-class _Strip:
-    __slots__ = 'x', 'y', 'max_height', 'y2'
-
-    def __init__(self, y, max_height):
-        self.x = 0
-        self.y = y
-        self.max_height = max_height
-        self.y2 = y
-
-    def add(self, width, height):
-        assert width > 0 and height > 0
-        assert height <= self.max_height
-
-        x, y = self.x, self.y
-        self.x += width
-        self.y2 = max(self.y + height, self.y2)
-        return x, y
-
-    def compact(self):
-        self.max_height = self.y2 - self.y
-
-
-class Allocator:
-    """Rectangular area allocation algorithm.
-
-    Initialise with a given ``width`` and ``height``, then repeatedly
-    call `alloc` to retrieve free regions of the area and protect that
-    area from future allocations.
-
-    `Allocator` uses a fairly simple strips-based algorithm.  It performs best
-    when rectangles are allocated in decreasing height order.
-    """
-    __slots__ = 'width', 'height', 'strips', 'used_area'
-
-    def __init__(self, width, height):
-        """Create an `Allocator` of the given size.
-
-        :Parameters:
-            `width` : int
-                Width of the allocation region.
-            `height` : int
-                Height of the allocation region.
-
-        """
-        assert width > 0 and height > 0
-        self.width = width
-        self.height = height
-        self.strips = [_Strip(0, height)]
-        self.used_area = 0
-
-    def alloc(self, width, height):
-        """Get a free area in the allocator of the given size.
-
-        After calling `alloc`, the requested area will no longer be used.
-        If there is not enough room to fit the given area `AllocatorException`
-        is raised.
-
-        :Parameters:
-            `width` : int
-                Width of the area to allocate.
-            `height` : int
-                Height of the area to allocate.
-
-        :rtype: int, int
-        :return: The X and Y coordinates of the bottom-left corner of the
-            allocated region.
-        """
-        for strip in self.strips:
-            if self.width - strip.x >= width and strip.max_height >= height:
-                self.used_area += width * height
-                return strip.add(width, height)
-
-        if self.width >= width and self.height - strip.y2 >= height:
-            self.used_area += width * height
-            strip.compact()
-            newstrip = _Strip(strip.y2, self.height - strip.y2)
-            self.strips.append(newstrip)
-            return newstrip.add(width, height)
-
-        raise AllocatorException('No more space in %r for box %dx%d' % (self, width, height))
-
-    def get_usage(self):
-        """Get the fraction of area already allocated.
-
-        This method is useful for debugging and profiling only.
-
-        :rtype: float
-        """
-        return self.used_area / float(self.width * self.height)
-
-    def get_fragmentation(self):
-        """Get the fraction of area that's unlikely to ever be used, based on
-        current allocation behaviour.
-
-        This method is useful for debugging and profiling only.
-
-        :rtype: float
-        """
-        # The total unused area in each compacted strip is summed.
-        if not self.strips:
-            return 0.0
-        possible_area = self.strips[-1].y2 * self.width
-        return 1.0 - self.used_area / float(possible_area)
-
-
-class TextureAtlas:
-    """Collection of images within a texture."""
-
-    def __init__(self, width=2048, height=2048):
-        """Create a texture atlas of the given size.
-
-        :Parameters:
-            `width` : int
-                Width of the underlying texture.
-            `height` : int
-                Height of the underlying texture.
-
-        """
-        max_texture_size = pyglet.image.get_max_texture_size()
-        width = min(width, max_texture_size)
-        height = min(height, max_texture_size)
-
-        self.texture = pyglet.image.Texture.create(width, height)
-        self.allocator = Allocator(width, height)
-
-    def add(self, img, border=0):
-        """Add an image to the atlas.
-
-        This method will fail if the given image cannot be transferred
-        directly to a texture (for example, if it is another texture).
-        :py:class:`~pyglet.image.ImageData` is the usual image type for this method.
-
-        `AllocatorException` will be raised if there is no room in the atlas
-        for the image.
-
-        :Parameters:
-            `img` : `~pyglet.image.AbstractImage`
-                The image to add.
-            `border` : int
-                Leaves specified pixels of blank space around
-                each image added to the Atlas.
-
-        :rtype: :py:class:`~pyglet.image.TextureRegion`
-        :return: The region of the atlas containing the newly added image.
-        """
-        x, y = self.allocator.alloc(img.width + border*2, img.height + border*2)
-        self.texture.blit_into(img, x+border, y+border, 0)
-        return self.texture.get_region(x+border, y+border, img.width, img.height)
-
-
-class TextureBin:
-    """Collection of texture atlases.
-
-    :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new
-    ones as necessary to accommodate images added to the bin.
-    """
-
-    def __init__(self, texture_width=2048, texture_height=2048):
-        """Create a texture bin for holding atlases of the given size.
-
-        :Parameters:
-            `texture_width` : int
-                Width of texture atlases to create.
-            `texture_height` : int
-                Height of texture atlases to create.
-            `border` : int
-                Leaves specified pixels of blank space around
-                each image added to the Atlases.
-
-        """
-        max_texture_size = pyglet.image.get_max_texture_size()
-        self.texture_width = min(texture_width, max_texture_size)
-        self.texture_height = min(texture_height, max_texture_size)
-        self.atlases = []
-
-    def add(self, img, border=0):
-        """Add an image into this texture bin.
-
-        This method calls `TextureAtlas.add` for the first atlas that has room
-        for the image.
-
-        `AllocatorException` is raised if the image exceeds the dimensions of
-        ``texture_width`` and ``texture_height``.
-
-        :Parameters:
-            `img` : `~pyglet.image.AbstractImage`
-                The image to add.
-            `border` : int
-                Leaves specified pixels of blank space around
-                each image added to the Atlas.
-
-        :rtype: :py:class:`~pyglet.image.TextureRegion`
-        :return: The region of an atlas containing the newly added image.
-        """
-        for atlas in list(self.atlases):
-            try:
-                return atlas.add(img, border)
-            except AllocatorException:
-                # Remove atlases that are no longer useful (so that their textures
-                # can later be freed if the images inside them get collected).
-                if img.width < 64 and img.height < 64:
-                    self.atlases.remove(atlas)
-
-        atlas = TextureAtlas(self.texture_width, self.texture_height)
-        self.atlases.append(atlas)
-        return atlas.add(img, border)
diff --git a/spaces/akhaliq/GPEN/face_model/face_gan.py b/spaces/akhaliq/GPEN/face_model/face_gan.py
deleted file mode 100644
index 163b3b3dfc54437e1fc150d8861a7097df2fb9b7..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/GPEN/face_model/face_gan.py
+++ /dev/null
@@ -1,57 +0,0 @@
-'''
-@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
-@author: yangxy (yangtao9009@gmail.com)
-'''
-import torch
-import os
-import cv2
-import glob
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-from torchvision import transforms, utils
-from gpen_model import FullGenerator
-
-class FaceGAN(object):
-    def __init__(self, base_dir='./', size=512, model=None, channel_multiplier=2, narrow=1, key=None, is_norm=True, device='cuda'):
-        self.mfile = os.path.join(base_dir, 'weights', model+'.pth')
-        self.n_mlp = 8
-        self.device = device
-        self.is_norm = is_norm
-        self.resolution = size
-        self.key = key
-        self.load_model(channel_multiplier, narrow)
-
-    def load_model(self, channel_multiplier=2, narrow=1):
-        self.model = FullGenerator(self.resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device)
-        pretrained_dict = torch.load(self.mfile, map_location=torch.device('cpu'))
-        if self.key is not None: pretrained_dict = pretrained_dict[self.key]
-        self.model.load_state_dict(pretrained_dict)
-        self.model.to(self.device)
-        self.model.eval()
-
-    def process(self, img):
-        img = cv2.resize(img, (self.resolution, self.resolution))
-        img_t = self.img2tensor(img)
-
-        with torch.no_grad():
-            out, __ = self.model(img_t)
-
-        out = self.tensor2img(out)
-
-        return out
-
-    def img2tensor(self, img):
-        img_t = torch.from_numpy(img).to(self.device)/255.
-        if self.is_norm:
-            img_t = (img_t - 0.5) / 0.5
-        img_t = img_t.permute(2, 0, 1).unsqueeze(0).flip(1) # BGR->RGB
-        return img_t
-
-    def tensor2img(self, img_t, pmax=255.0, imtype=np.uint8):
-        if self.is_norm:
-            img_t = img_t * 0.5 + 0.5
-        img_t = img_t.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
-        img_np = np.clip(img_t.float().cpu().numpy(), 0, 1) * pmax
-
-        return img_np.astype(imtype)
diff --git a/spaces/akhaliq/JoJoGAN/e4e/models/encoders/helpers.py b/spaces/akhaliq/JoJoGAN/e4e/models/encoders/helpers.py
deleted file mode 100644
index c4a58b34ea5ca6912fe53c63dede0a8696f5c024..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/models/encoders/helpers.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from collections import namedtuple
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
-    def forward(self, input):
-        return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
-    norm = torch.norm(input, 2, axis, True)
-    output = torch.div(input, norm)
-    return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
-    """ A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
-    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
-    if num_layers == 50:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=4),
-            get_block(in_channel=128, depth=256, num_units=14),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 100:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=13),
-            get_block(in_channel=128, depth=256, num_units=30),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 152:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=8),
-            get_block(in_channel=128, depth=256, num_units=36),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    else:
-        raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
-    return blocks
-
-
-class SEModule(Module):
-    def __init__(self, channels, reduction):
-        super(SEModule, self).__init__()
-        self.avg_pool = AdaptiveAvgPool2d(1)
-        self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
-        self.relu = ReLU(inplace=True)
-        self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
-        self.sigmoid = Sigmoid()
-
-    def forward(self, x):
-        module_input = x
-        x = self.avg_pool(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.sigmoid(x)
-        return module_input * x
-
-
-class bottleneck_IR(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR_SE, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
-            PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
-            BatchNorm2d(depth),
-            SEModule(depth, 16)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-def _upsample_add(x, y):
-    """Upsample and add two feature maps.
-    Args:
-      x: (Variable) top feature map to be upsampled.
-      y: (Variable) lateral feature map.
-    Returns:
-      (Variable) added feature map.
-    Note in PyTorch, when input size is odd, the upsampled feature map
-    with `F.upsample(..., scale_factor=2, mode='nearest')`
-    maybe not equal to the lateral feature map size.
-    e.g.
-    original input size: [N,_,15,15] ->
-    conv2d feature map size: [N,_,8,8] ->
-    upsampled feature map size: [N,_,16,16]
-    So we choose bilinear upsample which supports arbitrary output sizes.
-    """
-    _, _, H, W = y.size()
-    return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/spaces/akhaliq/SummerTime/evaluation/rouge_metric.py b/spaces/akhaliq/SummerTime/evaluation/rouge_metric.py
deleted file mode 100644
index 65c52db2fdbb344066393d9a3c8f17984d63ddba..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/evaluation/rouge_metric.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from summ_eval.rouge_metric import RougeMetric
-from evaluation.summeval_metric import SummEvalMetric
-from typing import List, Dict
-
-
-class Rouge(SummEvalMetric):
-    metric_name = "rouge"
-    range = (0, 1)
-    higher_is_better = True
-    requires_heavy_compute = False
-
-    def __init__(self):
-        se_metric = RougeMetric()
-        super(Rouge, self).__init__(se_metric)
-
-    def evaluate(
-        self,
-        inputs: List[str],
-        targets: List[str],
-        keys: List[str] = ["rouge_1_f_score", "rouge_2_f_score", "rouge_l_f_score"],
-    ) -> Dict[str, float]:
-        score_dict = self.se_metric.evaluate_batch(inputs, targets)
-        return {key: score_dict["rouge"][key] for key in keys}
diff --git a/spaces/akhaliq/deeplab2/tensorflow_ops/python/ops/merge_semantic_and_instance_maps_op.py b/spaces/akhaliq/deeplab2/tensorflow_ops/python/ops/merge_semantic_and_instance_maps_op.py
deleted file mode 100644
index 169681833f260c495bfe401176f15f4d86926516..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/tensorflow_ops/python/ops/merge_semantic_and_instance_maps_op.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Utility function for the C++ TensorFlow MergeSemanticAndInstanceMaps op."""
-
-import tensorflow as tf
-
-# Make the TensorFlow MergeSemanticAndInstanceMaps op accessible by importing
-# merge_semantic_and_instance_maps_op.py.
-from tensorflow.python.framework import load_library
-from tensorflow.python.platform import resource_loader
-gen_merge_semantic_and_instance_maps_op = load_library.load_op_library(resource_loader.get_path_to_datafile('../../kernels/merge_semantic_and_instance_maps_op.so'))
-
-merge_semantic_and_instance_maps = gen_merge_semantic_and_instance_maps_op.merge_semantic_and_instance_maps
-
-tf.no_gradient('MergeSemanticAndInstanceMaps')
diff --git a/spaces/akhaliq/sd2-dreambooth-ClaymationXmas/README.md b/spaces/akhaliq/sd2-dreambooth-ClaymationXmas/README.md
deleted file mode 100644
index 6885e7c7c71fc47422a1bc55b8d4bd5453ae4378..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/sd2-dreambooth-ClaymationXmas/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sd2 Dreambooth ClaymationXmas
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/requirements.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/requirements.py
deleted file mode 100644
index f561f1f1e270666ccd74c9d61f78c9c24f5c4c99..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/requirements.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from pip._vendor.packaging.specifiers import SpecifierSet
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-
-from pip._internal.req.req_install import InstallRequirement
-
-from .base import Candidate, CandidateLookup, Requirement, format_name
-
-
-class ExplicitRequirement(Requirement):
-    def __init__(self, candidate: Candidate) -> None:
-        self.candidate = candidate
-
-    def __str__(self) -> str:
-        return str(self.candidate)
-
-    def __repr__(self) -> str:
-        return "{class_name}({candidate!r})".format(
-            class_name=self.__class__.__name__,
-            candidate=self.candidate,
-        )
-
-    @property
-    def project_name(self) -> NormalizedName:
-        # No need to canonicalize - the candidate did this
-        return self.candidate.project_name
-
-    @property
-    def name(self) -> str:
-        # No need to canonicalize - the candidate did this
-        return self.candidate.name
-
-    def format_for_error(self) -> str:
-        return self.candidate.format_for_error()
-
-    def get_candidate_lookup(self) -> CandidateLookup:
-        return self.candidate, None
-
-    def is_satisfied_by(self, candidate: Candidate) -> bool:
-        return candidate == self.candidate
-
-
-class SpecifierRequirement(Requirement):
-    def __init__(self, ireq: InstallRequirement) -> None:
-        assert ireq.link is None, "This is a link, not a specifier"
-        self._ireq = ireq
-        self._extras = frozenset(ireq.extras)
-
-    def __str__(self) -> str:
-        return str(self._ireq.req)
-
-    def __repr__(self) -> str:
-        return "{class_name}({requirement!r})".format(
-            class_name=self.__class__.__name__,
-            requirement=str(self._ireq.req),
-        )
-
-    @property
-    def project_name(self) -> NormalizedName:
-        assert self._ireq.req, "Specifier-backed ireq is always PEP 508"
-        return canonicalize_name(self._ireq.req.name)
-
-    @property
-    def name(self) -> str:
-        return format_name(self.project_name, self._extras)
-
-    def format_for_error(self) -> str:
-
-        # Convert comma-separated specifiers into "A, B, ..., F and G"
-        # This makes the specifier a bit more "human readable", without
-        # risking a change in meaning. (Hopefully! Not all edge cases have
-        # been checked)
-        parts = [s.strip() for s in str(self).split(",")]
-        if len(parts) == 0:
-            return ""
-        elif len(parts) == 1:
-            return parts[0]
-
-        return ", ".join(parts[:-1]) + " and " + parts[-1]
-
-    def get_candidate_lookup(self) -> CandidateLookup:
-        return None, self._ireq
-
-    def is_satisfied_by(self, candidate: Candidate) -> bool:
-        assert candidate.name == self.name, (
-            f"Internal issue: Candidate is not for this requirement "
-            f"{candidate.name} vs {self.name}"
-        )
-        # We can safely always allow prereleases here since PackageFinder
-        # already implements the prerelease logic, and would have filtered out
-        # prerelease candidates if the user does not expect them.
-        assert self._ireq.req, "Specifier-backed ireq is always PEP 508"
-        spec = self._ireq.req.specifier
-        return spec.contains(candidate.version, prereleases=True)
-
-
-class RequiresPythonRequirement(Requirement):
-    """A requirement representing Requires-Python metadata."""
-
-    def __init__(self, specifier: SpecifierSet, match: Candidate) -> None:
-        self.specifier = specifier
-        self._candidate = match
-
-    def __str__(self) -> str:
-        return f"Python {self.specifier}"
-
-    def __repr__(self) -> str:
-        return "{class_name}({specifier!r})".format(
-            class_name=self.__class__.__name__,
-            specifier=str(self.specifier),
-        )
-
-    @property
-    def project_name(self) -> NormalizedName:
-        return self._candidate.project_name
-
-    @property
-    def name(self) -> str:
-        return self._candidate.name
-
-    def format_for_error(self) -> str:
-        return str(self)
-
-    def get_candidate_lookup(self) -> CandidateLookup:
-        if self.specifier.contains(self._candidate.version, prereleases=True):
-            return self._candidate, None
-        return None, None
-
-    def is_satisfied_by(self, candidate: Candidate) -> bool:
-        assert candidate.name == self._candidate.name, "Not Python candidate"
-        # We can safely always allow prereleases here since PackageFinder
-        # already implements the prerelease logic, and would have filtered out
-        # prerelease candidates if the user does not expect them.
-        return self.specifier.contains(candidate.version, prereleases=True)
-
-
-class UnsatisfiableRequirement(Requirement):
-    """A requirement that cannot be satisfied."""
-
-    def __init__(self, name: NormalizedName) -> None:
-        self._name = name
-
-    def __str__(self) -> str:
-        return f"{self._name} (unavailable)"
-
-    def __repr__(self) -> str:
-        return "{class_name}({name!r})".format(
-            class_name=self.__class__.__name__,
-            name=str(self._name),
-        )
-
-    @property
-    def project_name(self) -> NormalizedName:
-        return self._name
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    def format_for_error(self) -> str:
-        return str(self)
-
-    def get_candidate_lookup(self) -> CandidateLookup:
-        return None, None
-
-    def is_satisfied_by(self, candidate: Candidate) -> bool:
-        return False
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/resources.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/resources.py
deleted file mode 100644
index fef52aa103ea369c96567b9af2a5a0ba14db5cb9..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/resources.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import unicode_literals
-
-import bisect
-import io
-import logging
-import os
-import pkgutil
-import sys
-import types
-import zipimport
-
-from . import DistlibException
-from .util import cached_property, get_cache_base, Cache
-
-logger = logging.getLogger(__name__)
-
-
-cache = None    # created when needed
-
-
-class ResourceCache(Cache):
-    def __init__(self, base=None):
-        if base is None:
-            # Use native string to avoid issues on 2.x: see Python #20140.
-            base = os.path.join(get_cache_base(), str('resource-cache'))
-        super(ResourceCache, self).__init__(base)
-
-    def is_stale(self, resource, path):
-        """
-        Is the cache stale for the given resource?
-
-        :param resource: The :class:`Resource` being cached.
-        :param path: The path of the resource in the cache.
-        :return: True if the cache is stale.
-        """
-        # Cache invalidation is a hard problem :-)
-        return True
-
-    def get(self, resource):
-        """
-        Get a resource into the cache,
-
-        :param resource: A :class:`Resource` instance.
-        :return: The pathname of the resource in the cache.
-        """
-        prefix, path = resource.finder.get_cache_info(resource)
-        if prefix is None:
-            result = path
-        else:
-            result = os.path.join(self.base, self.prefix_to_dir(prefix), path)
-            dirname = os.path.dirname(result)
-            if not os.path.isdir(dirname):
-                os.makedirs(dirname)
-            if not os.path.exists(result):
-                stale = True
-            else:
-                stale = self.is_stale(resource, path)
-            if stale:
-                # write the bytes of the resource to the cache location
-                with open(result, 'wb') as f:
-                    f.write(resource.bytes)
-        return result
-
-
-class ResourceBase(object):
-    def __init__(self, finder, name):
-        self.finder = finder
-        self.name = name
-
-
-class Resource(ResourceBase):
-    """
-    A class representing an in-package resource, such as a data file. This is
-    not normally instantiated by user code, but rather by a
-    :class:`ResourceFinder` which manages the resource.
-    """
-    is_container = False        # Backwards compatibility
-
-    def as_stream(self):
-        """
-        Get the resource as a stream.
-
-        This is not a property to make it obvious that it returns a new stream
-        each time.
-        """
-        return self.finder.get_stream(self)
-
-    @cached_property
-    def file_path(self):
-        global cache
-        if cache is None:
-            cache = ResourceCache()
-        return cache.get(self)
-
-    @cached_property
-    def bytes(self):
-        return self.finder.get_bytes(self)
-
-    @cached_property
-    def size(self):
-        return self.finder.get_size(self)
-
-
-class ResourceContainer(ResourceBase):
-    is_container = True     # Backwards compatibility
-
-    @cached_property
-    def resources(self):
-        return self.finder.get_resources(self)
-
-
-class ResourceFinder(object):
-    """
-    Resource finder for file system resources.
-    """
-
-    if sys.platform.startswith('java'):
-        skipped_extensions = ('.pyc', '.pyo', '.class')
-    else:
-        skipped_extensions = ('.pyc', '.pyo')
-
-    def __init__(self, module):
-        self.module = module
-        self.loader = getattr(module, '__loader__', None)
-        self.base = os.path.dirname(getattr(module, '__file__', ''))
-
-    def _adjust_path(self, path):
-        return os.path.realpath(path)
-
-    def _make_path(self, resource_name):
-        # Issue #50: need to preserve type of path on Python 2.x
-        # like os.path._get_sep
-        if isinstance(resource_name, bytes):    # should only happen on 2.x
-            sep = b'/'
-        else:
-            sep = '/'
-        parts = resource_name.split(sep)
-        parts.insert(0, self.base)
-        result = os.path.join(*parts)
-        return self._adjust_path(result)
-
-    def _find(self, path):
-        return os.path.exists(path)
-
-    def get_cache_info(self, resource):
-        return None, resource.path
-
-    def find(self, resource_name):
-        path = self._make_path(resource_name)
-        if not self._find(path):
-            result = None
-        else:
-            if self._is_directory(path):
-                result = ResourceContainer(self, resource_name)
-            else:
-                result = Resource(self, resource_name)
-            result.path = path
-        return result
-
-    def get_stream(self, resource):
-        return open(resource.path, 'rb')
-
-    def get_bytes(self, resource):
-        with open(resource.path, 'rb') as f:
-            return f.read()
-
-    def get_size(self, resource):
-        return os.path.getsize(resource.path)
-
-    def get_resources(self, resource):
-        def allowed(f):
-            return (f != '__pycache__' and not
-                    f.endswith(self.skipped_extensions))
-        return set([f for f in os.listdir(resource.path) if allowed(f)])
-
-    def is_container(self, resource):
-        return self._is_directory(resource.path)
-
-    _is_directory = staticmethod(os.path.isdir)
-
-    def iterator(self, resource_name):
-        resource = self.find(resource_name)
-        if resource is not None:
-            todo = [resource]
-            while todo:
-                resource = todo.pop(0)
-                yield resource
-                if resource.is_container:
-                    rname = resource.name
-                    for name in resource.resources:
-                        if not rname:
-                            new_name = name
-                        else:
-                            new_name = '/'.join([rname, name])
-                        child = self.find(new_name)
-                        if child.is_container:
-                            todo.append(child)
-                        else:
-                            yield child
-
-
-class ZipResourceFinder(ResourceFinder):
-    """
-    Resource finder for resources in .zip files.
-    """
-    def __init__(self, module):
-        super(ZipResourceFinder, self).__init__(module)
-        archive = self.loader.archive
-        self.prefix_len = 1 + len(archive)
-        # PyPy doesn't have a _files attr on zipimporter, and you can't set one
-        if hasattr(self.loader, '_files'):
-            self._files = self.loader._files
-        else:
-            self._files = zipimport._zip_directory_cache[archive]
-        self.index = sorted(self._files)
-
-    def _adjust_path(self, path):
-        return path
-
-    def _find(self, path):
-        path = path[self.prefix_len:]
-        if path in self._files:
-            result = True
-        else:
-            if path and path[-1] != os.sep:
-                path = path + os.sep
-            i = bisect.bisect(self.index, path)
-            try:
-                result = self.index[i].startswith(path)
-            except IndexError:
-                result = False
-        if not result:
-            logger.debug('_find failed: %r %r', path, self.loader.prefix)
-        else:
-            logger.debug('_find worked: %r %r', path, self.loader.prefix)
-        return result
-
-    def get_cache_info(self, resource):
-        prefix = self.loader.archive
-        path = resource.path[1 + len(prefix):]
-        return prefix, path
-
-    def get_bytes(self, resource):
-        return self.loader.get_data(resource.path)
-
-    def get_stream(self, resource):
-        return io.BytesIO(self.get_bytes(resource))
-
-    def get_size(self, resource):
-        path = resource.path[self.prefix_len:]
-        return self._files[path][3]
-
-    def get_resources(self, resource):
-        path = resource.path[self.prefix_len:]
-        if path and path[-1] != os.sep:
-            path += os.sep
-        plen = len(path)
-        result = set()
-        i = bisect.bisect(self.index, path)
-        while i < len(self.index):
-            if not self.index[i].startswith(path):
-                break
-            s = self.index[i][plen:]
-            result.add(s.split(os.sep, 1)[0])   # only immediate children
-            i += 1
-        return result
-
-    def _is_directory(self, path):
-        path = path[self.prefix_len:]
-        if path and path[-1] != os.sep:
-            path += os.sep
-        i = bisect.bisect(self.index, path)
-        try:
-            result = self.index[i].startswith(path)
-        except IndexError:
-            result = False
-        return result
-
-
-_finder_registry = {
-    type(None): ResourceFinder,
-    zipimport.zipimporter: ZipResourceFinder
-}
-
-try:
-    # In Python 3.6, _frozen_importlib -> _frozen_importlib_external
-    try:
-        import _frozen_importlib_external as _fi
-    except ImportError:
-        import _frozen_importlib as _fi
-    _finder_registry[_fi.SourceFileLoader] = ResourceFinder
-    _finder_registry[_fi.FileFinder] = ResourceFinder
-    # See issue #146
-    _finder_registry[_fi.SourcelessFileLoader] = ResourceFinder
-    del _fi
-except (ImportError, AttributeError):
-    pass
-
-
-def register_finder(loader, finder_maker):
-    _finder_registry[type(loader)] = finder_maker
-
-
-_finder_cache = {}
-
-
-def finder(package):
-    """
-    Return a resource finder for a package.
-    :param package: The name of the package.
-    :return: A :class:`ResourceFinder` instance for the package.
-    """
-    if package in _finder_cache:
-        result = _finder_cache[package]
-    else:
-        if package not in sys.modules:
-            __import__(package)
-        module = sys.modules[package]
-        path = getattr(module, '__path__', None)
-        if path is None:
-            raise DistlibException('You cannot get a finder for a module, '
-                                   'only for a package')
-        loader = getattr(module, '__loader__', None)
-        finder_maker = _finder_registry.get(type(loader))
-        if finder_maker is None:
-            raise DistlibException('Unable to locate finder for %r' % package)
-        result = finder_maker(module)
-        _finder_cache[package] = result
-    return result
-
-
-_dummy_module = types.ModuleType(str('__dummy__'))
-
-
-def finder_for_path(path):
-    """
-    Return a resource finder for a path, which should represent a container.
-
-    :param path: The path.
-    :return: A :class:`ResourceFinder` instance for the path.
-    """
-    result = None
-    # calls any path hooks, gets importer into cache
-    pkgutil.get_importer(path)
-    loader = sys.path_importer_cache.get(path)
-    finder = _finder_registry.get(type(loader))
-    if finder:
-        module = _dummy_module
-        module.__file__ = os.path.join(path, '')
-        module.__loader__ = loader
-        result = finder(module)
-    return result
diff --git a/spaces/aliabd/SummerTime/download.py b/spaces/aliabd/SummerTime/download.py
deleted file mode 100644
index 3f59569e354853f0961315d42da1ab3226a96884..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/download.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import nltk
-
-nltk.download("stopwords")
diff --git a/spaces/aliabid94/gpt_who/README.md b/spaces/aliabid94/gpt_who/README.md
deleted file mode 100644
index 9c44f890715b9bd0d545a303b2970bceee2ae00d..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/gpt_who/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Gpt Who?
-emoji: 🦹🏼
-colorFrom: white
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: run.py
-pinned: false
-license: openrail
----
diff --git a/spaces/allandclive/Uganda_MMS/vits/README.md b/spaces/allandclive/Uganda_MMS/vits/README.md
deleted file mode 100644
index f7883f8c5badbece0887d48e41436a32e64c5935..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/README.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
-
-### Jaehyeon Kim, Jungil Kong, and Juhee Son
-
-In our recent [paper](https://arxiv.org/abs/2106.06103), we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.
-
-Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.
-
-Visit our [demo](https://jaywalnut310.github.io/vits-demo/index.html) for audio samples.
-
-We also provide the [pretrained models](https://drive.google.com/drive/folders/1ksarh-cJf3F5eKJjLVWY0X1j1qsQqiS2?usp=sharing).
-
-** Update note: Thanks to [Rishikesh (ऋषिकेश)](https://github.com/jaywalnut310/vits/issues/1), our interactive TTS demo is now available on [Colab Notebook](https://colab.research.google.com/drive/1CO61pZizDj7en71NQG_aqqKdGaA_SaBf?usp=sharing).
-
-<table style="width:100%">
-  <tr>
-    <th>VITS at training</th>
-    <th>VITS at inference</th>
-  </tr>
-  <tr>
-    <td><img src="resources/fig_1a.png" alt="VITS at training" height="400"></td>
-    <td><img src="resources/fig_1b.png" alt="VITS at inference" height="400"></td>
-  </tr>
-</table>
-
-
-## Pre-requisites
-0. Python >= 3.6
-0. Clone this repository
-0. Install python requirements. Please refer [requirements.txt](requirements.txt)
-    1. You may need to install espeak first: `apt-get install espeak`
-0. Download datasets
-    1. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: `ln -s /path/to/LJSpeech-1.1/wavs DUMMY1`
-    1. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: `ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2`
-0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
-```sh
-# Cython-version Monotonoic Alignment Search
-cd monotonic_align
-python setup.py build_ext --inplace
-
-# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
-# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt 
-# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt
-```
-
-
-## Training Exmaple
-```sh
-# LJ Speech
-python train.py -c configs/ljs_base.json -m ljs_base
-
-# VCTK
-python train_ms.py -c configs/vctk_base.json -m vctk_base
-```
-
-
-## Inference Example
-See [inference.ipynb](inference.ipynb)
diff --git a/spaces/alsrbdni/magic-to-diffusion/app.py b/spaces/alsrbdni/magic-to-diffusion/app.py
deleted file mode 100644
index 7d9008e6d18a615f3213d6e08ed65cdb9611e734..0000000000000000000000000000000000000000
--- a/spaces/alsrbdni/magic-to-diffusion/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import gradio as gr
-import os
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-text_gen = gr.Interface.load(name="spaces/Gustavosta/MagicPrompt-Stable-Diffusion")
-stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
-
-def get_images(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    sd_output = [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
-    return sd_output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-def get_prompts(prompt_text):
-    return text_gen(prompt_text)
-
-css = '''
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-a {text-decoration-line: underline;}
-footer {visibility: hidden}
-'''
-
-with gr.Blocks(css=css) 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;">
-                Magic Diffusion 🪄</h1>
-        </div>""")
-
-    with gr.Row():
-      with gr.Column():
-          input_text = gr.Textbox(label="Short text prompt", 
-                                lines=4, elem_id="input-text")
-          with gr.Row():
-            see_prompts = gr.Button("Feed in your text!")
-
-      with gr.Column():
-        text_output = gr.Textbox(
-                                label="Prettified text prompt", 
-                                lines=4,
-                                elem_id="translated"
-                            )
-        with gr.Row():
-            diffuse_btn = gr.Button(value="Diffuse the Prompt!")
-      with gr.Column(elem_id="generated-gallery"):
-        sd_output = gr.Gallery().style(grid=2, height="auto")
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-    see_prompts.click(get_prompts, 
-                            inputs = [input_text], 
-                            outputs = [
-                                text_output
-                            ])
-    diffuse_btn.click(get_images, 
-                          inputs = [
-                              text_output
-                              ], 
-                          outputs = [sd_output, community_icon, loading_icon, share_button]
-                          )
-    share_button.click(None, [], [], _js=share_js)
-
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/guided_diffusion/script_util.py b/spaces/amankishore/sjc/guided_diffusion/script_util.py
deleted file mode 100644
index 2bfdad9fce28193d8abeb439f7400eca90d4f728..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/guided_diffusion/script_util.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import argparse
-import inspect
-
-from . import gaussian_diffusion as gd
-from .respace import SpacedDiffusion, space_timesteps
-from .unet import SuperResModel, UNetModel, EncoderUNetModel
-
-NUM_CLASSES = 1000
-
-
-def diffusion_defaults():
-    """
-    Defaults for image and classifier training.
-    """
-    return dict(
-        learn_sigma=False,
-        diffusion_steps=1000,
-        noise_schedule="linear",
-        timestep_respacing="",
-        use_kl=False,
-        predict_xstart=False,
-        rescale_timesteps=False,
-        rescale_learned_sigmas=False,
-    )
-
-
-def classifier_defaults():
-    """
-    Defaults for classifier models.
-    """
-    return dict(
-        image_size=64,
-        classifier_use_fp16=False,
-        classifier_width=128,
-        classifier_depth=2,
-        classifier_attention_resolutions="32,16,8",  # 16
-        classifier_use_scale_shift_norm=True,  # False
-        classifier_resblock_updown=True,  # False
-        classifier_pool="attention",
-    )
-
-
-def model_and_diffusion_defaults():
-    """
-    Defaults for image training.
-    """
-    res = dict(
-        image_size=64,
-        num_channels=128,
-        num_res_blocks=2,
-        num_heads=4,
-        num_heads_upsample=-1,
-        num_head_channels=-1,
-        attention_resolutions="16,8",
-        channel_mult="",
-        dropout=0.0,
-        class_cond=False,
-        use_checkpoint=False,
-        use_scale_shift_norm=True,
-        resblock_updown=False,
-        use_fp16=False,
-        use_new_attention_order=False,
-    )
-    res.update(diffusion_defaults())
-    return res
-
-
-def classifier_and_diffusion_defaults():
-    res = classifier_defaults()
-    res.update(diffusion_defaults())
-    return res
-
-
-def create_model_and_diffusion(
-    image_size,
-    class_cond,
-    learn_sigma,
-    num_channels,
-    num_res_blocks,
-    channel_mult,
-    num_heads,
-    num_head_channels,
-    num_heads_upsample,
-    attention_resolutions,
-    dropout,
-    diffusion_steps,
-    noise_schedule,
-    timestep_respacing,
-    use_kl,
-    predict_xstart,
-    rescale_timesteps,
-    rescale_learned_sigmas,
-    use_checkpoint,
-    use_scale_shift_norm,
-    resblock_updown,
-    use_fp16,
-    use_new_attention_order,
-):
-    model = create_model(
-        image_size,
-        num_channels,
-        num_res_blocks,
-        channel_mult=channel_mult,
-        learn_sigma=learn_sigma,
-        class_cond=class_cond,
-        use_checkpoint=use_checkpoint,
-        attention_resolutions=attention_resolutions,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        dropout=dropout,
-        resblock_updown=resblock_updown,
-        use_fp16=use_fp16,
-        use_new_attention_order=use_new_attention_order,
-    )
-    diffusion = create_gaussian_diffusion(
-        steps=diffusion_steps,
-        learn_sigma=learn_sigma,
-        noise_schedule=noise_schedule,
-        use_kl=use_kl,
-        predict_xstart=predict_xstart,
-        rescale_timesteps=rescale_timesteps,
-        rescale_learned_sigmas=rescale_learned_sigmas,
-        timestep_respacing=timestep_respacing,
-    )
-    return model, diffusion
-
-
-def create_model(
-    image_size,
-    num_channels,
-    num_res_blocks,
-    channel_mult="",
-    learn_sigma=False,
-    class_cond=False,
-    use_checkpoint=False,
-    attention_resolutions="16",
-    num_heads=1,
-    num_head_channels=-1,
-    num_heads_upsample=-1,
-    use_scale_shift_norm=False,
-    dropout=0,
-    resblock_updown=False,
-    use_fp16=False,
-    use_new_attention_order=False,
-):
-    if channel_mult == "":
-        if image_size == 512:
-            channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
-        elif image_size == 256:
-            channel_mult = (1, 1, 2, 2, 4, 4)
-        elif image_size == 128:
-            channel_mult = (1, 1, 2, 3, 4)
-        elif image_size == 64:
-            channel_mult = (1, 2, 3, 4)
-        else:
-            raise ValueError(f"unsupported image size: {image_size}")
-    else:
-        channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
-
-    attention_ds = []
-    for res in attention_resolutions.split(","):
-        attention_ds.append(image_size // int(res))
-
-    return UNetModel(
-        image_size=image_size,
-        in_channels=3,
-        model_channels=num_channels,
-        out_channels=(3 if not learn_sigma else 6),
-        num_res_blocks=num_res_blocks,
-        attention_resolutions=tuple(attention_ds),
-        dropout=dropout,
-        channel_mult=channel_mult,
-        num_classes=(NUM_CLASSES if class_cond else None),
-        use_checkpoint=use_checkpoint,
-        use_fp16=use_fp16,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        resblock_updown=resblock_updown,
-        use_new_attention_order=use_new_attention_order,
-    )
-
-
-def create_classifier_and_diffusion(
-    image_size,
-    classifier_use_fp16,
-    classifier_width,
-    classifier_depth,
-    classifier_attention_resolutions,
-    classifier_use_scale_shift_norm,
-    classifier_resblock_updown,
-    classifier_pool,
-    learn_sigma,
-    diffusion_steps,
-    noise_schedule,
-    timestep_respacing,
-    use_kl,
-    predict_xstart,
-    rescale_timesteps,
-    rescale_learned_sigmas,
-):
-    classifier = create_classifier(
-        image_size,
-        classifier_use_fp16,
-        classifier_width,
-        classifier_depth,
-        classifier_attention_resolutions,
-        classifier_use_scale_shift_norm,
-        classifier_resblock_updown,
-        classifier_pool,
-    )
-    diffusion = create_gaussian_diffusion(
-        steps=diffusion_steps,
-        learn_sigma=learn_sigma,
-        noise_schedule=noise_schedule,
-        use_kl=use_kl,
-        predict_xstart=predict_xstart,
-        rescale_timesteps=rescale_timesteps,
-        rescale_learned_sigmas=rescale_learned_sigmas,
-        timestep_respacing=timestep_respacing,
-    )
-    return classifier, diffusion
-
-
-def create_classifier(
-    image_size,
-    classifier_use_fp16,
-    classifier_width,
-    classifier_depth,
-    classifier_attention_resolutions,
-    classifier_use_scale_shift_norm,
-    classifier_resblock_updown,
-    classifier_pool,
-):
-    if image_size == 512:
-        channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
-    elif image_size == 256:
-        channel_mult = (1, 1, 2, 2, 4, 4)
-    elif image_size == 128:
-        channel_mult = (1, 1, 2, 3, 4)
-    elif image_size == 64:
-        channel_mult = (1, 2, 3, 4)
-    else:
-        raise ValueError(f"unsupported image size: {image_size}")
-
-    attention_ds = []
-    for res in classifier_attention_resolutions.split(","):
-        attention_ds.append(image_size // int(res))
-
-    return EncoderUNetModel(
-        image_size=image_size,
-        in_channels=3,
-        model_channels=classifier_width,
-        out_channels=1000,
-        num_res_blocks=classifier_depth,
-        attention_resolutions=tuple(attention_ds),
-        channel_mult=channel_mult,
-        use_fp16=classifier_use_fp16,
-        num_head_channels=64,
-        use_scale_shift_norm=classifier_use_scale_shift_norm,
-        resblock_updown=classifier_resblock_updown,
-        pool=classifier_pool,
-    )
-
-
-def sr_model_and_diffusion_defaults():
-    res = model_and_diffusion_defaults()
-    res["large_size"] = 256
-    res["small_size"] = 64
-    arg_names = inspect.getfullargspec(sr_create_model_and_diffusion)[0]
-    for k in res.copy().keys():
-        if k not in arg_names:
-            del res[k]
-    return res
-
-
-def sr_create_model_and_diffusion(
-    large_size,
-    small_size,
-    class_cond,
-    learn_sigma,
-    num_channels,
-    num_res_blocks,
-    num_heads,
-    num_head_channels,
-    num_heads_upsample,
-    attention_resolutions,
-    dropout,
-    diffusion_steps,
-    noise_schedule,
-    timestep_respacing,
-    use_kl,
-    predict_xstart,
-    rescale_timesteps,
-    rescale_learned_sigmas,
-    use_checkpoint,
-    use_scale_shift_norm,
-    resblock_updown,
-    use_fp16,
-):
-    model = sr_create_model(
-        large_size,
-        small_size,
-        num_channels,
-        num_res_blocks,
-        learn_sigma=learn_sigma,
-        class_cond=class_cond,
-        use_checkpoint=use_checkpoint,
-        attention_resolutions=attention_resolutions,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        dropout=dropout,
-        resblock_updown=resblock_updown,
-        use_fp16=use_fp16,
-    )
-    diffusion = create_gaussian_diffusion(
-        steps=diffusion_steps,
-        learn_sigma=learn_sigma,
-        noise_schedule=noise_schedule,
-        use_kl=use_kl,
-        predict_xstart=predict_xstart,
-        rescale_timesteps=rescale_timesteps,
-        rescale_learned_sigmas=rescale_learned_sigmas,
-        timestep_respacing=timestep_respacing,
-    )
-    return model, diffusion
-
-
-def sr_create_model(
-    large_size,
-    small_size,
-    num_channels,
-    num_res_blocks,
-    learn_sigma,
-    class_cond,
-    use_checkpoint,
-    attention_resolutions,
-    num_heads,
-    num_head_channels,
-    num_heads_upsample,
-    use_scale_shift_norm,
-    dropout,
-    resblock_updown,
-    use_fp16,
-):
-    _ = small_size  # hack to prevent unused variable
-
-    if large_size == 512:
-        channel_mult = (1, 1, 2, 2, 4, 4)
-    elif large_size == 256:
-        channel_mult = (1, 1, 2, 2, 4, 4)
-    elif large_size == 64:
-        channel_mult = (1, 2, 3, 4)
-    else:
-        raise ValueError(f"unsupported large size: {large_size}")
-
-    attention_ds = []
-    for res in attention_resolutions.split(","):
-        attention_ds.append(large_size // int(res))
-
-    return SuperResModel(
-        image_size=large_size,
-        in_channels=3,
-        model_channels=num_channels,
-        out_channels=(3 if not learn_sigma else 6),
-        num_res_blocks=num_res_blocks,
-        attention_resolutions=tuple(attention_ds),
-        dropout=dropout,
-        channel_mult=channel_mult,
-        num_classes=(NUM_CLASSES if class_cond else None),
-        use_checkpoint=use_checkpoint,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        resblock_updown=resblock_updown,
-        use_fp16=use_fp16,
-    )
-
-
-def create_gaussian_diffusion(
-    *,
-    steps=1000,
-    learn_sigma=False,
-    sigma_small=False,
-    noise_schedule="linear",
-    use_kl=False,
-    predict_xstart=False,
-    rescale_timesteps=False,
-    rescale_learned_sigmas=False,
-    timestep_respacing="",
-):
-    betas = gd.get_named_beta_schedule(noise_schedule, steps)
-    if use_kl:
-        loss_type = gd.LossType.RESCALED_KL
-    elif rescale_learned_sigmas:
-        loss_type = gd.LossType.RESCALED_MSE
-    else:
-        loss_type = gd.LossType.MSE
-    if not timestep_respacing:
-        timestep_respacing = [steps]
-    return SpacedDiffusion(
-        use_timesteps=space_timesteps(steps, timestep_respacing),
-        betas=betas,
-        model_mean_type=(
-            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
-        ),
-        model_var_type=(
-            (
-                gd.ModelVarType.FIXED_LARGE
-                if not sigma_small
-                else gd.ModelVarType.FIXED_SMALL
-            )
-            if not learn_sigma
-            else gd.ModelVarType.LEARNED_RANGE
-        ),
-        loss_type=loss_type,
-        rescale_timesteps=rescale_timesteps,
-    )
-
-
-def add_dict_to_argparser(parser, default_dict):
-    for k, v in default_dict.items():
-        v_type = type(v)
-        if v is None:
-            v_type = str
-        elif isinstance(v, bool):
-            v_type = str2bool
-        parser.add_argument(f"--{k}", default=v, type=v_type)
-
-
-def args_to_dict(args, keys):
-    return {k: getattr(args, k) for k in keys}
-
-
-def str2bool(v):
-    """
-    https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
-    """
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise argparse.ArgumentTypeError("boolean value expected")
diff --git a/spaces/amgad59/Keras_cv_wedding_dress/README.md b/spaces/amgad59/Keras_cv_wedding_dress/README.md
deleted file mode 100644
index 79b4e32ffe24afc02bc6c2986897e7035f228df2..0000000000000000000000000000000000000000
--- a/spaces/amgad59/Keras_cv_wedding_dress/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Keras Cv Wedding Dress
-emoji: 👁
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/extra_networks.py b/spaces/aodianyun/stable-diffusion-webui/modules/extra_networks.py
deleted file mode 100644
index cf6aa7907156aefa62f183fdcd25fea3e17252b4..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/extra_networks.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import re
-from collections import defaultdict
-
-from modules import errors
-
-extra_network_registry = {}
-
-
-def initialize():
-    extra_network_registry.clear()
-
-
-def register_extra_network(extra_network):
-    extra_network_registry[extra_network.name] = extra_network
-
-
-class ExtraNetworkParams:
-    def __init__(self, items=None):
-        self.items = items or []
-
-
-class ExtraNetwork:
-    def __init__(self, name):
-        self.name = name
-
-    def activate(self, p, params_list):
-        """
-        Called by processing on every run. Whatever the extra network is meant to do should be activated here.
-        Passes arguments related to this extra network in params_list.
-        User passes arguments by specifying this in his prompt:
-
-        <name:arg1:arg2:arg3>
-
-        Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments
-        separated by colon.
-
-        Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list -
-        in this case, all effects of this extra networks should be disabled.
-
-        Can be called multiple times before deactivate() - each new call should override the previous call completely.
-
-        For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is:
-
-        > "1girl, <hypernet:agm:1.1> <extrasupernet:master:12:13:14> <hypernet:ray>"
-
-        params_list will be:
-
-        [
-            ExtraNetworkParams(items=["agm", "1.1"]),
-            ExtraNetworkParams(items=["ray"])
-        ]
-
-        """
-        raise NotImplementedError
-
-    def deactivate(self, p):
-        """
-        Called at the end of processing for housekeeping. No need to do anything here.
-        """
-
-        raise NotImplementedError
-
-
-def activate(p, extra_network_data):
-    """call activate for extra networks in extra_network_data in specified order, then call
-    activate for all remaining registered networks with an empty argument list"""
-
-    for extra_network_name, extra_network_args in extra_network_data.items():
-        extra_network = extra_network_registry.get(extra_network_name, None)
-        if extra_network is None:
-            print(f"Skipping unknown extra network: {extra_network_name}")
-            continue
-
-        try:
-            extra_network.activate(p, extra_network_args)
-        except Exception as e:
-            errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
-
-    for extra_network_name, extra_network in extra_network_registry.items():
-        args = extra_network_data.get(extra_network_name, None)
-        if args is not None:
-            continue
-
-        try:
-            extra_network.activate(p, [])
-        except Exception as e:
-            errors.display(e, f"activating extra network {extra_network_name}")
-
-
-def deactivate(p, extra_network_data):
-    """call deactivate for extra networks in extra_network_data in specified order, then call
-    deactivate for all remaining registered networks"""
-
-    for extra_network_name, extra_network_args in extra_network_data.items():
-        extra_network = extra_network_registry.get(extra_network_name, None)
-        if extra_network is None:
-            continue
-
-        try:
-            extra_network.deactivate(p)
-        except Exception as e:
-            errors.display(e, f"deactivating extra network {extra_network_name}")
-
-    for extra_network_name, extra_network in extra_network_registry.items():
-        args = extra_network_data.get(extra_network_name, None)
-        if args is not None:
-            continue
-
-        try:
-            extra_network.deactivate(p)
-        except Exception as e:
-            errors.display(e, f"deactivating unmentioned extra network {extra_network_name}")
-
-
-re_extra_net = re.compile(r"<(\w+):([^>]+)>")
-
-
-def parse_prompt(prompt):
-    res = defaultdict(list)
-
-    def found(m):
-        name = m.group(1)
-        args = m.group(2)
-
-        res[name].append(ExtraNetworkParams(items=args.split(":")))
-
-        return ""
-
-    prompt = re.sub(re_extra_net, found, prompt)
-
-    return prompt, res
-
-
-def parse_prompts(prompts):
-    res = []
-    extra_data = None
-
-    for prompt in prompts:
-        updated_prompt, parsed_extra_data = parse_prompt(prompt)
-
-        if extra_data is None:
-            extra_data = parsed_extra_data
-
-        res.append(updated_prompt)
-
-    return res, extra_data
-
diff --git a/spaces/aravind123456789/OPENAIAPP/README.md b/spaces/aravind123456789/OPENAIAPP/README.md
deleted file mode 100644
index 9a1a944ceb702f737428708bf9757dc76c4c3eb2..0000000000000000000000000000000000000000
--- a/spaces/aravind123456789/OPENAIAPP/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: OPENAIAPP
-emoji: 🚀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arch-123/bingo/src/lib/hooks/use-bing.ts b/spaces/arch-123/bingo/src/lib/hooks/use-bing.ts
deleted file mode 100644
index dcdb1667ced0cba299b0825c0e91c4732411308c..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/lib/hooks/use-bing.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-'use client'
-
-import { useState, useCallback, useEffect, useMemo } from 'react'
-import { useAtom, useAtomValue } from 'jotai'
-import { chatFamily, bingConversationStyleAtom, GreetMessages, hashAtom, voiceAtom } from '@/state'
-import { setConversationMessages } from './chat-history'
-import { ChatMessageModel, BotId, FileItem } from '@/lib/bots/bing/types'
-import { nanoid } from '../utils'
-import { TTS } from '../bots/bing/tts'
-
-export function useBing(botId: BotId = 'bing') {
-  const chatAtom = useMemo(() => chatFamily({ botId, page: 'singleton' }), [botId])
-  const [enableTTS] = useAtom(voiceAtom)
-  const speaker = useMemo(() => new TTS(), [])
-  const [hash, setHash] = useAtom(hashAtom)
-  const bingConversationStyle = useAtomValue(bingConversationStyleAtom)
-  const [chatState, setChatState] = useAtom(chatAtom)
-  const [input, setInput] = useState('')
-  const [attachmentList, setAttachmentList] = useState<FileItem[]>([])
-
-  const updateMessage = useCallback(
-    (messageId: string, updater: (message: ChatMessageModel) => void) => {
-      setChatState((draft) => {
-        const message = draft.messages.find((m) => m.id === messageId)
-        if (message) {
-          updater(message)
-        }
-      })
-    },
-    [setChatState],
-  )
-
-  const sendMessage = useCallback(
-    async (input: string, options = {}) => {
-      const botMessageId = nanoid()
-      const imageUrl = attachmentList?.[0]?.status === 'loaded' ? attachmentList[0].url : undefined
-      setChatState((draft) => {
-        const text = imageUrl ? `${input}\n\n![image](${imageUrl})` : input
-        draft.messages.push({ id: nanoid(), text, author: 'user' }, { id: botMessageId, text: '', author: 'bot' })
-        setAttachmentList([])
-      })
-      const abortController = new AbortController()
-      setChatState((draft) => {
-        draft.generatingMessageId = botMessageId
-        draft.abortController = abortController
-      })
-      speaker.reset()
-      await chatState.bot.sendMessage({
-        prompt: input,
-        imageUrl: /\?bcid=([^&]+)/.test(imageUrl ?? '') ? `https://www.bing.com/images/blob?bcid=${RegExp.$1}` : imageUrl,
-        options: {
-          ...options,
-          bingConversationStyle,
-        },
-        signal: abortController.signal,
-        onEvent(event) {
-          if (event.type === 'UPDATE_ANSWER') {
-            updateMessage(botMessageId, (message) => {
-              if (event.data.text.length > message.text.length) {
-                message.text = event.data.text
-              }
-
-              if (event.data.spokenText && enableTTS) {
-                speaker.speak(event.data.spokenText)
-              }
-
-              message.throttling = event.data.throttling || message.throttling
-              message.sourceAttributions = event.data.sourceAttributions || message.sourceAttributions
-              message.suggestedResponses = event.data.suggestedResponses || message.suggestedResponses
-            })
-          } else if (event.type === 'ERROR') {
-            updateMessage(botMessageId, (message) => {
-              message.error = event.error
-            })
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          } else if (event.type === 'DONE') {
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          }
-        },
-      })
-    },
-    [botId, attachmentList, chatState.bot, setChatState, updateMessage],
-  )
-
-  const uploadImage = useCallback(async (imgUrl: string) => {
-    setAttachmentList([{ url: imgUrl, status: 'loading' }])
-    const response = await chatState.bot.uploadImage(imgUrl, bingConversationStyle)
-    if (response?.blobId) {
-      setAttachmentList([{ url: `/api/blob?bcid=${response.blobId}`, status: 'loaded' }])
-    } else {
-      setAttachmentList([{ url: imgUrl, status: 'error' }])
-    }
-  }, [chatState.bot])
-
-  const resetConversation = useCallback(() => {
-    chatState.bot.resetConversation()
-    speaker.abort()
-    setChatState((draft) => {
-      draft.abortController = undefined
-      draft.generatingMessageId = ''
-      draft.messages = [{ author: 'bot', text: GreetMessages[Math.floor(GreetMessages.length * Math.random())], id: nanoid() }]
-      draft.conversationId = nanoid()
-    })
-  }, [chatState.bot, setChatState])
-
-  const stopGenerating = useCallback(() => {
-    chatState.abortController?.abort()
-    if (chatState.generatingMessageId) {
-      updateMessage(chatState.generatingMessageId, (message) => {
-        if (!message.text && !message.error) {
-          message.text = 'Cancelled'
-        }
-      })
-    }
-    setChatState((draft) => {
-      draft.generatingMessageId = ''
-    })
-  }, [chatState.abortController, chatState.generatingMessageId, setChatState, updateMessage])
-
-  useEffect(() => {
-    if (chatState.messages.length) {
-      setConversationMessages(botId, chatState.conversationId, chatState.messages)
-    }
-  }, [botId, chatState.conversationId, chatState.messages])
-
-  useEffect(() => {
-    if (hash === 'reset') {
-      resetConversation()
-      setHash('')
-    }
-  }, [hash, setHash])
-
-  const chat = useMemo(
-    () => ({
-      botId,
-      bot: chatState.bot,
-      isSpeaking: speaker.isSpeaking,
-      messages: chatState.messages,
-      sendMessage,
-      setInput,
-      input,
-      resetConversation,
-      generating: !!chatState.generatingMessageId,
-      stopGenerating,
-      uploadImage,
-      setAttachmentList,
-      attachmentList,
-    }),
-    [
-      botId,
-      bingConversationStyle,
-      chatState.bot,
-      chatState.generatingMessageId,
-      chatState.messages,
-      speaker.isSpeaking,
-      setInput,
-      input,
-      setAttachmentList,
-      attachmentList,
-      resetConversation,
-      sendMessage,
-      stopGenerating,
-    ],
-  )
-
-  return chat
-}
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/configs/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/configs/__init__.py
deleted file mode 100644
index b5e11b990c6d7294e7cb00c3e024bbb5f94a8105..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/configs/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import importlib
-import os
-from inspect import isclass
-
-# import all files under configs/
-configs_dir = os.path.dirname(__file__)
-for file in os.listdir(configs_dir):
-    path = os.path.join(configs_dir, file)
-    if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)):
-        config_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module("TTS.vocoder.configs." + config_name)
-        for attribute_name in dir(module):
-            attribute = getattr(module, attribute_name)
-
-            if isclass(attribute):
-                # Add the class to this package's variables
-                globals()[attribute_name] = attribute
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/multilingual/cml_yourtts/train_yourtts.py b/spaces/artificialguybr/video-dubbing/TTS/recipes/multilingual/cml_yourtts/train_yourtts.py
deleted file mode 100644
index 25a2fd0a4ba33f6c8be33ab7244555f45e4b9460..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/multilingual/cml_yourtts/train_yourtts.py
+++ /dev/null
@@ -1,337 +0,0 @@
-import os
-
-import torch
-from trainer import Trainer, TrainerArgs
-
-from TTS.bin.compute_embeddings import compute_embeddings
-from TTS.bin.resample import resample_files
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig
-from TTS.utils.downloaders import download_libri_tts
-
-torch.set_num_threads(24)
-
-# pylint: disable=W0105
-"""
-    This recipe replicates the first experiment proposed in the CML-TTS paper (https://arxiv.org/abs/2306.10097). It uses the YourTTS model.
-    YourTTS model is based on the VITS model however it uses external speaker embeddings extracted from a pre-trained speaker encoder and has small architecture changes.
-"""
-CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
-
-# Name of the run for the Trainer
-RUN_NAME = "YourTTS-CML-TTS"
-
-# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
-OUT_PATH = os.path.dirname(os.path.abspath(__file__))  # "/raid/coqui/Checkpoints/original-YourTTS/"
-
-# If you want to do transfer learning and speedup your training you can set here the path to the CML-TTS available checkpoint that cam be downloaded here:  https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
-RESTORE_PATH = "/raid/edresson/CML_YourTTS/checkpoints_yourtts_cml_tts_dataset/best_model.pth"  # Download the checkpoint here:  https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
-
-# This paramter is useful to debug, it skips the training epochs and just do the evaluation  and produce the test sentences
-SKIP_TRAIN_EPOCH = False
-
-# Set here the batch size to be used in training and evaluation
-BATCH_SIZE = 32
-
-# Training Sampling rate and the target sampling rate for resampling the downloaded dataset (Note: If you change this you might need to redownload the dataset !!)
-# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
-SAMPLE_RATE = 24000
-
-# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
-MAX_AUDIO_LEN_IN_SECONDS = float("inf")
-
-### Download CML-TTS dataset
-# You need to download the dataset for all languages manually and extract it to a path and then set the CML_DATASET_PATH to this path: https://github.com/freds0/CML-TTS-Dataset#download
-CML_DATASET_PATH = "./datasets/CML-TTS-Dataset/"
-
-
-### Download LibriTTS dataset
-# it will automatic download the dataset, if you have problems you can comment it and manually donwload and extract it ! Download link: https://www.openslr.org/resources/60/train-clean-360.tar.gz
-LIBRITTS_DOWNLOAD_PATH = "./datasets/LibriTTS/"
-# Check if LibriTTS dataset is not already downloaded, if not download it
-if not os.path.exists(LIBRITTS_DOWNLOAD_PATH):
-    print(">>> Downloading LibriTTS dataset:")
-    download_libri_tts(LIBRITTS_DOWNLOAD_PATH, subset="libri-tts-clean-360")
-
-# init LibriTTS configs
-libritts_config = BaseDatasetConfig(
-    formatter="libri_tts",
-    dataset_name="libri_tts",
-    meta_file_train="",
-    meta_file_val="",
-    path=os.path.join(LIBRITTS_DOWNLOAD_PATH, "train-clean-360/"),
-    language="en",
-)
-
-# init CML-TTS configs
-pt_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_portuguese_v0.1/"),
-    language="pt-br",
-)
-
-pl_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_polish_v0.1/"),
-    language="pl",
-)
-
-it_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_italian_v0.1/"),
-    language="it",
-)
-
-fr_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_french_v0.1/"),
-    language="fr",
-)
-
-du_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_dutch_v0.1/"),
-    language="du",
-)
-
-ge_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_german_v0.1/"),
-    language="ge",
-)
-
-sp_config = BaseDatasetConfig(
-    formatter="cml_tts",
-    dataset_name="cml_tts",
-    meta_file_train="train.csv",
-    meta_file_val="",
-    path=os.path.join(CML_DATASET_PATH, "cml_tts_dataset_spanish_v0.1/"),
-    language="sp",
-)
-
-# Add here all datasets configs Note: If you want to add new datasets, just add them here and it will automatically compute the speaker embeddings (d-vectors) for this new dataset :)
-DATASETS_CONFIG_LIST = [libritts_config, pt_config, pl_config, it_config, fr_config, du_config, ge_config, sp_config]
-
-### Extract speaker embeddings
-SPEAKER_ENCODER_CHECKPOINT_PATH = (
-    "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
-)
-SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
-
-D_VECTOR_FILES = []  # List of speaker embeddings/d-vectors to be used during the training
-
-# Iterates all the dataset configs checking if the speakers embeddings are already computated, if not compute it
-for dataset_conf in DATASETS_CONFIG_LIST:
-    # Check if the embeddings weren't already computed, if not compute it
-    embeddings_file = os.path.join(dataset_conf.path, "speakers.pth")
-    if not os.path.isfile(embeddings_file):
-        print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
-        compute_embeddings(
-            SPEAKER_ENCODER_CHECKPOINT_PATH,
-            SPEAKER_ENCODER_CONFIG_PATH,
-            embeddings_file,
-            old_speakers_file=None,
-            config_dataset_path=None,
-            formatter_name=dataset_conf.formatter,
-            dataset_name=dataset_conf.dataset_name,
-            dataset_path=dataset_conf.path,
-            meta_file_train=dataset_conf.meta_file_train,
-            meta_file_val=dataset_conf.meta_file_val,
-            disable_cuda=False,
-            no_eval=False,
-        )
-    D_VECTOR_FILES.append(embeddings_file)
-
-
-# Audio config used in training.
-audio_config = VitsAudioConfig(
-    sample_rate=SAMPLE_RATE,
-    hop_length=256,
-    win_length=1024,
-    fft_size=1024,
-    mel_fmin=0.0,
-    mel_fmax=None,
-    num_mels=80,
-)
-
-# Init VITSArgs setting the arguments that are needed for the YourTTS model
-model_args = VitsArgs(
-    spec_segment_size=62,
-    hidden_channels=192,
-    hidden_channels_ffn_text_encoder=768,
-    num_heads_text_encoder=2,
-    num_layers_text_encoder=10,
-    kernel_size_text_encoder=3,
-    dropout_p_text_encoder=0.1,
-    d_vector_file=D_VECTOR_FILES,
-    use_d_vector_file=True,
-    d_vector_dim=512,
-    speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
-    speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
-    resblock_type_decoder="2",  # In the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
-    # Useful parameters to enable the Speaker Consistency Loss (SCL) described in the paper
-    use_speaker_encoder_as_loss=False,
-    # Useful parameters to enable multilingual training
-    use_language_embedding=True,
-    embedded_language_dim=4,
-)
-
-# General training config, here you can change the batch size and others useful parameters
-config = VitsConfig(
-    output_path=OUT_PATH,
-    model_args=model_args,
-    run_name=RUN_NAME,
-    project_name="YourTTS",
-    run_description="""
-            - YourTTS trained using CML-TTS and LibriTTS datasets
-        """,
-    dashboard_logger="tensorboard",
-    logger_uri=None,
-    audio=audio_config,
-    batch_size=BATCH_SIZE,
-    batch_group_size=48,
-    eval_batch_size=BATCH_SIZE,
-    num_loader_workers=8,
-    eval_split_max_size=256,
-    print_step=50,
-    plot_step=100,
-    log_model_step=1000,
-    save_step=5000,
-    save_n_checkpoints=2,
-    save_checkpoints=True,
-    target_loss="loss_1",
-    print_eval=False,
-    use_phonemes=False,
-    phonemizer="espeak",
-    phoneme_language="en",
-    compute_input_seq_cache=True,
-    add_blank=True,
-    text_cleaner="multilingual_cleaners",
-    characters=CharactersConfig(
-        characters_class="TTS.tts.models.vits.VitsCharacters",
-        pad="_",
-        eos="&",
-        bos="*",
-        blank=None,
-        characters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\u00a1\u00a3\u00b7\u00b8\u00c0\u00c1\u00c2\u00c3\u00c4\u00c5\u00c7\u00c8\u00c9\u00ca\u00cb\u00cc\u00cd\u00ce\u00cf\u00d1\u00d2\u00d3\u00d4\u00d5\u00d6\u00d9\u00da\u00db\u00dc\u00df\u00e0\u00e1\u00e2\u00e3\u00e4\u00e5\u00e7\u00e8\u00e9\u00ea\u00eb\u00ec\u00ed\u00ee\u00ef\u00f1\u00f2\u00f3\u00f4\u00f5\u00f6\u00f9\u00fa\u00fb\u00fc\u0101\u0104\u0105\u0106\u0107\u010b\u0119\u0141\u0142\u0143\u0144\u0152\u0153\u015a\u015b\u0161\u0178\u0179\u017a\u017b\u017c\u020e\u04e7\u05c2\u1b20",
-        punctuations="\u2014!'(),-.:;?\u00bf ",
-        phonemes="iy\u0268\u0289\u026fu\u026a\u028f\u028ae\u00f8\u0258\u0259\u0275\u0264o\u025b\u0153\u025c\u025e\u028c\u0254\u00e6\u0250a\u0276\u0251\u0252\u1d7b\u0298\u0253\u01c0\u0257\u01c3\u0284\u01c2\u0260\u01c1\u029bpbtd\u0288\u0256c\u025fk\u0261q\u0262\u0294\u0274\u014b\u0272\u0273n\u0271m\u0299r\u0280\u2c71\u027e\u027d\u0278\u03b2fv\u03b8\u00f0sz\u0283\u0292\u0282\u0290\u00e7\u029dx\u0263\u03c7\u0281\u0127\u0295h\u0266\u026c\u026e\u028b\u0279\u027bj\u0270l\u026d\u028e\u029f\u02c8\u02cc\u02d0\u02d1\u028dw\u0265\u029c\u02a2\u02a1\u0255\u0291\u027a\u0267\u025a\u02de\u026b'\u0303' ",
-        is_unique=True,
-        is_sorted=True,
-    ),
-    phoneme_cache_path=None,
-    precompute_num_workers=12,
-    start_by_longest=True,
-    datasets=DATASETS_CONFIG_LIST,
-    cudnn_benchmark=False,
-    max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
-    mixed_precision=False,
-    test_sentences=[
-        ["Voc\u00ea ter\u00e1 a vista do topo da montanha que voc\u00ea escalar.", "9351", None, "pt-br"],
-        ["Quando voc\u00ea n\u00e3o corre nenhum risco, voc\u00ea arrisca tudo.", "12249", None, "pt-br"],
-        [
-            "S\u00e3o necess\u00e1rios muitos anos de trabalho para ter sucesso da noite para o dia.",
-            "2961",
-            None,
-            "pt-br",
-        ],
-        ["You'll have the view of the top of the mountain that you climb.", "LTTS_6574", None, "en"],
-        ["When you don\u2019t take any risks, you risk everything.", "LTTS_6206", None, "en"],
-        ["Are necessary too many years of work to succeed overnight.", "LTTS_5717", None, "en"],
-        ["Je hebt uitzicht op de top van de berg die je beklimt.", "960", None, "du"],
-        ["Als je geen risico neemt, riskeer je alles.", "2450", None, "du"],
-        ["Zijn te veel jaren werk nodig om van de ene op de andere dag te slagen.", "10984", None, "du"],
-        ["Vous aurez la vue sur le sommet de la montagne que vous gravirez.", "6381", None, "fr"],
-        ["Quand tu ne prends aucun risque, tu risques tout.", "2825", None, "fr"],
-        [
-            "Sont n\u00e9cessaires trop d'ann\u00e9es de travail pour r\u00e9ussir du jour au lendemain.",
-            "1844",
-            None,
-            "fr",
-        ],
-        ["Sie haben die Aussicht auf die Spitze des Berges, den Sie erklimmen.", "2314", None, "ge"],
-        ["Wer nichts riskiert, riskiert alles.", "7483", None, "ge"],
-        ["Es sind zu viele Jahre Arbeit notwendig, um \u00fcber Nacht erfolgreich zu sein.", "12461", None, "ge"],
-        ["Avrai la vista della cima della montagna che sali.", "4998", None, "it"],
-        ["Quando non corri alcun rischio, rischi tutto.", "6744", None, "it"],
-        ["Are necessary too many years of work to succeed overnight.", "1157", None, "it"],
-        [
-            "B\u0119dziesz mie\u0107 widok na szczyt g\u00f3ry, na kt\u00f3r\u0105 si\u0119 wspinasz.",
-            "7014",
-            None,
-            "pl",
-        ],
-        ["Kiedy nie podejmujesz \u017cadnego ryzyka, ryzykujesz wszystko.", "3492", None, "pl"],
-        [
-            "Potrzebne s\u0105 zbyt wiele lat pracy, aby odnie\u015b\u0107 sukces z dnia na dzie\u0144.",
-            "1890",
-            None,
-            "pl",
-        ],
-        ["Tendr\u00e1s la vista de la cima de la monta\u00f1a que subes", "101", None, "sp"],
-        ["Cuando no te arriesgas, lo arriesgas todo.", "5922", None, "sp"],
-        [
-            "Son necesarios demasiados a\u00f1os de trabajo para triunfar de la noche a la ma\u00f1ana.",
-            "10246",
-            None,
-            "sp",
-        ],
-    ],
-    # Enable the weighted sampler
-    use_weighted_sampler=True,
-    # Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
-    # weighted_sampler_attrs={"language": 1.0, "speaker_name": 1.0},
-    weighted_sampler_attrs={"language": 1.0},
-    weighted_sampler_multipliers={
-        # "speaker_name": {
-        # you can force the batching scheme to give a higher weight to a certain speaker and then this speaker will appears more frequently on the batch.
-        # It will speedup the speaker adaptation process. Considering the CML train dataset and "new_speaker" as the speaker name of the speaker that you want to adapt.
-        # The line above will make the balancer consider the "new_speaker" as 106 speakers so 1/4 of the number of speakers present on CML dataset.
-        # 'new_speaker': 106, # (CML tot. train speaker)/4 = (424/4) = 106
-        # }
-    },
-    # It defines the Speaker Consistency Loss (SCL) α to 9 like the YourTTS paper
-    speaker_encoder_loss_alpha=9.0,
-)
-
-# Load all the datasets samples and split traning and evaluation sets
-train_samples, eval_samples = load_tts_samples(
-    config.datasets,
-    eval_split=True,
-    eval_split_max_size=config.eval_split_max_size,
-    eval_split_size=config.eval_split_size,
-)
-
-# Init the model
-model = Vits.init_from_config(config)
-
-# Init the trainer and 🚀
-trainer = Trainer(
-    TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
-    config,
-    output_path=OUT_PATH,
-    model=model,
-    train_samples=train_samples,
-    eval_samples=eval_samples,
-)
-trainer.fit()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/TestUtils.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/TestUtils.py
deleted file mode 100644
index 9d6eb67fc3dacee7d257868eacef1ec6fc07463e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/TestUtils.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from __future__ import absolute_import
-
-import os
-import unittest
-import tempfile
-
-from .Compiler import Errors
-from .CodeWriter import CodeWriter
-from .Compiler.TreeFragment import TreeFragment, strip_common_indent
-from .Compiler.Visitor import TreeVisitor, VisitorTransform
-from .Compiler import TreePath
-
-
-class NodeTypeWriter(TreeVisitor):
-    def __init__(self):
-        super(NodeTypeWriter, self).__init__()
-        self._indents = 0
-        self.result = []
-
-    def visit_Node(self, node):
-        if not self.access_path:
-            name = u"(root)"
-        else:
-            tip = self.access_path[-1]
-            if tip[2] is not None:
-                name = u"%s[%d]" % tip[1:3]
-            else:
-                name = tip[1]
-
-        self.result.append(u"  " * self._indents +
-                           u"%s: %s" % (name, node.__class__.__name__))
-        self._indents += 1
-        self.visitchildren(node)
-        self._indents -= 1
-
-
-def treetypes(root):
-    """Returns a string representing the tree by class names.
-    There's a leading and trailing whitespace so that it can be
-    compared by simple string comparison while still making test
-    cases look ok."""
-    w = NodeTypeWriter()
-    w.visit(root)
-    return u"\n".join([u""] + w.result + [u""])
-
-
-class CythonTest(unittest.TestCase):
-
-    def setUp(self):
-        self.listing_file = Errors.listing_file
-        self.echo_file = Errors.echo_file
-        Errors.listing_file = Errors.echo_file = None
-
-    def tearDown(self):
-        Errors.listing_file = self.listing_file
-        Errors.echo_file = self.echo_file
-
-    def assertLines(self, expected, result):
-        "Checks that the given strings or lists of strings are equal line by line"
-        if not isinstance(expected, list):
-            expected = expected.split(u"\n")
-        if not isinstance(result, list):
-            result = result.split(u"\n")
-        for idx, (expected_line, result_line) in enumerate(zip(expected, result)):
-            self.assertEqual(expected_line, result_line,
-                             "Line %d:\nExp: %s\nGot: %s" % (idx, expected_line, result_line))
-        self.assertEqual(len(expected), len(result),
-                         "Unmatched lines. Got:\n%s\nExpected:\n%s" % ("\n".join(expected), u"\n".join(result)))
-
-    def codeToLines(self, tree):
-        writer = CodeWriter()
-        writer.write(tree)
-        return writer.result.lines
-
-    def codeToString(self, tree):
-        return "\n".join(self.codeToLines(tree))
-
-    def assertCode(self, expected, result_tree):
-        result_lines = self.codeToLines(result_tree)
-
-        expected_lines = strip_common_indent(expected.split("\n"))
-
-        for idx, (line, expected_line) in enumerate(zip(result_lines, expected_lines)):
-            self.assertEqual(expected_line, line,
-                             "Line %d:\nGot: %s\nExp: %s" % (idx, line, expected_line))
-        self.assertEqual(len(result_lines), len(expected_lines),
-                         "Unmatched lines. Got:\n%s\nExpected:\n%s" % ("\n".join(result_lines), expected))
-
-    def assertNodeExists(self, path, result_tree):
-        self.assertNotEqual(TreePath.find_first(result_tree, path), None,
-                            "Path '%s' not found in result tree" % path)
-
-    def fragment(self, code, pxds=None, pipeline=None):
-        "Simply create a tree fragment using the name of the test-case in parse errors."
-        if pxds is None:
-            pxds = {}
-        if pipeline is None:
-            pipeline = []
-        name = self.id()
-        if name.startswith("__main__."):
-            name = name[len("__main__."):]
-        name = name.replace(".", "_")
-        return TreeFragment(code, name, pxds, pipeline=pipeline)
-
-    def treetypes(self, root):
-        return treetypes(root)
-
-    def should_fail(self, func, exc_type=Exception):
-        """Calls "func" and fails if it doesn't raise the right exception
-        (any exception by default). Also returns the exception in question.
-        """
-        try:
-            func()
-            self.fail("Expected an exception of type %r" % exc_type)
-        except exc_type as e:
-            self.assertTrue(isinstance(e, exc_type))
-            return e
-
-    def should_not_fail(self, func):
-        """Calls func and succeeds if and only if no exception is raised
-        (i.e. converts exception raising into a failed testcase). Returns
-        the return value of func."""
-        try:
-            return func()
-        except Exception as exc:
-            self.fail(str(exc))
-
-
-class TransformTest(CythonTest):
-    """
-    Utility base class for transform unit tests. It is based around constructing
-    test trees (either explicitly or by parsing a Cython code string); running
-    the transform, serialize it using a customized Cython serializer (with
-    special markup for nodes that cannot be represented in Cython),
-    and do a string-comparison line-by-line of the result.
-
-    To create a test case:
-     - Call run_pipeline. The pipeline should at least contain the transform you
-       are testing; pyx should be either a string (passed to the parser to
-       create a post-parse tree) or a node representing input to pipeline.
-       The result will be a transformed result.
-
-     - Check that the tree is correct. If wanted, assertCode can be used, which
-       takes a code string as expected, and a ModuleNode in result_tree
-       (it serializes the ModuleNode to a string and compares line-by-line).
-
-    All code strings are first stripped for whitespace lines and then common
-    indentation.
-
-    Plans: One could have a pxd dictionary parameter to run_pipeline.
-    """
-
-    def run_pipeline(self, pipeline, pyx, pxds=None):
-        if pxds is None:
-            pxds = {}
-        tree = self.fragment(pyx, pxds).root
-        # Run pipeline
-        for T in pipeline:
-            tree = T(tree)
-        return tree
-
-
-class TreeAssertVisitor(VisitorTransform):
-    # actually, a TreeVisitor would be enough, but this needs to run
-    # as part of the compiler pipeline
-
-    def visit_CompilerDirectivesNode(self, node):
-        directives = node.directives
-        if 'test_assert_path_exists' in directives:
-            for path in directives['test_assert_path_exists']:
-                if TreePath.find_first(node, path) is None:
-                    Errors.error(
-                        node.pos,
-                        "Expected path '%s' not found in result tree" % path)
-        if 'test_fail_if_path_exists' in directives:
-            for path in directives['test_fail_if_path_exists']:
-                if TreePath.find_first(node, path) is not None:
-                    Errors.error(
-                        node.pos,
-                        "Unexpected path '%s' found in result tree" %  path)
-        self.visitchildren(node)
-        return node
-
-    visit_Node = VisitorTransform.recurse_to_children
-
-
-def unpack_source_tree(tree_file, dir=None):
-    if dir is None:
-        dir = tempfile.mkdtemp()
-    header = []
-    cur_file = None
-    f = open(tree_file)
-    try:
-        lines = f.readlines()
-    finally:
-        f.close()
-    del f
-    try:
-        for line in lines:
-            if line[:5] == '#####':
-                filename = line.strip().strip('#').strip().replace('/', os.path.sep)
-                path = os.path.join(dir, filename)
-                if not os.path.exists(os.path.dirname(path)):
-                    os.makedirs(os.path.dirname(path))
-                if cur_file is not None:
-                    f, cur_file = cur_file, None
-                    f.close()
-                cur_file = open(path, 'w')
-            elif cur_file is not None:
-                cur_file.write(line)
-            elif line.strip() and not line.lstrip().startswith('#'):
-                if line.strip() not in ('"""', "'''"):
-                    header.append(line)
-    finally:
-        if cur_file is not None:
-            cur_file.close()
-    return dir, ''.join(header)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/gpt2_bpe_utils.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/gpt2_bpe_utils.py
deleted file mode 100644
index 996d3d4a1175fb3841242c286f08faf43fc06e3e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/gpt2_bpe_utils.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""
-Byte pair encoding utilities from GPT-2.
-
-Original source: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-Original license: MIT
-"""
-
-import json
-from functools import lru_cache
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class Encoder:
-    def __init__(self, encoder, bpe_merges, errors="replace"):
-        self.encoder = encoder
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-
-        try:
-            import regex as re
-
-            self.re = re
-        except ImportError:
-            raise ImportError("Please install regex with: pip install regex")
-
-        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = self.re.compile(
-            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
-        )
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        for token in self.re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder.get(token, token) for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode(
-            "utf-8", errors=self.errors
-        )
-        return text
-
-
-def get_encoder(encoder_json_path, vocab_bpe_path):
-    with open(encoder_json_path, "r") as f:
-        encoder = json.load(f)
-    with open(vocab_bpe_path, "r", encoding="utf-8") as f:
-        bpe_data = f.read()
-    bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
-    return Encoder(
-        encoder=encoder,
-        bpe_merges=bpe_merges,
-    )
diff --git a/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/README.md b/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/README.md
deleted file mode 100644
index 73469a48eb29bee88e2e6b23771083a74999b923..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ⚕️Clinical Terminology Biomed AI NER 🩺
-emoji: ⚕️NER👩‍⚕️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/awacke1/QandAGenerator/app.py b/spaces/awacke1/QandAGenerator/app.py
deleted file mode 100644
index 8ca1d745aa16a9a8949e817225e38ecd8961392b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/QandAGenerator/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import nltk
-from qanom.qanom_end_to_end_pipeline import QANomEndToEndPipeline
-from typing import List
-
-models = ["kleinay/qanom-seq2seq-model-baseline", 
-          "kleinay/qanom-seq2seq-model-joint"]
-          
-pipelines = {model: QANomEndToEndPipeline(model) for model in models}
-
-
-description = f"""The QANom Pipeline identifies deverbal nominalizations, parsed with question-answer driven semantic role labeling (QASRL) """ 
-title="Question Answer Deverbal Nominalization"
-
-examples = [
-[models[1], "Physics is the lowest form of rationalization followed by chemistry which orders memory of the universe by bonds and selection.  Everything else is but a recommendation. People can break the law, but nobody can break the laws of physics. ", 0.45],   
-[models[1], "To boil something down to most fundamental principles, things that we are most confident of are true at a foundational level. That sets your axiomatic base, and then you reason up from there. And then you cross check your conclusion against the axiomatic truth.  Some basics in physics would be like are violating conservation of energy or momentum or something like that, then it's not gonna work.", 0.65],
-[models[0], "So first for any kind of technology problem you have to sort of just make sure you're not violating physics. First principles analysis, I think, is something that can be applied to really any walk of life, anything really.", 0.58],
-[models[1], "What is the perfect arrangement of atoms that would be the best possible product? And now let us try to figure out how to get the atoms in that shape. I mean, it sounds, its almost like Rick and Morty absurd until you start to really think about it. And you really should think about it in this way cause everything else is kind of, if you think you might fall victim to the momentum of the way things are done in the past", 0.5]
-]
-            
-input_sent_box_label = "Insert sentence here, or select from the examples below"
-links = """<p style='text-align: center'>
-<a href='https://www.qasrl.org' target='_blank'>QASRL Website</a>  |  <a href='https://huggingface.co/kleinay/qanom-seq2seq-model-baseline' target='_blank'>Model Repo at Huggingface Hub</a>
-</p>"""
-
-
-def call(model_name, sentence, detection_threshold):
-
-    pipeline = pipelines[model_name]
-    pred_infos = pipeline([sentence], detection_threshold=detection_threshold)[0]
-    def pretty_qas(pred_info) -> List[str]:
-        if not pred_info or not pred_info['QAs']: return []
-        return [f"{qa['question']} --- {';'.join(qa['answers'])}"
-                for qa in pred_info['QAs'] if qa is not None]
-    all_qas = [qa for pred_info in pred_infos for qa in pretty_qas(pred_info)]                      
-    if not pred_infos:
-        pretty_qa_output = "NO NOMINALIZATION FOUND"
-    elif not all_qas:
-        pretty_qa_output = "NO QA GENERATED"
-    else:
-        pretty_qa_output = "\n".join(all_qas) 
-    # also present highlighted predicates
-    positives = [pred_info['predicate_idx'] for pred_info in pred_infos] 
-    def color(idx):
-        if idx in positives: return "lightgreen"
-    idx2verb = {d["predicate_idx"] : d["verb_form"] for d in pred_infos}
-    idx2prob = {d["predicate_idx"] : d["predicate_detector_probability"] for d in pred_infos}
-    def word_span(word, idx):
-        tooltip = f'title=" probability={idx2prob[idx]:.2}&#010;verb={idx2verb[idx]}"' if idx in idx2verb else ''
-        return f'<span {tooltip} style="background-color: {color(idx)}">{word}</span>'
-    html = '<span>' + ' '.join(word_span(word, idx) for idx, word in enumerate(sentence.split(" "))) + '</span>'
-    return html, pretty_qa_output , pred_infos
-
-iface = gr.Interface(fn=call, 
-                     inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), 
-                             gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
-                             gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Nominalization Detection Threshold")], 
-                     outputs=[gr.outputs.HTML(label="Detected Nominalizations"),
-                              gr.outputs.Textbox(label="Generated QAs"), 
-                              gr.outputs.JSON(label="Raw Model Output")],
-                     title=title,
-                     description=description,
-                     article=links,
-                     examples=examples)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/awacke1/VotingCrowdsourceEvaluationApps/README.md b/spaces/awacke1/VotingCrowdsourceEvaluationApps/README.md
deleted file mode 100644
index 5b115b38f944a2ed95dc2516f626763365f18b3b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/VotingCrowdsourceEvaluationApps/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VotingCrowdsourceEvaluationApps
-emoji: 🔥
-colorFrom: indigo
-colorTo: pink
-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/ayaanzaveri/whisper-webui/src/vad.py b/spaces/ayaanzaveri/whisper-webui/src/vad.py
deleted file mode 100644
index 42c811ce1c7cc9c82c4cf6a50174a1bd65fcf0c2..0000000000000000000000000000000000000000
--- a/spaces/ayaanzaveri/whisper-webui/src/vad.py
+++ /dev/null
@@ -1,553 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import Counter, deque
-import time
-
-from typing import Any, Deque, Iterator, List, Dict
-
-from pprint import pprint
-from src.hooks.whisperProgressHook import ProgressListener, SubTaskProgressListener, create_progress_listener_handle
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-
-from src.segments import merge_timestamps
-from src.whisperContainer import WhisperCallback
-
-# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
-try:
-    import tensorflow as tf
-except ModuleNotFoundError:
-    # Error handling
-    pass
-
-import torch
-
-import ffmpeg
-import numpy as np
-
-from src.utils import format_timestamp
-from enum import Enum
-
-class NonSpeechStrategy(Enum):
-    """
-    Ignore non-speech frames segments.
-    """
-    SKIP = 1
-    """
-    Just treat non-speech segments as speech.
-    """
-    CREATE_SEGMENT = 2
-    """
-    Expand speech segments into subsequent non-speech segments.
-    """
-    EXPAND_SEGMENT = 3
-
-# Defaults for Silero
-SPEECH_TRESHOLD = 0.3
-
-# Minimum size of segments to process
-MIN_SEGMENT_DURATION = 1
-
-# The maximum time for texts from old segments to be used in the next segment 
-MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
-PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this
-
-VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio
-
-class TranscriptionConfig(ABC):
-    def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
-        self.non_speech_strategy = non_speech_strategy
-        self.segment_padding_left = segment_padding_left
-        self.segment_padding_right = segment_padding_right
-        self.max_silent_period = max_silent_period
-        self.max_merge_size = max_merge_size
-        self.max_prompt_window = max_prompt_window
-        self.initial_segment_index = initial_segment_index
-
-class PeriodicTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
-        super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window, initial_segment_index)
-        self.periodic_duration = periodic_duration
-
-class AbstractTranscription(ABC):
-    def __init__(self, sampling_rate: int = 16000):
-        self.sampling_rate = sampling_rate
-
-    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
-        return load_audio(str, self.sampling_rate, start_time, duration)
-
-    def is_transcribe_timestamps_fast(self):
-        """
-        Determine if get_transcribe_timestamps is fast enough to not need parallelization.
-        """
-        return False
-
-    @abstractmethod
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
-        """
-        Get the start and end timestamps of the sections that should be transcribed by this VAD method.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-        config: TranscriptionConfig
-            The transcription configuration.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-        return 
-
-    def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: TranscriptionConfig, total_duration: float):
-        """
-        Get the start and end timestamps of the sections that should be transcribed by this VAD method,
-        after merging the given segments using the specified configuration.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file. 
-        config: TranscriptionConfig
-            The transcription configuration.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-        merged = merge_timestamps(timestamps, config.max_silent_period, config.max_merge_size, 
-                                  config.segment_padding_left, config.segment_padding_right)
-
-        if config.non_speech_strategy != NonSpeechStrategy.SKIP:
-            # Expand segments to include the gaps between them
-            if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
-                # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
-                merged = self.fill_gaps(merged, total_duration=total_duration, max_expand_size=config.max_merge_size)
-            elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT: 
-                # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
-                merged = self.expand_gaps(merged, total_duration=total_duration)
-            else:
-                raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))
-
-            print("Transcribing non-speech:")
-            pprint(merged)
-        return merged
-
-    def transcribe(self, audio: str, whisperCallable: WhisperCallback, config: TranscriptionConfig, 
-                   progressListener: ProgressListener = None):
-        """
-        Transcribe the given audo file.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-        whisperCallable: WhisperCallback
-            A callback object to call to transcribe each segment.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-
-        try:
-            max_audio_duration = self.get_audio_duration(audio, config)
-            timestamp_segments = self.get_transcribe_timestamps(audio, config, 0, max_audio_duration)
-
-            # Get speech timestamps from full audio file
-            merged = self.get_merged_timestamps(timestamp_segments, config, max_audio_duration)
-
-            # A deque of transcribed segments that is passed to the next segment as a prompt
-            prompt_window = deque()
-
-            print("Processing timestamps:")
-            pprint(merged)
-
-            result = {
-                'text': "",
-                'segments': [],
-                'language': ""
-            }
-            languageCounter = Counter()
-            detected_language = None
-
-            segment_index = config.initial_segment_index
-
-            # Calculate progress 
-            progress_start_offset = merged[0]['start'] if len(merged) > 0 else 0
-            progress_total_duration = sum([segment['end'] - segment['start'] for segment in merged])
-
-            # For each time segment, run whisper
-            for segment in merged:
-                segment_index += 1
-                segment_start = segment['start']
-                segment_end = segment['end']
-                segment_expand_amount = segment.get('expand_amount', 0)
-                segment_gap = segment.get('gap', False)
-
-                segment_duration = segment_end - segment_start
-
-                if segment_duration < MIN_SEGMENT_DURATION:
-                    continue
-
-                # Audio to run on Whisper
-                segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
-                # Previous segments to use as a prompt
-                segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
-        
-                # Detected language
-                detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None
-
-                print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", 
-                    segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
-                
-                scaled_progress_listener = SubTaskProgressListener(progressListener, base_task_total=progress_total_duration, 
-                                                                   sub_task_start=segment_start - progress_start_offset, sub_task_total=segment_duration) 
-                segment_result = whisperCallable.invoke(segment_audio, segment_index, segment_prompt, detected_language, progress_listener=scaled_progress_listener)
-
-                adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)
-
-                # Propagate expand amount to the segments
-                if (segment_expand_amount > 0):
-                    segment_without_expansion = segment_duration - segment_expand_amount
-
-                    for adjusted_segment in adjusted_segments:
-                        adjusted_segment_end = adjusted_segment['end']
-
-                        # Add expand amount if the segment got expanded
-                        if (adjusted_segment_end > segment_without_expansion):
-                            adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion
-
-                # Append to output
-                result['text'] += segment_result['text']
-                result['segments'].extend(adjusted_segments)
-
-                # Increment detected language
-                if not segment_gap:
-                    languageCounter[segment_result['language']] += 1
-
-                # Update prompt window
-                self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
-                
-            if detected_language is not None:
-                result['language'] = detected_language
-        finally:
-            # Notify progress listener that we are done
-            if progressListener is not None:
-                progressListener.on_finished()
-        return result
-    
-    def get_audio_duration(self, audio: str, config: TranscriptionConfig):
-        return get_audio_duration(audio)
-
-    def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
-        if (config.max_prompt_window is not None and config.max_prompt_window > 0):
-            # Add segments to the current prompt window (unless it is a speech gap)
-            if not segment_gap:
-                for segment in adjusted_segments:
-                    if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
-                        prompt_window.append(segment)
-
-            while (len(prompt_window) > 0):
-                first_end_time = prompt_window[0].get('end', 0)
-                # Time expanded in the segments should be discounted from the prompt window
-                first_expand_time = prompt_window[0].get('expand_amount', 0)
-
-                if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
-                    prompt_window.popleft()
-                else:
-                    break
-
-    def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
-        result = []
-        last_end_time = 0
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            if (last_end_time != segment_start):
-                delta = segment_start - last_end_time
-
-                if (min_gap_length is None or delta >= min_gap_length):
-                    result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
-            
-            last_end_time = segment_end
-            result.append(segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None and last_end_time < total_duration):
-            delta = total_duration - segment_start
-
-            if (min_gap_length is None or delta >= min_gap_length):
-                result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )
-
-        return result
-
-    # Expand the end time of each segment to the start of the next segment
-    def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            # Expand if the gap actually exists
-            if (delta >= 0):
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-            
-            result.append(current_segment)
-
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            if (last_segment['end'] < total_duration):
-                last_segment = last_segment.copy()
-                last_segment['end'] = total_duration
-                result[-1] = last_segment
-
-        return result
-
-    def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            expanded = False
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            if (max_expand_size is not None and delta <= max_expand_size):
-                # Just expand the current segment
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-                expanded = True
-
-            result.append(current_segment)
-
-            # Add a gap to the next segment if needed
-            if (delta >= 0 and not expanded):
-                result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
-            
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            delta = total_duration - last_segment['end']
-
-            if (delta > 0):
-                if (max_expand_size is not None and delta <= max_expand_size):
-                    # Expand the last segment
-                    last_segment = last_segment.copy()
-                    last_segment['expand_amount'] = delta
-                    last_segment['end'] = total_duration
-                    result[-1] = last_segment
-                else:
-                    result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )
-
-        return result
-
-    def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
-        result = []
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            # Filter segments?
-            if (max_source_time is not None):
-                if (segment_start > max_source_time):
-                    continue
-                segment_end = min(max_source_time, segment_end)
-
-                new_segment = segment.copy()
-
-            # Add to start and end
-            new_segment['start'] = segment_start + adjust_seconds
-            new_segment['end'] = segment_end + adjust_seconds
-            result.append(new_segment)
-        return result
-
-    def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
-        result = []
-
-        for entry in timestamps:
-            start = entry['start']
-            end = entry['end']
-
-            result.append({
-                'start': start * factor,
-                'end': end * factor
-            })
-        return result
-
-
-class VadSileroTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000, cache: ModelCache = None):
-        super().__init__(sampling_rate=sampling_rate)
-        self.model = None
-        self.cache = cache
-        self._initialize_model()
-
-    def _initialize_model(self):
-        if (self.cache is not None):
-            model_key = "VadSileroTranscription"
-            self.model, self.get_speech_timestamps = self.cache.get(model_key, self._create_model)
-            print("Loaded Silerio model from cache.")
-        else:
-            self.model, self.get_speech_timestamps = self._create_model()
-            print("Created Silerio model")
-
-    def _create_model(self):
-        model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
-        
-        # Silero does not benefit from multi-threading
-        torch.set_num_threads(1) # JIT
-        (get_speech_timestamps, _, _, _, _) = utils
-
-        return model, get_speech_timestamps
-
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
-        result = []
-
-        print("Getting timestamps from audio file: {}, start: {}, duration: {}".format(audio, start_time, end_time))
-        perf_start_time = time.perf_counter()
-
-        # Divide procesisng of audio into chunks
-        chunk_start = start_time
-
-        while (chunk_start < end_time):
-            chunk_duration = min(end_time - chunk_start, VAD_MAX_PROCESSING_CHUNK)
-
-            print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
-            wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))
-
-            sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
-            seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) 
-            adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)
-
-            #pprint(adjusted)
-
-            result.extend(adjusted)
-            chunk_start += chunk_duration
-
-        perf_end_time = time.perf_counter()
-        print("VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-
-        return result
-
-    def __getstate__(self):
-        # We only need the sampling rate
-        return { 'sampling_rate': self.sampling_rate }
-
-    def __setstate__(self, state):
-        self.sampling_rate = state['sampling_rate']
-        self.model = None
-        # Use the global cache
-        self.cache = GLOBAL_MODEL_CACHE
-        self._initialize_model()
-
-# A very simple VAD that just marks every N seconds as speech
-class VadPeriodicTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def is_transcribe_timestamps_fast(self):
-        # This is a very fast VAD - no need to parallelize it
-        return True
-
-    def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig, start_time: float, end_time: float):
-        result = []
-
-        # Generate a timestamp every N seconds
-        start_timestamp = start_time
-
-        while (start_timestamp < end_time):
-            end_timestamp = min(start_timestamp + config.periodic_duration, end_time)
-            segment_duration = end_timestamp - start_timestamp
-
-            # Minimum duration is 1 second
-            if (segment_duration >= 1):
-                result.append( {  'start': start_timestamp, 'end': end_timestamp } )
-
-            start_timestamp = end_timestamp
-
-        return result
-
-def get_audio_duration(file: str):
-    return float(ffmpeg.probe(file)["format"]["duration"])
-
-def load_audio(file: str, sample_rate: int = 16000, 
-               start_time: str = None, duration: str = None):
-    """
-    Open an audio file and read as mono waveform, resampling as necessary
-
-    Parameters
-    ----------
-    file: str
-        The audio file to open
-
-    sr: int
-        The sample rate to resample the audio if necessary
-
-    start_time: str
-        The start time, using the standard FFMPEG time duration syntax, or None to disable.
-    
-    duration: str
-        The duration, using the standard FFMPEG time duration syntax, or None to disable.
-
-    Returns
-    -------
-    A NumPy array containing the audio waveform, in float32 dtype.
-    """
-    try:
-        inputArgs = {'threads': 0}
-
-        if (start_time is not None):
-            inputArgs['ss'] = start_time
-        if (duration is not None):
-            inputArgs['t'] = duration
-
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, **inputArgs)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
-            .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")
-
-    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/InputNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/InputNode.js
deleted file mode 100644
index 5257c4d5bb993b720e208dafee405b92a372f2a3..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/core/InputNode.js
+++ /dev/null
@@ -1,93 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { TempNode } from './TempNode.js';
-
-function InputNode( type, params ) {
-
-	params = params || {};
-	params.shared = params.shared !== undefined ? params.shared : false;
-
-	TempNode.call( this, type, params );
-
-	this.readonly = false;
-
-}
-
-InputNode.prototype = Object.create( TempNode.prototype );
-InputNode.prototype.constructor = InputNode;
-
-InputNode.prototype.setReadonly = function ( value ) {
-
-	this.readonly = value;
-
-	return this;
-
-};
-
-InputNode.prototype.getReadonly = function ( builder ) {
-
-	return this.readonly;
-
-};
-
-InputNode.prototype.copy = function ( source ) {
-
-	TempNode.prototype.copy.call( this, source );
-
-	if ( source.readonly !== undefined ) this.readonly = source.readonly;
-
-};
-
-InputNode.prototype.createJSONNode = function ( meta ) {
-
-	var data = TempNode.prototype.createJSONNode.call( this, meta );
-
-	if ( this.readonly === true ) data.readonly = this.readonly;
-
-	return data;
-
-};
-
-InputNode.prototype.generate = function ( builder, output, uuid, type, ns, needsUpdate ) {
-
-	uuid = builder.getUuid( uuid || this.getUuid() );
-	type = type || this.getType( builder );
-
-	var data = builder.getNodeData( uuid ),
-		readonly = this.getReadonly( builder ) && this.generateReadonly !== undefined;
-
-	if ( readonly ) {
-
-		return this.generateReadonly( builder, output, uuid, type, ns, needsUpdate );
-
-	} else {
-
-		if ( builder.isShader( 'vertex' ) ) {
-
-			if ( ! data.vertex ) {
-
-				data.vertex = builder.createVertexUniform( type, this, ns, needsUpdate, this.getLabel() );
-
-			}
-
-			return builder.format( data.vertex.name, type, output );
-
-		} else {
-
-			if ( ! data.fragment ) {
-
-				data.fragment = builder.createFragmentUniform( type, this, ns, needsUpdate, this.getLabel() );
-
-			}
-
-			return builder.format( data.fragment.name, type, output );
-
-		}
-
-	}
-
-};
-
-export { InputNode };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/objects/Bone.js b/spaces/banana-projects/web3d/node_modules/three/src/objects/Bone.js
deleted file mode 100644
index bbe66db0cc4f2120564fd87000c2295f0491ae32..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/objects/Bone.js
+++ /dev/null
@@ -1,26 +0,0 @@
-import { Object3D } from '../core/Object3D.js';
-
-/**
- * @author mikael emtinger / http://gomo.se/
- * @author alteredq / http://alteredqualia.com/
- * @author ikerr / http://verold.com
- */
-
-function Bone() {
-
-	Object3D.call( this );
-
-	this.type = 'Bone';
-
-}
-
-Bone.prototype = Object.assign( Object.create( Object3D.prototype ), {
-
-	constructor: Bone,
-
-	isBone: true
-
-} );
-
-
-export { Bone };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lightmap_pars_fragment.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lightmap_pars_fragment.glsl.js
deleted file mode 100644
index f5fd898d22b81d5aa8bc1194371d42aa81acd9de..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lightmap_pars_fragment.glsl.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export default /* glsl */`
-#ifdef USE_LIGHTMAP
-
-	uniform sampler2D lightMap;
-	uniform float lightMapIntensity;
-
-#endif
-`;
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_pars_fragment.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_pars_fragment.glsl.js
deleted file mode 100644
index 0a9b16174a10d3ac08ea066708693d65bc0dd623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/shadowmap_pars_fragment.glsl.js
+++ /dev/null
@@ -1,245 +0,0 @@
-export default /* glsl */`
-#ifdef USE_SHADOWMAP
-
-	#if NUM_DIR_LIGHTS > 0
-
-		uniform sampler2D directionalShadowMap[ NUM_DIR_LIGHTS ];
-		varying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHTS ];
-
-	#endif
-
-	#if NUM_SPOT_LIGHTS > 0
-
-		uniform sampler2D spotShadowMap[ NUM_SPOT_LIGHTS ];
-		varying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHTS ];
-
-	#endif
-
-	#if NUM_POINT_LIGHTS > 0
-
-		uniform sampler2D pointShadowMap[ NUM_POINT_LIGHTS ];
-		varying vec4 vPointShadowCoord[ NUM_POINT_LIGHTS ];
-
-	#endif
-
-	/*
-	#if NUM_RECT_AREA_LIGHTS > 0
-
-		// TODO (abelnation): create uniforms for area light shadows
-
-	#endif
-	*/
-
-	float texture2DCompare( sampler2D depths, vec2 uv, float compare ) {
-
-		return step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );
-
-	}
-
-	float texture2DShadowLerp( sampler2D depths, vec2 size, vec2 uv, float compare ) {
-
-		const vec2 offset = vec2( 0.0, 1.0 );
-
-		vec2 texelSize = vec2( 1.0 ) / size;
-		vec2 centroidUV = floor( uv * size + 0.5 ) / size;
-
-		float lb = texture2DCompare( depths, centroidUV + texelSize * offset.xx, compare );
-		float lt = texture2DCompare( depths, centroidUV + texelSize * offset.xy, compare );
-		float rb = texture2DCompare( depths, centroidUV + texelSize * offset.yx, compare );
-		float rt = texture2DCompare( depths, centroidUV + texelSize * offset.yy, compare );
-
-		vec2 f = fract( uv * size + 0.5 );
-
-		float a = mix( lb, lt, f.y );
-		float b = mix( rb, rt, f.y );
-		float c = mix( a, b, f.x );
-
-		return c;
-
-	}
-
-	float getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {
-
-		float shadow = 1.0;
-
-		shadowCoord.xyz /= shadowCoord.w;
-		shadowCoord.z += shadowBias;
-
-		// if ( something && something ) breaks ATI OpenGL shader compiler
-		// if ( all( something, something ) ) using this instead
-
-		bvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );
-		bool inFrustum = all( inFrustumVec );
-
-		bvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );
-
-		bool frustumTest = all( frustumTestVec );
-
-		if ( frustumTest ) {
-
-		#if defined( SHADOWMAP_TYPE_PCF )
-
-			vec2 texelSize = vec2( 1.0 ) / shadowMapSize;
-
-			float dx0 = - texelSize.x * shadowRadius;
-			float dy0 = - texelSize.y * shadowRadius;
-			float dx1 = + texelSize.x * shadowRadius;
-			float dy1 = + texelSize.y * shadowRadius;
-
-			shadow = (
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +
-				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )
-			) * ( 1.0 / 9.0 );
-
-		#elif defined( SHADOWMAP_TYPE_PCF_SOFT )
-
-			vec2 texelSize = vec2( 1.0 ) / shadowMapSize;
-
-			float dx0 = - texelSize.x * shadowRadius;
-			float dy0 = - texelSize.y * shadowRadius;
-			float dx1 = + texelSize.x * shadowRadius;
-			float dy1 = + texelSize.y * shadowRadius;
-
-			shadow = (
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy, shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +
-				texture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )
-			) * ( 1.0 / 9.0 );
-
-		#else // no percentage-closer filtering:
-
-			shadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );
-
-		#endif
-
-		}
-
-		return shadow;
-
-	}
-
-	// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D
-	// vector suitable for 2D texture mapping. This code uses the following layout for the
-	// 2D texture:
-	//
-	// xzXZ
-	//  y Y
-	//
-	// Y - Positive y direction
-	// y - Negative y direction
-	// X - Positive x direction
-	// x - Negative x direction
-	// Z - Positive z direction
-	// z - Negative z direction
-	//
-	// Source and test bed:
-	// https://gist.github.com/tschw/da10c43c467ce8afd0c4
-
-	vec2 cubeToUV( vec3 v, float texelSizeY ) {
-
-		// Number of texels to avoid at the edge of each square
-
-		vec3 absV = abs( v );
-
-		// Intersect unit cube
-
-		float scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );
-		absV *= scaleToCube;
-
-		// Apply scale to avoid seams
-
-		// two texels less per square (one texel will do for NEAREST)
-		v *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );
-
-		// Unwrap
-
-		// space: -1 ... 1 range for each square
-		//
-		// #X##		dim    := ( 4 , 2 )
-		//  # #		center := ( 1 , 1 )
-
-		vec2 planar = v.xy;
-
-		float almostATexel = 1.5 * texelSizeY;
-		float almostOne = 1.0 - almostATexel;
-
-		if ( absV.z >= almostOne ) {
-
-			if ( v.z > 0.0 )
-				planar.x = 4.0 - v.x;
-
-		} else if ( absV.x >= almostOne ) {
-
-			float signX = sign( v.x );
-			planar.x = v.z * signX + 2.0 * signX;
-
-		} else if ( absV.y >= almostOne ) {
-
-			float signY = sign( v.y );
-			planar.x = v.x + 2.0 * signY + 2.0;
-			planar.y = v.z * signY - 2.0;
-
-		}
-
-		// Transform to UV space
-
-		// scale := 0.5 / dim
-		// translate := ( center + 0.5 ) / dim
-		return vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );
-
-	}
-
-	float getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {
-
-		vec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );
-
-		// for point lights, the uniform @vShadowCoord is re-purposed to hold
-		// the vector from the light to the world-space position of the fragment.
-		vec3 lightToPosition = shadowCoord.xyz;
-
-		// dp = normalized distance from light to fragment position
-		float dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?
-		dp += shadowBias;
-
-		// bd3D = base direction 3D
-		vec3 bd3D = normalize( lightToPosition );
-
-		#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT )
-
-			vec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;
-
-			return (
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +
-				texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )
-			) * ( 1.0 / 9.0 );
-
-		#else // no percentage-closer filtering
-
-			return texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );
-
-		#endif
-
-	}
-
-#endif
-`;
diff --git a/spaces/bensonsantos/CANnet_Crowd_Counting/README.md b/spaces/bensonsantos/CANnet_Crowd_Counting/README.md
deleted file mode 100644
index 65ce256f29235f899d4a6504a49c6a7eff14c85b..0000000000000000000000000000000000000000
--- a/spaces/bensonsantos/CANnet_Crowd_Counting/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CANnet_Crowd_Counting
-emoji: 🔥
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/bigcode/bigcode-editor/app.py b/spaces/bigcode/bigcode-editor/app.py
deleted file mode 100644
index e5fe79415767c529b19c97b2023a56ef8960cd8c..0000000000000000000000000000000000000000
--- a/spaces/bigcode/bigcode-editor/app.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import sys
-from typing import List
-import traceback
-import os
-import base64
-import json
-import pprint
-
-from huggingface_hub import Repository
-from text_generation import Client
-
-from requests.exceptions import ReadTimeout
-
-PORT = 7860
-
-# TODO: implement maximum length (currently, each iteration is limited by the slider-specified max length, but this can be iterated, or long code entered into the editor, to get really long documents
-# if os.path.exists('unlock'):
-#     # create an 'unlock' file (not checked into Git) locally to get full context lengths
-#     MAX_LENGTH = 8192
-# else:
-#     # set to a shorter value to prevent long contexts and make the demo more efficient
-#     MAX_LENGTH = 1024
-# TRUNCATION_MESSAGE = f'warning: This demo is limited to {MAX_LENGTH} tokens in the document for efficiency.'
-TRUNCATION_MESSAGE = f'TODO'
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-API_URL = os.environ.get("API_URL")
-
-with open("./HHH_prompt.txt", "r") as f:
-    HHH_PROMPT = f.read() + "\n\n"
-
-# used by the model
-FIM_PREFIX = "<fim_prefix>"
-FIM_MIDDLE = "<fim_middle>"
-FIM_SUFFIX = "<fim_suffix>"
-END_OF_TEXT = "<|endoftext|>"
-
-# used to mark infill locations in the editor
-FIM_INDICATOR = "<infill>"
-
-client = Client(
-    API_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-
-from fastapi import FastAPI, Request
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse, StreamingResponse
-app = FastAPI(docs_url=None, redoc_url=None)
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-@app.head("/")
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="static/index.html", media_type="text/html")
-
-def generate(prefix, suffix=None, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    # TODO: deduplicate code between this and `infill`
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    fim_mode = suffix is not None
-    
-    if suffix is not None:
-        prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
-    else:
-        prompt = prefix
-    output = client.generate(prompt, **generate_kwargs)
-    generated_text = output.generated_text
-    # TODO: set this based on stop reason from client.generate
-    truncated = False
-    while generated_text.endswith(END_OF_TEXT):
-        generated_text = generated_text[:-len(END_OF_TEXT)]
-    generation = {
-        'truncated': truncated,
-    }
-    if fim_mode:
-        generation['type'] = 'infill'
-        generation['text'] = prefix + generated_text + suffix
-        generation['parts'] = [prefix, suffix]
-        generation['infills'] = [generated_text]
-    else:
-        generation['type'] = 'generate'
-        generation['text'] = prompt + generated_text
-        generation['parts'] = [prompt]
-    return generation
-
-@app.get('/generate')
-async def generate_maybe(info: str):
-    # info is a base64-encoded, url-escaped json string (since GET doesn't support a body, and POST leads to CORS issues)
-    # fix padding, following https://stackoverflow.com/a/9956217/1319683
-    info = base64.urlsafe_b64decode(info + '=' * (4 - len(info) % 4)).decode('utf-8')
-    form = json.loads(info)
-    prompt = form['prompt']
-    length_limit = int(form['length'])
-    temperature = float(form['temperature'])
-    try:
-        generation = generate(prompt, temperature=temperature, max_new_tokens=length_limit, top_p=0.95, repetition_penalty=1.0)
-        if generation['truncated']:
-            message = TRUNCATION_MESSAGE 
-        else:
-            message = ''
-        return {'result': 'success', 'type': 'generate', 'prompt': prompt, 'text': generation['text'], 'message': message}
-    except ReadTimeout as e:
-        print(e)
-        return {'result': 'error', 'type': 'generate', 'prompt': prompt, 'message': f'Request timed out.'}
-    except Exception as e:
-        traceback.print_exception(*sys.exc_info())
-        return {'result': 'error', 'type': 'generate', 'prompt': prompt, 'message': f'Error: {e}.'}
-
-@app.get('/infill')
-async def infill_maybe(info: str):
-    # info is a base64-encoded, url-escaped json string (since GET doesn't support a body, and POST leads to CORS issues)
-    # fix padding, following https://stackoverflow.com/a/9956217/1319683
-    info = base64.urlsafe_b64decode(info + '=' * (4 - len(info) % 4)).decode('utf-8')
-    form = json.loads(info)
-    length_limit = int(form['length'])
-    temperature = float(form['temperature'])
-    try:
-        if len(form['parts']) > 2:
-            return {'result': 'error', 'text': ''.join(form['parts']), 'type': 'infill', 'message': f"error: Only a single <infill> token is supported!"}
-        elif len(form['parts']) == 1:
-            return {'result': 'error', 'text': ''.join(form['parts']), 'type': 'infill', 'message': f"error: Must have an <infill> token present!"}
-        prefix, suffix = form['parts']
-        generation = generate(prefix, suffix=suffix, temperature=temperature, max_new_tokens=length_limit, top_p=0.95, repetition_penalty=1.0)
-        generation['result'] = 'success'
-        if generation['truncated']:
-            generation['message'] = TRUNCATION_MESSAGE
-        else:
-            generation['message'] = ''
-        return generation
-    except ReadTimeout as e:
-        print(e)
-        return {'result': 'error', 'type': 'generate', 'prompt': prompt, 'message': f'Request timed out.'}
-    except Exception as e:
-        traceback.print_exception(*sys.exc_info())
-        return {'result': 'error', 'type': 'infill', 'message': f'Error: {e}.'}
-
-if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=PORT, threaded=False)
diff --git a/spaces/bioriAsaeru/text-to-voice/Bein sport 1 canl izle antalya galatasaray Antalyaspor Galatasaray Nisan 2016dan beri evinde yenemiyor!.md b/spaces/bioriAsaeru/text-to-voice/Bein sport 1 canl izle antalya galatasaray Antalyaspor Galatasaray Nisan 2016dan beri evinde yenemiyor!.md
deleted file mode 100644
index 8e1ee8bc7f897a3a4705cf3281d336b3e2facb1c..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Bein sport 1 canl izle antalya galatasaray Antalyaspor Galatasaray Nisan 2016dan beri evinde yenemiyor!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bein sport 1 canl izle antalya galatasaray</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://urloso.com/2uyOdx">https://urloso.com/2uyOdx</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/Makefile b/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/Makefile
deleted file mode 100644
index 67e4d4dedb0353540206d98305f76006806fcca4..0000000000000000000000000000000000000000
--- a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/Makefile
+++ /dev/null
@@ -1,54 +0,0 @@
-#
-# Makefile
-# Jiayuan Mao, 2019-01-09 13:59
-#
-
-SRC_DIR = csrc
-INC_DIR = csrc
-OBJ_DIR = build/obj
-TARGET = libpatchmatch.so
-
-LIB_TARGET = $(TARGET)
-INCLUDE_DIR = -I $(SRC_DIR) -I $(INC_DIR)
-
-CXX = $(ENVIRONMENT_OPTIONS) g++
-CXXFLAGS = -std=c++14
-CXXFLAGS += -Ofast -ffast-math -w
-# CXXFLAGS += -g
-CXXFLAGS += $(shell pkg-config --cflags opencv) -fPIC
-CXXFLAGS += $(INCLUDE_DIR)
-LDFLAGS = $(shell pkg-config --cflags --libs opencv) -shared -fPIC
-
-
-CXXSOURCES = $(shell find $(SRC_DIR)/ -name "*.cpp")
-OBJS = $(addprefix $(OBJ_DIR)/,$(CXXSOURCES:.cpp=.o))
-DEPFILES = $(OBJS:.o=.d)
-
-.PHONY: all clean rebuild test
-
-all: $(LIB_TARGET)
-
-$(OBJ_DIR)/%.o: %.cpp
-	@echo "[CC] $< ..."
-	@$(CXX) -c $< $(CXXFLAGS) -o $@
-
-$(OBJ_DIR)/%.d: %.cpp
-	@mkdir -pv $(dir $@)
-	@echo "[dep] $< ..."
-	@$(CXX) $(INCLUDE_DIR) $(CXXFLAGS) -MM -MT "$(OBJ_DIR)/$(<:.cpp=.o) $(OBJ_DIR)/$(<:.cpp=.d)" "$<" > "$@"
-
-sinclude $(DEPFILES)
-
-$(LIB_TARGET): $(OBJS)
-	@echo "[link] $(LIB_TARGET) ..."
-	@$(CXX) $(OBJS) -o $@ $(CXXFLAGS) $(LDFLAGS)
-
-clean:
-	rm -rf $(OBJ_DIR) $(LIB_TARGET)
-
-rebuild:
-	+@make clean
-	+@make
-
-# vim:ft=make
-#
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py b/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py
deleted file mode 100644
index 43057a8eeed38c78183e26d21b74261eb4dbc1b9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.retinanet import model
-from ..common.train import train
-
-dataloader.train.mapper.use_instance_mask = False
-model.backbone.bottom_up.freeze_at = 2
-optimizer.lr = 0.01
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/tracking/hungarian_tracker.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/tracking/hungarian_tracker.py
deleted file mode 100644
index 5b3ce884d80d9cdc2e0da07194693dd1bf16dd61..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/tracking/hungarian_tracker.py
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2004-present Facebook. All Rights Reserved.
-import copy
-import numpy as np
-from typing import Dict
-import torch
-from scipy.optimize import linear_sum_assignment
-
-from detectron2.config import configurable
-from detectron2.structures import Boxes, Instances
-
-from ..config.config import CfgNode as CfgNode_
-from .base_tracker import BaseTracker
-
-
-class BaseHungarianTracker(BaseTracker):
-    """
-    A base class for all Hungarian trackers
-    """
-
-    @configurable
-    def __init__(
-        self,
-        video_height: int,
-        video_width: int,
-        max_num_instances: int = 200,
-        max_lost_frame_count: int = 0,
-        min_box_rel_dim: float = 0.02,
-        min_instance_period: int = 1,
-        **kwargs
-    ):
-        """
-        Args:
-        video_height: height the video frame
-        video_width: width of the video frame
-        max_num_instances: maximum number of id allowed to be tracked
-        max_lost_frame_count: maximum number of frame an id can lost tracking
-                              exceed this number, an id is considered as lost
-                              forever
-        min_box_rel_dim: a percentage, smaller than this dimension, a bbox is
-                         removed from tracking
-        min_instance_period: an instance will be shown after this number of period
-                             since its first showing up in the video
-        """
-        super().__init__(**kwargs)
-        self._video_height = video_height
-        self._video_width = video_width
-        self._max_num_instances = max_num_instances
-        self._max_lost_frame_count = max_lost_frame_count
-        self._min_box_rel_dim = min_box_rel_dim
-        self._min_instance_period = min_instance_period
-
-    @classmethod
-    def from_config(cls, cfg: CfgNode_) -> Dict:
-        raise NotImplementedError("Calling HungarianTracker::from_config")
-
-    def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray:
-        raise NotImplementedError("Calling HungarianTracker::build_matrix")
-
-    def update(self, instances: Instances) -> Instances:
-        if instances.has("pred_keypoints"):
-            raise NotImplementedError("Need to add support for keypoints")
-        instances = self._initialize_extra_fields(instances)
-        if self._prev_instances is not None:
-            self._untracked_prev_idx = set(range(len(self._prev_instances)))
-            cost_matrix = self.build_cost_matrix(instances, self._prev_instances)
-            matched_idx, matched_prev_idx = linear_sum_assignment(cost_matrix)
-            instances = self._process_matched_idx(instances, matched_idx, matched_prev_idx)
-            instances = self._process_unmatched_idx(instances, matched_idx)
-            instances = self._process_unmatched_prev_idx(instances, matched_prev_idx)
-        self._prev_instances = copy.deepcopy(instances)
-        return instances
-
-    def _initialize_extra_fields(self, instances: Instances) -> Instances:
-        """
-        If input instances don't have ID, ID_period, lost_frame_count fields,
-        this method is used to initialize these fields.
-
-        Args:
-            instances: D2 Instances, for predictions of the current frame
-        Return:
-            D2 Instances with extra fields added
-        """
-        if not instances.has("ID"):
-            instances.set("ID", [None] * len(instances))
-        if not instances.has("ID_period"):
-            instances.set("ID_period", [None] * len(instances))
-        if not instances.has("lost_frame_count"):
-            instances.set("lost_frame_count", [None] * len(instances))
-        if self._prev_instances is None:
-            instances.ID = list(range(len(instances)))
-            self._id_count += len(instances)
-            instances.ID_period = [1] * len(instances)
-            instances.lost_frame_count = [0] * len(instances)
-        return instances
-
-    def _process_matched_idx(
-        self, instances: Instances, matched_idx: np.ndarray, matched_prev_idx: np.ndarray
-    ) -> Instances:
-        assert matched_idx.size == matched_prev_idx.size
-        for i in range(matched_idx.size):
-            instances.ID[matched_idx[i]] = self._prev_instances.ID[matched_prev_idx[i]]
-            instances.ID_period[matched_idx[i]] = (
-                self._prev_instances.ID_period[matched_prev_idx[i]] + 1
-            )
-            instances.lost_frame_count[matched_idx[i]] = 0
-        return instances
-
-    def _process_unmatched_idx(self, instances: Instances, matched_idx: np.ndarray) -> Instances:
-        untracked_idx = set(range(len(instances))).difference(set(matched_idx))
-        for idx in untracked_idx:
-            instances.ID[idx] = self._id_count
-            self._id_count += 1
-            instances.ID_period[idx] = 1
-            instances.lost_frame_count[idx] = 0
-        return instances
-
-    def _process_unmatched_prev_idx(
-        self, instances: Instances, matched_prev_idx: np.ndarray
-    ) -> Instances:
-        untracked_instances = Instances(
-            image_size=instances.image_size,
-            pred_boxes=[],
-            pred_masks=[],
-            pred_classes=[],
-            scores=[],
-            ID=[],
-            ID_period=[],
-            lost_frame_count=[],
-        )
-        prev_bboxes = list(self._prev_instances.pred_boxes)
-        prev_classes = list(self._prev_instances.pred_classes)
-        prev_scores = list(self._prev_instances.scores)
-        prev_ID_period = self._prev_instances.ID_period
-        if instances.has("pred_masks"):
-            prev_masks = list(self._prev_instances.pred_masks)
-        untracked_prev_idx = set(range(len(self._prev_instances))).difference(set(matched_prev_idx))
-        for idx in untracked_prev_idx:
-            x_left, y_top, x_right, y_bot = prev_bboxes[idx]
-            if (
-                (1.0 * (x_right - x_left) / self._video_width < self._min_box_rel_dim)
-                or (1.0 * (y_bot - y_top) / self._video_height < self._min_box_rel_dim)
-                or self._prev_instances.lost_frame_count[idx] >= self._max_lost_frame_count
-                or prev_ID_period[idx] <= self._min_instance_period
-            ):
-                continue
-            untracked_instances.pred_boxes.append(list(prev_bboxes[idx].numpy()))
-            untracked_instances.pred_classes.append(int(prev_classes[idx]))
-            untracked_instances.scores.append(float(prev_scores[idx]))
-            untracked_instances.ID.append(self._prev_instances.ID[idx])
-            untracked_instances.ID_period.append(self._prev_instances.ID_period[idx])
-            untracked_instances.lost_frame_count.append(
-                self._prev_instances.lost_frame_count[idx] + 1
-            )
-            if instances.has("pred_masks"):
-                untracked_instances.pred_masks.append(prev_masks[idx].numpy().astype(np.uint8))
-
-        untracked_instances.pred_boxes = Boxes(torch.FloatTensor(untracked_instances.pred_boxes))
-        untracked_instances.pred_classes = torch.IntTensor(untracked_instances.pred_classes)
-        untracked_instances.scores = torch.FloatTensor(untracked_instances.scores)
-        if instances.has("pred_masks"):
-            untracked_instances.pred_masks = torch.IntTensor(untracked_instances.pred_masks)
-        else:
-            untracked_instances.remove("pred_masks")
-
-        return Instances.cat(
-            [
-                instances,
-                untracked_instances,
-            ]
-        )
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/roi_heads.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/roi_heads.py
deleted file mode 100644
index 74ccc34a1193c604fcc34b8deed5ece53fee3f19..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/roi_heads.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-
-from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
-
-
-@ROI_HEADS_REGISTRY.register()
-class PointRendROIHeads(StandardROIHeads):
-    """
-    Identical to StandardROIHeads, except for some weights conversion code to
-    handle old models.
-    """
-
-    _version = 2
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        version = local_metadata.get("version", None)
-        if version is None or version < 2:
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                "Weight format of PointRend models have changed! "
-                "Please upgrade your models. Applying automatic conversion now ..."
-            )
-            for k in list(state_dict.keys()):
-                newk = k
-                if k.startswith(prefix + "mask_point_head"):
-                    newk = k.replace(prefix + "mask_point_head", prefix + "mask_head.point_head")
-                if k.startswith(prefix + "mask_coarse_head"):
-                    newk = k.replace(prefix + "mask_coarse_head", prefix + "mask_head.coarse_head")
-                if newk != k:
-                    state_dict[newk] = state_dict[k]
-                    del state_dict[k]
-
-    @classmethod
-    def _init_mask_head(cls, cfg, input_shape):
-        if cfg.MODEL.MASK_ON and cfg.MODEL.ROI_MASK_HEAD.NAME != "PointRendMaskHead":
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                "Config of PointRend models have changed! "
-                "Please upgrade your models. Applying automatic conversion now ..."
-            )
-            assert cfg.MODEL.ROI_MASK_HEAD.NAME == "CoarseMaskHead"
-            cfg.defrost()
-            cfg.MODEL.ROI_MASK_HEAD.NAME = "PointRendMaskHead"
-            cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE = ""
-            cfg.freeze()
-        return super()._init_mask_head(cfg, input_shape)
diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/tests/unit/test_meshes.py b/spaces/brjathu/HMR2.0/vendor/pyrender/tests/unit/test_meshes.py
deleted file mode 100644
index 7070b01171c97069fa013c6eba8eee217017f08e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/pyrender/tests/unit/test_meshes.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import pytest
-import trimesh
-
-from pyrender import (Mesh, Primitive)
-
-
-def test_meshes():
-
-    with pytest.raises(TypeError):
-        x = Mesh()
-    with pytest.raises(TypeError):
-        x = Primitive()
-    with pytest.raises(ValueError):
-        x = Primitive([], mode=10)
-
-    # Basics
-    x = Mesh([])
-    assert x.name is None
-    assert x.is_visible
-    assert x.weights is None
-
-    x.name = 'str'
-
-    # From Trimesh
-    x = Mesh.from_trimesh(trimesh.creation.box())
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    assert not x.is_transparent
-
-    # Test some primitive functions
-    x = x.primitives[0]
-    with pytest.raises(ValueError):
-        x.normals = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.tangents = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_0 = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_1 = np.zeros(10)
-    with pytest.raises(TypeError):
-        x.material = np.zeros(10)
-    assert x.targets is None
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    x.material.baseColorFactor = np.array([0.0, 0.0, 0.0, 0.0])
-    assert x.is_transparent
-
-    # From two trimeshes
-    x = Mesh.from_trimesh([trimesh.creation.box(),
-                          trimesh.creation.cylinder(radius=0.1, height=2.0)],
-                          smooth=False)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 2
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -1.0],
-        [0.5, 0.5, 1.0]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, [1.0, 1.0, 2.0])
-    assert np.allclose(x.scale, np.sqrt(6))
-    assert not x.is_transparent
-
-    # From bad data
-    with pytest.raises(TypeError):
-        x = Mesh.from_trimesh(None)
-
-    # With instancing
-    poses = np.tile(np.eye(4), (5,1,1))
-    poses[:,0,3] = np.array([0,1,2,3,4])
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [4.5, 0.5, 0.5]
-    ]))
-    poses = np.eye(4)
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    poses = np.eye(3)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-
-    # From textured meshes
-    fm = trimesh.load('tests/data/fuze.obj')
-    x = Mesh.from_trimesh(fm)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert not x.is_transparent
-    assert x.primitives[0].material.baseColorTexture is not None
-
-    x = Mesh.from_trimesh(fm, smooth=False)
-    fm.visual = fm.visual.to_color()
-    fm.visual.face_colors = np.array([1.0, 0.0, 0.0, 1.0])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(fm, smooth=True)
-
-    fm.visual.vertex_colors = np.array([1.0, 0.0, 0.0, 0.5])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    x = Mesh.from_trimesh(fm, smooth=True)
-    assert x.primitives[0].color_0 is not None
-    assert x.is_transparent
-
-    bm = trimesh.load('tests/data/WaterBottle.glb').dump()[0]
-    x = Mesh.from_trimesh(bm)
-    assert x.primitives[0].material.baseColorTexture is not None
-    assert x.primitives[0].material.emissiveTexture is not None
-    assert x.primitives[0].material.metallicRoughnessTexture is not None
-
-    # From point cloud
-    x = Mesh.from_points(fm.vertices)
-
-# def test_duck():
-#     bm = trimesh.load('tests/data/Duck.glb').dump()[0]
-#     x = Mesh.from_trimesh(bm)
-#     assert x.primitives[0].material.baseColorTexture is not None
-#     pixel = x.primitives[0].material.baseColorTexture.source[100, 100]
-#     yellowish = np.array([1.0, 0.7411765, 0.0, 1.0])
-#     assert np.allclose(pixel, yellowish)
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/WmfImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/WmfImagePlugin.py
deleted file mode 100644
index 0ecab56a824fd3917067fd4b05c530f4abce75a3..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/WmfImagePlugin.py
+++ /dev/null
@@ -1,178 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# WMF stub codec
-#
-# history:
-# 1996-12-14 fl   Created
-# 2004-02-22 fl   Turned into a stub driver
-# 2004-02-23 fl   Added EMF support
-#
-# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-# WMF/EMF reference documentation:
-# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf
-# http://wvware.sourceforge.net/caolan/index.html
-# http://wvware.sourceforge.net/caolan/ora-wmf.html
-
-from . import Image, ImageFile
-from ._binary import i16le as word
-from ._binary import si16le as short
-from ._binary import si32le as _long
-
-_handler = None
-
-
-def register_handler(handler):
-    """
-    Install application-specific WMF image handler.
-
-    :param handler: Handler object.
-    """
-    global _handler
-    _handler = handler
-
-
-if hasattr(Image.core, "drawwmf"):
-    # install default handler (windows only)
-
-    class WmfHandler:
-        def open(self, im):
-            im.mode = "RGB"
-            self.bbox = im.info["wmf_bbox"]
-
-        def load(self, im):
-            im.fp.seek(0)  # rewind
-            return Image.frombytes(
-                "RGB",
-                im.size,
-                Image.core.drawwmf(im.fp.read(), im.size, self.bbox),
-                "raw",
-                "BGR",
-                (im.size[0] * 3 + 3) & -4,
-                -1,
-            )
-
-    register_handler(WmfHandler())
-
-#
-# --------------------------------------------------------------------
-# Read WMF file
-
-
-def _accept(prefix):
-    return (
-        prefix[:6] == b"\xd7\xcd\xc6\x9a\x00\x00" or prefix[:4] == b"\x01\x00\x00\x00"
-    )
-
-
-##
-# Image plugin for Windows metafiles.
-
-
-class WmfStubImageFile(ImageFile.StubImageFile):
-    format = "WMF"
-    format_description = "Windows Metafile"
-
-    def _open(self):
-        self._inch = None
-
-        # check placable header
-        s = self.fp.read(80)
-
-        if s[:6] == b"\xd7\xcd\xc6\x9a\x00\x00":
-            # placeable windows metafile
-
-            # get units per inch
-            self._inch = word(s, 14)
-
-            # get bounding box
-            x0 = short(s, 6)
-            y0 = short(s, 8)
-            x1 = short(s, 10)
-            y1 = short(s, 12)
-
-            # normalize size to 72 dots per inch
-            self.info["dpi"] = 72
-            size = (
-                (x1 - x0) * self.info["dpi"] // self._inch,
-                (y1 - y0) * self.info["dpi"] // self._inch,
-            )
-
-            self.info["wmf_bbox"] = x0, y0, x1, y1
-
-            # sanity check (standard metafile header)
-            if s[22:26] != b"\x01\x00\t\x00":
-                msg = "Unsupported WMF file format"
-                raise SyntaxError(msg)
-
-        elif s[:4] == b"\x01\x00\x00\x00" and s[40:44] == b" EMF":
-            # enhanced metafile
-
-            # get bounding box
-            x0 = _long(s, 8)
-            y0 = _long(s, 12)
-            x1 = _long(s, 16)
-            y1 = _long(s, 20)
-
-            # get frame (in 0.01 millimeter units)
-            frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36)
-
-            size = x1 - x0, y1 - y0
-
-            # calculate dots per inch from bbox and frame
-            xdpi = 2540.0 * (x1 - y0) / (frame[2] - frame[0])
-            ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1])
-
-            self.info["wmf_bbox"] = x0, y0, x1, y1
-
-            if xdpi == ydpi:
-                self.info["dpi"] = xdpi
-            else:
-                self.info["dpi"] = xdpi, ydpi
-
-        else:
-            msg = "Unsupported file format"
-            raise SyntaxError(msg)
-
-        self.mode = "RGB"
-        self._size = size
-
-        loader = self._load()
-        if loader:
-            loader.open(self)
-
-    def _load(self):
-        return _handler
-
-    def load(self, dpi=None):
-        if dpi is not None and self._inch is not None:
-            self.info["dpi"] = dpi
-            x0, y0, x1, y1 = self.info["wmf_bbox"]
-            self._size = (
-                (x1 - x0) * self.info["dpi"] // self._inch,
-                (y1 - y0) * self.info["dpi"] // self._inch,
-            )
-        return super().load()
-
-
-def _save(im, fp, filename):
-    if _handler is None or not hasattr(_handler, "save"):
-        msg = "WMF save handler not installed"
-        raise OSError(msg)
-    _handler.save(im, fp, filename)
-
-
-#
-# --------------------------------------------------------------------
-# Registry stuff
-
-
-Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept)
-Image.register_save(WmfStubImageFile.format, _save)
-
-Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"])
diff --git a/spaces/candlend/vits-hoshimi/sovits/vdecoder/hifigan/models.py b/spaces/candlend/vits-hoshimi/sovits/vdecoder/hifigan/models.py
deleted file mode 100644
index bdc3fa2c3447f360472d94c2fad9bd74993f6410..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/sovits/vdecoder/hifigan/models.py
+++ /dev/null
@@ -1,500 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
-    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    generator = Generator(h).to(device)
-
-    cp_dict = torch.load(model_path)
-    generator.load_state_dict(cp_dict['generator'])
-    generator.eval()
-    generator.remove_weight_norm()
-    del cp_dict
-    return generator, h
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class 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
-        self.flag_for_pulse = flag_for_pulse
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = (f0 > self.voiced_threshold).type(torch.float32)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The interger part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-            # for normal case
-
-            # To prevent torch.cumsum numerical overflow,
-            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-            # Buffer tmp_over_one_idx indicates the time step to add -1.
-            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-            tmp_over_one = torch.cumsum(rad_values, 1) % 1
-            tmp_over_one_idx = (torch.diff(tmp_over_one, dim=1)) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
-                              * 2 * np.pi)
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ 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_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
-                                 device=f0.device)
-            # fundamental component
-            fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
-
-            # generate sine waveforms
-            sine_waves = self._f02sine(fn) * self.sine_amp
-
-            # generate uv signal
-            # uv = torch.ones(f0.shape)
-            # uv = uv * (f0 > self.voiced_threshold)
-            uv = self._f02uv(f0)
-
-            # noise: for unvoiced should be similar to sine_amp
-            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-            # .       for voiced regions is self.noise_std
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-
-            # first: set the unvoiced part to 0 by uv
-            # then: additive noise
-            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):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # 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):
-        """
-        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-        F0_sampled (batchsize, length, 1)
-        Sine_source (batchsize, length, 1)
-        noise_source (batchsize, length 1)
-        """
-        # source for harmonic branch
-        sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.sine_amp / 3
-        return sine_merge, noise, uv
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-
-        self.num_kernels = len(h["resblock_kernel_sizes"])
-        self.num_upsamples = len(h["upsample_rates"])
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=h["sampling_rate"],
-            harmonic_num=8)
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
-        resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
-            c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-            if i + 1 < len(h["upsample_rates"]):  #
-                stride_f0 = np.prod(h["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 = h["upsample_initial_channel"] // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
-
-    def forward(self, x, f0, g=None):
-        # print(1,x.shape,f0.shape,f0[:, None].shape)
-        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
-        # print(2,f0.shape)
-        har_source, noi_source, uv = self.m_source(f0)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        x = x + self.cond(g)
-        # print(124,x.shape,har_source.shape)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            # print(3,x.shape)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            # print(4,x_source.shape,har_source.shape,x.shape)
-            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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, periods=None):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
-        self.discriminators = nn.ModuleList()
-        for period in self.periods:
-            self.discriminators.append(DiscriminatorP(period))
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/retinanet.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/retinanet.py
deleted file mode 100644
index 784e5317f594db966dac02792e9c9db1774623d6..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/retinanet.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.meta_arch import RetinaNet
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone.fpn import LastLevelP6P7
-from detectron2.modeling.backbone import BasicStem, FPN, ResNet
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.meta_arch.retinanet import RetinaNetHead
-
-from ..data.constants import constants
-
-model = L(RetinaNet)(
-    backbone=L(FPN)(
-        bottom_up=L(ResNet)(
-            stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
-            stages=L(ResNet.make_default_stages)(
-                depth=50,
-                stride_in_1x1=True,
-                norm="FrozenBN",
-            ),
-            out_features=["res3", "res4", "res5"],
-        ),
-        in_features=["res3", "res4", "res5"],
-        out_channels=256,
-        top_block=L(LastLevelP6P7)(in_channels=2048, out_channels="${..out_channels}"),
-    ),
-    head=L(RetinaNetHead)(
-        # Shape for each input feature map
-        input_shape=[ShapeSpec(channels=256)] * 5,
-        num_classes="${..num_classes}",
-        conv_dims=[256, 256, 256, 256],
-        prior_prob=0.01,
-        num_anchors=9,
-    ),
-    anchor_generator=L(DefaultAnchorGenerator)(
-        sizes=[[x, x * 2 ** (1.0 / 3), x * 2 ** (2.0 / 3)] for x in [32, 64, 128, 256, 512]],
-        aspect_ratios=[0.5, 1.0, 2.0],
-        strides=[8, 16, 32, 64, 128],
-        offset=0.0,
-    ),
-    box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
-    anchor_matcher=L(Matcher)(
-        thresholds=[0.4, 0.5], labels=[0, -1, 1], allow_low_quality_matches=True
-    ),
-    num_classes=80,
-    head_in_features=["p3", "p4", "p5", "p6", "p7"],
-    focal_loss_alpha=0.25,
-    focal_loss_gamma=2.0,
-    pixel_mean=constants.imagenet_bgr256_mean,
-    pixel_std=constants.imagenet_bgr256_std,
-    input_format="BGR",
-)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/densepose_data_points.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/densepose_data_points.py
deleted file mode 100644
index b6839a984a4df5691f31314efd4a815d75121b3b..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/densepose_data_points.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Iterable, Optional, Tuple
-import cv2
-
-from densepose.structures import DensePoseDataRelative
-
-from .base import Boxes, Image, MatrixVisualizer, PointsVisualizer
-
-
-class DensePoseDataCoarseSegmentationVisualizer(object):
-    """
-    Visualizer for ground truth segmentation
-    """
-
-    def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs):
-        self.mask_visualizer = MatrixVisualizer(
-            inplace=inplace,
-            cmap=cmap,
-            val_scale=255.0 / DensePoseDataRelative.N_BODY_PARTS,
-            alpha=alpha,
-        )
-
-    def visualize(
-        self,
-        image_bgr: Image,
-        bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]],
-    ) -> Image:
-        if bbox_densepose_datas is None:
-            return image_bgr
-        for bbox_xywh, densepose_data in zip(*bbox_densepose_datas):
-            matrix = densepose_data.segm.numpy()
-            mask = np.zeros(matrix.shape, dtype=np.uint8)
-            mask[matrix > 0] = 1
-            image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh.numpy())
-        return image_bgr
-
-
-class DensePoseDataPointsVisualizer(object):
-    def __init__(self, densepose_data_to_value_fn=None, cmap=cv2.COLORMAP_PARULA, **kwargs):
-        self.points_visualizer = PointsVisualizer()
-        self.densepose_data_to_value_fn = densepose_data_to_value_fn
-        self.cmap = cmap
-
-    def visualize(
-        self,
-        image_bgr: Image,
-        bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]],
-    ) -> Image:
-        if bbox_densepose_datas is None:
-            return image_bgr
-        for bbox_xywh, densepose_data in zip(*bbox_densepose_datas):
-            x0, y0, w, h = bbox_xywh.numpy()
-            x = densepose_data.x.numpy() * w / 255.0 + x0
-            y = densepose_data.y.numpy() * h / 255.0 + y0
-            pts_xy = zip(x, y)
-            if self.densepose_data_to_value_fn is None:
-                image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy)
-            else:
-                v = self.densepose_data_to_value_fn(densepose_data)
-                img_colors_bgr = cv2.applyColorMap(v, self.cmap)
-                colors_bgr = [
-                    [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr
-                ]
-                image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy, colors_bgr)
-        return image_bgr
-
-
-def _densepose_data_u_for_cmap(densepose_data):
-    u = np.clip(densepose_data.u.numpy(), 0, 1) * 255.0
-    return u.astype(np.uint8)
-
-
-def _densepose_data_v_for_cmap(densepose_data):
-    v = np.clip(densepose_data.v.numpy(), 0, 1) * 255.0
-    return v.astype(np.uint8)
-
-
-def _densepose_data_i_for_cmap(densepose_data):
-    i = (
-        np.clip(densepose_data.i.numpy(), 0.0, DensePoseDataRelative.N_PART_LABELS)
-        * 255.0
-        / DensePoseDataRelative.N_PART_LABELS
-    )
-    return i.astype(np.uint8)
-
-
-class DensePoseDataPointsUVisualizer(DensePoseDataPointsVisualizer):
-    def __init__(self, **kwargs):
-        super(DensePoseDataPointsUVisualizer, self).__init__(
-            densepose_data_to_value_fn=_densepose_data_u_for_cmap, **kwargs
-        )
-
-
-class DensePoseDataPointsVVisualizer(DensePoseDataPointsVisualizer):
-    def __init__(self, **kwargs):
-        super(DensePoseDataPointsVVisualizer, self).__init__(
-            densepose_data_to_value_fn=_densepose_data_v_for_cmap, **kwargs
-        )
-
-
-class DensePoseDataPointsIVisualizer(DensePoseDataPointsVisualizer):
-    def __init__(self, **kwargs):
-        super(DensePoseDataPointsIVisualizer, self).__init__(
-            densepose_data_to_value_fn=_densepose_data_i_for_cmap, **kwargs
-        )
diff --git a/spaces/chats-bug/ai-image-captioning/model.py b/spaces/chats-bug/ai-image-captioning/model.py
deleted file mode 100644
index de223f021d5763f463961b8a1dccdeacdb64723a..0000000000000000000000000000000000000000
--- a/spaces/chats-bug/ai-image-captioning/model.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from transformers import AutoProcessor, AutoModelForCausalLM, BlipForConditionalGeneration
-
-class ImageCaptionModel:
-	def __init__(
-		self,
-		device,
-		processor,
-		model,
-	) -> None:
-		"""
-		Initializes the model for generating captions for images.
-
-		-----
-		Parameters:
-		device: str
-			The device to use for the model. Must be either "cpu" or "cuda".
-		processor: transformers.AutoProcessor
-			The preprocessor to use for the model.
-		model: transformers.AutoModelForCausalLM or transformers.BlipForConditionalGeneration
-			The model to use for generating captions.
-
-		-----
-		Returns:
-		None
-		"""
-		self.device = device
-		self.processor = processor
-		self.model = model
-		self.model.to(self.device)
-
-	def generate(
-		self,
-		image,
-		num_captions: int = 1,
-		max_length: int = 50,
-		temperature: float = 1.0,
-		top_k: int = 50,
-		top_p: float = 1.0,
-		repetition_penalty: float = 1.0,
-		diversity_penalty: float = 0.0,
-	):
-		"""
-		Generates captions for the given image.
-
-		-----
-		Parameters:
-		preprocessor: transformers.PreTrainedTokenizerFast
-			The preprocessor to use for the model.
-		model: transformers.PreTrainedModel	
-			The model to use for generating captions.
-		image: PIL.Image
-			The image to generate captions for.
-		num_captions: int
-			The number of captions to generate.
-		temperature: float
-			The temperature to use for sampling. The value used to module the next token probabilities that will be used by default in the generate method of the model. Must be strictly positive. Defaults to 1.0.
-		top_k: int
-			The number of highest probability vocabulary tokens to keep for top-k-filtering. A large value of top_k will keep more probabilities for each token leading to a better but slower generation. Defaults to 50.
-		top_p: float
-			The value that will be used by default in the generate method of the model for top_p. If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
-		repetition_penalty: float
-			The parameter for repetition penalty. 1.0 means no penalty. Defaults to 1.0.
-		diversity_penalty: float
-			The parameter for diversity penalty. 0.0 means no penalty. Defaults to 0.0.
-
-		"""
-		# Type checking and making sure the values are valid.
-		assert type(num_captions) == int and num_captions > 0, "num_captions must be a positive integer."
-		assert type(max_length) == int and max_length > 0, "max_length must be a positive integer."
-		assert type(temperature) == float and temperature > 0.0, "temperature must be a positive float."
-		assert type(top_k) == int and top_k > 0, "top_k must be a positive integer."
-		assert type(top_p) == float and top_p > 0.0, "top_p must be a positive float."
-		assert type(repetition_penalty) == float and repetition_penalty >= 1.0, "repetition_penalty must be a positive float greater than or equal to 1."
-		assert type(diversity_penalty) == float and diversity_penalty >= 0.0, "diversity_penalty must be a non negative float."
-
-		pixel_values = self.processor(images=image, return_tensors="pt").pixel_values.to(self.device) # Convert the image to pixel values.
-
-		# Generate captions ids.
-		if num_captions == 1:
-			generated_ids = self.model.generate(
-				pixel_values=pixel_values,
-				max_length=max_length,
-				num_return_sequences=1,
-				temperature=temperature,
-				top_k=top_k,
-				top_p=top_p,
-			)
-		else:
-			generated_ids = self.model.generate(
-				pixel_values=pixel_values,
-				max_length=max_length,
-				num_beams=num_captions, # num_beams must be greater than or equal to num_captions and must be divisible by num_beam_groups.
-				num_beam_groups=num_captions, # num_beam_groups is set to equal to num_captions so that all the captions are diverse
-				num_return_sequences=num_captions, # generate multiple captions which are very similar to each other due to the grouping effect of beam search.
-				temperature=temperature,
-				top_k=top_k,
-				top_p=top_p,
-				repetition_penalty=repetition_penalty,
-				diversity_penalty=diversity_penalty,
-			)
-			
-		# Decode the generated ids to get the captions.
-		generated_caption = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
-
-		return generated_caption
-
-
-class GitBaseCocoModel(ImageCaptionModel):
-	def __init__(self, device):
-		"""
-		A wrapper class for the Git-Base-COCO model. It is a pretrained model for image captioning.
-
-		-----
-		Parameters:
-		device: str
-			The device to run the model on, either "cpu" or "cuda".
-		checkpoint: str
-			The checkpoint to load the model from.
-
-		-----
-		Returns:
-		None
-		"""
-		checkpoint = "microsoft/git-base-coco"
-		processor = AutoProcessor.from_pretrained(checkpoint)
-		model = AutoModelForCausalLM.from_pretrained(checkpoint)
-		super().__init__(device, processor, model)
-
-
-class BlipBaseModel(ImageCaptionModel):
-	def __init__(self, device):
-		"""
-		A wrapper class for the Blip-Base model. It is a pretrained model for image captioning.
-
-		-----
-		Parameters:
-		device: str
-			The device to run the model on, either "cpu" or "cuda".
-		checkpoint: str
-			The checkpoint to load the model from.
-
-		-----
-		Returns:
-		None
-		"""
-		self.checkpoint = "Salesforce/blip-image-captioning-base"
-		processor = AutoProcessor.from_pretrained(self.checkpoint)
-		model = BlipForConditionalGeneration.from_pretrained(self.checkpoint)
-		super().__init__(device, processor, model)
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/docker/transformers-tensorflow-gpu/Dockerfile b/spaces/chendl/compositional_test/transformers/docker/transformers-tensorflow-gpu/Dockerfile
deleted file mode 100644
index 09e8512f2ce83133e948385a457357c01d373b29..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/docker/transformers-tensorflow-gpu/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
-LABEL maintainer="Hugging Face"
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-RUN apt update
-RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
-RUN python3 -m pip install --no-cache-dir --upgrade pip
-
-ARG REF=main
-RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing]
-
-# If set to nothing, will install the latest version
-ARG TENSORFLOW='2.11'
-
-RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' ||  VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION
-RUN python3 -m pip uninstall -y torch flax
-RUN python3 -m pip install -U "itsdangerous<2.1.0"
-
-RUN python3 -m pip install --no-cache-dir -U tensorflow_probability
-
-# When installing in editable mode, `transformers` is not recognized as a package.
-# this line must be added in order for python to be aware of transformers.
-RUN cd transformers && python3 setup.py develop
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/train_deebert.sh b/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/train_deebert.sh
deleted file mode 100644
index 32cdf5730f204e63ba9acf22c9d71656b701741a..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/train_deebert.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0
-
-PATH_TO_DATA=/h/xinji/projects/GLUE
-
-MODEL_TYPE=bert  # bert or roberta
-MODEL_SIZE=base  # base or large
-DATASET=MRPC  # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
-
-MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
-EPOCHS=10
-if [ $MODEL_TYPE = 'bert' ]
-then
-  EPOCHS=3
-  MODEL_NAME=${MODEL_NAME}-uncased
-fi
-
-
-python -u run_glue_deebert.py \
-  --model_type $MODEL_TYPE \
-  --model_name_or_path $MODEL_NAME \
-  --task_name $DATASET \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $PATH_TO_DATA/$DATASET \
-  --max_seq_length 128 \
-  --per_gpu_eval_batch_size=1 \
-  --per_gpu_train_batch_size=8 \
-  --learning_rate 2e-5 \
-  --num_train_epochs $EPOCHS \
-  --overwrite_output_dir \
-  --seed 42 \
-  --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
-  --plot_data_dir ./results/ \
-  --save_steps 0 \
-  --overwrite_cache \
-  --eval_after_first_stage
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/modeling_flax_pytorch_utils.py b/spaces/chendl/compositional_test/transformers/src/transformers/modeling_flax_pytorch_utils.py
deleted file mode 100644
index 5de6a985df4116dc9037a59f5622b383a633ee44..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/modeling_flax_pytorch_utils.py
+++ /dev/null
@@ -1,421 +0,0 @@
-# coding=utf-8
-# Copyright 2021 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.
-""" PyTorch - Flax general utilities."""
-
-
-import os
-from pickle import UnpicklingError
-from typing import Dict, Tuple
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.serialization import from_bytes
-from flax.traverse_util import flatten_dict, unflatten_dict
-
-import transformers
-
-from .utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-#####################
-# PyTorch => Flax #
-#####################
-
-
-def load_pytorch_checkpoint_in_flax_state_dict(
-    flax_model, pytorch_checkpoint_path, is_sharded, allow_missing_keys=False
-):
-    """Load pytorch checkpoints in a flax model"""
-    try:
-        import torch  # noqa: F401
-    except ImportError:
-        logger.error(
-            "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
-            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
-            " instructions."
-        )
-        raise
-
-    if not is_sharded:
-        pt_path = os.path.abspath(pytorch_checkpoint_path)
-        logger.info(f"Loading PyTorch weights from {pt_path}")
-
-        pt_state_dict = torch.load(pt_path, map_location="cpu")
-        logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")
-
-        flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
-    else:
-        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
-        flax_state_dict = convert_pytorch_sharded_state_dict_to_flax(pytorch_checkpoint_path, flax_model)
-    return flax_state_dict
-
-
-def rename_key_and_reshape_tensor(
-    pt_tuple_key: Tuple[str],
-    pt_tensor: np.ndarray,
-    random_flax_state_dict: Dict[str, jnp.ndarray],
-    model_prefix: str,
-) -> (Tuple[str], np.ndarray):
-    """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
-
-    def is_key_or_prefix_key_in_dict(key: Tuple[str]) -> bool:
-        """Checks if `key` of `(prefix,) + key` is in random_flax_state_dict"""
-        return len(set(random_flax_state_dict) & {key, (model_prefix,) + key}) > 0
-
-    # layer norm
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
-    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
-        return renamed_pt_tuple_key, pt_tensor
-
-    # batch norm layer mean
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("mean",)
-    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
-        return renamed_pt_tuple_key, pt_tensor
-
-    # batch norm layer var
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("var",)
-    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
-        return renamed_pt_tuple_key, pt_tensor
-
-    # embedding
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
-    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
-        return renamed_pt_tuple_key, pt_tensor
-
-    # conv layer
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
-    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(pt_tuple_key):
-        pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
-        return renamed_pt_tuple_key, pt_tensor
-
-    # linear layer
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
-    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
-        pt_tensor = pt_tensor.T
-        return renamed_pt_tuple_key, pt_tensor
-
-    # old PyTorch layer norm weight
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
-    if pt_tuple_key[-1] == "gamma":
-        return renamed_pt_tuple_key, pt_tensor
-
-    # old PyTorch layer norm bias
-    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
-    if pt_tuple_key[-1] == "beta":
-        return renamed_pt_tuple_key, pt_tensor
-
-    return pt_tuple_key, pt_tensor
-
-
-def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
-    # convert pytorch tensor to numpy
-    pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
-
-    model_prefix = flax_model.base_model_prefix
-
-    # use params dict if the model contains batch norm layers
-    if "params" in flax_model.params:
-        flax_model_params = flax_model.params["params"]
-    else:
-        flax_model_params = flax_model.params
-    random_flax_state_dict = flatten_dict(flax_model_params)
-
-    # add batch_stats keys,values to dict
-    if "batch_stats" in flax_model.params:
-        flax_batch_stats = flatten_dict(flax_model.params["batch_stats"])
-        random_flax_state_dict.update(flax_batch_stats)
-
-    flax_state_dict = {}
-
-    load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
-        model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
-    )
-    load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
-        model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
-    )
-
-    # Need to change some parameters name to match Flax names
-    for pt_key, pt_tensor in pt_state_dict.items():
-        pt_tuple_key = tuple(pt_key.split("."))
-
-        # remove base model prefix if necessary
-        has_base_model_prefix = pt_tuple_key[0] == model_prefix
-        if load_model_with_head_into_base_model and has_base_model_prefix:
-            pt_tuple_key = pt_tuple_key[1:]
-
-        # Correctly rename weight parameters
-        flax_key, flax_tensor = rename_key_and_reshape_tensor(
-            pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
-        )
-
-        # add model prefix if necessary
-        require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
-        if load_base_model_into_model_with_head and require_base_model_prefix:
-            flax_key = (model_prefix,) + flax_key
-
-        if flax_key in random_flax_state_dict:
-            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
-                raise ValueError(
-                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
-                    f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
-                )
-
-        # add batch stats if the model contains batchnorm layers
-        if "batch_stats" in flax_model.params:
-            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
-                flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
-                continue
-            # remove num_batches_tracked key
-            if "num_batches_tracked" in flax_key[-1]:
-                flax_state_dict.pop(flax_key, None)
-                continue
-
-            # also add unexpected weight so that warning is thrown
-            flax_state_dict[("params",) + flax_key] = jnp.asarray(flax_tensor)
-
-        else:
-            # also add unexpected weight so that warning is thrown
-            flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
-
-    return unflatten_dict(flax_state_dict)
-
-
-############################
-# Sharded Pytorch => Flax #
-############################
-
-
-def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
-    import torch
-
-    # Load the index
-    flax_state_dict = {}
-    for shard_file in shard_filenames:
-        # load using msgpack utils
-        pt_state_dict = torch.load(shard_file)
-        pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
-
-        model_prefix = flax_model.base_model_prefix
-
-        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
-        if "batch_stats" in flax_model.params:
-            flax_model_params = flax_model.params["params"]
-
-            random_flax_state_dict = flatten_dict(flax_model_params)
-            random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]))
-        else:
-            flax_model_params = flax_model.params
-            random_flax_state_dict = flatten_dict(flax_model_params)
-
-        load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
-            model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
-        )
-        load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
-            model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
-        )
-        # Need to change some parameters name to match Flax names
-        for pt_key, pt_tensor in pt_state_dict.items():
-            pt_tuple_key = tuple(pt_key.split("."))
-
-            # remove base model prefix if necessary
-            has_base_model_prefix = pt_tuple_key[0] == model_prefix
-            if load_model_with_head_into_base_model and has_base_model_prefix:
-                pt_tuple_key = pt_tuple_key[1:]
-
-            # Correctly rename weight parameters
-            flax_key, flax_tensor = rename_key_and_reshape_tensor(
-                pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
-            )
-            # add model prefix if necessary
-            require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
-            if load_base_model_into_model_with_head and require_base_model_prefix:
-                flax_key = (model_prefix,) + flax_key
-
-            if flax_key in random_flax_state_dict:
-                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
-                    raise ValueError(
-                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
-                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
-                    )
-
-            # add batch stats if the model contains batchnorm layers
-            if "batch_stats" in flax_model.params:
-                if "mean" in flax_key[-1]:
-                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
-                    continue
-                if "var" in flax_key[-1]:
-                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
-                    continue
-                # remove num_batches_tracked key
-                if "num_batches_tracked" in flax_key[-1]:
-                    flax_state_dict.pop(flax_key, None)
-                    continue
-
-                # also add unexpected weight so that warning is thrown
-                flax_state_dict[("params",) + flax_key] = jnp.asarray(flax_tensor)
-
-            else:
-                # also add unexpected weight so that warning is thrown
-                flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
-    return unflatten_dict(flax_state_dict)
-
-
-#####################
-# Flax => PyTorch #
-#####################
-
-
-def load_flax_checkpoint_in_pytorch_model(model, flax_checkpoint_path):
-    """Load flax checkpoints in a PyTorch model"""
-    flax_checkpoint_path = os.path.abspath(flax_checkpoint_path)
-    logger.info(f"Loading Flax weights from {flax_checkpoint_path}")
-
-    # import correct flax class
-    flax_cls = getattr(transformers, "Flax" + model.__class__.__name__)
-
-    # load flax weight dict
-    with open(flax_checkpoint_path, "rb") as state_f:
-        try:
-            flax_state_dict = from_bytes(flax_cls, state_f.read())
-        except UnpicklingError:
-            raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")
-
-    return load_flax_weights_in_pytorch_model(model, flax_state_dict)
-
-
-def load_flax_weights_in_pytorch_model(pt_model, flax_state):
-    """Load flax checkpoints in a PyTorch model"""
-
-    try:
-        import torch  # noqa: F401
-    except ImportError:
-        logger.error(
-            "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
-            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
-            " instructions."
-        )
-        raise
-
-    # check if we have bf16 weights
-    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
-    if any(is_type_bf16):
-        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
-        # and bf16 is not fully supported in PT yet.
-        logger.warning(
-            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
-            "before loading those in PyTorch model."
-        )
-        flax_state = jax.tree_util.tree_map(
-            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
-        )
-
-    flax_state_dict = flatten_dict(flax_state)
-    pt_model_dict = pt_model.state_dict()
-
-    load_model_with_head_into_base_model = (pt_model.base_model_prefix in flax_state) and (
-        pt_model.base_model_prefix not in {k.split(".")[0] for k in pt_model_dict.keys()}
-    )
-    load_base_model_into_model_with_head = (pt_model.base_model_prefix not in flax_state) and (
-        pt_model.base_model_prefix in {k.split(".")[0] for k in pt_model_dict.keys()}
-    )
-
-    # keep track of unexpected & missing keys
-    unexpected_keys = []
-    missing_keys = set(pt_model_dict.keys())
-
-    for flax_key_tuple, flax_tensor in flax_state_dict.items():
-        has_base_model_prefix = flax_key_tuple[0] == pt_model.base_model_prefix
-        require_base_model_prefix = ".".join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict
-
-        # adapt flax_key to prepare for loading from/to base model only
-        if load_model_with_head_into_base_model and has_base_model_prefix:
-            flax_key_tuple = flax_key_tuple[1:]
-        elif load_base_model_into_model_with_head and require_base_model_prefix:
-            flax_key_tuple = (pt_model.base_model_prefix,) + flax_key_tuple
-
-        # rename flax weights to PyTorch format
-        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(flax_key_tuple) not in pt_model_dict:
-            # conv layer
-            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
-            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
-        elif flax_key_tuple[-1] == "kernel" and ".".join(flax_key_tuple) not in pt_model_dict:
-            # linear layer
-            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
-            flax_tensor = flax_tensor.T
-        elif flax_key_tuple[-1] in ["scale", "embedding"]:
-            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
-
-        # adding batch stats from flax batch norm to pt
-        elif "mean" in flax_key_tuple[-1]:
-            flax_key_tuple = flax_key_tuple[:-1] + ("running_mean",)
-        elif "var" in flax_key_tuple[-1]:
-            flax_key_tuple = flax_key_tuple[:-1] + ("running_var",)
-
-        if "batch_stats" in flax_state:
-            flax_key = ".".join(flax_key_tuple[1:])  # Remove the params/batch_stats header
-        else:
-            flax_key = ".".join(flax_key_tuple)
-
-        if flax_key in pt_model_dict:
-            if flax_tensor.shape != pt_model_dict[flax_key].shape:
-                raise ValueError(
-                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
-                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
-                )
-            else:
-                # add weight to pytorch dict
-                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
-                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
-                # remove from missing keys
-                missing_keys.remove(flax_key)
-        else:
-            # weight is not expected by PyTorch model
-            unexpected_keys.append(flax_key)
-
-    pt_model.load_state_dict(pt_model_dict)
-
-    # re-transform missing_keys to list
-    missing_keys = list(missing_keys)
-
-    if len(unexpected_keys) > 0:
-        logger.warning(
-            "Some weights of the Flax model were not used when initializing the PyTorch model"
-            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
-            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
-            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
-            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
-            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
-            " FlaxBertForSequenceClassification model)."
-        )
-    else:
-        logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
-    if len(missing_keys) > 0:
-        logger.warning(
-            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
-            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
-            " use it for predictions and inference."
-        )
-    else:
-        logger.warning(
-            f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
-            "If your task is similar to the task the model of the checkpoint was trained on, "
-            f"you can already use {pt_model.__class__.__name__} for predictions without further training."
-        )
-
-    return pt_model
diff --git a/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/attention.py b/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/attention.py
deleted file mode 100644
index d38d90764accd6729441dc715f90845da1d8b513..0000000000000000000000000000000000000000
--- a/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/attention.py
+++ /dev/null
@@ -1,482 +0,0 @@
-# code mostly taken from https://github.com/huggingface/diffusers
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.modeling_utils import ModelMixin
-from diffusers.models.attention import FeedForward, CrossAttention, AdaLayerNorm
-from diffusers.utils import BaseOutput
-from diffusers.utils.import_utils import is_xformers_available
-
-from einops import rearrange
-
-
-@dataclass
-class SpatioTemporalTransformerModelOutput(BaseOutput):
-    """torch.FloatTensor of shape [batch x channel x frames x height x width]"""
-
-    sample: torch.FloatTensor
-
-
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
-
-
-class SpatioTemporalTransformerModel(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        upcast_attention: bool = False,
-        model_config: dict = {},
-        **transformer_kwargs,
-    ):
-        super().__init__()
-        self.use_linear_projection = use_linear_projection
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-
-        # Define input layers
-        self.in_channels = in_channels
-
-        self.norm = torch.nn.GroupNorm(
-            num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
-        )
-        if use_linear_projection:
-            self.proj_in = nn.Linear(in_channels, inner_dim)
-        else:
-            self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-
-        # Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                SpatioTemporalTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    num_embeds_ada_norm=num_embeds_ada_norm,
-                    attention_bias=attention_bias,
-                    only_cross_attention=only_cross_attention,
-                    upcast_attention=upcast_attention,
-                    model_config=model_config,
-                    **transformer_kwargs,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        # Define output layers
-        if use_linear_projection:
-            self.proj_out = nn.Linear(in_channels, inner_dim)
-        else:
-            self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(
-        self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True
-    ):
-        # 1. Input
-        clip_length = None
-        is_video = hidden_states.ndim == 5
-        if is_video:
-            clip_length = hidden_states.shape[2]
-            hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
-            encoder_hidden_states = encoder_hidden_states.repeat_interleave(clip_length, 0)
-        else:
-            # To adapt to classifier-free guidance where encoder_hidden_states=2
-            batch_size = hidden_states.shape[0]//encoder_hidden_states.shape[0]
-            encoder_hidden_states = encoder_hidden_states.repeat_interleave(batch_size, 0)
-        *_, h, w = hidden_states.shape
-        residual = hidden_states
-
-        hidden_states = self.norm(hidden_states)
-        if not self.use_linear_projection:
-            hidden_states = self.proj_in(hidden_states)
-            hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c") # (bf) (hw) c
-        else:
-            hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c")
-            hidden_states = self.proj_in(hidden_states)
-
-        # 2. Blocks
-        for block in self.transformer_blocks:
-            hidden_states = block(
-                hidden_states, # [16, 4096, 320]
-                encoder_hidden_states=encoder_hidden_states, # ([1, 77, 768]
-                timestep=timestep,
-                clip_length=clip_length,
-            )
-
-        # 3. Output
-        if not self.use_linear_projection:
-            hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=h, w=w).contiguous()
-            hidden_states = self.proj_out(hidden_states)
-        else:
-            hidden_states = self.proj_out(hidden_states)
-            hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=h, w=w).contiguous()
-
-        output = hidden_states + residual
-        if is_video:
-            output = rearrange(output, "(b f) c h w -> b c f h w", f=clip_length)
-
-        if not return_dict:
-            return (output,)
-
-        return SpatioTemporalTransformerModelOutput(sample=output)
-
-import copy
-class SpatioTemporalTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        attention_bias: bool = False,
-        only_cross_attention: bool = False,
-        upcast_attention: bool = False,
-        use_sparse_causal_attention: bool = True,
-        temporal_attention_position: str = "after_feedforward",
-        model_config: dict = {}
-    ):
-        super().__init__()
-
-        self.only_cross_attention = only_cross_attention
-        self.use_ada_layer_norm = num_embeds_ada_norm is not None
-        self.use_sparse_causal_attention = use_sparse_causal_attention
-        # For safety, freeze the model_config
-        self.model_config = copy.deepcopy(model_config)
-        if 'least_sc_channel' in model_config:
-            if dim< model_config['least_sc_channel']:
-                self.model_config['SparseCausalAttention_index'] = []
-        
-        self.temporal_attention_position = temporal_attention_position
-        temporal_attention_positions = ["after_spatial", "after_cross", "after_feedforward"]
-        if temporal_attention_position not in temporal_attention_positions:
-            raise ValueError(
-                f"`temporal_attention_position` must be one of {temporal_attention_positions}"
-            )
-
-        # 1. Spatial-Attn
-        spatial_attention = SparseCausalAttention if use_sparse_causal_attention else CrossAttention
-        self.attn1 = spatial_attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
-            upcast_attention=upcast_attention,
-        )  # is a self-attention
-        self.norm1 = (
-            AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None:
-            self.attn2 = CrossAttention(
-                query_dim=dim,
-                cross_attention_dim=cross_attention_dim,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                upcast_attention=upcast_attention,
-            )  # is self-attn if encoder_hidden_states is none
-            self.norm2 = (
-                AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
-            )
-        else:
-            self.attn2 = None
-            self.norm2 = None
-
-        # 3. Temporal-Attn
-        self.attn_temporal = CrossAttention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            upcast_attention=upcast_attention,
-        )
-        nn.init.zeros_(self.attn_temporal.to_out[0].weight.data)  # initialize as an identity function
-        self.norm_temporal = (
-            AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
-        )
-        # efficient_attention_backward_cutlass is not implemented for large channels
-        self.use_xformers = (dim <= 320) or "3090" not in torch.cuda.get_device_name(0)
-
-        # 4. Feed-forward
-        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
-        self.norm3 = nn.LayerNorm(dim)
-
-    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
-        if not is_xformers_available():
-            print("Here is how to install it")
-            raise ModuleNotFoundError(
-                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                " xformers",
-                name="xformers",
-            )
-        elif not torch.cuda.is_available():
-            raise ValueError(
-                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
-                " available for GPU "
-            )
-        else:
-            try:
-                # Make sure we can run the memory efficient attention
-                if use_memory_efficient_attention_xformers is True:
-                    
-                    _ = xformers.ops.memory_efficient_attention(
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                    )
-                else:
-                    
-                    pass
-            except Exception as e:
-                raise e
-            # self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
-            # self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
-            self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers and self.use_xformers
-            self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers and self.use_xformers
-            # self.attn_temporal._use_memory_efficient_attention_xformers = (
-            #     use_memory_efficient_attention_xformers
-            # ),  # FIXME: enabling this raises CUDA ERROR. Gotta dig in.
-
-    def forward(
-        self,
-        hidden_states,
-        encoder_hidden_states=None,
-        timestep=None,
-        attention_mask=None,
-        clip_length=None,
-    ):
-        # 1. Self-Attention
-        norm_hidden_states = (
-            self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
-        )
-
-        kwargs = dict(
-            hidden_states=norm_hidden_states,
-            attention_mask=attention_mask,
-        )
-        if self.only_cross_attention:
-            kwargs.update(encoder_hidden_states=encoder_hidden_states)
-        if self.use_sparse_causal_attention:
-            kwargs.update(clip_length=clip_length)
-        if 'SparseCausalAttention_index' in self.model_config.keys():
-            kwargs.update(SparseCausalAttention_index = self.model_config['SparseCausalAttention_index'])
-        
-        hidden_states = hidden_states + self.attn1(**kwargs)
-
-        if clip_length is not None and self.temporal_attention_position == "after_spatial":
-            hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length)
-
-        if self.attn2 is not None:
-            # 2. Cross-Attention
-            norm_hidden_states = (
-                self.norm2(hidden_states, timestep)
-                if self.use_ada_layer_norm
-                else self.norm2(hidden_states)
-            )
-            hidden_states = (
-                self.attn2(
-                    norm_hidden_states, # [16, 4096, 320]
-                    encoder_hidden_states=encoder_hidden_states, # [1, 77, 768]
-                    attention_mask=attention_mask,
-                )
-                + hidden_states
-            )
-
-        if clip_length is not None and self.temporal_attention_position == "after_cross":
-            hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length)
-
-        # 3. Feed-forward
-        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
-
-        if clip_length is not None and self.temporal_attention_position == "after_feedforward":
-            hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length)
-
-        return hidden_states
-
-    def apply_temporal_attention(self, hidden_states, timestep, clip_length):
-        d = hidden_states.shape[1]
-        hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=clip_length)
-        norm_hidden_states = (
-            self.norm_temporal(hidden_states, timestep)
-            if self.use_ada_layer_norm
-            else self.norm_temporal(hidden_states)
-        )
-        hidden_states = self.attn_temporal(norm_hidden_states) + hidden_states
-        hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
-        return hidden_states
-
-
-class SparseCausalAttention(CrossAttention):
-    def forward(
-        self,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        clip_length: int = None,
-        SparseCausalAttention_index: list = [-1, 'first']
-    ):
-        if (
-            self.added_kv_proj_dim is not None
-            or encoder_hidden_states is not None
-            or attention_mask is not None
-        ):
-            raise NotImplementedError
-
-        if self.group_norm is not None:
-            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = self.to_q(hidden_states)
-        dim = query.shape[-1]
-        query = self.reshape_heads_to_batch_dim(query)
-
-        key = self.to_k(hidden_states)
-        value = self.to_v(hidden_states)
-
-        if clip_length is not None:
-            key = rearrange(key, "(b f) d c -> b f d c", f=clip_length)
-            value = rearrange(value, "(b f) d c -> b f d c", f=clip_length)
-
-
-            #  ***********************Start of SparseCausalAttention_index**********
-            frame_index_list = []
-            # print(f'SparseCausalAttention_index {str(SparseCausalAttention_index)}')
-            if len(SparseCausalAttention_index) > 0:
-                for index in SparseCausalAttention_index:
-                    if isinstance(index, str):
-                        if index == 'first':
-                            frame_index = [0] * clip_length
-                        if index == 'last':
-                            frame_index = [clip_length-1] * clip_length
-                        if (index == 'mid') or (index == 'middle'):
-                            frame_index = [int(clip_length-1)//2] * clip_length
-                    else:
-                        assert isinstance(index, int), 'relative index must be int'
-                        frame_index = torch.arange(clip_length) + index
-                        frame_index = frame_index.clip(0, clip_length-1)
-                        
-                    frame_index_list.append(frame_index)
-                
-                key = torch.cat([   key[:, frame_index] for frame_index in frame_index_list
-                                    ], dim=2)
-                value = torch.cat([ value[:, frame_index] for frame_index in frame_index_list
-                                    ], dim=2)
-
-
-            #  ***********************End of SparseCausalAttention_index**********
-            key = rearrange(key, "b f d c -> (b f) d c", f=clip_length)
-            value = rearrange(value, "b f d c -> (b f) d c", f=clip_length)
-        
-        
-        key = self.reshape_heads_to_batch_dim(key)
-        value = self.reshape_heads_to_batch_dim(value)
-        
-        # attention, what we cannot get enough of
-        if self._use_memory_efficient_attention_xformers:
-            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
-            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
-            hidden_states = hidden_states.to(query.dtype)
-        else:
-            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
-                hidden_states = self._attention(query, key, value, attention_mask)
-            else:
-                hidden_states = self._sliced_attention(
-                    query, key, value, hidden_states.shape[1], dim, attention_mask
-                )
-
-        # linear proj
-        hidden_states = self.to_out[0](hidden_states)
-
-        # dropout
-        hidden_states = self.to_out[1](hidden_states)
-        return hidden_states
-
-# FIXME
-class SparseCausalAttention_fixme(CrossAttention):
-    def forward(
-        self,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        clip_length: int = None,
-    ):
-        if (
-            self.added_kv_proj_dim is not None
-            or encoder_hidden_states is not None
-            or attention_mask is not None
-        ):
-            raise NotImplementedError
-
-        if self.group_norm is not None:
-            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = self.to_q(hidden_states)
-        dim = query.shape[-1]
-        query = self.reshape_heads_to_batch_dim(query)
-
-        key = self.to_k(hidden_states)
-        value = self.to_v(hidden_states)
-
-        prev_frame_index = torch.arange(clip_length) - 1
-        prev_frame_index[0] = 0
-
-        key = rearrange(key, "(b f) d c -> b f d c", f=clip_length)
-        key = torch.cat([key[:, [0] * clip_length], key[:, prev_frame_index]], dim=2)
-        key = rearrange(key, "b f d c -> (b f) d c", f=clip_length)
-
-        value = rearrange(value, "(b f) d c -> b f d c", f=clip_length)
-        value = torch.cat([value[:, [0] * clip_length], value[:, prev_frame_index]], dim=2)
-        value = rearrange(value, "b f d c -> (b f) d c", f=clip_length)
-
-        key = self.reshape_heads_to_batch_dim(key)
-        value = self.reshape_heads_to_batch_dim(value)
-        
-        
-        if self._use_memory_efficient_attention_xformers:
-            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
-            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
-            hidden_states = hidden_states.to(query.dtype)
-        else:
-            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
-                hidden_states = self._attention(query, key, value, attention_mask)
-            else:
-                hidden_states = self._sliced_attention(
-                    query, key, value, hidden_states.shape[1], dim, attention_mask
-                )
-
-        # linear proj
-        hidden_states = self.to_out[0](hidden_states)
-
-        # dropout
-        hidden_states = self.to_out[1](hidden_states)
-        return hidden_states
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/streams/file.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/streams/file.py
deleted file mode 100644
index 2840d40ab6a2fa222d6594d6980d8234df17eade..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/streams/file.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from __future__ import annotations
-
-from io import SEEK_SET, UnsupportedOperation
-from os import PathLike
-from pathlib import Path
-from typing import Any, BinaryIO, Callable, Mapping, cast
-
-from .. import (
-    BrokenResourceError,
-    ClosedResourceError,
-    EndOfStream,
-    TypedAttributeSet,
-    to_thread,
-    typed_attribute,
-)
-from ..abc import ByteReceiveStream, ByteSendStream
-
-
-class FileStreamAttribute(TypedAttributeSet):
-    #: the open file descriptor
-    file: BinaryIO = typed_attribute()
-    #: the path of the file on the file system, if available (file must be a real file)
-    path: Path = typed_attribute()
-    #: the file number, if available (file must be a real file or a TTY)
-    fileno: int = typed_attribute()
-
-
-class _BaseFileStream:
-    def __init__(self, file: BinaryIO):
-        self._file = file
-
-    async def aclose(self) -> None:
-        await to_thread.run_sync(self._file.close)
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        attributes: dict[Any, Callable[[], Any]] = {
-            FileStreamAttribute.file: lambda: self._file,
-        }
-
-        if hasattr(self._file, "name"):
-            attributes[FileStreamAttribute.path] = lambda: Path(self._file.name)
-
-        try:
-            self._file.fileno()
-        except UnsupportedOperation:
-            pass
-        else:
-            attributes[FileStreamAttribute.fileno] = lambda: self._file.fileno()
-
-        return attributes
-
-
-class FileReadStream(_BaseFileStream, ByteReceiveStream):
-    """
-    A byte stream that reads from a file in the file system.
-
-    :param file: a file that has been opened for reading in binary mode
-
-    .. versionadded:: 3.0
-    """
-
-    @classmethod
-    async def from_path(cls, path: str | PathLike[str]) -> FileReadStream:
-        """
-        Create a file read stream by opening the given file.
-
-        :param path: path of the file to read from
-
-        """
-        file = await to_thread.run_sync(Path(path).open, "rb")
-        return cls(cast(BinaryIO, file))
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        try:
-            data = await to_thread.run_sync(self._file.read, max_bytes)
-        except ValueError:
-            raise ClosedResourceError from None
-        except OSError as exc:
-            raise BrokenResourceError from exc
-
-        if data:
-            return data
-        else:
-            raise EndOfStream
-
-    async def seek(self, position: int, whence: int = SEEK_SET) -> int:
-        """
-        Seek the file to the given position.
-
-        .. seealso:: :meth:`io.IOBase.seek`
-
-        .. note:: Not all file descriptors are seekable.
-
-        :param position: position to seek the file to
-        :param whence: controls how ``position`` is interpreted
-        :return: the new absolute position
-        :raises OSError: if the file is not seekable
-
-        """
-        return await to_thread.run_sync(self._file.seek, position, whence)
-
-    async def tell(self) -> int:
-        """
-        Return the current stream position.
-
-        .. note:: Not all file descriptors are seekable.
-
-        :return: the current absolute position
-        :raises OSError: if the file is not seekable
-
-        """
-        return await to_thread.run_sync(self._file.tell)
-
-
-class FileWriteStream(_BaseFileStream, ByteSendStream):
-    """
-    A byte stream that writes to a file in the file system.
-
-    :param file: a file that has been opened for writing in binary mode
-
-    .. versionadded:: 3.0
-    """
-
-    @classmethod
-    async def from_path(
-        cls, path: str | PathLike[str], append: bool = False
-    ) -> FileWriteStream:
-        """
-        Create a file write stream by opening the given file for writing.
-
-        :param path: path of the file to write to
-        :param append: if ``True``, open the file for appending; if ``False``, any existing file
-            at the given path will be truncated
-
-        """
-        mode = "ab" if append else "wb"
-        file = await to_thread.run_sync(Path(path).open, mode)
-        return cls(cast(BinaryIO, file))
-
-    async def send(self, item: bytes) -> None:
-        try:
-            await to_thread.run_sync(self._file.write, item)
-        except ValueError:
-            raise ClosedResourceError from None
-        except OSError as exc:
-            raise BrokenResourceError from exc
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/attr/validators.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/attr/validators.py
deleted file mode 100644
index 1488554f789526d8d85eb467250a64a64489362d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/attr/validators.py
+++ /dev/null
@@ -1,720 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Commonly useful validators.
-"""
-
-
-import operator
-import re
-
-from contextlib import contextmanager
-from re import Pattern
-
-from ._config import get_run_validators, set_run_validators
-from ._make import _AndValidator, and_, attrib, attrs
-from .converters import default_if_none
-from .exceptions import NotCallableError
-
-
-__all__ = [
-    "and_",
-    "deep_iterable",
-    "deep_mapping",
-    "disabled",
-    "ge",
-    "get_disabled",
-    "gt",
-    "in_",
-    "instance_of",
-    "is_callable",
-    "le",
-    "lt",
-    "matches_re",
-    "max_len",
-    "min_len",
-    "not_",
-    "optional",
-    "provides",
-    "set_disabled",
-]
-
-
-def set_disabled(disabled):
-    """
-    Globally disable or enable running validators.
-
-    By default, they are run.
-
-    :param disabled: If ``True``, disable running all validators.
-    :type disabled: bool
-
-    .. warning::
-
-        This function is not thread-safe!
-
-    .. versionadded:: 21.3.0
-    """
-    set_run_validators(not disabled)
-
-
-def get_disabled():
-    """
-    Return a bool indicating whether validators are currently disabled or not.
-
-    :return: ``True`` if validators are currently disabled.
-    :rtype: bool
-
-    .. versionadded:: 21.3.0
-    """
-    return not get_run_validators()
-
-
-@contextmanager
-def disabled():
-    """
-    Context manager that disables running validators within its context.
-
-    .. warning::
-
-        This context manager is not thread-safe!
-
-    .. versionadded:: 21.3.0
-    """
-    set_run_validators(False)
-    try:
-        yield
-    finally:
-        set_run_validators(True)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _InstanceOfValidator:
-    type = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not isinstance(value, self.type):
-            raise TypeError(
-                "'{name}' must be {type!r} (got {value!r} that is a "
-                "{actual!r}).".format(
-                    name=attr.name,
-                    type=self.type,
-                    actual=value.__class__,
-                    value=value,
-                ),
-                attr,
-                self.type,
-                value,
-            )
-
-    def __repr__(self):
-        return "<instance_of validator for type {type!r}>".format(
-            type=self.type
-        )
-
-
-def instance_of(type):
-    """
-    A validator that raises a `TypeError` if the initializer is called
-    with a wrong type for this particular attribute (checks are performed using
-    `isinstance` therefore it's also valid to pass a tuple of types).
-
-    :param type: The type to check for.
-    :type type: type or tuple of type
-
-    :raises TypeError: With a human readable error message, the attribute
-        (of type `attrs.Attribute`), the expected type, and the value it
-        got.
-    """
-    return _InstanceOfValidator(type)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MatchesReValidator:
-    pattern = attrib()
-    match_func = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not self.match_func(value):
-            raise ValueError(
-                "'{name}' must match regex {pattern!r}"
-                " ({value!r} doesn't)".format(
-                    name=attr.name, pattern=self.pattern.pattern, value=value
-                ),
-                attr,
-                self.pattern,
-                value,
-            )
-
-    def __repr__(self):
-        return "<matches_re validator for pattern {pattern!r}>".format(
-            pattern=self.pattern
-        )
-
-
-def matches_re(regex, flags=0, func=None):
-    r"""
-    A validator that raises `ValueError` if the initializer is called
-    with a string that doesn't match *regex*.
-
-    :param regex: a regex string or precompiled pattern to match against
-    :param int flags: flags that will be passed to the underlying re function
-        (default 0)
-    :param callable func: which underlying `re` function to call. Valid options
-        are `re.fullmatch`, `re.search`, and `re.match`; the default ``None``
-        means `re.fullmatch`. For performance reasons, the pattern is always
-        precompiled using `re.compile`.
-
-    .. versionadded:: 19.2.0
-    .. versionchanged:: 21.3.0 *regex* can be a pre-compiled pattern.
-    """
-    valid_funcs = (re.fullmatch, None, re.search, re.match)
-    if func not in valid_funcs:
-        raise ValueError(
-            "'func' must be one of {}.".format(
-                ", ".join(
-                    sorted(
-                        e and e.__name__ or "None" for e in set(valid_funcs)
-                    )
-                )
-            )
-        )
-
-    if isinstance(regex, Pattern):
-        if flags:
-            raise TypeError(
-                "'flags' can only be used with a string pattern; "
-                "pass flags to re.compile() instead"
-            )
-        pattern = regex
-    else:
-        pattern = re.compile(regex, flags)
-
-    if func is re.match:
-        match_func = pattern.match
-    elif func is re.search:
-        match_func = pattern.search
-    else:
-        match_func = pattern.fullmatch
-
-    return _MatchesReValidator(pattern, match_func)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _ProvidesValidator:
-    interface = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not self.interface.providedBy(value):
-            raise TypeError(
-                "'{name}' must provide {interface!r} which {value!r} "
-                "doesn't.".format(
-                    name=attr.name, interface=self.interface, value=value
-                ),
-                attr,
-                self.interface,
-                value,
-            )
-
-    def __repr__(self):
-        return "<provides validator for interface {interface!r}>".format(
-            interface=self.interface
-        )
-
-
-def provides(interface):
-    """
-    A validator that raises a `TypeError` if the initializer is called
-    with an object that does not provide the requested *interface* (checks are
-    performed using ``interface.providedBy(value)`` (see `zope.interface
-    <https://zopeinterface.readthedocs.io/en/latest/>`_).
-
-    :param interface: The interface to check for.
-    :type interface: ``zope.interface.Interface``
-
-    :raises TypeError: With a human readable error message, the attribute
-        (of type `attrs.Attribute`), the expected interface, and the
-        value it got.
-
-    .. deprecated:: 23.1.0
-    """
-    import warnings
-
-    warnings.warn(
-        "attrs's zope-interface support is deprecated and will be removed in, "
-        "or after, April 2024.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return _ProvidesValidator(interface)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _OptionalValidator:
-    validator = attrib()
-
-    def __call__(self, inst, attr, value):
-        if value is None:
-            return
-
-        self.validator(inst, attr, value)
-
-    def __repr__(self):
-        return "<optional validator for {what} or None>".format(
-            what=repr(self.validator)
-        )
-
-
-def optional(validator):
-    """
-    A validator that makes an attribute optional.  An optional attribute is one
-    which can be set to ``None`` in addition to satisfying the requirements of
-    the sub-validator.
-
-    :param Callable | tuple[Callable] | list[Callable] validator: A validator
-        (or validators) that is used for non-``None`` values.
-
-    .. versionadded:: 15.1.0
-    .. versionchanged:: 17.1.0 *validator* can be a list of validators.
-    .. versionchanged:: 23.1.0 *validator* can also be a tuple of validators.
-    """
-    if isinstance(validator, (list, tuple)):
-        return _OptionalValidator(_AndValidator(validator))
-
-    return _OptionalValidator(validator)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _InValidator:
-    options = attrib()
-
-    def __call__(self, inst, attr, value):
-        try:
-            in_options = value in self.options
-        except TypeError:  # e.g. `1 in "abc"`
-            in_options = False
-
-        if not in_options:
-            raise ValueError(
-                "'{name}' must be in {options!r} (got {value!r})".format(
-                    name=attr.name, options=self.options, value=value
-                ),
-                attr,
-                self.options,
-                value,
-            )
-
-    def __repr__(self):
-        return "<in_ validator with options {options!r}>".format(
-            options=self.options
-        )
-
-
-def in_(options):
-    """
-    A validator that raises a `ValueError` if the initializer is called
-    with a value that does not belong in the options provided.  The check is
-    performed using ``value in options``.
-
-    :param options: Allowed options.
-    :type options: list, tuple, `enum.Enum`, ...
-
-    :raises ValueError: With a human readable error message, the attribute (of
-       type `attrs.Attribute`), the expected options, and the value it
-       got.
-
-    .. versionadded:: 17.1.0
-    .. versionchanged:: 22.1.0
-       The ValueError was incomplete until now and only contained the human
-       readable error message. Now it contains all the information that has
-       been promised since 17.1.0.
-    """
-    return _InValidator(options)
-
-
-@attrs(repr=False, slots=False, hash=True)
-class _IsCallableValidator:
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not callable(value):
-            message = (
-                "'{name}' must be callable "
-                "(got {value!r} that is a {actual!r})."
-            )
-            raise NotCallableError(
-                msg=message.format(
-                    name=attr.name, value=value, actual=value.__class__
-                ),
-                value=value,
-            )
-
-    def __repr__(self):
-        return "<is_callable validator>"
-
-
-def is_callable():
-    """
-    A validator that raises a `attrs.exceptions.NotCallableError` if the
-    initializer is called with a value for this particular attribute
-    that is not callable.
-
-    .. versionadded:: 19.1.0
-
-    :raises attrs.exceptions.NotCallableError: With a human readable error
-        message containing the attribute (`attrs.Attribute`) name,
-        and the value it got.
-    """
-    return _IsCallableValidator()
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _DeepIterable:
-    member_validator = attrib(validator=is_callable())
-    iterable_validator = attrib(
-        default=None, validator=optional(is_callable())
-    )
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if self.iterable_validator is not None:
-            self.iterable_validator(inst, attr, value)
-
-        for member in value:
-            self.member_validator(inst, attr, member)
-
-    def __repr__(self):
-        iterable_identifier = (
-            ""
-            if self.iterable_validator is None
-            else f" {self.iterable_validator!r}"
-        )
-        return (
-            "<deep_iterable validator for{iterable_identifier}"
-            " iterables of {member!r}>"
-        ).format(
-            iterable_identifier=iterable_identifier,
-            member=self.member_validator,
-        )
-
-
-def deep_iterable(member_validator, iterable_validator=None):
-    """
-    A validator that performs deep validation of an iterable.
-
-    :param member_validator: Validator(s) to apply to iterable members
-    :param iterable_validator: Validator to apply to iterable itself
-        (optional)
-
-    .. versionadded:: 19.1.0
-
-    :raises TypeError: if any sub-validators fail
-    """
-    if isinstance(member_validator, (list, tuple)):
-        member_validator = and_(*member_validator)
-    return _DeepIterable(member_validator, iterable_validator)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _DeepMapping:
-    key_validator = attrib(validator=is_callable())
-    value_validator = attrib(validator=is_callable())
-    mapping_validator = attrib(default=None, validator=optional(is_callable()))
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if self.mapping_validator is not None:
-            self.mapping_validator(inst, attr, value)
-
-        for key in value:
-            self.key_validator(inst, attr, key)
-            self.value_validator(inst, attr, value[key])
-
-    def __repr__(self):
-        return (
-            "<deep_mapping validator for objects mapping {key!r} to {value!r}>"
-        ).format(key=self.key_validator, value=self.value_validator)
-
-
-def deep_mapping(key_validator, value_validator, mapping_validator=None):
-    """
-    A validator that performs deep validation of a dictionary.
-
-    :param key_validator: Validator to apply to dictionary keys
-    :param value_validator: Validator to apply to dictionary values
-    :param mapping_validator: Validator to apply to top-level mapping
-        attribute (optional)
-
-    .. versionadded:: 19.1.0
-
-    :raises TypeError: if any sub-validators fail
-    """
-    return _DeepMapping(key_validator, value_validator, mapping_validator)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _NumberValidator:
-    bound = attrib()
-    compare_op = attrib()
-    compare_func = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not self.compare_func(value, self.bound):
-            raise ValueError(
-                "'{name}' must be {op} {bound}: {value}".format(
-                    name=attr.name,
-                    op=self.compare_op,
-                    bound=self.bound,
-                    value=value,
-                )
-            )
-
-    def __repr__(self):
-        return "<Validator for x {op} {bound}>".format(
-            op=self.compare_op, bound=self.bound
-        )
-
-
-def lt(val):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a number larger or equal to *val*.
-
-    :param val: Exclusive upper bound for values
-
-    .. versionadded:: 21.3.0
-    """
-    return _NumberValidator(val, "<", operator.lt)
-
-
-def le(val):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a number greater than *val*.
-
-    :param val: Inclusive upper bound for values
-
-    .. versionadded:: 21.3.0
-    """
-    return _NumberValidator(val, "<=", operator.le)
-
-
-def ge(val):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a number smaller than *val*.
-
-    :param val: Inclusive lower bound for values
-
-    .. versionadded:: 21.3.0
-    """
-    return _NumberValidator(val, ">=", operator.ge)
-
-
-def gt(val):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a number smaller or equal to *val*.
-
-    :param val: Exclusive lower bound for values
-
-    .. versionadded:: 21.3.0
-    """
-    return _NumberValidator(val, ">", operator.gt)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MaxLengthValidator:
-    max_length = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if len(value) > self.max_length:
-            raise ValueError(
-                "Length of '{name}' must be <= {max}: {len}".format(
-                    name=attr.name, max=self.max_length, len=len(value)
-                )
-            )
-
-    def __repr__(self):
-        return f"<max_len validator for {self.max_length}>"
-
-
-def max_len(length):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a string or iterable that is longer than *length*.
-
-    :param int length: Maximum length of the string or iterable
-
-    .. versionadded:: 21.3.0
-    """
-    return _MaxLengthValidator(length)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MinLengthValidator:
-    min_length = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if len(value) < self.min_length:
-            raise ValueError(
-                "Length of '{name}' must be => {min}: {len}".format(
-                    name=attr.name, min=self.min_length, len=len(value)
-                )
-            )
-
-    def __repr__(self):
-        return f"<min_len validator for {self.min_length}>"
-
-
-def min_len(length):
-    """
-    A validator that raises `ValueError` if the initializer is called
-    with a string or iterable that is shorter than *length*.
-
-    :param int length: Minimum length of the string or iterable
-
-    .. versionadded:: 22.1.0
-    """
-    return _MinLengthValidator(length)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _SubclassOfValidator:
-    type = attrib()
-
-    def __call__(self, inst, attr, value):
-        """
-        We use a callable class to be able to change the ``__repr__``.
-        """
-        if not issubclass(value, self.type):
-            raise TypeError(
-                "'{name}' must be a subclass of {type!r} "
-                "(got {value!r}).".format(
-                    name=attr.name,
-                    type=self.type,
-                    value=value,
-                ),
-                attr,
-                self.type,
-                value,
-            )
-
-    def __repr__(self):
-        return "<subclass_of validator for type {type!r}>".format(
-            type=self.type
-        )
-
-
-def _subclass_of(type):
-    """
-    A validator that raises a `TypeError` if the initializer is called
-    with a wrong type for this particular attribute (checks are performed using
-    `issubclass` therefore it's also valid to pass a tuple of types).
-
-    :param type: The type to check for.
-    :type type: type or tuple of types
-
-    :raises TypeError: With a human readable error message, the attribute
-        (of type `attrs.Attribute`), the expected type, and the value it
-        got.
-    """
-    return _SubclassOfValidator(type)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _NotValidator:
-    validator = attrib()
-    msg = attrib(
-        converter=default_if_none(
-            "not_ validator child '{validator!r}' "
-            "did not raise a captured error"
-        )
-    )
-    exc_types = attrib(
-        validator=deep_iterable(
-            member_validator=_subclass_of(Exception),
-            iterable_validator=instance_of(tuple),
-        ),
-    )
-
-    def __call__(self, inst, attr, value):
-        try:
-            self.validator(inst, attr, value)
-        except self.exc_types:
-            pass  # suppress error to invert validity
-        else:
-            raise ValueError(
-                self.msg.format(
-                    validator=self.validator,
-                    exc_types=self.exc_types,
-                ),
-                attr,
-                self.validator,
-                value,
-                self.exc_types,
-            )
-
-    def __repr__(self):
-        return (
-            "<not_ validator wrapping {what!r}, " "capturing {exc_types!r}>"
-        ).format(
-            what=self.validator,
-            exc_types=self.exc_types,
-        )
-
-
-def not_(validator, *, msg=None, exc_types=(ValueError, TypeError)):
-    """
-    A validator that wraps and logically 'inverts' the validator passed to it.
-    It will raise a `ValueError` if the provided validator *doesn't* raise a
-    `ValueError` or `TypeError` (by default), and will suppress the exception
-    if the provided validator *does*.
-
-    Intended to be used with existing validators to compose logic without
-    needing to create inverted variants, for example, ``not_(in_(...))``.
-
-    :param validator: A validator to be logically inverted.
-    :param msg: Message to raise if validator fails.
-        Formatted with keys ``exc_types`` and ``validator``.
-    :type msg: str
-    :param exc_types: Exception type(s) to capture.
-        Other types raised by child validators will not be intercepted and
-        pass through.
-
-    :raises ValueError: With a human readable error message,
-        the attribute (of type `attrs.Attribute`),
-        the validator that failed to raise an exception,
-        the value it got,
-        and the expected exception types.
-
-    .. versionadded:: 22.2.0
-    """
-    try:
-        exc_types = tuple(exc_types)
-    except TypeError:
-        exc_types = (exc_types,)
-    return _NotValidator(validator, msg, exc_types)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/tests/test_catalogue.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/tests/test_catalogue.py
deleted file mode 100644
index c8e83cf406e84f11615ecee86ebee6e8c38ea2ef..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/tests/test_catalogue.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import pytest
-import sys
-from pathlib import Path
-import catalogue
-
-
-@pytest.fixture(autouse=True)
-def cleanup():
-    catalogue.REGISTRY = {}
-    yield
-
-
-def test_get_set():
-    catalogue._set(("a", "b", "c"), "test")
-    assert len(catalogue.REGISTRY) == 1
-    assert ("a", "b", "c") in catalogue.REGISTRY
-    assert catalogue.check_exists("a", "b", "c")
-    assert catalogue.REGISTRY[("a", "b", "c")] == "test"
-    assert catalogue._get(("a", "b", "c")) == "test"
-    with pytest.raises(catalogue.RegistryError):
-        catalogue._get(("a", "b", "d"))
-    with pytest.raises(catalogue.RegistryError):
-        catalogue._get(("a", "b", "c", "d"))
-    catalogue._set(("x", "y", "z1"), "test1")
-    catalogue._set(("x", "y", "z2"), "test2")
-    assert catalogue._remove(("a", "b", "c")) == "test"
-    catalogue._set(("x", "y2"), "test3")
-    with pytest.raises(catalogue.RegistryError):
-        catalogue._remove(("x", "y"))
-    assert catalogue._remove(("x", "y", "z2")) == "test2"
-
-
-def test_registry_get_set():
-    test_registry = catalogue.create("test")
-    with pytest.raises(catalogue.RegistryError):
-        test_registry.get("foo")
-    test_registry.register("foo", func=lambda x: x)
-    assert "foo" in test_registry
-
-
-def test_registry_call():
-    test_registry = catalogue.create("test")
-    test_registry("foo", func=lambda x: x)
-    assert "foo" in test_registry
-
-
-def test_get_all():
-    catalogue._set(("a", "b", "c"), "test")
-    catalogue._set(("a", "b", "d"), "test")
-    catalogue._set(("a", "b"), "test")
-    catalogue._set(("b", "a"), "test")
-    all_items = catalogue._get_all(("a", "b"))
-    assert len(all_items) == 3
-    assert ("a", "b", "c") in all_items
-    assert ("a", "b", "d") in all_items
-    assert ("a", "b") in all_items
-    all_items = catalogue._get_all(("a", "b", "c"))
-    assert len(all_items) == 1
-    assert ("a", "b", "c") in all_items
-    assert len(catalogue._get_all(("a", "b", "c", "d"))) == 0
-
-
-def test_create_single_namespace():
-    test_registry = catalogue.create("test")
-    assert catalogue.REGISTRY == {}
-
-    @test_registry.register("a")
-    def a():
-        pass
-
-    def b():
-        pass
-
-    test_registry.register("b", func=b)
-    items = test_registry.get_all()
-    assert len(items) == 2
-    assert items["a"] == a
-    assert items["b"] == b
-    assert catalogue.check_exists("test", "a")
-    assert catalogue.check_exists("test", "b")
-    assert catalogue._get(("test", "a")) == a
-    assert catalogue._get(("test", "b")) == b
-
-    with pytest.raises(TypeError):
-        # The decorator only accepts one argument
-        @test_registry.register("x", "y")
-        def x():
-            pass
-
-
-def test_create_multi_namespace():
-    test_registry = catalogue.create("x", "y")
-
-    @test_registry.register("z")
-    def z():
-        pass
-
-    items = test_registry.get_all()
-    assert len(items) == 1
-    assert items["z"] == z
-    assert catalogue.check_exists("x", "y", "z")
-    assert catalogue._get(("x", "y", "z")) == z
-
-
-def _check_entry_points():
-    # Check entry points for test_entry_points_older() and test_entry_points_newer().
-    assert catalogue.REGISTRY == {}
-    test_registry = catalogue.create("test", "foo", entry_points=True)
-    entry_points = test_registry.get_entry_points()
-    assert "bar" in entry_points
-    assert entry_points["bar"] == catalogue.check_exists
-    assert test_registry.get_entry_point("bar") == catalogue.check_exists
-    assert catalogue.REGISTRY == {}
-    assert test_registry.get("bar") == catalogue.check_exists
-    assert test_registry.get_all() == {"bar": catalogue.check_exists}
-    assert "bar" in test_registry
-
-
-@pytest.mark.skipif(
-    sys.version_info >= (3, 10),
-    reason="Test does not support >=3.10 importlib_metadata API",
-)
-def test_entry_points_older():
-    # Create a new EntryPoint object by pretending we have a setup.cfg and
-    # use one of catalogue's util functions as the advertised function
-    ep_string = "[options.entry_points]test_foo\n    bar = catalogue:check_exists"
-    ep = catalogue.importlib_metadata.EntryPoint._from_text(ep_string)
-    catalogue.AVAILABLE_ENTRY_POINTS["test_foo"] = ep
-    _check_entry_points()
-
-
-@pytest.mark.skipif(
-    sys.version_info < (3, 10),
-    reason="Test does not support <3.10 importlib_metadata API",
-)
-def test_entry_points_newer():
-    # Create a new EntryPoint object by pretending we have a setup.cfg and
-    # use one of catalogue's util functions as the advertised function
-    ep = catalogue.importlib_metadata.EntryPoint(
-        "bar", "catalogue:check_exists", "test_foo"
-    )
-    catalogue.AVAILABLE_ENTRY_POINTS[
-        "test_foo"
-    ] = catalogue.importlib_metadata.EntryPoints([ep])
-    _check_entry_points()
-
-
-def test_registry_find():
-    test_registry = catalogue.create("test_registry_find")
-    name = "a"
-
-    @test_registry.register(name)
-    def a():
-        """This is a registered function."""
-        pass
-
-    info = test_registry.find(name)
-    assert info["module"] == "catalogue.tests.test_catalogue"
-    assert info["file"] == str(Path(__file__))
-    assert info["docstring"] == "This is a registered function."
-    assert info["line_no"]
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/vector.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/vector.py
deleted file mode 100644
index 666ff15cf8be0a0c17de4f86b74584d2bb27244f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/vector.py
+++ /dev/null
@@ -1,148 +0,0 @@
-from numbers import Number
-import math
-import operator
-import warnings
-
-
-__all__ = ["Vector"]
-
-
-class Vector(tuple):
-
-    """A math-like vector.
-
-    Represents an n-dimensional numeric vector. ``Vector`` objects support
-    vector addition and subtraction, scalar multiplication and division,
-    negation, rounding, and comparison tests.
-    """
-
-    __slots__ = ()
-
-    def __new__(cls, values, keep=False):
-        if keep is not False:
-            warnings.warn(
-                "the 'keep' argument has been deprecated",
-                DeprecationWarning,
-            )
-        if type(values) == Vector:
-            # No need to create a new object
-            return values
-        return super().__new__(cls, values)
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}({super().__repr__()})"
-
-    def _vectorOp(self, other, op):
-        if isinstance(other, Vector):
-            assert len(self) == len(other)
-            return self.__class__(op(a, b) for a, b in zip(self, other))
-        if isinstance(other, Number):
-            return self.__class__(op(v, other) for v in self)
-        raise NotImplementedError()
-
-    def _scalarOp(self, other, op):
-        if isinstance(other, Number):
-            return self.__class__(op(v, other) for v in self)
-        raise NotImplementedError()
-
-    def _unaryOp(self, op):
-        return self.__class__(op(v) for v in self)
-
-    def __add__(self, other):
-        return self._vectorOp(other, operator.add)
-
-    __radd__ = __add__
-
-    def __sub__(self, other):
-        return self._vectorOp(other, operator.sub)
-
-    def __rsub__(self, other):
-        return self._vectorOp(other, _operator_rsub)
-
-    def __mul__(self, other):
-        return self._scalarOp(other, operator.mul)
-
-    __rmul__ = __mul__
-
-    def __truediv__(self, other):
-        return self._scalarOp(other, operator.truediv)
-
-    def __rtruediv__(self, other):
-        return self._scalarOp(other, _operator_rtruediv)
-
-    def __pos__(self):
-        return self._unaryOp(operator.pos)
-
-    def __neg__(self):
-        return self._unaryOp(operator.neg)
-
-    def __round__(self, *, round=round):
-        return self._unaryOp(round)
-
-    def __eq__(self, other):
-        if isinstance(other, list):
-            # bw compat Vector([1, 2, 3]) == [1, 2, 3]
-            other = tuple(other)
-        return super().__eq__(other)
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __bool__(self):
-        return any(self)
-
-    __nonzero__ = __bool__
-
-    def __abs__(self):
-        return math.sqrt(sum(x * x for x in self))
-
-    def length(self):
-        """Return the length of the vector. Equivalent to abs(vector)."""
-        return abs(self)
-
-    def normalized(self):
-        """Return the normalized vector of the vector."""
-        return self / abs(self)
-
-    def dot(self, other):
-        """Performs vector dot product, returning the sum of
-        ``a[0] * b[0], a[1] * b[1], ...``"""
-        assert len(self) == len(other)
-        return sum(a * b for a, b in zip(self, other))
-
-    # Deprecated methods/properties
-
-    def toInt(self):
-        warnings.warn(
-            "the 'toInt' method has been deprecated, use round(vector) instead",
-            DeprecationWarning,
-        )
-        return self.__round__()
-
-    @property
-    def values(self):
-        warnings.warn(
-            "the 'values' attribute has been deprecated, use "
-            "the vector object itself instead",
-            DeprecationWarning,
-        )
-        return list(self)
-
-    @values.setter
-    def values(self, values):
-        raise AttributeError(
-            "can't set attribute, the 'values' attribute has been deprecated",
-        )
-
-    def isclose(self, other: "Vector", **kwargs) -> bool:
-        """Return True if the vector is close to another Vector."""
-        assert len(self) == len(other)
-        return all(math.isclose(a, b, **kwargs) for a, b in zip(self, other))
-
-
-def _operator_rsub(a, b):
-    return operator.sub(b, a)
-
-
-def _operator_rtruediv(a, b):
-    return operator.truediv(b, a)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/duplicate_button.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/duplicate_button.py
deleted file mode 100644
index e4ecc25c9b3d1f405dcd8cbbaa96c6b536e96d80..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/duplicate_button.py
+++ /dev/null
@@ -1,61 +0,0 @@
-""" Predefined buttons with bound events that can be included in a gr.Blocks for convenience. """
-
-from __future__ import annotations
-
-from typing import Literal
-
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components import Button
-from gradio.utils import get_space
-
-set_documentation_group("component")
-
-
-@document()
-class DuplicateButton(Button):
-    """
-    Button that triggers a Spaces Duplication, when the demo is on Hugging Face Spaces. Does nothing locally.
-    Preprocessing: passes the button value as a {str} into the function
-    Postprocessing: expects a {str} to be returned from a function, which is set as the label of the button
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        *,
-        value: str = "Duplicate Space",
-        variant: Literal["primary", "secondary", "stop"] = "secondary",
-        size: Literal["sm", "lg"] | None = "sm",
-        visible: bool = True,
-        interactive: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        scale: int | None = 0,
-        min_width: int | None = None,
-        _activate: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value,
-            variant=variant,
-            size=size,
-            visible=visible,
-            interactive=interactive,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            scale=scale,
-            min_width=min_width,
-            **kwargs,
-        )
-        if _activate:
-            self.activate()
-
-    def activate(self):
-        space_name = get_space()
-        if space_name is not None:
-            self.click(
-                fn=None,
-                _js=f"() => {{ window.open(`https://huggingface.co/spaces/{space_name}?duplicate=true`, '_blank') }}",
-            )
diff --git a/spaces/cihyFjudo/fairness-paper-search/Argumentative Essay Abortion Thesis Statement How Abortion Empowers Women and Promotes Gender Equality.md b/spaces/cihyFjudo/fairness-paper-search/Argumentative Essay Abortion Thesis Statement How Abortion Empowers Women and Promotes Gender Equality.md
deleted file mode 100644
index 3dd0c40589bce13387f363f537aa2b48821e5143..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Argumentative Essay Abortion Thesis Statement How Abortion Empowers Women and Promotes Gender Equality.md	
+++ /dev/null
@@ -1,37 +0,0 @@
-<br />
-<p>You begin your essay with the <strong>introduction</strong>. Here you give the main definitions in case the reader is not aware of the topic. You also include some background information on the problem, describe the reason for your work and end the entry with a thesis. As a rule, a thesis contains your point of view on the subject.</p>
-<p>In <strong>the main body </strong> of your college research paper, you express all the points for and against the abortions. That means you will have two paragraphs for each group of statements. In this part, you place all the ideas you have.</p>
-<h2>argumentative essay abortion thesis statement</h2><br /><p><b><b>DOWNLOAD</b> &#9733; <a href="https://tinurli.com/2uwjCt">https://tinurli.com/2uwjCt</a></b></p><br /><br />
-<p>Abortion has been a complicated issue for many individuals, debating whether it should be illegal or not. Various medical and health situations have consequently supported the inhumane procedures used so that the act can be successful. This has therefore made it crucial for students to find out more about the practice despite the multiple opinions that people have towards it. In light of this, the guidelines below will be of great assistance as you write a thesis statement on abortion.</p>
-<p>Examiners look at many things apart from the content that you give in your paper. To avoid any unnecessary penalties, you should follow the points given above strictly in the coming up of an abortion thesis statement.</p>
-<p>In the introduction, a person should inform the reader what their essay is constituted of. In such a case, you can state what the article may cover or in other words what it contains (your thesis statement`s general take on abortion). Therefore, it is essential to have a thesis statement about abortion as the introduction and make sure that it is not too long. It should either cover half a page or less unless otherwise stated.</p>
-<p>In a paragraph, it is essential to state the document where the information used to come up with the thesis statement`s content was derived from. The references used will often come from published sources including books, electronic sources such as websites, e-books, articles and research papers, audio or video media.</p>
-<p>In a thesis statement for abortion, one needs to highlight the causes and effects of abortion. As the writer, you will need first to introduce the causes then describe implications that are possible. To start with, you should aim to be thorough on precisely what should be the result. If the termination of a pregnancy is the cause, then you will have to describe the possible outcome of the procedure.</p>
-<p>The best approach to coming up with a thesis statement is by using the argument of your essay. For a perfect thesis statement, you need to tailor in a manner that will give the reader a feel of what to expect in the rest of your essay.</p>
-<p>The conclusion here is intended for the summary of the entire project. This is where you are supposed to revise your hook sentence, thesis, and significant ideas. There is a wide range of topics concerning abortion to choose from and here are some of the abortion thesis statement samples:</p>
-<p>Writing of an essay can be made easier once the guidelines have been adhered to. Always remember that writing demands that one should express their stand to the readers hence making it crystal clear and brief. If you are well conversant with how to write an abortion thesis statement, feel free to share some additional tips that have worked for you.</p>
-<p></p>
-<p>A student must consider many facts about birth laws, elaborate an idea of whether abortions should be legal or illegal, and write a summary for a research paper on abortion based on the information. If you need something to base on, you can familiarize yourself with free argumentative essay on abortion and abortion thesis statement examples on our website.</p>
-<p>This debate is likely to go on for several years unless the sturdy stands taken by both pro-lifers and pro-abortionists are softened. People have to find ways of accommodating the views of each other regarding the subject. The author will evaluate the issues surrounding the abortion debate. Analyzing the pros and examining the cons of abortion, the essay will seek to find solutions to the conflicting ideas.</p>
-<p>Abortion essay is one of the most controversial essays students are ever asked to write. The debate around the issue is so sensitive that professors sometimes discourage students from choosing an abortion essay topic for their open topic essay. Students, on the other hand, argue for their point with so much vigor that it no longer looks like scientific and impartial academic writing.</p>
-<p>Much of academic writing success depends on how well you understand the peculiarities of essay types. They guide your research efforts, determine the structure of an essay, and inform the creation of a thesis statement. Here are the essay types suitable for essays on abortion and tips on how to write them:</p>
-<p>An argumentative essay is the most common essay type for the topic. Here, after a short lead-in, you should include an abortion argumentative essay thesis. The latter should clearly show your position on abortions: either it is a fundamental right of a woman and should be allowed, or it is bad (for the reasons you name) and should be banned.</p>
-<p>A persuasive essay typically has the same structure as an argumentative one. The difference is that you may present your own ideas and do not necessarily have to back up all your statements with solid facts. Also, you may use more rhetorical appeals to persuade the readers in your point.</p>
-<p>*Note that it is also common to approach debate essays as argumentative ones associating it with debates where you also prove your point; thus, read carefully the instruction of your professors to understand what they expect to see.</p>
-<p>One topic for a cause and effect essay may be the diverse causes for choosing to abort an unborn baby or the effects of doing so for a woman, a family, medical workers involved in abortion, or society at large. Another option is analyzing the impact of the legislature that makes abortions legal or illegal.</p>
-<p>To adopt the topic to a compare and contrast essay, you may compare the past and present perception, safety, and prevalence of abortions or analyze how these differ for various cultures or countries.</p>
-<p>An introduction is an important element in the essay structure that introduces your audience to the context, grasps their attention, sets the tone of our message and, thus, leads to the key message of your essay, summarised in a thesis statement.</p>
-<p>The essay about abortion should not be regarded as an argumentative paper only; you can choose cause-effect paper or a descriptive one. Nevertheless, if you are interested in an argumentative abortion essay, you can check our example here.</p>
-<p>The thesis or thesis statement is a comprehensive summary of everything you say in the essay. It is the main point you are making in the essay. Therefore, it may be best to formulate your thesis afteryou have written the rest of the essay, so you are aware of what point you want to make. You may, of course, set out with a tentative thesis, allowing it to change as the content in your essay changes.</p>
-<p>In a literary essay, do not confuse the thesis with the theme. The theme is the message, the truth (about life or human relationships) that the author of the story or play wants to convey. The thesisis your theory of how the author conveys that theme through the device of irony, point-of-view, contrast (or conflict), setting, motif, or symbol.</p>
-<p>Whatever type of essay you are writing, the thesis should be a definite summary of the essay, allowing the reader to anticipate the rest of the essay. Usually appearing at the end of the first paragraph, the thesis should be provocative, a punchline that makes the reader want to read the essay. The thesis is one of the most important parts of an essay. Make it strong, and make it say something.</p>
-<p>A thesis statement's main purpose is to help a reader identify the main idea of an essay, summary, or research paper. Another purpose of a thesis statement is to help a writer develop a plan for writing a paper. Most writers will change their thesis...</p>
-<p>Prewriting is preparation process that you can complete before you actually write your paper, essay or summary. Prewriting helps you organize your thoughts, plan your research or writing, and clarify your thesis.</p>
-<p>Just as a personal anecdote can be a disorganized mess, an essay can fall into the same trap of being out of order and confusing. That is why writers need a thesis statement to provide a specific focus for their essay and to organize what they are about to discuss in the body.</p>
-<p>Specificity.</strong> A thesis statement must concentrate on a specific area of a general topic. As you may recall, the creation of a thesis statement begins when you choose a broad subject and then narrow down its parts until you pinpoint a specific aspect of that topic. For example, health care is a broad topic, but a proper thesis statement would focus on a specific area of that topic, such as options for individuals without health care coverage.</p>
-<p>Precision.</strong> A strong thesis statement must be precise enough to allow for a coherent argument and to remain focused on the topic. If the specific topic is options for individuals without health care coverage, then your precise thesis statement must make an exact claim about it, such as that limited options exist for those who are uninsured by their employers. You must further pinpoint what you are going to discuss regarding these limited effects, such as whom they affect and what the cause is.</p>
-<p>Ability to be argued.</strong> A thesis statement must present a relevant and specific argument. A factual statement often is not considered arguable. Be sure your thesis statement contains a point of view that can be supported with evidence.</p>
-<p>Even in a personal essay that allows the use of first person, your thesis should not contain phrases such as in my opinion or I believe. These statements reduce your credibility and weaken your argument. Your opinion is more convincing when you use a firm attitude.</p>
-<p>Read the following thesis statements. On a separate piece of paper, identify each as weak or strong. For those that are weak, list the reasons why. Then revise the weak statements so that they conform to the requirements of a strong thesis.</p> aaccfb2cb3<br />
-<br />
-<br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Lubert Stryer Biochemistry 7th Edition PDF Free Download Discover the Latest Advances and Research in Biochemistry.md b/spaces/cihyFjudo/fairness-paper-search/Lubert Stryer Biochemistry 7th Edition PDF Free Download Discover the Latest Advances and Research in Biochemistry.md
deleted file mode 100644
index 7fc2a1799cef5fdab9460d69d978006f629aba8d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Lubert Stryer Biochemistry 7th Edition PDF Free Download Discover the Latest Advances and Research in Biochemistry.md	
+++ /dev/null
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Thehotboysguerillawarfarezip Learn the Lyrics and Tracklist of the Album.md b/spaces/cihyFjudo/fairness-paper-search/Thehotboysguerillawarfarezip Learn the Lyrics and Tracklist of the Album.md
deleted file mode 100644
index 93a94480c04f70c883bb574ae1945648b75c07db..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Thehotboysguerillawarfarezip Learn the Lyrics and Tracklist of the Album.md	
+++ /dev/null
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-<h2>thehotboysguerillawarfarezip</h2><br /><p><b><b>DOWNLOAD</b> &#10084; <a href="https://tinurli.com/2uwkKQ">https://tinurli.com/2uwkKQ</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Vinod Rai Not Just An Accountant Pdf Free A Critical Analysis of the Governance and Accountability Issues in India.md b/spaces/cihyFjudo/fairness-paper-search/Vinod Rai Not Just An Accountant Pdf Free A Critical Analysis of the Governance and Accountability Issues in India.md
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index dac08dd029dcc79e756916a1a4f68d2266e88c1e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Vinod Rai Not Just An Accountant Pdf Free A Critical Analysis of the Governance and Accountability Issues in India.md	
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diff --git a/spaces/cjayic/soft-vc-widowmaker/hifigan/discriminator.py b/spaces/cjayic/soft-vc-widowmaker/hifigan/discriminator.py
deleted file mode 100644
index 8f53c3d970e68a5ce68ef52c21e39ff553536f73..0000000000000000000000000000000000000000
--- a/spaces/cjayic/soft-vc-widowmaker/hifigan/discriminator.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Tuple, List
-
-from hifigan.utils import get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class PeriodDiscriminator(torch.nn.Module):
-    """HiFiGAN Period Discriminator"""
-
-    def __init__(
-        self,
-        period: int,
-        kernel_size: int = 5,
-        stride: int = 3,
-        use_spectral_norm: bool = False,
-    ) -> None:
-        super().__init__()
-        self.period = period
-        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    nn.Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-            ]
-        )
-        self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        """
-        Args:
-            x (Tensor): input waveform.
-        Returns:
-            [Tensor]: discriminator scores per sample in the batch.
-            [List[Tensor]]: list of features from each convolutional layer.
-        """
-        feat = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            feat.append(x)
-        x = self.conv_post(x)
-        feat.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, feat
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    """HiFiGAN Multi-Period Discriminator (MPD)"""
-
-    def __init__(self):
-        super().__init__()
-        self.discriminators = nn.ModuleList(
-            [
-                PeriodDiscriminator(2),
-                PeriodDiscriminator(3),
-                PeriodDiscriminator(5),
-                PeriodDiscriminator(7),
-                PeriodDiscriminator(11),
-            ]
-        )
-
-    def forward(
-        self, x: torch.Tensor
-    ) -> Tuple[List[torch.Tensor], List[List[torch.Tensor]]]:
-        """
-        Args:
-            x (Tensor): input waveform.
-        Returns:
-            [List[Tensor]]: list of scores from each discriminator.
-            [List[List[Tensor]]]: list of features from each discriminator's convolutional layers.
-        """
-        scores = []
-        feats = []
-        for _, d in enumerate(self.discriminators):
-            score, feat = d(x)
-            scores.append(score)
-            feats.append(feat)
-        return scores, feats
-
-
-class ScaleDiscriminator(torch.nn.Module):
-    """HiFiGAN Scale Discriminator."""
-
-    def __init__(self, use_spectral_norm: bool = False) -> None:
-        super().__init__()
-        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)),
-                norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-                norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-                norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-                norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-                norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-                norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        """
-        Args:
-            x (Tensor): input waveform.
-        Returns:
-            Tensor: discriminator scores.
-            List[Tensor]: list of features from the convolutional layers.
-        """
-        feat = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            feat.append(x)
-        x = self.conv_post(x)
-        feat.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, feat
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    """HiFiGAN Multi-Scale Discriminator."""
-
-    def __init__(self):
-        super().__init__()
-        self.discriminators = nn.ModuleList(
-            [
-                ScaleDiscriminator(use_spectral_norm=True),
-                ScaleDiscriminator(),
-                ScaleDiscriminator(),
-            ]
-        )
-        self.meanpools = nn.ModuleList(
-            [nn.AvgPool1d(4, 2, padding=2), nn.AvgPool1d(4, 2, padding=2)]
-        )
-
-    def forward(
-        self, x: torch.Tensor
-    ) -> Tuple[List[torch.Tensor], List[List[torch.Tensor]]]:
-        """
-        Args:
-            x (Tensor): input waveform.
-        Returns:
-            List[Tensor]: discriminator scores.
-            List[List[Tensor]]: list of features from each discriminator's convolutional layers.
-        """
-        scores = []
-        feats = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                x = self.meanpools[i - 1](x)
-            score, feat = d(x)
-            scores.append(score)
-            feats.append(feat)
-        return scores, feats
-
-
-class HifiganDiscriminator(nn.Module):
-    """HiFiGAN discriminator"""
-
-    def __init__(self):
-        super().__init__()
-        self.mpd = MultiPeriodDiscriminator()
-        self.msd = MultiScaleDiscriminator()
-
-    def forward(
-        self, x: torch.Tensor
-    ) -> Tuple[List[torch.Tensor], List[List[torch.Tensor]]]:
-        """
-        Args:
-            x (Tensor): input waveform.
-        Returns:
-            List[Tensor]: discriminator scores.
-            List[List[Tensor]]: list of features from from each discriminator's convolutional layers.
-        """
-        scores, feats = self.mpd(x)
-        scores_, feats_ = self.msd(x)
-        return scores + scores_, feats + feats_
-
-
-def feature_loss(
-    features_real: List[List[torch.Tensor]], features_generate: List[List[torch.Tensor]]
-) -> float:
-    loss = 0
-    for r, g in zip(features_real, features_generate):
-        for rl, gl in zip(r, g):
-            loss += torch.mean(torch.abs(rl - gl))
-    return loss * 2
-
-
-def discriminator_loss(real, generated):
-    loss = 0
-    real_losses = []
-    generated_losses = []
-    for r, g in zip(real, generated):
-        r_loss = torch.mean((1 - r) ** 2)
-        g_loss = torch.mean(g ** 2)
-        loss += r_loss + g_loss
-        real_losses.append(r_loss.item())
-        generated_losses.append(g_loss.item())
-
-    return loss, real_losses, generated_losses
-
-
-def generator_loss(discriminator_outputs):
-    loss = 0
-    generator_losses = []
-    for x in discriminator_outputs:
-        l = torch.mean((1 - x) ** 2)
-        generator_losses.append(l)
-        loss += l
-
-    return loss, generator_losses
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py
deleted file mode 100644
index 27cb89f735e2a1883b2b52ee42fd9ba34c5805fb..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py
+++ /dev/null
@@ -1,399 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# macOS icns file decoder, based on icns.py by Bob Ippolito.
-#
-# history:
-# 2004-10-09 fl   Turned into a PIL plugin; removed 2.3 dependencies.
-# 2020-04-04      Allow saving on all operating systems.
-#
-# Copyright (c) 2004 by Bob Ippolito.
-# Copyright (c) 2004 by Secret Labs.
-# Copyright (c) 2004 by Fredrik Lundh.
-# Copyright (c) 2014 by Alastair Houghton.
-# Copyright (c) 2020 by Pan Jing.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import os
-import struct
-import sys
-
-from . import Image, ImageFile, PngImagePlugin, features
-
-enable_jpeg2k = features.check_codec("jpg_2000")
-if enable_jpeg2k:
-    from . import Jpeg2KImagePlugin
-
-MAGIC = b"icns"
-HEADERSIZE = 8
-
-
-def nextheader(fobj):
-    return struct.unpack(">4sI", fobj.read(HEADERSIZE))
-
-
-def read_32t(fobj, start_length, size):
-    # The 128x128 icon seems to have an extra header for some reason.
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(4)
-    if sig != b"\x00\x00\x00\x00":
-        msg = "Unknown signature, expecting 0x00000000"
-        raise SyntaxError(msg)
-    return read_32(fobj, (start + 4, length - 4), size)
-
-
-def read_32(fobj, start_length, size):
-    """
-    Read a 32bit RGB icon resource.  Seems to be either uncompressed or
-    an RLE packbits-like scheme.
-    """
-    (start, length) = start_length
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    if length == sizesq * 3:
-        # uncompressed ("RGBRGBGB")
-        indata = fobj.read(length)
-        im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1)
-    else:
-        # decode image
-        im = Image.new("RGB", pixel_size, None)
-        for band_ix in range(3):
-            data = []
-            bytesleft = sizesq
-            while bytesleft > 0:
-                byte = fobj.read(1)
-                if not byte:
-                    break
-                byte = byte[0]
-                if byte & 0x80:
-                    blocksize = byte - 125
-                    byte = fobj.read(1)
-                    for i in range(blocksize):
-                        data.append(byte)
-                else:
-                    blocksize = byte + 1
-                    data.append(fobj.read(blocksize))
-                bytesleft -= blocksize
-                if bytesleft <= 0:
-                    break
-            if bytesleft != 0:
-                msg = f"Error reading channel [{repr(bytesleft)} left]"
-                raise SyntaxError(msg)
-            band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1)
-            im.im.putband(band.im, band_ix)
-    return {"RGB": im}
-
-
-def read_mk(fobj, start_length, size):
-    # Alpha masks seem to be uncompressed
-    start = start_length[0]
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1)
-    return {"A": band}
-
-
-def read_png_or_jpeg2000(fobj, start_length, size):
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(12)
-    if sig[:8] == b"\x89PNG\x0d\x0a\x1a\x0a":
-        fobj.seek(start)
-        im = PngImagePlugin.PngImageFile(fobj)
-        Image._decompression_bomb_check(im.size)
-        return {"RGBA": im}
-    elif (
-        sig[:4] == b"\xff\x4f\xff\x51"
-        or sig[:4] == b"\x0d\x0a\x87\x0a"
-        or sig == b"\x00\x00\x00\x0cjP  \x0d\x0a\x87\x0a"
-    ):
-        if not enable_jpeg2k:
-            msg = (
-                "Unsupported icon subimage format (rebuild PIL "
-                "with JPEG 2000 support to fix this)"
-            )
-            raise ValueError(msg)
-        # j2k, jpc or j2c
-        fobj.seek(start)
-        jp2kstream = fobj.read(length)
-        f = io.BytesIO(jp2kstream)
-        im = Jpeg2KImagePlugin.Jpeg2KImageFile(f)
-        Image._decompression_bomb_check(im.size)
-        if im.mode != "RGBA":
-            im = im.convert("RGBA")
-        return {"RGBA": im}
-    else:
-        msg = "Unsupported icon subimage format"
-        raise ValueError(msg)
-
-
-class IcnsFile:
-    SIZES = {
-        (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)],
-        (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)],
-        (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)],
-        (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)],
-        (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)],
-        (128, 128, 1): [
-            (b"ic07", read_png_or_jpeg2000),
-            (b"it32", read_32t),
-            (b"t8mk", read_mk),
-        ],
-        (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)],
-        (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)],
-        (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)],
-        (32, 32, 1): [
-            (b"icp5", read_png_or_jpeg2000),
-            (b"il32", read_32),
-            (b"l8mk", read_mk),
-        ],
-        (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)],
-        (16, 16, 1): [
-            (b"icp4", read_png_or_jpeg2000),
-            (b"is32", read_32),
-            (b"s8mk", read_mk),
-        ],
-    }
-
-    def __init__(self, fobj):
-        """
-        fobj is a file-like object as an icns resource
-        """
-        # signature : (start, length)
-        self.dct = dct = {}
-        self.fobj = fobj
-        sig, filesize = nextheader(fobj)
-        if not _accept(sig):
-            msg = "not an icns file"
-            raise SyntaxError(msg)
-        i = HEADERSIZE
-        while i < filesize:
-            sig, blocksize = nextheader(fobj)
-            if blocksize <= 0:
-                msg = "invalid block header"
-                raise SyntaxError(msg)
-            i += HEADERSIZE
-            blocksize -= HEADERSIZE
-            dct[sig] = (i, blocksize)
-            fobj.seek(blocksize, io.SEEK_CUR)
-            i += blocksize
-
-    def itersizes(self):
-        sizes = []
-        for size, fmts in self.SIZES.items():
-            for fmt, reader in fmts:
-                if fmt in self.dct:
-                    sizes.append(size)
-                    break
-        return sizes
-
-    def bestsize(self):
-        sizes = self.itersizes()
-        if not sizes:
-            msg = "No 32bit icon resources found"
-            raise SyntaxError(msg)
-        return max(sizes)
-
-    def dataforsize(self, size):
-        """
-        Get an icon resource as {channel: array}.  Note that
-        the arrays are bottom-up like windows bitmaps and will likely
-        need to be flipped or transposed in some way.
-        """
-        dct = {}
-        for code, reader in self.SIZES[size]:
-            desc = self.dct.get(code)
-            if desc is not None:
-                dct.update(reader(self.fobj, desc, size))
-        return dct
-
-    def getimage(self, size=None):
-        if size is None:
-            size = self.bestsize()
-        if len(size) == 2:
-            size = (size[0], size[1], 1)
-        channels = self.dataforsize(size)
-
-        im = channels.get("RGBA", None)
-        if im:
-            return im
-
-        im = channels.get("RGB").copy()
-        try:
-            im.putalpha(channels["A"])
-        except KeyError:
-            pass
-        return im
-
-
-##
-# Image plugin for Mac OS icons.
-
-
-class IcnsImageFile(ImageFile.ImageFile):
-    """
-    PIL image support for Mac OS .icns files.
-    Chooses the best resolution, but will possibly load
-    a different size image if you mutate the size attribute
-    before calling 'load'.
-
-    The info dictionary has a key 'sizes' that is a list
-    of sizes that the icns file has.
-    """
-
-    format = "ICNS"
-    format_description = "Mac OS icns resource"
-
-    def _open(self):
-        self.icns = IcnsFile(self.fp)
-        self.mode = "RGBA"
-        self.info["sizes"] = self.icns.itersizes()
-        self.best_size = self.icns.bestsize()
-        self.size = (
-            self.best_size[0] * self.best_size[2],
-            self.best_size[1] * self.best_size[2],
-        )
-
-    @property
-    def size(self):
-        return self._size
-
-    @size.setter
-    def size(self, value):
-        info_size = value
-        if info_size not in self.info["sizes"] and len(info_size) == 2:
-            info_size = (info_size[0], info_size[1], 1)
-        if (
-            info_size not in self.info["sizes"]
-            and len(info_size) == 3
-            and info_size[2] == 1
-        ):
-            simple_sizes = [
-                (size[0] * size[2], size[1] * size[2]) for size in self.info["sizes"]
-            ]
-            if value in simple_sizes:
-                info_size = self.info["sizes"][simple_sizes.index(value)]
-        if info_size not in self.info["sizes"]:
-            msg = "This is not one of the allowed sizes of this image"
-            raise ValueError(msg)
-        self._size = value
-
-    def load(self):
-        if len(self.size) == 3:
-            self.best_size = self.size
-            self.size = (
-                self.best_size[0] * self.best_size[2],
-                self.best_size[1] * self.best_size[2],
-            )
-
-        px = Image.Image.load(self)
-        if self.im is not None and self.im.size == self.size:
-            # Already loaded
-            return px
-        self.load_prepare()
-        # This is likely NOT the best way to do it, but whatever.
-        im = self.icns.getimage(self.best_size)
-
-        # If this is a PNG or JPEG 2000, it won't be loaded yet
-        px = im.load()
-
-        self.im = im.im
-        self.mode = im.mode
-        self.size = im.size
-
-        return px
-
-
-def _save(im, fp, filename):
-    """
-    Saves the image as a series of PNG files,
-    that are then combined into a .icns file.
-    """
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-    sizes = {
-        b"ic07": 128,
-        b"ic08": 256,
-        b"ic09": 512,
-        b"ic10": 1024,
-        b"ic11": 32,
-        b"ic12": 64,
-        b"ic13": 256,
-        b"ic14": 512,
-    }
-    provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])}
-    size_streams = {}
-    for size in set(sizes.values()):
-        image = (
-            provided_images[size]
-            if size in provided_images
-            else im.resize((size, size))
-        )
-
-        temp = io.BytesIO()
-        image.save(temp, "png")
-        size_streams[size] = temp.getvalue()
-
-    entries = []
-    for type, size in sizes.items():
-        stream = size_streams[size]
-        entries.append(
-            {"type": type, "size": HEADERSIZE + len(stream), "stream": stream}
-        )
-
-    # Header
-    fp.write(MAGIC)
-    file_length = HEADERSIZE  # Header
-    file_length += HEADERSIZE + 8 * len(entries)  # TOC
-    file_length += sum(entry["size"] for entry in entries)
-    fp.write(struct.pack(">i", file_length))
-
-    # TOC
-    fp.write(b"TOC ")
-    fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE))
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-
-    # Data
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-        fp.write(entry["stream"])
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-def _accept(prefix):
-    return prefix[:4] == MAGIC
-
-
-Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept)
-Image.register_extension(IcnsImageFile.format, ".icns")
-
-Image.register_save(IcnsImageFile.format, _save)
-Image.register_mime(IcnsImageFile.format, "image/icns")
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 IcnsImagePlugin.py [file]")
-        sys.exit()
-
-    with open(sys.argv[1], "rb") as fp:
-        imf = IcnsImageFile(fp)
-        for size in imf.info["sizes"]:
-            imf.size = size
-            imf.save("out-%s-%s-%s.png" % size)
-        with Image.open(sys.argv[1]) as im:
-            im.save("out.png")
-        if sys.platform == "windows":
-            os.startfile("out.png")
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_O_S_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_O_S_.py
deleted file mode 100644
index ca8290bab440e31196dd009c5125e022a079d7af..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_O_S_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_G_P_O_S_(BaseTTXConverter):
-    pass
diff --git a/spaces/codelion/Grounding_DINO_demo/groundingdino/util/misc.py b/spaces/codelion/Grounding_DINO_demo/groundingdino/util/misc.py
deleted file mode 100644
index d64b84ef24bea0c98e76824feb1903f6bfebe7a5..0000000000000000000000000000000000000000
--- a/spaces/codelion/Grounding_DINO_demo/groundingdino/util/misc.py
+++ /dev/null
@@ -1,717 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import colorsys
-import datetime
-import functools
-import io
-import json
-import os
-import pickle
-import subprocess
-import time
-from collections import OrderedDict, defaultdict, deque
-from typing import List, Optional
-
-import numpy as np
-import torch
-import torch.distributed as dist
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-from torch import Tensor
-
-__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
-if __torchvision_need_compat_flag:
-    from torchvision.ops import _new_empty_tensor
-    from torchvision.ops.misc import _output_size
-
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        if d.shape[0] == 0:
-            return 0
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        if os.environ.get("SHILONG_AMP", None) == "1":
-            eps = 1e-4
-        else:
-            eps = 1e-6
-        return self.total / (self.count + eps)
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value,
-        )
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-
-    return dist.group.WORLD
-
-
-def all_gather_cpu(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    cpu_group = _get_global_gloo_group()
-
-    buffer = io.BytesIO()
-    torch.save(data, buffer)
-    data_view = buffer.getbuffer()
-    device = "cuda" if cpu_group is None else "cpu"
-    tensor = torch.ByteTensor(data_view).to(device)
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
-    if cpu_group is None:
-        dist.all_gather(size_list, local_size)
-    else:
-        print("gathering on cpu")
-        dist.all_gather(size_list, local_size, group=cpu_group)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-    assert isinstance(local_size.item(), int)
-    local_size = int(local_size.item())
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    if cpu_group is None:
-        dist.all_gather(tensor_list, tensor)
-    else:
-        dist.all_gather(tensor_list, tensor, group=cpu_group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
-        buffer = io.BytesIO(tensor.cpu().numpy())
-        obj = torch.load(buffer)
-        data_list.append(obj)
-
-    return data_list
-
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    if os.getenv("CPU_REDUCE") == "1":
-        return all_gather_cpu(data)
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            # print(name, str(meter))
-            # import ipdb;ipdb.set_trace()
-            if meter.count > 0:
-                loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None, logger=None):
-        if logger is None:
-            print_func = print
-        else:
-            print_func = logger.info
-
-        i = 0
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        if torch.cuda.is_available():
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                    "max mem: {memory:.0f}",
-                ]
-            )
-        else:
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                ]
-            )
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            # import ipdb; ipdb.set_trace()
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / MB,
-                        )
-                    )
-                else:
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print_func(
-            "{} Total time: {} ({:.4f} s / it)".format(
-                header, total_time_str, total_time / len(iterable)
-            )
-        )
-
-
-def get_sha():
-    cwd = os.path.dirname(os.path.abspath(__file__))
-
-    def _run(command):
-        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
-
-    sha = "N/A"
-    diff = "clean"
-    branch = "N/A"
-    try:
-        sha = _run(["git", "rev-parse", "HEAD"])
-        subprocess.check_output(["git", "diff"], cwd=cwd)
-        diff = _run(["git", "diff-index", "HEAD"])
-        diff = "has uncommited changes" if diff else "clean"
-        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
-    except Exception:
-        pass
-    message = f"sha: {sha}, status: {diff}, branch: {branch}"
-    return message
-
-
-def collate_fn(batch):
-    # import ipdb; ipdb.set_trace()
-    batch = list(zip(*batch))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    return tuple(batch)
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-        if mask == "auto":
-            self.mask = torch.zeros_like(tensors).to(tensors.device)
-            if self.mask.dim() == 3:
-                self.mask = self.mask.sum(0).to(bool)
-            elif self.mask.dim() == 4:
-                self.mask = self.mask.sum(1).to(bool)
-            else:
-                raise ValueError(
-                    "tensors dim must be 3 or 4 but {}({})".format(
-                        self.tensors.dim(), self.tensors.shape
-                    )
-                )
-
-    def imgsize(self):
-        res = []
-        for i in range(self.tensors.shape[0]):
-            mask = self.mask[i]
-            maxH = (~mask).sum(0).max()
-            maxW = (~mask).sum(1).max()
-            res.append(torch.Tensor([maxH, maxW]))
-        return res
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def to_img_list_single(self, tensor, mask):
-        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
-        maxH = (~mask).sum(0).max()
-        maxW = (~mask).sum(1).max()
-        img = tensor[:, :maxH, :maxW]
-        return img
-
-    def to_img_list(self):
-        """remove the padding and convert to img list
-
-        Returns:
-            [type]: [description]
-        """
-        if self.tensors.dim() == 3:
-            return self.to_img_list_single(self.tensors, self.mask)
-        else:
-            res = []
-            for i in range(self.tensors.shape[0]):
-                tensor_i = self.tensors[i]
-                mask_i = self.mask[i]
-                res.append(self.to_img_list_single(tensor_i, mask_i))
-            return res
-
-    @property
-    def device(self):
-        return self.tensors.device
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-    @property
-    def shape(self):
-        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
-
-        # launch by torch.distributed.launch
-        # Single node
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
-        # Multi nodes
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
-        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
-        # args.world_size = args.world_size * local_world_size
-        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
-        # args.rank = args.rank * local_world_size + args.local_rank
-        print(
-            "world size: {}, rank: {}, local rank: {}".format(
-                args.world_size, args.rank, args.local_rank
-            )
-        )
-        print(json.dumps(dict(os.environ), indent=2))
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
-        args.world_size = int(os.environ["SLURM_NPROCS"])
-
-        print(
-            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
-                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
-            )
-        )
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        args.world_size = 1
-        args.rank = 0
-        args.local_rank = 0
-        return
-
-    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
-    args.distributed = True
-    torch.cuda.set_device(args.local_rank)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-
-    torch.distributed.init_process_group(
-        backend=args.dist_backend,
-        world_size=args.world_size,
-        rank=args.rank,
-        init_method=args.dist_url,
-    )
-
-    print("Before torch.distributed.barrier()")
-    torch.distributed.barrier()
-    print("End torch.distributed.barrier()")
-    setup_for_distributed(args.rank == 0)
-
-
-@torch.no_grad()
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    if target.numel() == 0:
-        return [torch.zeros([], device=output.device)]
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-@torch.no_grad()
-def accuracy_onehot(pred, gt):
-    """_summary_
-
-    Args:
-        pred (_type_): n, c
-        gt (_type_): n, c
-    """
-    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
-    acc = tp / gt.shape[0] * 100
-    return acc
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if __torchvision_need_compat_flag < 0.7:
-        if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
-
-        output_shape = _output_size(2, input, size, scale_factor)
-        output_shape = list(input.shape[:-2]) + list(output_shape)
-        return _new_empty_tensor(input, output_shape)
-    else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-class color_sys:
-    def __init__(self, num_colors) -> None:
-        self.num_colors = num_colors
-        colors = []
-        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
-            hue = i / 360.0
-            lightness = (50 + np.random.rand() * 10) / 100.0
-            saturation = (90 + np.random.rand() * 10) / 100.0
-            colors.append(
-                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
-            )
-        self.colors = colors
-
-    def __call__(self, idx):
-        return self.colors[idx]
-
-
-def inverse_sigmoid(x, eps=1e-3):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict
diff --git a/spaces/codertoro/gpt-academic/crazy_functions/test_project/cpp/cppipc/shm.cpp b/spaces/codertoro/gpt-academic/crazy_functions/test_project/cpp/cppipc/shm.cpp
deleted file mode 100644
index 593ce3129dc1574dbc8fc8b088cf595df215de93..0000000000000000000000000000000000000000
--- a/spaces/codertoro/gpt-academic/crazy_functions/test_project/cpp/cppipc/shm.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-
-#include <string>
-#include <utility>
-
-#include "libipc/shm.h"
-
-#include "libipc/utility/pimpl.h"
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace shm {
-
-class handle::handle_ : public pimpl<handle_> {
-public:
-    shm::id_t id_ = nullptr;
-    void*     m_  = nullptr;
-
-    ipc::string n_;
-    std::size_t s_ = 0;
-};
-
-handle::handle()
-    : p_(p_->make()) {
-}
-
-handle::handle(char const * name, std::size_t size, unsigned mode)
-    : handle() {
-    acquire(name, size, mode);
-}
-
-handle::handle(handle&& rhs)
-    : handle() {
-    swap(rhs);
-}
-
-handle::~handle() {
-    release();
-    p_->clear();
-}
-
-void handle::swap(handle& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-handle& handle::operator=(handle rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool handle::valid() const noexcept {
-    return impl(p_)->m_ != nullptr;
-}
-
-std::size_t handle::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-char const * handle::name() const noexcept {
-    return impl(p_)->n_.c_str();
-}
-
-std::int32_t handle::ref() const noexcept {
-    return shm::get_ref(impl(p_)->id_);
-}
-
-void handle::sub_ref() noexcept {
-    shm::sub_ref(impl(p_)->id_);
-}
-
-bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
-    release();
-    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-    return valid();
-}
-
-std::int32_t handle::release() {
-    if (impl(p_)->id_ == nullptr) return -1;
-    return shm::release(detach());
-}
-
-void* handle::get() const {
-    return impl(p_)->m_;
-}
-
-void handle::attach(id_t id) {
-    if (id == nullptr) return;
-    release();
-    impl(p_)->id_ = id;
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-}
-
-id_t handle::detach() {
-    auto old = impl(p_)->id_;
-    impl(p_)->id_ = nullptr;
-    impl(p_)->m_  = nullptr;
-    impl(p_)->s_  = 0;
-    impl(p_)->n_.clear();
-    return old;
-}
-
-} // namespace shm
-} // namespace ipc
diff --git a/spaces/codesue/streamlit-tfx/streamlit_tfx/visualization.py b/spaces/codesue/streamlit-tfx/streamlit_tfx/visualization.py
deleted file mode 100644
index 0d3de6061578d33458dc539d97842249e0c3639e..0000000000000000000000000000000000000000
--- a/spaces/codesue/streamlit-tfx/streamlit_tfx/visualization.py
+++ /dev/null
@@ -1,348 +0,0 @@
-import base64
-from typing import Any, Callable, Dict, List, Optional, Text, Union
-from textwrap import dedent
-
-from ipywidgets.embed import embed_snippet
-import pandas as pd
-import streamlit as st
-from tensorflow_data_validation.types import FeaturePath
-from tensorflow_data_validation.utils.display_util import _get_combined_statistics
-from tensorflow_data_validation.utils.display_util import (
-    get_anomalies_dataframe, get_schema_dataframe)
-from tensorflow_metadata.proto.v0.anomalies_pb2 import Anomalies
-from tensorflow_metadata.proto.v0.schema_pb2 import Schema
-from tensorflow_metadata.proto.v0.statistics_pb2 import DatasetFeatureStatisticsList
-from tensorflow_model_analysis.proto.config_pb2 import SlicingSpec
-from tensorflow_model_analysis.slicer.slicer_lib import SingleSliceSpec
-from tensorflow_model_analysis.view import (
-    render_plot,
-    render_slicing_attributions,
-    render_slicing_metrics,
-    render_time_series
-)
-from tensorflow_model_analysis.view.view_types import EvalResult, EvalResults
-
-WRAPPER = dedent("""
-    <div style="overflow-x: auto; border: 1px solid #e6e9ef;
-      border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">
-      {html}
-    </div>
-""")
-
-FOOTER = dedent("""
-    <span style="font-size: 0.75em">
-      &hearts; Built with [`streamlit-tfx`](https://github.com/codesue/streamlit-tfx)
-    </span>
-""")
-
-def wrap_html(html: str):
-  """Wraps HTML components to make visualizations look more uniform."""
-  return WRAPPER.format(html=html)
-
-def get_widget_view_html(widget_view: Any) -> str:
-  """Produces minimal HTML with an IPython widget view embedded."""
-  snippet = embed_snippet(views=[widget_view])
-  return wrap_html(snippet)
-
-def display_widget_view(
-    widget_view: Any,
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays a widget view.
-  Args:
-    widget_view: The widget view to display.
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-      element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  st.components.v1.html(
-      html=get_widget_view_html(widget_view),
-      width=width,
-      height=height,
-      scrolling=True
-  )
-
-def get_statistics_html(
-    lhs_statistics: DatasetFeatureStatisticsList,
-    rhs_statistics: Optional[DatasetFeatureStatisticsList] = None,
-    lhs_name: Text = 'lhs_statistics',
-    rhs_name: Text = 'rhs_statistics',
-    allowlist_features: Optional[List[FeaturePath]] = None,
-    denylist_features: Optional[List[FeaturePath]] = None
-) -> Text:
-  """Produces HTML for displaying input statistics using Facets.
-  Args:
-    lhs_statistics: A `DatasetFeatureStatisticsList` protocol buffer.
-    rhs_statistics: A `DatasetFeatureStatisticsList` protocol buffer to
-      compare with lhs_statistics.
-    lhs_name: Name of the lhs_statistics dataset.
-    rhs_name: Name of the rhs_statistics dataset.
-    allowlist_features: Set of features to be visualized.
-    denylist_features: Set of features to ignore for visualization.
-  Returns:
-    HTML to be embedded for visualization.
-  Raises:
-    TypeError: If the input argument is not of the expected type.
-    ValueError: If the input statistics protocol buffer has more than one dataset.
-  """
-  template = wrap_html("""
-      <script src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js"></script>
-      <link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html">
-      <facets-overview proto-input="{protostr}"></facets-overview>
-  """)
-  combined_statistics = _get_combined_statistics(
-      lhs_statistics, rhs_statistics, lhs_name, rhs_name, allowlist_features,
-      denylist_features
-  )
-  protostr = base64.b64encode(
-      combined_statistics.SerializeToString()
-  ).decode('utf-8')
-
-  return template.format(protostr=protostr)
-
-def display_statistics(
-    lhs_statistics: DatasetFeatureStatisticsList,
-    rhs_statistics: Optional[DatasetFeatureStatisticsList] = None,
-    lhs_name: Text = 'lhs_statistics',
-    rhs_name: Text = 'rhs_statistics',
-    allowlist_features: Optional[List[FeaturePath]] = None,
-    denylist_features: Optional[List[FeaturePath]] = None,
-    title: Optional[str] = 'Statistics',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays input statistics using Facets.
-  Args:
-    lhs_statistics: A `DatasetFeatureStatisticsList` protocol buffer.
-    rhs_statistics: A `DatasetFeatureStatisticsList` protocol buffer to
-      compare with lhs_statistics.
-    lhs_name: Name of the lhs_statistics dataset.
-    rhs_name: Name of the rhs_statistics dataset.
-    allowlist_features: Set of features to be visualized.
-    denylist_features: Set of features to ignore for visualization.
-    title: Title for the visualization. Defaults to "Statistics".
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-      element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  Raises:
-    TypeError: If the input argument is not of the expected type.
-    ValueError: If the input statistics protocol buffer has more than one dataset.
-  """
-  assert (not allowlist_features or not denylist_features), (
-      'Only specify one of allowlist_features and denylist_features.')
-
-  if title:
-    st.subheader(title)
-  html = get_statistics_html(
-      lhs_statistics,
-      rhs_statistics,
-      lhs_name, rhs_name,
-      allowlist_features,
-      denylist_features
-  )
-  st.components.v1.html(
-      html=html,
-      width=width,
-      height=height,
-      scrolling=True
-  )
-
-def display_schema(
-    schema: Schema,
-    title: Optional[str] = 'Schema',
-    features_title: Optional[str] = 'Features',
-    domains_title: Optional[str] = 'Domains',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays an input Schema.
-  Args:
-  schema: A `Schema` protocol buffer.
-  title: Title for the visualization. Defaults to "Schema".
-  features_title: Title for the features visualization. Defaults to "Features".
-  domains_title: Title for the domain's visualization. Defaults to "Domains".
-  width: Width of the frame in CSS pixels. Defaults to the app's default
-    element width.
-  height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  features_df, domains_df = get_schema_dataframe(schema)
-  st.markdown(f'#### {features_title}')
-  st.dataframe(features_df.style, width=width, height=height)
-  if not domains_df.empty:
-    pd.set_option('max_colwidth', None)
-    st.markdown(f'#### {domains_title}')
-    st.dataframe(domains_df.style, width=width, height=height)
-
-def display_anomalies(
-    anomalies: Anomalies,
-    title: Optional[str] = 'Anomalies',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays input anomalies.
-  Args:
-  anomalies: An `Anomalies` protocol buffer.
-  title: Title for the visualization. Defaults to "Anomalies".
-  width: Width of the frame in CSS pixels. Defaults to the app's default
-    element width.
-  height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  anomalies_df = get_anomalies_dataframe(anomalies)
-  if anomalies_df.empty:
-    st.markdown('<h4 style="color:green;">No anomalies found.</h4>',
-        unsafe_allow_html=True)
-  else:
-    st.markdown('<h4 style="color:red;">Found anomalies.</h4>',
-        unsafe_allow_html=True)
-    st.dataframe(anomalies_df.style, width=width, height=height)
-
-def display_eval_result_plot(
-    result: EvalResult,
-    slicing_spec: Optional[Union[SingleSliceSpec, SlicingSpec]] = None,
-    output_name: Optional[str] = None,
-    class_id: Optional[int] = None,
-    top_k: Optional[int] = None,
-    k: Optional[int] = None,
-    label: Optional[str] = None,
-    title: Optional[str] = 'Evaluation Result Plot',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays an evaluation result plot.
-  Args:
-    result: A `tfma.EvalResult`.
-    slicing_spec: The `tfma.SlicingSpec` to identify the slice. Show overall if
-      unset.
-    output_name: A string representing the output name.
-    class_id: A number representing the class id if multi class.
-    top_k: The k used to compute prediction in the top k position.
-    k: The k used to compute the prediction at the kth position.
-    label: A partial label used to match a set of plots in the results.
-    title: Title for the visualization. Defaults to "Evaluation Result Plot".
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-    element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  widget_view = render_plot(
-      result=result,
-      slicing_spec=slicing_spec,
-      output_name=output_name,
-      class_id=class_id,
-      top_k=top_k,
-      k=k,
-      label=label,
-  )
-  display_widget_view(widget_view, height=height, width=width)
-
-def display_eval_result_slicing_attributions(
-    result: EvalResult,
-    slicing_column: Optional[str] = None,
-    slicing_spec: Optional[Union[SingleSliceSpec, SlicingSpec]] = None,
-    metric_name: Optional[str] = None,
-    weighted_example_column: Optional[str] = None,
-    event_handlers: Optional[Callable[[Dict[str, Union[str, float]]], None]] = None,
-    title: Optional[str] = 'Evaluation Result Slicing Attributions',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays evaluation result slicing attributions.
-  Args:
-    result: A `tfma.EvalResult`.
-    slicing_column: The column to slice on.
-    slicing_spec: The `tfma.SlicingSpec` to filter results. If neither column nor
-      spec is set, show overall.
-    metric_name: Name of attributions metric to show attributions for. Optional
-      if only one metric used.
-    weighted_example_column: Override for the weighted example column. This can
-      be used when different weights are applied in different parts of the model
-      (eg: multi-head).
-    event_handlers: The event handlers.
-    title: Title for the visualization. Defaults to "Evaluation Result Slicing
-      Attributions".
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-      element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  widget_view = render_slicing_attributions(
-      result=result,
-      slicing_column=slicing_column,
-      slicing_spec=slicing_spec,
-      metric_name=metric_name,
-      weighted_example_column=weighted_example_column,
-      event_handlers=event_handlers
-  )
-  display_widget_view(widget_view, height=height, width=width)
-
-def display_eval_result_slicing_metrics(
-    result: EvalResult,
-    slicing_column: Optional[str] = None,
-    slicing_spec: Optional[Union[SingleSliceSpec, SlicingSpec]] = None,
-    weighted_example_column: Optional[str] = None,
-    event_handlers: Optional[Callable[[Dict[str, Union[str, float]]], None]] = None,
-    title: Optional[str] = 'Evaluation Result Slicing Metrics',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays evaluation result slicing metrics.
-  Args:
-    result: A `tfma.EvalResult`.
-    slicing_column: The column to slice on.
-    slicing_spec: The `tfma.SlicingSpec` to filter results. If neither column nor
-      spec is set, show overall.
-    weighted_example_column: Override for the weighted example column. This can
-      be used when different weights are applied in different parts of the model
-      (eg: multi-head).
-    event_handlers: The event handlers.
-    title: Title for the visualization. Defaults to "Evaluation Result Slicing
-      Metrics".
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-    element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  widget_view = render_slicing_metrics(
-      result=result,
-      slicing_column=slicing_column,
-      slicing_spec=slicing_spec,
-      weighted_example_column=weighted_example_column,
-      event_handlers=event_handlers
-  )
-  display_widget_view(widget_view, height=height, width=width)
-
-def display_eval_results_time_series(
-    results: EvalResults,
-    slicing_spec: Optional[Union[SingleSliceSpec, SlicingSpec]] = None,
-    display_full_path: bool = False,
-    title: Optional[str] = 'Evaluation Results Time Series',
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-) -> None:
-  """Displays evaluation results time series.
-  Args:
-    results: A `tfma.EvalResults`.
-    slicing_spec: A `tfma.SlicingSpec` determining the slice to show time series
-      on. Show overall if not set.
-    display_full_path: Whether to display the full path to the model / data in the
-      visualization or just show a file name.
-    width: Width of the frame in CSS pixels. Defaults to the app's default
-      element width.
-    height: Height of the frame in CSS pixels. Defaults to 150.
-  """
-  if title:
-    st.subheader(title)
-  widget_view = render_time_series(
-      results=results,
-      slicing_spec=slicing_spec,
-      display_full_path=display_full_path
-  )
-  display_widget_view(widget_view, height=height, width=width)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intelh263dec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intelh263dec.c
deleted file mode 100644
index 2c216b00a62ebe6c168e8ab435c5adac3525e4c4..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intelh263dec.c
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- * H.263i decoder
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "codec_internal.h"
-#include "mpegutils.h"
-#include "mpegvideo.h"
-#include "mpegvideodec.h"
-#include "h263data.h"
-#include "h263dec.h"
-#include "mpegvideodata.h"
-
-/* don't understand why they choose a different header ! */
-int ff_intel_h263_decode_picture_header(MpegEncContext *s)
-{
-    int format;
-
-    if (get_bits_left(&s->gb) == 64) { /* special dummy frames */
-        return FRAME_SKIPPED;
-    }
-
-    /* picture header */
-    if (get_bits(&s->gb, 22) != 0x20) {
-        av_log(s->avctx, AV_LOG_ERROR, "Bad picture start code\n");
-        return -1;
-    }
-    s->picture_number = get_bits(&s->gb, 8); /* picture timestamp */
-
-    if (check_marker(s->avctx, &s->gb, "after picture_number") != 1) {
-        return -1;      /* marker */
-    }
-    if (get_bits1(&s->gb) != 0) {
-        av_log(s->avctx, AV_LOG_ERROR, "Bad H.263 id\n");
-        return -1;      /* H.263 id */
-    }
-    skip_bits1(&s->gb);         /* split screen off */
-    skip_bits1(&s->gb);         /* camera  off */
-    skip_bits1(&s->gb);         /* freeze picture release off */
-
-    format = get_bits(&s->gb, 3);
-    if (format == 0 || format == 6) {
-        av_log(s->avctx, AV_LOG_ERROR, "Intel H.263 free format not supported\n");
-        return -1;
-    }
-    s->h263_plus = 0;
-
-    s->pict_type = AV_PICTURE_TYPE_I + get_bits1(&s->gb);
-
-    s->h263_long_vectors = get_bits1(&s->gb);
-
-    if (get_bits1(&s->gb) != 0) {
-        av_log(s->avctx, AV_LOG_ERROR, "SAC not supported\n");
-        return -1;      /* SAC: off */
-    }
-    s->obmc= get_bits1(&s->gb);
-    s->pb_frame = get_bits1(&s->gb);
-
-    if (format < 6) {
-        s->width = ff_h263_format[format][0];
-        s->height = ff_h263_format[format][1];
-        s->avctx->sample_aspect_ratio.num = 12;
-        s->avctx->sample_aspect_ratio.den = 11;
-    } else {
-        format = get_bits(&s->gb, 3);
-        if(format == 0 || format == 7){
-            av_log(s->avctx, AV_LOG_ERROR, "Wrong Intel H.263 format\n");
-            return -1;
-        }
-        if(get_bits(&s->gb, 2))
-            av_log(s->avctx, AV_LOG_ERROR, "Bad value for reserved field\n");
-        s->loop_filter = get_bits1(&s->gb) * !s->avctx->lowres;
-        if(get_bits1(&s->gb))
-            av_log(s->avctx, AV_LOG_ERROR, "Bad value for reserved field\n");
-        if(get_bits1(&s->gb))
-            s->pb_frame = 2;
-        if(get_bits(&s->gb, 5))
-            av_log(s->avctx, AV_LOG_ERROR, "Bad value for reserved field\n");
-        if(get_bits(&s->gb, 5) != 1)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid marker\n");
-    }
-    if(format == 6){
-        int ar = get_bits(&s->gb, 4);
-        skip_bits(&s->gb, 9); // display width
-        check_marker(s->avctx, &s->gb, "in dimensions");
-        skip_bits(&s->gb, 9); // display height
-        if(ar == 15){
-            s->avctx->sample_aspect_ratio.num = get_bits(&s->gb, 8); // aspect ratio - width
-            s->avctx->sample_aspect_ratio.den = get_bits(&s->gb, 8); // aspect ratio - height
-        } else {
-            s->avctx->sample_aspect_ratio = ff_h263_pixel_aspect[ar];
-        }
-        if (s->avctx->sample_aspect_ratio.num == 0)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid aspect ratio.\n");
-    }
-
-    s->chroma_qscale= s->qscale = get_bits(&s->gb, 5);
-    skip_bits1(&s->gb); /* Continuous Presence Multipoint mode: off */
-
-    if(s->pb_frame){
-        skip_bits(&s->gb, 3); //temporal reference for B-frame
-        skip_bits(&s->gb, 2); //dbquant
-    }
-
-    /* PEI */
-    if (skip_1stop_8data_bits(&s->gb) < 0)
-        return AVERROR_INVALIDDATA;
-    s->f_code = 1;
-
-    ff_h263_show_pict_info(s);
-
-    return 0;
-}
-
-const FFCodec ff_h263i_decoder = {
-    .p.name         = "h263i",
-    CODEC_LONG_NAME("Intel H.263"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_H263I,
-    .priv_data_size = sizeof(MpegEncContext),
-    .init           = ff_h263_decode_init,
-    .close          = ff_h263_decode_end,
-    FF_CODEC_DECODE_CB(ff_h263_decode_frame),
-    .p.capabilities = AV_CODEC_CAP_DRAW_HORIZ_BAND | AV_CODEC_CAP_DR1,
-    .caps_internal  = FF_CODEC_CAP_SKIP_FRAME_FILL_PARAM,
-    .p.pix_fmts     = (const enum AVPixelFormat[]) {
-        AV_PIX_FMT_YUV420P,
-        AV_PIX_FMT_NONE
-    },
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.c
deleted file mode 100644
index 33bde8112ed437948a23a416f81d74ed27d32d96..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.c
+++ /dev/null
@@ -1,148 +0,0 @@
-/*
- * Android MediaCodec public API functions
- *
- * Copyright (c) 2016 Matthieu Bouron <matthieu.bouron stupeflix.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "config.h"
-
-#include "libavutil/error.h"
-
-#include "mediacodec.h"
-
-#if CONFIG_MEDIACODEC
-
-#include <jni.h>
-
-#include "libavcodec/avcodec.h"
-#include "libavutil/mem.h"
-
-#include "ffjni.h"
-#include "mediacodecdec_common.h"
-
-AVMediaCodecContext *av_mediacodec_alloc_context(void)
-{
-    return av_mallocz(sizeof(AVMediaCodecContext));
-}
-
-int av_mediacodec_default_init(AVCodecContext *avctx, AVMediaCodecContext *ctx, void *surface)
-{
-    int ret = 0;
-    JNIEnv *env = NULL;
-
-    env = ff_jni_get_env(avctx);
-    if (!env) {
-        return AVERROR_EXTERNAL;
-    }
-
-    ctx->surface = (*env)->NewGlobalRef(env, surface);
-    if (ctx->surface) {
-        avctx->hwaccel_context = ctx;
-    } else {
-        av_log(avctx, AV_LOG_ERROR, "Could not create new global reference\n");
-        ret = AVERROR_EXTERNAL;
-    }
-
-    return ret;
-}
-
-void av_mediacodec_default_free(AVCodecContext *avctx)
-{
-    JNIEnv *env = NULL;
-
-    AVMediaCodecContext *ctx = avctx->hwaccel_context;
-
-    if (!ctx) {
-        return;
-    }
-
-    env = ff_jni_get_env(avctx);
-    if (!env) {
-        return;
-    }
-
-    if (ctx->surface) {
-        (*env)->DeleteGlobalRef(env, ctx->surface);
-        ctx->surface = NULL;
-    }
-
-    av_freep(&avctx->hwaccel_context);
-}
-
-int av_mediacodec_release_buffer(AVMediaCodecBuffer *buffer, int render)
-{
-    MediaCodecDecContext *ctx = buffer->ctx;
-    int released = atomic_fetch_add(&buffer->released, 1);
-
-    if (!released && (ctx->delay_flush || buffer->serial == atomic_load(&ctx->serial))) {
-        atomic_fetch_sub(&ctx->hw_buffer_count, 1);
-        av_log(ctx->avctx, AV_LOG_DEBUG,
-               "Releasing output buffer %zd (%p) ts=%"PRId64" with render=%d [%d pending]\n",
-               buffer->index, buffer, buffer->pts, render, atomic_load(&ctx->hw_buffer_count));
-        return ff_AMediaCodec_releaseOutputBuffer(ctx->codec, buffer->index, render);
-    }
-
-    return 0;
-}
-
-int av_mediacodec_render_buffer_at_time(AVMediaCodecBuffer *buffer, int64_t time)
-{
-    MediaCodecDecContext *ctx = buffer->ctx;
-    int released = atomic_fetch_add(&buffer->released, 1);
-
-    if (!released && (ctx->delay_flush || buffer->serial == atomic_load(&ctx->serial))) {
-        atomic_fetch_sub(&ctx->hw_buffer_count, 1);
-        av_log(ctx->avctx, AV_LOG_DEBUG,
-               "Rendering output buffer %zd (%p) ts=%"PRId64" with time=%"PRId64" [%d pending]\n",
-               buffer->index, buffer, buffer->pts, time, atomic_load(&ctx->hw_buffer_count));
-        return ff_AMediaCodec_releaseOutputBufferAtTime(ctx->codec, buffer->index, time);
-    }
-
-    return 0;
-}
-
-#else
-
-#include <stdlib.h>
-
-AVMediaCodecContext *av_mediacodec_alloc_context(void)
-{
-    return NULL;
-}
-
-int av_mediacodec_default_init(AVCodecContext *avctx, AVMediaCodecContext *ctx, void *surface)
-{
-    return AVERROR(ENOSYS);
-}
-
-void av_mediacodec_default_free(AVCodecContext *avctx)
-{
-}
-
-int av_mediacodec_release_buffer(AVMediaCodecBuffer *buffer, int render)
-{
-    return AVERROR(ENOSYS);
-}
-
-int av_mediacodec_render_buffer_at_time(AVMediaCodecBuffer *buffer, int64_t time)
-{
-    return AVERROR(ENOSYS);
-}
-
-#endif
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Clash of Clans APK and Join Millions of Players Worldwide.md b/spaces/congsaPfin/Manga-OCR/logs/Download Clash of Clans APK and Join Millions of Players Worldwide.md
deleted file mode 100644
index b247873695a6b1b75aa12e94e96087d01b61629a..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Clash of Clans APK and Join Millions of Players Worldwide.md	
+++ /dev/null
@@ -1,201 +0,0 @@
-<br />
-<h1>Clash of Clans Indir Apkcombo: How to Download and Play the Popular Strategy Game</h1>
-<p>If you are looking for a fun and addictive strategy game that will challenge your skills and creativity, you should try <strong>Clash of Clans</strong>. This game has been one of the most popular games in the world for years, with millions of players joining clans and competing in epic wars. In this article, we will show you how to download and play Clash of Clans from <strong>Apkcombo</strong>, a website that offers free APK files for Android games and apps. We will also give you some tips and tricks to help you win in this game.</p>
- <h2>What is Clash of Clans?</h2>
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-<p>Clash of Clans has many features that make it an exciting and diverse game. Here are some of them:</p>
-<ul>
-<li><strong>Village:</strong> This is where you build your base, which consists of various buildings, such as gold mines, elixir collectors, barracks, army camps, defenses, walls, town hall, clan castle, laboratory, etc. You can also customize your village with decorations, obstacles, hero skins, and sceneries.</li>
-<li><strong>Troops:</strong> These are the units that you use to attack or defend in battles. There are different types of troops, such as barbarians, archers, giants, goblins, wizards, dragons, etc. Each troop has its own strengths and weaknesses, as well as different levels that you can upgrade in the laboratory.</li>
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-<ul>
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-<li><strong>No need for Google Play Store:</strong> Apkcombo does not require you to have Google Play Store installed on your device, which may be useful if you have a device that does not support Google Play Store, or if you want to save some storage space.</li>
-</ul>
-<p>However, there are also some risks and drawbacks of downloading APK files from Apkcombo, such as:</p>
-<ul>
-<li><strong>Potential malware or viruses:</strong> Apkcombo claims that all the APK files are scanned and verified by antivirus software, but there is still a possibility that some malicious code or software may be hidden in the APK files. Therefore, you should always check the source and reputation of the APK file before downloading it, and use a reliable antivirus app to scan it before installing it.</li>
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-</ul>
-<p>Therefore, you should always be careful and responsible when downloading and installing APK files from Apkcombo. You should only download APK files from trusted sources, and only for personal use. You should also avoid using modded APK files that may give you unfair advantages or harm other players in Clash of Clans.</p>
- <h2>How to Download and Install Clash of Clans from Apkcombo</h2>
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- <h3>The steps to download the APK file from Apkcombo website</h3>
-<ol>
-<li>Go to <a href="(^5^)">Apkcombo website</a> on your browser.</li>
-<li>Type "Clash of Clans" in the search box and press enter.</li>
-<li>Select the version of Clash of Clans that you want to download. You can choose between the original version or the modded version.</li>
-<li>Select the region of Clash of Clans that you want to download. You can choose between different regions, such as global, China, Japan, etc.</li>
-<li>Select the architecture of your device. You can choose between armeabi-v7a, arm64-v8a, x86, or x86_x64.</li>
-<li>Click on the "Download" button and wait for the download to finish.</li>
-</ol>
- <h3>The steps to install the APK file on your Android device</h3>
-<ol>
-<li>Before installing the APK file, make sure that you have enabled "Unknown Sources" option on your device settings. This will allow you to install apps from sources other than Google Play Store.</li>
-<li>Locate the downloaded APK file on your device storage using a file manager app.</li>
-<li>Tap on the APK file and follow the instructions on the screen to install it.</li>
-<li>Wait for the installation to complete and launch Clash of Clans from your app drawer <h3>The steps to update the game and fix any issues</h3>
-<ol>
-<li>To update the game, you can either download the latest APK file from Apkcombo and install it over the existing one, or you can use the in-game update option if available. You should always update the game to enjoy the new features and improvements.</li>
-<li>To fix any issues, such as crashes, errors, or glitches, you can try some of these solutions:   <ul>
-  <li>Clear the cache and data of the game from your device settings.</li>
-  <li>Uninstall and reinstall the game from Apkcombo.</li>
-  <li>Check your internet connection and make sure it is stable and fast.</li>
-  <li>Check your device storage and make sure it has enough space for the game.</li>
-  <li>Contact Supercell support team from the game settings or their website for further assistance.</li>
-  </ul>
-</li>
-</ol>
- <h2>How to Play Clash of Clans and Win</h2>
-<p>Now that you have downloaded and installed Clash of Clans from Apkcombo, you are ready to play and win. Here are some tips and tricks to help you succeed in this game:</p>
- <h3>The basics of building your village and raising your clan</h3>
-<p>The first thing you need to do in Clash of Clans is to build your village and raise your clan. Here are some basic steps to follow:</p>
-<ol>
-<li>Start by building and upgrading your town hall, which is the heart of your village. Your town hall level determines what buildings and troops you can unlock and use.</li>
-<li>Build and upgrade your resource buildings, such as gold mines, elixir collectors, gold storages, elixir storages, dark elixir drills, and dark elixir storages. These buildings will provide you with the resources you need to build and upgrade other buildings and troops.</li>
-<li>Build and upgrade your defense buildings, such as cannons, archer towers, mortars, air defenses, wizard towers, hidden teslas, bomb towers, inferno towers, eagle artillery, etc. These buildings will protect your village from enemy attacks.</li>
-<li>Build and upgrade your walls, which will act as a barrier against enemy troops. You can also place traps, such as bombs, spring traps, air bombs, giant bombs, seeking air mines, skeleton traps, etc. to surprise and damage enemy troops.</li>
-<li>Build and upgrade your army buildings, such as barracks, dark barracks, army camps, spell factory, dark spell factory, siege workshop, etc. These buildings will allow you to train and store your troops and spells for battles.</li>
-<li>Build and upgrade your clan castle, which will allow you to join or create a clan with other players. You can also request and donate troops and spells to your clan members, which will help you in battles.</li>
-<li>Build and upgrade your hero buildings, such as barbarian king altar, archer queen altar, grand warden altar, and royal champion altar. These buildings will allow you to unlock and use the heroes in battles.</li>
-<li>Build and upgrade your builder base buildings, such as builder hall, gold mine, elixir collector , gold storage, elixir storage, gem mine, clock tower, etc. These buildings will allow you to access the builder base mode, where you can build a second village and compete in versus battles with other players.</li>
-</ol>
-<p>You should always try to balance your village development, and not neglect any aspect of it. You should also follow the recommended upgrade order, which you can find in various guides and websites online.</p>
- <h3>The tips and tricks to attack and defend in clan wars and multiplayer battles</h3>
-<p>One of the main attractions of Clash of Clans is the clan wars and multiplayer battles, where you can test your skills and strategies against other players. Here are some tips and tricks to help you attack and defend in these modes:</p>
-<ul>
-<li><strong>Scout your enemy:</strong> Before you attack, you should always scout your enemy's village and analyze their layout, defenses, traps, clan castle troops, heroes, etc. You should also check their profile and see their attack and defense history, trophies, league, clan, etc. This will help you plan your attack and choose the best troops and spells for it.</li>
-<li><strong>Use the right army composition:</strong> Depending on your enemy's village and your strategy, you should use the right army composition for your attack. You should consider the cost, training time, housing space, damage, health, speed, range, target preference, special ability, etc. of each troop and spell. You should also have a variety of troops and spells to deal with different situations and obstacles.</li>
-<li><strong>Use the right deployment technique:</strong> Depending on your army composition and your strategy, you should use the right deployment technique for your attack. You should consider the timing, location, direction, spacing, grouping, funneling, etc. of each troop and spell. You should also use the hero abilities and the clan castle troops wisely.</li>
-<li><strong>Use the right attack strategy:</strong> Depending on your army composition and your strategy, you should use the right attack strategy for your attack. You should consider the objective, priority, sequence, phase, etc. of each troop and spell. You should also adapt to the enemy's defenses and reactions.</li>
-<li><strong>Practice and learn:</strong> The best way to improve your attacking skills is to practice and learn from your own and others' attacks. You can use the friendly challenge feature to practice with your clan mates or the practice mode to learn some basic strategies. You can also watch replays of your own and others' attacks to see what worked and what didn't.</li>
-<li><strong>Design your base:</strong> To defend your village from enemy attacks, you should design your base carefully and strategically. You should consider the layout, placement , and synergy of each building, wall, trap, clan castle troop, hero, etc. You should also follow the recommended base design principles, which you can find in various guides and websites online.</li>
-<li><strong>Upgrade your defenses:</strong> To defend your village from enemy attacks, you should upgrade your defenses regularly and strategically. You should consider the cost, time, effect, priority, etc. of each upgrade. You should also follow the recommended upgrade order, which you can find in various guides and websites online.</li>
-<li><strong>Test your base:</strong> To defend your village from enemy attacks, you should test your base frequently and realistically. You can use the friendly challenge feature to test your base with your clan mates or the base layout editor to test your base with different scenarios. You can also watch replays of enemy attacks to see how your base performed and what you can improve.</li>
-</ul>
- <h3>The resources and strategies to upgrade your troops, spells, and heroes</h3>
-<p>To succeed in Clash of Clans, you need to upgrade your troops, spells, and heroes constantly and strategically. Here are some resources and strategies to help you do that:</p>
-<ul>
-<li><strong>Gold and elixir:</strong> These are the main resources that you need to upgrade your troops, spells, and most buildings. You can get gold and elixir from gold mines and elixir collectors, raiding other players' villages, completing achievements and events, opening loot carts and star bonus chests, etc.</li>
-<li><strong>Dark elixir:</strong> This is a special resource that you need to upgrade your dark troops, dark spells, heroes, and some buildings. You can get dark elixir from dark elixir drills, raiding other players' villages, completing achievements and events, opening loot carts and star bonus chests, etc.</li>
-<li><strong>Gems:</strong> This is a premium resource that you can use to speed up upgrades, buy resources, boost buildings, train troops and spells instantly, etc. You can get gems from clearing obstacles, completing achievements and events, opening gem boxes and gem mine carts , buying them with real money, etc.</li>
-<li><strong>Builder base gold and elixir:</strong> These are the resources that you need to upgrade your builder base troops, buildings, and walls. You can get builder base gold and elixir from gold mines and elixir collectors, winning versus battles, completing achievements and events, opening loot carts and star bonus chests, etc.</li>
-<li><strong>Builder base gems:</strong> This is a resource that you can use to speed up upgrades, buy resources, boost buildings, etc. in your builder base. You can get builder base gems from clearing obstacles, completing achievements and events, opening gem boxes and gem mine carts, buying them with real money, etc.</li>
-<li><strong>Magic items:</strong> These are special items that you can use to boost your progress in various ways, such as increasing your resource production, reducing your upgrade time or cost, enhancing your troops or spells, etc. You can get magic items from completing clan games, reaching certain league levels, buying them with gems or real money, etc.</li>
-<li><strong>Strategy:</strong> To upgrade your troops, spells, and heroes effectively and efficiently, you should follow a strategy that suits your goals and play style. You should consider the cost, time, benefit, priority, etc. of each upgrade. You should also follow the recommended upgrade order, which you can find in various guides and websites online.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Clash of Clans is a game that will keep you entertained and engaged for hours. You can download and play it from Apkcombo, a website that offers free APK files for Android games and apps. However, you should be careful and responsible when downloading and installing APK files from Apkcombo. You should also follow some tips and tricks to help you build your village, raise your clan, and win in clan wars and multiplayer battles. You should also upgrade your troops, spells, and heroes regularly and strategically. We hope this article has helped you learn more about Clash of Clans indir Apkcombo. Now go ahead and enjoy the game!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Clash of Clans free to play?</h3>
-<p>A1: Yes, Clash of Clans is free to download and play. However, it also offers some optional in-game purchases with real money, such as gems, magic items , or special offers. You can disable these purchases from your device settings if you want to.</p>
- <h3>Q2: Is Clash of Clans safe to download from Apkcombo?</h3>
-<p>A2: Apkcombo claims that all the APK files on its website are scanned and verified by antivirus software, but there is still a risk of malware or viruses. Therefore, you should always check the source and reputation of the APK file before downloading it, and use a reliable antivirus app to scan it before installing it. You should also only download APK files from trusted sources, and only for personal use.</p>
- <h3>Q3: How can I join or create a clan in Clash of Clans?</h3>
-<p>A3: To join or create a clan in Clash of Clans, you need to have a clan castle, which you can build after reaching town hall level 3. You can then tap on the clan castle and choose the option to join or create a clan. You can search for clans by name, tag, location, level, members, etc. or browse the recommended clans. You can also invite or accept other players to join your clan. You can then chat, donate, request, and war with your clan members.</p>
- <h3>Q4: What are the best troops and spells to use in Clash of Clans?</h3>
-<p>A4: There is no definitive answer to this question, as different troops and spells may work better for different situations and strategies. However, some of the most popular and effective troops and spells are:</p>
-<table>
-<tr>
-<th>Troops</th>
-<th>Spells</th>
-</tr>
-<tr>
-<td>Miners</td>
-<td>Healing Spell</td>
-</tr>
-<tr>
-<td>Bowlers</td>
-<td>Rage Spell</td>
-</tr>
-<tr>
-<td>Hog Riders</td>
-<td>Freeze Spell</td>
-</tr>
-<tr>
-<td>Electro Dragons</td>
-<td>Bat Spell</td>
-</tr>
-<tr>
-<td>Lava Hounds</td>
-<td>Haste Spell</td>
-</tr>
-<tr>
-<td>Balloons</td>
-<td>Clone Spell</td>
-</tr>
-<tr>
-<td>Golems</td>
-<td>Poison Spell</td>
-</tr>
-<tr>
-<td>Witches</td <td>Earthquake Spell</td>
-</tr>
-</table>
-<p>You can experiment with different combinations of troops and spells to find the ones that suit your style and goals.</p>
- <h3>Q5: How can I contact Supercell for support or feedback?</h3>
-<p>A5: If you have any issues, questions, or suggestions regarding Clash of Clans, you can contact Supercell for support or feedback. You can do this by:</p>
-<ul>
-<li>Tapping on the settings icon in the game and choosing the "Help and Support" option. You can then browse the FAQs, report an issue, or send a message to the support team.</li>
-<li>Visiting the official website of Clash of Clans and choosing the "Contact Us" option. You can then fill out a form with your details and query.</li>
-<li>Visiting the official forums of Clash of Clans and posting your query or feedback in the relevant section. You can also interact with other players and moderators there.</li>
-<li>Visiting the official social media pages of Clash of Clans, such as Facebook, Twitter, Instagram, YouTube, etc. and leaving a comment or message there. You can also follow the latest news and updates there.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Episode Apk Mod and Experience the Best Stories Ever.md b/spaces/congsaPfin/Manga-OCR/logs/Download Episode Apk Mod and Experience the Best Stories Ever.md
deleted file mode 100644
index 0d08f8a9a0a09600c0b3ddcf72102066293a9af6..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Episode Apk Mod and Experience the Best Stories Ever.md	
+++ /dev/null
@@ -1,111 +0,0 @@
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- <p>Episode is a mobile app that allows you to create and play interactive stories in various genres, such as romance, drama, comedy, fantasy, horror, and more. You can choose from thousands of stories created by other players or by professional writers, or you can create your own stories using the app's tools and resources. You can also customize your own characters, outfits, hairstyles, and backgrounds, and make choices that affect the outcome of the story. You can also interact with other players, rate and review stories, and join communities and contests.</p>
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- <p>In conclusion, Episode Download APK Mod is a modified version of the popular storytelling app that lets you enjoy unlimited gems and passes without spending any money. This means that you can unlock all the premium choices and episodes that you want without any restrictions. You can also access all the features of the original app, such as creating your own stories and characters, interacting with other players, joining communities and contests, and more. However, Episode Download APK Mod also comes with some risks that you should be aware of before using it, such as compatibility issues, security threats, and account bans. Therefore, you should use it at your own risk and discretion.</p>
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- <p>Here are some frequently asked questions about Episode Download APK Mod:</p>
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-<li>Episode Download APK Mod is not an official app and is not endorsed by the developers of Episode. Therefore, it may not be safe to use and may contain viruses, malware, or spyware that can harm your device or compromise your privacy. Therefore, you should only download it from trusted sources and scan it with an antivirus before installing it.</li>
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- <p>I hope this article has helped you understand more about Episode Download APK Mod and how to use it. If you have any questions or feedback, please leave a comment below. Thank you for reading!</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Red Embrace Hollywood APK and Become a Vampire Star.md b/spaces/congsaPfin/Manga-OCR/logs/Download Red Embrace Hollywood APK and Become a Vampire Star.md
deleted file mode 100644
index 7586195f7a7584498d5fe3733e7927a623c51333..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Red Embrace Hollywood APK and Become a Vampire Star.md	
+++ /dev/null
@@ -1,88 +0,0 @@
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-<h1>Red Embrace: Hollywood - A Vampire Visual Novel for Android</h1>
-<p>Do you love vampires, romance, and drama? Do you want to experience a thrilling and immersive story where you can shape your own destiny? If you answered yes, then you should check out Red Embrace: Hollywood, a unique vampire visual novel that is available for Android devices. In this article, we will tell you what this game is about, how to download and install it on your device, and what are its features.</p>
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-<h2>How to download and install Red Embrace: Hollywood APK?</h2>
-<p>If you are interested in playing Red Embrace: Hollywood on your Android device, you will need to download and install its APK file. An APK file is an application package file that contains all the data and files needed to run an app on Android. Here are the steps to download and install Red Embrace: Hollywood APK:</p>
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-<p>The next step is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, you need to go to your device settings and look for the security or privacy option. There, you will find a toggle or checkbox that says "allow installation of apps from unknown sources" or something similar. Turn it on and confirm your choice.</p>
-<h3>Install the APK file and enjoy the game</h3>
-<p>The final step is to install the APK file and enjoy the game. To install the APK file, you need to locate it on your device using a file manager app. Tap on the file and follow the instructions on the screen. The installation process may take a few minutes, depending on your device and the size of the file. Once the installation is complete, you can launch the game from your app drawer or home screen. You can now enjoy Red Embrace: Hollywood on your Android device.</p>
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-<p>Red Embrace: Hollywood APK is a great way to play this amazing game on your Android device. Here are some of the features that you can expect from this APK:</p>
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-<p>The game has multiple endings and replay value that make it worth playing more than once. The game has over 25 endings that depend on your choices, actions, morality, reputation, and relationships. Each ending has its own epilogue that reveals the consequences of your decisions and the fate of Los Angeles. You can also unlock bonus scenes and content by completing certain achievements or fulfilling certain conditions. The game also has a gallery where you can view all the images, scenes, and endings that you have unlocked.</p>
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-<p>The game has interactive romance and drama that make it engaging and emotional. The game allows you to romance four different characters: Markus, Randal, Isaac, and Sheryl. Each character has their own personality, background, secrets, and preferences. You can interact with them through dialogue choices, actions, gifts, dates, and more. You can also influence their feelings and opinions of you by being honest, loyal, charming, or manipulative. The game also has drama and conflict that arise from your interactions with other characters, factions, and events. You can experience betrayal, jealousy, rivalry, friendship, loyalty, love, and more.</p>
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-<p>Red Embrace: Hollywood is a vampire visual novel that is available for Android devices through its APK file. It is a unique story-driven game that lets you customize your vampire protagonist and choose your own path in a rich and diverse setting. It has high-quality graphics and sound, multiple endings and replay value, and interactive romance and drama. If you are looking for a thrilling and immersive game that will keep you hooked for hours, you should download Red Embrace: Hollywood APK today.</p>
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-<tr><td>Is Red Embrace: Hollywood APK free?</td><td>Yes, Red Embrace: Hollywood APK is free to download and play. However, some in-game content may require in-app purchases.</td></tr>
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-</table></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/FTS 23 Apk Obb Data File - Everything You Need to Know About First Touch Soccer 2023.md b/spaces/congsaPfin/Manga-OCR/logs/FTS 23 Apk Obb Data File - Everything You Need to Know About First Touch Soccer 2023.md
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-<p>If you are a fan of football management games, you might have heard of First Touch Soccer or FTS 2023. This is the latest entry in the popular series that has been ruling the world of mobile sports games for years. This game lets you create your own dream team, play against the best clubs in the world, and enjoy a realistic and immersive soccer experience on your mobile device. In this article, we will tell you everything you need to know about FTS 2023, including what it is, why you should play it, how to download and install it, how to play it on PC, and some tips and tricks to help you become a successful manager. Let's get started!</p>
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-<p>FTS 2023 is a football management game for mobile devices developed by First Touch Games. It is the latest installment in the long-running series that started in 2011. The game features over 4,000 licensed players from around the world, stunning graphics, realistic animations, immersive commentary, and a variety of game modes. You can build your own team from scratch, sign top players, upgrade your stadium and facilities, train your players, compete in different leagues and cups, and challenge other players online. FTS 2023 is one of the most comprehensive and enjoyable football management games for mobile devices.</p>
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-<p>There are many reasons why you should play FTS 2023 if you love football management games. Here are some of them:</p>
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-<p>If you want to play FTS 2023 on your PC, you will need to use an emulator. An emulator is a software that allows you to run mobile apps on your computer. There are many emulators available online, but we recommend using BlueStacks. BlueStacks is one of the most popular and reliable emulators that supports FTS 2023 and other mobile games. Here is how to use BlueStacks to play FTS 2023 on PC:</p>
-<ul>
-<li>Download and install BlueStacks on your PC from its official website: [BlueStacks].</li>
-<li>Launch BlueStacks and sign in with your Google account.</li>
-<li>Go to the search bar and type "FTS 2023".</li>
-<li>Select FTS 2023 from the results and click on install.</li>
-<li>Wait for the installation to finish and then click on open.</li>
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-<p>To help you become a better manager in FTS 2023, here are some tips and tricks that you should know:</p>
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- <h2>Conclusion</h2>
-<p>FTS 2023 is a fantastic football management game for mobile devices that will keep you entertained and engaged for hours. You can download and play it for free, without an internet connection, and with easy controls. You can also play it on your PC with an emulator. You can create your own team, sign top players, upgrade your stadium and facilities, train your players, choose your formation and tactics, watch your matches live or in highlights, and challenge other players online. FTS 2023 is a must-play game for any football fan. Download it now and start your football management career!</p>
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-<p>Here are some frequently asked questions and their answers about FTS 2023:</p>
-<ol>
-<li>Q: How do I update FTS 2023?<br>A: To update FTS 2023, you need to go to the official download page and download the latest version of the game. Then, you need to uninstall the previous version of the game and install the new one. You can also check for updates within the game settings.</li>
-<li>Q: How do I get more coins in FTS 2023?<br>A: You can get more coins in FTS 2023 by playing matches, winning trophies, completing achievements, watching ads, or buying them with real money.</li>
-<li>Q: How do I transfer players in FTS 2023?<br>A: To transfer players in FTS 2023, you need to go to the transfer market and search for the player you want. Then, you need to make an offer and wait for the response. You can also sell your own players or loan them out.</li>
-<li>Q: How do I change the language in FTS 2023?<br>A: To change the language in FTS 2023, you need to go to the settings menu and select the language option. Then, you can choose from over 10 languages available.</li>
-<li>Q: How do I contact the developer of FTS 2023?<br>A: To contact the developer of FTS 2023, you can visit their website: [First Touch Games] or follow them on their social media accounts: [Facebook], [Twitter], [Instagram].</li>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Unlock Unlimited Stella and Gold in Heroes Infinity Premium Mod APK.md b/spaces/congsaPfin/Manga-OCR/logs/How to Unlock Unlimited Stella and Gold in Heroes Infinity Premium Mod APK.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/How to Unlock Unlimited Stella and Gold in Heroes Infinity Premium Mod APK.md	
+++ /dev/null
@@ -1,97 +0,0 @@
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-<li>Fifth, you need to launch the game and enjoy the premium mod APK unlimited stella features and benefits.</li>
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- <h2>Tips and tricks for playing Heroes Infinity</h2>
-<p>Now that you have downloaded and installed the premium mod APK unlimited stella version of Heroes Infinity, you might want to know some tips and tricks that can help you play the game better. Here are some of them:</p>
- <h3>Choose your heroes wisely</h3>
-<p>One of the most important aspects of Heroes Infinity is choosing your heroes. You can have up to five heroes in your team, each with their own roles and abilities. You should choose your heroes based on their compatibility, synergy, and suitability for different situations. For example, you might want to have a balanced team that consists of a tank, a healer, a damage dealer, a support, and a crowd controller. You should also consider the elements of your heroes, as they have advantages and disadvantages against certain enemies. For example, fire heroes are strong against earth enemies but weak against water enemies.</p>
- <h3>Upgrade your heroes and items</h3>
-<p>Another important aspect of Heroes Infinity is upgrading your heroes and items. You can upgrade your heroes by using gold, gems, stella, or other resources. Upgrading your heroes can increase their level, rank, star level, skill level, or talent level. Each upgrade can improve your heroes' stats and abilities, making them more powerful and effective. You can also upgrade your items by using gold or other resources. Upgrading your items can increase their quality, level, or enchantment level. Each upgrade can enhance your items' attributes and effects, making them more useful and valuable.</p>
- <h3>Use your skills and strategies</h3>
-<p>The last important aspect of Heroes Infinity is using your skills and strategies. You can use your skills by tapping on the buttons on the right side of the screen. Each skill has a cooldown time that limits how often you can use it. You should use your skills wisely and strategically, depending on the situation and the enemy. For example, you might want to use a healing skill when your health is low, or a stun skill when the enemy is about to attack. You should also use your strategies by planning ahead and adapting to different scenarios. For example, you might want to change your formation or switch your heroes depending on the enemy's composition or behavior.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Heroes Infinity is a fun and exciting action RPG game that offers a lot of features and modes for you to enjoy. However, if you want to experience the game at its best, you should download the premium mod APK unlimited stella version that gives you access to some exclusive benefits and advantages that are not available in the original version. By downloading and installing the premium mod APK unlimited stella version of Heroes Infinity, you can get unlimited stella, gold, gems, energy, VIP points, as well as all the heroes, items, [user](#message and features unlocked and upgraded. You can also learn some tips and tricks that can help you play the game better, such as choosing your heroes wisely, upgrading your heroes and items, and using your skills and strategies. By following this guide, you can become a master of Heroes Infinity and enjoy the game to the fullest.</p>
- <h3>Summary of the article</h3>
-<p>This article has covered the following topics:</p>
-<ul>
-<li>What is Heroes Infinity and what are its features and gameplay modes?</li>
-<li>Why download Heroes Infinity Premium Mod APK Unlimited Stella and what are its benefits?</li>
-<li>How to download and install Heroes Infinity Premium Mod APK Unlimited Stella?</li>
-<li>Tips and tricks for playing Heroes Infinity like a pro.</li>
-</ul>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Heroes Infinity Premium Mod APK Unlimited Stella:</p>
-<table>
-<tr><td><b>Q: Is Heroes Infinity Premium Mod APK Unlimited Stella safe to download and install?</b></td><td><b>A: Yes, it is safe as long as you download it from a reliable source. However, you should always be careful when downloading and installing any modded or hacked apps, as they might contain viruses or malware that can harm your device or compromise your privacy.</b></td></tr>
-<tr><td><b>Q: Will I get banned from playing Heroes Infinity if I use the premium mod APK unlimited stella version?</b></td><td><b>A: No, you will not get banned from playing Heroes Infinity if you use the premium mod APK unlimited stella version. The modded version is designed to bypass the detection system of the game and prevent any ban or suspension. However, you should always use the modded version at your own risk, as there is no guarantee that it will work flawlessly or indefinitely.</b></td></tr>
-<tr><td><b>Q: Can I play Heroes Infinity online with other players if I use the premium mod APK unlimited stella version?</b></td><td><b>A: Yes, you can play Heroes Infinity online with other players if you use the premium mod APK unlimited stella version. The modded version does not affect the online multiplayer mode of the game, and you can still join or create rooms, chat with other players, and participate in PvP battles. However, you should be aware that some players might not appreciate your use of the modded version, as they might consider it unfair or cheating.</b></td></tr>
-<tr><td><b>Q: Can I update Heroes Infinity if I use the premium mod APK unlimited stella version?</b></td><td><b>A: No, you cannot update Heroes Infinity if you use the premium mod APK unlimited stella version. The modded version is based on a specific version of the game, and updating it might cause it to stop working or lose its features. If you want to update Heroes Infinity, you need to uninstall the modded version and install the original version from the official app store.</b></td></tr>
-<tr><td><b>Q: Can I restore my progress in Heroes Infinity if I uninstall the premium mod APK unlimited stella version?</b></td><td><b>A: Yes, you can restore your progress in Heroes Infinity if you uninstall the premium mod APK unlimited stella version. The modded version does not affect your game data or account information, and you can still access them if you switch back to the original version. However, you might lose some of the items or resources that you obtained using the modded version, as they might not be compatible with the original version.</b></td></tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Store Allah A Fun and Educational App for Chess Qawwali Enthusiasts.md b/spaces/congsaPfin/Manga-OCR/logs/Play Store Allah A Fun and Educational App for Chess Qawwali Enthusiasts.md
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-<br />
-<h1>Play Store Allah Download Chess Qawwali: A Guide to Enjoying Islamic Devotional Music Through Chess</h1> | | H2: Introduction | <h2>Introduction</h2><p>If you are looking for a way to combine your passion for chess with your love for Islamic devotional music, then you might be interested in downloading chess qawwali from the Play Store. Chess qawwali is a genre of music that blends the traditional Sufi singing of qawwali with the strategic game of chess. In this article, we will explain what chess qawwali is, how it originated, how it can benefit your brain, and how you can download it from the Play Store.</p>
-<h2>play store allah download chess qawwali</h2><br /><p><b><b>Download</b> &middot;&middot;&middot; <a href="https://urlca.com/2uObCu">https://urlca.com/2uObCu</a></b></p><br /><br /> | | H2: What is chess qawwali? | <h2>What is chess qawwali?</h2><p>Chess qawwali is a form of Sufi Islamic devotional singing that incorporates elements of chess into its lyrics and performance. Qawwali is a musical vehicle by which a group of male singers deliver inspirational Sufi messages to an audience of devotees. Qawwali originated in South Asia in the 13th century by the poet and composer Amir Khusrow, who fused Persian, Arabic, Turkish, and Indian traditions. Chess is a two-player strategy game that originated in India around the 6th century AD. Chess qawwali combines these two cultural expressions by using chess metaphors and references to convey spiritual meanings and teachings.</p> | | H3: How did chess qawwali develop? | <h3>How did chess qawwali develop?</h3><p>The exact origin of chess qawwali is not clear, but some historians suggest that it may have been influenced by the Persian poet Hafez, who wrote poems about chess and wine in the 14th century. Another possible source of inspiration is the legend of Sultan Ibrahim Adil Shah II of Bijapur, who was an avid chess player and patron of arts and music in the 16th century. He is said to have composed songs about chess and played chess with his court musicians. However, the most widely accepted theory is that chess qawwali emerged in the 20th century as a result of the popularity of both chess and qawwali among the masses in India and Pakistan. Some of the most famous chess qawwals include Nusrat Fateh Ali Khan, Aziz Mian, Sabri Brothers, Rahat Fateh Ali Khan, and Amjad Sabri.</p> | | H3: What are some examples of chess qawwali? | <h3>What are some examples of chess qawwali?</h3><p>Chess qawwali songs often use chess terminology and imagery to describe various aspects of life, love, faith, and mysticism. For instance, some songs compare God to the king on the chessboard, who is the most important but also the most vulnerable piece. Other songs use chess moves and strategies to illustrate human emotions and dilemmas. Some songs even narrate stories or dialogues between chess players or pieces. Here are some examples of popular chess qawwali songs:</p><ul><li>"Shah Mat" by Nusrat Fateh Ali Khan and Party: This song is a tribute to the legendary chess player Bobby Fischer, who defeated Boris Spassky in the 1972 World Chess Championship. The song praises Fischer's skill and intelligence, and compares his victory to a checkmate (shah mat) against his opponent.</li><li>"Mera Dil Bhi Shatranj Ka Khel Hai" by Aziz Mian: This song is a lament of a lover who feels that his heart is like a chess game, where he is constantly losing and suffering. The song uses chess metaphors to express the pain and confusion of love, such as being trapped in a stalemate, being sacrificed like a pawn, and being deceived by a false move.</li><li>"Shatranj Ke Khiladi" by Sabri Brothers: This song is based on the short story "The Chess Players" by Munshi Premchand, which was also adapted into a film by Satyajit Ray. The song narrates the story of two chess enthusiasts who are so engrossed in their game that they ignore the political turmoil and the invasion of their kingdom by the British.</li></ul> | | H2: How can chess qawwali benefit your brain? | <h2>How can chess qawwali benefit your brain?</h2><p>Listening to chess qawwali can have positive effects on your brain, as it can stimulate both your logical and creative faculties. Chess qawwali can help you improve your chess skills, as it can expose you to different chess scenarios and strategies, and inspire you to think more deeply and creatively about the game. Chess qawwali can also help you enhance your memory, concentration, and problem-solving abilities, as it can challenge you to recall and understand the complex lyrics and meanings of the songs. Moreover, chess qawwali can also help you relax and enjoy the music, as it can evoke emotions and sensations that can soothe your mind and body. Chess qawwali can also help you connect with your spiritual side, as it can convey messages and teachings that can enrich your faith and soul.</p>
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-play store allah download chess qawwali rating</p> | | H2: How can you download chess qawwali from the Play Store? | <h2>How can you download chess qawwali from the Play Store?</h2><p>If you want to listen to chess qawwali on your Android device, you can easily download it from the Play Store. There are many apps and websites that offer chess qawwali songs for free or for a fee. Some of the most popular ones are:</p><table><tr><th>App/Website</th><th>Description</th></tr><tr><td>Chess Qawwali</td><td>This app provides a collection of over 100 chess qawwali songs by various artists. You can listen to them online or offline, and create your own playlists. You can also read the lyrics and translations of the songs, and learn more about the history and culture of chess qawwali.</td></tr><tr><td>Qawwali Online</td><td>This website offers a wide range of qawwali songs, including chess qawwali. You can browse by artist, genre, or theme, and stream or download the songs for free. You can also watch videos of live performances, and join the online community of qawwali fans.</td></tr><tr><td>Qawwalify</td><td>This app allows you to create your own chess qawwali songs by mixing different tracks and sounds. You can choose from various instruments, vocals, beats, and effects, and customize your own composition. You can also share your creations with other users, and discover new chess qawwali songs.</td></tr></table> | | H2: Conclusion | <h2>Conclusion</h2><p>Chess qawwali is a unique and fascinating genre of music that combines the ancient game of chess with the spiritual singing of qawwali. Chess qawwali can offer you a lot of benefits, such as improving your chess skills, enhancing your brain functions, relaxing your mind and body, and connecting with your soul. If you want to experience chess qawwali for yourself, you can download it from the Play Store using various apps and websites. Chess qawwali is a great way to enjoy Islamic devotional music through chess.</p> | | H2: FAQs | <h2>FAQs</h2><ol><li>What is the difference between chess qawwali and regular qawwali?</li><p>Chess qawwali is a subgenre of regular qawwali that uses chess terminology and imagery to convey spiritual meanings and teachings. Regular qawwali does not necessarily use chess references, but it may use other metaphors and symbols to express Sufi messages.</p><li>Who are the best chess qawwali singers?</li><p>There are many talented and renowned chess qawwali singers, but some of the most famous ones are Nusrat Fateh Ali Khan, Aziz Mian, Sabri Brothers, Rahat Fateh Ali Khan, and Amjad Sabri. They have performed chess qawwali songs in various languages, such as Urdu, Punjabi, Hindi, Persian, and Arabic.</p><li>How can I learn to play chess qawwali?</li><p>If you want to learn to play chess qawwali, you will need to have some basic knowledge of both chess and qawwali. You can learn chess by reading books, watching videos, or taking online courses. You can learn qawwali by listening to songs, reading lyrics and translations, or taking lessons from a teacher. You can also practice playing chess qawwali with a friend or a partner, by singing along to the songs or creating your own lyrics.</p><li>Where can I watch chess qawwali live?</li><p>Chess qawwali is usually performed in special events or festivals, such as Sufi music festivals, chess tournaments, or cultural programs. You can check the local listings or online platforms to find out when and where chess qawwali is being performed near you. You can also watch chess qawwali live online, by streaming the performances on YouTube, Facebook, or other websites.</p><li>Why is chess qawwali popular?</li><p>Chess qawwali is popular because it appeals to a wide range of audiences who enjoy both chess and qawwali. Chess qawwali is also popular because it showcases the diversity and richness of Islamic culture and art. Chess qawwali is also popular because it provides a unique and enjoyable way to learn and appreciate the game of chess and the music of qawwali.</p></ol> | | Custom message |  |</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Pokmon GO APK Everything You Need to Know About the Game.md b/spaces/congsaPfin/Manga-OCR/logs/Pokmon GO APK Everything You Need to Know About the Game.md
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-<h1>Pokémon GO Original APK: How to Download and Play the Game on Your Android Device</h1>
- <p>Do you want to join millions of trainers around the world who are discovering and catching Pokémon as they explore their surroundings? Do you want to experience the thrill of finding, battling, and trading Pokémon in real life? If you answered yes, then you need to download and play Pokémon GO, the global gaming sensation that has taken the world by storm.</p>
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- <p>But what if you can't find the game on your Google Play Store or you want to access some features that are not available in your region? Don't worry, there is a solution for that. You can download and install the Pokémon GO original APK file on your Android device and enjoy the game without any restrictions.</p>
- <p>In this article, we will explain what Pokémon GO is, what an APK file is, how to download and install the Pokémon GO original APK on your Android device, and how to play the game like a pro. Let's get started!</p>
- <h2>What is Pokémon GO?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Pokémon GO is a mobile game developed by Niantic, Inc. in collaboration with The Pokémon Company. It was released in July 2016 for iOS and Android devices. The game uses augmented reality (AR) technology to overlay digital images of Pokémon on your device's camera view. This allows you to see and interact with Pokémon as if they were in your real environment.</p>
- <p>The game's main objective is to catch as many Pokémon as you can by walking around your neighborhood, visiting different locations, and using your device's GPS and camera. You can also battle other trainers, join teams, participate in raids, trade Pokémon, complete quests, collect items, earn badges, and more.</p>
- <p>The game features over 600 different species of Pokémon from various generations of the franchise. Some of them are exclusive to certain regions or events. You can also encounter shiny, shadow, purified, lucky, mega evolved, or costumed Pokémon with different appearances or abilities.</p>
- <h3>Why is it so popular and addictive?</h3>
- <p>Pokémon GO is not just a game, it's a phenomenon. It has been downloaded over 1 billion times and has won multiple awards for its innovation and impact. It has also created a huge community of fans who share their experiences and stories online.</p>
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- <p>There are many reasons why Pokémon GO is so popular and addictive. Here are some of them:</p>
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-<li>It offers a variety of gameplay modes, challenges, events, and rewards that keep players engaged.</li>
-<li>It appeals to people of all ages, backgrounds, and preferences.</li>
-<li>It taps into the nostalgia, nostalgia factor for older fans who remember the original games or anime.</li>
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- <h2>What is an APK file and why do you need it?</h2>
- <h3>The difference between an APK and a regular app</h3>
- <p>An APK file is an Android Package Kit, which is a file format that contains all the components of an Android app. It is similar to an executable file (.exe) for Windows or a package file (.pkg) for Mac. When you download an app from the Google Play Store, you are actually downloading an APK file that is then installed on your device. A regular app is an app that is officially available on the Google Play Store and has been verified by Google for its security and compatibility. A regular app can be easily updated and uninstalled from your device. <h3>The benefits and risks of using an APK file</h3>
- <p>An APK file is an alternative way of installing an app on your Android device. You can download an APK file from various sources on the internet, such as websites, forums, or file-sharing platforms. However, not all APK files are safe or legal to use. Some of them may contain malware, viruses, or pirated content that can harm your device or violate the terms of service of the app.</p>
- <p>There are some benefits and risks of using an APK file. Here are some of them:</p>
- <table>
-<tr>
-<th>Benefits</th>
-<th>Risks</th>
-</tr>
-<tr>
-<td>You can access apps that are not available in your region or on your device.</td>
-<td>You may violate the intellectual property rights of the app developers or publishers.</td>
-</tr>
-<tr>
-<td>You can enjoy features that are not yet released or updated on the Google Play Store.</td>
-<td>You may expose your device to security threats or performance issues.</td>
-</tr>
-<tr>
-<td>You can customize or modify the app to suit your preferences.</td>
-<td>You may lose the support or warranty from the app developers or Google.</td>
-</tr>
-</table>
- <h2>How to download and install the Pokémon GO original APK on your Android device</h2>
- <h3>The steps to follow and the requirements to meet</h3>
- <p>If you want to download and install the Pokémon GO original APK on your Android device, you need to follow these steps and meet these requirements:</p>
- <ol>
-<li>Make sure your device has enough storage space and battery life.</li>
-<li>Enable the option to install apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Download the Pokémon GO original APK file from a reliable source. You can use this link to get the latest version of the game (0.227.1) as of June 2022.</li>
-<li>Locate the downloaded APK file on your device using a file manager app or your browser's downloads folder.</li>
-<li>Tap on the APK file and follow the instructions to install it on your device.</li>
-<li>Launch the Pokémon GO app and sign in with your Google account or Pokémon Trainer Club account.</li>
-<li>Grant the necessary permissions to the app, such as location, camera, and storage.</li>
-<li>Enjoy playing Pokémon GO on your Android device!</li>
-</ol>
- <h3>How to update the APK file and troubleshoot common issues</h3>
- <p>To update the Pokémon GO original APK file, you need to repeat the same steps as above whenever a new version of the game is released. You can check for updates by visiting the same source where you downloaded the APK file or by following Pokémon GO's official social media accounts.</p>
- <p>If you encounter any issues while using the Pokémon GO original APK file, such as crashes, errors, or glitches, you can try these solutions:</p>
- <ul>
-<li>Restart your device and relaunch the app.</li>
-<li>Clear the cache and data of the app. To do this, go to Settings > Apps > Pokémon GO > Storage > Clear Cache and Clear Data.</li>
-<li>Uninstall and reinstall the app using the latest APK file.</li>
-<li>Contact Niantic's customer support team through their website or email (pokemon-go-support@nianticlabs.com).</li>
-</ul>
- <h2>How to play Pokémon GO on your Android device</h2>
- <h3>The basics of the gameplay and the interface</h3>
- <p>Pokémon GO is a game that requires you to move around in real life to catch Pokémon, visit PokéStops, battle Gyms, and more. The game uses a map-based interface that shows your location and nearby points of interest. You can also access various menus and options by tapping on icons or buttons on the screen.</p>
- <p>Here are some of the basic elements of the gameplay and the interface:</p>
- <ul>
-<li>To catch a Pokémon, you need to tap on it when it appears on your map. This will take you to a capture screen where you can throw Poké Balls at it. You can also use items, such as berries, to make it easier to catch. Some Pokémon may require you to perform certain actions, such as walking, spinning, or tapping, to catch them.</li>
-<li>To visit a PokéStop, you need to walk near it and tap on it when it turns blue. This will open a disc that you can spin to get items, such as Poké Balls, potions, revives, eggs, and more. Some PokéStops may also have quests, lures, or stickers that you can obtain.</li>
-<li>To battle a Gym, you need to walk near it and tap on it when it turns yellow, blue, or red depending on the team that controls it. You can then choose up to six Pokémon to fight against the defenders of the Gym. You can also use items, such as potions and revives, to heal your Pokémon. You can earn Gym badges, coins, and items by battling Gyms.</li>
-<li>To participate in a raid, you need to walk near a Gym that has a timer and an egg on top of it. When the timer reaches zero, the egg will hatch and reveal a powerful Pokémon that you can battle with up to 20 other trainers. You can use a raid pass to join a raid and earn rewards, such as rare candies, golden razz berries, TMs, and a chance to catch the raid boss.</li>
-<li>To trade Pokémon with another trainer, you need to be friends with them and be within 100 meters of each other. You can then select the Pokémon you want to trade and agree on the terms. You can earn candy, stardust, and friendship points by trading Pokémon.</li>
-</ul>
- <h3>The tips and tricks to catch more Pokémon and level up faster</h3>
- <p>Pokémon GO is a game that rewards you for your efforts and skills. The more you play, the more you learn and improve. However, there are some tips and tricks that can help you catch more Pokémon and level up faster. Here are some of them:</p>
- <ul>
-<li>Use the nearby radar or the PokéTracker app to find out which Pokémon are around you and where they are located.</li>
-<li>Use incense or lures to attract more Pokémon to your location or a specific PokéStop.</li>
-<li>Use curveballs, great throws, excellent throws, or first throw bonuses to increase your chances of catching a Pokémon and earning more XP.</li>
-<li>Use the appropriate type of Poké Ball or berry for the Pokémon you are trying to catch. For example, use a Great Ball or an Ultra Ball for higher-level or rarer Pokémon, or use a Nanab Berry or a Razz Berry to calm down or make them easier to catch.</li>
-<li>Catch as many Pokémon as you can, especially those that are common or have low evolution costs. You can then transfer them to Professor Willow for candy or evolve them for XP.</li>
-<li>Use lucky eggs or star pieces to double your XP or stardust gains for 30 minutes.</li>
-<li>Complete quests, achievements, research tasks, field research tasks, special research tasks, or timed research tasks to earn rewards, such as items, XP, stardust, or encounters with Pokémon.</li>
-<li>Join a team and cooperate with other trainers to take over Gyms, defend Gyms, or participate in raids.</li>
-<li>Make friends with other trainers and send or receive gifts, trade Pokémon, battle together, or join raids together. You can also increase your friendship level with them and unlock bonuses.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Pokémon GO is a game that lets you experience the joy of catching and collecting Pokémon in real life. It is fun, exciting, and addictive. However, if you want to play the game on your Android device without any limitations or restrictions, you may need to download and install the Pokémon GO original APK file from a trusted source. This will allow you to access features that are not available in your region or on your device.</p>
- <p>In this article, we have explained what Pokémon GO is, what an APK file is, how to download and install the Pokémon GO original APK on your Android device, and how to play the game like a pro. We have also shared some tips and tricks to catch more Pokémon and level up faster. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy hunting! <h2>FAQs</h2>
- <h3>Q1: Is Pokémon GO free to play?</h3>
- <p>A1: Yes, Pokémon GO is free to download and play on your Android device. However, the game also offers some optional in-app purchases that can enhance your gameplay or speed up your progress. You can buy items, such as Poké Balls, incense, lures, lucky eggs, star pieces, raid passes, or coins, with real money. You can also use coins to buy cosmetic items, such as outfits, accessories, or stickers, for your avatar or your Pokémon.</p>
- <h3>Q2: Is Pokémon GO safe for kids?</h3>
- <p>A2: Pokémon GO is rated E for Everyone by the ESRB, which means it is suitable for players of all ages. However, the game does involve some risks and challenges that may not be appropriate for younger or inexperienced players. For example, the game requires players to walk around in real life, which may expose them to traffic, strangers, or other hazards. The game also uses location data, camera access, and internet connection, which may compromise the privacy or security of the players. Therefore, parents or guardians should supervise and guide their kids when playing Pokémon GO and set some rules and boundaries for their safety and well-being.</p>
- <h3>Q3: Can I play Pokémon GO offline?</h3>
- <p>A3: No, you cannot play Pokémon GO offline. The game requires a constant internet connection to access the game servers and update the map data. You also need a GPS signal to track your location and movement. Without these features, you will not be able to see or catch any Pokémon, visit any PokéStops or Gyms, or participate in any raids or events. Therefore, make sure you have a stable Wi-Fi or cellular data connection and a good GPS signal when playing Pokémon GO.</p>
- <h3>Q4: How can I get rare and legendary Pokémon in Pokémon GO?</h3>
- <p>A4: Rare and legendary Pokémon are Pokémon that are very hard to find or catch in Pokémon GO. They usually have higher stats, unique abilities, or special appearances than other Pokémon. Some examples of rare and legendary Pokémon are Ditto, Unown, Mewtwo, Zapdos, Lugia, Ho-Oh, Rayquaza, Kyogre, Groudon, Dialga, Palkia, Giratina, Reshiram, Zekrom, Kyurem, Xerneas, Yveltal, and more. There are several ways to get rare and legendary Pokémon in Pokémon GO. Here are some of them: - Participate in raids. Raids are cooperative battles that take place at Gyms. They feature powerful Pokémon that you can fight and catch with other trainers. Some raids feature rare or legendary Pokémon that are only available for a limited time or require a special invitation. You can use a raid pass to join a raid and earn rewards, such as rare candies, golden razz berries, TMs, and a chance to catch the raid boss. - Complete research tasks. Research tasks are quests that you can get from PokéStops, Professor Willow, or special events. They require you to perform certain actions, such as catching Pokémon, spinning PokéStops, battling Gyms, or walking a certain distance. You can earn rewards, such as items, XP, stardust, or encounters with Pokémon. Some research tasks may lead you to rare or legendary Pokémon, especially the special research tasks or the breakthrough rewards. - Hatch eggs. Eggs are items that you can get from PokéStops, gifts, or quests. They contain Pokémon that you can hatch by walking a certain distance with them in an incubator. You can get different types of eggs, such as 2 km, 5 km, 7 km, 10 km, or 12 km eggs. Some eggs may contain rare or legendary Pokémon that are not easy to find in the wild. - Use incense or lures. Incense and lures are items that you can use to attract more Pokémon to your location or a specific PokéStop. They last for 30 minutes and can increase your chances of encountering rare or legendary Pokémon. You can also use special types of incense or lures, such as magnetic, mossy, or glacial lures, to attract specific types of Pokémon. - Trade with other trainers. Trading is a feature that allows you to exchange Pokémon with another trainer who is your friend and within 100 meters of you. You can trade any Pokémon that you have caught or hatched, except for mythical Pokémon. You can also trade legendary Pokémon, but only if you have already caught them or if you are best friends with the other trainer. Trading can help you get rare or legendary Pokémon that you don't have or that are not available in your region. <h3>Q5: How can I contact the developers of Pokémon GO?</h3>
- <p>A5: If you have any questions, feedback, suggestions, complaints, or issues regarding Pokémon GO, you can contact the developers of the game through various channels. Here are some of them:</p>
- <ul>
-<li>You can visit their official website (https://pokemongolive.com/) and check their news section, blog posts, help center, terms of service, privacy policy, or social media links.</li>
-<li>You can email them at pokemon-go-support@nianticlabs.com and provide them with your details and your inquiry.</li>
-<li>You can report a bug or a problem through the app itself. To do this, go to Settings > Get Support > Report a Bug or Report a Problem.</li>
-<li>You can submit a request or a feedback through their online form (https://niantic.helpshift.com/a/pokemon-go/?p=web&contact=1) and choose the appropriate category and subcategory for your issue.</li>
-</ul></p> 197e85843d<br />
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-<br />
-<h1>What is Skatteverket and why is it important?</h1>
-<p>Skatteverket, or the Swedish Tax Agency, is a government agency that manages civil registration of private individuals and collects taxes such as personal income tax, corporate tax, VAT and excise tax. It also performs other services and tasks related to population registration, identity documents, real estate transactions, inheritance and gift taxes, gambling taxes, etc. Skatteverket is one of the most important agencies in Sweden, as it affects almost every aspect of life and business in the country. In this article, we will explain what Skatteverket does, how to interact with it, and some tips and advice for dealing with it.</p>
- <h2>Skatteverket's main functions and responsibilities</h2>
-<p>Skatteverket has three main functions and responsibilities: civil registration of private individuals, tax collection and administration, and other services and tasks. Let's look at each of them in more detail.</p>
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- <h3>Civil registration of private individuals</h3>
-<p>Skatteverket is responsible for maintaining the population register, which contains information about all people who are or have been registered in Sweden. The population register includes data such as name, date of birth, gender, citizenship, marital status, address, family relations, etc. Skatteverket also issues personal identity numbers (personnummer) or coordination numbers (samordningsnummer) to people who are registered in Sweden. These numbers are used as identifiers for various purposes, such as opening a bank account, applying for a job, accessing health care, etc. Skatteverket also issues identity documents, such as ID cards and passports, to Swedish citizens and residents.</p>
- <h3>Tax collection and administration</h3>
-<p>Skatteverket is responsible for collecting and administering various taxes in Sweden, such as personal income tax, corporate tax, VAT, excise tax, property tax, inheritance and gift tax, gambling tax, etc. Skatteverket also provides information and guidance on tax matters to individuals and businesses. Skatteverket prepares tax returns for most individuals based on the information it receives from employers, banks, authorities, etc. Individuals can review their tax returns online or on paper and make any changes or additions if needed. Skatteverket also handles tax refunds or payments for individuals and businesses. Skatteverket also conducts audits and investigations to ensure compliance with tax laws and regulations.</p>
- <h3>Other services and tasks</h3>
-<p>Skatteverket also performs other services and tasks related to population registration and tax collection. For example, Skatteverket registers real estate transactions and issues certificates of registration (lagfart) to property owners. Skatteverket also registers marriages, divorces, births, deaths, adoptions, name changes, etc. Skatteverket also cooperates with other authorities and organizations in Sweden and abroad to exchange information and prevent fraud and crime. Skatteverket also participates in public awareness campaigns to inform people about their rights and obligations regarding taxes and population registration.</p>
- <h2>How to interact with Skatteverket</h2>
-<p>Skatteverket offers various ways for individuals and businesses to interact with it. Here are some of the most common ones:</p>
- <h3>Online services and e-tjänster</h3>
-<p>Skatteverket has a website (www.skatteverket.se) where you can find information about taxes, population registration, identity documents, real estate transactions, etc. in your personal or financial situation</h3>
-<p>One more thing you need to do when dealing with Skatteverket is to report any changes in your personal or financial situation. For example, if you change your name, address, marital status, family relations, income, deductions, etc., you need to inform Skatteverket as soon as possible. You can use the online services or the app to report most changes, or you can visit a service center or send a form by mail. Reporting changes is important to ensure that your information in the population register and your tax return is correct and up to date.</p>
- <h2>Conclusion and FAQs</h2>
-<p>Skatteverket is a government agency that manages civil registration of private individuals and collects taxes in Sweden. It also performs other services and tasks related to population registration and tax collection. Skatteverket offers various ways for individuals and businesses to interact with it, such as online services, phone and email support, and service centers. Dealing with Skatteverket can be easy and smooth if you follow some tips and advice, such as declaring and paying your taxes on time, checking your tax rate and deductions, applying for a personal identity number or coordination number, and reporting any changes in your personal or financial situation.</p>
-<p>Here are some frequently asked questions (FAQs) about Skatteverket:</p>
-<table>
-<tr><td><b>Q: How can I contact Skatteverket?</b></td><td><b>A: You can contact Skatteverket by phone (0771-567 567), email (via the contact form on the website), or visiting a service center.</b></td></tr>
-<tr><td><b>Q: How can I check my tax return?</b></td><td><b>A: You can check your tax return online (via the website or the app), by phone (0771-567 567), or by mail (if you receive a paper tax return).</b></td></tr>
-<tr><td><b>Q: How can I get an ID card or passport?</b></td><td><b>A: You can apply for or collect an ID card or passport at a service center. You need to book an appointment in advance and bring your identity document and other relevant documents.</b></td></tr>
-<tr><td><b>Q: How can I register a property transaction?</b></td><td><b>A: You can register a property transaction online (via the website) or by mail (by sending the application form and the deed of sale to Skatteverket). You need to pay a stamp duty of 1.5% of the purchase price.</b></td></tr>
-<tr><td><b>Q: How can I get a certificate of registration for a property?</b></td><td><b>A: You can order a certificate of registration for a property online (via the website) or by phone (0771-567 567). You need to pay a fee of SEK 375 per certificate.</b></td></tr>
-</table></p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Tips and tricks to download additional content from google play store.md b/spaces/congsaPfin/Manga-OCR/logs/Tips and tricks to download additional content from google play store.md
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-<h1>How to Download Additional Content from Google Play Store</h1>
-<p>Google Play Store is the official app store for Android devices, where you can find and download millions of apps, games, and digital content for your device. Whether you want to play the latest games, read books, watch movies, listen to music, or learn something new, Google Play Store has something for everyone. In this article, we will show you how to download additional content from Google Play Store, including how to access the store, how to install or purchase content, how to manage your downloads and updates, and how to download APK files from the store.</p>
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- <h2>What is Google Play Store and Why You Need It</h2>
-<h3>Google Play Store Features and Benefits</h3>
-<p>Google Play Store is more than just an app store. It is a digital distribution platform that offers a variety of services and features for Android users. Some of the features and benefits of Google Play Store are:</p>
-<ul>
-<li>You can access over 3 million apps and games, ranging from free to paid, from popular to niche, from casual to hardcore.</li>
-<li>You can discover and download digital content such as books, movies, TV shows, music, podcasts, magazines, and more.</li>
-<li>You can enjoy instant apps that don't require installation. You can try them out before downloading them or use them without taking up space on your device.</li>
-<li>You can get personalized recommendations based on your preferences and usage history.</li>
-<li>You can join Google Play Pass, a subscription service that gives you access to hundreds of premium apps and games without ads or in-app purchases.</li>
-<li>You can use Google Play Protect, a security feature that scans your device for harmful apps and malware.</li>
-<li>You can use Google Play Family Library, a feature that lets you share your purchases with up to five family members across devices.</li>
-<li>You can use Google Play Points, a loyalty program that rewards you for using the store. You can earn points by downloading apps and games, making purchases, or subscribing to services. You can redeem your points for discounts, coupons, in-app items, or donations.</li>
-</ul>
- <h3>How to Access Google Play Store on Your Device</h3>
-<p>If you have an Android device that supports Google Play, you should already have the Google Play Store app pre-installed on your device. You can find it in the Apps section of your device. If you don't have it or you deleted it by mistake, you can download it from the official website.</p>
-<p>To access Google Play Store on your device, you need a Google Account. If you don't have one, you can create one for free. You can sign in with your Google Account on multiple devices and sync your data across them. You also need an internet connection, either Wi-Fi or mobile data.</p>
- <h2>How to Download Apps, Games, and Digital Content from Google Play Store</h2>
-<h3>How to Search and Browse for Content</h3>
-<p>To find the content you want on Google Play Store, you can use the search feature or browse by categories. You can also use filters and sorting options to narrow down your results. To search for content:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Tap the search bar at the top of the screen.</li>
-<li>Type in the name of the app, game, or content you want or use voice search.</li>
-<li>Tap the magnifying glass icon or press Enter.</li>
-<li>Scroll through the results and tap the one you want.</li>
-</ol>
-<p>To browse for content:</p <p>Open the Google Play Store app on your device.</p>
-<p>Tap the menu icon at the top left corner of the screen.</p>
-<p>How to download apps and games from google play store<br />
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-<p>Select a category from the list, such as Apps, Games, Movies & TV, Books, etc.</p>
-<p>Scroll through the featured, top charts, or new and updated sections or tap See more to view more options.</p>
-<p>Tap the content you want to explore.</p>
- <h3>How to Install or Purchase Content</h3>
-<p>Once you find the content you want on Google Play Store, you can install it or purchase it depending on whether it is free or paid. To install or purchase content:</p>
-<ol>
-<li>Tap the content you want to download from the search or browse results.</li>
-<li>On the content's detail page, tap Install for free content or the price for paid content.</li>
-<li>If you are purchasing content, you will need to select a payment method and confirm your purchase. You can also use Google Play gift cards or promo codes to pay for your content.</li>
-<li>Wait for the download to complete. You can see the progress on the notification bar or on the My apps & games section of the store.</li>
-<li>Once the download is finished, you can open the content from the notification bar, the app drawer, or the My apps & games section of the store.</li>
-</ol>
- <h3>How to Manage Your Downloads and Updates</h3>
-<p>To keep your apps, games, and digital content up to date and running smoothly, you need to manage your downloads and updates on Google Play Store. You can also uninstall or disable content that you don't use anymore. To manage your downloads and updates:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Tap the menu icon at the top left corner of the screen.</li>
-<li>Select My apps & games from the list.</li>
-<li>On the Updates tab, you can see which apps and games have updates available. You can tap Update All to update all of them at once or tap Update next to each one individually.</li>
-<li>On the Installed tab, you can see all the apps and games that are installed on your device. You can tap Open to launch them or tap Uninstall to remove them. You can also tap Disable to turn off pre-installed apps that you don't use.</li>
-<li>On the Library tab, you can see all the apps and games that you have ever downloaded but are not currently installed on your device. You can tap Install to reinstall them or tap X to remove them from your library.</li>
-</ol>
- <h2>How to Download APK Files from Google Play Store</h2>
-<h3>What are APK Files and Why You Might Need Them</h3>
-<p>APK files are Android application packages that contain all the files and data needed to run an app or game on your device. Normally, when you download an app or game from Google Play Store, it automatically installs the APK file on your device. However, sometimes you might need to download APK files manually for various reasons, such as:</p>
-<ul>
-<li>You want to install an app or game that is not available in your region or country.</li>
-<li>You want to install an older version of an app or game that has been updated with unwanted changes or bugs.</li>
-<li>You want to install a modified or hacked version of an app or game that offers extra features or benefits.</li>
-<li>You want to backup an app or game that you have purchased or downloaded before.</li>
-</ul>
- <h3>How to Use APKCombo Downloader to Get APK Files</h3>
-<p>One of the easiest and safest ways to download APK files from Google Play Store is to use APKCombo Downloader, a free online tool that lets you get any app or game's APK file with a few clicks. To use APKCombo Downloader:</p <p>One of the easiest and safest ways to download APK files from Google Play Store is to use APKCombo Downloader, a free online tool that lets you get any app or game's APK file with a few clicks. To use APKCombo Downloader:</p>
-<ol>
-<li>Go to the APKCombo Downloader website on your browser.</li>
-<li>Type in the name of the app or game you want to download or paste its Google Play Store URL in the search box.</li>
-<li>Select the version and variant of the app or game you want from the drop-down menu.</li>
-<li>Click on Download APK to start downloading the APK file to your device or computer.</li>
-<li>You can also scan the QR code to download the APK file directly to your device.</li>
-</ol>
- <h3>How to Install APK Files on Your Device</h3>
-<p>Before you can install APK files on your device, you need to enable the option to install apps from unknown sources. This option may vary depending on your device model and Android version, but you can usually find it in the Settings > Security > Unknown sources or Settings > Apps & notifications > Special app access > Install unknown apps. Once you enable this option, you can install APK files on your device by following these steps:</p>
-<ol>
-<li>Locate the APK file you downloaded on your device or computer. You can use a file manager app or connect your device to your computer via USB cable.</li>
-<li>Tap on the APK file to open it. You may see a warning message that says "This type of file can harm your device". Tap OK to proceed.</li>
-<li>You may see a pop-up window that asks you to grant permissions to the app or game. Tap Install to continue.</li>
-<li>Wait for the installation to complete. You may see a confirmation message that says "App installed". Tap Open to launch the app or game or Done to exit.</li>
-</ol>
- <h2>Conclusion and FAQs</h2>
-<h3>Summary of the Main Points</h3>
-<p>In this article, we have shown you how to download additional content from Google Play Store, including how to access the store, how to install or purchase content, how to manage your downloads and updates, and how to download APK files from the store. Google Play Store is a great source of apps, games, and digital content for your Android device, but sometimes you may need to download APK files manually for various reasons. We hope this article has helped you learn how to do that safely and easily with APKCombo Downloader.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about downloading additional content from Google Play Store:</p>
-<table>
-<tr><th>Question</th><th>Answer</th></tr>
-<tr><td>Is Google Play Store safe?</td><td>Google Play Store is generally safe as it has strict policies and guidelines for app developers and users. It also has Google Play Protect, a security feature that scans your device for harmful apps and malware. However, you should always be careful about what you download and install on your device, as some apps may contain viruses, spyware, or other malicious code.</td></tr>
-<tr><td>Is APKCombo Downloader safe?</td><td>APKCombo Downloader is safe as it downloads APK files directly from Google Play Store without any modifications or alterations. It also uses SSL encryption to protect your data and privacy. However, you should always check the source and permissions of the APK files before installing them on your device, as some apps may have hidden features or functions that may harm your device or compromise your security.</td></tr>
-<tr><td>Can I update apps and games that I downloaded as APK files?</td><td>You can update apps and games that you downloaded as APK files by downloading and installing their latest versions from Google Play Store or APKCombo Downloader. However, some apps and games may not be compatible with newer versions or may lose some features or functions after updating. You should always backup your data before updating any app or game on your device.</td></tr>
-<tr><td>Can I download paid apps and games for free as APK files?</td><td>You should not download paid apps and games for free as APK files as this is illegal and unethical. You may also expose your device to malware or viruses that may steal your personal information or damage your device. You should always support the developers by purchasing their apps and games from Google Play Store or other legitimate sources.</td></tr>
-<tr><td>Can I download apps and games that are not available in my region or country as APK files?</td><td>You can download apps and games that are not available in your region or country as APK files by using a VPN service or a proxy server that changes your IP address and location. However, some apps and games may not work properly or may have restricted content or features depending on your region or country. You should always respect the regional and legal restrictions of the app and game developers and publishers.</td></tr>
-</table></p> 197e85843d<br />
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-
-<h1>WhatsApp Messenger iPhone APK Download: How to Do It and What Are the Alternatives?</h1>
-<p>WhatsApp is one of the most popular messaging apps in the world, with over 2 billion users in 180 countries. It offers free, secure, and easy-to-use communication features, such as voice and video calls, text messages, group chats, media sharing, and more. WhatsApp is owned by Meta (formerly Facebook), which means it can integrate with other Meta products and services, such as Instagram, Messenger, and Portal.</p>
-<p>But what if you want to install WhatsApp on your iPhone using an APK file? An APK file is a file format used by Android devices to install apps and games. It is not compatible with iOS devices, which use a different file format called IPA. However, there are some ways to install APK files on iPhone, either by jailbreaking your device or by finding the IPA equivalent of the APK file. In this article, we will explain how to do both methods, as well as introduce some of the best WhatsApp alternatives and competitors for Android and iPhone.</p>
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- <h2>How to Install APK File on iPhone</h2>
-<p>To install an APK file on your iPhone, you have two options: perform jailbreaking or find the IPA equivalent. Both methods have their pros and cons, so you should weigh them carefully before proceeding.</p>
- <h3>Method 1: Perform Jailbreaking</h3>
-<p>Jailbreaking your iPhone means modifying its software to remove some of the restrictions imposed by Apple. This way, you can install apps that are not available on the App Store, customize your device's appearance and settings, and access more features and functions. However, jailbreaking also comes with some risks, such as voiding your warranty, exposing your device to malware and viruses, losing data or functionality, and making your device incompatible with future updates.</p>
-<p>If you decide to jailbreak your iPhone, you will need to follow these steps:</p>
-<ul>
-<li>Download a jailbreak tool that is compatible with your device model and iOS version. Some of the popular jailbreak tools are Checkra1n, Unc0ver, Chimera, Odyssey, etc.</li>
-<li>Connect your iPhone to your computer and run the jailbreak tool. Follow the instructions on the screen to complete the jailbreak process.</li>
-<li>After jailbreaking your device, you will see a new app called Cydia on your home screen. Cydia is a store that offers apps for jailbroken devices.</li>
-<li>Open Cydia and search for an app that can install APK files on your iPhone. Some of the apps that can do this are AppCake, iFile, Filza File Manager, etc.</li>
-<li>Download and install the app of your choice from Cydia.</li>
-<li>Download the APK file of WhatsApp or any other app you want to install on your iPhone.</li>
-<li>Open the app that can install APK files and locate the downloaded APK file.</li>
-<li>Select the APK file and tap on Install. Wait for the installation process to finish.</li>
-<li>You should now see the app icon on your home screen. Tap on it to launch it.</li>
-</ul>
- <h3>Method 2: Find the IPA Equivalent</h3>
-<p>If you don't want to jailbreak your iPhone, you can try to find the IPA equivalent of the APK file you want to install. An IPA file is a file format that is used by iOS devices to install apps and games. It is similar to APK, but it is designed for Apple devices. However, not all APK files have an IPA equivalent, and vice versa. Some apps are exclusive to Android or iOS, and some apps have different versions for different platforms.</p>
-<p>If you want to install an IPA file on your iPhone, you will need to follow these steps:</p>
-<ul>
-<li>Download the IPA file of WhatsApp or any other app you want to install on your iPhone. You can search for IPA files on websites like iPA Library, iPABox, AppValley, etc.</li>
-<li>Download and install a tool that can sideload IPA files on your iPhone. Sideload means installing apps from sources other than the App Store. Some of the tools that can do this are Cydia Impactor, AltStore, 3uTools, etc.</li>
-<li>Connect your iPhone to your computer and run the tool of your choice.</li>
-<li>Drag and drop the IPA file into the tool and enter your Apple ID and password. This is required to sign the app with your account.</li>
-<li>Wait for the tool to install the app on your iPhone.</li>
-<li>You should now see the app icon on your home screen. Tap on it to launch it.</li>
-</ul>
- <h2>WhatsApp Alternatives and Competitors</h2>
-<p>WhatsApp is not the only messaging app available for Android and iPhone. There are many other apps that offer similar or better features and benefits. Here are some of the best WhatsApp alternatives and competitors for Android and iPhone:</p>
- <table>
-<tr>
-<th>App Name</th>
-<th>Features</th>
-<th>Benefits</th>
-</tr>
-<tr>
-<td>Telegram</td>
-<td>- End-to-end encryption for messages and calls<br>- Cloud-based storage for media and files<br>- Self-destructing messages and secret chats<br>- Group chats with up to 200,000 members<br>- Channels for broadcasting messages to unlimited subscribers<br>- Bots for automation and integration with other services</td>
-<td>- Fast, secure, and reliable<br>- Supports multiple devices and platforms<br>- Offers more privacy and control over your data<br>- Allows customization and personalization of chats<br>- Has a large and active community of users and developers</td>
-</tr>
-<tr>
-<td>Signal</td>
-<td>- End-to-end encryption for messages and calls<br>- Open-source and peer-reviewed code<br>- Disappearing messages and screen security<br>- Group chats with up to 1000 members<br>- Stickers, GIFs, voice notes, and media sharing<br>- Relay calls through Signal servers to hide your IP address</td>
-<td>- Safe, simple, and transparent<br>- Protects your data from third parties and advertisers<br>- Offers advanced security features and settings<br>- Supports voice and video calls with high quality<br>- Has a minimalist and user-friendly interface</td>
-</tr>
-<tr>
-<td>Viber</td>
-<td>- End-to-end encryption for messages and calls<br>- Group chats with up to 250 members<br>- Communities for public discussions with unlimited members<br>- Stickers, GIFs, emojis, voice notes, and media sharing<br>- Viber Out for low-cost calls to non-Viber users<br>- Viber Games for playing fun games with friends</td>
-<td>- Fun, colorful, and expressive<br>- Supports HD voice and video calls<br>- Offers a variety of content and services<br>- Integrates with other apps like Spotify, YouTube, etc.<br>- Has a loyal and engaged user base</td>
-</tr>
-<tr>
-<td>Skype</td>
-<td>- Free voice and video calls with up to 50 people<br>- Instant messaging with emojis, stickers, GIFs, etc.<br>- Screen sharing and file sharing up to 300 MB<br>- Skype Number for receiving calls from landlines and mobiles<br>- Skype Credit for making low-cost calls to non-Skype users<br>- Skype Translator for real-time translation of voice and text</td>
-<td>- Versatile, convenient, and professional<br>- Supports multiple devices and platforms<br>- Offers high-quality audio and video calls<br>- Allows cross-platform communication with other apps like Outlook, OneDrive, etc.<br>- Has a wide range of features and options</td>
-</tr>
- <tr><td colspan="3">Source:    </td></tr>
- </table>
- <h2>Conclusion</h2>
- <p>In this article, we have explained how to install WhatsApp on your iPhone using an APK file. We have also introduced some of the best WhatsApp alternatives and competitors for Android and iPhone. We hope you have found this article helpful and informative. Here are some tips and recommendations for using WhatsApp or its alternatives:</p>
-<ul>
-<li>Always update your app to the latest version to enjoy the new features and bug fixes.</li>
-<li>Check your app settings and permissions to make sure you are comfortable with how your data is used and shared.</li>
-<li>Backup your chats and media regularly to avoid losing them in case of device loss, theft, or damage.</li>
-<li>Use strong passwords and two-factor authentication to protect your account from unauthorized access.</li>
-<li>Be careful of phishing, spam, and scam messages that may try to trick you into revealing your personal or financial information.</li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers related to the topic of WhatsApp messenger iPhone APK download:</p>
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-<ol>
-<li><b>What is the difference between WhatsApp and WhatsApp Business?</b><br>
-WhatsApp Business is a separate app that is designed for small and medium businesses. It allows them to create a business profile, showcase their products and services, communicate with their customers, and access analytics and insights. WhatsApp Business is compatible with WhatsApp, which means customers can use their regular WhatsApp app to chat with businesses that use WhatsApp Business.</li>
-<li><b>Can I use WhatsApp on my computer or tablet?</b><br>
-Yes, you can use WhatsApp on your computer or tablet by using WhatsApp Web or WhatsApp Desktop. WhatsApp Web is a web-based version of WhatsApp that you can access from any browser. WhatsApp Desktop is a downloadable app that you can install on your Windows or Mac computer. To use either of them, you need to scan a QR code from your phone to link your devices.</li>
-<li><b>How can I delete or unsend a message on WhatsApp?</b><br>
-You can delete or unsend a message on WhatsApp by tapping and holding the message, then selecting Delete. You will have two options: Delete for Me or Delete for Everyone. Delete for Me will remove the message from your chat, but not from the recipient's chat. Delete for Everyone will remove the message from both chats, but only if you do it within an hour of sending the message.</li>
-<li><b>How can I make a group video call on WhatsApp?</b><br>
-You can make a group video call on WhatsApp by starting a video call with one of your contacts, then tapping on the Add Participant icon at the top right corner of the screen. You can add up to seven more participants, for a total of eight people in a group video call. You can also create a group chat with the people you want to video call, then tap on the Video Call icon at the top right corner of the screen.</li>
-<li><b>How can I backup or restore my WhatsApp data?</b><br>
-You can backup or restore your WhatsApp data by using iCloud (for iPhone) or Google Drive (for Android). To backup your data, go to Settings > Chats > Chat Backup, then tap on Back Up Now. You can also enable Auto Backup to backup your data automatically at regular intervals. To restore your data, you need to reinstall WhatsApp on your device, then follow the prompts to restore your data from iCloud or Google Drive.</li>
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diff --git a/spaces/contluForse/HuggingGPT/assets/Corel VideoStudio Ultimate 2019 22.2.0.402 Crack Full Version.md b/spaces/contluForse/HuggingGPT/assets/Corel VideoStudio Ultimate 2019 22.2.0.402 Crack Full Version.md
deleted file mode 100644
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-<p>Some of the pros and cons of Corel VideoStudio Ultimate 2019 22.2.0.402 Crack Full Version are:</p>
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-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>You can use all the features and tools of the original software for free.</td><td>You might face legal issues or malware risks by using a cracked version of the software.</td></tr>
-<tr><td>You can edit any type of video project with ease and creativity.</td><td>You might experience some bugs or glitches in the software performance.</td></tr>
-<tr><td>You can export your videos in various formats and resolutions.</td><td>You might not get any updates or support from the official developers.</td></tr>
-</table>
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-<p>Corel VideoStudio Ultimate 2019 22.2.0.402 Crack Full Version is a great option for video editing enthusiasts who want to use a professional and full-featured software without paying for a license. However, it also comes with some risks and drawbacks that you should be aware of before downloading and installing it.</p>
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-<p>Next, you can enhance your videos with the color correction tools, which let you adjust the color balance, exposure, contrast, and more. You can also apply filters and LUTs to give your videos a cinematic look.</p>
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-<p>If you want to use Corel VideoStudio Ultimate 2019 legally and safely, you should buy it from the official website or from an authorized reseller. By doing so, you will get a valid license key that will activate the software and grant you access to all the features and tools.</p>
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-<p>You will also get free updates and support from the official developers, as well as a 30-day money-back guarantee if you are not satisfied with the product. You can choose between a one-time purchase or a subscription plan, depending on your needs and budget.</p>
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-<p>The current price of Corel VideoStudio Ultimate 2019 is $99.99 for a one-time purchase or $79.99 per year for a subscription plan. You can also get a free trial version for 30 days to test the software before buying it.</p>
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-<p>Corel VideoStudio Ultimate 2019 has received mostly positive reviews from customers who have used it for their video editing projects. According to the reviews on Best Buy, Amazon, and PCMag, some of the pros and cons of Corel VideoStudio Ultimate 2019 are:</p>
-
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>Easy to use and learn</td><td>Audio editing capabilities are comparatively weaker than video</td></tr>
-<tr><td>Loads of snazzy effects and creative tools</td><td>Lackluster support for high-DPI monitors</td></tr>
-<tr><td>Extremely fast rendering and performance</td><td>Too many easy-edit tools that may confuse some users</td></tr>
-<tr><td>Support for 360-degree VR, 4K Ultra HD, and 3D media</td><td>Some bugs and glitches in the software performance</td></tr>
-</table>
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-<p>Overall, most customers are happy with Corel VideoStudio Ultimate 2019 and recommend it to others who are looking for a feature-packed and intuitive consumer video editing program.</p>
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-<h2>Conclusion</h2>
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-<p>Corel VideoStudio Ultimate 2019 22.2.0.402 Crack Full Version is a remarkable video editing software that can help you create stunning movies with ease and creativity. It has all the features and tools you need to edit any type of video project, from basic trimming and cropping to advanced color grading and video masking. It also has some fun and innovative features, such as stop motion, face effects, GIF creation, and speech-to-text auto-captioning.</p>
-
-<p>However, using a cracked version of Corel VideoStudio Ultimate 2019 is not a wise decision, as it can expose you to legal issues or malware risks, as well as affect the performance and quality of the software. You will also miss out on the updates and support from the official developers, which can enhance your software experience.</p>
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\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/vit.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/vit.py
deleted file mode 100644
index 07b5e2073ae80859be59d1142394929b504cf427..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/vit.py
+++ /dev/null
@@ -1,524 +0,0 @@
-import logging
-import math
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn as nn
-
-from annotator.oneformer.detectron2.layers import CNNBlockBase, Conv2d, get_norm
-from annotator.oneformer.detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous
-
-from .backbone import Backbone
-from .utils import (
-    PatchEmbed,
-    add_decomposed_rel_pos,
-    get_abs_pos,
-    window_partition,
-    window_unpartition,
-)
-
-logger = logging.getLogger(__name__)
-
-
-__all__ = ["ViT", "SimpleFeaturePyramid", "get_vit_lr_decay_rate"]
-
-
-class Attention(nn.Module):
-    """Multi-head Attention block with relative position embeddings."""
-
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=True,
-        use_rel_pos=False,
-        rel_pos_zero_init=True,
-        input_size=None,
-    ):
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads.
-            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
-            rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (int or None): Input resolution for calculating the relative positional
-                parameter size.
-        """
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim**-0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.proj = nn.Linear(dim, dim)
-
-        self.use_rel_pos = use_rel_pos
-        if self.use_rel_pos:
-            # initialize relative positional embeddings
-            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
-            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
-            if not rel_pos_zero_init:
-                nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
-                nn.init.trunc_normal_(self.rel_pos_w, std=0.02)
-
-    def forward(self, x):
-        B, H, W, _ = x.shape
-        # qkv with shape (3, B, nHead, H * W, C)
-        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        # q, k, v with shape (B * nHead, H * W, C)
-        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
-        attn = (q * self.scale) @ k.transpose(-2, -1)
-
-        if self.use_rel_pos:
-            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
-        attn = attn.softmax(dim=-1)
-        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
-        x = self.proj(x)
-
-        return x
-
-
-class ResBottleneckBlock(CNNBlockBase):
-    """
-    The standard bottleneck residual block without the last activation layer.
-    It contains 3 conv layers with kernels 1x1, 3x3, 1x1.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        bottleneck_channels,
-        norm="LN",
-        act_layer=nn.GELU,
-    ):
-        """
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            bottleneck_channels (int): number of output channels for the 3x3
-                "bottleneck" conv layers.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format.
-            act_layer (callable): activation for all conv layers.
-        """
-        super().__init__(in_channels, out_channels, 1)
-
-        self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False)
-        self.norm1 = get_norm(norm, bottleneck_channels)
-        self.act1 = act_layer()
-
-        self.conv2 = Conv2d(
-            bottleneck_channels,
-            bottleneck_channels,
-            3,
-            padding=1,
-            bias=False,
-        )
-        self.norm2 = get_norm(norm, bottleneck_channels)
-        self.act2 = act_layer()
-
-        self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False)
-        self.norm3 = get_norm(norm, out_channels)
-
-        for layer in [self.conv1, self.conv2, self.conv3]:
-            weight_init.c2_msra_fill(layer)
-        for layer in [self.norm1, self.norm2]:
-            layer.weight.data.fill_(1.0)
-            layer.bias.data.zero_()
-        # zero init last norm layer.
-        self.norm3.weight.data.zero_()
-        self.norm3.bias.data.zero_()
-
-    def forward(self, x):
-        out = x
-        for layer in self.children():
-            out = layer(out)
-
-        out = x + out
-        return out
-
-
-class Block(nn.Module):
-    """Transformer blocks with support of window attention and residual propagation blocks"""
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        act_layer=nn.GELU,
-        use_rel_pos=False,
-        rel_pos_zero_init=True,
-        window_size=0,
-        use_residual_block=False,
-        input_size=None,
-    ):
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            drop_path (float): Stochastic depth rate.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks. If it equals 0, then not
-                use window attention.
-            use_residual_block (bool): If True, use a residual block after the MLP block.
-            input_size (int or None): Input resolution for calculating the relative positional
-                parameter size.
-        """
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            use_rel_pos=use_rel_pos,
-            rel_pos_zero_init=rel_pos_zero_init,
-            input_size=input_size if window_size == 0 else (window_size, window_size),
-        )
-
-        from timm.models.layers import DropPath, Mlp
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer)
-
-        self.window_size = window_size
-
-        self.use_residual_block = use_residual_block
-        if use_residual_block:
-            # Use a residual block with bottleneck channel as dim // 2
-            self.residual = ResBottleneckBlock(
-                in_channels=dim,
-                out_channels=dim,
-                bottleneck_channels=dim // 2,
-                norm="LN",
-                act_layer=act_layer,
-            )
-
-    def forward(self, x):
-        shortcut = x
-        x = self.norm1(x)
-        # Window partition
-        if self.window_size > 0:
-            H, W = x.shape[1], x.shape[2]
-            x, pad_hw = window_partition(x, self.window_size)
-
-        x = self.attn(x)
-        # Reverse window partition
-        if self.window_size > 0:
-            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        if self.use_residual_block:
-            x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
-
-        return x
-
-
-class ViT(Backbone):
-    """
-    This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
-    "Exploring Plain Vision Transformer Backbones for Object Detection",
-    https://arxiv.org/abs/2203.16527
-    """
-
-    def __init__(
-        self,
-        img_size=1024,
-        patch_size=16,
-        in_chans=3,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop_path_rate=0.0,
-        norm_layer=nn.LayerNorm,
-        act_layer=nn.GELU,
-        use_abs_pos=True,
-        use_rel_pos=False,
-        rel_pos_zero_init=True,
-        window_size=0,
-        window_block_indexes=(),
-        residual_block_indexes=(),
-        use_act_checkpoint=False,
-        pretrain_img_size=224,
-        pretrain_use_cls_token=True,
-        out_feature="last_feat",
-    ):
-        """
-        Args:
-            img_size (int): Input image size.
-            patch_size (int): Patch size.
-            in_chans (int): Number of input image channels.
-            embed_dim (int): Patch embedding dimension.
-            depth (int): Depth of ViT.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            drop_path_rate (float): Stochastic depth rate.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_abs_pos (bool): If True, use absolute positional embeddings.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks.
-            window_block_indexes (list): Indexes for blocks using window attention.
-            residual_block_indexes (list): Indexes for blocks using conv propagation.
-            use_act_checkpoint (bool): If True, use activation checkpointing.
-            pretrain_img_size (int): input image size for pretraining models.
-            pretrain_use_cls_token (bool): If True, pretrainig models use class token.
-            out_feature (str): name of the feature from the last block.
-        """
-        super().__init__()
-        self.pretrain_use_cls_token = pretrain_use_cls_token
-
-        self.patch_embed = PatchEmbed(
-            kernel_size=(patch_size, patch_size),
-            stride=(patch_size, patch_size),
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-
-        if use_abs_pos:
-            # Initialize absolute positional embedding with pretrain image size.
-            num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
-            num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
-            self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
-        else:
-            self.pos_embed = None
-
-        # stochastic depth decay rule
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
-
-        self.blocks = nn.ModuleList()
-        for i in range(depth):
-            block = Block(
-                dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                drop_path=dpr[i],
-                norm_layer=norm_layer,
-                act_layer=act_layer,
-                use_rel_pos=use_rel_pos,
-                rel_pos_zero_init=rel_pos_zero_init,
-                window_size=window_size if i in window_block_indexes else 0,
-                use_residual_block=i in residual_block_indexes,
-                input_size=(img_size // patch_size, img_size // patch_size),
-            )
-            if use_act_checkpoint:
-                # TODO: use torch.utils.checkpoint
-                from fairscale.nn.checkpoint import checkpoint_wrapper
-
-                block = checkpoint_wrapper(block)
-            self.blocks.append(block)
-
-        self._out_feature_channels = {out_feature: embed_dim}
-        self._out_feature_strides = {out_feature: patch_size}
-        self._out_features = [out_feature]
-
-        if self.pos_embed is not None:
-            nn.init.trunc_normal_(self.pos_embed, std=0.02)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            nn.init.trunc_normal_(m.weight, std=0.02)
-            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)
-
-    def forward(self, x):
-        x = self.patch_embed(x)
-        if self.pos_embed is not None:
-            x = x + get_abs_pos(
-                self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
-            )
-
-        for blk in self.blocks:
-            x = blk(x)
-
-        outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)}
-        return outputs
-
-
-class SimpleFeaturePyramid(Backbone):
-    """
-    This module implements SimpleFeaturePyramid in :paper:`vitdet`.
-    It creates pyramid features built on top of the input feature map.
-    """
-
-    def __init__(
-        self,
-        net,
-        in_feature,
-        out_channels,
-        scale_factors,
-        top_block=None,
-        norm="LN",
-        square_pad=0,
-    ):
-        """
-        Args:
-            net (Backbone): module representing the subnetwork backbone.
-                Must be a subclass of :class:`Backbone`.
-            in_feature (str): names of the input feature maps coming
-                from the net.
-            out_channels (int): number of channels in the output feature maps.
-            scale_factors (list[float]): list of scaling factors to upsample or downsample
-                the input features for creating pyramid features.
-            top_block (nn.Module or None): if provided, an extra operation will
-                be performed on the output of the last (smallest resolution)
-                pyramid output, and the result will extend the result list. The top_block
-                further downsamples the feature map. It must have an attribute
-                "num_levels", meaning the number of extra pyramid levels added by
-                this block, and "in_feature", which is a string representing
-                its input feature (e.g., p5).
-            norm (str): the normalization to use.
-            square_pad (int): If > 0, require input images to be padded to specific square size.
-        """
-        super(SimpleFeaturePyramid, self).__init__()
-        assert isinstance(net, Backbone)
-
-        self.scale_factors = scale_factors
-
-        input_shapes = net.output_shape()
-        strides = [int(input_shapes[in_feature].stride / scale) for scale in scale_factors]
-        _assert_strides_are_log2_contiguous(strides)
-
-        dim = input_shapes[in_feature].channels
-        self.stages = []
-        use_bias = norm == ""
-        for idx, scale in enumerate(scale_factors):
-            out_dim = dim
-            if scale == 4.0:
-                layers = [
-                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
-                    get_norm(norm, dim // 2),
-                    nn.GELU(),
-                    nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2),
-                ]
-                out_dim = dim // 4
-            elif scale == 2.0:
-                layers = [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)]
-                out_dim = dim // 2
-            elif scale == 1.0:
-                layers = []
-            elif scale == 0.5:
-                layers = [nn.MaxPool2d(kernel_size=2, stride=2)]
-            else:
-                raise NotImplementedError(f"scale_factor={scale} is not supported yet.")
-
-            layers.extend(
-                [
-                    Conv2d(
-                        out_dim,
-                        out_channels,
-                        kernel_size=1,
-                        bias=use_bias,
-                        norm=get_norm(norm, out_channels),
-                    ),
-                    Conv2d(
-                        out_channels,
-                        out_channels,
-                        kernel_size=3,
-                        padding=1,
-                        bias=use_bias,
-                        norm=get_norm(norm, out_channels),
-                    ),
-                ]
-            )
-            layers = nn.Sequential(*layers)
-
-            stage = int(math.log2(strides[idx]))
-            self.add_module(f"simfp_{stage}", layers)
-            self.stages.append(layers)
-
-        self.net = net
-        self.in_feature = in_feature
-        self.top_block = top_block
-        # Return feature names are "p<stage>", like ["p2", "p3", ..., "p6"]
-        self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
-        # top block output feature maps.
-        if self.top_block is not None:
-            for s in range(stage, stage + self.top_block.num_levels):
-                self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1)
-
-        self._out_features = list(self._out_feature_strides.keys())
-        self._out_feature_channels = {k: out_channels for k in self._out_features}
-        self._size_divisibility = strides[-1]
-        self._square_pad = square_pad
-
-    @property
-    def padding_constraints(self):
-        return {
-            "size_divisiblity": self._size_divisibility,
-            "square_size": self._square_pad,
-        }
-
-    def forward(self, x):
-        """
-        Args:
-            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
-
-        Returns:
-            dict[str->Tensor]:
-                mapping from feature map name to pyramid feature map tensor
-                in high to low resolution order. Returned feature names follow the FPN
-                convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
-                ["p2", "p3", ..., "p6"].
-        """
-        bottom_up_features = self.net(x)
-        features = bottom_up_features[self.in_feature]
-        results = []
-
-        for stage in self.stages:
-            results.append(stage(features))
-
-        if self.top_block is not None:
-            if self.top_block.in_feature in bottom_up_features:
-                top_block_in_feature = bottom_up_features[self.top_block.in_feature]
-            else:
-                top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)]
-            results.extend(self.top_block(top_block_in_feature))
-        assert len(self._out_features) == len(results)
-        return {f: res for f, res in zip(self._out_features, results)}
-
-
-def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
-    """
-    Calculate lr decay rate for different ViT blocks.
-    Args:
-        name (string): parameter name.
-        lr_decay_rate (float): base lr decay rate.
-        num_layers (int): number of ViT blocks.
-
-    Returns:
-        lr decay rate for the given parameter.
-    """
-    layer_id = num_layers + 1
-    if name.startswith("backbone"):
-        if ".pos_embed" in name or ".patch_embed" in name:
-            layer_id = 0
-        elif ".blocks." in name and ".residual." not in name:
-            layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
-
-    return lr_decay_rate ** (num_layers + 1 - layer_id)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/vkitti2.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/vkitti2.py
deleted file mode 100644
index 9bcfb0414b7f3f21859f30ae34bd71689516a3e7..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/vkitti2.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# 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.
-
-# File author: Shariq Farooq Bhat
-
-import os
-
-import cv2
-import numpy as np
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms
-
-
-class ToTensor(object):
-    def __init__(self):
-        # self.normalize = transforms.Normalize(
-        #     mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        self.normalize = lambda x: x
-        # self.resize = transforms.Resize((375, 1242))
-
-    def __call__(self, sample):
-        image, depth = sample['image'], sample['depth']
-
-        image = self.to_tensor(image)
-        image = self.normalize(image)
-        depth = self.to_tensor(depth)
-
-        # image = self.resize(image)
-
-        return {'image': image, 'depth': depth, 'dataset': "vkitti"}
-
-    def to_tensor(self, pic):
-
-        if isinstance(pic, np.ndarray):
-            img = torch.from_numpy(pic.transpose((2, 0, 1)))
-            return img
-
-        #         # handle PIL Image
-        if pic.mode == 'I':
-            img = torch.from_numpy(np.array(pic, np.int32, copy=False))
-        elif pic.mode == 'I;16':
-            img = torch.from_numpy(np.array(pic, np.int16, copy=False))
-        else:
-            img = torch.ByteTensor(
-                torch.ByteStorage.from_buffer(pic.tobytes()))
-        # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
-        if pic.mode == 'YCbCr':
-            nchannel = 3
-        elif pic.mode == 'I;16':
-            nchannel = 1
-        else:
-            nchannel = len(pic.mode)
-        img = img.view(pic.size[1], pic.size[0], nchannel)
-
-        img = img.transpose(0, 1).transpose(0, 2).contiguous()
-        if isinstance(img, torch.ByteTensor):
-            return img.float()
-        else:
-            return img
-
-
-class VKITTI2(Dataset):
-    def __init__(self, data_dir_root, do_kb_crop=True, split="test"):
-        import glob
-
-        # image paths are of the form <data_dir_root>/rgb/<scene>/<variant>/frames/<rgb,depth>/Camera<0,1>/rgb_{}.jpg
-        self.image_files = glob.glob(os.path.join(
-            data_dir_root, "rgb", "**", "frames", "rgb", "Camera_0", '*.jpg'), recursive=True)
-        self.depth_files = [r.replace("/rgb/", "/depth/").replace(
-            "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
-        self.do_kb_crop = True
-        self.transform = ToTensor()
-
-        # If train test split is not created, then create one.
-        # Split is such that 8% of the frames from each scene are used for testing.
-        if not os.path.exists(os.path.join(data_dir_root, "train.txt")):
-            import random
-            scenes = set([os.path.basename(os.path.dirname(
-                os.path.dirname(os.path.dirname(f)))) for f in self.image_files])
-            train_files = []
-            test_files = []
-            for scene in scenes:
-                scene_files = [f for f in self.image_files if os.path.basename(
-                    os.path.dirname(os.path.dirname(os.path.dirname(f)))) == scene]
-                random.shuffle(scene_files)
-                train_files.extend(scene_files[:int(len(scene_files) * 0.92)])
-                test_files.extend(scene_files[int(len(scene_files) * 0.92):])
-            with open(os.path.join(data_dir_root, "train.txt"), "w") as f:
-                f.write("\n".join(train_files))
-            with open(os.path.join(data_dir_root, "test.txt"), "w") as f:
-                f.write("\n".join(test_files))
-
-        if split == "train":
-            with open(os.path.join(data_dir_root, "train.txt"), "r") as f:
-                self.image_files = f.read().splitlines()
-            self.depth_files = [r.replace("/rgb/", "/depth/").replace(
-                "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
-        elif split == "test":
-            with open(os.path.join(data_dir_root, "test.txt"), "r") as f:
-                self.image_files = f.read().splitlines()
-            self.depth_files = [r.replace("/rgb/", "/depth/").replace(
-                "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
-
-    def __getitem__(self, idx):
-        image_path = self.image_files[idx]
-        depth_path = self.depth_files[idx]
-
-        image = Image.open(image_path)
-        # depth = Image.open(depth_path)
-        depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR |
-                           cv2.IMREAD_ANYDEPTH) / 100.0  # cm to m
-        depth = Image.fromarray(depth)
-        # print("dpeth min max", depth.min(), depth.max())
-
-        # print(np.shape(image))
-        # print(np.shape(depth))
-
-        if self.do_kb_crop:
-            if idx == 0:
-                print("Using KB input crop")
-            height = image.height
-            width = image.width
-            top_margin = int(height - 352)
-            left_margin = int((width - 1216) / 2)
-            depth = depth.crop(
-                (left_margin, top_margin, left_margin + 1216, top_margin + 352))
-            image = image.crop(
-                (left_margin, top_margin, left_margin + 1216, top_margin + 352))
-            # uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216]
-
-        image = np.asarray(image, dtype=np.float32) / 255.0
-        # depth = np.asarray(depth, dtype=np.uint16) /1.
-        depth = np.asarray(depth, dtype=np.float32) / 1.
-        depth[depth > 80] = -1
-
-        depth = depth[..., None]
-        sample = dict(image=image, depth=depth)
-
-        # return sample
-        sample = self.transform(sample)
-
-        if idx == 0:
-            print(sample["image"].shape)
-
-        return sample
-
-    def __len__(self):
-        return len(self.image_files)
-
-
-def get_vkitti2_loader(data_dir_root, batch_size=1, **kwargs):
-    dataset = VKITTI2(data_dir_root)
-    return DataLoader(dataset, batch_size, **kwargs)
-
-
-if __name__ == "__main__":
-    loader = get_vkitti2_loader(
-        data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti2")
-    print("Total files", len(loader.dataset))
-    for i, sample in enumerate(loader):
-        print(sample["image"].shape)
-        print(sample["depth"].shape)
-        print(sample["dataset"])
-        print(sample['depth'].min(), sample['depth'].max())
-        if i > 5:
-            break
diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/poser/modes/standard_half.py b/spaces/cymic/Talking_Head_Anime_3/tha3/poser/modes/standard_half.py
deleted file mode 100644
index aa6a6de20e480ddb6452828a8a54b00e497bbe0d..0000000000000000000000000000000000000000
--- a/spaces/cymic/Talking_Head_Anime_3/tha3/poser/modes/standard_half.py
+++ /dev/null
@@ -1,363 +0,0 @@
-from enum import Enum
-from typing import List, Dict, Optional
-
-import torch
-from torch import Tensor
-from torch.nn import Module
-from torch.nn.functional import interpolate
-
-from tha3.nn.eyebrow_decomposer.eyebrow_decomposer_00 import EyebrowDecomposer00, \
-    EyebrowDecomposer00Factory, EyebrowDecomposer00Args
-from tha3.nn.eyebrow_morphing_combiner.eyebrow_morphing_combiner_00 import \
-    EyebrowMorphingCombiner00Factory, EyebrowMorphingCombiner00Args, EyebrowMorphingCombiner00
-from tha3.nn.face_morpher.face_morpher_08 import FaceMorpher08Args, FaceMorpher08Factory
-from tha3.poser.general_poser_02 import GeneralPoser02
-from tha3.poser.poser import PoseParameterCategory, PoseParameters
-from tha3.nn.editor.editor_07 import Editor07, Editor07Args
-from tha3.nn.two_algo_body_rotator.two_algo_face_body_rotator_05 import TwoAlgoFaceBodyRotator05, \
-    TwoAlgoFaceBodyRotator05Args
-from tha3.util import torch_load
-from tha3.compute.cached_computation_func import TensorListCachedComputationFunc
-from tha3.compute.cached_computation_protocol import CachedComputationProtocol
-from tha3.nn.nonlinearity_factory import ReLUFactory, LeakyReLUFactory
-from tha3.nn.normalization import InstanceNorm2dFactory
-from tha3.nn.util import BlockArgs
-
-
-class Network(Enum):
-    eyebrow_decomposer = 1
-    eyebrow_morphing_combiner = 2
-    face_morpher = 3
-    two_algo_face_body_rotator = 4
-    editor = 5
-
-    @property
-    def outputs_key(self):
-        return f"{self.name}_outputs"
-
-
-class Branch(Enum):
-    face_morphed_half = 1
-    face_morphed_full = 2
-    all_outputs = 3
-
-
-NUM_EYEBROW_PARAMS = 12
-NUM_FACE_PARAMS = 27
-NUM_ROTATION_PARAMS = 6
-
-
-class FiveStepPoserComputationProtocol(CachedComputationProtocol):
-    def __init__(self, eyebrow_morphed_image_index: int):
-        super().__init__()
-        self.eyebrow_morphed_image_index = eyebrow_morphed_image_index
-        self.cached_batch_0 = None
-        self.cached_eyebrow_decomposer_output = None
-
-    def compute_func(self) -> TensorListCachedComputationFunc:
-        def func(modules: Dict[str, Module],
-                 batch: List[Tensor],
-                 outputs: Dict[str, List[Tensor]]):
-            if self.cached_batch_0 is None:
-                new_batch_0 = True
-            elif batch[0].shape[0] != self.cached_batch_0.shape[0]:
-                new_batch_0 = True
-            else:
-                new_batch_0 = torch.max((batch[0] - self.cached_batch_0).abs()).item() > 0
-            if not new_batch_0:
-                outputs[Network.eyebrow_decomposer.outputs_key] = self.cached_eyebrow_decomposer_output
-            output = self.get_output(Branch.all_outputs.name, modules, batch, outputs)
-            if new_batch_0:
-                self.cached_batch_0 = batch[0]
-                self.cached_eyebrow_decomposer_output = outputs[Network.eyebrow_decomposer.outputs_key]
-            return output
-
-        return func
-
-    def compute_output(self, key: str, modules: Dict[str, Module], batch: List[Tensor],
-                       outputs: Dict[str, List[Tensor]]) -> List[Tensor]:
-        if key == Network.eyebrow_decomposer.outputs_key:
-            input_image = batch[0][:, :, 64:192, 64 + 128:192 + 128]
-            return modules[Network.eyebrow_decomposer.name].forward(input_image)
-        elif key == Network.eyebrow_morphing_combiner.outputs_key:
-            eyebrow_decomposer_output = self.get_output(Network.eyebrow_decomposer.outputs_key, modules, batch, outputs)
-            background_layer = eyebrow_decomposer_output[EyebrowDecomposer00.BACKGROUND_LAYER_INDEX]
-            eyebrow_layer = eyebrow_decomposer_output[EyebrowDecomposer00.EYEBROW_LAYER_INDEX]
-            eyebrow_pose = batch[1][:, :NUM_EYEBROW_PARAMS]
-            return modules[Network.eyebrow_morphing_combiner.name].forward(
-                background_layer,
-                eyebrow_layer,
-                eyebrow_pose)
-        elif key == Network.face_morpher.outputs_key:
-            eyebrow_morphing_combiner_output = self.get_output(
-                Network.eyebrow_morphing_combiner.outputs_key, modules, batch, outputs)
-            eyebrow_morphed_image = eyebrow_morphing_combiner_output[self.eyebrow_morphed_image_index]
-            input_image = batch[0][:, :, 32:32 + 192, (32 + 128):(32 + 192 + 128)].clone()
-            input_image[:, :, 32:32 + 128, 32:32 + 128] = eyebrow_morphed_image
-            face_pose = batch[1][:, NUM_EYEBROW_PARAMS:NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS]
-            return modules[Network.face_morpher.name].forward(input_image, face_pose)
-        elif key == Branch.face_morphed_full.name:
-            face_morpher_output = self.get_output(Network.face_morpher.outputs_key, modules, batch, outputs)
-            face_morphed_image = face_morpher_output[0]
-            input_image = batch[0].clone()
-            input_image[:, :, 32:32 + 192, 32 + 128:32 + 192 + 128] = face_morphed_image
-            return [input_image]
-        elif key == Branch.face_morphed_half.name:
-            face_morphed_full = self.get_output(Branch.face_morphed_full.name, modules, batch, outputs)[0]
-            return [
-                interpolate(face_morphed_full, size=(256, 256), mode='bilinear', align_corners=False)
-            ]
-        elif key == Network.two_algo_face_body_rotator.outputs_key:
-            face_morphed_half = self.get_output(Branch.face_morphed_half.name, modules, batch, outputs)[0]
-            rotation_pose = batch[1][:, NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS:]
-            return modules[Network.two_algo_face_body_rotator.name].forward(face_morphed_half, rotation_pose)
-        elif key == Network.editor.outputs_key:
-            input_original_image = self.get_output(Branch.face_morphed_full.name, modules, batch, outputs)[0]
-            rotator_outputs = self.get_output(
-                Network.two_algo_face_body_rotator.outputs_key, modules, batch, outputs)
-            half_warped_image = rotator_outputs[TwoAlgoFaceBodyRotator05.WARPED_IMAGE_INDEX]
-            full_warped_image = interpolate(
-                half_warped_image, size=(512, 512), mode='bilinear', align_corners=False)
-            half_grid_change = rotator_outputs[TwoAlgoFaceBodyRotator05.GRID_CHANGE_INDEX]
-            full_grid_change = interpolate(
-                half_grid_change, size=(512, 512), mode='bilinear', align_corners=False)
-            rotation_pose = batch[1][:, NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS:]
-            return modules[Network.editor.name].forward(
-                input_original_image, full_warped_image, full_grid_change, rotation_pose)
-        elif key == Branch.all_outputs.name:
-            editor_output = self.get_output(Network.editor.outputs_key, modules, batch, outputs)
-            rotater_output = self.get_output(Network.two_algo_face_body_rotator.outputs_key, modules, batch, outputs)
-            face_morpher_output = self.get_output(Network.face_morpher.outputs_key, modules, batch, outputs)
-            eyebrow_morphing_combiner_output = self.get_output(
-                Network.eyebrow_morphing_combiner.outputs_key, modules, batch, outputs)
-            eyebrow_decomposer_output = self.get_output(
-                Network.eyebrow_decomposer.outputs_key, modules, batch, outputs)
-            output = editor_output \
-                     + rotater_output \
-                     + face_morpher_output \
-                     + eyebrow_morphing_combiner_output \
-                     + eyebrow_decomposer_output
-            return output
-        else:
-            raise RuntimeError("Unsupported key: " + key)
-
-
-def load_eyebrow_decomposer(file_name: str):
-    factory = EyebrowDecomposer00Factory(
-        EyebrowDecomposer00Args(
-            image_size=128,
-            image_channels=4,
-            start_channels=64,
-            bottleneck_image_size=16,
-            num_bottleneck_blocks=6,
-            max_channels=512,
-            block_args=BlockArgs(
-                initialization_method='he',
-                use_spectral_norm=False,
-                normalization_layer_factory=InstanceNorm2dFactory(),
-                nonlinearity_factory=ReLUFactory(inplace=True))))
-    print("Loading the eyebrow decomposer ... ", end="")
-    module = factory.create().half()
-    module.load_state_dict(torch_load(file_name))
-    print("DONE!!!")
-    return module
-
-
-def load_eyebrow_morphing_combiner(file_name: str):
-    factory = EyebrowMorphingCombiner00Factory(
-        EyebrowMorphingCombiner00Args(
-            image_size=128,
-            image_channels=4,
-            start_channels=64,
-            num_pose_params=12,
-            bottleneck_image_size=16,
-            num_bottleneck_blocks=6,
-            max_channels=512,
-            block_args=BlockArgs(
-                initialization_method='he',
-                use_spectral_norm=False,
-                normalization_layer_factory=InstanceNorm2dFactory(),
-                nonlinearity_factory=ReLUFactory(inplace=True))))
-    print("Loading the eyebrow morphing conbiner ... ", end="")
-    module = factory.create().half()
-    module.load_state_dict(torch_load(file_name))
-    print("DONE!!!")
-    return module
-
-
-def load_face_morpher(file_name: str):
-    factory = FaceMorpher08Factory(
-        FaceMorpher08Args(
-            image_size=192,
-            image_channels=4,
-            num_expression_params=27,
-            start_channels=64,
-            bottleneck_image_size=24,
-            num_bottleneck_blocks=6,
-            max_channels=512,
-            block_args=BlockArgs(
-                initialization_method='he',
-                use_spectral_norm=False,
-                normalization_layer_factory=InstanceNorm2dFactory(),
-                nonlinearity_factory=ReLUFactory(inplace=False))))
-    print("Loading the face morpher ... ", end="")
-    module = factory.create().half()
-    module.load_state_dict(torch_load(file_name))
-    print("DONE!!!")
-    return module
-
-
-def load_two_algo_generator(file_name) -> Module:
-    module = TwoAlgoFaceBodyRotator05(
-        TwoAlgoFaceBodyRotator05Args(
-            image_size=256,
-            image_channels=4,
-            start_channels=64,
-            num_pose_params=6,
-            bottleneck_image_size=32,
-            num_bottleneck_blocks=6,
-            max_channels=512,
-            upsample_mode='nearest',
-            block_args=BlockArgs(
-                initialization_method='he',
-                use_spectral_norm=False,
-                normalization_layer_factory=InstanceNorm2dFactory(),
-                nonlinearity_factory=LeakyReLUFactory(inplace=False, negative_slope=0.1)))).half()
-    print("Loading the face-body rotator ... ", end="")
-    module.load_state_dict(torch_load(file_name))
-    print("DONE!!!")
-    return module
-
-
-def load_editor(file_name) -> Module:
-    module = Editor07(
-        Editor07Args(
-            image_size=512,
-            image_channels=4,
-            num_pose_params=6,
-            start_channels=32,
-            bottleneck_image_size=64,
-            num_bottleneck_blocks=6,
-            max_channels=512,
-            upsampling_mode='nearest',
-            block_args=BlockArgs(
-                initialization_method='he',
-                use_spectral_norm=False,
-                normalization_layer_factory=InstanceNorm2dFactory(),
-                nonlinearity_factory=LeakyReLUFactory(inplace=False, negative_slope=0.1)))).half()
-    print("Loading the combiner ... ", end="")
-    module.load_state_dict(torch_load(file_name))
-    print("DONE!!!")
-    return module
-
-
-def get_pose_parameters():
-    return PoseParameters.Builder() \
-        .add_parameter_group("eyebrow_troubled", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eyebrow_angry", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eyebrow_lowered", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eyebrow_raised", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eyebrow_happy", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eyebrow_serious", PoseParameterCategory.EYEBROW, arity=2) \
-        .add_parameter_group("eye_wink", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("eye_happy_wink", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("eye_surprised", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("eye_relaxed", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("eye_unimpressed", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("eye_raised_lower_eyelid", PoseParameterCategory.EYE, arity=2) \
-        .add_parameter_group("iris_small", PoseParameterCategory.IRIS_MORPH, arity=2) \
-        .add_parameter_group("mouth_aaa", PoseParameterCategory.MOUTH, arity=1, default_value=1.0) \
-        .add_parameter_group("mouth_iii", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("mouth_uuu", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("mouth_eee", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("mouth_ooo", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("mouth_delta", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("mouth_lowered_corner", PoseParameterCategory.MOUTH, arity=2) \
-        .add_parameter_group("mouth_raised_corner", PoseParameterCategory.MOUTH, arity=2) \
-        .add_parameter_group("mouth_smirk", PoseParameterCategory.MOUTH, arity=1) \
-        .add_parameter_group("iris_rotation_x", PoseParameterCategory.IRIS_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("iris_rotation_y", PoseParameterCategory.IRIS_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("head_x", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("head_y", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("neck_z", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("body_y", PoseParameterCategory.BODY_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("body_z", PoseParameterCategory.BODY_ROTATION, arity=1, range=(-1.0, 1.0)) \
-        .add_parameter_group("breathing", PoseParameterCategory.BREATHING, arity=1, range=(0.0, 1.0)) \
-        .build()
-
-
-def create_poser(
-        device: torch.device,
-        module_file_names: Optional[Dict[str, str]] = None,
-        eyebrow_morphed_image_index: int = EyebrowMorphingCombiner00.EYEBROW_IMAGE_NO_COMBINE_ALPHA_INDEX,
-        default_output_index: int = 0) -> GeneralPoser02:
-    if module_file_names is None:
-        module_file_names = {}
-    if Network.eyebrow_decomposer.name not in module_file_names:
-        dir = "data/models/standard_half"
-        file_name = dir + "/eyebrow_decomposer.pt"
-        module_file_names[Network.eyebrow_decomposer.name] = file_name
-    if Network.eyebrow_morphing_combiner.name not in module_file_names:
-        dir = "data/models/standard_half"
-        file_name = dir + "/eyebrow_morphing_combiner.pt"
-        module_file_names[Network.eyebrow_morphing_combiner.name] = file_name
-    if Network.face_morpher.name not in module_file_names:
-        dir = "data/models/standard_half"
-        file_name = dir + "/face_morpher.pt"
-        module_file_names[Network.face_morpher.name] = file_name
-    if Network.two_algo_face_body_rotator.name not in module_file_names:
-        dir = "data/models/standard_half"
-        file_name = dir + "/two_algo_face_body_rotator.pt"
-        module_file_names[Network.two_algo_face_body_rotator.name] = file_name
-    if Network.editor.name not in module_file_names:
-        dir = "data/models/standard_half"
-        file_name = dir + "/editor.pt"
-        module_file_names[Network.editor.name] = file_name
-
-    loaders = {
-        Network.eyebrow_decomposer.name:
-            lambda: load_eyebrow_decomposer(module_file_names[Network.eyebrow_decomposer.name]),
-        Network.eyebrow_morphing_combiner.name:
-            lambda: load_eyebrow_morphing_combiner(module_file_names[Network.eyebrow_morphing_combiner.name]),
-        Network.face_morpher.name:
-            lambda: load_face_morpher(module_file_names[Network.face_morpher.name]),
-        Network.two_algo_face_body_rotator.name:
-            lambda: load_two_algo_generator(module_file_names[Network.two_algo_face_body_rotator.name]),
-        Network.editor.name:
-            lambda: load_editor(module_file_names[Network.editor.name]),
-    }
-    return GeneralPoser02(
-        image_size=512,
-        module_loaders=loaders,
-        pose_parameters=get_pose_parameters().get_pose_parameter_groups(),
-        output_list_func=FiveStepPoserComputationProtocol(eyebrow_morphed_image_index).compute_func(),
-        subrect=None,
-        device=device,
-        output_length=29,
-        dtype=torch.half,
-        default_output_index=default_output_index)
-
-
-if __name__ == "__main__":
-    device = torch.device('cuda')
-    poser = create_poser(device)
-
-    image = torch.zeros(1, 4, 512, 512, device=device, dtype=torch.half)
-    pose = torch.zeros(1, 45, device=device, dtype=torch.half)
-
-    repeat = 100
-    acc = 0.0
-    for i in range(repeat + 2):
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-
-        start.record()
-        poser.pose(image, pose)
-        end.record()
-        torch.cuda.synchronize()
-        if i >= 2:
-            elapsed_time = start.elapsed_time(end)
-            print("%d:" % i, elapsed_time)
-            acc = acc + elapsed_time
-
-    print("average:", acc / repeat)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_next_gen.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_next_gen.py
deleted file mode 100644
index 8f7c0b9a46b7a0ee008f94b8054baf5807df043a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_next_gen.py
+++ /dev/null
@@ -1,232 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-These are keyword-only APIs that call `attr.s` and `attr.ib` with different
-default values.
-"""
-
-
-from functools import partial
-
-from . import setters
-from ._funcs import asdict as _asdict
-from ._funcs import astuple as _astuple
-from ._make import (
-    NOTHING,
-    _frozen_setattrs,
-    _ng_default_on_setattr,
-    attrib,
-    attrs,
-)
-from .exceptions import UnannotatedAttributeError
-
-
-def define(
-    maybe_cls=None,
-    *,
-    these=None,
-    repr=None,
-    unsafe_hash=None,
-    hash=None,
-    init=None,
-    slots=True,
-    frozen=False,
-    weakref_slot=True,
-    str=False,
-    auto_attribs=None,
-    kw_only=False,
-    cache_hash=False,
-    auto_exc=True,
-    eq=None,
-    order=False,
-    auto_detect=True,
-    getstate_setstate=None,
-    on_setattr=None,
-    field_transformer=None,
-    match_args=True,
-):
-    r"""
-    Define an *attrs* class.
-
-    Differences to the classic `attr.s` that it uses underneath:
-
-    - Automatically detect whether or not *auto_attribs* should be `True` (c.f.
-      *auto_attribs* parameter).
-    - If *frozen* is `False`, run converters and validators when setting an
-      attribute by default.
-    - *slots=True*
-
-      .. caution::
-
-         Usually this has only upsides and few visible effects in everyday
-         programming. But it *can* lead to some suprising behaviors, so please
-         make sure to read :term:`slotted classes`.
-    - *auto_exc=True*
-    - *auto_detect=True*
-    - *order=False*
-    - Some options that were only relevant on Python 2 or were kept around for
-      backwards-compatibility have been removed.
-
-    Please note that these are all defaults and you can change them as you
-    wish.
-
-    :param Optional[bool] auto_attribs: If set to `True` or `False`, it behaves
-       exactly like `attr.s`. If left `None`, `attr.s` will try to guess:
-
-       1. If any attributes are annotated and no unannotated `attrs.fields`\ s
-          are found, it assumes *auto_attribs=True*.
-       2. Otherwise it assumes *auto_attribs=False* and tries to collect
-          `attrs.fields`\ s.
-
-    For now, please refer to `attr.s` for the rest of the parameters.
-
-    .. versionadded:: 20.1.0
-    .. versionchanged:: 21.3.0 Converters are also run ``on_setattr``.
-    .. versionadded:: 22.2.0
-       *unsafe_hash* as an alias for *hash* (for :pep:`681` compliance).
-    """
-
-    def do_it(cls, auto_attribs):
-        return attrs(
-            maybe_cls=cls,
-            these=these,
-            repr=repr,
-            hash=hash,
-            unsafe_hash=unsafe_hash,
-            init=init,
-            slots=slots,
-            frozen=frozen,
-            weakref_slot=weakref_slot,
-            str=str,
-            auto_attribs=auto_attribs,
-            kw_only=kw_only,
-            cache_hash=cache_hash,
-            auto_exc=auto_exc,
-            eq=eq,
-            order=order,
-            auto_detect=auto_detect,
-            collect_by_mro=True,
-            getstate_setstate=getstate_setstate,
-            on_setattr=on_setattr,
-            field_transformer=field_transformer,
-            match_args=match_args,
-        )
-
-    def wrap(cls):
-        """
-        Making this a wrapper ensures this code runs during class creation.
-
-        We also ensure that frozen-ness of classes is inherited.
-        """
-        nonlocal frozen, on_setattr
-
-        had_on_setattr = on_setattr not in (None, setters.NO_OP)
-
-        # By default, mutable classes convert & validate on setattr.
-        if frozen is False and on_setattr is None:
-            on_setattr = _ng_default_on_setattr
-
-        # However, if we subclass a frozen class, we inherit the immutability
-        # and disable on_setattr.
-        for base_cls in cls.__bases__:
-            if base_cls.__setattr__ is _frozen_setattrs:
-                if had_on_setattr:
-                    raise ValueError(
-                        "Frozen classes can't use on_setattr "
-                        "(frozen-ness was inherited)."
-                    )
-
-                on_setattr = setters.NO_OP
-                break
-
-        if auto_attribs is not None:
-            return do_it(cls, auto_attribs)
-
-        try:
-            return do_it(cls, True)
-        except UnannotatedAttributeError:
-            return do_it(cls, False)
-
-    # maybe_cls's type depends on the usage of the decorator.  It's a class
-    # if it's used as `@attrs` but ``None`` if used as `@attrs()`.
-    if maybe_cls is None:
-        return wrap
-    else:
-        return wrap(maybe_cls)
-
-
-mutable = define
-frozen = partial(define, frozen=True, on_setattr=None)
-
-
-def field(
-    *,
-    default=NOTHING,
-    validator=None,
-    repr=True,
-    hash=None,
-    init=True,
-    metadata=None,
-    type=None,
-    converter=None,
-    factory=None,
-    kw_only=False,
-    eq=None,
-    order=None,
-    on_setattr=None,
-    alias=None,
-):
-    """
-    Identical to `attr.ib`, except keyword-only and with some arguments
-    removed.
-
-    .. versionadded:: 23.1.0
-       The *type* parameter has been re-added; mostly for
-       {func}`attrs.make_class`. Please note that type checkers ignore this
-       metadata.
-    .. versionadded:: 20.1.0
-    """
-    return attrib(
-        default=default,
-        validator=validator,
-        repr=repr,
-        hash=hash,
-        init=init,
-        metadata=metadata,
-        type=type,
-        converter=converter,
-        factory=factory,
-        kw_only=kw_only,
-        eq=eq,
-        order=order,
-        on_setattr=on_setattr,
-        alias=alias,
-    )
-
-
-def asdict(inst, *, recurse=True, filter=None, value_serializer=None):
-    """
-    Same as `attr.asdict`, except that collections types are always retained
-    and dict is always used as *dict_factory*.
-
-    .. versionadded:: 21.3.0
-    """
-    return _asdict(
-        inst=inst,
-        recurse=recurse,
-        filter=filter,
-        value_serializer=value_serializer,
-        retain_collection_types=True,
-    )
-
-
-def astuple(inst, *, recurse=True, filter=None):
-    """
-    Same as `attr.astuple`, except that collections types are always retained
-    and `tuple` is always used as the *tuple_factory*.
-
-    .. versionadded:: 21.3.0
-    """
-    return _astuple(
-        inst=inst, recurse=recurse, filter=filter, retain_collection_types=True
-    )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/tz/tz.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/tz/tz.py
deleted file mode 100644
index c67f56d4659f17aab4540dfd42511bb850871a77..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/tz/tz.py
+++ /dev/null
@@ -1,1849 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers timezone implementations subclassing the abstract
-:py:class:`datetime.tzinfo` type. There are classes to handle tzfile format
-files (usually are in :file:`/etc/localtime`, :file:`/usr/share/zoneinfo`,
-etc), TZ environment string (in all known formats), given ranges (with help
-from relative deltas), local machine timezone, fixed offset timezone, and UTC
-timezone.
-"""
-import datetime
-import struct
-import time
-import sys
-import os
-import bisect
-import weakref
-from collections import OrderedDict
-
-import six
-from six import string_types
-from six.moves import _thread
-from ._common import tzname_in_python2, _tzinfo
-from ._common import tzrangebase, enfold
-from ._common import _validate_fromutc_inputs
-
-from ._factories import _TzSingleton, _TzOffsetFactory
-from ._factories import _TzStrFactory
-try:
-    from .win import tzwin, tzwinlocal
-except ImportError:
-    tzwin = tzwinlocal = None
-
-# For warning about rounding tzinfo
-from warnings import warn
-
-ZERO = datetime.timedelta(0)
-EPOCH = datetime.datetime.utcfromtimestamp(0)
-EPOCHORDINAL = EPOCH.toordinal()
-
-
-@six.add_metaclass(_TzSingleton)
-class tzutc(datetime.tzinfo):
-    """
-    This is a tzinfo object that represents the UTC time zone.
-
-    **Examples:**
-
-    .. doctest::
-
-        >>> from datetime import *
-        >>> from dateutil.tz import *
-
-        >>> datetime.now()
-        datetime.datetime(2003, 9, 27, 9, 40, 1, 521290)
-
-        >>> datetime.now(tzutc())
-        datetime.datetime(2003, 9, 27, 12, 40, 12, 156379, tzinfo=tzutc())
-
-        >>> datetime.now(tzutc()).tzname()
-        'UTC'
-
-    .. versionchanged:: 2.7.0
-        ``tzutc()`` is now a singleton, so the result of ``tzutc()`` will
-        always return the same object.
-
-        .. doctest::
-
-            >>> from dateutil.tz import tzutc, UTC
-            >>> tzutc() is tzutc()
-            True
-            >>> tzutc() is UTC
-            True
-    """
-    def utcoffset(self, dt):
-        return ZERO
-
-    def dst(self, dt):
-        return ZERO
-
-    @tzname_in_python2
-    def tzname(self, dt):
-        return "UTC"
-
-    def is_ambiguous(self, dt):
-        """
-        Whether or not the "wall time" of a given datetime is ambiguous in this
-        zone.
-
-        :param dt:
-            A :py:class:`datetime.datetime`, naive or time zone aware.
-
-
-        :return:
-            Returns ``True`` if ambiguous, ``False`` otherwise.
-
-        .. versionadded:: 2.6.0
-        """
-        return False
-
-    @_validate_fromutc_inputs
-    def fromutc(self, dt):
-        """
-        Fast track version of fromutc() returns the original ``dt`` object for
-        any valid :py:class:`datetime.datetime` object.
-        """
-        return dt
-
-    def __eq__(self, other):
-        if not isinstance(other, (tzutc, tzoffset)):
-            return NotImplemented
-
-        return (isinstance(other, tzutc) or
-                (isinstance(other, tzoffset) and other._offset == ZERO))
-
-    __hash__ = None
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __repr__(self):
-        return "%s()" % self.__class__.__name__
-
-    __reduce__ = object.__reduce__
-
-
-#: Convenience constant providing a :class:`tzutc()` instance
-#:
-#: .. versionadded:: 2.7.0
-UTC = tzutc()
-
-
-@six.add_metaclass(_TzOffsetFactory)
-class tzoffset(datetime.tzinfo):
-    """
-    A simple class for representing a fixed offset from UTC.
-
-    :param name:
-        The timezone name, to be returned when ``tzname()`` is called.
-    :param offset:
-        The time zone offset in seconds, or (since version 2.6.0, represented
-        as a :py:class:`datetime.timedelta` object).
-    """
-    def __init__(self, name, offset):
-        self._name = name
-
-        try:
-            # Allow a timedelta
-            offset = offset.total_seconds()
-        except (TypeError, AttributeError):
-            pass
-
-        self._offset = datetime.timedelta(seconds=_get_supported_offset(offset))
-
-    def utcoffset(self, dt):
-        return self._offset
-
-    def dst(self, dt):
-        return ZERO
-
-    @tzname_in_python2
-    def tzname(self, dt):
-        return self._name
-
-    @_validate_fromutc_inputs
-    def fromutc(self, dt):
-        return dt + self._offset
-
-    def is_ambiguous(self, dt):
-        """
-        Whether or not the "wall time" of a given datetime is ambiguous in this
-        zone.
-
-        :param dt:
-            A :py:class:`datetime.datetime`, naive or time zone aware.
-        :return:
-            Returns ``True`` if ambiguous, ``False`` otherwise.
-
-        .. versionadded:: 2.6.0
-        """
-        return False
-
-    def __eq__(self, other):
-        if not isinstance(other, tzoffset):
-            return NotImplemented
-
-        return self._offset == other._offset
-
-    __hash__ = None
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __repr__(self):
-        return "%s(%s, %s)" % (self.__class__.__name__,
-                               repr(self._name),
-                               int(self._offset.total_seconds()))
-
-    __reduce__ = object.__reduce__
-
-
-class tzlocal(_tzinfo):
-    """
-    A :class:`tzinfo` subclass built around the ``time`` timezone functions.
-    """
-    def __init__(self):
-        super(tzlocal, self).__init__()
-
-        self._std_offset = datetime.timedelta(seconds=-time.timezone)
-        if time.daylight:
-            self._dst_offset = datetime.timedelta(seconds=-time.altzone)
-        else:
-            self._dst_offset = self._std_offset
-
-        self._dst_saved = self._dst_offset - self._std_offset
-        self._hasdst = bool(self._dst_saved)
-        self._tznames = tuple(time.tzname)
-
-    def utcoffset(self, dt):
-        if dt is None and self._hasdst:
-            return None
-
-        if self._isdst(dt):
-            return self._dst_offset
-        else:
-            return self._std_offset
-
-    def dst(self, dt):
-        if dt is None and self._hasdst:
-            return None
-
-        if self._isdst(dt):
-            return self._dst_offset - self._std_offset
-        else:
-            return ZERO
-
-    @tzname_in_python2
-    def tzname(self, dt):
-        return self._tznames[self._isdst(dt)]
-
-    def is_ambiguous(self, dt):
-        """
-        Whether or not the "wall time" of a given datetime is ambiguous in this
-        zone.
-
-        :param dt:
-            A :py:class:`datetime.datetime`, naive or time zone aware.
-
-
-        :return:
-            Returns ``True`` if ambiguous, ``False`` otherwise.
-
-        .. versionadded:: 2.6.0
-        """
-        naive_dst = self._naive_is_dst(dt)
-        return (not naive_dst and
-                (naive_dst != self._naive_is_dst(dt - self._dst_saved)))
-
-    def _naive_is_dst(self, dt):
-        timestamp = _datetime_to_timestamp(dt)
-        return time.localtime(timestamp + time.timezone).tm_isdst
-
-    def _isdst(self, dt, fold_naive=True):
-        # We can't use mktime here. It is unstable when deciding if
-        # the hour near to a change is DST or not.
-        #
-        # timestamp = time.mktime((dt.year, dt.month, dt.day, dt.hour,
-        #                         dt.minute, dt.second, dt.weekday(), 0, -1))
-        # return time.localtime(timestamp).tm_isdst
-        #
-        # The code above yields the following result:
-        #
-        # >>> import tz, datetime
-        # >>> t = tz.tzlocal()
-        # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
-        # 'BRDT'
-        # >>> datetime.datetime(2003,2,16,0,tzinfo=t).tzname()
-        # 'BRST'
-        # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
-        # 'BRST'
-        # >>> datetime.datetime(2003,2,15,22,tzinfo=t).tzname()
-        # 'BRDT'
-        # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
-        # 'BRDT'
-        #
-        # Here is a more stable implementation:
-        #
-        if not self._hasdst:
-            return False
-
-        # Check for ambiguous times:
-        dstval = self._naive_is_dst(dt)
-        fold = getattr(dt, 'fold', None)
-
-        if self.is_ambiguous(dt):
-            if fold is not None:
-                return not self._fold(dt)
-            else:
-                return True
-
-        return dstval
-
-    def __eq__(self, other):
-        if isinstance(other, tzlocal):
-            return (self._std_offset == other._std_offset and
-                    self._dst_offset == other._dst_offset)
-        elif isinstance(other, tzutc):
-            return (not self._hasdst and
-                    self._tznames[0] in {'UTC', 'GMT'} and
-                    self._std_offset == ZERO)
-        elif isinstance(other, tzoffset):
-            return (not self._hasdst and
-                    self._tznames[0] == other._name and
-                    self._std_offset == other._offset)
-        else:
-            return NotImplemented
-
-    __hash__ = None
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __repr__(self):
-        return "%s()" % self.__class__.__name__
-
-    __reduce__ = object.__reduce__
-
-
-class _ttinfo(object):
-    __slots__ = ["offset", "delta", "isdst", "abbr",
-                 "isstd", "isgmt", "dstoffset"]
-
-    def __init__(self):
-        for attr in self.__slots__:
-            setattr(self, attr, None)
-
-    def __repr__(self):
-        l = []
-        for attr in self.__slots__:
-            value = getattr(self, attr)
-            if value is not None:
-                l.append("%s=%s" % (attr, repr(value)))
-        return "%s(%s)" % (self.__class__.__name__, ", ".join(l))
-
-    def __eq__(self, other):
-        if not isinstance(other, _ttinfo):
-            return NotImplemented
-
-        return (self.offset == other.offset and
-                self.delta == other.delta and
-                self.isdst == other.isdst and
-                self.abbr == other.abbr and
-                self.isstd == other.isstd and
-                self.isgmt == other.isgmt and
-                self.dstoffset == other.dstoffset)
-
-    __hash__ = None
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __getstate__(self):
-        state = {}
-        for name in self.__slots__:
-            state[name] = getattr(self, name, None)
-        return state
-
-    def __setstate__(self, state):
-        for name in self.__slots__:
-            if name in state:
-                setattr(self, name, state[name])
-
-
-class _tzfile(object):
-    """
-    Lightweight class for holding the relevant transition and time zone
-    information read from binary tzfiles.
-    """
-    attrs = ['trans_list', 'trans_list_utc', 'trans_idx', 'ttinfo_list',
-             'ttinfo_std', 'ttinfo_dst', 'ttinfo_before', 'ttinfo_first']
-
-    def __init__(self, **kwargs):
-        for attr in self.attrs:
-            setattr(self, attr, kwargs.get(attr, None))
-
-
-class tzfile(_tzinfo):
-    """
-    This is a ``tzinfo`` subclass that allows one to use the ``tzfile(5)``
-    format timezone files to extract current and historical zone information.
-
-    :param fileobj:
-        This can be an opened file stream or a file name that the time zone
-        information can be read from.
-
-    :param filename:
-        This is an optional parameter specifying the source of the time zone
-        information in the event that ``fileobj`` is a file object. If omitted
-        and ``fileobj`` is a file stream, this parameter will be set either to
-        ``fileobj``'s ``name`` attribute or to ``repr(fileobj)``.
-
-    See `Sources for Time Zone and Daylight Saving Time Data
-    <https://data.iana.org/time-zones/tz-link.html>`_ for more information.
-    Time zone files can be compiled from the `IANA Time Zone database files
-    <https://www.iana.org/time-zones>`_ with the `zic time zone compiler
-    <https://www.freebsd.org/cgi/man.cgi?query=zic&sektion=8>`_
-
-    .. note::
-
-        Only construct a ``tzfile`` directly if you have a specific timezone
-        file on disk that you want to read into a Python ``tzinfo`` object.
-        If you want to get a ``tzfile`` representing a specific IANA zone,
-        (e.g. ``'America/New_York'``), you should call
-        :func:`dateutil.tz.gettz` with the zone identifier.
-
-
-    **Examples:**
-
-    Using the US Eastern time zone as an example, we can see that a ``tzfile``
-    provides time zone information for the standard Daylight Saving offsets:
-
-    .. testsetup:: tzfile
-
-        from dateutil.tz import gettz
-        from datetime import datetime
-
-    .. doctest:: tzfile
-
-        >>> NYC = gettz('America/New_York')
-        >>> NYC
-        tzfile('/usr/share/zoneinfo/America/New_York')
-
-        >>> print(datetime(2016, 1, 3, tzinfo=NYC))     # EST
-        2016-01-03 00:00:00-05:00
-
-        >>> print(datetime(2016, 7, 7, tzinfo=NYC))     # EDT
-        2016-07-07 00:00:00-04:00
-
-
-    The ``tzfile`` structure contains a fully history of the time zone,
-    so historical dates will also have the right offsets. For example, before
-    the adoption of the UTC standards, New York used local solar  mean time:
-
-    .. doctest:: tzfile
-
-       >>> print(datetime(1901, 4, 12, tzinfo=NYC))    # LMT
-       1901-04-12 00:00:00-04:56
-
-    And during World War II, New York was on "Eastern War Time", which was a
-    state of permanent daylight saving time:
-
-    .. doctest:: tzfile
-
-        >>> print(datetime(1944, 2, 7, tzinfo=NYC))    # EWT
-        1944-02-07 00:00:00-04:00
-
-    """
-
-    def __init__(self, fileobj, filename=None):
-        super(tzfile, self).__init__()
-
-        file_opened_here = False
-        if isinstance(fileobj, string_types):
-            self._filename = fileobj
-            fileobj = open(fileobj, 'rb')
-            file_opened_here = True
-        elif filename is not None:
-            self._filename = filename
-        elif hasattr(fileobj, "name"):
-            self._filename = fileobj.name
-        else:
-            self._filename = repr(fileobj)
-
-        if fileobj is not None:
-            if not file_opened_here:
-                fileobj = _nullcontext(fileobj)
-
-            with fileobj as file_stream:
-                tzobj = self._read_tzfile(file_stream)
-
-            self._set_tzdata(tzobj)
-
-    def _set_tzdata(self, tzobj):
-        """ Set the time zone data of this object from a _tzfile object """
-        # Copy the relevant attributes over as private attributes
-        for attr in _tzfile.attrs:
-            setattr(self, '_' + attr, getattr(tzobj, attr))
-
-    def _read_tzfile(self, fileobj):
-        out = _tzfile()
-
-        # From tzfile(5):
-        #
-        # The time zone information files used by tzset(3)
-        # begin with the magic characters "TZif" to identify
-        # them as time zone information files, followed by
-        # sixteen bytes reserved for future use, followed by
-        # six four-byte values of type long, written in a
-        # ``standard'' byte order (the high-order  byte
-        # of the value is written first).
-        if fileobj.read(4).decode() != "TZif":
-            raise ValueError("magic not found")
-
-        fileobj.read(16)
-
-        (
-            # The number of UTC/local indicators stored in the file.
-            ttisgmtcnt,
-
-            # The number of standard/wall indicators stored in the file.
-            ttisstdcnt,
-
-            # The number of leap seconds for which data is
-            # stored in the file.
-            leapcnt,
-
-            # The number of "transition times" for which data
-            # is stored in the file.
-            timecnt,
-
-            # The number of "local time types" for which data
-            # is stored in the file (must not be zero).
-            typecnt,
-
-            # The  number  of  characters  of "time zone
-            # abbreviation strings" stored in the file.
-            charcnt,
-
-        ) = struct.unpack(">6l", fileobj.read(24))
-
-        # The above header is followed by tzh_timecnt four-byte
-        # values  of  type long,  sorted  in ascending order.
-        # These values are written in ``standard'' byte order.
-        # Each is used as a transition time (as  returned  by
-        # time(2)) at which the rules for computing local time
-        # change.
-
-        if timecnt:
-            out.trans_list_utc = list(struct.unpack(">%dl" % timecnt,
-                                                    fileobj.read(timecnt*4)))
-        else:
-            out.trans_list_utc = []
-
-        # Next come tzh_timecnt one-byte values of type unsigned
-        # char; each one tells which of the different types of
-        # ``local time'' types described in the file is associated
-        # with the same-indexed transition time. These values
-        # serve as indices into an array of ttinfo structures that
-        # appears next in the file.
-
-        if timecnt:
-            out.trans_idx = struct.unpack(">%dB" % timecnt,
-                                          fileobj.read(timecnt))
-        else:
-            out.trans_idx = []
-
-        # Each ttinfo structure is written as a four-byte value
-        # for tt_gmtoff  of  type long,  in  a  standard  byte
-        # order, followed  by a one-byte value for tt_isdst
-        # and a one-byte  value  for  tt_abbrind.   In  each
-        # structure, tt_gmtoff  gives  the  number  of
-        # seconds to be added to UTC, tt_isdst tells whether
-        # tm_isdst should be set by  localtime(3),  and
-        # tt_abbrind serves  as an index into the array of
-        # time zone abbreviation characters that follow the
-        # ttinfo structure(s) in the file.
-
-        ttinfo = []
-
-        for i in range(typecnt):
-            ttinfo.append(struct.unpack(">lbb", fileobj.read(6)))
-
-        abbr = fileobj.read(charcnt).decode()
-
-        # Then there are tzh_leapcnt pairs of four-byte
-        # values, written in  standard byte  order;  the
-        # first  value  of  each pair gives the time (as
-        # returned by time(2)) at which a leap second
-        # occurs;  the  second  gives the  total  number of
-        # leap seconds to be applied after the given time.
-        # The pairs of values are sorted in ascending order
-        # by time.
-
-        # Not used, for now (but seek for correct file position)
-        if leapcnt:
-            fileobj.seek(leapcnt * 8, os.SEEK_CUR)
-
-        # Then there are tzh_ttisstdcnt standard/wall
-        # indicators, each stored as a one-byte value;
-        # they tell whether the transition times associated
-        # with local time types were specified as standard
-        # time or wall clock time, and are used when
-        # a time zone file is used in handling POSIX-style
-        # time zone environment variables.
-
-        if ttisstdcnt:
-            isstd = struct.unpack(">%db" % ttisstdcnt,
-                                  fileobj.read(ttisstdcnt))
-
-        # Finally, there are tzh_ttisgmtcnt UTC/local
-        # indicators, each stored as a one-byte value;
-        # they tell whether the transition times associated
-        # with local time types were specified as UTC or
-        # local time, and are used when a time zone file
-        # is used in handling POSIX-style time zone envi-
-        # ronment variables.
-
-        if ttisgmtcnt:
-            isgmt = struct.unpack(">%db" % ttisgmtcnt,
-                                  fileobj.read(ttisgmtcnt))
-
-        # Build ttinfo list
-        out.ttinfo_list = []
-        for i in range(typecnt):
-            gmtoff, isdst, abbrind = ttinfo[i]
-            gmtoff = _get_supported_offset(gmtoff)
-            tti = _ttinfo()
-            tti.offset = gmtoff
-            tti.dstoffset = datetime.timedelta(0)
-            tti.delta = datetime.timedelta(seconds=gmtoff)
-            tti.isdst = isdst
-            tti.abbr = abbr[abbrind:abbr.find('\x00', abbrind)]
-            tti.isstd = (ttisstdcnt > i and isstd[i] != 0)
-            tti.isgmt = (ttisgmtcnt > i and isgmt[i] != 0)
-            out.ttinfo_list.append(tti)
-
-        # Replace ttinfo indexes for ttinfo objects.
-        out.trans_idx = [out.ttinfo_list[idx] for idx in out.trans_idx]
-
-        # Set standard, dst, and before ttinfos. before will be
-        # used when a given time is before any transitions,
-        # and will be set to the first non-dst ttinfo, or to
-        # the first dst, if all of them are dst.
-        out.ttinfo_std = None
-        out.ttinfo_dst = None
-        out.ttinfo_before = None
-        if out.ttinfo_list:
-            if not out.trans_list_utc:
-                out.ttinfo_std = out.ttinfo_first = out.ttinfo_list[0]
-            else:
-                for i in range(timecnt-1, -1, -1):
-                    tti = out.trans_idx[i]
-                    if not out.ttinfo_std and not tti.isdst:
-                        out.ttinfo_std = tti
-                    elif not out.ttinfo_dst and tti.isdst:
-                        out.ttinfo_dst = tti
-
-                    if out.ttinfo_std and out.ttinfo_dst:
-                        break
-                else:
-                    if out.ttinfo_dst and not out.ttinfo_std:
-                        out.ttinfo_std = out.ttinfo_dst
-
-                for tti in out.ttinfo_list:
-                    if not tti.isdst:
-                        out.ttinfo_before = tti
-                        break
-                else:
-                    out.ttinfo_before = out.ttinfo_list[0]
-
-        # Now fix transition times to become relative to wall time.
-        #
-        # I'm not sure about this. In my tests, the tz source file
-        # is setup to wall time, and in the binary file isstd and
-        # isgmt are off, so it should be in wall time. OTOH, it's
-        # always in gmt time. Let me know if you have comments
-        # about this.
-        lastdst = None
-        lastoffset = None
-        lastdstoffset = None
-        lastbaseoffset = None
-        out.trans_list = []
-
-        for i, tti in enumerate(out.trans_idx):
-            offset = tti.offset
-            dstoffset = 0
-
-            if lastdst is not None:
-                if tti.isdst:
-                    if not lastdst:
-                        dstoffset = offset - lastoffset
-
-                    if not dstoffset and lastdstoffset:
-                        dstoffset = lastdstoffset
-
-                    tti.dstoffset = datetime.timedelta(seconds=dstoffset)
-                    lastdstoffset = dstoffset
-
-            # If a time zone changes its base offset during a DST transition,
-            # then you need to adjust by the previous base offset to get the
-            # transition time in local time. Otherwise you use the current
-            # base offset. Ideally, I would have some mathematical proof of
-            # why this is true, but I haven't really thought about it enough.
-            baseoffset = offset - dstoffset
-            adjustment = baseoffset
-            if (lastbaseoffset is not None and baseoffset != lastbaseoffset
-                    and tti.isdst != lastdst):
-                # The base DST has changed
-                adjustment = lastbaseoffset
-
-            lastdst = tti.isdst
-            lastoffset = offset
-            lastbaseoffset = baseoffset
-
-            out.trans_list.append(out.trans_list_utc[i] + adjustment)
-
-        out.trans_idx = tuple(out.trans_idx)
-        out.trans_list = tuple(out.trans_list)
-        out.trans_list_utc = tuple(out.trans_list_utc)
-
-        return out
-
-    def _find_last_transition(self, dt, in_utc=False):
-        # If there's no list, there are no transitions to find
-        if not self._trans_list:
-            return None
-
-        timestamp = _datetime_to_timestamp(dt)
-
-        # Find where the timestamp fits in the transition list - if the
-        # timestamp is a transition time, it's part of the "after" period.
-        trans_list = self._trans_list_utc if in_utc else self._trans_list
-        idx = bisect.bisect_right(trans_list, timestamp)
-
-        # We want to know when the previous transition was, so subtract off 1
-        return idx - 1
-
-    def _get_ttinfo(self, idx):
-        # For no list or after the last transition, default to _ttinfo_std
-        if idx is None or (idx + 1) >= len(self._trans_list):
-            return self._ttinfo_std
-
-        # If there is a list and the time is before it, return _ttinfo_before
-        if idx < 0:
-            return self._ttinfo_before
-
-        return self._trans_idx[idx]
-
-    def _find_ttinfo(self, dt):
-        idx = self._resolve_ambiguous_time(dt)
-
-        return self._get_ttinfo(idx)
-
-    def fromutc(self, dt):
-        """
-        The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`.
-
-        :param dt:
-            A :py:class:`datetime.datetime` object.
-
-        :raises TypeError:
-            Raised if ``dt`` is not a :py:class:`datetime.datetime` object.
-
-        :raises ValueError:
-            Raised if this is called with a ``dt`` which does not have this
-            ``tzinfo`` attached.
-
-        :return:
-            Returns a :py:class:`datetime.datetime` object representing the
-            wall time in ``self``'s time zone.
-        """
-        # These isinstance checks are in datetime.tzinfo, so we'll preserve
-        # them, even if we don't care about duck typing.
-        if not isinstance(dt, datetime.datetime):
-            raise TypeError("fromutc() requires a datetime argument")
-
-        if dt.tzinfo is not self:
-            raise ValueError("dt.tzinfo is not self")
-
-        # First treat UTC as wall time and get the transition we're in.
-        idx = self._find_last_transition(dt, in_utc=True)
-        tti = self._get_ttinfo(idx)
-
-        dt_out = dt + datetime.timedelta(seconds=tti.offset)
-
-        fold = self.is_ambiguous(dt_out, idx=idx)
-
-        return enfold(dt_out, fold=int(fold))
-
-    def is_ambiguous(self, dt, idx=None):
-        """
-        Whether or not the "wall time" of a given datetime is ambiguous in this
-        zone.
-
-        :param dt:
-            A :py:class:`datetime.datetime`, naive or time zone aware.
-
-
-        :return:
-            Returns ``True`` if ambiguous, ``False`` otherwise.
-
-        .. versionadded:: 2.6.0
-        """
-        if idx is None:
-            idx = self._find_last_transition(dt)
-
-        # Calculate the difference in offsets from current to previous
-        timestamp = _datetime_to_timestamp(dt)
-        tti = self._get_ttinfo(idx)
-
-        if idx is None or idx <= 0:
-            return False
-
-        od = self._get_ttinfo(idx - 1).offset - tti.offset
-        tt = self._trans_list[idx]          # Transition time
-
-        return timestamp < tt + od
-
-    def _resolve_ambiguous_time(self, dt):
-        idx = self._find_last_transition(dt)
-
-        # If we have no transitions, return the index
-        _fold = self._fold(dt)
-        if idx is None or idx == 0:
-            return idx
-
-        # If it's ambiguous and we're in a fold, shift to a different index.
-        idx_offset = int(not _fold and self.is_ambiguous(dt, idx))
-
-        return idx - idx_offset
-
-    def utcoffset(self, dt):
-        if dt is None:
-            return None
-
-        if not self._ttinfo_std:
-            return ZERO
-
-        return self._find_ttinfo(dt).delta
-
-    def dst(self, dt):
-        if dt is None:
-            return None
-
-        if not self._ttinfo_dst:
-            return ZERO
-
-        tti = self._find_ttinfo(dt)
-
-        if not tti.isdst:
-            return ZERO
-
-        # The documentation says that utcoffset()-dst() must
-        # be constant for every dt.
-        return tti.dstoffset
-
-    @tzname_in_python2
-    def tzname(self, dt):
-        if not self._ttinfo_std or dt is None:
-            return None
-        return self._find_ttinfo(dt).abbr
-
-    def __eq__(self, other):
-        if not isinstance(other, tzfile):
-            return NotImplemented
-        return (self._trans_list == other._trans_list and
-                self._trans_idx == other._trans_idx and
-                self._ttinfo_list == other._ttinfo_list)
-
-    __hash__ = None
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __repr__(self):
-        return "%s(%s)" % (self.__class__.__name__, repr(self._filename))
-
-    def __reduce__(self):
-        return self.__reduce_ex__(None)
-
-    def __reduce_ex__(self, protocol):
-        return (self.__class__, (None, self._filename), self.__dict__)
-
-
-class tzrange(tzrangebase):
-    """
-    The ``tzrange`` object is a time zone specified by a set of offsets and
-    abbreviations, equivalent to the way the ``TZ`` variable can be specified
-    in POSIX-like systems, but using Python delta objects to specify DST
-    start, end and offsets.
-
-    :param stdabbr:
-        The abbreviation for standard time (e.g. ``'EST'``).
-
-    :param stdoffset:
-        An integer or :class:`datetime.timedelta` object or equivalent
-        specifying the base offset from UTC.
-
-        If unspecified, +00:00 is used.
-
-    :param dstabbr:
-        The abbreviation for DST / "Summer" time (e.g. ``'EDT'``).
-
-        If specified, with no other DST information, DST is assumed to occur
-        and the default behavior or ``dstoffset``, ``start`` and ``end`` is
-        used. If unspecified and no other DST information is specified, it
-        is assumed that this zone has no DST.
-
-        If this is unspecified and other DST information is *is* specified,
-        DST occurs in the zone but the time zone abbreviation is left
-        unchanged.
-
-    :param dstoffset:
-        A an integer or :class:`datetime.timedelta` object or equivalent
-        specifying the UTC offset during DST. If unspecified and any other DST
-        information is specified, it is assumed to be the STD offset +1 hour.
-
-    :param start:
-        A :class:`relativedelta.relativedelta` object or equivalent specifying
-        the time and time of year that daylight savings time starts. To
-        specify, for example, that DST starts at 2AM on the 2nd Sunday in
-        March, pass:
-
-            ``relativedelta(hours=2, month=3, day=1, weekday=SU(+2))``
-
-        If unspecified and any other DST information is specified, the default
-        value is 2 AM on the first Sunday in April.
-
-    :param end:
-        A :class:`relativedelta.relativedelta` object or equivalent
-        representing the time and time of year that daylight savings time
-        ends, with the same specification method as in ``start``. One note is
-        that this should point to the first time in the *standard* zone, so if
-        a transition occurs at 2AM in the DST zone and the clocks are set back
-        1 hour to 1AM, set the ``hours`` parameter to +1.
-
-
-    **Examples:**
-
-    .. testsetup:: tzrange
-
-        from dateutil.tz import tzrange, tzstr
-
-    .. doctest:: tzrange
-
-        >>> tzstr('EST5EDT') == tzrange("EST", -18000, "EDT")
-        True
-
-        >>> from dateutil.relativedelta import *
-        >>> range1 = tzrange("EST", -18000, "EDT")
-        >>> range2 = tzrange("EST", -18000, "EDT", -14400,
-        ...                  relativedelta(hours=+2, month=4, day=1,
-        ...                                weekday=SU(+1)),
-        ...                  relativedelta(hours=+1, month=10, day=31,
-        ...                                weekday=SU(-1)))
-        >>> tzstr('EST5EDT') == range1 == range2
-        True
-
-    """
-    def __init__(self, stdabbr, stdoffset=None,
-                 dstabbr=None, dstoffset=None,
-                 start=None, end=None):
-
-        global relativedelta
-        from dateutil import relativedelta
-
-        self._std_abbr = stdabbr
-        self._dst_abbr = dstabbr
-
-        try:
-            stdoffset = stdoffset.total_seconds()
-        except (TypeError, AttributeError):
-            pass
-
-        try:
-            dstoffset = dstoffset.total_seconds()
-        except (TypeError, AttributeError):
-            pass
-
-        if stdoffset is not None:
-            self._std_offset = datetime.timedelta(seconds=stdoffset)
-        else:
-            self._std_offset = ZERO
-
-        if dstoffset is not None:
-            self._dst_offset = datetime.timedelta(seconds=dstoffset)
-        elif dstabbr and stdoffset is not None:
-            self._dst_offset = self._std_offset + datetime.timedelta(hours=+1)
-        else:
-            self._dst_offset = ZERO
-
-        if dstabbr and start is None:
-            self._start_delta = relativedelta.relativedelta(
-                hours=+2, month=4, day=1, weekday=relativedelta.SU(+1))
-        else:
-            self._start_delta = start
-
-        if dstabbr and end is None:
-            self._end_delta = relativedelta.relativedelta(
-                hours=+1, month=10, day=31, weekday=relativedelta.SU(-1))
-        else:
-            self._end_delta = end
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = bool(self._start_delta)
-
-    def transitions(self, year):
-        """
-        For a given year, get the DST on and off transition times, expressed
-        always on the standard time side. For zones with no transitions, this
-        function returns ``None``.
-
-        :param year:
-            The year whose transitions you would like to query.
-
-        :return:
-            Returns a :class:`tuple` of :class:`datetime.datetime` objects,
-            ``(dston, dstoff)`` for zones with an annual DST transition, or
-            ``None`` for fixed offset zones.
-        """
-        if not self.hasdst:
-            return None
-
-        base_year = datetime.datetime(year, 1, 1)
-
-        start = base_year + self._start_delta
-        end = base_year + self._end_delta
-
-        return (start, end)
-
-    def __eq__(self, other):
-        if not isinstance(other, tzrange):
-            return NotImplemented
-
-        return (self._std_abbr == other._std_abbr and
-                self._dst_abbr == other._dst_abbr and
-                self._std_offset == other._std_offset and
-                self._dst_offset == other._dst_offset and
-                self._start_delta == other._start_delta and
-                self._end_delta == other._end_delta)
-
-    @property
-    def _dst_base_offset(self):
-        return self._dst_base_offset_
-
-
-@six.add_metaclass(_TzStrFactory)
-class tzstr(tzrange):
-    """
-    ``tzstr`` objects are time zone objects specified by a time-zone string as
-    it would be passed to a ``TZ`` variable on POSIX-style systems (see
-    the `GNU C Library: TZ Variable`_ for more details).
-
-    There is one notable exception, which is that POSIX-style time zones use an
-    inverted offset format, so normally ``GMT+3`` would be parsed as an offset
-    3 hours *behind* GMT. The ``tzstr`` time zone object will parse this as an
-    offset 3 hours *ahead* of GMT. If you would like to maintain the POSIX
-    behavior, pass a ``True`` value to ``posix_offset``.
-
-    The :class:`tzrange` object provides the same functionality, but is
-    specified using :class:`relativedelta.relativedelta` objects. rather than
-    strings.
-
-    :param s:
-        A time zone string in ``TZ`` variable format. This can be a
-        :class:`bytes` (2.x: :class:`str`), :class:`str` (2.x:
-        :class:`unicode`) or a stream emitting unicode characters
-        (e.g. :class:`StringIO`).
-
-    :param posix_offset:
-        Optional. If set to ``True``, interpret strings such as ``GMT+3`` or
-        ``UTC+3`` as being 3 hours *behind* UTC rather than ahead, per the
-        POSIX standard.
-
-    .. caution::
-
-        Prior to version 2.7.0, this function also supported time zones
-        in the format:
-
-            * ``EST5EDT,4,0,6,7200,10,0,26,7200,3600``
-            * ``EST5EDT,4,1,0,7200,10,-1,0,7200,3600``
-
-        This format is non-standard and has been deprecated; this function
-        will raise a :class:`DeprecatedTZFormatWarning` until
-        support is removed in a future version.
-
-    .. _`GNU C Library: TZ Variable`:
-        https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html
-    """
-    def __init__(self, s, posix_offset=False):
-        global parser
-        from dateutil.parser import _parser as parser
-
-        self._s = s
-
-        res = parser._parsetz(s)
-        if res is None or res.any_unused_tokens:
-            raise ValueError("unknown string format")
-
-        # Here we break the compatibility with the TZ variable handling.
-        # GMT-3 actually *means* the timezone -3.
-        if res.stdabbr in ("GMT", "UTC") and not posix_offset:
-            res.stdoffset *= -1
-
-        # We must initialize it first, since _delta() needs
-        # _std_offset and _dst_offset set. Use False in start/end
-        # to avoid building it two times.
-        tzrange.__init__(self, res.stdabbr, res.stdoffset,
-                         res.dstabbr, res.dstoffset,
-                         start=False, end=False)
-
-        if not res.dstabbr:
-            self._start_delta = None
-            self._end_delta = None
-        else:
-            self._start_delta = self._delta(res.start)
-            if self._start_delta:
-                self._end_delta = self._delta(res.end, isend=1)
-
-        self.hasdst = bool(self._start_delta)
-
-    def _delta(self, x, isend=0):
-        from dateutil import relativedelta
-        kwargs = {}
-        if x.month is not None:
-            kwargs["month"] = x.month
-            if x.weekday is not None:
-                kwargs["weekday"] = relativedelta.weekday(x.weekday, x.week)
-                if x.week > 0:
-                    kwargs["day"] = 1
-                else:
-                    kwargs["day"] = 31
-            elif x.day:
-                kwargs["day"] = x.day
-        elif x.yday is not None:
-            kwargs["yearday"] = x.yday
-        elif x.jyday is not None:
-            kwargs["nlyearday"] = x.jyday
-        if not kwargs:
-            # Default is to start on first sunday of april, and end
-            # on last sunday of october.
-            if not isend:
-                kwargs["month"] = 4
-                kwargs["day"] = 1
-                kwargs["weekday"] = relativedelta.SU(+1)
-            else:
-                kwargs["month"] = 10
-                kwargs["day"] = 31
-                kwargs["weekday"] = relativedelta.SU(-1)
-        if x.time is not None:
-            kwargs["seconds"] = x.time
-        else:
-            # Default is 2AM.
-            kwargs["seconds"] = 7200
-        if isend:
-            # Convert to standard time, to follow the documented way
-            # of working with the extra hour. See the documentation
-            # of the tzinfo class.
-            delta = self._dst_offset - self._std_offset
-            kwargs["seconds"] -= delta.seconds + delta.days * 86400
-        return relativedelta.relativedelta(**kwargs)
-
-    def __repr__(self):
-        return "%s(%s)" % (self.__class__.__name__, repr(self._s))
-
-
-class _tzicalvtzcomp(object):
-    def __init__(self, tzoffsetfrom, tzoffsetto, isdst,
-                 tzname=None, rrule=None):
-        self.tzoffsetfrom = datetime.timedelta(seconds=tzoffsetfrom)
-        self.tzoffsetto = datetime.timedelta(seconds=tzoffsetto)
-        self.tzoffsetdiff = self.tzoffsetto - self.tzoffsetfrom
-        self.isdst = isdst
-        self.tzname = tzname
-        self.rrule = rrule
-
-
-class _tzicalvtz(_tzinfo):
-    def __init__(self, tzid, comps=[]):
-        super(_tzicalvtz, self).__init__()
-
-        self._tzid = tzid
-        self._comps = comps
-        self._cachedate = []
-        self._cachecomp = []
-        self._cache_lock = _thread.allocate_lock()
-
-    def _find_comp(self, dt):
-        if len(self._comps) == 1:
-            return self._comps[0]
-
-        dt = dt.replace(tzinfo=None)
-
-        try:
-            with self._cache_lock:
-                return self._cachecomp[self._cachedate.index(
-                    (dt, self._fold(dt)))]
-        except ValueError:
-            pass
-
-        lastcompdt = None
-        lastcomp = None
-
-        for comp in self._comps:
-            compdt = self._find_compdt(comp, dt)
-
-            if compdt and (not lastcompdt or lastcompdt < compdt):
-                lastcompdt = compdt
-                lastcomp = comp
-
-        if not lastcomp:
-            # RFC says nothing about what to do when a given
-            # time is before the first onset date. We'll look for the
-            # first standard component, or the first component, if
-            # none is found.
-            for comp in self._comps:
-                if not comp.isdst:
-                    lastcomp = comp
-                    break
-            else:
-                lastcomp = comp[0]
-
-        with self._cache_lock:
-            self._cachedate.insert(0, (dt, self._fold(dt)))
-            self._cachecomp.insert(0, lastcomp)
-
-            if len(self._cachedate) > 10:
-                self._cachedate.pop()
-                self._cachecomp.pop()
-
-        return lastcomp
-
-    def _find_compdt(self, comp, dt):
-        if comp.tzoffsetdiff < ZERO and self._fold(dt):
-            dt -= comp.tzoffsetdiff
-
-        compdt = comp.rrule.before(dt, inc=True)
-
-        return compdt
-
-    def utcoffset(self, dt):
-        if dt is None:
-            return None
-
-        return self._find_comp(dt).tzoffsetto
-
-    def dst(self, dt):
-        comp = self._find_comp(dt)
-        if comp.isdst:
-            return comp.tzoffsetdiff
-        else:
-            return ZERO
-
-    @tzname_in_python2
-    def tzname(self, dt):
-        return self._find_comp(dt).tzname
-
-    def __repr__(self):
-        return "<tzicalvtz %s>" % repr(self._tzid)
-
-    __reduce__ = object.__reduce__
-
-
-class tzical(object):
-    """
-    This object is designed to parse an iCalendar-style ``VTIMEZONE`` structure
-    as set out in `RFC 5545`_ Section 4.6.5 into one or more `tzinfo` objects.
-
-    :param `fileobj`:
-        A file or stream in iCalendar format, which should be UTF-8 encoded
-        with CRLF endings.
-
-    .. _`RFC 5545`: https://tools.ietf.org/html/rfc5545
-    """
-    def __init__(self, fileobj):
-        global rrule
-        from dateutil import rrule
-
-        if isinstance(fileobj, string_types):
-            self._s = fileobj
-            # ical should be encoded in UTF-8 with CRLF
-            fileobj = open(fileobj, 'r')
-        else:
-            self._s = getattr(fileobj, 'name', repr(fileobj))
-            fileobj = _nullcontext(fileobj)
-
-        self._vtz = {}
-
-        with fileobj as fobj:
-            self._parse_rfc(fobj.read())
-
-    def keys(self):
-        """
-        Retrieves the available time zones as a list.
-        """
-        return list(self._vtz.keys())
-
-    def get(self, tzid=None):
-        """
-        Retrieve a :py:class:`datetime.tzinfo` object by its ``tzid``.
-
-        :param tzid:
-            If there is exactly one time zone available, omitting ``tzid``
-            or passing :py:const:`None` value returns it. Otherwise a valid
-            key (which can be retrieved from :func:`keys`) is required.
-
-        :raises ValueError:
-            Raised if ``tzid`` is not specified but there are either more
-            or fewer than 1 zone defined.
-
-        :returns:
-            Returns either a :py:class:`datetime.tzinfo` object representing
-            the relevant time zone or :py:const:`None` if the ``tzid`` was
-            not found.
-        """
-        if tzid is None:
-            if len(self._vtz) == 0:
-                raise ValueError("no timezones defined")
-            elif len(self._vtz) > 1:
-                raise ValueError("more than one timezone available")
-            tzid = next(iter(self._vtz))
-
-        return self._vtz.get(tzid)
-
-    def _parse_offset(self, s):
-        s = s.strip()
-        if not s:
-            raise ValueError("empty offset")
-        if s[0] in ('+', '-'):
-            signal = (-1, +1)[s[0] == '+']
-            s = s[1:]
-        else:
-            signal = +1
-        if len(s) == 4:
-            return (int(s[:2]) * 3600 + int(s[2:]) * 60) * signal
-        elif len(s) == 6:
-            return (int(s[:2]) * 3600 + int(s[2:4]) * 60 + int(s[4:])) * signal
-        else:
-            raise ValueError("invalid offset: " + s)
-
-    def _parse_rfc(self, s):
-        lines = s.splitlines()
-        if not lines:
-            raise ValueError("empty string")
-
-        # Unfold
-        i = 0
-        while i < len(lines):
-            line = lines[i].rstrip()
-            if not line:
-                del lines[i]
-            elif i > 0 and line[0] == " ":
-                lines[i-1] += line[1:]
-                del lines[i]
-            else:
-                i += 1
-
-        tzid = None
-        comps = []
-        invtz = False
-        comptype = None
-        for line in lines:
-            if not line:
-                continue
-            name, value = line.split(':', 1)
-            parms = name.split(';')
-            if not parms:
-                raise ValueError("empty property name")
-            name = parms[0].upper()
-            parms = parms[1:]
-            if invtz:
-                if name == "BEGIN":
-                    if value in ("STANDARD", "DAYLIGHT"):
-                        # Process component
-                        pass
-                    else:
-                        raise ValueError("unknown component: "+value)
-                    comptype = value
-                    founddtstart = False
-                    tzoffsetfrom = None
-                    tzoffsetto = None
-                    rrulelines = []
-                    tzname = None
-                elif name == "END":
-                    if value == "VTIMEZONE":
-                        if comptype:
-                            raise ValueError("component not closed: "+comptype)
-                        if not tzid:
-                            raise ValueError("mandatory TZID not found")
-                        if not comps:
-                            raise ValueError(
-                                "at least one component is needed")
-                        # Process vtimezone
-                        self._vtz[tzid] = _tzicalvtz(tzid, comps)
-                        invtz = False
-                    elif value == comptype:
-                        if not founddtstart:
-                            raise ValueError("mandatory DTSTART not found")
-                        if tzoffsetfrom is None:
-                            raise ValueError(
-                                "mandatory TZOFFSETFROM not found")
-                        if tzoffsetto is None:
-                            raise ValueError(
-                                "mandatory TZOFFSETFROM not found")
-                        # Process component
-                        rr = None
-                        if rrulelines:
-                            rr = rrule.rrulestr("\n".join(rrulelines),
-                                                compatible=True,
-                                                ignoretz=True,
-                                                cache=True)
-                        comp = _tzicalvtzcomp(tzoffsetfrom, tzoffsetto,
-                                              (comptype == "DAYLIGHT"),
-                                              tzname, rr)
-                        comps.append(comp)
-                        comptype = None
-                    else:
-                        raise ValueError("invalid component end: "+value)
-                elif comptype:
-                    if name == "DTSTART":
-                        # DTSTART in VTIMEZONE takes a subset of valid RRULE
-                        # values under RFC 5545.
-                        for parm in parms:
-                            if parm != 'VALUE=DATE-TIME':
-                                msg = ('Unsupported DTSTART param in ' +
-                                       'VTIMEZONE: ' + parm)
-                                raise ValueError(msg)
-                        rrulelines.append(line)
-                        founddtstart = True
-                    elif name in ("RRULE", "RDATE", "EXRULE", "EXDATE"):
-                        rrulelines.append(line)
-                    elif name == "TZOFFSETFROM":
-                        if parms:
-                            raise ValueError(
-                                "unsupported %s parm: %s " % (name, parms[0]))
-                        tzoffsetfrom = self._parse_offset(value)
-                    elif name == "TZOFFSETTO":
-                        if parms:
-                            raise ValueError(
-                                "unsupported TZOFFSETTO parm: "+parms[0])
-                        tzoffsetto = self._parse_offset(value)
-                    elif name == "TZNAME":
-                        if parms:
-                            raise ValueError(
-                                "unsupported TZNAME parm: "+parms[0])
-                        tzname = value
-                    elif name == "COMMENT":
-                        pass
-                    else:
-                        raise ValueError("unsupported property: "+name)
-                else:
-                    if name == "TZID":
-                        if parms:
-                            raise ValueError(
-                                "unsupported TZID parm: "+parms[0])
-                        tzid = value
-                    elif name in ("TZURL", "LAST-MODIFIED", "COMMENT"):
-                        pass
-                    else:
-                        raise ValueError("unsupported property: "+name)
-            elif name == "BEGIN" and value == "VTIMEZONE":
-                tzid = None
-                comps = []
-                invtz = True
-
-    def __repr__(self):
-        return "%s(%s)" % (self.__class__.__name__, repr(self._s))
-
-
-if sys.platform != "win32":
-    TZFILES = ["/etc/localtime", "localtime"]
-    TZPATHS = ["/usr/share/zoneinfo",
-               "/usr/lib/zoneinfo",
-               "/usr/share/lib/zoneinfo",
-               "/etc/zoneinfo"]
-else:
-    TZFILES = []
-    TZPATHS = []
-
-
-def __get_gettz():
-    tzlocal_classes = (tzlocal,)
-    if tzwinlocal is not None:
-        tzlocal_classes += (tzwinlocal,)
-
-    class GettzFunc(object):
-        """
-        Retrieve a time zone object from a string representation
-
-        This function is intended to retrieve the :py:class:`tzinfo` subclass
-        that best represents the time zone that would be used if a POSIX
-        `TZ variable`_ were set to the same value.
-
-        If no argument or an empty string is passed to ``gettz``, local time
-        is returned:
-
-        .. code-block:: python3
-
-            >>> gettz()
-            tzfile('/etc/localtime')
-
-        This function is also the preferred way to map IANA tz database keys
-        to :class:`tzfile` objects:
-
-        .. code-block:: python3
-
-            >>> gettz('Pacific/Kiritimati')
-            tzfile('/usr/share/zoneinfo/Pacific/Kiritimati')
-
-        On Windows, the standard is extended to include the Windows-specific
-        zone names provided by the operating system:
-
-        .. code-block:: python3
-
-            >>> gettz('Egypt Standard Time')
-            tzwin('Egypt Standard Time')
-
-        Passing a GNU ``TZ`` style string time zone specification returns a
-        :class:`tzstr` object:
-
-        .. code-block:: python3
-
-            >>> gettz('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3')
-            tzstr('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3')
-
-        :param name:
-            A time zone name (IANA, or, on Windows, Windows keys), location of
-            a ``tzfile(5)`` zoneinfo file or ``TZ`` variable style time zone
-            specifier. An empty string, no argument or ``None`` is interpreted
-            as local time.
-
-        :return:
-            Returns an instance of one of ``dateutil``'s :py:class:`tzinfo`
-            subclasses.
-
-        .. versionchanged:: 2.7.0
-
-            After version 2.7.0, any two calls to ``gettz`` using the same
-            input strings will return the same object:
-
-            .. code-block:: python3
-
-                >>> tz.gettz('America/Chicago') is tz.gettz('America/Chicago')
-                True
-
-            In addition to improving performance, this ensures that
-            `"same zone" semantics`_ are used for datetimes in the same zone.
-
-
-        .. _`TZ variable`:
-            https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html
-
-        .. _`"same zone" semantics`:
-            https://blog.ganssle.io/articles/2018/02/aware-datetime-arithmetic.html
-        """
-        def __init__(self):
-
-            self.__instances = weakref.WeakValueDictionary()
-            self.__strong_cache_size = 8
-            self.__strong_cache = OrderedDict()
-            self._cache_lock = _thread.allocate_lock()
-
-        def __call__(self, name=None):
-            with self._cache_lock:
-                rv = self.__instances.get(name, None)
-
-                if rv is None:
-                    rv = self.nocache(name=name)
-                    if not (name is None
-                            or isinstance(rv, tzlocal_classes)
-                            or rv is None):
-                        # tzlocal is slightly more complicated than the other
-                        # time zone providers because it depends on environment
-                        # at construction time, so don't cache that.
-                        #
-                        # We also cannot store weak references to None, so we
-                        # will also not store that.
-                        self.__instances[name] = rv
-                    else:
-                        # No need for strong caching, return immediately
-                        return rv
-
-                self.__strong_cache[name] = self.__strong_cache.pop(name, rv)
-
-                if len(self.__strong_cache) > self.__strong_cache_size:
-                    self.__strong_cache.popitem(last=False)
-
-            return rv
-
-        def set_cache_size(self, size):
-            with self._cache_lock:
-                self.__strong_cache_size = size
-                while len(self.__strong_cache) > size:
-                    self.__strong_cache.popitem(last=False)
-
-        def cache_clear(self):
-            with self._cache_lock:
-                self.__instances = weakref.WeakValueDictionary()
-                self.__strong_cache.clear()
-
-        @staticmethod
-        def nocache(name=None):
-            """A non-cached version of gettz"""
-            tz = None
-            if not name:
-                try:
-                    name = os.environ["TZ"]
-                except KeyError:
-                    pass
-            if name is None or name in ("", ":"):
-                for filepath in TZFILES:
-                    if not os.path.isabs(filepath):
-                        filename = filepath
-                        for path in TZPATHS:
-                            filepath = os.path.join(path, filename)
-                            if os.path.isfile(filepath):
-                                break
-                        else:
-                            continue
-                    if os.path.isfile(filepath):
-                        try:
-                            tz = tzfile(filepath)
-                            break
-                        except (IOError, OSError, ValueError):
-                            pass
-                else:
-                    tz = tzlocal()
-            else:
-                try:
-                    if name.startswith(":"):
-                        name = name[1:]
-                except TypeError as e:
-                    if isinstance(name, bytes):
-                        new_msg = "gettz argument should be str, not bytes"
-                        six.raise_from(TypeError(new_msg), e)
-                    else:
-                        raise
-                if os.path.isabs(name):
-                    if os.path.isfile(name):
-                        tz = tzfile(name)
-                    else:
-                        tz = None
-                else:
-                    for path in TZPATHS:
-                        filepath = os.path.join(path, name)
-                        if not os.path.isfile(filepath):
-                            filepath = filepath.replace(' ', '_')
-                            if not os.path.isfile(filepath):
-                                continue
-                        try:
-                            tz = tzfile(filepath)
-                            break
-                        except (IOError, OSError, ValueError):
-                            pass
-                    else:
-                        tz = None
-                        if tzwin is not None:
-                            try:
-                                tz = tzwin(name)
-                            except (WindowsError, UnicodeEncodeError):
-                                # UnicodeEncodeError is for Python 2.7 compat
-                                tz = None
-
-                        if not tz:
-                            from dateutil.zoneinfo import get_zonefile_instance
-                            tz = get_zonefile_instance().get(name)
-
-                        if not tz:
-                            for c in name:
-                                # name is not a tzstr unless it has at least
-                                # one offset. For short values of "name", an
-                                # explicit for loop seems to be the fastest way
-                                # To determine if a string contains a digit
-                                if c in "0123456789":
-                                    try:
-                                        tz = tzstr(name)
-                                    except ValueError:
-                                        pass
-                                    break
-                            else:
-                                if name in ("GMT", "UTC"):
-                                    tz = UTC
-                                elif name in time.tzname:
-                                    tz = tzlocal()
-            return tz
-
-    return GettzFunc()
-
-
-gettz = __get_gettz()
-del __get_gettz
-
-
-def datetime_exists(dt, tz=None):
-    """
-    Given a datetime and a time zone, determine whether or not a given datetime
-    would fall in a gap.
-
-    :param dt:
-        A :class:`datetime.datetime` (whose time zone will be ignored if ``tz``
-        is provided.)
-
-    :param tz:
-        A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If
-        ``None`` or not provided, the datetime's own time zone will be used.
-
-    :return:
-        Returns a boolean value whether or not the "wall time" exists in
-        ``tz``.
-
-    .. versionadded:: 2.7.0
-    """
-    if tz is None:
-        if dt.tzinfo is None:
-            raise ValueError('Datetime is naive and no time zone provided.')
-        tz = dt.tzinfo
-
-    dt = dt.replace(tzinfo=None)
-
-    # This is essentially a test of whether or not the datetime can survive
-    # a round trip to UTC.
-    dt_rt = dt.replace(tzinfo=tz).astimezone(UTC).astimezone(tz)
-    dt_rt = dt_rt.replace(tzinfo=None)
-
-    return dt == dt_rt
-
-
-def datetime_ambiguous(dt, tz=None):
-    """
-    Given a datetime and a time zone, determine whether or not a given datetime
-    is ambiguous (i.e if there are two times differentiated only by their DST
-    status).
-
-    :param dt:
-        A :class:`datetime.datetime` (whose time zone will be ignored if ``tz``
-        is provided.)
-
-    :param tz:
-        A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If
-        ``None`` or not provided, the datetime's own time zone will be used.
-
-    :return:
-        Returns a boolean value whether or not the "wall time" is ambiguous in
-        ``tz``.
-
-    .. versionadded:: 2.6.0
-    """
-    if tz is None:
-        if dt.tzinfo is None:
-            raise ValueError('Datetime is naive and no time zone provided.')
-
-        tz = dt.tzinfo
-
-    # If a time zone defines its own "is_ambiguous" function, we'll use that.
-    is_ambiguous_fn = getattr(tz, 'is_ambiguous', None)
-    if is_ambiguous_fn is not None:
-        try:
-            return tz.is_ambiguous(dt)
-        except Exception:
-            pass
-
-    # If it doesn't come out and tell us it's ambiguous, we'll just check if
-    # the fold attribute has any effect on this particular date and time.
-    dt = dt.replace(tzinfo=tz)
-    wall_0 = enfold(dt, fold=0)
-    wall_1 = enfold(dt, fold=1)
-
-    same_offset = wall_0.utcoffset() == wall_1.utcoffset()
-    same_dst = wall_0.dst() == wall_1.dst()
-
-    return not (same_offset and same_dst)
-
-
-def resolve_imaginary(dt):
-    """
-    Given a datetime that may be imaginary, return an existing datetime.
-
-    This function assumes that an imaginary datetime represents what the
-    wall time would be in a zone had the offset transition not occurred, so
-    it will always fall forward by the transition's change in offset.
-
-    .. doctest::
-
-        >>> from dateutil import tz
-        >>> from datetime import datetime
-        >>> NYC = tz.gettz('America/New_York')
-        >>> print(tz.resolve_imaginary(datetime(2017, 3, 12, 2, 30, tzinfo=NYC)))
-        2017-03-12 03:30:00-04:00
-
-        >>> KIR = tz.gettz('Pacific/Kiritimati')
-        >>> print(tz.resolve_imaginary(datetime(1995, 1, 1, 12, 30, tzinfo=KIR)))
-        1995-01-02 12:30:00+14:00
-
-    As a note, :func:`datetime.astimezone` is guaranteed to produce a valid,
-    existing datetime, so a round-trip to and from UTC is sufficient to get
-    an extant datetime, however, this generally "falls back" to an earlier time
-    rather than falling forward to the STD side (though no guarantees are made
-    about this behavior).
-
-    :param dt:
-        A :class:`datetime.datetime` which may or may not exist.
-
-    :return:
-        Returns an existing :class:`datetime.datetime`. If ``dt`` was not
-        imaginary, the datetime returned is guaranteed to be the same object
-        passed to the function.
-
-    .. versionadded:: 2.7.0
-    """
-    if dt.tzinfo is not None and not datetime_exists(dt):
-
-        curr_offset = (dt + datetime.timedelta(hours=24)).utcoffset()
-        old_offset = (dt - datetime.timedelta(hours=24)).utcoffset()
-
-        dt += curr_offset - old_offset
-
-    return dt
-
-
-def _datetime_to_timestamp(dt):
-    """
-    Convert a :class:`datetime.datetime` object to an epoch timestamp in
-    seconds since January 1, 1970, ignoring the time zone.
-    """
-    return (dt.replace(tzinfo=None) - EPOCH).total_seconds()
-
-
-if sys.version_info >= (3, 6):
-    def _get_supported_offset(second_offset):
-        return second_offset
-else:
-    def _get_supported_offset(second_offset):
-        # For python pre-3.6, round to full-minutes if that's not the case.
-        # Python's datetime doesn't accept sub-minute timezones. Check
-        # http://python.org/sf/1447945 or https://bugs.python.org/issue5288
-        # for some information.
-        old_offset = second_offset
-        calculated_offset = 60 * ((second_offset + 30) // 60)
-        return calculated_offset
-
-
-try:
-    # Python 3.7 feature
-    from contextlib import nullcontext as _nullcontext
-except ImportError:
-    class _nullcontext(object):
-        """
-        Class for wrapping contexts so that they are passed through in a
-        with statement.
-        """
-        def __init__(self, context):
-            self.context = context
-
-        def __enter__(self):
-            return self.context
-
-        def __exit__(*args, **kwargs):
-            pass
-
-# vim:ts=4:sw=4:et
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/builder.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/builder.py
deleted file mode 100644
index 2a02c20045904915b50bdd9f1ef3644e3db5258f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/builder.py
+++ /dev/null
@@ -1,2916 +0,0 @@
-from collections import namedtuple, OrderedDict
-import os
-from fontTools.misc.fixedTools import fixedToFloat
-from fontTools import ttLib
-from fontTools.ttLib.tables import otTables as ot
-from fontTools.ttLib.tables.otBase import (
-    ValueRecord,
-    valueRecordFormatDict,
-    OTTableWriter,
-    CountReference,
-)
-from fontTools.ttLib.tables import otBase
-from fontTools.feaLib.ast import STATNameStatement
-from fontTools.otlLib.optimize.gpos import (
-    _compression_level_from_env,
-    compact_lookup,
-)
-from fontTools.otlLib.error import OpenTypeLibError
-from functools import reduce
-import logging
-import copy
-
-
-log = logging.getLogger(__name__)
-
-
-def buildCoverage(glyphs, glyphMap):
-    """Builds a coverage table.
-
-    Coverage tables (as defined in the `OpenType spec <https://docs.microsoft.com/en-gb/typography/opentype/spec/chapter2#coverage-table>`__)
-    are used in all OpenType Layout lookups apart from the Extension type, and
-    define the glyphs involved in a layout subtable. This allows shaping engines
-    to compare the glyph stream with the coverage table and quickly determine
-    whether a subtable should be involved in a shaping operation.
-
-    This function takes a list of glyphs and a glyphname-to-ID map, and
-    returns a ``Coverage`` object representing the coverage table.
-
-    Example::
-
-        glyphMap = font.getReverseGlyphMap()
-        glyphs = [ "A", "B", "C" ]
-        coverage = buildCoverage(glyphs, glyphMap)
-
-    Args:
-        glyphs: a sequence of glyph names.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.Coverage`` object or ``None`` if there are no glyphs
-        supplied.
-    """
-
-    if not glyphs:
-        return None
-    self = ot.Coverage()
-    self.glyphs = sorted(set(glyphs), key=glyphMap.__getitem__)
-    return self
-
-
-LOOKUP_FLAG_RIGHT_TO_LEFT = 0x0001
-LOOKUP_FLAG_IGNORE_BASE_GLYPHS = 0x0002
-LOOKUP_FLAG_IGNORE_LIGATURES = 0x0004
-LOOKUP_FLAG_IGNORE_MARKS = 0x0008
-LOOKUP_FLAG_USE_MARK_FILTERING_SET = 0x0010
-
-
-def buildLookup(subtables, flags=0, markFilterSet=None):
-    """Turns a collection of rules into a lookup.
-
-    A Lookup (as defined in the `OpenType Spec <https://docs.microsoft.com/en-gb/typography/opentype/spec/chapter2#lookupTbl>`__)
-    wraps the individual rules in a layout operation (substitution or
-    positioning) in a data structure expressing their overall lookup type -
-    for example, single substitution, mark-to-base attachment, and so on -
-    as well as the lookup flags and any mark filtering sets. You may import
-    the following constants to express lookup flags:
-
-    - ``LOOKUP_FLAG_RIGHT_TO_LEFT``
-    - ``LOOKUP_FLAG_IGNORE_BASE_GLYPHS``
-    - ``LOOKUP_FLAG_IGNORE_LIGATURES``
-    - ``LOOKUP_FLAG_IGNORE_MARKS``
-    - ``LOOKUP_FLAG_USE_MARK_FILTERING_SET``
-
-    Args:
-        subtables: A list of layout subtable objects (e.g.
-            ``MultipleSubst``, ``PairPos``, etc.) or ``None``.
-        flags (int): This lookup's flags.
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-
-    Returns:
-        An ``otTables.Lookup`` object or ``None`` if there are no subtables
-        supplied.
-    """
-    if subtables is None:
-        return None
-    subtables = [st for st in subtables if st is not None]
-    if not subtables:
-        return None
-    assert all(
-        t.LookupType == subtables[0].LookupType for t in subtables
-    ), "all subtables must have the same LookupType; got %s" % repr(
-        [t.LookupType for t in subtables]
-    )
-    self = ot.Lookup()
-    self.LookupType = subtables[0].LookupType
-    self.LookupFlag = flags
-    self.SubTable = subtables
-    self.SubTableCount = len(self.SubTable)
-    if markFilterSet is not None:
-        self.LookupFlag |= LOOKUP_FLAG_USE_MARK_FILTERING_SET
-        assert isinstance(markFilterSet, int), markFilterSet
-        self.MarkFilteringSet = markFilterSet
-    else:
-        assert (self.LookupFlag & LOOKUP_FLAG_USE_MARK_FILTERING_SET) == 0, (
-            "if markFilterSet is None, flags must not set "
-            "LOOKUP_FLAG_USE_MARK_FILTERING_SET; flags=0x%04x" % flags
-        )
-    return self
-
-
-class LookupBuilder(object):
-    SUBTABLE_BREAK_ = "SUBTABLE_BREAK"
-
-    def __init__(self, font, location, table, lookup_type):
-        self.font = font
-        self.glyphMap = font.getReverseGlyphMap()
-        self.location = location
-        self.table, self.lookup_type = table, lookup_type
-        self.lookupflag = 0
-        self.markFilterSet = None
-        self.lookup_index = None  # assigned when making final tables
-        assert table in ("GPOS", "GSUB")
-
-    def equals(self, other):
-        return (
-            isinstance(other, self.__class__)
-            and self.table == other.table
-            and self.lookupflag == other.lookupflag
-            and self.markFilterSet == other.markFilterSet
-        )
-
-    def inferGlyphClasses(self):
-        """Infers glyph glasses for the GDEF table, such as {"cedilla":3}."""
-        return {}
-
-    def getAlternateGlyphs(self):
-        """Helper for building 'aalt' features."""
-        return {}
-
-    def buildLookup_(self, subtables):
-        return buildLookup(subtables, self.lookupflag, self.markFilterSet)
-
-    def buildMarkClasses_(self, marks):
-        """{"cedilla": ("BOTTOM", ast.Anchor), ...} --> {"BOTTOM":0, "TOP":1}
-
-        Helper for MarkBasePostBuilder, MarkLigPosBuilder, and
-        MarkMarkPosBuilder. Seems to return the same numeric IDs
-        for mark classes as the AFDKO makeotf tool.
-        """
-        ids = {}
-        for mark in sorted(marks.keys(), key=self.font.getGlyphID):
-            markClassName, _markAnchor = marks[mark]
-            if markClassName not in ids:
-                ids[markClassName] = len(ids)
-        return ids
-
-    def setBacktrackCoverage_(self, prefix, subtable):
-        subtable.BacktrackGlyphCount = len(prefix)
-        subtable.BacktrackCoverage = []
-        for p in reversed(prefix):
-            coverage = buildCoverage(p, self.glyphMap)
-            subtable.BacktrackCoverage.append(coverage)
-
-    def setLookAheadCoverage_(self, suffix, subtable):
-        subtable.LookAheadGlyphCount = len(suffix)
-        subtable.LookAheadCoverage = []
-        for s in suffix:
-            coverage = buildCoverage(s, self.glyphMap)
-            subtable.LookAheadCoverage.append(coverage)
-
-    def setInputCoverage_(self, glyphs, subtable):
-        subtable.InputGlyphCount = len(glyphs)
-        subtable.InputCoverage = []
-        for g in glyphs:
-            coverage = buildCoverage(g, self.glyphMap)
-            subtable.InputCoverage.append(coverage)
-
-    def setCoverage_(self, glyphs, subtable):
-        subtable.GlyphCount = len(glyphs)
-        subtable.Coverage = []
-        for g in glyphs:
-            coverage = buildCoverage(g, self.glyphMap)
-            subtable.Coverage.append(coverage)
-
-    def build_subst_subtables(self, mapping, klass):
-        substitutions = [{}]
-        for key in mapping:
-            if key[0] == self.SUBTABLE_BREAK_:
-                substitutions.append({})
-            else:
-                substitutions[-1][key] = mapping[key]
-        subtables = [klass(s) for s in substitutions]
-        return subtables
-
-    def add_subtable_break(self, location):
-        """Add an explicit subtable break.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this break, or ``None`` if
-                no location is provided.
-        """
-        log.warning(
-            OpenTypeLibError(
-                'unsupported "subtable" statement for lookup type', location
-            )
-        )
-
-
-class AlternateSubstBuilder(LookupBuilder):
-    """Builds an Alternate Substitution (GSUB3) lookup.
-
-    Users are expected to manually add alternate glyph substitutions to
-    the ``alternates`` attribute after the object has been initialized,
-    e.g.::
-
-        builder.alternates["A"] = ["A.alt1", "A.alt2"]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        alternates: An ordered dictionary of alternates, mapping glyph names
-            to a list of names of alternates.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 3)
-        self.alternates = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.alternates == other.alternates
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the alternate
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(
-            self.alternates, buildAlternateSubstSubtable
-        )
-        return self.buildLookup_(subtables)
-
-    def getAlternateGlyphs(self):
-        return self.alternates
-
-    def add_subtable_break(self, location):
-        self.alternates[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class ChainContextualRule(
-    namedtuple("ChainContextualRule", ["prefix", "glyphs", "suffix", "lookups"])
-):
-    @property
-    def is_subtable_break(self):
-        return self.prefix == LookupBuilder.SUBTABLE_BREAK_
-
-
-class ChainContextualRuleset:
-    def __init__(self):
-        self.rules = []
-
-    def addRule(self, rule):
-        self.rules.append(rule)
-
-    @property
-    def hasPrefixOrSuffix(self):
-        # Do we have any prefixes/suffixes? If this is False for all
-        # rulesets, we can express the whole lookup as GPOS5/GSUB7.
-        for rule in self.rules:
-            if len(rule.prefix) > 0 or len(rule.suffix) > 0:
-                return True
-        return False
-
-    @property
-    def hasAnyGlyphClasses(self):
-        # Do we use glyph classes anywhere in the rules? If this is False
-        # we can express this subtable as a Format 1.
-        for rule in self.rules:
-            for coverage in (rule.prefix, rule.glyphs, rule.suffix):
-                if any(len(x) > 1 for x in coverage):
-                    return True
-        return False
-
-    def format2ClassDefs(self):
-        PREFIX, GLYPHS, SUFFIX = 0, 1, 2
-        classDefBuilders = []
-        for ix in [PREFIX, GLYPHS, SUFFIX]:
-            context = []
-            for r in self.rules:
-                context.append(r[ix])
-            classes = self._classBuilderForContext(context)
-            if not classes:
-                return None
-            classDefBuilders.append(classes)
-        return classDefBuilders
-
-    def _classBuilderForContext(self, context):
-        classdefbuilder = ClassDefBuilder(useClass0=False)
-        for position in context:
-            for glyphset in position:
-                glyphs = set(glyphset)
-                if not classdefbuilder.canAdd(glyphs):
-                    return None
-                classdefbuilder.add(glyphs)
-        return classdefbuilder
-
-
-class ChainContextualBuilder(LookupBuilder):
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.rules == other.rules
-
-    def rulesets(self):
-        # Return a list of ChainContextRuleset objects, taking explicit
-        # subtable breaks into account
-        ruleset = [ChainContextualRuleset()]
-        for rule in self.rules:
-            if rule.is_subtable_break:
-                ruleset.append(ChainContextualRuleset())
-                continue
-            ruleset[-1].addRule(rule)
-        # Squish any empty subtables
-        return [x for x in ruleset if len(x.rules) > 0]
-
-    def getCompiledSize_(self, subtables):
-        size = 0
-        for st in subtables:
-            w = OTTableWriter()
-            w["LookupType"] = CountReference(
-                {"LookupType": st.LookupType}, "LookupType"
-            )
-            # We need to make a copy here because compiling
-            # modifies the subtable (finalizing formats etc.)
-            copy.deepcopy(st).compile(w, self.font)
-            size += len(w.getAllData())
-        return size
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the chained
-            contextual positioning lookup.
-        """
-        subtables = []
-
-        rulesets = self.rulesets()
-        chaining = any(ruleset.hasPrefixOrSuffix for ruleset in rulesets)
-
-        # https://github.com/fonttools/fonttools/issues/2539
-        #
-        # Unfortunately, as of 2022-03-07, Apple's CoreText renderer does not
-        # correctly process GPOS7 lookups, so for now we force contextual
-        # positioning lookups to be chaining (GPOS8).
-        #
-        # This seems to be fixed as of macOS 13.2, but we keep disabling this
-        # for now until we are no longer concerned about old macOS versions.
-        # But we allow people to opt-out of this with the config key below.
-        write_gpos7 = self.font.cfg.get("fontTools.otlLib.builder:WRITE_GPOS7")
-        # horrible separation of concerns breach
-        if not write_gpos7 and self.subtable_type == "Pos":
-            chaining = True
-
-        for ruleset in rulesets:
-            # Determine format strategy. We try to build formats 1, 2 and 3
-            # subtables and then work out which is best. candidates list holds
-            # the subtables in each format for this ruleset (including a dummy
-            # "format 0" to make the addressing match the format numbers).
-
-            # We can always build a format 3 lookup by accumulating each of
-            # the rules into a list, so start with that.
-            candidates = [None, None, None, []]
-            for rule in ruleset.rules:
-                candidates[3].append(self.buildFormat3Subtable(rule, chaining))
-
-            # Can we express the whole ruleset as a format 2 subtable?
-            classdefs = ruleset.format2ClassDefs()
-            if classdefs:
-                candidates[2] = [
-                    self.buildFormat2Subtable(ruleset, classdefs, chaining)
-                ]
-
-            if not ruleset.hasAnyGlyphClasses:
-                candidates[1] = [self.buildFormat1Subtable(ruleset, chaining)]
-
-            for i in [1, 2, 3]:
-                if candidates[i]:
-                    try:
-                        self.getCompiledSize_(candidates[i])
-                    except Exception as e:
-                        log.warning(
-                            "Contextual format %i at %s overflowed (%s)"
-                            % (i, str(self.location), e)
-                        )
-                        candidates[i] = None
-
-            candidates = [x for x in candidates if x is not None]
-            if not candidates:
-                raise OpenTypeLibError("All candidates overflowed", self.location)
-
-            winner = min(candidates, key=self.getCompiledSize_)
-            subtables.extend(winner)
-
-        # If we are not chaining, lookup type will be automatically fixed by
-        # buildLookup_
-        return self.buildLookup_(subtables)
-
-    def buildFormat1Subtable(self, ruleset, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 1
-        st.populateDefaults()
-        coverage = set()
-        rulesetsByFirstGlyph = {}
-        ruleAttr = self.ruleAttr_(format=1, chaining=chaining)
-
-        for rule in ruleset.rules:
-            ruleAsSubtable = self.newRule_(format=1, chaining=chaining)
-
-            if chaining:
-                ruleAsSubtable.BacktrackGlyphCount = len(rule.prefix)
-                ruleAsSubtable.LookAheadGlyphCount = len(rule.suffix)
-                ruleAsSubtable.Backtrack = [list(x)[0] for x in reversed(rule.prefix)]
-                ruleAsSubtable.LookAhead = [list(x)[0] for x in rule.suffix]
-
-                ruleAsSubtable.InputGlyphCount = len(rule.glyphs)
-            else:
-                ruleAsSubtable.GlyphCount = len(rule.glyphs)
-
-            ruleAsSubtable.Input = [list(x)[0] for x in rule.glyphs[1:]]
-
-            self.buildLookupList(rule, ruleAsSubtable)
-
-            firstGlyph = list(rule.glyphs[0])[0]
-            if firstGlyph not in rulesetsByFirstGlyph:
-                coverage.add(firstGlyph)
-                rulesetsByFirstGlyph[firstGlyph] = []
-            rulesetsByFirstGlyph[firstGlyph].append(ruleAsSubtable)
-
-        st.Coverage = buildCoverage(coverage, self.glyphMap)
-        ruleSets = []
-        for g in st.Coverage.glyphs:
-            ruleSet = self.newRuleSet_(format=1, chaining=chaining)
-            setattr(ruleSet, ruleAttr, rulesetsByFirstGlyph[g])
-            setattr(ruleSet, f"{ruleAttr}Count", len(rulesetsByFirstGlyph[g]))
-            ruleSets.append(ruleSet)
-
-        setattr(st, self.ruleSetAttr_(format=1, chaining=chaining), ruleSets)
-        setattr(
-            st, self.ruleSetAttr_(format=1, chaining=chaining) + "Count", len(ruleSets)
-        )
-
-        return st
-
-    def buildFormat2Subtable(self, ruleset, classdefs, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 2
-        st.populateDefaults()
-
-        if chaining:
-            (
-                st.BacktrackClassDef,
-                st.InputClassDef,
-                st.LookAheadClassDef,
-            ) = [c.build() for c in classdefs]
-        else:
-            st.ClassDef = classdefs[1].build()
-
-        inClasses = classdefs[1].classes()
-
-        classSets = []
-        for _ in inClasses:
-            classSet = self.newRuleSet_(format=2, chaining=chaining)
-            classSets.append(classSet)
-
-        coverage = set()
-        classRuleAttr = self.ruleAttr_(format=2, chaining=chaining)
-
-        for rule in ruleset.rules:
-            ruleAsSubtable = self.newRule_(format=2, chaining=chaining)
-            if chaining:
-                ruleAsSubtable.BacktrackGlyphCount = len(rule.prefix)
-                ruleAsSubtable.LookAheadGlyphCount = len(rule.suffix)
-                # The glyphs in the rule may be list, tuple, odict_keys...
-                # Order is not important anyway because they are guaranteed
-                # to be members of the same class.
-                ruleAsSubtable.Backtrack = [
-                    st.BacktrackClassDef.classDefs[list(x)[0]]
-                    for x in reversed(rule.prefix)
-                ]
-                ruleAsSubtable.LookAhead = [
-                    st.LookAheadClassDef.classDefs[list(x)[0]] for x in rule.suffix
-                ]
-
-                ruleAsSubtable.InputGlyphCount = len(rule.glyphs)
-                ruleAsSubtable.Input = [
-                    st.InputClassDef.classDefs[list(x)[0]] for x in rule.glyphs[1:]
-                ]
-                setForThisRule = classSets[
-                    st.InputClassDef.classDefs[list(rule.glyphs[0])[0]]
-                ]
-            else:
-                ruleAsSubtable.GlyphCount = len(rule.glyphs)
-                ruleAsSubtable.Class = [  # The spec calls this InputSequence
-                    st.ClassDef.classDefs[list(x)[0]] for x in rule.glyphs[1:]
-                ]
-                setForThisRule = classSets[
-                    st.ClassDef.classDefs[list(rule.glyphs[0])[0]]
-                ]
-
-            self.buildLookupList(rule, ruleAsSubtable)
-            coverage |= set(rule.glyphs[0])
-
-            getattr(setForThisRule, classRuleAttr).append(ruleAsSubtable)
-            setattr(
-                setForThisRule,
-                f"{classRuleAttr}Count",
-                getattr(setForThisRule, f"{classRuleAttr}Count") + 1,
-            )
-        setattr(st, self.ruleSetAttr_(format=2, chaining=chaining), classSets)
-        setattr(
-            st, self.ruleSetAttr_(format=2, chaining=chaining) + "Count", len(classSets)
-        )
-        st.Coverage = buildCoverage(coverage, self.glyphMap)
-        return st
-
-    def buildFormat3Subtable(self, rule, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 3
-        if chaining:
-            self.setBacktrackCoverage_(rule.prefix, st)
-            self.setLookAheadCoverage_(rule.suffix, st)
-            self.setInputCoverage_(rule.glyphs, st)
-        else:
-            self.setCoverage_(rule.glyphs, st)
-        self.buildLookupList(rule, st)
-        return st
-
-    def buildLookupList(self, rule, st):
-        for sequenceIndex, lookupList in enumerate(rule.lookups):
-            if lookupList is not None:
-                if not isinstance(lookupList, list):
-                    # Can happen with synthesised lookups
-                    lookupList = [lookupList]
-                for l in lookupList:
-                    if l.lookup_index is None:
-                        if isinstance(self, ChainContextPosBuilder):
-                            other = "substitution"
-                        else:
-                            other = "positioning"
-                        raise OpenTypeLibError(
-                            "Missing index of the specified "
-                            f"lookup, might be a {other} lookup",
-                            self.location,
-                        )
-                    rec = self.newLookupRecord_(st)
-                    rec.SequenceIndex = sequenceIndex
-                    rec.LookupListIndex = l.lookup_index
-
-    def add_subtable_break(self, location):
-        self.rules.append(
-            ChainContextualRule(
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                [self.SUBTABLE_BREAK_],
-            )
-        )
-
-    def newSubtable_(self, chaining=True):
-        subtablename = f"Context{self.subtable_type}"
-        if chaining:
-            subtablename = "Chain" + subtablename
-        st = getattr(ot, subtablename)()  # ot.ChainContextPos()/ot.ChainSubst()/etc.
-        setattr(st, f"{self.subtable_type}Count", 0)
-        setattr(st, f"{self.subtable_type}LookupRecord", [])
-        return st
-
-    # Format 1 and format 2 GSUB5/GSUB6/GPOS7/GPOS8 rulesets and rules form a family:
-    #
-    #       format 1 ruleset      format 1 rule      format 2 ruleset      format 2 rule
-    # GSUB5 SubRuleSet            SubRule            SubClassSet           SubClassRule
-    # GSUB6 ChainSubRuleSet       ChainSubRule       ChainSubClassSet      ChainSubClassRule
-    # GPOS7 PosRuleSet            PosRule            PosClassSet           PosClassRule
-    # GPOS8 ChainPosRuleSet       ChainPosRule       ChainPosClassSet      ChainPosClassRule
-    #
-    # The following functions generate the attribute names and subtables according
-    # to this naming convention.
-    def ruleSetAttr_(self, format=1, chaining=True):
-        if format == 1:
-            formatType = "Rule"
-        elif format == 2:
-            formatType = "Class"
-        else:
-            raise AssertionError(formatType)
-        subtablename = f"{self.subtable_type[0:3]}{formatType}Set"  # Sub, not Subst.
-        if chaining:
-            subtablename = "Chain" + subtablename
-        return subtablename
-
-    def ruleAttr_(self, format=1, chaining=True):
-        if format == 1:
-            formatType = ""
-        elif format == 2:
-            formatType = "Class"
-        else:
-            raise AssertionError(formatType)
-        subtablename = f"{self.subtable_type[0:3]}{formatType}Rule"  # Sub, not Subst.
-        if chaining:
-            subtablename = "Chain" + subtablename
-        return subtablename
-
-    def newRuleSet_(self, format=1, chaining=True):
-        st = getattr(
-            ot, self.ruleSetAttr_(format, chaining)
-        )()  # ot.ChainPosRuleSet()/ot.SubRuleSet()/etc.
-        st.populateDefaults()
-        return st
-
-    def newRule_(self, format=1, chaining=True):
-        st = getattr(
-            ot, self.ruleAttr_(format, chaining)
-        )()  # ot.ChainPosClassRule()/ot.SubClassRule()/etc.
-        st.populateDefaults()
-        return st
-
-    def attachSubtableWithCount_(
-        self, st, subtable_name, count_name, existing=None, index=None, chaining=False
-    ):
-        if chaining:
-            subtable_name = "Chain" + subtable_name
-            count_name = "Chain" + count_name
-
-        if not hasattr(st, count_name):
-            setattr(st, count_name, 0)
-            setattr(st, subtable_name, [])
-
-        if existing:
-            new_subtable = existing
-        else:
-            # Create a new, empty subtable from otTables
-            new_subtable = getattr(ot, subtable_name)()
-
-        setattr(st, count_name, getattr(st, count_name) + 1)
-
-        if index:
-            getattr(st, subtable_name).insert(index, new_subtable)
-        else:
-            getattr(st, subtable_name).append(new_subtable)
-
-        return new_subtable
-
-    def newLookupRecord_(self, st):
-        return self.attachSubtableWithCount_(
-            st,
-            f"{self.subtable_type}LookupRecord",
-            f"{self.subtable_type}Count",
-            chaining=False,
-        )  # Oddly, it isn't ChainSubstLookupRecord
-
-
-class ChainContextPosBuilder(ChainContextualBuilder):
-    """Builds a Chained Contextual Positioning (GPOS8) lookup.
-
-    Users are expected to manually add rules to the ``rules`` attribute after
-    the object has been initialized, e.g.::
-
-        # pos [A B] [C D] x' lookup lu1 y' z' lookup lu2 E;
-
-        prefix  = [ ["A", "B"], ["C", "D"] ]
-        suffix  = [ ["E"] ]
-        glyphs  = [ ["x"], ["y"], ["z"] ]
-        lookups = [ [lu1], None,  [lu2] ]
-        builder.rules.append( (prefix, glyphs, suffix, lookups) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        rules: A list of tuples representing the rules in this lookup.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 8)
-        self.rules = []
-        self.subtable_type = "Pos"
-
-    def find_chainable_single_pos(self, lookups, glyphs, value):
-        """Helper for add_single_pos_chained_()"""
-        res = None
-        for lookup in lookups[::-1]:
-            if lookup == self.SUBTABLE_BREAK_:
-                return res
-            if isinstance(lookup, SinglePosBuilder) and all(
-                lookup.can_add(glyph, value) for glyph in glyphs
-            ):
-                res = lookup
-        return res
-
-
-class ChainContextSubstBuilder(ChainContextualBuilder):
-    """Builds a Chained Contextual Substitution (GSUB6) lookup.
-
-    Users are expected to manually add rules to the ``rules`` attribute after
-    the object has been initialized, e.g.::
-
-        # sub [A B] [C D] x' lookup lu1 y' z' lookup lu2 E;
-
-        prefix  = [ ["A", "B"], ["C", "D"] ]
-        suffix  = [ ["E"] ]
-        glyphs  = [ ["x"], ["y"], ["z"] ]
-        lookups = [ [lu1], None,  [lu2] ]
-        builder.rules.append( (prefix, glyphs, suffix, lookups) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        rules: A list of tuples representing the rules in this lookup.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 6)
-        self.rules = []  # (prefix, input, suffix, lookups)
-        self.subtable_type = "Subst"
-
-    def getAlternateGlyphs(self):
-        result = {}
-        for rule in self.rules:
-            if rule.is_subtable_break:
-                continue
-            for lookups in rule.lookups:
-                if not isinstance(lookups, list):
-                    lookups = [lookups]
-                for lookup in lookups:
-                    if lookup is not None:
-                        alts = lookup.getAlternateGlyphs()
-                        for glyph, replacements in alts.items():
-                            result.setdefault(glyph, set()).update(replacements)
-        return result
-
-    def find_chainable_single_subst(self, mapping):
-        """Helper for add_single_subst_chained_()"""
-        res = None
-        for rule in self.rules[::-1]:
-            if rule.is_subtable_break:
-                return res
-            for sub in rule.lookups:
-                if isinstance(sub, SingleSubstBuilder) and not any(
-                    g in mapping and mapping[g] != sub.mapping[g] for g in sub.mapping
-                ):
-                    res = sub
-        return res
-
-
-class LigatureSubstBuilder(LookupBuilder):
-    """Builds a Ligature Substitution (GSUB4) lookup.
-
-    Users are expected to manually add ligatures to the ``ligatures``
-    attribute after the object has been initialized, e.g.::
-
-        # sub f i by f_i;
-        builder.ligatures[("f","f","i")] = "f_f_i"
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        ligatures: An ordered dictionary mapping a tuple of glyph names to the
-            ligature glyphname.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 4)
-        self.ligatures = OrderedDict()  # {('f','f','i'): 'f_f_i'}
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.ligatures == other.ligatures
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the ligature
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(
-            self.ligatures, buildLigatureSubstSubtable
-        )
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        self.ligatures[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class MultipleSubstBuilder(LookupBuilder):
-    """Builds a Multiple Substitution (GSUB2) lookup.
-
-    Users are expected to manually add substitutions to the ``mapping``
-    attribute after the object has been initialized, e.g.::
-
-        # sub uni06C0 by uni06D5.fina hamza.above;
-        builder.mapping["uni06C0"] = [ "uni06D5.fina", "hamza.above"]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: An ordered dictionary mapping a glyph name to a list of
-            substituted glyph names.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 2)
-        self.mapping = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        subtables = self.build_subst_subtables(self.mapping, buildMultipleSubstSubtable)
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        self.mapping[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class CursivePosBuilder(LookupBuilder):
-    """Builds a Cursive Positioning (GPOS3) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        attachments: An ordered dictionary mapping a glyph name to a two-element
-            tuple of ``otTables.Anchor`` objects.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 3)
-        self.attachments = {}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other) and self.attachments == other.attachments
-        )
-
-    def add_attachment(self, location, glyphs, entryAnchor, exitAnchor):
-        """Adds attachment information to the cursive positioning lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this lookup. (Unused.)
-            glyphs: A list of glyph names sharing these entry and exit
-                anchor locations.
-            entryAnchor: A ``otTables.Anchor`` object representing the
-                entry anchor, or ``None`` if no entry anchor is present.
-            exitAnchor: A ``otTables.Anchor`` object representing the
-                exit anchor, or ``None`` if no exit anchor is present.
-        """
-        for glyph in glyphs:
-            self.attachments[glyph] = (entryAnchor, exitAnchor)
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the cursive
-            positioning lookup.
-        """
-        st = buildCursivePosSubtable(self.attachments, self.glyphMap)
-        return self.buildLookup_([st])
-
-
-class MarkBasePosBuilder(LookupBuilder):
-    """Builds a Mark-To-Base Positioning (GPOS4) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``bases`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]   = (0, a1)
-        builder.marks["grave"]   = (0, a1)
-        builder.marks["cedilla"] = (1, a2)
-        builder.bases["a"] = {0: a3, 1: a5}
-        builder.bases["b"] = {0: a4, 1: a5}
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        bases: An dictionary mapping a glyph name to a dictionary of
-            mark class IDs and ``otTables.Anchor`` object.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 4)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.bases = {}  # glyphName -> {markClassName: anchor}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.bases == other.bases
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 1 for glyph in self.bases}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-base
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        marks = {}
-        for mark, (mc, anchor) in self.marks.items():
-            if mc not in markClasses:
-                raise ValueError(
-                    "Mark class %s not found for mark glyph %s" % (mc, mark)
-                )
-            marks[mark] = (markClasses[mc], anchor)
-        bases = {}
-        for glyph, anchors in self.bases.items():
-            bases[glyph] = {}
-            for mc, anchor in anchors.items():
-                if mc not in markClasses:
-                    raise ValueError(
-                        "Mark class %s not found for base glyph %s" % (mc, glyph)
-                    )
-                bases[glyph][markClasses[mc]] = anchor
-        subtables = buildMarkBasePos(marks, bases, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-class MarkLigPosBuilder(LookupBuilder):
-    """Builds a Mark-To-Ligature Positioning (GPOS5) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``ligatures`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]   = (0, a1)
-        builder.marks["grave"]   = (0, a1)
-        builder.marks["cedilla"] = (1, a2)
-        builder.ligatures["f_i"] = [
-            { 0: a3, 1: a5 }, # f
-            { 0: a4, 1: a5 }  # i
-        ]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        ligatures: An dictionary mapping a glyph name to an array with one
-            element for each ligature component. Each array element should be
-            a dictionary mapping mark class IDs to ``otTables.Anchor`` objects.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 5)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.ligatures = {}  # glyphName -> [{markClassName: anchor}, ...]
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.ligatures == other.ligatures
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 2 for glyph in self.ligatures}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-ligature
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        marks = {
-            mark: (markClasses[mc], anchor) for mark, (mc, anchor) in self.marks.items()
-        }
-        ligs = {}
-        for lig, components in self.ligatures.items():
-            ligs[lig] = []
-            for c in components:
-                ligs[lig].append({markClasses[mc]: a for mc, a in c.items()})
-        subtables = buildMarkLigPos(marks, ligs, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-class MarkMarkPosBuilder(LookupBuilder):
-    """Builds a Mark-To-Mark Positioning (GPOS6) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``baseMarks`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]     = (0, a1)
-        builder.marks["grave"]     = (0, a1)
-        builder.marks["cedilla"]   = (1, a2)
-        builder.baseMarks["acute"] = {0: a3}
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        baseMarks: An dictionary mapping a glyph name to a dictionary
-            containing one item: a mark class ID and a ``otTables.Anchor`` object.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 6)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.baseMarks = {}  # glyphName -> {markClassName: anchor}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.baseMarks == other.baseMarks
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 3 for glyph in self.baseMarks}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-mark
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        markClassList = sorted(markClasses.keys(), key=markClasses.get)
-        marks = {
-            mark: (markClasses[mc], anchor) for mark, (mc, anchor) in self.marks.items()
-        }
-
-        st = ot.MarkMarkPos()
-        st.Format = 1
-        st.ClassCount = len(markClasses)
-        st.Mark1Coverage = buildCoverage(marks, self.glyphMap)
-        st.Mark2Coverage = buildCoverage(self.baseMarks, self.glyphMap)
-        st.Mark1Array = buildMarkArray(marks, self.glyphMap)
-        st.Mark2Array = ot.Mark2Array()
-        st.Mark2Array.Mark2Count = len(st.Mark2Coverage.glyphs)
-        st.Mark2Array.Mark2Record = []
-        for base in st.Mark2Coverage.glyphs:
-            anchors = [self.baseMarks[base].get(mc) for mc in markClassList]
-            st.Mark2Array.Mark2Record.append(buildMark2Record(anchors))
-        return self.buildLookup_([st])
-
-
-class ReverseChainSingleSubstBuilder(LookupBuilder):
-    """Builds a Reverse Chaining Contextual Single Substitution (GSUB8) lookup.
-
-    Users are expected to manually add substitutions to the ``substitutions``
-    attribute after the object has been initialized, e.g.::
-
-        # reversesub [a e n] d' by d.alt;
-        prefix = [ ["a", "e", "n"] ]
-        suffix = []
-        mapping = { "d": "d.alt" }
-        builder.substitutions.append( (prefix, suffix, mapping) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        substitutions: A three-element tuple consisting of a prefix sequence,
-            a suffix sequence, and a dictionary of single substitutions.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 8)
-        self.rules = []  # (prefix, suffix, mapping)
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.rules == other.rules
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the chained
-            contextual substitution lookup.
-        """
-        subtables = []
-        for prefix, suffix, mapping in self.rules:
-            st = ot.ReverseChainSingleSubst()
-            st.Format = 1
-            self.setBacktrackCoverage_(prefix, st)
-            self.setLookAheadCoverage_(suffix, st)
-            st.Coverage = buildCoverage(mapping.keys(), self.glyphMap)
-            st.GlyphCount = len(mapping)
-            st.Substitute = [mapping[g] for g in st.Coverage.glyphs]
-            subtables.append(st)
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        # Nothing to do here, each substitution is in its own subtable.
-        pass
-
-
-class SingleSubstBuilder(LookupBuilder):
-    """Builds a Single Substitution (GSUB1) lookup.
-
-    Users are expected to manually add substitutions to the ``mapping``
-    attribute after the object has been initialized, e.g.::
-
-        # sub x by y;
-        builder.mapping["x"] = "y"
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: A dictionary mapping a single glyph name to another glyph name.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 1)
-        self.mapping = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the multiple
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(self.mapping, buildSingleSubstSubtable)
-        return self.buildLookup_(subtables)
-
-    def getAlternateGlyphs(self):
-        return {glyph: set([repl]) for glyph, repl in self.mapping.items()}
-
-    def add_subtable_break(self, location):
-        self.mapping[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class ClassPairPosSubtableBuilder(object):
-    """Builds class-based Pair Positioning (GPOS2 format 2) subtables.
-
-    Note that this does *not* build a GPOS2 ``otTables.Lookup`` directly,
-    but builds a list of ``otTables.PairPos`` subtables. It is used by the
-    :class:`PairPosBuilder` below.
-
-    Attributes:
-        builder (PairPosBuilder): A pair positioning lookup builder.
-    """
-
-    def __init__(self, builder):
-        self.builder_ = builder
-        self.classDef1_, self.classDef2_ = None, None
-        self.values_ = {}  # (glyphclass1, glyphclass2) --> (value1, value2)
-        self.forceSubtableBreak_ = False
-        self.subtables_ = []
-
-    def addPair(self, gc1, value1, gc2, value2):
-        """Add a pair positioning rule.
-
-        Args:
-            gc1: A set of glyph names for the "left" glyph
-            value1: An ``otTables.ValueRecord`` object for the left glyph's
-                positioning.
-            gc2: A set of glyph names for the "right" glyph
-            value2: An ``otTables.ValueRecord`` object for the right glyph's
-                positioning.
-        """
-        mergeable = (
-            not self.forceSubtableBreak_
-            and self.classDef1_ is not None
-            and self.classDef1_.canAdd(gc1)
-            and self.classDef2_ is not None
-            and self.classDef2_.canAdd(gc2)
-        )
-        if not mergeable:
-            self.flush_()
-            self.classDef1_ = ClassDefBuilder(useClass0=True)
-            self.classDef2_ = ClassDefBuilder(useClass0=False)
-            self.values_ = {}
-        self.classDef1_.add(gc1)
-        self.classDef2_.add(gc2)
-        self.values_[(gc1, gc2)] = (value1, value2)
-
-    def addSubtableBreak(self):
-        """Add an explicit subtable break at this point."""
-        self.forceSubtableBreak_ = True
-
-    def subtables(self):
-        """Return the list of ``otTables.PairPos`` subtables constructed."""
-        self.flush_()
-        return self.subtables_
-
-    def flush_(self):
-        if self.classDef1_ is None or self.classDef2_ is None:
-            return
-        st = buildPairPosClassesSubtable(self.values_, self.builder_.glyphMap)
-        if st.Coverage is None:
-            return
-        self.subtables_.append(st)
-        self.forceSubtableBreak_ = False
-
-
-class PairPosBuilder(LookupBuilder):
-    """Builds a Pair Positioning (GPOS2) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        pairs: An array of class-based pair positioning tuples. Usually
-            manipulated with the :meth:`addClassPair` method below.
-        glyphPairs: A dictionary mapping a tuple of glyph names to a tuple
-            of ``otTables.ValueRecord`` objects. Usually manipulated with the
-            :meth:`addGlyphPair` method below.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 2)
-        self.pairs = []  # [(gc1, value1, gc2, value2)*]
-        self.glyphPairs = {}  # (glyph1, glyph2) --> (value1, value2)
-        self.locations = {}  # (gc1, gc2) --> (filepath, line, column)
-
-    def addClassPair(self, location, glyphclass1, value1, glyphclass2, value2):
-        """Add a class pair positioning rule to the current lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this rule. Unused.
-            glyphclass1: A set of glyph names for the "left" glyph in the pair.
-            value1: A ``otTables.ValueRecord`` for positioning the left glyph.
-            glyphclass2: A set of glyph names for the "right" glyph in the pair.
-            value2: A ``otTables.ValueRecord`` for positioning the right glyph.
-        """
-        self.pairs.append((glyphclass1, value1, glyphclass2, value2))
-
-    def addGlyphPair(self, location, glyph1, value1, glyph2, value2):
-        """Add a glyph pair positioning rule to the current lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this rule.
-            glyph1: A glyph name for the "left" glyph in the pair.
-            value1: A ``otTables.ValueRecord`` for positioning the left glyph.
-            glyph2: A glyph name for the "right" glyph in the pair.
-            value2: A ``otTables.ValueRecord`` for positioning the right glyph.
-        """
-        key = (glyph1, glyph2)
-        oldValue = self.glyphPairs.get(key, None)
-        if oldValue is not None:
-            # the Feature File spec explicitly allows specific pairs generated
-            # by an 'enum' rule to be overridden by preceding single pairs
-            otherLoc = self.locations[key]
-            log.debug(
-                "Already defined position for pair %s %s at %s; "
-                "choosing the first value",
-                glyph1,
-                glyph2,
-                otherLoc,
-            )
-        else:
-            self.glyphPairs[key] = (value1, value2)
-            self.locations[key] = location
-
-    def add_subtable_break(self, location):
-        self.pairs.append(
-            (
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-            )
-        )
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.glyphPairs == other.glyphPairs
-            and self.pairs == other.pairs
-        )
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the pair positioning
-            lookup.
-        """
-        builders = {}
-        builder = ClassPairPosSubtableBuilder(self)
-        for glyphclass1, value1, glyphclass2, value2 in self.pairs:
-            if glyphclass1 is self.SUBTABLE_BREAK_:
-                builder.addSubtableBreak()
-                continue
-            builder.addPair(glyphclass1, value1, glyphclass2, value2)
-        subtables = []
-        if self.glyphPairs:
-            subtables.extend(buildPairPosGlyphs(self.glyphPairs, self.glyphMap))
-        subtables.extend(builder.subtables())
-        lookup = self.buildLookup_(subtables)
-
-        # Compact the lookup
-        # This is a good moment to do it because the compaction should create
-        # smaller subtables, which may prevent overflows from happening.
-        # Keep reading the value from the ENV until ufo2ft switches to the config system
-        level = self.font.cfg.get(
-            "fontTools.otlLib.optimize.gpos:COMPRESSION_LEVEL",
-            default=_compression_level_from_env(),
-        )
-        if level != 0:
-            log.info("Compacting GPOS...")
-            compact_lookup(self.font, level, lookup)
-
-        return lookup
-
-
-class SinglePosBuilder(LookupBuilder):
-    """Builds a Single Positioning (GPOS1) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: A dictionary mapping a glyph name to a ``otTables.ValueRecord``
-            objects. Usually manipulated with the :meth:`add_pos` method below.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 1)
-        self.locations = {}  # glyph -> (filename, line, column)
-        self.mapping = {}  # glyph -> ot.ValueRecord
-
-    def add_pos(self, location, glyph, otValueRecord):
-        """Add a single positioning rule.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this lookup.
-            glyph: A glyph name.
-            otValueRection: A ``otTables.ValueRecord`` used to position the
-                glyph.
-        """
-        if not self.can_add(glyph, otValueRecord):
-            otherLoc = self.locations[glyph]
-            raise OpenTypeLibError(
-                'Already defined different position for glyph "%s" at %s'
-                % (glyph, otherLoc),
-                location,
-            )
-        if otValueRecord:
-            self.mapping[glyph] = otValueRecord
-        self.locations[glyph] = location
-
-    def can_add(self, glyph, value):
-        assert isinstance(value, ValueRecord)
-        curValue = self.mapping.get(glyph)
-        return curValue is None or curValue == value
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the single positioning
-            lookup.
-        """
-        subtables = buildSinglePos(self.mapping, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-# GSUB
-
-
-def buildSingleSubstSubtable(mapping):
-    """Builds a single substitution (GSUB1) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SingleSubstBuilder` instead.
-
-    Args:
-        mapping: A dictionary mapping input glyph names to output glyph names.
-
-    Returns:
-        An ``otTables.SingleSubst`` object, or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.SingleSubst()
-    self.mapping = dict(mapping)
-    return self
-
-
-def buildMultipleSubstSubtable(mapping):
-    """Builds a multiple substitution (GSUB2) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MultipleSubstBuilder` instead.
-
-    Example::
-
-        # sub uni06C0 by uni06D5.fina hamza.above
-        # sub uni06C2 by uni06C1.fina hamza.above;
-
-        subtable = buildMultipleSubstSubtable({
-            "uni06C0": [ "uni06D5.fina", "hamza.above"],
-            "uni06C2": [ "uni06D1.fina", "hamza.above"]
-        })
-
-    Args:
-        mapping: A dictionary mapping input glyph names to a list of output
-            glyph names.
-
-    Returns:
-        An ``otTables.MultipleSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.MultipleSubst()
-    self.mapping = dict(mapping)
-    return self
-
-
-def buildAlternateSubstSubtable(mapping):
-    """Builds an alternate substitution (GSUB3) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.AlternateSubstBuilder` instead.
-
-    Args:
-        mapping: A dictionary mapping input glyph names to a list of output
-            glyph names.
-
-    Returns:
-        An ``otTables.AlternateSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.AlternateSubst()
-    self.alternates = dict(mapping)
-    return self
-
-
-def _getLigatureKey(components):
-    # Computes a key for ordering ligatures in a GSUB Type-4 lookup.
-
-    # When building the OpenType lookup, we need to make sure that
-    # the longest sequence of components is listed first, so we
-    # use the negative length as the primary key for sorting.
-    # To make buildLigatureSubstSubtable() deterministic, we use the
-    # component sequence as the secondary key.
-
-    # For example, this will sort (f,f,f) < (f,f,i) < (f,f) < (f,i) < (f,l).
-    return (-len(components), components)
-
-
-def buildLigatureSubstSubtable(mapping):
-    """Builds a ligature substitution (GSUB4) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.LigatureSubstBuilder` instead.
-
-    Example::
-
-        # sub f f i by f_f_i;
-        # sub f i by f_i;
-
-        subtable = buildLigatureSubstSubtable({
-            ("f", "f", "i"): "f_f_i",
-            ("f", "i"): "f_i",
-        })
-
-    Args:
-        mapping: A dictionary mapping tuples of glyph names to output
-            glyph names.
-
-    Returns:
-        An ``otTables.LigatureSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-
-    if not mapping:
-        return None
-    self = ot.LigatureSubst()
-    # The following single line can replace the rest of this function
-    # with fontTools >= 3.1:
-    # self.ligatures = dict(mapping)
-    self.ligatures = {}
-    for components in sorted(mapping.keys(), key=_getLigatureKey):
-        ligature = ot.Ligature()
-        ligature.Component = components[1:]
-        ligature.CompCount = len(ligature.Component) + 1
-        ligature.LigGlyph = mapping[components]
-        firstGlyph = components[0]
-        self.ligatures.setdefault(firstGlyph, []).append(ligature)
-    return self
-
-
-# GPOS
-
-
-def buildAnchor(x, y, point=None, deviceX=None, deviceY=None):
-    """Builds an Anchor table.
-
-    This determines the appropriate anchor format based on the passed parameters.
-
-    Args:
-        x (int): X coordinate.
-        y (int): Y coordinate.
-        point (int): Index of glyph contour point, if provided.
-        deviceX (``otTables.Device``): X coordinate device table, if provided.
-        deviceY (``otTables.Device``): Y coordinate device table, if provided.
-
-    Returns:
-        An ``otTables.Anchor`` object.
-    """
-    self = ot.Anchor()
-    self.XCoordinate, self.YCoordinate = x, y
-    self.Format = 1
-    if point is not None:
-        self.AnchorPoint = point
-        self.Format = 2
-    if deviceX is not None or deviceY is not None:
-        assert (
-            self.Format == 1
-        ), "Either point, or both of deviceX/deviceY, must be None."
-        self.XDeviceTable = deviceX
-        self.YDeviceTable = deviceY
-        self.Format = 3
-    return self
-
-
-def buildBaseArray(bases, numMarkClasses, glyphMap):
-    """Builds a base array record.
-
-    As part of building mark-to-base positioning rules, you will need to define
-    a ``BaseArray`` record, which "defines for each base glyph an array of
-    anchors, one for each mark class." This function builds the base array
-    subtable.
-
-    Example::
-
-        bases = {"a": {0: a3, 1: a5}, "b": {0: a4, 1: a5}}
-        basearray = buildBaseArray(bases, 2, font.getReverseGlyphMap())
-
-    Args:
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class.
-        numMarkClasses (int): The total number of mark classes for which anchors
-            are defined.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.BaseArray`` object.
-    """
-    self = ot.BaseArray()
-    self.BaseRecord = []
-    for base in sorted(bases, key=glyphMap.__getitem__):
-        b = bases[base]
-        anchors = [b.get(markClass) for markClass in range(numMarkClasses)]
-        self.BaseRecord.append(buildBaseRecord(anchors))
-    self.BaseCount = len(self.BaseRecord)
-    return self
-
-
-def buildBaseRecord(anchors):
-    # [otTables.Anchor, otTables.Anchor, ...] --> otTables.BaseRecord
-    self = ot.BaseRecord()
-    self.BaseAnchor = anchors
-    return self
-
-
-def buildComponentRecord(anchors):
-    """Builds a component record.
-
-    As part of building mark-to-ligature positioning rules, you will need to
-    define ``ComponentRecord`` objects, which contain "an array of offsets...
-    to the Anchor tables that define all the attachment points used to attach
-    marks to the component." This function builds the component record.
-
-    Args:
-        anchors: A list of ``otTables.Anchor`` objects or ``None``.
-
-    Returns:
-        A ``otTables.ComponentRecord`` object or ``None`` if no anchors are
-        supplied.
-    """
-    if not anchors:
-        return None
-    self = ot.ComponentRecord()
-    self.LigatureAnchor = anchors
-    return self
-
-
-def buildCursivePosSubtable(attach, glyphMap):
-    """Builds a cursive positioning (GPOS3) subtable.
-
-    Cursive positioning lookups are made up of a coverage table of glyphs,
-    and a set of ``EntryExitRecord`` records containing the anchors for
-    each glyph. This function builds the cursive positioning subtable.
-
-    Example::
-
-        subtable = buildCursivePosSubtable({
-            "AlifIni": (None, buildAnchor(0, 50)),
-            "BehMed": (buildAnchor(500,250), buildAnchor(0,50)),
-            # ...
-        }, font.getReverseGlyphMap())
-
-    Args:
-        attach (dict): A mapping between glyph names and a tuple of two
-            ``otTables.Anchor`` objects representing entry and exit anchors.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.CursivePos`` object, or ``None`` if the attachment
-        dictionary was empty.
-    """
-    if not attach:
-        return None
-    self = ot.CursivePos()
-    self.Format = 1
-    self.Coverage = buildCoverage(attach.keys(), glyphMap)
-    self.EntryExitRecord = []
-    for glyph in self.Coverage.glyphs:
-        entryAnchor, exitAnchor = attach[glyph]
-        rec = ot.EntryExitRecord()
-        rec.EntryAnchor = entryAnchor
-        rec.ExitAnchor = exitAnchor
-        self.EntryExitRecord.append(rec)
-    self.EntryExitCount = len(self.EntryExitRecord)
-    return self
-
-
-def buildDevice(deltas):
-    """Builds a Device record as part of a ValueRecord or Anchor.
-
-    Device tables specify size-specific adjustments to value records
-    and anchors to reflect changes based on the resolution of the output.
-    For example, one could specify that an anchor's Y position should be
-    increased by 1 pixel when displayed at 8 pixels per em. This routine
-    builds device records.
-
-    Args:
-        deltas: A dictionary mapping pixels-per-em sizes to the delta
-            adjustment in pixels when the font is displayed at that size.
-
-    Returns:
-        An ``otTables.Device`` object if any deltas were supplied, or
-        ``None`` otherwise.
-    """
-    if not deltas:
-        return None
-    self = ot.Device()
-    keys = deltas.keys()
-    self.StartSize = startSize = min(keys)
-    self.EndSize = endSize = max(keys)
-    assert 0 <= startSize <= endSize
-    self.DeltaValue = deltaValues = [
-        deltas.get(size, 0) for size in range(startSize, endSize + 1)
-    ]
-    maxDelta = max(deltaValues)
-    minDelta = min(deltaValues)
-    assert minDelta > -129 and maxDelta < 128
-    if minDelta > -3 and maxDelta < 2:
-        self.DeltaFormat = 1
-    elif minDelta > -9 and maxDelta < 8:
-        self.DeltaFormat = 2
-    else:
-        self.DeltaFormat = 3
-    return self
-
-
-def buildLigatureArray(ligs, numMarkClasses, glyphMap):
-    """Builds a LigatureArray subtable.
-
-    As part of building a mark-to-ligature lookup, you will need to define
-    the set of anchors (for each mark class) on each component of the ligature
-    where marks can be attached. For example, for an Arabic divine name ligature
-    (lam lam heh), you may want to specify mark attachment positioning for
-    superior marks (fatha, etc.) and inferior marks (kasra, etc.) on each glyph
-    of the ligature. This routine builds the ligature array record.
-
-    Example::
-
-        buildLigatureArray({
-            "lam-lam-heh": [
-                { 0: superiorAnchor1, 1: inferiorAnchor1 }, # attach points for lam1
-                { 0: superiorAnchor2, 1: inferiorAnchor2 }, # attach points for lam2
-                { 0: superiorAnchor3, 1: inferiorAnchor3 }, # attach points for heh
-            ]
-        }, 2, font.getReverseGlyphMap())
-
-    Args:
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors.
-        numMarkClasses (int): The number of mark classes.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.LigatureArray`` object if deltas were supplied.
-    """
-    self = ot.LigatureArray()
-    self.LigatureAttach = []
-    for lig in sorted(ligs, key=glyphMap.__getitem__):
-        anchors = []
-        for component in ligs[lig]:
-            anchors.append([component.get(mc) for mc in range(numMarkClasses)])
-        self.LigatureAttach.append(buildLigatureAttach(anchors))
-    self.LigatureCount = len(self.LigatureAttach)
-    return self
-
-
-def buildLigatureAttach(components):
-    # [[Anchor, Anchor], [Anchor, Anchor, Anchor]] --> LigatureAttach
-    self = ot.LigatureAttach()
-    self.ComponentRecord = [buildComponentRecord(c) for c in components]
-    self.ComponentCount = len(self.ComponentRecord)
-    return self
-
-
-def buildMarkArray(marks, glyphMap):
-    """Builds a mark array subtable.
-
-    As part of building mark-to-* positioning rules, you will need to define
-    a MarkArray subtable, which "defines the class and the anchor point
-    for a mark glyph." This function builds the mark array subtable.
-
-    Example::
-
-        mark = {
-            "acute": (0, buildAnchor(300,712)),
-            # ...
-        }
-        markarray = buildMarkArray(marks, font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.MarkArray`` object.
-    """
-    self = ot.MarkArray()
-    self.MarkRecord = []
-    for mark in sorted(marks.keys(), key=glyphMap.__getitem__):
-        markClass, anchor = marks[mark]
-        markrec = buildMarkRecord(markClass, anchor)
-        self.MarkRecord.append(markrec)
-    self.MarkCount = len(self.MarkRecord)
-    return self
-
-
-def buildMarkBasePos(marks, bases, glyphMap):
-    """Build a list of MarkBasePos (GPOS4) subtables.
-
-    This routine turns a set of marks and bases into a list of mark-to-base
-    positioning subtables. Currently the list will contain a single subtable
-    containing all marks and bases, although at a later date it may return the
-    optimal list of subtables subsetting the marks and bases into groups which
-    save space. See :func:`buildMarkBasePosSubtable` below.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MarkBasePosBuilder` instead.
-
-    Example::
-
-        # a1, a2, a3, a4, a5 = buildAnchor(500, 100), ...
-
-        marks = {"acute": (0, a1), "grave": (0, a1), "cedilla": (1, a2)}
-        bases = {"a": {0: a3, 1: a5}, "b": {0: a4, 1: a5}}
-        markbaseposes = buildMarkBasePos(marks, bases, font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class. (See :func:`buildBaseArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.MarkBasePos`` objects.
-    """
-    # TODO: Consider emitting multiple subtables to save space.
-    # Partition the marks and bases into disjoint subsets, so that
-    # MarkBasePos rules would only access glyphs from a single
-    # subset. This would likely lead to smaller mark/base
-    # matrices, so we might be able to omit many of the empty
-    # anchor tables that we currently produce. Of course, this
-    # would only work if the MarkBasePos rules of real-world fonts
-    # allow partitioning into multiple subsets. We should find out
-    # whether this is the case; if so, implement the optimization.
-    # On the other hand, a very large number of subtables could
-    # slow down layout engines; so this would need profiling.
-    return [buildMarkBasePosSubtable(marks, bases, glyphMap)]
-
-
-def buildMarkBasePosSubtable(marks, bases, glyphMap):
-    """Build a single MarkBasePos (GPOS4) subtable.
-
-    This builds a mark-to-base lookup subtable containing all of the referenced
-    marks and bases. See :func:`buildMarkBasePos`.
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class. (See :func:`buildBaseArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.MarkBasePos`` object.
-    """
-    self = ot.MarkBasePos()
-    self.Format = 1
-    self.MarkCoverage = buildCoverage(marks, glyphMap)
-    self.MarkArray = buildMarkArray(marks, glyphMap)
-    self.ClassCount = max([mc for mc, _ in marks.values()]) + 1
-    self.BaseCoverage = buildCoverage(bases, glyphMap)
-    self.BaseArray = buildBaseArray(bases, self.ClassCount, glyphMap)
-    return self
-
-
-def buildMarkLigPos(marks, ligs, glyphMap):
-    """Build a list of MarkLigPos (GPOS5) subtables.
-
-    This routine turns a set of marks and ligatures into a list of mark-to-ligature
-    positioning subtables. Currently the list will contain a single subtable
-    containing all marks and ligatures, although at a later date it may return
-    the optimal list of subtables subsetting the marks and ligatures into groups
-    which save space. See :func:`buildMarkLigPosSubtable` below.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MarkLigPosBuilder` instead.
-
-    Example::
-
-        # a1, a2, a3, a4, a5 = buildAnchor(500, 100), ...
-        marks = {
-            "acute": (0, a1),
-            "grave": (0, a1),
-            "cedilla": (1, a2)
-        }
-        ligs = {
-            "f_i": [
-                { 0: a3, 1: a5 }, # f
-                { 0: a4, 1: a5 }  # i
-                ],
-        #   "c_t": [{...}, {...}]
-        }
-        markligposes = buildMarkLigPos(marks, ligs,
-            font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors. (See :func:`buildLigatureArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.MarkLigPos`` objects.
-
-    """
-    # TODO: Consider splitting into multiple subtables to save space,
-    # as with MarkBasePos, this would be a trade-off that would need
-    # profiling. And, depending on how typical fonts are structured,
-    # it might not be worth doing at all.
-    return [buildMarkLigPosSubtable(marks, ligs, glyphMap)]
-
-
-def buildMarkLigPosSubtable(marks, ligs, glyphMap):
-    """Build a single MarkLigPos (GPOS5) subtable.
-
-    This builds a mark-to-base lookup subtable containing all of the referenced
-    marks and bases. See :func:`buildMarkLigPos`.
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors. (See :func:`buildLigatureArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.MarkLigPos`` object.
-    """
-    self = ot.MarkLigPos()
-    self.Format = 1
-    self.MarkCoverage = buildCoverage(marks, glyphMap)
-    self.MarkArray = buildMarkArray(marks, glyphMap)
-    self.ClassCount = max([mc for mc, _ in marks.values()]) + 1
-    self.LigatureCoverage = buildCoverage(ligs, glyphMap)
-    self.LigatureArray = buildLigatureArray(ligs, self.ClassCount, glyphMap)
-    return self
-
-
-def buildMarkRecord(classID, anchor):
-    assert isinstance(classID, int)
-    assert isinstance(anchor, ot.Anchor)
-    self = ot.MarkRecord()
-    self.Class = classID
-    self.MarkAnchor = anchor
-    return self
-
-
-def buildMark2Record(anchors):
-    # [otTables.Anchor, otTables.Anchor, ...] --> otTables.Mark2Record
-    self = ot.Mark2Record()
-    self.Mark2Anchor = anchors
-    return self
-
-
-def _getValueFormat(f, values, i):
-    # Helper for buildPairPos{Glyphs|Classes}Subtable.
-    if f is not None:
-        return f
-    mask = 0
-    for value in values:
-        if value is not None and value[i] is not None:
-            mask |= value[i].getFormat()
-    return mask
-
-
-def buildPairPosClassesSubtable(pairs, glyphMap, valueFormat1=None, valueFormat2=None):
-    """Builds a class pair adjustment (GPOS2 format 2) subtable.
-
-    Kerning tables are generally expressed as pair positioning tables using
-    class-based pair adjustments. This routine builds format 2 PairPos
-    subtables.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.ClassPairPosSubtableBuilder`
-    instead, as this takes care of ensuring that the supplied pairs can be
-    formed into non-overlapping classes and emitting individual subtables
-    whenever the non-overlapping requirement means that a new subtable is
-    required.
-
-    Example::
-
-        pairs = {}
-
-        pairs[(
-            [ "K", "X" ],
-            [ "W", "V" ]
-        )] = ( buildValue(xAdvance=+5), buildValue() )
-        # pairs[(... , ...)] = (..., ...)
-
-        pairpos = buildPairPosClassesSubtable(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of lists of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-        valueFormat1: Force the "left" value records to the given format.
-        valueFormat2: Force the "right" value records to the given format.
-
-    Returns:
-        A ``otTables.PairPos`` object.
-    """
-    coverage = set()
-    classDef1 = ClassDefBuilder(useClass0=True)
-    classDef2 = ClassDefBuilder(useClass0=False)
-    for gc1, gc2 in sorted(pairs):
-        coverage.update(gc1)
-        classDef1.add(gc1)
-        classDef2.add(gc2)
-    self = ot.PairPos()
-    self.Format = 2
-    valueFormat1 = self.ValueFormat1 = _getValueFormat(valueFormat1, pairs.values(), 0)
-    valueFormat2 = self.ValueFormat2 = _getValueFormat(valueFormat2, pairs.values(), 1)
-    self.Coverage = buildCoverage(coverage, glyphMap)
-    self.ClassDef1 = classDef1.build()
-    self.ClassDef2 = classDef2.build()
-    classes1 = classDef1.classes()
-    classes2 = classDef2.classes()
-    self.Class1Record = []
-    for c1 in classes1:
-        rec1 = ot.Class1Record()
-        rec1.Class2Record = []
-        self.Class1Record.append(rec1)
-        for c2 in classes2:
-            rec2 = ot.Class2Record()
-            val1, val2 = pairs.get((c1, c2), (None, None))
-            rec2.Value1 = (
-                ValueRecord(src=val1, valueFormat=valueFormat1)
-                if valueFormat1
-                else None
-            )
-            rec2.Value2 = (
-                ValueRecord(src=val2, valueFormat=valueFormat2)
-                if valueFormat2
-                else None
-            )
-            rec1.Class2Record.append(rec2)
-    self.Class1Count = len(self.Class1Record)
-    self.Class2Count = len(classes2)
-    return self
-
-
-def buildPairPosGlyphs(pairs, glyphMap):
-    """Builds a list of glyph-based pair adjustment (GPOS2 format 1) subtables.
-
-    This organises a list of pair positioning adjustments into subtables based
-    on common value record formats.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.PairPosBuilder`
-    instead.
-
-    Example::
-
-        pairs = {
-            ("K", "W"): ( buildValue(xAdvance=+5), buildValue() ),
-            ("K", "V"): ( buildValue(xAdvance=+5), buildValue() ),
-            # ...
-        }
-
-        subtables = buildPairPosGlyphs(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.PairPos`` objects.
-    """
-
-    p = {}  # (formatA, formatB) --> {(glyphA, glyphB): (valA, valB)}
-    for (glyphA, glyphB), (valA, valB) in pairs.items():
-        formatA = valA.getFormat() if valA is not None else 0
-        formatB = valB.getFormat() if valB is not None else 0
-        pos = p.setdefault((formatA, formatB), {})
-        pos[(glyphA, glyphB)] = (valA, valB)
-    return [
-        buildPairPosGlyphsSubtable(pos, glyphMap, formatA, formatB)
-        for ((formatA, formatB), pos) in sorted(p.items())
-    ]
-
-
-def buildPairPosGlyphsSubtable(pairs, glyphMap, valueFormat1=None, valueFormat2=None):
-    """Builds a single glyph-based pair adjustment (GPOS2 format 1) subtable.
-
-    This builds a PairPos subtable from a dictionary of glyph pairs and
-    their positioning adjustments. See also :func:`buildPairPosGlyphs`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.PairPosBuilder` instead.
-
-    Example::
-
-        pairs = {
-            ("K", "W"): ( buildValue(xAdvance=+5), buildValue() ),
-            ("K", "V"): ( buildValue(xAdvance=+5), buildValue() ),
-            # ...
-        }
-
-        pairpos = buildPairPosGlyphsSubtable(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-        valueFormat1: Force the "left" value records to the given format.
-        valueFormat2: Force the "right" value records to the given format.
-
-    Returns:
-        A ``otTables.PairPos`` object.
-    """
-    self = ot.PairPos()
-    self.Format = 1
-    valueFormat1 = self.ValueFormat1 = _getValueFormat(valueFormat1, pairs.values(), 0)
-    valueFormat2 = self.ValueFormat2 = _getValueFormat(valueFormat2, pairs.values(), 1)
-    p = {}
-    for (glyphA, glyphB), (valA, valB) in pairs.items():
-        p.setdefault(glyphA, []).append((glyphB, valA, valB))
-    self.Coverage = buildCoverage({g for g, _ in pairs.keys()}, glyphMap)
-    self.PairSet = []
-    for glyph in self.Coverage.glyphs:
-        ps = ot.PairSet()
-        ps.PairValueRecord = []
-        self.PairSet.append(ps)
-        for glyph2, val1, val2 in sorted(p[glyph], key=lambda x: glyphMap[x[0]]):
-            pvr = ot.PairValueRecord()
-            pvr.SecondGlyph = glyph2
-            pvr.Value1 = (
-                ValueRecord(src=val1, valueFormat=valueFormat1)
-                if valueFormat1
-                else None
-            )
-            pvr.Value2 = (
-                ValueRecord(src=val2, valueFormat=valueFormat2)
-                if valueFormat2
-                else None
-            )
-            ps.PairValueRecord.append(pvr)
-        ps.PairValueCount = len(ps.PairValueRecord)
-    self.PairSetCount = len(self.PairSet)
-    return self
-
-
-def buildSinglePos(mapping, glyphMap):
-    """Builds a list of single adjustment (GPOS1) subtables.
-
-    This builds a list of SinglePos subtables from a dictionary of glyph
-    names and their positioning adjustments. The format of the subtables are
-    determined to optimize the size of the resulting subtables.
-    See also :func:`buildSinglePosSubtable`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SinglePosBuilder` instead.
-
-    Example::
-
-        mapping = {
-            "V": buildValue({ "xAdvance" : +5 }),
-            # ...
-        }
-
-        subtables = buildSinglePos(pairs, font.getReverseGlyphMap())
-
-    Args:
-        mapping (dict): A mapping between glyphnames and
-            ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.SinglePos`` objects.
-    """
-    result, handled = [], set()
-    # In SinglePos format 1, the covered glyphs all share the same ValueRecord.
-    # In format 2, each glyph has its own ValueRecord, but these records
-    # all have the same properties (eg., all have an X but no Y placement).
-    coverages, masks, values = {}, {}, {}
-    for glyph, value in mapping.items():
-        key = _getSinglePosValueKey(value)
-        coverages.setdefault(key, []).append(glyph)
-        masks.setdefault(key[0], []).append(key)
-        values[key] = value
-
-    # If a ValueRecord is shared between multiple glyphs, we generate
-    # a SinglePos format 1 subtable; that is the most compact form.
-    for key, glyphs in coverages.items():
-        # 5 ushorts is the length of introducing another sublookup
-        if len(glyphs) * _getSinglePosValueSize(key) > 5:
-            format1Mapping = {g: values[key] for g in glyphs}
-            result.append(buildSinglePosSubtable(format1Mapping, glyphMap))
-            handled.add(key)
-
-    # In the remaining ValueRecords, look for those whose valueFormat
-    # (the set of used properties) is shared between multiple records.
-    # These will get encoded in format 2.
-    for valueFormat, keys in masks.items():
-        f2 = [k for k in keys if k not in handled]
-        if len(f2) > 1:
-            format2Mapping = {}
-            for k in f2:
-                format2Mapping.update((g, values[k]) for g in coverages[k])
-            result.append(buildSinglePosSubtable(format2Mapping, glyphMap))
-            handled.update(f2)
-
-    # The remaining ValueRecords are only used by a few glyphs, normally
-    # one. We encode these in format 1 again.
-    for key, glyphs in coverages.items():
-        if key not in handled:
-            for g in glyphs:
-                st = buildSinglePosSubtable({g: values[key]}, glyphMap)
-            result.append(st)
-
-    # When the OpenType layout engine traverses the subtables, it will
-    # stop after the first matching subtable.  Therefore, we sort the
-    # resulting subtables by decreasing coverage size; this increases
-    # the chance that the layout engine can do an early exit. (Of course,
-    # this would only be true if all glyphs were equally frequent, which
-    # is not really the case; but we do not know their distribution).
-    # If two subtables cover the same number of glyphs, we sort them
-    # by glyph ID so that our output is deterministic.
-    result.sort(key=lambda t: _getSinglePosTableKey(t, glyphMap))
-    return result
-
-
-def buildSinglePosSubtable(values, glyphMap):
-    """Builds a single adjustment (GPOS1) subtable.
-
-    This builds a list of SinglePos subtables from a dictionary of glyph
-    names and their positioning adjustments. The format of the subtable is
-    determined to optimize the size of the output.
-    See also :func:`buildSinglePos`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SinglePosBuilder` instead.
-
-    Example::
-
-        mapping = {
-            "V": buildValue({ "xAdvance" : +5 }),
-            # ...
-        }
-
-        subtable = buildSinglePos(pairs, font.getReverseGlyphMap())
-
-    Args:
-        mapping (dict): A mapping between glyphnames and
-            ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.SinglePos`` object.
-    """
-    self = ot.SinglePos()
-    self.Coverage = buildCoverage(values.keys(), glyphMap)
-    valueFormat = self.ValueFormat = reduce(
-        int.__or__, [v.getFormat() for v in values.values()], 0
-    )
-    valueRecords = [
-        ValueRecord(src=values[g], valueFormat=valueFormat)
-        for g in self.Coverage.glyphs
-    ]
-    if all(v == valueRecords[0] for v in valueRecords):
-        self.Format = 1
-        if self.ValueFormat != 0:
-            self.Value = valueRecords[0]
-        else:
-            self.Value = None
-    else:
-        self.Format = 2
-        self.Value = valueRecords
-        self.ValueCount = len(self.Value)
-    return self
-
-
-def _getSinglePosTableKey(subtable, glyphMap):
-    assert isinstance(subtable, ot.SinglePos), subtable
-    glyphs = subtable.Coverage.glyphs
-    return (-len(glyphs), glyphMap[glyphs[0]])
-
-
-def _getSinglePosValueKey(valueRecord):
-    # otBase.ValueRecord --> (2, ("YPlacement": 12))
-    assert isinstance(valueRecord, ValueRecord), valueRecord
-    valueFormat, result = 0, []
-    for name, value in valueRecord.__dict__.items():
-        if isinstance(value, ot.Device):
-            result.append((name, _makeDeviceTuple(value)))
-        else:
-            result.append((name, value))
-        valueFormat |= valueRecordFormatDict[name][0]
-    result.sort()
-    result.insert(0, valueFormat)
-    return tuple(result)
-
-
-_DeviceTuple = namedtuple("_DeviceTuple", "DeltaFormat StartSize EndSize DeltaValue")
-
-
-def _makeDeviceTuple(device):
-    # otTables.Device --> tuple, for making device tables unique
-    return _DeviceTuple(
-        device.DeltaFormat,
-        device.StartSize,
-        device.EndSize,
-        () if device.DeltaFormat & 0x8000 else tuple(device.DeltaValue),
-    )
-
-
-def _getSinglePosValueSize(valueKey):
-    # Returns how many ushorts this valueKey (short form of ValueRecord) takes up
-    count = 0
-    for _, v in valueKey[1:]:
-        if isinstance(v, _DeviceTuple):
-            count += len(v.DeltaValue) + 3
-        else:
-            count += 1
-    return count
-
-
-def buildValue(value):
-    """Builds a positioning value record.
-
-    Value records are used to specify coordinates and adjustments for
-    positioning and attaching glyphs. Many of the positioning functions
-    in this library take ``otTables.ValueRecord`` objects as arguments.
-    This function builds value records from dictionaries.
-
-    Args:
-        value (dict): A dictionary with zero or more of the following keys:
-            - ``xPlacement``
-            - ``yPlacement``
-            - ``xAdvance``
-            - ``yAdvance``
-            - ``xPlaDevice``
-            - ``yPlaDevice``
-            - ``xAdvDevice``
-            - ``yAdvDevice``
-
-    Returns:
-        An ``otTables.ValueRecord`` object.
-    """
-    self = ValueRecord()
-    for k, v in value.items():
-        setattr(self, k, v)
-    return self
-
-
-# GDEF
-
-
-def buildAttachList(attachPoints, glyphMap):
-    """Builds an AttachList subtable.
-
-    A GDEF table may contain an Attachment Point List table (AttachList)
-    which stores the contour indices of attachment points for glyphs with
-    attachment points. This routine builds AttachList subtables.
-
-    Args:
-        attachPoints (dict): A mapping between glyph names and a list of
-            contour indices.
-
-    Returns:
-        An ``otTables.AttachList`` object if attachment points are supplied,
-            or ``None`` otherwise.
-    """
-    if not attachPoints:
-        return None
-    self = ot.AttachList()
-    self.Coverage = buildCoverage(attachPoints.keys(), glyphMap)
-    self.AttachPoint = [buildAttachPoint(attachPoints[g]) for g in self.Coverage.glyphs]
-    self.GlyphCount = len(self.AttachPoint)
-    return self
-
-
-def buildAttachPoint(points):
-    # [4, 23, 41] --> otTables.AttachPoint
-    # Only used by above.
-    if not points:
-        return None
-    self = ot.AttachPoint()
-    self.PointIndex = sorted(set(points))
-    self.PointCount = len(self.PointIndex)
-    return self
-
-
-def buildCaretValueForCoord(coord):
-    # 500 --> otTables.CaretValue, format 1
-    # (500, DeviceTable) --> otTables.CaretValue, format 3
-    self = ot.CaretValue()
-    if isinstance(coord, tuple):
-        self.Format = 3
-        self.Coordinate, self.DeviceTable = coord
-    else:
-        self.Format = 1
-        self.Coordinate = coord
-    return self
-
-
-def buildCaretValueForPoint(point):
-    # 4 --> otTables.CaretValue, format 2
-    self = ot.CaretValue()
-    self.Format = 2
-    self.CaretValuePoint = point
-    return self
-
-
-def buildLigCaretList(coords, points, glyphMap):
-    """Builds a ligature caret list table.
-
-    Ligatures appear as a single glyph representing multiple characters; however
-    when, for example, editing text containing a ``f_i`` ligature, the user may
-    want to place the cursor between the ``f`` and the ``i``. The ligature caret
-    list in the GDEF table specifies the position to display the "caret" (the
-    character insertion indicator, typically a flashing vertical bar) "inside"
-    the ligature to represent an insertion point. The insertion positions may
-    be specified either by coordinate or by contour point.
-
-    Example::
-
-        coords = {
-            "f_f_i": [300, 600] # f|fi cursor at 300 units, ff|i cursor at 600.
-        }
-        points = {
-            "c_t": [28] # c|t cursor appears at coordinate of contour point 28.
-        }
-        ligcaretlist = buildLigCaretList(coords, points, font.getReverseGlyphMap())
-
-    Args:
-        coords: A mapping between glyph names and a list of coordinates for
-            the insertion point of each ligature component after the first one.
-        points: A mapping between glyph names and a list of contour points for
-            the insertion point of each ligature component after the first one.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.LigCaretList`` object if any carets are present, or
-            ``None`` otherwise."""
-    glyphs = set(coords.keys()) if coords else set()
-    if points:
-        glyphs.update(points.keys())
-    carets = {g: buildLigGlyph(coords.get(g), points.get(g)) for g in glyphs}
-    carets = {g: c for g, c in carets.items() if c is not None}
-    if not carets:
-        return None
-    self = ot.LigCaretList()
-    self.Coverage = buildCoverage(carets.keys(), glyphMap)
-    self.LigGlyph = [carets[g] for g in self.Coverage.glyphs]
-    self.LigGlyphCount = len(self.LigGlyph)
-    return self
-
-
-def buildLigGlyph(coords, points):
-    # ([500], [4]) --> otTables.LigGlyph; None for empty coords/points
-    carets = []
-    if coords:
-        coords = sorted(coords, key=lambda c: c[0] if isinstance(c, tuple) else c)
-        carets.extend([buildCaretValueForCoord(c) for c in coords])
-    if points:
-        carets.extend([buildCaretValueForPoint(p) for p in sorted(points)])
-    if not carets:
-        return None
-    self = ot.LigGlyph()
-    self.CaretValue = carets
-    self.CaretCount = len(self.CaretValue)
-    return self
-
-
-def buildMarkGlyphSetsDef(markSets, glyphMap):
-    """Builds a mark glyph sets definition table.
-
-    OpenType Layout lookups may choose to use mark filtering sets to consider
-    or ignore particular combinations of marks. These sets are specified by
-    setting a flag on the lookup, but the mark filtering sets are defined in
-    the ``GDEF`` table. This routine builds the subtable containing the mark
-    glyph set definitions.
-
-    Example::
-
-        set0 = set("acute", "grave")
-        set1 = set("caron", "grave")
-
-        markglyphsets = buildMarkGlyphSetsDef([set0, set1], font.getReverseGlyphMap())
-
-    Args:
-
-        markSets: A list of sets of glyphnames.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns
-        An ``otTables.MarkGlyphSetsDef`` object.
-    """
-    if not markSets:
-        return None
-    self = ot.MarkGlyphSetsDef()
-    self.MarkSetTableFormat = 1
-    self.Coverage = [buildCoverage(m, glyphMap) for m in markSets]
-    self.MarkSetCount = len(self.Coverage)
-    return self
-
-
-class ClassDefBuilder(object):
-    """Helper for building ClassDef tables."""
-
-    def __init__(self, useClass0):
-        self.classes_ = set()
-        self.glyphs_ = {}
-        self.useClass0_ = useClass0
-
-    def canAdd(self, glyphs):
-        if isinstance(glyphs, (set, frozenset)):
-            glyphs = sorted(glyphs)
-        glyphs = tuple(glyphs)
-        if glyphs in self.classes_:
-            return True
-        for glyph in glyphs:
-            if glyph in self.glyphs_:
-                return False
-        return True
-
-    def add(self, glyphs):
-        if isinstance(glyphs, (set, frozenset)):
-            glyphs = sorted(glyphs)
-        glyphs = tuple(glyphs)
-        if glyphs in self.classes_:
-            return
-        self.classes_.add(glyphs)
-        for glyph in glyphs:
-            if glyph in self.glyphs_:
-                raise OpenTypeLibError(
-                    f"Glyph {glyph} is already present in class.", None
-                )
-            self.glyphs_[glyph] = glyphs
-
-    def classes(self):
-        # In ClassDef1 tables, class id #0 does not need to be encoded
-        # because zero is the default. Therefore, we use id #0 for the
-        # glyph class that has the largest number of members. However,
-        # in other tables than ClassDef1, 0 means "every other glyph"
-        # so we should not use that ID for any real glyph classes;
-        # we implement this by inserting an empty set at position 0.
-        #
-        # TODO: Instead of counting the number of glyphs in each class,
-        # we should determine the encoded size. If the glyphs in a large
-        # class form a contiguous range, the encoding is actually quite
-        # compact, whereas a non-contiguous set might need a lot of bytes
-        # in the output file. We don't get this right with the key below.
-        result = sorted(self.classes_, key=lambda s: (len(s), s), reverse=True)
-        if not self.useClass0_:
-            result.insert(0, frozenset())
-        return result
-
-    def build(self):
-        glyphClasses = {}
-        for classID, glyphs in enumerate(self.classes()):
-            if classID == 0:
-                continue
-            for glyph in glyphs:
-                glyphClasses[glyph] = classID
-        classDef = ot.ClassDef()
-        classDef.classDefs = glyphClasses
-        return classDef
-
-
-AXIS_VALUE_NEGATIVE_INFINITY = fixedToFloat(-0x80000000, 16)
-AXIS_VALUE_POSITIVE_INFINITY = fixedToFloat(0x7FFFFFFF, 16)
-
-
-def buildStatTable(
-    ttFont, axes, locations=None, elidedFallbackName=2, windowsNames=True, macNames=True
-):
-    """Add a 'STAT' table to 'ttFont'.
-
-    'axes' is a list of dictionaries describing axes and their
-    values.
-
-    Example::
-
-        axes = [
-            dict(
-                tag="wght",
-                name="Weight",
-                ordering=0,  # optional
-                values=[
-                    dict(value=100, name='Thin'),
-                    dict(value=300, name='Light'),
-                    dict(value=400, name='Regular', flags=0x2),
-                    dict(value=900, name='Black'),
-                ],
-            )
-        ]
-
-    Each axis dict must have 'tag' and 'name' items. 'tag' maps
-    to the 'AxisTag' field. 'name' can be a name ID (int), a string,
-    or a dictionary containing multilingual names (see the
-    addMultilingualName() name table method), and will translate to
-    the AxisNameID field.
-
-    An axis dict may contain an 'ordering' item that maps to the
-    AxisOrdering field. If omitted, the order of the axes list is
-    used to calculate AxisOrdering fields.
-
-    The axis dict may contain a 'values' item, which is a list of
-    dictionaries describing AxisValue records belonging to this axis.
-
-    Each value dict must have a 'name' item, which can be a name ID
-    (int), a string, or a dictionary containing multilingual names,
-    like the axis name. It translates to the ValueNameID field.
-
-    Optionally the value dict can contain a 'flags' item. It maps to
-    the AxisValue Flags field, and will be 0 when omitted.
-
-    The format of the AxisValue is determined by the remaining contents
-    of the value dictionary:
-
-    If the value dict contains a 'value' item, an AxisValue record
-    Format 1 is created. If in addition to the 'value' item it contains
-    a 'linkedValue' item, an AxisValue record Format 3 is built.
-
-    If the value dict contains a 'nominalValue' item, an AxisValue
-    record Format 2 is built. Optionally it may contain 'rangeMinValue'
-    and 'rangeMaxValue' items. These map to -Infinity and +Infinity
-    respectively if omitted.
-
-    You cannot specify Format 4 AxisValue tables this way, as they are
-    not tied to a single axis, and specify a name for a location that
-    is defined by multiple axes values. Instead, you need to supply the
-    'locations' argument.
-
-    The optional 'locations' argument specifies AxisValue Format 4
-    tables. It should be a list of dicts, where each dict has a 'name'
-    item, which works just like the value dicts above, an optional
-    'flags' item (defaulting to 0x0), and a 'location' dict. A
-    location dict key is an axis tag, and the associated value is the
-    location on the specified axis. They map to the AxisIndex and Value
-    fields of the AxisValueRecord.
-
-    Example::
-
-        locations = [
-            dict(name='Regular ABCD', location=dict(wght=300, ABCD=100)),
-            dict(name='Bold ABCD XYZ', location=dict(wght=600, ABCD=200)),
-        ]
-
-    The optional 'elidedFallbackName' argument can be a name ID (int),
-    a string, a dictionary containing multilingual names, or a list of
-    STATNameStatements. It translates to the ElidedFallbackNameID field.
-
-    The 'ttFont' argument must be a TTFont instance that already has a
-    'name' table. If a 'STAT' table already exists, it will be
-    overwritten by the newly created one.
-    """
-    ttFont["STAT"] = ttLib.newTable("STAT")
-    statTable = ttFont["STAT"].table = ot.STAT()
-    nameTable = ttFont["name"]
-    statTable.ElidedFallbackNameID = _addName(
-        nameTable, elidedFallbackName, windows=windowsNames, mac=macNames
-    )
-
-    # 'locations' contains data for AxisValue Format 4
-    axisRecords, axisValues = _buildAxisRecords(
-        axes, nameTable, windowsNames=windowsNames, macNames=macNames
-    )
-    if not locations:
-        statTable.Version = 0x00010001
-    else:
-        # We'll be adding Format 4 AxisValue records, which
-        # requires a higher table version
-        statTable.Version = 0x00010002
-        multiAxisValues = _buildAxisValuesFormat4(
-            locations, axes, nameTable, windowsNames=windowsNames, macNames=macNames
-        )
-        axisValues = multiAxisValues + axisValues
-    nameTable.names.sort()
-
-    # Store AxisRecords
-    axisRecordArray = ot.AxisRecordArray()
-    axisRecordArray.Axis = axisRecords
-    # XXX these should not be hard-coded but computed automatically
-    statTable.DesignAxisRecordSize = 8
-    statTable.DesignAxisRecord = axisRecordArray
-    statTable.DesignAxisCount = len(axisRecords)
-
-    statTable.AxisValueCount = 0
-    statTable.AxisValueArray = None
-    if axisValues:
-        # Store AxisValueRecords
-        axisValueArray = ot.AxisValueArray()
-        axisValueArray.AxisValue = axisValues
-        statTable.AxisValueArray = axisValueArray
-        statTable.AxisValueCount = len(axisValues)
-
-
-def _buildAxisRecords(axes, nameTable, windowsNames=True, macNames=True):
-    axisRecords = []
-    axisValues = []
-    for axisRecordIndex, axisDict in enumerate(axes):
-        axis = ot.AxisRecord()
-        axis.AxisTag = axisDict["tag"]
-        axis.AxisNameID = _addName(
-            nameTable, axisDict["name"], 256, windows=windowsNames, mac=macNames
-        )
-        axis.AxisOrdering = axisDict.get("ordering", axisRecordIndex)
-        axisRecords.append(axis)
-
-        for axisVal in axisDict.get("values", ()):
-            axisValRec = ot.AxisValue()
-            axisValRec.AxisIndex = axisRecordIndex
-            axisValRec.Flags = axisVal.get("flags", 0)
-            axisValRec.ValueNameID = _addName(
-                nameTable, axisVal["name"], windows=windowsNames, mac=macNames
-            )
-
-            if "value" in axisVal:
-                axisValRec.Value = axisVal["value"]
-                if "linkedValue" in axisVal:
-                    axisValRec.Format = 3
-                    axisValRec.LinkedValue = axisVal["linkedValue"]
-                else:
-                    axisValRec.Format = 1
-            elif "nominalValue" in axisVal:
-                axisValRec.Format = 2
-                axisValRec.NominalValue = axisVal["nominalValue"]
-                axisValRec.RangeMinValue = axisVal.get(
-                    "rangeMinValue", AXIS_VALUE_NEGATIVE_INFINITY
-                )
-                axisValRec.RangeMaxValue = axisVal.get(
-                    "rangeMaxValue", AXIS_VALUE_POSITIVE_INFINITY
-                )
-            else:
-                raise ValueError("Can't determine format for AxisValue")
-
-            axisValues.append(axisValRec)
-    return axisRecords, axisValues
-
-
-def _buildAxisValuesFormat4(
-    locations, axes, nameTable, windowsNames=True, macNames=True
-):
-    axisTagToIndex = {}
-    for axisRecordIndex, axisDict in enumerate(axes):
-        axisTagToIndex[axisDict["tag"]] = axisRecordIndex
-
-    axisValues = []
-    for axisLocationDict in locations:
-        axisValRec = ot.AxisValue()
-        axisValRec.Format = 4
-        axisValRec.ValueNameID = _addName(
-            nameTable, axisLocationDict["name"], windows=windowsNames, mac=macNames
-        )
-        axisValRec.Flags = axisLocationDict.get("flags", 0)
-        axisValueRecords = []
-        for tag, value in axisLocationDict["location"].items():
-            avr = ot.AxisValueRecord()
-            avr.AxisIndex = axisTagToIndex[tag]
-            avr.Value = value
-            axisValueRecords.append(avr)
-        axisValueRecords.sort(key=lambda avr: avr.AxisIndex)
-        axisValRec.AxisCount = len(axisValueRecords)
-        axisValRec.AxisValueRecord = axisValueRecords
-        axisValues.append(axisValRec)
-    return axisValues
-
-
-def _addName(nameTable, value, minNameID=0, windows=True, mac=True):
-    if isinstance(value, int):
-        # Already a nameID
-        return value
-    if isinstance(value, str):
-        names = dict(en=value)
-    elif isinstance(value, dict):
-        names = value
-    elif isinstance(value, list):
-        nameID = nameTable._findUnusedNameID()
-        for nameRecord in value:
-            if isinstance(nameRecord, STATNameStatement):
-                nameTable.setName(
-                    nameRecord.string,
-                    nameID,
-                    nameRecord.platformID,
-                    nameRecord.platEncID,
-                    nameRecord.langID,
-                )
-            else:
-                raise TypeError("value must be a list of STATNameStatements")
-        return nameID
-    else:
-        raise TypeError("value must be int, str, dict or list")
-    return nameTable.addMultilingualName(
-        names, windows=windows, mac=mac, minNameID=minNameID
-    )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py
deleted file mode 100644
index 1323b670d0c2e7a51e553ee8aa341af789898b1d..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table__l_c_a_r(BaseTTXConverter):
-    pass
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/state.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/state.py
deleted file mode 100644
index 9722fa31e5240b8975af313d58bbcd83bb235fcd..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/state.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""gr.State() component."""
-
-from __future__ import annotations
-
-from copy import deepcopy
-from typing import Any
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import SimpleSerializable
-
-from gradio.components.base import IOComponent
-
-set_documentation_group("component")
-
-
-@document()
-class State(IOComponent, SimpleSerializable):
-    """
-    Special hidden component that stores session state across runs of the demo by the
-    same user. The value of the State variable is cleared when the user refreshes the page.
-
-    Preprocessing: No preprocessing is performed
-    Postprocessing: No postprocessing is performed
-    Demos: blocks_simple_squares
-    Guides: real-time-speech-recognition
-    """
-
-    allow_string_shortcut = False
-
-    def __init__(
-        self,
-        value: Any = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: the initial value (of arbitrary type) of the state. The provided argument is deepcopied. If a callable is provided, the function will be called whenever the app loads to set the initial value of the state.
-        """
-        self.stateful = True
-        IOComponent.__init__(self, value=deepcopy(value), **kwargs)
-
-
-class Variable(State):
-    """Variable was renamed to State. This class is kept for backwards compatibility."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def get_block_name(self):
-        return "state"
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/state_core.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/state_core.py
deleted file mode 100644
index a938041d992fdf7ae3f2843a2e0f9ef298c45790..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/state_core.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from ..ruler import StateBase
-from ..token import Token
-from ..utils import EnvType
-
-if TYPE_CHECKING:
-    from markdown_it import MarkdownIt
-
-
-class StateCore(StateBase):
-    def __init__(
-        self,
-        src: str,
-        md: MarkdownIt,
-        env: EnvType,
-        tokens: list[Token] | None = None,
-    ) -> None:
-        self.src = src
-        self.md = md  # link to parser instance
-        self.env = env
-        self.tokens: list[Token] = tokens or []
-        self.inlineMode = False
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/logging.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/logging.py
deleted file mode 100644
index 3308d117e994d95d5dd7cb494d88512a61847fd6..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/logging.py
+++ /dev/null
@@ -1,342 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Optuna, Hugging Face
-#
-# 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.
-""" Logging utilities."""
-
-import logging
-import os
-import sys
-import threading
-from logging import (
-    CRITICAL,  # NOQA
-    DEBUG,  # NOQA
-    ERROR,  # NOQA
-    FATAL,  # NOQA
-    INFO,  # NOQA
-    NOTSET,  # NOQA
-    WARN,  # NOQA
-    WARNING,  # NOQA
-)
-from typing import Optional
-
-from tqdm import auto as tqdm_lib
-
-
-_lock = threading.Lock()
-_default_handler: Optional[logging.Handler] = None
-
-log_levels = {
-    "debug": logging.DEBUG,
-    "info": logging.INFO,
-    "warning": logging.WARNING,
-    "error": logging.ERROR,
-    "critical": logging.CRITICAL,
-}
-
-_default_log_level = logging.WARNING
-
-_tqdm_active = True
-
-
-def _get_default_logging_level():
-    """
-    If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
-    not - fall back to `_default_log_level`
-    """
-    env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
-    if env_level_str:
-        if env_level_str in log_levels:
-            return log_levels[env_level_str]
-        else:
-            logging.getLogger().warning(
-                f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
-                f"has to be one of: { ', '.join(log_levels.keys()) }"
-            )
-    return _default_log_level
-
-
-def _get_library_name() -> str:
-    return __name__.split(".")[0]
-
-
-def _get_library_root_logger() -> logging.Logger:
-    return logging.getLogger(_get_library_name())
-
-
-def _configure_library_root_logger() -> None:
-    global _default_handler
-
-    with _lock:
-        if _default_handler:
-            # This library has already configured the library root logger.
-            return
-        _default_handler = logging.StreamHandler()  # Set sys.stderr as stream.
-        _default_handler.flush = sys.stderr.flush
-
-        # Apply our default configuration to the library root logger.
-        library_root_logger = _get_library_root_logger()
-        library_root_logger.addHandler(_default_handler)
-        library_root_logger.setLevel(_get_default_logging_level())
-        library_root_logger.propagate = False
-
-
-def _reset_library_root_logger() -> None:
-    global _default_handler
-
-    with _lock:
-        if not _default_handler:
-            return
-
-        library_root_logger = _get_library_root_logger()
-        library_root_logger.removeHandler(_default_handler)
-        library_root_logger.setLevel(logging.NOTSET)
-        _default_handler = None
-
-
-def get_log_levels_dict():
-    return log_levels
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
-    """
-    Return a logger with the specified name.
-
-    This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
-    """
-
-    if name is None:
-        name = _get_library_name()
-
-    _configure_library_root_logger()
-    return logging.getLogger(name)
-
-
-def get_verbosity() -> int:
-    """
-    Return the current level for the 🤗 Diffusers' root logger as an int.
-
-    Returns:
-        `int`: The logging level.
-
-    <Tip>
-
-    🤗 Diffusers has following logging levels:
-
-    - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
-    - 40: `diffusers.logging.ERROR`
-    - 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
-    - 20: `diffusers.logging.INFO`
-    - 10: `diffusers.logging.DEBUG`
-
-    </Tip>"""
-
-    _configure_library_root_logger()
-    return _get_library_root_logger().getEffectiveLevel()
-
-
-def set_verbosity(verbosity: int) -> None:
-    """
-    Set the verbosity level for the 🤗 Diffusers' root logger.
-
-    Args:
-        verbosity (`int`):
-            Logging level, e.g., one of:
-
-            - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
-            - `diffusers.logging.ERROR`
-            - `diffusers.logging.WARNING` or `diffusers.logging.WARN`
-            - `diffusers.logging.INFO`
-            - `diffusers.logging.DEBUG`
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().setLevel(verbosity)
-
-
-def set_verbosity_info():
-    """Set the verbosity to the `INFO` level."""
-    return set_verbosity(INFO)
-
-
-def set_verbosity_warning():
-    """Set the verbosity to the `WARNING` level."""
-    return set_verbosity(WARNING)
-
-
-def set_verbosity_debug():
-    """Set the verbosity to the `DEBUG` level."""
-    return set_verbosity(DEBUG)
-
-
-def set_verbosity_error():
-    """Set the verbosity to the `ERROR` level."""
-    return set_verbosity(ERROR)
-
-
-def disable_default_handler() -> None:
-    """Disable the default handler of the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert _default_handler is not None
-    _get_library_root_logger().removeHandler(_default_handler)
-
-
-def enable_default_handler() -> None:
-    """Enable the default handler of the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert _default_handler is not None
-    _get_library_root_logger().addHandler(_default_handler)
-
-
-def add_handler(handler: logging.Handler) -> None:
-    """adds a handler to the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert handler is not None
-    _get_library_root_logger().addHandler(handler)
-
-
-def remove_handler(handler: logging.Handler) -> None:
-    """removes given handler from the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert handler is not None and handler not in _get_library_root_logger().handlers
-    _get_library_root_logger().removeHandler(handler)
-
-
-def disable_propagation() -> None:
-    """
-    Disable propagation of the library log outputs. Note that log propagation is disabled by default.
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().propagate = False
-
-
-def enable_propagation() -> None:
-    """
-    Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
-    double logging if the root logger has been configured.
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().propagate = True
-
-
-def enable_explicit_format() -> None:
-    """
-    Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
-    ```
-        [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
-    ```
-    All handlers currently bound to the root logger are affected by this method.
-    """
-    handlers = _get_library_root_logger().handlers
-
-    for handler in handlers:
-        formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
-        handler.setFormatter(formatter)
-
-
-def reset_format() -> None:
-    """
-    Resets the formatting for HuggingFace Diffusers' loggers.
-
-    All handlers currently bound to the root logger are affected by this method.
-    """
-    handlers = _get_library_root_logger().handlers
-
-    for handler in handlers:
-        handler.setFormatter(None)
-
-
-def warning_advice(self, *args, **kwargs):
-    """
-    This method is identical to `logger.warning()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
-    warning will not be printed
-    """
-    no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
-    if no_advisory_warnings:
-        return
-    self.warning(*args, **kwargs)
-
-
-logging.Logger.warning_advice = warning_advice
-
-
-class EmptyTqdm:
-    """Dummy tqdm which doesn't do anything."""
-
-    def __init__(self, *args, **kwargs):  # pylint: disable=unused-argument
-        self._iterator = args[0] if args else None
-
-    def __iter__(self):
-        return iter(self._iterator)
-
-    def __getattr__(self, _):
-        """Return empty function."""
-
-        def empty_fn(*args, **kwargs):  # pylint: disable=unused-argument
-            return
-
-        return empty_fn
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type_, value, traceback):
-        return
-
-
-class _tqdm_cls:
-    def __call__(self, *args, **kwargs):
-        if _tqdm_active:
-            return tqdm_lib.tqdm(*args, **kwargs)
-        else:
-            return EmptyTqdm(*args, **kwargs)
-
-    def set_lock(self, *args, **kwargs):
-        self._lock = None
-        if _tqdm_active:
-            return tqdm_lib.tqdm.set_lock(*args, **kwargs)
-
-    def get_lock(self):
-        if _tqdm_active:
-            return tqdm_lib.tqdm.get_lock()
-
-
-tqdm = _tqdm_cls()
-
-
-def is_progress_bar_enabled() -> bool:
-    """Return a boolean indicating whether tqdm progress bars are enabled."""
-    global _tqdm_active
-    return bool(_tqdm_active)
-
-
-def enable_progress_bar():
-    """Enable tqdm progress bar."""
-    global _tqdm_active
-    _tqdm_active = True
-
-
-def disable_progress_bar():
-    """Disable tqdm progress bar."""
-    global _tqdm_active
-    _tqdm_active = False
diff --git a/spaces/deepghs/anime_object_detection/face.py b/spaces/deepghs/anime_object_detection/face.py
deleted file mode 100644
index 22d03082016fb728261feb20829296ca7bf42aea..0000000000000000000000000000000000000000
--- a/spaces/deepghs/anime_object_detection/face.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from functools import lru_cache
-from typing import List, Tuple
-
-from huggingface_hub import hf_hub_download
-from imgutils.data import ImageTyping, load_image, rgb_encode
-
-from onnx_ import _open_onnx_model
-from plot import detection_visualize
-from yolo_ import _image_preprocess, _data_postprocess
-
-_FACE_MODELS = [
-    'face_detect_v1.4_s',
-    'face_detect_v1.4_n',
-    'face_detect_v1.3_s',
-    'face_detect_v1.3_n',
-    'face_detect_v1.2_s',
-    'face_detect_v1.1_s',
-    'face_detect_v1.1_n',
-    'face_detect_v1_s',
-    'face_detect_v1_n',
-    'face_detect_v0_s',
-    'face_detect_v0_n',
-]
-_DEFAULT_FACE_MODEL = _FACE_MODELS[0]
-
-
-@lru_cache()
-def _open_face_detect_model(model_name):
-    return _open_onnx_model(hf_hub_download(
-        f'deepghs/anime_face_detection',
-        f'{model_name}/model.onnx',
-    ))
-
-
-_LABELS = ['face']
-
-
-def detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
-                 conf_threshold: float = 0.25, iou_threshold: float = 0.7) \
-        -> List[Tuple[Tuple[int, int, int, int], str, float]]:
-    image = load_image(image, mode='RGB')
-    new_image, old_size, new_size = _image_preprocess(image, max_infer_size)
-
-    data = rgb_encode(new_image)[None, ...]
-    output, = _open_face_detect_model(model_name).run(['output0'], {'images': data})
-    return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, _LABELS)
-
-
-def _gr_detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
-                     conf_threshold: float = 0.25, iou_threshold: float = 0.7):
-    ret = detect_faces(image, model_name, max_infer_size, conf_threshold, iou_threshold)
-    return detection_visualize(image, ret, _LABELS)
diff --git a/spaces/dfm42/orangeloaf/app.py b/spaces/dfm42/orangeloaf/app.py
deleted file mode 100644
index fe3ad5b25b29aa891232b6385fc803c237e6a8a8..0000000000000000000000000000000000000000
--- a/spaces/dfm42/orangeloaf/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-learn = load_learner('model.pkl')
-
-categories = ['Orange cat','White bread loaf']
-
-def classify_image(img):
-    _,_,probs = learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
-
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-
-title = "Orange cat vs Bread loaf classifier"
-description = "Who doesn't know the problem! You want to eat a delicious slice of bread, and you accidentally end up toasting your orange cat! Bummer! This app will help. Based on lecture 2 of the fastai course and Tanishq Abraham's blog post (https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial)."
-
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, title = title, description = description)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Download Madras Cafe Movies 1080p Torrent.md b/spaces/diacanFperku/AutoGPT/Download Madras Cafe Movies 1080p Torrent.md
deleted file mode 100644
index e7a734904ceba026c162bf890785db0a77c873f5..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Download Madras Cafe Movies 1080p Torrent.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>download Madras Cafe movies 1080p torrent</h2><br /><p><b><b>Download Zip</b> &#9193; <a href="https://gohhs.com/2uFTXh">https://gohhs.com/2uFTXh</a></b></p><br /><br />
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diff --git a/spaces/diacanFperku/AutoGPT/Khun Ploypailin Jensen Pictures.md b/spaces/diacanFperku/AutoGPT/Khun Ploypailin Jensen Pictures.md
deleted file mode 100644
index 56ffe3748c8a2677cce9c7246c02906b712dd371..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Khun Ploypailin Jensen Pictures.md	
+++ /dev/null
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diff --git a/spaces/diacanFperku/AutoGPT/Mon Ami 3000 Crack 58.md b/spaces/diacanFperku/AutoGPT/Mon Ami 3000 Crack 58.md
deleted file mode 100644
index 258e32c248ad01dfed4755158a2162a588c3d89c..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Mon Ami 3000 Crack 58.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>the researchers emphasize that crack cocaine is a major public health problem for the united states; it accounts for almost a third of all drug arrests and 60 percent of all drug-related arrests. between 1990 and 2010, the total number of arrests for crack increased by 673 percent. moreover, between 1992 and 2005, the number of blacks arrested for crack offenses jumped from 106,000 to 333,000. notably, crack cocaine arrests in the us have increased sharply in recent years, particularly among young black males. in 2011, blacks made up about 18 percent of all drug arrests, but accounted for 32 percent of drug arrests involving crack. they were also nearly four times more likely than whites to be arrested for crack.</p>
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-<p>the researchers find that although crack has a major impact on health and social inequality, it is not the direct cause of these disparities. rather, crack users are more likely to be poor, young, and black. for example, two-thirds of crack users are black and less than half have a high school education. however, crack is not as likely to lead to violent crimes and it does not increase the likelihood of arrest for violent crimes. crack users are more likely to have drug and alcohol abuse problems, to be homeless, and to be poor. crack also impacts blacks more than any other ethnic group; when researchers control for other socioeconomic factors, the disparity in incarceration rates for crack cocaine is even higher.</p> 899543212b<br />
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diff --git a/spaces/diacanFperku/AutoGPT/Resident Evil 5 Lmt Tool V2 0 19.md b/spaces/diacanFperku/AutoGPT/Resident Evil 5 Lmt Tool V2 0 19.md
deleted file mode 100644
index d48dab4cd978537f539b3a18622020a98666cd65..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Resident Evil 5 Lmt Tool V2 0 19.md	
+++ /dev/null
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diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/README.md b/spaces/digitalxingtong/Nailv-Bert-Vits2/README.md
deleted file mode 100644
index d7b9b8837242bb9e8f8fee027b971cef6b322efd..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI奶绿
-emoji: 🌟
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.36.1
-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/digitalxingtong/Taffy-Bert-VITS2/text/cleaner.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Taffy-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/dmeck/RVC-Speakers/vits/modules/transforms/transforms.py b/spaces/dmeck/RVC-Speakers/vits/modules/transforms/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/modules/transforms/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/dogincharge/Shap-ER/README.md b/spaces/dogincharge/Shap-ER/README.md
deleted file mode 100644
index 3f4789db88c2fd1b9777918e1beeb719bddbc858..0000000000000000000000000000000000000000
--- a/spaces/dogincharge/Shap-ER/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: Shap-E
-emoji: 🧢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.38.0
-python_version: 3.10.11
-app_file: app.py
-pinned: false
-license: mit
-suggested_hardware: t4-small
-duplicated_from: hysts/Shap-E
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-https://arxiv.org/abs/2305.02463
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py
deleted file mode 100644
index eac7e896bbe85a670824bfe8ef487d0535d5bd99..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py
+++ /dev/null
@@ -1,186 +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]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-from groundingdino.util.misc import NestedTensor
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            # if os.environ.get("SHILONG_AMP", None) == '1':
-            #     eps = 1e-4
-            # else:
-            #     eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-class PositionEmbeddingSineHW(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperatureH = temperatureH
-        self.temperatureW = temperatureW
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-
-        # import ipdb; ipdb.set_trace()
-
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_x = x_embed[:, :, :, None] / dim_tx
-
-        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_y = y_embed[:, :, :, None] / dim_ty
-
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-
-        # import ipdb; ipdb.set_trace()
-
-        return pos
-
-
-class PositionEmbeddingLearned(nn.Module):
-    """
-    Absolute pos embedding, learned.
-    """
-
-    def __init__(self, num_pos_feats=256):
-        super().__init__()
-        self.row_embed = nn.Embedding(50, num_pos_feats)
-        self.col_embed = nn.Embedding(50, num_pos_feats)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.uniform_(self.row_embed.weight)
-        nn.init.uniform_(self.col_embed.weight)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        h, w = x.shape[-2:]
-        i = torch.arange(w, device=x.device)
-        j = torch.arange(h, device=x.device)
-        x_emb = self.col_embed(i)
-        y_emb = self.row_embed(j)
-        pos = (
-            torch.cat(
-                [
-                    x_emb.unsqueeze(0).repeat(h, 1, 1),
-                    y_emb.unsqueeze(1).repeat(1, w, 1),
-                ],
-                dim=-1,
-            )
-            .permute(2, 0, 1)
-            .unsqueeze(0)
-            .repeat(x.shape[0], 1, 1, 1)
-        )
-        return pos
-
-
-def build_position_encoding(args):
-    N_steps = args.hidden_dim // 2
-    if args.position_embedding in ("v2", "sine"):
-        # TODO find a better way of exposing other arguments
-        position_embedding = PositionEmbeddingSineHW(
-            N_steps,
-            temperatureH=args.pe_temperatureH,
-            temperatureW=args.pe_temperatureW,
-            normalize=True,
-        )
-    elif args.position_embedding in ("v3", "learned"):
-        position_embedding = PositionEmbeddingLearned(N_steps)
-    else:
-        raise ValueError(f"not supported {args.position_embedding}")
-
-    return position_embedding
diff --git a/spaces/emc348/faces-through-time/utils/ImagesDataset.py b/spaces/emc348/faces-through-time/utils/ImagesDataset.py
deleted file mode 100644
index 20aa43e9b9ad242a5e1fd4309f83ab182021cbc6..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/utils/ImagesDataset.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-
-from torch.utils.data import Dataset
-from PIL import Image
-
-from utils.data_utils import make_dataset
-
-
-class ImagesDataset(Dataset):
-
-    def __init__(self, source_root, source_transform=None):
-        self.source_paths = sorted(make_dataset(source_root))
-        self.source_transform = source_transform
-
-    def __len__(self):
-        return len(self.source_paths)
-
-    def __getitem__(self, index):
-        fname, from_path = self.source_paths[index]
-        from_im = Image.open(from_path).convert('RGB')
-
-        if self.source_transform:
-            from_im = self.source_transform(from_im)
-
-        return fname, from_im
diff --git a/spaces/etweedy/pet_breeds/app.py b/spaces/etweedy/pet_breeds/app.py
deleted file mode 100644
index 843ccb93ae9ae15adf0a3fd47fc698be90627624..0000000000000000000000000000000000000000
--- a/spaces/etweedy/pet_breeds/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-# Load in our trained model
-learn = load_learner('model1.pkl')
-
-# pull the output category strings off of our model
-categories = learn.dls.vocab
-
-# define a function for classifying an image.  Input is an image file, and output is a dictionary giving probabilities for each output category
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
-
-# Build the gradio interface
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['abys.jpeg','beag.jpeg', 'rag.jpeg','newf.jpeg', 'shiba2.jpeg','blue.jpeg']
-title = "What's that breed?"
-description = "This app will try to guess the breed of the cat or dog in the photo it's given. Keep in mind that it only knows certain breeds - hopefully, it knows yours. Try one of the examples, or upload your own photo!"
-intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples, title = title, description = description)
-intf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex
deleted file mode 100644
index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ /dev/null
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
-    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
-    A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
diff --git a/spaces/falterWliame/Face_Mask_Detection/Alien.Skin.Eye.Candy.v5.1.Impact.Retail.for.Adobe.Photoshop.Incl 64 Bit HOT!.md b/spaces/falterWliame/Face_Mask_Detection/Alien.Skin.Eye.Candy.v5.1.Impact.Retail.for.Adobe.Photoshop.Incl 64 Bit HOT!.md
deleted file mode 100644
index 255f6ca4d39789b3bd6ba9a308e6bdd12b6c9e63..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Alien.Skin.Eye.Candy.v5.1.Impact.Retail.for.Adobe.Photoshop.Incl 64 Bit HOT!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/falterWliame/Face_Mask_Detection/CharacterGenerator2011KeygensonlyXFORCE3264bitsRHdownloadpc.md b/spaces/falterWliame/Face_Mask_Detection/CharacterGenerator2011KeygensonlyXFORCE3264bitsRHdownloadpc.md
deleted file mode 100644
index 278905b3bd37ec1d45e891900873fddeaa122b63..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/CharacterGenerator2011KeygensonlyXFORCE3264bitsRHdownloadpc.md
+++ /dev/null
@@ -1,47 +0,0 @@
-<br />
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-<ol>
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-<ol>
-<li>Open your AutoCAD software and click on the "Activate" button.</li>
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-</ol>
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Bricks King APK Enjoy Smooth Gameplay and Beautiful Graphics.md b/spaces/fatiXbelha/sd/Bricks King APK Enjoy Smooth Gameplay and Beautiful Graphics.md
deleted file mode 100644
index d8b19baddeb3878c48aae17264e4adc9ae9a5cee..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Bricks King APK Enjoy Smooth Gameplay and Beautiful Graphics.md	
+++ /dev/null
@@ -1,127 +0,0 @@
-
-<h1>Bricks King Download APK: A Fun and Engaging Brick Breaker Game</h1>
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- <h3>Customizable game balls and paddles</h3>
-<p>The game also allows you to customize your game balls and paddles with different colors and designs. You can unlock more game balls and paddles by collecting stars or coins, or by watching ads. You can choose from various game balls and paddles, such as soccer balls, basketballs, tennis balls, baseballs, golf balls, footballs, ping pong balls, billiard balls, emojis, fruits, animals, flowers, stars, hearts, diamonds, and more. You can also mix and match different game balls and paddles to create your own style.</p>
- <h2>What are some tips and tricks for playing Bricks King?</h2>
-<p>Bricks King is a game that requires skill and strategy to play well. Here are some tips and tricks for playing Bricks King:</p>
- <h3>Aim your shots carefully and use the walls to bounce the ball</h3>
-<p>One of the most important skills in Bricks King is aiming your shots carefully. You need to consider the angle and the direction of the ball when you launch it. You also need to use the walls to bounce the ball and hit more bricks. You can tilt your device to adjust the angle of the ball. You can also swipe your finger on the screen to change the direction of the ball. You should try to hit as many bricks as possible with each shot and avoid wasting your shots.</p>
- <h3>Collect stars to unlock more power-ups and game balls</h3>
-<p>Another important skill in Bricks King is collecting stars. Stars are essential for unlocking more power-ups and game balls. Stars appear when you break bricks, and you need to catch them with your paddle before they disappear. You should try to collect as many stars as possible in each level and use them wisely. You can also watch ads to get more stars or buy them with real money.</p>
- <h3>Use the pause button to adjust your strategy and resume the game</h3>
-<p>A useful feature in Bricks King is the pause button. The pause button allows you to stop the game at any time and resume it later. You can use the pause button to adjust your strategy and plan your next move. You can also use the pause button to take a break from the game or switch to another app without losing your progress. To use the pause button, you need to tap on the icon at the top right corner of the screen.</p>
- <h2>What are some reviews of Bricks King?</h2>
-<p>Bricks King is a game that has received many reviews from users who have played it. Here are some of them:</p>
- <h3>Positive reviews from users who enjoy the game</h3>
-<p>Some users have given positive reviews of Bricks King, praising its gameplay, graphics, sounds, and features. Here are some examples of positive reviews:</p>
- <table>
-<tr><td><b>User</b></td><td><b>Review</b></td></tr>
-<tr><td>Jane</td><td>I love this game! It's so fun and addictive! The graphics are beautiful and the sounds are relaxing. The levels are challenging but not frustrating. The power-ups are awesome and make the game more exciting. I highly recommend this game!</td></tr>
-<tr><td>John</td><td>This is one of the best brick breaker games I have ever played! It has smooth controls, amazing graphics, and satisfying power-ups. The levels are varied and interesting. The game is relaxing and entertaining at the same time. I can't stop playing it!</td></tr>
-<tr><td>Lisa</td><td>This game is awesome! It has everything I want in a brick breaker game: levels of increasing difficulty, varied brick designs, amazing power-ups, customizable game balls and paddles, and relaxing sounds. The game is easy to play but hard to master. I love it!</td></tr>
-</table>
- <h3>Negative reviews from users who encounter bugs or ads</h3>
-<p>Some users have given negative reviews of Bricks King, complaining about its bugs or ads. Here are some examples of negative reviews:</p>
- <table>
-<tr><td><b>User</b></td><td><b>Review</b></td></tr>
-<tr><td>Mike</td><td>This game is terrible! It has so many bugs and glitches that make it unplayable. The game freezes, crashes, or resets randomly. The ball gets stuck or disappears. The bricks don't break or move. The power-ups don't work or have negative effects. The game is a waste of time and space!</td></tr>
-<tr><td>Amy</td><td>This game is annoying! It has so many ads that ruin the game experience. The ads pop up every few seconds or minutes, interrupting the game flow. The ads are loud, long, or inappropriate. The ads also drain the battery and use the data. The game is a scam and a rip-off!</td></tr>
-<tr><td>Tom</td><td>This game is boring! It has no variety or challenge in the game play. The levels are repetitive and easy. The bricks are dull and plain. The power-ups are lame and useless. The game has no story or goal. The game is a snooze fest and a disappointment!</td></tr>
-</table>
- <h3>Suggestions for improvement from users who want more features or challenges</h3>
-<p>Some users have given suggestions for improvement of Bricks King, requesting more features or challenges in the game. Here are some examples of suggestions for improvement:</p>
- <table>
-<tr><td><b>User</b></td><td><b>Suggestion</b></td></tr>
-<tr><td>Mary</td><td>I like this game, but I wish it had more features, such as a leaderboard, achievements, statistics, or a shop. I want to see how I rank against other players, earn rewards for my progress, track my performance, or buy more items with my coins.</td></tr>
-<tr><td>Bob</td><td>I enjoy this game, but I wish it had more challenges, such as a timer, a score limit, a life limit, or a boss level. I want to feel more pressure and excitement while playing, test my skills and speed, or face a bigger obstacle.</td></tr>
-<tr><td>Sara</td><td>I love this game, but I wish it had more options, such as a difficulty level, a theme selection, a sound control, or a language choice. I want to customize the game to suit my preference and mood, adjust the volume or mute the sound, or change the language of the game.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Bricks King is a fun and engaging brick breaker game that you can download and play on your Android device. It has over 300 levels of increasing difficulty and varied brick designs, amazing power-ups to help you clear the bricks, customizable game balls and paddles to suit your style, smooth controls, beautiful graphics, relaxing sounds and satisfying chain reactions. It is a game that anyone can play and enjoy, whether you are looking for a casual game to pass the time or a challenging game to test your skills. You can download the APK file of Bricks King from a trusted source and install it on your device following the steps we have provided in this article. We hope you have fun playing Bricks King!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Bricks King:</p>
- <h3>Q: Is Bricks King free to play?</h3>
-<p>A: Yes, Bricks King is free to play. However, it contains ads that may interrupt your gameplay or consume your data. You can also make in-app purchases with real money to get more stars or coins.</p>
- <h3>Q: Is Bricks King safe to download and install?</h3>
-<p>A: Yes, Bricks King is safe to download and install if you get the APK file from a trusted source. However, you should always be careful when downloading apps from unknown sources and check the permissions and reviews before installing them.</p>
- <h3>Q: How can I contact the developer of Bricks King?</h3>
-<p>A: You can contact the developer of Bricks King by sending an email to protagames@gmail.com or visiting their website at http://protagames.com/. You can also follow them on Facebook at https://www.facebook.com/protagames/.</p>
- <h3>Q: How can I update Bricks King?</h3>
-<p>A: You A: You can update Bricks King by downloading and installing the latest version of the APK file from the same source you got the previous version. You can also check for updates on the developer's website or Facebook page. You should always update Bricks King to enjoy the new features, levels, and bug fixes.</p>
- <h3>Q: How can I uninstall Bricks King?</h3>
-<p>A: You can uninstall Bricks King by going to your device settings and looking for the apps or applications option. Then, you need to find Bricks King on the list of installed apps and tap or click on it. You will see an option to uninstall or remove the app. Tap or click on it and confirm your action. You may also see a pop-up window asking you to rate or review the app before uninstalling it. You can choose to do so or skip it.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/data/template_dataset.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/data/template_dataset.py
deleted file mode 100644
index bfdf16be2a8a834b204c45d88c86857b37b9bd25..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/data/template_dataset.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Dataset class template
-
-This module provides a template for users to implement custom datasets.
-You can specify '--dataset_mode template' to use this dataset.
-The class name should be consistent with both the filename and its dataset_mode option.
-The filename should be <dataset_mode>_dataset.py
-The class name should be <Dataset_mode>Dataset.py
-You need to implement the following functions:
-    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
-    -- <__init__>: Initialize this dataset class.
-    -- <__getitem__>: Return a data point and its metadata information.
-    -- <__len__>: Return the number of images.
-"""
-from data.base_dataset import BaseDataset, get_transform
-# from data.image_folder import make_dataset
-# from PIL import Image
-
-
-class TemplateDataset(BaseDataset):
-    """A template dataset class for you to implement custom datasets."""
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
-        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        A few things can be done here.
-        - save the options (have been done in BaseDataset)
-        - get image paths and meta information of the dataset.
-        - define the image transformation.
-        """
-        # save the option and dataset root
-        BaseDataset.__init__(self, opt)
-        # get the image paths of your dataset;
-        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
-        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
-        self.transform = get_transform(opt)
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index -- a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It usually contains the data itself and its metadata information.
-
-        Step 1: get a random image path: e.g., path = self.image_paths[index]
-        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
-        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
-        Step 4: return a data point as a dictionary.
-        """
-        path = 'temp'    # needs to be a string
-        data_A = None    # needs to be a tensor
-        data_B = None    # needs to be a tensor
-        return {'data_A': data_A, 'data_B': data_B, 'path': path}
-
-    def __len__(self):
-        """Return the total number of images."""
-        return len(self.image_paths)
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/base_model.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/base_model.py
deleted file mode 100644
index cfe64a7f739ad8f8cfbf3073a2bf49e1468127fd..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/base_model.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""This script defines the base network model for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch
-from collections import OrderedDict
-from abc import ABC, abstractmethod
-from . import networks
-
-
-class BaseModel(ABC):
-    """This class is an abstract base class (ABC) for models.
-    To create a subclass, you need to implement the following five functions:
-        -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-        -- <set_input>:                     unpack data from dataset and apply preprocessing.
-        -- <forward>:                       produce intermediate results.
-        -- <optimize_parameters>:           calculate losses, gradients, and update network weights.
-        -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-    """
-
-    def __init__(self, opt):
-        """Initialize the BaseModel class.
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        When creating your custom class, you need to implement your own initialization.
-        In this fucntion, you should first call <BaseModel.__init__(self, opt)>
-        Then, you need to define four lists:
-            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-            -- self.model_names (str list):         specify the images that you want to display and save.
-            -- self.visual_names (str list):        define networks used in our training.
-            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
-        """
-        self.opt = opt
-        self.isTrain = False
-        self.device = torch.device('cpu') 
-        self.save_dir = " " # os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
-        self.loss_names = []
-        self.model_names = []
-        self.visual_names = []
-        self.parallel_names = []
-        self.optimizers = []
-        self.image_paths = []
-        self.metric = 0  # used for learning rate policy 'plateau'
-
-    @staticmethod
-    def dict_grad_hook_factory(add_func=lambda x: x):
-        saved_dict = dict()
-
-        def hook_gen(name):
-            def grad_hook(grad):
-                saved_vals = add_func(grad)
-                saved_dict[name] = saved_vals
-            return grad_hook
-        return hook_gen, saved_dict
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new model-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        return parser
-
-    @abstractmethod
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
-        Parameters:
-            input (dict): includes the data itself and its metadata information.
-        """
-        pass
-
-    @abstractmethod
-    def forward(self):
-        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        pass
-
-    @abstractmethod
-    def optimize_parameters(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-        pass
-
-    def setup(self, opt):
-        """Load and print networks; create schedulers
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        if self.isTrain:
-            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
-        
-        if not self.isTrain or opt.continue_train:
-            load_suffix = opt.epoch
-            self.load_networks(load_suffix)
- 
-            
-        # self.print_networks(opt.verbose)
-
-    def parallelize(self, convert_sync_batchnorm=True):
-        if not self.opt.use_ddp:
-            for name in self.parallel_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-        else:
-            for name in self.model_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    if convert_sync_batchnorm:
-                        module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module)
-                    setattr(self, name, torch.nn.parallel.DistributedDataParallel(module.to(self.device),
-                        device_ids=[self.device.index], 
-                        find_unused_parameters=True, broadcast_buffers=True))
-            
-            # DistributedDataParallel is not needed when a module doesn't have any parameter that requires a gradient.
-            for name in self.parallel_names:
-                if isinstance(name, str) and name not in self.model_names:
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-            
-        # put state_dict of optimizer to gpu device
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                for optim in self.optimizers:
-                    for state in optim.state.values():
-                        for k, v in state.items():
-                            if isinstance(v, torch.Tensor):
-                                state[k] = v.to(self.device)
-
-    def data_dependent_initialize(self, data):
-        pass
-
-    def train(self):
-        """Make models train mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.train()
-
-    def eval(self):
-        """Make models eval mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.eval()
-
-    def test(self):
-        """Forward function used in test time.
-
-        This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
-        It also calls <compute_visuals> to produce additional visualization results
-        """
-        with torch.no_grad():
-            self.forward()
-            self.compute_visuals()
-
-    def compute_visuals(self):
-        """Calculate additional output images for visdom and HTML visualization"""
-        pass
-
-    def get_image_paths(self, name='A'):
-        """ Return image paths that are used to load current data"""
-        return self.image_paths if name =='A' else self.image_paths_B
-
-    def update_learning_rate(self):
-        """Update learning rates for all the networks; called at the end of every epoch"""
-        for scheduler in self.schedulers:
-            if self.opt.lr_policy == 'plateau':
-                scheduler.step(self.metric)
-            else:
-                scheduler.step()
-
-        lr = self.optimizers[0].param_groups[0]['lr']
-        print('learning rate = %.7f' % lr)
-
-    def get_current_visuals(self):
-        """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
-        visual_ret = OrderedDict()
-        for name in self.visual_names:
-            if isinstance(name, str):
-                visual_ret[name] = getattr(self, name)[:, :3, ...]
-        return visual_ret
-
-    def get_current_losses(self):
-        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
-        errors_ret = OrderedDict()
-        for name in self.loss_names:
-            if isinstance(name, str):
-                errors_ret[name] = float(getattr(self, 'loss_' + name))  # float(...) works for both scalar tensor and float number
-        return errors_ret
-
-    def save_networks(self, epoch):
-        """Save all the networks to the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if not os.path.isdir(self.save_dir):
-            os.makedirs(self.save_dir)
-
-        save_filename = 'epoch_%s.pth' % (epoch)
-        save_path = os.path.join(self.save_dir, save_filename)
-        
-        save_dict = {}
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel) or isinstance(net,
-                        torch.nn.parallel.DistributedDataParallel):
-                    net = net.module
-                save_dict[name] = net.state_dict()
-                
-
-        for i, optim in enumerate(self.optimizers):
-            save_dict['opt_%02d'%i] = optim.state_dict()
-
-        for i, sched in enumerate(self.schedulers):
-            save_dict['sched_%02d'%i] = sched.state_dict()
-        
-        torch.save(save_dict, save_path)
-
-    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
-        """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
-        key = keys[i]
-        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-                    (key == 'running_mean' or key == 'running_var'):
-                if getattr(module, key) is None:
-                    state_dict.pop('.'.join(keys))
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-               (key == 'num_batches_tracked'):
-                state_dict.pop('.'.join(keys))
-        else:
-            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
-
-    def load_networks(self, epoch):
-        """Load all the networks from the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if self.opt.isTrain and self.opt.pretrained_name is not None:
-            load_dir = os.path.join(self.opt.checkpoints_dir, self.opt.pretrained_name)
-        else:
-            load_dir = self.save_dir    
-        load_filename = 'epoch_%s.pth' % (epoch)
-        load_path = os.path.join(load_dir, load_filename)
-        state_dict = torch.load(load_path, map_location=self.device)
-        print('loading the model from %s' % load_path)
-
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel):
-                    net = net.module
-                net.load_state_dict(state_dict[name])
-        
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                print('loading the optim from %s' % load_path)
-                for i, optim in enumerate(self.optimizers):
-                    optim.load_state_dict(state_dict['opt_%02d'%i])
-
-                try:
-                    print('loading the sched from %s' % load_path)
-                    for i, sched in enumerate(self.schedulers):
-                        sched.load_state_dict(state_dict['sched_%02d'%i])
-                except:
-                    print('Failed to load schedulers, set schedulers according to epoch count manually')
-                    for i, sched in enumerate(self.schedulers):
-                        sched.last_epoch = self.opt.epoch_count - 1
-                    
-
-            
-
-    def print_networks(self, verbose):
-        """Print the total number of parameters in the network and (if verbose) network architecture
-
-        Parameters:
-            verbose (bool) -- if verbose: print the network architecture
-        """
-        print('---------- Networks initialized -------------')
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                num_params = 0
-                for param in net.parameters():
-                    num_params += param.numel()
-                if verbose:
-                    print(net)
-                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
-        print('-----------------------------------------------')
-
-    def set_requires_grad(self, nets, requires_grad=False):
-        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
-        Parameters:
-            nets (network list)   -- a list of networks
-            requires_grad (bool)  -- whether the networks require gradients or not
-        """
-        if not isinstance(nets, list):
-            nets = [nets]
-        for net in nets:
-            if net is not None:
-                for param in net.parameters():
-                    param.requires_grad = requires_grad
-
-    def generate_visuals_for_evaluation(self, data, mode):
-        return {}
diff --git a/spaces/fclong/summary/fengshen/data/mmap_dataloader/mmap_index_dataset.py b/spaces/fclong/summary/fengshen/data/mmap_dataloader/mmap_index_dataset.py
deleted file mode 100644
index 53b290c12a8825a483f14ca0535a813b36477fa1..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/mmap_dataloader/mmap_index_dataset.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-import torch
-from typing import List
-from torch.utils.data import Dataset
-
-
-class MMapIndexDataset(Dataset):
-    # datapaths 是所有的内存映射文件的路径
-    # input_tensor_name 是输入的tensor的名字 例如 ['input_ids'] 会存储在对应的文件里面
-    def __init__(self, datapaths: List[str], input_tensor_name: List[str]):
-        dict_idx_fp = {}
-        dict_bin_fp = {}
-        idx_len = []
-        for tensor_name in input_tensor_name:
-            idx_fp = []
-            bin_fp = []
-            len = 0
-            for data_path in datapaths:
-                idx_fp += [np.load(
-                    data_path + '_' + tensor_name + '.npy', mmap_mode='r')]
-                bin_fp += [np.memmap(
-                    data_path + '_' + tensor_name + '.bin',
-                    dtype='long',
-                    mode='r')]
-                len += idx_fp[-1].shape[0]
-                idx_len += [idx_fp[-1].shape[0]]
-            dict_idx_fp[tensor_name] = idx_fp
-            dict_bin_fp[tensor_name] = bin_fp
-            #  通常情况下不同的tensor的长度是一样的
-            self._len = len
-
-        self._input_tensor_name = input_tensor_name
-        self._dict_idx_fp = dict_idx_fp
-        self._dict_bin_fp = dict_bin_fp
-        self._idx_len = idx_len
-
-    def __len__(self):
-        return self._len
-
-    def __getitem__(self, idx):
-        sample = {}
-        for i in range(len(self._idx_len)):
-            if idx >= self._idx_len[i]:
-                idx -= self._idx_len[i]
-            else:
-                break
-        for tensor_name in self._input_tensor_name:
-            sample[tensor_name] = torch.tensor(self._dict_bin_fp[tensor_name][i][
-                self._dict_idx_fp[tensor_name][i][idx, 0]:
-                    self._dict_idx_fp[tensor_name][i][idx, 1]
-            ], dtype=torch.long)
-        # print(sample)
-        return sample
diff --git a/spaces/feregVcuzo/sanity-test-midi/app.py b/spaces/feregVcuzo/sanity-test-midi/app.py
deleted file mode 100644
index d70003953bab82997099781f7e4c6849763722f3..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-import gradio as gr
-
-# Load the saved model and tokenizer
-model_path = "trained_model"
-model = T5ForConditionalGeneration.from_pretrained(model_path)
-tokenizer = T5Tokenizer.from_pretrained(model_path)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model.to(device)
-
-# Define the function to generate a sequence based on the input text
-def generate_sequence(input_text):
-    test_input_tokens = tokenizer(input_text, return_tensors='pt').to(device)
-
-    model.eval()
-    with torch.no_grad():
-        test_output_tokens = model.generate(test_input_tokens['input_ids'], num_return_sequences=1)
-
-    test_output_sequence = tokenizer.decode(test_output_tokens[0], skip_special_tokens=True)
-    return test_output_sequence
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=generate_sequence,
-    inputs=gr.inputs.Textbox(lines=3, label="Input Text"),
-    outputs=gr.outputs.Textbox(label="Generated Sequence"),
-    title="MIDI Sequence Generator",
-    description="Generate a MIDI sequence based on a text description",
-    examples=[
-        "A popular classical piano piece composed by Ludwig van Beethoven",
-        "A beautiful and melancholic classical piano piece composed by Frédéric Chopin",
-    ]
-)
-
-# Launch the Gradio interface
-iface.launch(debug=True)
diff --git a/spaces/fffiloni/Image-to-MusicGen/tests/common_utils/__init__.py b/spaces/fffiloni/Image-to-MusicGen/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-MusicGen/tests/common_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav
diff --git a/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_test_val_prepare.sh b/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_test_val_prepare.sh
deleted file mode 100644
index 6017e29aa1593c1c66affa4b9081afac2b9fb000..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/fetch_data/places_standard_test_val_prepare.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-mkdir -p places_standard_dataset/original/test/
-tar -xvf test_large.tar --transform='s/.*\///' -C places_standard_dataset/original/test/
-
-mkdir -p places_standard_dataset/original/val/
-tar -xvf val_large.tar --transform='s/.*\///' -C places_standard_dataset/original/val/
diff --git a/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/training/modules/fake_fakes.py b/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/training/modules/fake_fakes.py
deleted file mode 100644
index 45c4ad559cef2730b771a709197e00ae1c87683c..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/training/modules/fake_fakes.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from kornia import SamplePadding
-from kornia.augmentation import RandomAffine, CenterCrop
-
-
-class FakeFakesGenerator:
-    def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
-        self.grad_aug = RandomAffine(degrees=360,
-                                     translate=0.2,
-                                     padding_mode=SamplePadding.REFLECTION,
-                                     keepdim=False,
-                                     p=1)
-        self.img_aug = RandomAffine(degrees=img_aug_degree,
-                                    translate=img_aug_translate,
-                                    padding_mode=SamplePadding.REFLECTION,
-                                    keepdim=True,
-                                    p=1)
-        self.aug_proba = aug_proba
-
-    def __call__(self, input_images, masks):
-        blend_masks = self._fill_masks_with_gradient(masks)
-        blend_target = self._make_blend_target(input_images)
-        result = input_images * (1 - blend_masks) + blend_target * blend_masks
-        return result, blend_masks
-
-    def _make_blend_target(self, input_images):
-        batch_size = input_images.shape[0]
-        permuted = input_images[torch.randperm(batch_size)]
-        augmented = self.img_aug(input_images)
-        is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
-        result = augmented * is_aug + permuted * (1 - is_aug)
-        return result
-
-    def _fill_masks_with_gradient(self, masks):
-        batch_size, _, height, width = masks.shape
-        grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
-            .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
-        grad = self.grad_aug(grad)
-        grad = CenterCrop((height, width))(grad)
-        grad *= masks
-
-        grad_for_min = grad + (1 - masks) * 10
-        grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
-        grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
-        grad.clamp_(min=0, max=1)
-
-        return grad
diff --git a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/diffusion_models/__init__.py b/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/diffusion_models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/flowers-team/SocialAISchool/display_LLM_evaluations.py b/spaces/flowers-team/SocialAISchool/display_LLM_evaluations.py
deleted file mode 100644
index 2fd3745ddb4ab240cc67566329093d4fa86bc423..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/display_LLM_evaluations.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-from pathlib import Path
-# Opening JSON file
-def load_json(path):
-    with open(path) as f:
-        data = json.load(f)
-    return data
-
-random_asocial = load_json(Path("llm_log/random_asocial_04_01_2023_14:28:53/evaluation_log.json"))
-random_boxes = load_json(Path("llm_log/random_boxes_04_01_2023_14:32:17/evaluation_log.json"))
-
-ada_asocial = load_json(Path("llm_log/ada_asocial_3_04_01_2023_14:53:16/evaluation_log.json"))
-ada_boxes = load_json(Path("llm_log/ada_3st_boxes_04_01_2023_18:55:38/evaluation_log.json")) # no caretaker
-ada_boxes_c = load_json(Path("llm_log/ada_3st_boxes_caretaker_04_01_2023_20:18:18/evaluation_log.json"))  # caretaker
-
-davinci_asocial = load_json(Path("llm_log/davinci_asocial_3st_04_01_2023_21:27:23/evaluation_log.json"))
-davinci_boxes = load_json(Path("llm_log/davinci_3st_boxes_04_01_2023_20:37:28/evaluation_log.json"))
-davinci_boxes_c = load_json(Path("llm_log/davinci_3st_boxes_caretaker_04_01_2023_21:17:44/evaluation_log.json"))
-
-bloom_560_asocial = load_json(Path("llm_log/bloom_560m_asocial_3st_04_01_2023_14:59:44/evaluation_log.json"))
-bloom_560_boxes = load_json(Path("llm_log/bloom_560_3st_boxes_04_01_2023_20:14:13/evaluation_log.json"))  # no caretaker
-bloom_560_boxes_c = load_json(Path("llm_log/bloom_560_3st_boxes_caretaker_04_01_2023_20:05:08/evaluation_log.json")) #  caretaker
-
-
-data = [
-    random_asocial,
-    random_boxes,
-
-    ada_asocial,
-    # ada_boxes,
-    ada_boxes_c,
-
-    davinci_asocial,
-    # davinci_boxes,
-    davinci_boxes_c,
-
-    bloom_560_asocial,
-    # bloom_560_boxes,
-    bloom_560_boxes_c,
-
-]
-
-for d in data:
-    print(f'Model: {d["model"]} Env: {d["env_name"]} {"hist" if d["feed_full_ep"] else ""} ---> {d["mean_success_rate"]} ({len(d["success_rates"])})')
-
diff --git a/spaces/flowers-team/SocialAISchool/scripts/manual_control.py b/spaces/flowers-team/SocialAISchool/scripts/manual_control.py
deleted file mode 100644
index 00b1715252b71c43a1888aee43964c9a1e535d2b..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/scripts/manual_control.py
+++ /dev/null
@@ -1,450 +0,0 @@
-#!/usr/bin/env python3
-
-import time
-import argparse
-import numpy as np
-import gym
-import gym_minigrid
-from gym_minigrid.wrappers import *
-from gym_minigrid.window import Window
-from utils import *
-from models import MultiModalBaby11ACModel
-from collections import Counter
-import torch_ac
-import json
-from termcolor import colored, COLORS
-
-from functools import partial
-from tkinter import *
-
-from torch.distributions import Categorical
-
-inter_acl = False
-draw_tree = True
-
-class InteractiveACL:
-
-    def choose(self, node):
-
-        def pop_up(options):
-            pop_data = {}
-
-            def setVar(value):
-                pop_data["var"] = value
-                root.destroy()
-
-            root = Tk()
-            root.title(node.label)
-            root.geometry('600x{}'.format(50*len(options)))
-
-            for i, o in enumerate(options):
-                fn = partial(setVar, value=i)
-                Button(root, text='{}'.format(o), command=fn).pack()
-
-            root.mainloop()
-
-            return pop_data["var"]
-
-        chosen_ind = pop_up([n.label for n in node.children])
-
-        ch = node.children[chosen_ind]
-
-        return ch
-
-
-if inter_acl:
-    interactive_acl = InteractiveACL()
-else:
-    interactive_acl = None
-
-
-def redraw(img):
-    if not args.agent_view:
-        img = env.render('human', tile_size=args.tile_size, mask_unobserved=args.mask_unobserved)
-
-    window.show_img(img)
-
-def reset():
-    # if args.seed != -1:
-    #     env.seed(args.seed)
-
-    obs = env.reset()
-
-    if hasattr(env, 'mission'):
-        print('Mission: %s' % env.mission)
-        window.set_caption(env.mission)
-
-    redraw(obs)
-
-    if draw_tree:
-        # draw tree
-        params = env.current_env.parameters
-        env.parameter_tree.draw_tree(
-            filename="viz/SocialAIParam/parameters_{}_{}".format(params["Env_type"], hash(str(params))),
-            ignore_labels=["Num_of_colors"],
-            selected_parameters=params
-        )
-
-        with open('viz/SocialAIParam/parameters_{}_{}.json'.format(params["Env_type"], hash(str(params))), 'w') as fp:
-            json.dump(params, fp)
-
-
-tot_bonus = [0]
-
-prev = {
-    "prev_obs": None,
-    "prev_info": {},
-}
-shortened_obj_names = {
-    'lockablebox'      : 'loc_box',
-    'applegenerator'   : 'app_gen',
-    'generatorplatform': 'gen_pl',
-    'marbletee'        : 'tee',
-    'remotedoor'       : 'rem_door',
-}
-
-IDX_TO_OBJECT = {v: shortened_obj_names.get(k, k) for k, v in OBJECT_TO_IDX.items()}
-# no duplicates
-assert len(IDX_TO_OBJECT) == len(OBJECT_TO_IDX)
-
-IDX_TO_COLOR = {v: k for k, v in COLOR_TO_IDX.items()}
-assert len(IDX_TO_COLOR) == len(COLOR_TO_IDX)
-
-
-def to_string(enc):
-    s =  "{:<8} {} {} {} {} {:3} {:3} {}\t".format(
-        IDX_TO_OBJECT.get(enc[0], enc[0]),  # obj
-        *enc[1:3],  # x, y
-        IDX_TO_COLOR.get(enc[3], enc[3])[:1].upper(),  # color
-        *enc[4:]  #
-    )
-
-    if IDX_TO_OBJECT.get(enc[0], enc[0]) == "unseen":
-        pass
-        # s = colored(s, "on_grey")
-
-    elif IDX_TO_OBJECT.get(enc[0], enc[0]) != "empty":
-        col = IDX_TO_COLOR.get(enc[3], enc[3])
-        if col in COLORS:
-            s = colored(s, col)
-
-    return s
-
-
-def step(action):
-    if type(action) == np.ndarray:
-        obs, reward, done, info = env.step(action)
-    else:
-        action = [int(action), np.nan, np.nan]
-        obs, reward, done, info = env.step(action)
-
-    print('\nStep=%s' % (env.step_count))
-
-    # print("".join(info["descriptions"]))
-    print(obs['utterance_history'])
-    print("")
-    # print("Your possible actions are:")
-    # print("a) move forward")
-    # print("b) turn left")
-    # print("c) turn right")
-    # print("d) toggle")
-    # print("e) no_op")
-    # print("Your next action is: ")
-
-    if args.print_grid:
-        grid = obs['image'].transpose((1, 0, 2))
-        for row_i, row in enumerate(grid):
-
-            # if row_i == 0:
-            #     for _ in row:
-            #         print(to_string(["OBJECT", "X", "Y", "C", "-", "---", "---", "-"]), end="")
-            #         # print("{:<8} {} {} {} {:2} {:2} {} {}\t".format("Object", "X", "Y", "C", "", "", "", ""), end="")
-            # print(end="\n")
-
-            for col_i, enc in enumerate(row):
-                print(str(enc), end=" | ")
-                # if row_i == len(grid) - 1 and col_i == len(row) // 2:
-                #     # gent
-                #     print(to_string(["^^^^^^", "^", "^", "^", "^", "^^^", "^^^", "^"]), end="")
-                # else:
-                #     print(to_string(enc), end="")
-            print(end="\n")
-
-    if not args.agent_view:
-
-        nvec = algo.acmodel.model_raw_action_space.nvec
-
-        raw_action = (
-            5 if np.isnan(action[0]) else 1,  # speak switch
-            0 if np.isnan(action[1]) else 1,  # speak switch
-            0 if np.isnan(action[1]) else action[1],  # template
-            0 if np.isnan(action[2]) else action[2],  # word
-        )
-
-
-        dist = []
-        for a, n in zip(raw_action, nvec):
-            logits = torch.ones(n)[None, :]
-            logits[0][int(a)] *= 10
-
-            d = Categorical(logits=logits)
-            dist.append(d)
-        if args.calc_bonus:
-            bonus = algo.calculate_exploration_bonus(
-                obs=[obs],
-                embeddings=torch.zeros([1,128]),
-                done=[done],
-                prev_obs=[prev["prev_obs"]],
-                prev_info=[prev["prev_info"]],
-                agent_actions=torch.tensor([raw_action]),
-                dist=dist,
-                i_step=0,
-            )
-
-        else:
-            bonus = [0]
-
-        prev["prev_obs"] = obs
-        prev["prev_info"] = info
-
-        tot_bonus[0] = tot_bonus[0]+bonus[0]
-        print('expl_bonus_step=%.2f' % (bonus[0]))
-        print('tot_bonus=%.2f' % (tot_bonus[0]))
-
-        if done:
-            for v in algo.visitation_counter.values():
-                v[0] = Counter()
-
-    print('Full reward (undiminshed)=%.2f' % (reward))
-
-    redraw(obs)
-
-    if done:
-        print('done!')
-        print('Reward=%.2f' % (reward))
-        print('Exploration_bonus=%.2f' % (tot_bonus[0]))
-        tot_bonus[0] = 0
-
-        if draw_tree:
-            # draw tree
-            params = env.current_env.parameters
-            env.parameter_tree.draw_tree(
-                filename="viz/SocialAIParam/parameters_{}_{}".format(params["Env_type"], hash(str(params))),
-                ignore_labels=[],
-                selected_parameters=params,
-            )
-
-            with open('viz/SocialAIParam/parameters_{}_{}.json'.format(params["Env_type"], hash(str(params))),
-                      'w') as fp:
-                json.dump(params, fp)
-
-        reset()
-
-def key_handler(event):
-
-    # if hasattr(event.canvas, "_event_loop") and event.canvas._event_loop.isRunning():
-    #     return
-
-    print('pressed', event.key)
-
-    action_dict = {
-        "up": "a) move forward",
-        "left": "b) turn left",
-        "right": "c) turn right",
-        " ": "d) toggle",
-        "shift": "e) no_op",
-    }
-
-    if event.key in action_dict:
-        your_action = action_dict[event.key]
-        print("Your next action is: {}".format(your_action))
-
-    if event.key == 'escape':
-        window.close()
-        return
-
-    if event.key == 'r':
-        reset()
-        return
-
-    if event.key == 'tab':
-        step(np.array([np.nan, np.nan, np.nan]))
-        return
-
-    if event.key == 'shift':
-        step(np.array([np.nan, np.nan, np.nan]))
-        return
-
-    if event.key == 'left':
-        step(env.actions.left)
-        return
-    if event.key == 'right':
-        step(env.actions.right)
-        return
-    if event.key == 'up':
-        step(env.actions.forward)
-        return
-    if event.key == 't':
-        step(env.actions.speak)
-        return
-
-    if event.key == '1':
-        step(np.array([np.nan, 0, 0]))
-        return
-    if event.key == '2':
-        step(np.array([np.nan, 0, 1]))
-        return
-    if event.key == '3':
-        step(np.array([np.nan, 1, 0]))
-        return
-    if event.key == '4':
-        step(np.array([np.nan, 1, 1]))
-        return
-    if event.key == '5':
-        step(np.array([np.nan, 2, 2]))
-        return
-    if event.key == '6':
-        step(np.array([np.nan, 1, 2]))
-        return
-    if event.key == '7':
-        step(np.array([np.nan, 2, 1]))
-        return
-    if event.key == '8':
-        step(np.array([np.nan, 1, 3]))
-        return
-    if event.key == 'p':
-        step(np.array([np.nan, 3, 3]))
-        return
-
-    # Spacebar
-    if event.key == ' ':
-        step(env.actions.toggle)
-        return
-    if event.key == '9':
-        step(env.actions.pickup)
-        return
-    if event.key == '0':
-        step(env.actions.drop)
-        return
-
-    if event.key == 'enter':
-        step(env.actions.done)
-        return
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--env",
-    help="gym environment to load",
-    default='SocialAI-ELangColorBoxesTestInformationSeekingParamEnv-v1',
-)
-parser.add_argument(
-    "--seed",
-    type=int,
-    help="random seed to generate the environment with",
-    default=-1
-)
-parser.add_argument(
-    "--tile_size",
-    type=int,
-    help="size at which to render tiles",
-    default=32
-)
-parser.add_argument(
-    '--agent_view',
-    default=False,
-    help="draw the agent sees (partially observable view)",
-    action='store_true'
-)
-parser.add_argument(
-    '--print_grid',
-    default=False,
-    help="print the grid with symbols",
-    action='store_true'
-)
-parser.add_argument(
-    '--calc-bonus',
-    default=False,
-    help="calculate explo bonus",
-    action='store_true'
-)
-parser.add_argument(
-    '--mask-unobserved',
-    default=False,
-    help="mask cells that are not observed by the agent",
-    action='store_true'
-)
-
-# Put all env related arguments after --env_args, e.g. --env_args nb_foo 1 is_bar True
-parser.add_argument("--env-args", nargs='*', default=None)
-
-parser.add_argument("--exploration-bonus", action="store_true", default=False,
-                    help="Use a count based exploration bonus")
-parser.add_argument("--exploration-bonus-type", nargs="+", default=["lang"],
-                    help="modality on which to use the bonus (lang/grid/cell)")
-parser.add_argument("--exploration-bonus-params", nargs="+", type=float, default=(30., 50.),  # lang
-                    help="parameters for a count based exploration bonus (C, M)")
-# parser.add_argument("--exploration-bonus-params", nargs="+", type=float, default=(3, 50.),  # cell
-#                     help="parameters for a count based exploration bonus (C, M)")
-# parser.add_argument("--exploration-bonus-params", nargs="+", type=float, default=(1.5, 50.), # grid
-#                     help="parameters for a count based exploration bonus (C, M)")
-parser.add_argument("--exploration-bonus-tanh", nargs="+", type=float, default=None,
-                    help="tanh expl bonus scale, None means no tanh")
-parser.add_argument("--intrinsic-reward-coef", type=float, default=0.1,
-                    help="tanh expl bonus scale, None means no tanh")
-
-args = parser.parse_args()
-
-if interactive_acl:
-    env = gym.make(args.env, curriculum=interactive_acl, **env_args_str_to_dict(args.env_args))
-else:
-    env = gym.make(args.env, **env_args_str_to_dict(args.env_args))
-
-if draw_tree:
-    # draw tree
-    env.parameter_tree.draw_tree(
-        filename="viz/SocialAIParam/{}_raw_tree".format(args.env),
-        ignore_labels=["Num_of_colors"],
-    )
-
-
-# if hasattr(env, "draw_tree"):
-#     env.draw_tree(ignore_labels=["Num_of_colors"])
-
-# if hasattr(env, "print_tree"):
-#     env.print_tree()
-
-if args.seed >= 0:
-    env.seed(args.seed)
-
-# dummy just algo instance just to enable exploration bonus calculation
-algo = torch_ac.PPOAlgo(
-    envs=[env],
-    acmodel=MultiModalBaby11ACModel(
-        obs_space=utils.get_obss_preprocessor(
-            obs_space=env.observation_space,
-            text=False,
-            dialogue_current=False,
-            dialogue_history=True,
-        )[0],
-        action_space=env.action_space,
-    ),
-    exploration_bonus=True,
-    exploration_bonus_tanh=args.exploration_bonus_tanh,
-    exploration_bonus_type=args.exploration_bonus_type,
-    exploration_bonus_params=args.exploration_bonus_params,
-    expert_exploration_bonus=False,
-    episodic_exploration_bonus=True,
-    intrinsic_reward_coef=args.intrinsic_reward_coef,
-    num_frames_per_proc=40,
-)
-
-# if args.agent_view:
-#     env = RGBImgPartialObsWrapper(env)
-#     env = ImgObsWrapper(env)
-
-window = Window('gym_minigrid - ' + args.env, figsize=(4, 4))
-window.reg_key_handler(key_handler)
-env.window = window
-
-# Blocking event loop
-window.show(block=True)
diff --git a/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/models/skff.py b/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/models/skff.py
deleted file mode 100644
index ee1b60ed898328981f2955abdd967becaa437b1f..0000000000000000000000000000000000000000
--- a/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/models/skff.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras import layers
-
-
-def selective_kernel_feature_fusion(
-    multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3
-):
-    channels = list(multi_scale_feature_1.shape)[-1]
-    combined_feature = layers.Add()(
-        [multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3]
-    )
-    gap = layers.GlobalAveragePooling2D()(combined_feature)
-    channel_wise_statistics = tf.reshape(gap, shape=(-1, 1, 1, channels))
-    compact_feature_representation = layers.Conv2D(
-        filters=channels // 8, kernel_size=(1, 1), activation="relu"
-    )(channel_wise_statistics)
-    feature_descriptor_1 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_descriptor_2 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_descriptor_3 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_1 = multi_scale_feature_1 * feature_descriptor_1
-    feature_2 = multi_scale_feature_2 * feature_descriptor_2
-    feature_3 = multi_scale_feature_3 * feature_descriptor_3
-    aggregated_feature = layers.Add()([feature_1, feature_2, feature_3])
-    return aggregated_feature
diff --git a/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/__init__.py b/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/__init__.py
deleted file mode 100644
index 8b3c9cdc35a03a4e4585bd6bbc9c793331eb1723..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/__init__.py
+++ /dev/null
@@ -1,161 +0,0 @@
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-#from skimage.measure import compare_ssim
-from skimage.metrics import structural_similarity as compare_ssim
-import torch
-from torch.autograd import Variable
-
-from model.stylegan.lpips import dist_model
-
-class PerceptualLoss(torch.nn.Module):
-    def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric)
-    # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
-        super(PerceptualLoss, self).__init__()
-        print('Setting up Perceptual loss...')
-        self.use_gpu = use_gpu
-        self.spatial = spatial
-        self.gpu_ids = gpu_ids
-        self.model = dist_model.DistModel()
-        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids)
-        print('...[%s] initialized'%self.model.name())
-        print('...Done')
-
-    def forward(self, pred, target, normalize=False):
-        """
-        Pred and target are Variables.
-        If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
-        If normalize is False, assumes the images are already between [-1,+1]
-
-        Inputs pred and target are Nx3xHxW
-        Output pytorch Variable N long
-        """
-
-        if normalize:
-            target = 2 * target  - 1
-            pred = 2 * pred  - 1
-
-        return self.model.forward(target, pred)
-
-def normalize_tensor(in_feat,eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))
-    return in_feat/(norm_factor+eps)
-
-def l2(p0, p1, range=255.):
-    return .5*np.mean((p0 / range - p1 / range)**2)
-
-def psnr(p0, p1, peak=255.):
-    return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
-
-def dssim(p0, p1, range=255.):
-    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
-
-def rgb2lab(in_img,mean_cent=False):
-    from skimage import color
-    img_lab = color.rgb2lab(in_img)
-    if(mean_cent):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-    return img_lab
-
-def tensor2np(tensor_obj):
-    # change dimension of a tensor object into a numpy array
-    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
-
-def np2tensor(np_obj):
-     # change dimenion of np array into tensor array
-    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
-    # image tensor to lab tensor
-    from skimage import color
-
-    img = tensor2im(image_tensor)
-    img_lab = color.rgb2lab(img)
-    if(mc_only):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-    if(to_norm and not mc_only):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-        img_lab = img_lab/100.
-
-    return np2tensor(img_lab)
-
-def tensorlab2tensor(lab_tensor,return_inbnd=False):
-    from skimage import color
-    import warnings
-    warnings.filterwarnings("ignore")
-
-    lab = tensor2np(lab_tensor)*100.
-    lab[:,:,0] = lab[:,:,0]+50
-
-    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
-    if(return_inbnd):
-        # convert back to lab, see if we match
-        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
-        mask = 1.*np.isclose(lab_back,lab,atol=2.)
-        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
-        return (im2tensor(rgb_back),mask)
-    else:
-        return im2tensor(rgb_back)
-
-def rgb2lab(input):
-    from skimage import color
-    return color.rgb2lab(input / 255.)
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
-    image_numpy = image_tensor[0].cpu().float().numpy()
-    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
-    return image_numpy.astype(imtype)
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
-    return torch.Tensor((image / factor - cent)
-                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-def tensor2vec(vector_tensor):
-    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
-
-def voc_ap(rec, prec, use_07_metric=False):
-    """ ap = voc_ap(rec, prec, [use_07_metric])
-    Compute VOC AP given precision and recall.
-    If use_07_metric is true, uses the
-    VOC 07 11 point method (default:False).
-    """
-    if use_07_metric:
-        # 11 point metric
-        ap = 0.
-        for t in np.arange(0., 1.1, 0.1):
-            if np.sum(rec >= t) == 0:
-                p = 0
-            else:
-                p = np.max(prec[rec >= t])
-            ap = ap + p / 11.
-    else:
-        # correct AP calculation
-        # first append sentinel values at the end
-        mrec = np.concatenate(([0.], rec, [1.]))
-        mpre = np.concatenate(([0.], prec, [0.]))
-
-        # compute the precision envelope
-        for i in range(mpre.size - 1, 0, -1):
-            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
-
-        # to calculate area under PR curve, look for points
-        # where X axis (recall) changes value
-        i = np.where(mrec[1:] != mrec[:-1])[0]
-
-        # and sum (\Delta recall) * prec
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
-    return ap
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
-# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
-    image_numpy = image_tensor[0].cpu().float().numpy()
-    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
-    return image_numpy.astype(imtype)
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
-# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
-    return torch.Tensor((image / factor - cent)
-                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Chatrak Bengali Movie Full Download Utorrent 325 How to Get the Torrent File and Enjoy the Movie.md b/spaces/gotiQspiryo/whisper-ui/examples/Chatrak Bengali Movie Full Download Utorrent 325 How to Get the Torrent File and Enjoy the Movie.md
deleted file mode 100644
index 56844e6087eaeef2b933076b22be4e7932188643..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Chatrak Bengali Movie Full Download Utorrent 325 How to Get the Torrent File and Enjoy the Movie.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Clash of Clans MOD APK v13.0.28 (Unlimited Money TH13 100 Work) Free Download Why You Should Try It Now.md b/spaces/gotiQspiryo/whisper-ui/examples/Clash of Clans MOD APK v13.0.28 (Unlimited Money TH13 100 Work) Free Download Why You Should Try It Now.md
deleted file mode 100644
index e762d7e558c30c2ea28f55360d08102902295c11..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Clash of Clans MOD APK v13.0.28 (Unlimited Money TH13 100 Work) Free Download Why You Should Try It Now.md	
+++ /dev/null
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-<h2>Clash of Clans MOD APK v13.0.28 (Unlimited Money TH13 100% Work) Free Download</h2><br /><p><b><b>Download Zip</b> &harr; <a href="https://urlgoal.com/2uyMEG">https://urlgoal.com/2uyMEG</a></b></p><br /><br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Orcad 9.2 WORK Full Crack Vn-zoom.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Orcad 9.2 WORK Full Crack Vn-zoom.md
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index de0f0540917814e57deafbd10e5e6081ff1bce27..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Orcad 9.2 WORK Full Crack Vn-zoom.md	
+++ /dev/null
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-<h2>download orcad 9.2 full crack vn-zoom</h2><br /><p><b><b>Download</b> ->->->-> <a href="https://urlgoal.com/2uyLZG">https://urlgoal.com/2uyLZG</a></b></p><br /><br />
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diff --git a/spaces/gputrain/UrbanSounds8K/README.md b/spaces/gputrain/UrbanSounds8K/README.md
deleted file mode 100644
index eaea700a7bee5bbdd54de68059ca6a9ec74c05aa..0000000000000000000000000000000000000000
--- a/spaces/gputrain/UrbanSounds8K/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: UrbanSounds8K
-emoji: ⚡
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.3
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/gradio/HuBERT/examples/laser/laser_src/laser_lstm.py b/spaces/gradio/HuBERT/examples/laser/laser_src/laser_lstm.py
deleted file mode 100644
index 10df90e002d5a7dd74a571dbc3b328c130c57a0a..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/laser/laser_src/laser_lstm.py
+++ /dev/null
@@ -1,585 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from fairseq import options, utils
-
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqIncrementalDecoder,
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-
-
-@register_model("laser_lstm")
-class LSTMModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens=None,
-        tgt_tokens=None,
-        tgt_lengths=None,
-        target_language_id=None,
-        dataset_name="",
-    ):
-        assert target_language_id is not None
-
-        src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name)
-        return self.decoder(
-            prev_output_tokens, src_encoder_out, lang_id=target_language_id
-        )
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--dropout",
-            default=0.1,
-            type=float,
-            metavar="D",
-            help="dropout probability",
-        )
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained encoder embedding",
-        )
-        parser.add_argument(
-            "--encoder-hidden-size", type=int, metavar="N", help="encoder hidden size"
-        )
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="N", help="number of encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-bidirectional",
-            action="store_true",
-            help="make all layers of encoder bidirectional",
-        )
-        parser.add_argument(
-            "--decoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained decoder embedding",
-        )
-        parser.add_argument(
-            "--decoder-hidden-size", type=int, metavar="N", help="decoder hidden size"
-        )
-        parser.add_argument(
-            "--decoder-layers", type=int, metavar="N", help="number of decoder layers"
-        )
-        parser.add_argument(
-            "--decoder-out-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder output embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-zero-init",
-            type=str,
-            metavar="BOOL",
-            help="initialize the decoder hidden/cell state to zero",
-        )
-        parser.add_argument(
-            "--decoder-lang-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder language embedding dimension",
-        )
-        parser.add_argument(
-            "--fixed-embeddings",
-            action="store_true",
-            help="keep embeddings fixed (ENCODER ONLY)",
-        )  # TODO Also apply to decoder embeddings?
-
-        # Granular dropout settings (if not specified these default to --dropout)
-        parser.add_argument(
-            "--encoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder input embedding",
-        )
-        parser.add_argument(
-            "--encoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder output",
-        )
-        parser.add_argument(
-            "--decoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder input embedding",
-        )
-        parser.add_argument(
-            "--decoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder output",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        # make sure that all args are properly defaulted (in case there are any new ones)
-        base_architecture(args)
-
-        def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
-            num_embeddings = len(dictionary)
-            padding_idx = dictionary.pad()
-            embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-            embed_dict = utils.parse_embedding(embed_path)
-            utils.print_embed_overlap(embed_dict, dictionary)
-            return utils.load_embedding(embed_dict, dictionary, embed_tokens)
-
-        pretrained_encoder_embed = None
-        if args.encoder_embed_path:
-            pretrained_encoder_embed = load_pretrained_embedding_from_file(
-                args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
-            )
-        pretrained_decoder_embed = None
-        if args.decoder_embed_path:
-            pretrained_decoder_embed = load_pretrained_embedding_from_file(
-                args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim
-            )
-
-        num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0
-
-        encoder = LSTMEncoder(
-            dictionary=task.source_dictionary,
-            embed_dim=args.encoder_embed_dim,
-            hidden_size=args.encoder_hidden_size,
-            num_layers=args.encoder_layers,
-            dropout_in=args.encoder_dropout_in,
-            dropout_out=args.encoder_dropout_out,
-            bidirectional=args.encoder_bidirectional,
-            pretrained_embed=pretrained_encoder_embed,
-            fixed_embeddings=args.fixed_embeddings,
-        )
-        decoder = LSTMDecoder(
-            dictionary=task.target_dictionary,
-            embed_dim=args.decoder_embed_dim,
-            hidden_size=args.decoder_hidden_size,
-            out_embed_dim=args.decoder_out_embed_dim,
-            num_layers=args.decoder_layers,
-            dropout_in=args.decoder_dropout_in,
-            dropout_out=args.decoder_dropout_out,
-            zero_init=options.eval_bool(args.decoder_zero_init),
-            encoder_embed_dim=args.encoder_embed_dim,
-            encoder_output_units=encoder.output_units,
-            pretrained_embed=pretrained_decoder_embed,
-            num_langs=num_langs,
-            lang_embed_dim=args.decoder_lang_embed_dim,
-        )
-        return cls(encoder, decoder)
-
-
-class LSTMEncoder(FairseqEncoder):
-    """LSTM encoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        bidirectional=False,
-        left_pad=True,
-        pretrained_embed=None,
-        padding_value=0.0,
-        fixed_embeddings=False,
-    ):
-        super().__init__(dictionary)
-        self.num_layers = num_layers
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.bidirectional = bidirectional
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        self.padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-        if fixed_embeddings:
-            self.embed_tokens.weight.requires_grad = False
-
-        self.lstm = LSTM(
-            input_size=embed_dim,
-            hidden_size=hidden_size,
-            num_layers=num_layers,
-            dropout=self.dropout_out if num_layers > 1 else 0.0,
-            bidirectional=bidirectional,
-        )
-        self.left_pad = left_pad
-        self.padding_value = padding_value
-
-        self.output_units = hidden_size
-        if bidirectional:
-            self.output_units *= 2
-
-    def forward(self, src_tokens, src_lengths, dataset_name):
-        if self.left_pad:
-            # convert left-padding to right-padding
-            src_tokens = utils.convert_padding_direction(
-                src_tokens,
-                self.padding_idx,
-                left_to_right=True,
-            )
-
-        bsz, seqlen = src_tokens.size()
-
-        # embed tokens
-        x = self.embed_tokens(src_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # pack embedded source tokens into a PackedSequence
-        try:
-            packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist())
-        except BaseException:
-            raise Exception(f"Packing failed in dataset {dataset_name}")
-
-        # apply LSTM
-        if self.bidirectional:
-            state_size = 2 * self.num_layers, bsz, self.hidden_size
-        else:
-            state_size = self.num_layers, bsz, self.hidden_size
-        h0 = x.data.new(*state_size).zero_()
-        c0 = x.data.new(*state_size).zero_()
-        packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
-
-        # unpack outputs and apply dropout
-        x, _ = nn.utils.rnn.pad_packed_sequence(
-            packed_outs, padding_value=self.padding_value
-        )
-        x = F.dropout(x, p=self.dropout_out, training=self.training)
-        assert list(x.size()) == [seqlen, bsz, self.output_units]
-
-        if self.bidirectional:
-
-            def combine_bidir(outs):
-                return torch.cat(
-                    [
-                        torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view(
-                            1, bsz, self.output_units
-                        )
-                        for i in range(self.num_layers)
-                    ],
-                    dim=0,
-                )
-
-            final_hiddens = combine_bidir(final_hiddens)
-            final_cells = combine_bidir(final_cells)
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
-
-        # Set padded outputs to -inf so they are not selected by max-pooling
-        padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1)
-        if padding_mask.any():
-            x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x)
-
-        # Build the sentence embedding by max-pooling over the encoder outputs
-        sentemb = x.max(dim=0)[0]
-
-        return {
-            "sentemb": sentemb,
-            "encoder_out": (x, final_hiddens, final_cells),
-            "encoder_padding_mask": encoder_padding_mask
-            if encoder_padding_mask.any()
-            else None,
-        }
-
-    def reorder_encoder_out(self, encoder_out_dict, new_order):
-        encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select(
-            0, new_order
-        )
-        encoder_out_dict["encoder_out"] = tuple(
-            eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"]
-        )
-        if encoder_out_dict["encoder_padding_mask"] is not None:
-            encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[
-                "encoder_padding_mask"
-            ].index_select(1, new_order)
-        return encoder_out_dict
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-class LSTMDecoder(FairseqIncrementalDecoder):
-    """LSTM decoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        out_embed_dim=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        zero_init=False,
-        encoder_embed_dim=512,
-        encoder_output_units=512,
-        pretrained_embed=None,
-        num_langs=1,
-        lang_embed_dim=0,
-    ):
-        super().__init__(dictionary)
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-
-        self.layers = nn.ModuleList(
-            [
-                LSTMCell(
-                    input_size=encoder_output_units + embed_dim + lang_embed_dim
-                    if layer == 0
-                    else hidden_size,
-                    hidden_size=hidden_size,
-                )
-                for layer in range(num_layers)
-            ]
-        )
-        if hidden_size != out_embed_dim:
-            self.additional_fc = Linear(hidden_size, out_embed_dim)
-        self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
-
-        if zero_init:
-            self.sentemb2init = None
-        else:
-            self.sentemb2init = Linear(
-                encoder_output_units, 2 * num_layers * hidden_size
-            )
-
-        if lang_embed_dim == 0:
-            self.embed_lang = None
-        else:
-            self.embed_lang = nn.Embedding(num_langs, lang_embed_dim)
-            nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1)
-
-    def forward(
-        self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0
-    ):
-        sentemb = encoder_out_dict["sentemb"]
-        encoder_out = encoder_out_dict["encoder_out"]
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-        bsz, seqlen = prev_output_tokens.size()
-
-        # get outputs from encoder
-        encoder_outs, _, _ = encoder_out[:3]
-        srclen = encoder_outs.size(0)
-
-        # embed tokens
-        x = self.embed_tokens(prev_output_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # embed language identifier
-        if self.embed_lang is not None:
-            lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id)
-            langemb = self.embed_lang(lang_ids)
-            # TODO Should we dropout here???
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # initialize previous states (or get from cache during incremental generation)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is not None:
-            prev_hiddens, prev_cells, input_feed = cached_state
-        else:
-            num_layers = len(self.layers)
-            if self.sentemb2init is None:
-                prev_hiddens = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-                prev_cells = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-            else:
-                init = self.sentemb2init(sentemb)
-                prev_hiddens = [
-                    init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size]
-                    for i in range(num_layers)
-                ]
-                prev_cells = [
-                    init[
-                        :,
-                        (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size,
-                    ]
-                    for i in range(num_layers)
-                ]
-            input_feed = x.data.new(bsz, self.hidden_size).zero_()
-
-        attn_scores = x.data.new(srclen, seqlen, bsz).zero_()
-        outs = []
-        for j in range(seqlen):
-            if self.embed_lang is None:
-                input = torch.cat((x[j, :, :], sentemb), dim=1)
-            else:
-                input = torch.cat((x[j, :, :], sentemb, langemb), dim=1)
-
-            for i, rnn in enumerate(self.layers):
-                # recurrent cell
-                hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
-
-                # hidden state becomes the input to the next layer
-                input = F.dropout(hidden, p=self.dropout_out, training=self.training)
-
-                # save state for next time step
-                prev_hiddens[i] = hidden
-                prev_cells[i] = cell
-
-            out = hidden
-            out = F.dropout(out, p=self.dropout_out, training=self.training)
-
-            # input feeding
-            input_feed = out
-
-            # save final output
-            outs.append(out)
-
-        # cache previous states (no-op except during incremental generation)
-        utils.set_incremental_state(
-            self,
-            incremental_state,
-            "cached_state",
-            (prev_hiddens, prev_cells, input_feed),
-        )
-
-        # collect outputs across time steps
-        x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(1, 0)
-
-        # srclen x tgtlen x bsz -> bsz x tgtlen x srclen
-        attn_scores = attn_scores.transpose(0, 2)
-
-        # project back to size of vocabulary
-        if hasattr(self, "additional_fc"):
-            x = self.additional_fc(x)
-            x = F.dropout(x, p=self.dropout_out, training=self.training)
-        x = self.fc_out(x)
-
-        return x, attn_scores
-
-    def reorder_incremental_state(self, incremental_state, new_order):
-        super().reorder_incremental_state(incremental_state, new_order)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is None:
-            return
-
-        def reorder_state(state):
-            if isinstance(state, list):
-                return [reorder_state(state_i) for state_i in state]
-            return state.index_select(0, new_order)
-
-        new_state = tuple(map(reorder_state, cached_state))
-        utils.set_incremental_state(self, incremental_state, "cached_state", new_state)
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.uniform_(m.weight, -0.1, 0.1)
-    nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def LSTM(input_size, hidden_size, **kwargs):
-    m = nn.LSTM(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def LSTMCell(input_size, hidden_size, **kwargs):
-    m = nn.LSTMCell(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def Linear(in_features, out_features, bias=True, dropout=0):
-    """Weight-normalized Linear layer (input: N x T x C)"""
-    m = nn.Linear(in_features, out_features, bias=bias)
-    m.weight.data.uniform_(-0.1, 0.1)
-    if bias:
-        m.bias.data.uniform_(-0.1, 0.1)
-    return m
-
-
-@register_model_architecture("laser_lstm", "laser_lstm")
-def base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_hidden_size = getattr(
-        args, "encoder_hidden_size", args.encoder_embed_dim
-    )
-    args.encoder_layers = getattr(args, "encoder_layers", 1)
-    args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
-    args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
-    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_hidden_size = getattr(
-        args, "decoder_hidden_size", args.decoder_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 1)
-    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
-    args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
-    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
-    args.decoder_zero_init = getattr(args, "decoder_zero_init", "0")
-    args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0)
-    args.fixed_embeddings = getattr(args, "fixed_embeddings", False)
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/models/__init__.py b/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/models/__init__.py
deleted file mode 100644
index 3e3039b7081a9e3228c8abefb6391a75b4864439..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/models/__init__.py
+++ /dev/null
@@ -1,11 +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 .wav2vec_u import Wav2vec_U
-
-
-__all__ = [
-    "Wav2vec_U",
-]
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_manifest.py b/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_manifest.py
deleted file mode 100644
index 9b8aa180e88d9ee98bdca7089aed5046ec0d9cb9..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/wav2vec_manifest.py
+++ /dev/null
@@ -1,87 +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.
-"""
-Data pre-processing: build vocabularies and binarize training data.
-"""
-
-import argparse
-import glob
-import os
-import random
-
-import soundfile
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "root", metavar="DIR", help="root directory containing flac files to index"
-    )
-    parser.add_argument(
-        "--valid-percent",
-        default=0.01,
-        type=float,
-        metavar="D",
-        help="percentage of data to use as validation set (between 0 and 1)",
-    )
-    parser.add_argument(
-        "--dest", default=".", type=str, metavar="DIR", help="output directory"
-    )
-    parser.add_argument(
-        "--ext", default="flac", type=str, metavar="EXT", help="extension to look for"
-    )
-    parser.add_argument("--seed", default=42, type=int, metavar="N", help="random seed")
-    parser.add_argument(
-        "--path-must-contain",
-        default=None,
-        type=str,
-        metavar="FRAG",
-        help="if set, path must contain this substring for a file to be included in the manifest",
-    )
-    return parser
-
-
-def main(args):
-    assert args.valid_percent >= 0 and args.valid_percent <= 1.0
-
-    if not os.path.exists(args.dest):
-        os.makedirs(args.dest)
-
-    dir_path = os.path.realpath(args.root)
-    search_path = os.path.join(dir_path, "**/*." + args.ext)
-    rand = random.Random(args.seed)
-
-    valid_f = (
-        open(os.path.join(args.dest, "valid.tsv"), "w")
-        if args.valid_percent > 0
-        else None
-    )
-
-    with open(os.path.join(args.dest, "train.tsv"), "w") as train_f:
-        print(dir_path, file=train_f)
-
-        if valid_f is not None:
-            print(dir_path, file=valid_f)
-
-        for fname in glob.iglob(search_path, recursive=True):
-            file_path = os.path.realpath(fname)
-
-            if args.path_must_contain and args.path_must_contain not in file_path:
-                continue
-
-            frames = soundfile.info(fname).frames
-            dest = train_f if rand.random() > args.valid_percent else valid_f
-            print(
-                "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=dest
-            )
-    if valid_f is not None:
-        valid_f.close()
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    main(args)
diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/hooks/useFetch.ts b/spaces/gsaivinay/Llama-2-13B-GGML-UI/hooks/useFetch.ts
deleted file mode 100644
index 3e5c7cc86cdd6f13ff246718f861cb8d526edced..0000000000000000000000000000000000000000
--- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/hooks/useFetch.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-export type RequestModel = {
-  params?: object;
-  headers?: object;
-  signal?: AbortSignal;
-};
-
-export type RequestWithBodyModel = RequestModel & {
-  body?: object | FormData;
-};
-
-export const useFetch = () => {
-  const handleFetch = async (
-    url: string,
-    request: any,
-    signal?: AbortSignal,
-  ) => {
-    const requestUrl = request?.params ? `${url}${request.params}` : url;
-
-    const requestBody = request?.body
-      ? request.body instanceof FormData
-        ? { ...request, body: request.body }
-        : { ...request, body: JSON.stringify(request.body) }
-      : request;
-
-    const headers = {
-      ...(request?.headers
-        ? request.headers
-        : request?.body && request.body instanceof FormData
-        ? {}
-        : { 'Content-type': 'application/json' }),
-    };
-
-    return fetch(requestUrl, { ...requestBody, headers, signal })
-      .then((response) => {
-        if (!response.ok) throw response;
-
-        const contentType = response.headers.get('content-type');
-        const contentDisposition = response.headers.get('content-disposition');
-
-        const headers = response.headers;
-
-        const result =
-          contentType &&
-          (contentType?.indexOf('application/json') !== -1 ||
-            contentType?.indexOf('text/plain') !== -1)
-            ? response.json()
-            : contentDisposition?.indexOf('attachment') !== -1
-            ? response.blob()
-            : response;
-
-        return result;
-      })
-      .catch(async (err) => {
-        const contentType = err.headers.get('content-type');
-
-        const errResult =
-          contentType && contentType?.indexOf('application/problem+json') !== -1
-            ? await err.json()
-            : err;
-
-        throw errResult;
-      });
-  };
-
-  return {
-    get: async <T>(url: string, request?: RequestModel): Promise<T> => {
-      return handleFetch(url, { ...request, method: 'get' });
-    },
-    post: async <T>(
-      url: string,
-      request?: RequestWithBodyModel,
-    ): Promise<T> => {
-      return handleFetch(url, { ...request, method: 'post' });
-    },
-    put: async <T>(url: string, request?: RequestWithBodyModel): Promise<T> => {
-      return handleFetch(url, { ...request, method: 'put' });
-    },
-    patch: async <T>(
-      url: string,
-      request?: RequestWithBodyModel,
-    ): Promise<T> => {
-      return handleFetch(url, { ...request, method: 'patch' });
-    },
-    delete: async <T>(url: string, request?: RequestModel): Promise<T> => {
-      return handleFetch(url, { ...request, method: 'delete' });
-    },
-  };
-};
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/custom_ops.py b/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/custom_ops.py
deleted file mode 100644
index ed2524f47ab3d5b8750cfb868cc14012f424acc8..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,159 +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 glob
-import hashlib
-import importlib
-import os
-import re
-import shutil
-import uuid
-
-import torch
-import torch.utils.cpp_extension
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-#----------------------------------------------------------------------------
-
-def _get_mangled_gpu_name():
-    name = torch.cuda.get_device_name().lower()
-    out = []
-    for c in name:
-        if re.match('[a-z0-9_-]+', c):
-            out.append(c)
-        else:
-            out.append('-')
-    return ''.join(out)
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-    if headers is None:
-        headers = []
-    if source_dir is not None:
-        sources = [os.path.join(source_dir, fname) for fname in sources]
-        headers = [os.path.join(source_dir, fname) for fname in headers]
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-    verbose_build = (verbosity == 'full')
-
-    # Compile and load.
-    try: # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
-        # break the build or unnecessarily restrict what's available to nvcc.
-        # Unset it to let nvcc decide based on what's available on the
-        # machine.
-        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        #
-        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
-        # around the *.cu dependency bug in ninja config.
-        #
-        all_source_files = sorted(sources + headers)
-        all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files)
-        if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ):
-
-            # Compute combined hash digest for all source files.
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-
-            # Select cached build directory name.
-            source_digest = hash_md5.hexdigest()
-            build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
-            cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
-
-            if not os.path.isdir(cached_build_dir):
-                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
-                os.makedirs(tmpdir)
-                for src in all_source_files:
-                    shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src)))
-                try:
-                    os.replace(tmpdir, cached_build_dir) # atomic
-                except OSError:
-                    # source directory already exists, delete tmpdir and its contents.
-                    shutil.rmtree(tmpdir)
-                    if not os.path.isdir(cached_build_dir): raise
-
-            # Compile.
-            cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
-                verbose=verbose_build, sources=cached_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-
-        # Load.
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache dict.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/networks.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/networks.py
deleted file mode 100644
index 40ce9f9974267da87505b9c0a5e929b12c644801..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/networks.py
+++ /dev/null
@@ -1,521 +0,0 @@
-"""This script defines deep neural networks for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch.nn.functional as F
-from torch.nn import init
-import functools
-from torch.optim import lr_scheduler
-import torch
-from torch import Tensor
-import torch.nn as nn
-try:
-    from torch.hub import load_state_dict_from_url
-except ImportError:
-    from torch.utils.model_zoo import load_url as load_state_dict_from_url
-from typing import Type, Any, Callable, Union, List, Optional
-from .arcface_torch.backbones import get_model
-from kornia.geometry import warp_affine
-
-def resize_n_crop(image, M, dsize=112):
-    # image: (b, c, h, w)
-    # M   :  (b, 2, 3)
-    return warp_affine(image, M, dsize=(dsize, dsize))
-
-def filter_state_dict(state_dict, remove_name='fc'):
-    new_state_dict = {}
-    for key in state_dict:
-        if remove_name in key:
-            continue
-        new_state_dict[key] = state_dict[key]
-    return new_state_dict
-
-def get_scheduler(optimizer, opt):
-    """Return a learning rate scheduler
-
-    Parameters:
-        optimizer          -- the optimizer of the network
-        opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
-                              opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
-
-    For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
-    See https://pytorch.org/docs/stable/optim.html for more details.
-    """
-    if opt.lr_policy == 'linear':
-        def lambda_rule(epoch):
-            lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs + 1)
-            return lr_l
-        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
-    elif opt.lr_policy == 'step':
-        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_epochs, gamma=0.2)
-    elif opt.lr_policy == 'plateau':
-        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
-    elif opt.lr_policy == 'cosine':
-        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
-    else:
-        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
-    return scheduler
-
-
-def define_net_recon(net_recon, use_last_fc=False, init_path=None):
-    return ReconNetWrapper(net_recon, use_last_fc=use_last_fc, init_path=init_path)
-
-def define_net_recog(net_recog, pretrained_path=None):
-    net = RecogNetWrapper(net_recog=net_recog, pretrained_path=pretrained_path)
-    net.eval()
-    return net
-
-class ReconNetWrapper(nn.Module):
-    fc_dim=257
-    def __init__(self, net_recon, use_last_fc=False, init_path=None):
-        super(ReconNetWrapper, self).__init__()
-        self.use_last_fc = use_last_fc
-        if net_recon not in func_dict:
-            return  NotImplementedError('network [%s] is not implemented', net_recon)
-        func, last_dim = func_dict[net_recon]
-        backbone = func(use_last_fc=use_last_fc, num_classes=self.fc_dim)
-        if init_path and os.path.isfile(init_path):
-            state_dict = filter_state_dict(torch.load(init_path, map_location='cpu'))
-            backbone.load_state_dict(state_dict)
-            print("loading init net_recon %s from %s" %(net_recon, init_path))
-        self.backbone = backbone
-        if not use_last_fc:
-            self.final_layers = nn.ModuleList([
-                conv1x1(last_dim, 80, bias=True), # id layer
-                conv1x1(last_dim, 64, bias=True), # exp layer
-                conv1x1(last_dim, 80, bias=True), # tex layer
-                conv1x1(last_dim, 3, bias=True),  # angle layer
-                conv1x1(last_dim, 27, bias=True), # gamma layer
-                conv1x1(last_dim, 2, bias=True),  # tx, ty
-                conv1x1(last_dim, 1, bias=True)   # tz
-            ])
-            for m in self.final_layers:
-                nn.init.constant_(m.weight, 0.)
-                nn.init.constant_(m.bias, 0.)
-
-    def forward(self, x):
-        x = self.backbone(x)
-        if not self.use_last_fc:
-            output = []
-            for layer in self.final_layers:
-                output.append(layer(x))
-            x = torch.flatten(torch.cat(output, dim=1), 1)
-        return x
-
-
-class RecogNetWrapper(nn.Module):
-    def __init__(self, net_recog, pretrained_path=None, input_size=112):
-        super(RecogNetWrapper, self).__init__()
-        net = get_model(name=net_recog, fp16=False)
-        if pretrained_path:
-            state_dict = torch.load(pretrained_path, map_location='cpu')
-            net.load_state_dict(state_dict)
-            print("loading pretrained net_recog %s from %s" %(net_recog, pretrained_path))
-        for param in net.parameters():
-            param.requires_grad = False
-        self.net = net
-        self.preprocess = lambda x: 2 * x - 1
-        self.input_size=input_size
-        
-    def forward(self, image, M):
-        image = self.preprocess(resize_n_crop(image, M, self.input_size))
-        id_feature = F.normalize(self.net(image), dim=-1, p=2)
-        return id_feature
-
-
-# adapted from https://github.com/pytorch/vision/edit/master/torchvision/models/resnet.py
-__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
-           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
-           'wide_resnet50_2', 'wide_resnet101_2']
-
-
-model_urls = {
-    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
-    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
-    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
-    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
-    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
-    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
-    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
-    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
-    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
-}
-
-
-def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=dilation, groups=groups, bias=False, dilation=dilation)
-
-
-def conv1x1(in_planes: int, out_planes: int, stride: int = 1, bias: bool = False) -> nn.Conv2d:
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=bias)
-
-
-class BasicBlock(nn.Module):
-    expansion: int = 1
-
-    def __init__(
-        self,
-        inplanes: int,
-        planes: int,
-        stride: int = 1,
-        downsample: Optional[nn.Module] = None,
-        groups: int = 1,
-        base_width: int = 64,
-        dilation: int = 1,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x: Tensor) -> Tensor:
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
-    expansion: int = 4
-
-    def __init__(
-        self,
-        inplanes: int,
-        planes: int,
-        stride: int = 1,
-        downsample: Optional[nn.Module] = None,
-        groups: int = 1,
-        base_width: int = 64,
-        dilation: int = 1,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(Bottleneck, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, width)
-        self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
-        self.bn2 = norm_layer(width)
-        self.conv3 = conv1x1(width, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x: Tensor) -> Tensor:
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(
-        self,
-        block: Type[Union[BasicBlock, Bottleneck]],
-        layers: List[int],
-        num_classes: int = 1000,
-        zero_init_residual: bool = False,
-        use_last_fc: bool = False,
-        groups: int = 1,
-        width_per_group: int = 64,
-        replace_stride_with_dilation: Optional[List[bool]] = None,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(ResNet, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self._norm_layer = norm_layer
-
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            # each element in the tuple indicates if we should replace
-            # the 2x2 stride with a dilated convolution instead
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.use_last_fc = use_last_fc
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        
-        if self.use_last_fc:
-            self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        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.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-
-
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]
-
-    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
-                    stride: int = 1, dilate: bool = False) -> nn.Sequential:
-        norm_layer = self._norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def _forward_impl(self, x: Tensor) -> Tensor:
-        # See note [TorchScript super()]
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        if self.use_last_fc:
-            x = torch.flatten(x, 1)
-            x = self.fc(x)
-        return x
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self._forward_impl(x)
-
-
-def _resnet(
-    arch: str,
-    block: Type[Union[BasicBlock, Bottleneck]],
-    layers: List[int],
-    pretrained: bool,
-    progress: bool,
-    **kwargs: Any
-) -> ResNet:
-    model = ResNet(block, layers, **kwargs)
-    if pretrained:
-        state_dict = load_state_dict_from_url(model_urls[arch],
-                                              progress=progress)
-        model.load_state_dict(state_dict)
-    return model
-
-
-def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
-                   **kwargs)
-
-
-def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 4
-    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 8
-    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""Wide ResNet-50-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""Wide ResNet-101-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
-
-
-func_dict = {
-    'resnet18': (resnet18, 512),
-    'resnet50': (resnet50, 2048)
-}
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/openpose/src/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/openpose/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.cpp b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.cpp
deleted file mode 100644
index 42bdd483490a555266c8f9b9dd6684464b2088bc..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.cpp
+++ /dev/null
@@ -1,105 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/plot_bokeh_script.py b/spaces/h2oai/wave-tour/examples/plot_bokeh_script.py
deleted file mode 100644
index 046abb78ac8b49404473e33d65880a650017b0a1..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_bokeh_script.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Plot / Bokeh / Script
-# Embed Bokeh components into a page using Javascript.
-# ---
-
-import json
-from h2o_wave import site, ui
-from bokeh.resources import CDN
-from bokeh.plotting import figure
-from bokeh.embed import json_item
-from bokeh.sampledata.iris import flowers
-
-# Make a plot
-plot = figure(title="Iris Morphology")
-plot.xaxis.axis_label = 'Petal Length'
-plot.yaxis.axis_label = 'Petal Width'
-
-colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
-colors = [colormap[x] for x in flowers['species']]
-plot.circle(flowers["petal_length"], flowers["petal_width"], color=colors, fill_alpha=0.2, size=10)
-
-# Serialize the plot as JSON.
-# See https://docs.bokeh.org/en/latest/docs/user_guide/embed.html#json-items
-plot_id = 'my_plot'
-plot_data = json.dumps(json_item(plot, plot_id))
-
-page = site['/demo']
-
-page['meta'] = ui.meta_card(
-    box='',
-    # Import Bokeh Javascript libraries from CDN
-    scripts=[ui.script(path=f) for f in CDN.js_files],
-    # Execute custom Javascript
-    script=ui.inline_script(
-        # Call Bakeh's renderer using Javascript
-        content=f'Bokeh.embed.embed_item({plot_data});',
-        # Ensure that the Bokeh Javascript library is available
-        requires=['Bokeh'],
-        # Ensure that the target HTML container element is available
-        targets=[plot_id],
-    ),
-)
-
-# Create a HTML container element to hold our plot.
-page['example'] = ui.markup_card(
-    box='1 1 5 8',
-    title='',
-    content=f'<div id="{plot_id}"></div>',
-)
-
-page.save()
diff --git a/spaces/hackathon-pln-es/clasificador-de-tesis/app.py b/spaces/hackathon-pln-es/clasificador-de-tesis/app.py
deleted file mode 100644
index 58842289a0f89cbce4a74afd25e865b456b2dff9..0000000000000000000000000000000000000000
--- a/spaces/hackathon-pln-es/clasificador-de-tesis/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, pipeline
-
-title = "Clasificador de Tesis"
-
-description = "Clasifica el texto de una tesis con la carrera a la que le pertenece [Actuaría, Derecho, Economía, Psicología, Química Farmacéutico Biológica]."
-
-article = """
-## Obtención de los datos
-    
-### Motivos y Objetivo de la Obtención de los Datos
-    
-La creación de este dataset ha sido motivado por la participación en el Hackathon 2022 de PLN en Español organizado por Somos NLP, con el objetivo de democratizar el NLP en español y promover su aplicación a buenas causas y, debido a que no existe un dataset de tesis en español.
-    
-### Proceso de Obtención
-    
-Se optó realizar un [scraper](https://github.com/IsaacIsaias/NLP-clasificador-tesis/blob/main/main.py) para conseguir la información. Se decidió usar la base de datos [TESIUNAM](https://tesiunam.dgb.unam.mx/F?func=find-b-0&local_base=TES01), la cual es un catálogo en donde se pueden visualizar las tesis de los sustentantes que obtuvieron un grado en la Universidad Nacional Autónoma de México (UNAM), así como de las tesis de licenciatura de escuelas incorporadas a ella.
-        
-Para ello, en primer lugar se consultó la [Oferta Académica](http://oferta.unam.mx/indice-alfabetico.html) de la Universidad, sitio de donde se extrajo cada una de las 131 licenciaturas en forma de lista. Después, se analizó cada uno de los casos presente en la base de datos, debido a que existen carreras con más de 10 tesis, otras con menos de 10, o con solo una o ninguna tesis disponible. Se usó Selenium para la interacción con un navegador Web (Edge) y está actualmente configurado para obtener las primeras 20 tesis, o menos, por carrera.
-      
-Este scraper obtiene de esta base de datos:
-- Nombres del Autor
-- Apellidos del Autor
-- Título de la Tesis
-- Año de la Tesis
-- Carrera de la Tesis
-        
-A la vez, este scraper descarga cada una de las tesis en la carpeta *Downloads* del equipo local.
-        
-En el csv formado por el scraper se añadió el Resumen/Introduccion/Conclusion de la tesis, dependiendo cual primero estuviera disponible, ya que la complejidad recae en la diferencia de la estructura y formato de cada una de las tesis.
-        
-Posteriormente, se le realizó un procesado al dataset con las siguientes tareas:
-- Conversión a minúsculas
-- Tokenización
-- Eliminación de palabras que no son alfanuméricas
-- Eliminación de palabras vacías
-- Stemming: eliminación de plurales
-        
-## Impacto Social
-    
-El presente conjunto de datos favorecerá la búsqueda e investigación relacionada con tesis en español, a partir de su categorización automática por un modelo entrenado con este dataset. Esta tarea favorece el cumplimiento del Objetivo 4 de Desarrollo Sostenible de la ONU: Educación y Calidad (https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/).
-    
-## Miembros del Equipo:
-    
-- Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
-- Dionis López Ramos ([inoid](https://huggingface.co/inoid))
-- Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel))
-- Ximena Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer))
-"""
-
-tokenizer = AutoTokenizer.from_pretrained('hiiamsid/BETO_es_binary_classification', use_fast=False)
-model = AutoModelForSequenceClassification.from_pretrained(
-    'hackathon-pln-es/unam_tesis_BETO_finnetuning', num_labels=5, output_attentions=False, output_hidden_states=False)
-#pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
-pipe = pipeline("text-classification", model='hackathon-pln-es/unam_tesis_BETO_finnetuning', tokenizer=tokenizer) 
-#classificationResult = pipe("El objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa.")
-
-
-def thesis_prediction(input):
-    classificationResult = pipe(input)
-    classificationResult[0].sort(reverse=True, key=lambda x:x['score'])
-    keyClass = classificationResult[0][0]['label']
-    return keyClass
-              
-
-examples = [
-    ["Introducción al análisis de riesgos competitivos bajo el enfoque de la función de incidencia acumulada (FIA) y su aplicación con R"],
-    ["Análisis de regresión logística para indicadores de salud sexual y reproductiva"],
-    ["El matrimonio civil sin distinción de género y las garantías de su reconocimiento y protección"],
-    ["establece que la acusación deberá contener la enunciación de los hechos  que  se  le  atribuyen  al  acusado"],
-    ["Notas sobre deuda pública en México"],
-    ["Análisis de las condiciones del aprendizaje desde casa en los alumnos de preescolar y primaria del municipio de Nicolás Romero"],
-    ["La importancia de la motivación para el clima laboral"],
-    ["pues  surgen  pensamientos  que  se  relacionan  con  la  evaluación  de  la  vida misma"],
-    ["Asociación del polimorfismo rs1256031 del receptor beta de estrógenos en pacientes con diabetes tipo 2"],
-    ["los  derivados  de la  serie  LCG presentaron actividad  citotóxica contra  las  líneas celulares SK-LU-1 y SW620"]
-]
-
-#gr.Interface(fn=thesis_prediction, inputs=[gr.inputs.Textbox(label="Título o frase:", placeholder="Ingrese de favor el título de la tesis o un fragmento de esta."),], outputs=[gr.outputs.HTML(label="La tesis pertenece a la carrera de: ",)], title=title, description=description, article=article, examples=examples).launch()
-#gr.Interface(fn=thesis_prediction, inputs=[gr.inputs.Textbox(label="Título o frase:", placeholder="Ingrese de favor el título de la tesis o un fragmento de esta."),], outputs="keyClass", title=title, description=description, article=article, examples=examples).launch()
-#gr.Interface.load('huggingface/hackathon-pln-es/unam_tesis_BETO_finnetuning', placeholder="Ingrese de favor el título de la tesis o un fragmento de esta.", title=title, description=description, article=article, examples=examples).launch()
-gr.Interface.from_pipeline(pipe, title=title, description=description, article=article, examples=examples).launch()
\ No newline at end of file
diff --git a/spaces/hamacojr/CAT-Seg/open_clip/README.md b/spaces/hamacojr/CAT-Seg/open_clip/README.md
deleted file mode 100644
index c69d288d57c670c27374cfbdbb0c34ef1a79df3f..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/open_clip/README.md
+++ /dev/null
@@ -1,635 +0,0 @@
-# OpenCLIP
-
-[[Paper]](https://arxiv.org/abs/2212.07143) [[Colab]](https://colab.research.google.com/github/mlfoundations/open_clip/blob/master/docs/Interacting_with_open_clip.ipynb)
-[![pypi](https://img.shields.io/pypi/v/open_clip_torch.svg)](https://pypi.python.org/pypi/open_clip_torch)
-
-Welcome to an open source implementation of OpenAI's [CLIP](https://arxiv.org/abs/2103.00020) (Contrastive Language-Image Pre-training).
-
-The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. Our starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset.
-Specifically, a ResNet-50 model trained with our codebase on OpenAI's [15 million image subset of YFCC](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) achieves **32.7%** top-1 accuracy on ImageNet. OpenAI's CLIP model reaches **31.3%** when trained on the same subset of YFCC. For ease of experimentation, we also provide code for training on the 3 million images in the [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/download) dataset, where a ResNet-50x4 trained with our codebase reaches 22.2% top-1 ImageNet accuracy.
-
-We further this with a replication study on a dataset of comparable size to OpenAI's, [LAION-400M](https://arxiv.org/abs/2111.02114), and with the larger [LAION-2B](https://laion.ai/blog/laion-5b/) superset. In addition, we study scaling behavior in a paper on [reproducible scaling laws for contrastive language-image learning](https://arxiv.org/abs/2212.07143).
-
-We have trained:
-  * ViT-B/32 on LAION-400M with a accuracy of **62.9%**, comparable to OpenAI's **63.2%**, zero-shot top-1 on ImageNet1k
-  * ViT-B/32 on LAION-2B with a accuracy of **66.6%**.
-  * ViT-B/16 on LAION-400M achieving an accuracy of **67.1%**, lower than OpenAI's **68.3%** (as measured here, 68.6% in paper)
-  * ViT-B/16+ 240x240 (~50% more FLOPS than B/16 224x224) on LAION-400M achieving an accuracy of **69.2%**
-  * ViT-L/14 on LAION-400M with an accuracy of **72.77%**, vs OpenAI's **75.5%** (as measured here, 75.3% in paper)
-  * ViT-L/14 on LAION-2B with an accuracy of **75.3%**, vs OpenAI's **75.5%** (as measured here, 75.3% in paper)
-  * ViT-H/14 on LAION-2B with an accuracy of **78.0**. The second best in1k zero-shot for released, open-source weights thus far.
-  * ViT-g/14 on LAION-2B with an accuracy of **76.6**. This was trained on reduced schedule, same samples seen as 400M models.
-  * ViT-G/14 on LAION-2B with an accuracy of **80.1**. The best in1k zero-shot for released, open-source weights thus far.
-
-As we describe in more detail [below](#why-are-low-accuracy-clip-models-interesting), CLIP models in a medium accuracy regime already allow us to draw conclusions about the robustness of larger CLIP models since the models follow [reliable scaling laws](https://arxiv.org/abs/2107.04649).
-
-This codebase is work in progress, and we invite all to contribute in making it more accessible and useful. In the future, we plan to add support for TPU training and release larger models. We hope this codebase facilitates and promotes further research in contrastive image-text learning. Please submit an issue or send an email if you have any other requests or suggestions.
-
-Note that portions of `src/open_clip/` modelling and tokenizer code are adaptations of OpenAI's official [repository](https://github.com/openai/CLIP).
-
-## Approach
-
-| ![CLIP](https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/CLIP.png) |
-|:--:|
-| Image Credit: https://github.com/openai/CLIP |
-
-## Usage
-
-```
-pip install open_clip_torch
-```
-
-```python
-import torch
-from PIL import Image
-import open_clip
-
-model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32-quickgelu', pretrained='laion400m_e32')
-tokenizer = open_clip.get_tokenizer('ViT-B-32-quickgelu')
-
-image = preprocess(Image.open("CLIP.png")).unsqueeze(0)
-text = tokenizer(["a diagram", "a dog", "a cat"])
-
-with torch.no_grad(), torch.cuda.amp.autocast():
-    image_features = model.encode_image(image)
-    text_features = model.encode_text(text)
-    image_features /= image_features.norm(dim=-1, keepdim=True)
-    text_features /= text_features.norm(dim=-1, keepdim=True)
-
-    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-
-print("Label probs:", text_probs)  # prints: [[1., 0., 0.]]
-```
-
-To compute billions of embeddings efficiently, you can use [clip-retrieval](https://github.com/rom1504/clip-retrieval) which has openclip support.
-
-## Fine-tuning on classification tasks
-
-This repository is focused on training CLIP models. To fine-tune a *trained* zero-shot model on a downstream classification task such as ImageNet, please see [our other repository: WiSE-FT](https://github.com/mlfoundations/wise-ft). The [WiSE-FT repository](https://github.com/mlfoundations/wise-ft) contains code for our paper on [Robust Fine-tuning of Zero-shot Models](https://arxiv.org/abs/2109.01903), in which we introduce a technique for fine-tuning zero-shot models while preserving robustness under distribution shift.
-
-## Data
-
-To download datasets as webdataset, we recommend [img2dataset](https://github.com/rom1504/img2dataset)
-
-### Conceptual Captions
-
-See [cc3m img2dataset example](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/cc3m.md)
-
-### YFCC and other datasets
-
-In addition to specifying the training data via CSV files as mentioned above, our codebase also supports [webdataset](https://github.com/webdataset/webdataset), which is recommended for larger scale datasets. The expected format is a series of `.tar` files. Each of these `.tar` files should contain two files for each training example, one for the image and one for the corresponding text. Both files should have the same name but different extensions. For instance, `shard_001.tar` could contain files such as `abc.jpg` and `abc.txt`. You can learn more about `webdataset` at [https://github.com/webdataset/webdataset](https://github.com/webdataset/webdataset). We use `.tar` files with 1,000 data points each, which we create using [tarp](https://github.com/webdataset/tarp).
-
-You can download the YFCC dataset from [Multimedia Commons](http://mmcommons.org/).
-Similar to OpenAI, we used a subset of YFCC to reach the aforementioned accuracy numbers.
-The indices of images in this subset are in [OpenAI's CLIP repository](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md).
-
-
-## Training CLIP
-
-### Install
-
-We advise you first create a virtual environment with:
-
-```
-python3 -m venv .env
-source .env/bin/activate
-pip install -U pip
-```
-
-You can then install openclip for training with `pip install 'open_clip_torch[training]'`.
-
-#### Development
-
-If you want to make changes to contribute code, you can close openclip then run `make install` in openclip folder (after creating a virtualenv)
-
-Install pip PyTorch as per https://pytorch.org/get-started/locally/
-
-You may run `make install-training` to install training deps
-
-#### Testing
-
-Test can be run with `make install-test` then `make test`
-
-`python -m pytest -x -s -v tests -k "training"` to run a specific test
-
-Running regression tests against a specific git revision or tag:
-1. Generate testing data
-    ```sh
-    python tests/util_test.py --model RN50 RN101 --save_model_list models.txt --git_revision 9d31b2ec4df6d8228f370ff20c8267ec6ba39383
-    ```
-    **_WARNING_: This will invoke git and modify your working tree, but will reset it to the current state after data has been generated! \
-    Don't modify your working tree while test data is being generated this way.**
-
-2. Run regression tests
-    ```sh
-    OPEN_CLIP_TEST_REG_MODELS=models.txt python -m pytest -x -s -v -m regression_test
-    ```
-
-### Sample single-process running code:
-
-```bash
-python -m training.main \
-    --save-frequency 1 \
-    --zeroshot-frequency 1 \
-    --report-to tensorboard \
-    --train-data="/path/to/train_data.csv"  \
-    --val-data="/path/to/validation_data.csv"  \
-    --csv-img-key filepath \
-    --csv-caption-key title \
-    --imagenet-val=/path/to/imagenet/root/val/ \
-    --warmup 10000 \
-    --batch-size=128 \
-    --lr=1e-3 \
-    --wd=0.1 \
-    --epochs=30 \
-    --workers=8 \
-    --model RN50
-```
-
-Note: `imagenet-val` is the path to the *validation* set of ImageNet for zero-shot evaluation, not the training set!
-You can remove this argument if you do not want to perform zero-shot evaluation on ImageNet throughout training. Note that the `val` folder should contain subfolders. If it doest not, please use [this script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh).
-
-### Multi-GPU and Beyond
-
-This code has been battle tested up to 1024 A100s and offers a variety of solutions
-for distributed training. We include native support for SLURM clusters.
-
-As the number of devices used to train increases, so does the space complexity of
-the the logit matrix. Using a naïve all-gather scheme, space complexity will be
-`O(n^2)`. Instead, complexity may become effectively linear if the flags
-`--gather-with-grad` and `--local-loss` are used. This alteration results in one-to-one
-numerical results as the naïve method.
-
-#### Epochs
-
-For larger datasets (eg Laion2B), we recommend setting --train-num-samples to a lower value than the full epoch, for example `--train-num-samples 135646078` to 1/16 of an epoch in conjunction with --dataset-resampled to do sampling with replacement. This allows having frequent checkpoints to evaluate more often.
-
-#### Patch Dropout
-
-<a href="https://arxiv.org/abs/2212.00794">Recent research</a> has shown that one can dropout half to three-quarters of the visual tokens, leading to up to 2-3x training speeds without loss of accuracy.
-
-You can set this on your visual transformer config with the key `patch_dropout`.
-
-In the paper, they also finetuned without the patch dropout at the end. You can do this with the command-line argument `--force-patch-dropout 0.`
-
-#### Single-Node
-
-We make use of `torchrun` to launch distributed jobs. The following launches a
-a job on a node of 4 GPUs:
-
-```bash
-cd open_clip/src
-torchrun --nproc_per_node 4 -m training.main \
-    --train-data '/data/cc12m/cc12m-train-{0000..2175}.tar' \
-    --train-num-samples 10968539 \
-    --dataset-type webdataset \
-    --batch-size 320 \
-    --precision amp \
-    --workers 4 \
-    --imagenet-val /data/imagenet/validation/
-```
-
-#### Multi-Node
-
-The same script above works, so long as users include information about the number
-of nodes and host node.
-
-```bash
-cd open_clip/src
-torchrun --nproc_per_node=4 \
-    --rdzv_endpoint=$HOSTE_NODE_ADDR \
-    -m training.main \
-    --train-data '/data/cc12m/cc12m-train-{0000..2175}.tar' \
-    --train-num-samples 10968539 \
-    --dataset-type webdataset \
-    --batch-size 320 \
-    --precision amp \
-    --workers 4 \
-    --imagenet-val /data/imagenet/validation/
-```
-
-#### SLURM
-
-This is likely the easiest solution to utilize. The following script was used to
-train our largest models:
-
-```bash
-#!/bin/bash -x
-#SBATCH --nodes=32
-#SBATCH --gres=gpu:4
-#SBATCH --ntasks-per-node=4
-#SBATCH --cpus-per-task=6
-#SBATCH --wait-all-nodes=1
-#SBATCH --job-name=open_clip
-#SBATCH --account=ACCOUNT_NAME
-#SBATCH --partition PARTITION_NAME
-
-eval "$(/path/to/conda/bin/conda shell.bash hook)" # init conda
-conda activate open_clip
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-export MASTER_PORT=12802
-
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-cd /shared/open_clip
-export PYTHONPATH="$PYTHONPATH:$PWD/src"
-srun --cpu_bind=v --accel-bind=gn python -u src/training/main.py \
-    --save-frequency 1 \
-    --report-to tensorboard \
-    --train-data="/data/LAION-400M/{00000..41455}.tar" \
-    --warmup 2000 \
-    --batch-size=256 \
-    --epochs=32 \
-    --workers=8 \
-    --model ViT-B-32 \
-    --name "ViT-B-32-Vanilla" \
-    --seed 0 \
-    --local-loss \
-    --gather-with-grad
-```
-
-### Resuming from a checkpoint:
-
-```bash
-python -m training.main \
-    --train-data="/path/to/train_data.csv" \
-    --val-data="/path/to/validation_data.csv"  \
-    --resume /path/to/checkpoints/epoch_K.pt
-```
-
-### Training with pre-trained language models as text encoder:
-
-If you wish to use different language models as the text encoder for CLIP you can do so by using one of the Hugging Face model configs in ```src/open_clip/model_configs``` and passing in it's tokenizer as the ```--model``` and ```--hf-tokenizer-name``` parameters respectively. Currently we only support RoBERTa ("test-roberta" config), however adding new models should be trivial. You can also determine how many layers, from the end, to leave unfrozen with the ```--lock-text-unlocked-layers``` parameter. Here's an example command to train CLIP with the RoBERTa LM that has it's last 10 layers unfrozen:
-```bash
-python -m training.main \
-         --train-data="pipe:aws s3 cp s3://s-mas/cc3m/{00000..00329}.tar -" \
-         --train-num-samples 3000000 \
-         --val-data="pipe:aws s3 cp s3://s-mas/cc3m/{00330..00331}.tar -" \
-         --val-num-samples 10000 \
-         --dataset-type webdataset \
-         --batch-size 256 \
-         --warmup 2000 \
-         --epochs 10 \
-         --lr 5e-4 \
-         --precision amp \
-         --workers 6 \
-         --model "roberta-ViT-B-32" \
-         --lock-text \
-         --lock-text-unlocked-layers 10 \
-         --name "10_unfrozen" \
-         --report-to "tensorboard" \
-```
-
-### Loss Curves
-
-When run on a machine with 8 GPUs the command should produce the following training curve for Conceptual Captions:
-
-![CLIP zero shot training curve](https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/clip_zeroshot.png)
-
-More detailed curves for Conceptual Captions are given at [/docs/clip_conceptual_captions.md](/docs/clip_conceptual_captions.md).
-
-When training a RN50 on YFCC the same hyperparameters as above are used, with the exception of `lr=5e-4` and `epochs=32`.
-
-Note that to use another model, like `ViT-B/32` or `RN50x4` or `RN50x16` or `ViT-B/16`, specify with `--model RN50x4`.
-
-### Launch tensorboard:
-```bash
-tensorboard --logdir=logs/tensorboard/ --port=7777
-```
-
-## Evaluation / Zero-Shot
-
-### Evaluating local checkpoint:
-
-```bash
-python -m training.main \
-    --val-data="/path/to/validation_data.csv"  \
-    --model RN101 \
-    --pretrained /path/to/checkpoints/epoch_K.pt
-```
-
-### Evaluating hosted pretrained checkpoint on ImageNet zero-shot prediction:
-
-```bash
-python -m training.main \
-    --imagenet-val /path/to/imagenet/validation \
-    --model ViT-B-32-quickgelu \
-    --pretrained laion400m_e32
-```
-
-## Pretrained model details
-
-### LAION-400M - https://laion.ai/laion-400-open-dataset
-
-We are working on reproducing OpenAI's ViT results with the comparably sized (and open) LAION-400M dataset. Trained
-weights may be found in release [v0.2](https://github.com/mlfoundations/open_clip/releases/tag/v0.2-weights).
-
-The LAION400M weights have been trained on the JUWELS supercomputer (see acknowledgements section below).
-
-#### ViT-B/32 224x224
-
-We replicate OpenAI's results on ViT-B/32, reaching a top-1 ImageNet-1k zero-shot accuracy of 62.96%.
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot.png" width="700">
-
-__Zero-shot comparison (courtesy of Andreas Fürst)__
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_openai_compare_b32.jpg" width="700">
-
-ViT-B/32 was trained with 128 A100 (40 GB) GPUs for ~36 hours, 4600 GPU-hours. The per-GPU batch size was 256 for a global batch size of 32768. 256 is much lower than it could have been (~320-384) due to being sized initially before moving to 'local' contrastive loss.
-
-#### ViT-B/16 224x224
-
-The B/16 LAION400M training reached a top-1 ImageNet-1k zero-shot validation score of 67.07.
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_b16.png" width="700">
-
-This was the first major train session using the updated webdataset 0.2.x code. A bug was found that prevented shards from being shuffled properly between nodes/workers each epoch. This was fixed part way through training (epoch 26) but likely had an impact.
-
-ViT-B/16 was trained with 176 A100 (40 GB) GPUS for ~61 hours, 10700 GPU-hours. Batch size per GPU was 192 for a global batch size of 33792.
-
-#### ViT-B/16+ 240x240
-
-The B/16+ 240x240 LAION400M training reached a top-1 ImageNet-1k zero-shot validation score of 69.21.
-
-This model is the same depth as the B/16, but increases the
-  * vision width from 768 -> 896
-  * text width from 512 -> 640
-  * the resolution 224x224 -> 240x240 (196 -> 225 tokens)
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_b16_plus_240.png" width="700">
-
-Unlike the B/16 run above, this model was a clean run with no dataset shuffling issues.
-
-ViT-B/16+ was trained with 224 A100 (40 GB) GPUS for ~61 hours, 13620 GPU-hours. Batch size per GPU was 160 for a global batch size of 35840.
-
-#### ViT-L/14 224x224
-
-The L/14 LAION-400M training reached a top-1 ImageNet-1k zero-shot validation score of 72.77.
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_l14.png" width="700">
-
-ViT-L/14 was trained with 400 A100 (40 GB) GPUS for ~127 hours, 50800 GPU-hours. Batch size per GPU was 96 for a global batch size of 38400. Grad checkpointing was enabled.
-
-### LAION-2B (en) - https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/
-
-A ~2B sample subset of LAION-5B with english captions (https://huggingface.co/datasets/laion/laion2B-en)
-
-#### ViT-B/32 224x224
-A ViT-B/32 trained on LAION-2B, reaching a top-1 ImageNet-1k zero-shot accuracy of 65.62%.
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion2b_clip_zeroshot_b32.png" width="700">
-
-ViT-B/32 was trained with 112 A100 (40 GB) GPUs. The per-GPU batch size was 416 for a global batch size of 46592. Compute generously provided by [stability.ai](https://stability.ai/).
-
-A second iteration of B/32 was trained on stability.ai cluster with a larger global batch size and learning rate, hitting 66.6% top-1. See https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K
-
-#### ViT-L/14 224x224
-
-A ViT-L/14 with a 75.3% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K
-
-These weights use a different dataset mean and std than others. Instead of using the OpenAI mean & std, inception style normalization `[-1, 1]` is used via a mean and std of `[0.5, 0.5, 0.5]`. This is handled automatically if using `open_clip.create_model_and_transforms` from pretrained weights.
-
-#### ViT-H/14 224x224
-
-A ViT-H/14 with a 78.0% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
-
-#### ViT-g/14 224x224
-
-A ViT-g/14 with a 76.6% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K
-
-This model was trained with a shorted schedule than other LAION-2B models with 12B samples seen instead of 32+B. It matches LAION-400M training in samples seen. Many zero-shot results are lower as a result, but despite this it performs very well in some OOD zero-shot and retrieval tasks.
-
-
-#### ViT-B/32 roberta base
-
-A ViT-B/32 with roberta base encoder with a 61.7% top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k
-This is the first openclip model using a HF text tower. It has better performance on a range of tasks compared to the standard text encoder, see [metrics](https://huggingface.co/laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/blob/main/unknown.png)
-
-#### ViT-B/32 xlm roberta base
-
-A ViT-B/32 with xlm roberta base encoder with a 62.33% top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k
-This is the first openclip model trained on the full laion5B dataset; hence the first multilingual clip trained with openclip. It has better performance on a range of tasks compared to the standard text encoder, see [metrics](https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/blob/main/metrics.png)
-A preliminary multilingual evaluation was run: 43% on imagenet1k italian (vs 21% for english B/32), 37% for imagenet1k japanese (vs 1% for english B/32 and 50% for B/16 clip japanese). It shows the multilingual property is indeed there as expected. Larger models will get even better performance.
-
-#### ViT-H/14 xlm roberta large
-
-A ViT-H/14 with xlm roberta large encoder with a 77.0% (vs 78% for the english equivalent) top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k
-
-This model was trained following the [LiT](https://arxiv.org/abs/2111.07991) methodology: the image tower was frozen (initialized from english openclip ViT-H/14), the text tower was initialized from [xlm roberta large](https://huggingface.co/xlm-roberta-large) and unfrozen. This reduced training cost by a 3x factor.
-
-See full english [metrics](https://huggingface.co/laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/resolve/main/results_xlm_roberta_large.png)
-
-On zero shot classification on imagenet with translated prompts this model reaches:
-
-* 56% in italian (vs 21% for https://github.com/clip-italian/clip-italian)
-* 53% in japanese (vs 54.6% for https://github.com/rinnakk/japanese-clip)
-* 55.7% in chinese (to be compared with https://github.com/OFA-Sys/Chinese-CLIP)
-
-
-#### YFCC-15M
-
-Below are checkpoints of models trained on YFCC-15M, along with their zero-shot top-1 accuracies on ImageNet and ImageNetV2. These models were trained using 8 GPUs and the same hyperparameters described in the "Sample running code" section, with the exception of `lr=5e-4` and `epochs=32`.
-
-* [ResNet-50](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt) (32.7% / 27.9%)
-* [ResNet-101](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt) (34.8% / 30.0%)
-
-#### CC12M - https://github.com/google-research-datasets/conceptual-12m
-
-* [ResNet-50](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt) (36.45%)
-
-### Pretrained Model Interface
-
-We offer a simple model interface to instantiate both pre-trained and untrained models.
-
-NOTE: Many existing checkpoints use the QuickGELU activation from the original OpenAI models. This activation is actually less efficient than native torch.nn.GELU in recent versions of PyTorch. The model defaults are now nn.GELU, so one should use model definitions with `-quickgelu` postfix for the OpenCLIP pretrained weights. All OpenAI pretrained weights will always default to QuickGELU. One can also use the non `-quickgelu` model definitions with pretrained weights using QuickGELU but there will be an accuracy drop, for fine-tune that will likely vanish for longer runs.
-
-Future trained models will use nn.GELU.
-
-```python
->>> import open_clip
->>> open_clip.list_pretrained()
-[('RN50', 'openai'),
- ('RN50', 'yfcc15m'),
- ('RN50', 'cc12m'),
- ('RN50-quickgelu', 'openai'),
- ('RN50-quickgelu', 'yfcc15m'),
- ('RN50-quickgelu', 'cc12m'),
- ('RN101', 'openai'),
- ('RN101', 'yfcc15m'),
- ('RN101-quickgelu', 'openai'),
- ('RN101-quickgelu', 'yfcc15m'),
- ('RN50x4', 'openai'),
- ('RN50x16', 'openai'),
- ('RN50x64', 'openai'),
- ('ViT-B-32', 'openai'),
- ('ViT-B-32', 'laion400m_e31'),
- ('ViT-B-32', 'laion400m_e32'),
- ('ViT-B-32', 'laion2b_e16'),
- ('ViT-B-32', 'laion2b_s34b_b79k'),
- ('ViT-B-32-quickgelu', 'openai'),
- ('ViT-B-32-quickgelu', 'laion400m_e31'),
- ('ViT-B-32-quickgelu', 'laion400m_e32'),
- ('ViT-B-16', 'openai'),
- ('ViT-B-16', 'laion400m_e31'),
- ('ViT-B-16', 'laion400m_e32'),
- ('ViT-B-16-plus-240', 'laion400m_e31'),
- ('ViT-B-16-plus-240', 'laion400m_e32'),
- ('ViT-L-14', 'openai'),
- ('ViT-L-14', 'laion400m_e31'),
- ('ViT-L-14', 'laion400m_e32'),
- ('ViT-L-14', 'laion2b_s32b_b82k'),
- ('ViT-L-14-336', 'openai'),
- ('ViT-H-14', 'laion2b_s32b_b79k'),
- ('ViT-g-14', 'laion2b_s12b_b42k'),
- ('ViT-bigG-14', 'laion2b_s39b_b160k'),
- ('roberta-ViT-B-32', 'laion2b_s12b_b32k'),
- ('xlm-roberta-base-ViT-B-32', 'laion5b_s13b_b90k'),
- ('xlm-roberta-large-ViT-H-14', 'frozen_laion5b_s13b_b90k'),]
-
->>> model, train_transform, eval_transform = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
-```
-### Gradient accumulation
-
-To simulate larger batches use `--accum-freq k`. If per gpu batch size, `--batch-size`, is `m`, then the effective batch size will be `k * m * num_gpus`.
-
-When increasing `--accum-freq` from its default of 1, samples/s will remain approximately constant (batch size will double, as will time-per-batch). It is recommended to use other features to reduce batch size such as `--grad-checkpointing --local-loss --gather-with-grad` before increasing `--accum-freq`. `--accum-freq` can be used in addition to these features.
-
-Instead of 1 forward pass per example, there are now 2 forward passes per-example. However, the first is done with `torch.no_grad`.
-
-There is some additional GPU memory required --- the features and data from all `m` batches are stored in memory.
-
-There are also `m` loss computations instead of the usual 1.
-
-For more information see Cui et al. (https://arxiv.org/abs/2112.09331) or Pham et al. (https://arxiv.org/abs/2111.10050). 
-
-### Support for remote loading/training
-
-It is always possible to resume directly from a remote file, e.g., a file in an s3 bucket. Just set `--resume s3://<path-to-checkpoint> `.
-This will work with any filesystem supported by `fsspec`.
-
-It is also possible to train `open_clip` models while continuously backing up to s3. This can help to avoid slow local file systems.
-
-Say that your node has a local ssd `/scratch`, an s3 bucket `s3://<path-to-bucket>`.
-
-In that case, set `--logs /scratch` and `--remote-sync s3://<path-to-bucket>`. Then, a background process will sync `/scratch/<run-name>` to `s3://<path-to-bucket>/<run-name>`. After syncing, the background process will sleep for `--remote-sync-frequency` seconds, which defaults to 5 minutes.
-
-There is also experimental support for syncing to other remote file systems, not just s3. To do so, specify `--remote-sync-protocol fsspec`. However, this is currently very slow and not recommended.
-
-Also, to optionally avoid saving too many checkpoints locally when using these features, you can use `--delete-previous-checkpoint` which deletes the previous checkpoint after saving a new one.
-
-Note: if you are using this feature with `--resume latest`, there are a few warnings. First, use with `--save-most-recent` is not supported. Second, only `s3` is supported. Finally, since the sync happens in the background, it is possible that the most recent checkpoint may not be finished syncing to the remote.
-
-## Scaling trends
-
-The plot below shows how zero-shot performance of CLIP models varies as we scale the number of samples used for training. Zero-shot performance increases steadily for both ImageNet and [ImageNetV2](https://arxiv.org/abs/1902.10811), and is far from saturated at ~15M samples.
-
-<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/scaling.png" width="700">
-
-## Why are low-accuracy CLIP models interesting?
-
-**TL;DR:** CLIP models have high effective robustness, even at small scales.
-
-CLIP models are particularly intriguing because they are more robust to natural distribution shifts (see Section 3.3 in the [CLIP paper](https://arxiv.org/abs/2103.00020)).
-This phenomena is illustrated by the figure below, with ImageNet accuracy on the x-axis
-and [ImageNetV2](https://arxiv.org/abs/1902.10811) (a reproduction of the ImageNet validation set with distribution shift) accuracy on the y-axis.
-Standard training denotes training on the ImageNet train set and the CLIP zero-shot models
-are shown as stars.
-
-![CLIP scatter plot](https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/effective_robustness.png)
-
-As observed by [Taori et al., 2020](https://arxiv.org/abs/2007.00644) and [Miller et al., 2021](https://arxiv.org/abs/2107.04649), the in-distribution
-and out-of-distribution accuracies of models trained on ImageNet follow a predictable linear trend (the red line in the above plot). *Effective robustness*
-quantifies robustness as accuracy beyond this baseline, i.e., how far a model lies above the red line. Ideally a model would not suffer from distribution shift and fall on the y = x line ([trained human labelers are within a percentage point of the y = x line](http://proceedings.mlr.press/v119/shankar20c.html)).
-
-Even though the CLIP models trained with
-this codebase achieve much lower accuracy than those trained by OpenAI, our models still lie on the same
-trend of improved effective robustness (the purple line). Therefore, we can study what makes
-CLIP robust without requiring industrial-scale compute.
-
-For more information on effective robustness, please see:
-
-- [Recht et al., 2019](https://arxiv.org/abs/1902.10811).
-- [Taori et al., 2020](https://arxiv.org/abs/2007.00644).
-- [Miller et al., 2021](https://arxiv.org/abs/2107.04649).
-
-To know more about the factors that contribute to CLIP's robustness refer to [Fang et al., 2022](https://arxiv.org/abs/2205.01397).
-
-## Acknowledgments
-
-We gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC).
-
-## The Team
-
-Current development of this repository is led by [Ross Wightman](https://rwightman.com/), [Cade Gordon](http://cadegordon.io/), and [Vaishaal Shankar](http://vaishaal.com/).
-
-The original version of this repository is from a group of researchers at UW, Google, Stanford, Amazon, Columbia, and Berkeley.
-
-[Gabriel Ilharco*](http://gabrielilharco.com/), [Mitchell Wortsman*](https://mitchellnw.github.io/), [Nicholas Carlini](https://nicholas.carlini.com/), [Rohan Taori](https://www.rohantaori.com/), [Achal Dave](http://www.achaldave.com/), [Vaishaal Shankar](http://vaishaal.com/), [John Miller](https://people.eecs.berkeley.edu/~miller_john/), [Hongseok Namkoong](https://hsnamkoong.github.io/), [Hannaneh Hajishirzi](https://homes.cs.washington.edu/~hannaneh/), [Ali Farhadi](https://homes.cs.washington.edu/~ali/), [Ludwig Schmidt](https://people.csail.mit.edu/ludwigs/)
-
-Special thanks to [Jong Wook Kim](https://jongwook.kim/) and [Alec Radford](https://github.com/Newmu) for help with reproducing CLIP!
-
-## Citing
-
-If you found this repository useful, please consider citing:
-```bibtex
-@software{ilharco_gabriel_2021_5143773,
-  author       = {Ilharco, Gabriel and
-                  Wortsman, Mitchell and
-                  Wightman, Ross and
-                  Gordon, Cade and
-                  Carlini, Nicholas and
-                  Taori, Rohan and
-                  Dave, Achal and
-                  Shankar, Vaishaal and
-                  Namkoong, Hongseok and
-                  Miller, John and
-                  Hajishirzi, Hannaneh and
-                  Farhadi, Ali and
-                  Schmidt, Ludwig},
-  title        = {OpenCLIP},
-  month        = jul,
-  year         = 2021,
-  note         = {If you use this software, please cite it as below.},
-  publisher    = {Zenodo},
-  version      = {0.1},
-  doi          = {10.5281/zenodo.5143773},
-  url          = {https://doi.org/10.5281/zenodo.5143773}
-}
-```
-
-```bibtex
-@inproceedings{Radford2021LearningTV,
-  title={Learning Transferable Visual Models From Natural Language Supervision},
-  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
-  booktitle={ICML},
-  year={2021}
-}
-```
-
-```bibtex
-@inproceedings{schuhmann2022laionb,
-  title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
-  author={Christoph Schuhmann and
-          Romain Beaumont and
-          Richard Vencu and
-          Cade W Gordon and
-          Ross Wightman and
-          Mehdi Cherti and
-          Theo Coombes and
-          Aarush Katta and
-          Clayton Mullis and
-          Mitchell Wortsman and
-          Patrick Schramowski and
-          Srivatsa R Kundurthy and
-          Katherine Crowson and
-          Ludwig Schmidt and
-          Robert Kaczmarczyk and
-          Jenia Jitsev},
-  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
-  year={2022},
-  url={https://openreview.net/forum?id=M3Y74vmsMcY}
-}
-```
-
-[![DOI](https://zenodo.org/badge/390536799.svg)](https://zenodo.org/badge/latestdoi/390536799)
diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/upfirdn2d.cpp b/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/upfirdn2d.cpp
deleted file mode 100644
index 44fa337d8d4c34dfa010a59cd27d86857db671aa..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/upfirdn2d.cpp
+++ /dev/null
@@ -1,107 +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.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.numel() > 0, "x has zero size");
-    TORCH_CHECK(f.numel() > 0, "f has zero size");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-    TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/hannahaa/MovieAI/info.md b/spaces/hannahaa/MovieAI/info.md
deleted file mode 100644
index 9b1ffca81f1a298ad5ef4cf84d9931c8c59ed64f..0000000000000000000000000000000000000000
--- a/spaces/hannahaa/MovieAI/info.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# 😌 [Let's Watch A Movie !]
-
-### 🧐 Problem Statement and Research Summary
-[Have you ever gotten tired of watching the same genre for a long time? We want to solve this problem by using AI. With AI, you would only need to answer a few simple questions to find out the right genre for you to watch next, this will be a very time-saving initiative that the creators want to bring for the users. Instead of scrolling on Netflix, Hulu, Amazon Prime for hours and hours, now, with just 10 questions, you can figure out which movie genre you will be watching next, how convenient !!.]
-### 🎣 Data Collection Plan
-[Our questions are mostly related to your preferences about movies and what factor in a movie that attracts you so we can generate a high accuracy result in the end. We also ask about personal but not too sensitive information such as age so our AI recommendation system can provide to you an appropriate movie with your age and avoid any problem about censory content.]
-
-### 💥 Ethical Considerations (Data Privacy and Bias)
-* Data privacy: [Thank you for taking your time completing our survey, we hope that you will find our recommendation system helpful. This survey was made to help you choosing and trying new movie genres that maybe you’ve never tried before. Our survey might ask some personal questions in purpose to understand our user better and to produce a higher accuracy answer, and we will ensure that the users’ data is going to be private and is protected. All of our questions will be use towards data collection in order to provide a high accuracy result, there will be no personal purpose for collecting user's personal information and we guarantee that we will not use or publish our user's personal information for any third parties.] 
-* Bias: [Disclaimer: Relating to our anti-biased plan, we will target a diverse audience in order to create a diversity of data points. Also, we will test our AI for any sign of bias and make modifications whenever necessary. ]
-
-### 👻 Our Team
-[Hey everyone! We are the creators of this survey - Hannah and Amy. Hannah loves to watch movies and interested in creating AI system. Amy likes AI. This survey was made in order to suggest and recommend what movie genre everyone should try to watch next. We hope you enjoy this! 
-]
-
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/transforms/build.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/transforms/build.py
deleted file mode 100644
index 3ed88faea6d328c3ce7e4a9a6361eea6b2646099..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/transforms/build.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from . import transforms as T
-
-
-def build_transforms(cfg, is_train=True):
-    if is_train:
-        if len(cfg.AUGMENT.MULT_MIN_SIZE_TRAIN)>0:
-            min_size = cfg.AUGMENT.MULT_MIN_SIZE_TRAIN
-        else:
-            min_size = cfg.INPUT.MIN_SIZE_TRAIN
-        max_size = cfg.INPUT.MAX_SIZE_TRAIN
-        flip_horizontal_prob = cfg.AUGMENT.FLIP_PROB_TRAIN
-        flip_vertical_prob = cfg.AUGMENT.VERTICAL_FLIP_PROB_TRAIN
-        brightness = cfg.AUGMENT.BRIGHTNESS
-        contrast = cfg.AUGMENT.CONTRAST
-        saturation = cfg.AUGMENT.SATURATION
-        hue = cfg.AUGMENT.HUE
-
-        crop_prob = cfg.AUGMENT.CROP_PROB
-        min_ious = cfg.AUGMENT.CROP_MIN_IOUS
-        min_crop_size = cfg.AUGMENT.CROP_MIN_SIZE
-
-    else:
-        min_size = cfg.INPUT.MIN_SIZE_TEST
-        max_size = cfg.INPUT.MAX_SIZE_TEST
-        flip_horizontal_prob = 0.0
-
-    fix_res = cfg.INPUT.FIX_RES
-    if cfg.INPUT.FORMAT is not '':
-        input_format = cfg.INPUT.FORMAT
-    elif cfg.INPUT.TO_BGR255:
-        input_format = 'bgr255'
-    normalize_transform = T.Normalize(
-        mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format
-    )
- 
-    transform = T.Compose(
-        [
-            T.Resize(min_size, max_size, restrict=fix_res),
-            T.RandomHorizontalFlip(flip_horizontal_prob),
-            T.ToTensor(),
-            normalize_transform,
-        ]
-    )
-    return transform
diff --git "a/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" "b/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
deleted file mode 100644
index 2f4201438c4d8597c251726fe99c02d40f0cadf0..0000000000000000000000000000000000000000
--- "a/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
+++ /dev/null
@@ -1,166 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-import re
-import unicodedata
-fast_debug = False
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def is_paragraph_break(match):
-    """
-    根据给定的匹配结果来判断换行符是否表示段落分隔。
-    如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
-    也可以根据之前的内容长度来判断段落是否已经足够长。
-    """
-    prev_char, next_char = match.groups()
-
-    # 句子结束标志
-    sentence_endings = ".!?"
-
-    # 设定一个最小段落长度阈值
-    min_paragraph_length = 140
-
-    if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
-        return "\n\n" 
-    else:
-        return " "
-
-def normalize_text(text):
-    """
-    通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
-    例如，将连字 "fi" 转换为 "f" 和 "i"。
-    """
-    # 对文本进行归一化处理，分解连字
-    normalized_text = unicodedata.normalize("NFKD", text)
-
-    # 替换其他特殊字符
-    cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
-
-    return cleaned_text
-
-def clean_text(raw_text):
-    """
-    对从 PDF 提取出的原始文本进行清洗和格式化处理。
-    1. 对原始文本进行归一化处理。
-    2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
-    3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
-    """
-    # 对文本进行归一化处理
-    normalized_text = normalize_text(raw_text)
-
-    # 替换跨行的连词
-    text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
-
-    # 根据前后相邻字符的特点，找到原文本中的换行符
-    newlines = re.compile(r'(\S)\n(\S)')
-
-    # 根据 heuristic 规则，用空格或段落分隔符替换原换行符
-    final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
-
-    return final_text.strip()
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os, fitz
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with fitz.open(fp) as doc:
-            file_content = ""
-            for page in doc:
-                file_content += page.get_text()
-            file_content = clean_text(file_content)
-            print(file_content)
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-                
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, 
-            inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, 
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-@CatchException
-def 批量总结PDF文档(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？",
-        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/hebert2099/MusicGen/tests/quantization/test_vq.py b/spaces/hebert2099/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/hebert2099/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
-    def test_rvq(self):
-        x = torch.randn(1, 16, 2048)
-        vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
-        res = vq(x, 1.)
-        assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/herosly/open-reverse-proxy/README.md b/spaces/herosly/open-reverse-proxy/README.md
deleted file mode 100644
index 7bbd9662cac3ab3d63dd57cec70400187b6d49fa..0000000000000000000000000000000000000000
--- a/spaces/herosly/open-reverse-proxy/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Open Reverse Proxy
-emoji: 🦀
-colorFrom: purple
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huggan/cryptopunk-captcha/README.md b/spaces/huggan/cryptopunk-captcha/README.md
deleted file mode 100644
index ff09b5afeae1becdcd4a0844a9efc9a6274eb366..0000000000000000000000000000000000000000
--- a/spaces/huggan/cryptopunk-captcha/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cryptopunk Captcha
-emoji: 📊
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/huggingface/text-data-filtering/app.py b/spaces/huggingface/text-data-filtering/app.py
deleted file mode 100644
index 371541e49ccd3a8673eb58b96c7b5f3fc981ab55..0000000000000000000000000000000000000000
--- a/spaces/huggingface/text-data-filtering/app.py
+++ /dev/null
@@ -1,916 +0,0 @@
-# Run with: streamlit run visualization.py
-
-import streamlit as st
-
-import os
-
-from io import StringIO
-import base64
-import json
-import pandas as pd
-
-pd.options.mode.chained_assignment = None
-
-import numpy as np
-
-import matplotlib.pyplot as plt
-
-from filtering import LoadParameters, ModifyingDocuments, Filtering
-from languages_id import langs_id
-
-
-class Visualization_for_lang:
-    def __init__(
-        self,
-        path_data,
-        lang,
-        num_docs,
-        num_docs_for_words,
-        max_len_text_display,
-        lang_dataset_id,
-        path_fasttext_model,
-        path_sentencepiece_model,
-        path_kenlm_model,
-    ):
-        self.path_data = path_data
-        self.lang = lang
-        self.num_docs = num_docs
-        self.num_docs_for_words = num_docs_for_words
-        self.max_len_text_display = max_len_text_display
-
-        self.lang_dataset_id = lang_dataset_id
-        self.param = LoadParameters.load_parameters(lang_dataset_id)
-        self.stopwords = LoadParameters.load_stopwords(lang_dataset_id)
-        self.flagged_words = LoadParameters.load_flagged_words(lang_dataset_id)
-        self.model_lang_id = LoadParameters.load_model_lang_id(
-            lang_dataset_id, path_fasttext_model
-        )
-        self.sentencepiece_model = LoadParameters.load_sentencepiece_model(
-            lang_dataset_id, path_sentencepiece_model
-        )
-        self.sentencepiece_model_tok = (
-            self.sentencepiece_model if self.param["tokenization"] else None
-        )
-        self.kenlm_model = LoadParameters.load_kenlm_model(
-            lang_dataset_id, path_kenlm_model
-        )
-
-    def set_title(self):
-        st.title(f"Filtering visualization for {self.lang}")
-
-    def open_data(self):
-        with open(self.path_data) as json_file:
-            data = json.load(json_file)
-
-        self.num_docs = min(self.num_docs, len(data))
-        self.num_docs_for_words = min(self.num_docs_for_words, len(data))
-
-        if "words" in data[0]:
-            words = [doc["words"] for doc in data[: self.num_docs_for_words]]
-            words = [word for doc in words for word in doc]
-            self.words = pd.DataFrame(words)
-        else:
-            self.words = None
-
-        docs = data[: self.num_docs]
-        for doc in docs:
-            if not (self.words is None):
-                del doc["words"]
-            if len(doc["text"]) > self.max_len_text_display:
-                doc["text"] = (
-                    doc["text"][: self.max_len_text_display]
-                    + " [...] [THIS LONG TEXT HAS BEEN TRUNCATED FOR DISPLAY REASONS]"
-                )
-        self.docs_checkpoint = pd.DataFrame(docs)
-        self.docs = self.docs_checkpoint
-
-    @staticmethod
-    def print_discarded_by_cond(cond):
-        st.caption(
-            f"{(len(cond) - np.sum(1*cond)) / len(cond) * 100:.2f}% of the total is discarded with this filter."
-        )
-
-    @staticmethod
-    def plot_hist(dataframe, key, num_bins=50):
-        checkbox = st.checkbox(
-            "Diplay distribution", value=True, key=f"display_distribution_{key[0]}"
-        )
-        if checkbox:
-            fig, ax = plt.subplots()
-            val = dataframe[key[0]].values
-            if np.median(val) != 0:
-                val = val[
-                    abs(val - np.median(val))
-                    < 9 * np.median(np.absolute(val - np.median(val)))
-                ]
-            ax.hist(val, bins=num_bins, density=True)
-            ax.set_title(" ".join(key[0].split("_")))
-            ax.axvline(x=key[1], color="r", linestyle="dashed")
-            st.pyplot(fig)
-
-    @staticmethod
-    def display_dataset(dataframe, cond, description, type_of_examples):
-        displayed_examples = dataframe.loc[cond]
-        st.subheader(
-            f"{description}: {len(displayed_examples)} {type_of_examples} ({len(displayed_examples) / len(dataframe.index) * 100:.2f}%)"
-        )
-        st.markdown(
-            "Click on a column to sort by it, place the cursor on the text to display it."
-        )
-        st.dataframe(displayed_examples)
-
-    def filtering_of_docs(self):
-        def set_sliders():
-            columns = list(self.docs)
-            keys = []
-            conds = {}
-
-            def get_cond(key, cutoff, max_cutoff):
-                if max_cutoff:
-                    return self.docs[key] <= cutoff
-                return self.docs[key] >= cutoff
-
-            if "number_words" in columns:
-                with st.sidebar.expander("Number of words"):
-                    cutoff_def = "If the number of words of a document is lower than this number, the document is removed."
-                    max_nb_words = int(np.max(self.docs["number_words"])) + 1
-                    cutoff_min_number_words = st.slider(
-                        cutoff_def, 0, min(max_nb_words, 500), 0
-                    )
-                    new_key = ("number_words", cutoff_min_number_words, False)
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond_1 = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond_1)
-
-                    cutoff_def = "If the number of words of a document is higher than this number, the document is removed."
-                    cutoff_max_number_words = st.slider(
-                        cutoff_def, 0, max_nb_words, max_nb_words
-                    )
-                    new_key = ("number_words", cutoff_max_number_words, True)
-                    keys.append(new_key)
-                    cond_2 = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond_2)
-
-                    conds["number_words"] = [cond_1, cond_2]
-
-            if "character_repetition_ratio" in columns:
-                with st.sidebar.expander("Character repetition ratio"):
-                    val_repetitions_lengths = list(
-                        self.docs["character_repetition_ratio"].iloc[0].keys()
-                    )
-                    default_index = (
-                        val_repetitions_lengths.index("10")
-                        if "10" in val_repetitions_lengths
-                        else 0
-                    )
-                    label_selectbox = "Length of repetitions in characters (that will influence the character repetition ratio)."
-                    repetitions_length = st.selectbox(
-                        label=label_selectbox,
-                        options=val_repetitions_lengths,
-                        index=default_index,
-                    )
-                    st.caption(
-                        "Choosing a higher or lower number does not mean that the filtering "
-                        "is stronger or weaker. Be careful, choosing a low number (below 5 for languages like English) "
-                        "tends to associate a high character repetition ratio to very long documents (like book chapters), but with "
-                        "few or no repetitions, simply because their length gives them more diversity, and we do "
-                        "not want to discard such documents. It is generally better to increase this number, so that false "
-                        "positives are very short documents (which we want to delete anyway) rather than long ones. However, "
-                        "a low number can be useful for Chinese, where a character can designate a whole word."
-                    )
-                    self.docs["character_repetition_ratio"] = self.docs_checkpoint[
-                        "character_repetition_ratio"
-                    ]
-                    for i in range(len(self.docs["character_repetition_ratio"])):
-                        self.docs["character_repetition_ratio"].iloc[i] = self.docs[
-                            "character_repetition_ratio"
-                        ].iloc[i][repetitions_length]
-
-                    cutoff_def = "If the character repetition ratio of a document is higher than this number, the document is removed."
-                    cutoff_character_repetition_ratio = st.slider(
-                        cutoff_def, 0.0, 1.0, 1.0, step=0.01
-                    )
-                    new_key = (
-                        "character_repetition_ratio",
-                        cutoff_character_repetition_ratio,
-                        True,
-                        repetitions_length,
-                    )
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["character_repetition_ratio"] = [cond]
-
-            if "word_repetition_ratio" in columns:
-                with st.sidebar.expander("Word repetition ratio"):
-                    val_repetitions_lengths = list(
-                        self.docs["word_repetition_ratio"].iloc[0].keys()
-                    )
-                    default_index = (
-                        val_repetitions_lengths.index("5")
-                        if "5" in val_repetitions_lengths
-                        else 0
-                    )
-                    label_selectbox = "Length of repetitions in words (that will influence the word repetition ratio)."
-                    repetitions_length = st.selectbox(
-                        label=label_selectbox,
-                        options=val_repetitions_lengths,
-                        index=default_index,
-                    )
-                    st.caption(
-                        "Choosing a higher or lower number does not mean that the filtering "
-                        "is stronger or weaker. Be careful, choosing a low number (like 3) could "
-                        "tend to associate a high word repetition ratio to very long documents (like book chapters), but with "
-                        "few or no repetitions, simply because their length gives them more diversity, and we do "
-                        "not want to discard such documents. It is generally better to increase a bit this number, so that false "
-                        "positives are very short documents (which we want to delete anyway) rather than long ones."
-                    )
-                    self.docs["word_repetition_ratio"] = self.docs_checkpoint[
-                        "word_repetition_ratio"
-                    ]
-                    for i in range(len(self.docs["word_repetition_ratio"])):
-                        self.docs["word_repetition_ratio"].iloc[i] = self.docs[
-                            "word_repetition_ratio"
-                        ].iloc[i][repetitions_length]
-
-                    cutoff_def = "If the word repetition ratio of a document is higher than this number, the document is removed."
-                    cutoff_word_repetition_ratio = st.slider(
-                        cutoff_def, 0.0, 1.0, 1.0, step=0.01
-                    )
-                    new_key = (
-                        "word_repetition_ratio",
-                        cutoff_word_repetition_ratio,
-                        True,
-                        repetitions_length,
-                    )
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["word_repetition_ratio"] = [cond]
-
-            if "special_characters_ratio" in columns:
-                with st.sidebar.expander("Special characters ratio"):
-                    cutoff_def = "If the special characters ratio of a document is higher than this number, the document is removed."
-                    cutoff_special_characters_ratio = st.slider(
-                        cutoff_def, 0.0, 1.0, 1.0, step=0.01
-                    )
-                    new_key = (
-                        "special_characters_ratio",
-                        cutoff_special_characters_ratio,
-                        True,
-                    )
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["special_characters_ratio"] = [cond]
-
-            if "stopwords_ratio" in columns:
-                with st.sidebar.expander("Stop words ratio"):
-                    stopwords_file = st.file_uploader(
-                        "Upload your own list of stop words (one per line). If there is none, the default one is used."
-                    )
-                    if stopwords_file:
-                        new_stopwords = StringIO(
-                            stopwords_file.getvalue().decode("utf-8")
-                        ).read()
-                        new_stopwords = set(new_stopwords.split("\n"))
-                        self.docs["stopwords_ratio"] = self.docs_checkpoint[
-                            "stopwords_ratio"
-                        ]
-                        for i in range(len(self.docs["stopwords_ratio"])):
-                            self.docs["stopwords_ratio"].iloc[
-                                i
-                            ] = Filtering.compute_stopwords_ratio(
-                                self.docs["text"].iloc[i],
-                                self.sentencepiece_model_tok,
-                                self.param["strip_characters"],
-                                self.param["cond_words_augmentation"],
-                                self.param["words_augmentation_group_sizes"],
-                                self.param["words_augmentation_join_char"],
-                                new_stopwords,
-                            )
-                    cutoff_def = "If the stop words ratio of a document is lower than this number, the document is removed."
-                    cutoff_stopwords_ratio = st.slider(
-                        cutoff_def, 0.0, 1.0, 0.0, step=0.01
-                    )
-                    new_key = ("stopwords_ratio", cutoff_stopwords_ratio, False)
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["stopwords_ratio"] = [cond]
-
-            if "flagged_words_ratio" in columns:
-                with st.sidebar.expander("Flagged words ratio"):
-                    flagged_words_file = st.file_uploader(
-                        "Upload your own list of flagged words (one per line). If there is none, the default one is used."
-                    )
-                    if flagged_words_file:
-                        new_flagged_words = StringIO(
-                            flagged_words_file.getvalue().decode("utf-8")
-                        ).read()
-                        new_flagged_words = set(new_flagged_words.split("\n"))
-                        self.docs["flagged_words_ratio"] = self.docs_checkpoint[
-                            "flagged_words_ratio"
-                        ]
-                        for i in range(len(self.docs["flagged_words_ratio"])):
-                            self.docs["flagged_words_ratio"].iloc[
-                                i
-                            ] = Filtering.compute_flagged_words_ratio(
-                                self.docs["text"].iloc[i],
-                                self.sentencepiece_model_tok,
-                                self.param["strip_characters"],
-                                self.param["cond_words_augmentation"],
-                                self.param["words_augmentation_group_sizes"],
-                                self.param["words_augmentation_join_char"],
-                                new_flagged_words,
-                            )
-                    cutoff_def = "If the flagged words ratio of a document is higher than this number, the document is removed."
-                    max_fwr = np.max(self.docs["flagged_words_ratio"])
-                    max_fwr = np.ceil(max_fwr * 1000) / 1000
-                    max_fwr = float(max_fwr)
-                    cutoff_flagged_words_ratio = st.slider(
-                        cutoff_def,
-                        0.000,
-                        max_fwr,
-                        max_fwr,
-                        step=0.001,
-                        format="%f",
-                    )
-                    new_key = ("flagged_words_ratio", cutoff_flagged_words_ratio, True)
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["flagged_words_ratio"] = [cond]
-
-            if "lang_id_score" in columns:
-                with st.sidebar.expander("Language ID confidence score"):
-                    cutoff_def = "If the confidence score for the language identification prediction of a document is lower than this number, the document is removed."
-                    cutoff_lang_id_score = st.slider(
-                        cutoff_def, 0.0, 1.0, 0.0, step=0.01
-                    )
-                    new_key = ("lang_id_score", cutoff_lang_id_score, False)
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["lang_id_score"] = [cond]
-
-            if "perplexity_score" in columns:
-                with st.sidebar.expander("Perplexity score"):
-                    cutoff_def = "If the perplexity score of a document is higher than this number, the document is removed."
-                    max_pp = int(np.max(self.docs["perplexity_score"])) + 1
-                    cutoff_perplexity_score = st.slider(cutoff_def, 0, max_pp, max_pp)
-                    new_key = ("perplexity_score", cutoff_perplexity_score, True)
-                    keys.append(new_key)
-                    Visualization_for_lang.plot_hist(self.docs, new_key)
-                    cond = get_cond(new_key[0], new_key[1], new_key[2])
-                    Visualization_for_lang.print_discarded_by_cond(cond)
-                    conds["perplexity_score"] = [cond]
-
-            return keys, conds
-
-        with st.expander(
-            f"Filtering on documents, for {self.num_docs} {self.lang} documents"
-        ):
-            st.header(
-                f"Filtering on documents, for {self.num_docs} {self.lang} documents"
-            )
-
-            if "labels" in list(self.docs):
-                chosen_label = st.selectbox(
-                    label="Consider only documents that include the following label",
-                    options=[
-                        "All",
-                        "NA: Narrative",
-                        "IN: Informational Description",
-                        "OP: Opinion",
-                        "ID: Interactive Discussion",
-                        "HI: How-to/Instruction",
-                        "IP: Informational Persuasion",
-                        "LY: Lyrical",
-                        "SP: Spoken",
-                    ],
-                )
-                chosen_label = chosen_label.split(":")[0]
-                if chosen_label != "All":
-                    cond_label = list(
-                        self.docs["labels"].apply(
-                            lambda x: True if chosen_label in x else False
-                        )
-                    )
-                    self.docs = self.docs[cond_label]
-
-            if self.docs.empty:
-                st.markdown(
-                    "No document to display, please try to select a different label."
-                )
-                self.keys = []
-                self.parameters = []
-
-            else:
-                st.sidebar.subheader("Parameters of the filtering on documents")
-                self.keys, conds = set_sliders()
-                self.parameters = self.keys * 1
-
-                all_conds = [
-                    subcond for cond in list(conds.values()) for subcond in cond
-                ]
-                all_conds = np.all(all_conds, axis=0)
-
-                Visualization_for_lang.display_dataset(
-                    self.docs, np.invert(all_conds), "Discarded documents", "docs"
-                )
-
-                # st.subheader("Display discarded documents by filter")
-                display_discarded_documents_by_filter = st.checkbox(
-                    "Display discarded documents by filter"
-                )
-
-                if display_discarded_documents_by_filter:
-                    columns = list(self.docs)
-
-                    if "number_words" in columns:
-                        cond_filter = np.invert(np.all(conds["number_words"], axis=0))
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the number of words",
-                            "docs",
-                        )
-
-                    if "character_repetition_ratio" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["character_repetition_ratio"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the character repetition ratio",
-                            "docs",
-                        )
-
-                    if "word_repetition_ratio" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["word_repetition_ratio"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the word repetition ratio",
-                            "docs",
-                        )
-
-                    if "special_characters_ratio" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["special_characters_ratio"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the special characters ratio",
-                            "docs",
-                        )
-
-                    if "stopwords_ratio" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["stopwords_ratio"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the stop words ratio",
-                            "docs",
-                        )
-
-                    if "flagged_words_ratio" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["flagged_words_ratio"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the flagged words ratio",
-                            "docs",
-                        )
-
-                    if "lang_id_score" in columns:
-                        cond_filter = np.invert(np.all(conds["lang_id_score"], axis=0))
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the language identification confidence score",
-                            "docs",
-                        )
-
-                    if "perplexity_score" in columns:
-                        cond_filter = np.invert(
-                            np.all(conds["perplexity_score"], axis=0)
-                        )
-                        Visualization_for_lang.display_dataset(
-                            self.docs,
-                            cond_filter,
-                            "Discarded documents for the filter on the perplexity score",
-                            "docs",
-                        )
-
-                Visualization_for_lang.display_dataset(
-                    self.docs, all_conds, "Retained documents", "docs"
-                )
-
-            st.header("Download data")
-
-            with open(self.path_data) as json_file:
-                btn = st.download_button(
-                    label="Download data as json",
-                    data=json_file,
-                    file_name="data.json",
-                )
-
-    def filtering_of_words(self):
-        if not (self.words is None):
-            columns = list(self.words)
-
-            st.sidebar.subheader("Parameter of the filtering on words")
-
-            conds_words = {}
-
-            if "len_word" in columns:
-                with st.sidebar.expander("Length of words"):
-                    cutoff_def = "If the length of a word is higher than this number, the word is removed."
-                    max_len_word = min(int(np.max(self.words["len_word"])) + 1, 200)
-                    cutoff_word = st.slider(cutoff_def, 0, max_len_word, max_len_word)
-                    new_key = ("len_word", cutoff_word, True)
-                    self.parameters.append(new_key)
-                    Visualization_for_lang.plot_hist(self.words, new_key)
-                    cond_len_words = self.words["len_word"] <= cutoff_word
-                    Visualization_for_lang.print_discarded_by_cond(cond_len_words)
-                    conds_words["len_word"] = cond_len_words
-
-            if "incorrect_substrings" in columns:
-                with st.sidebar.expander("Words with incorrect substrings"):
-                    incorrect_substrings = st.checkbox(
-                        "Remove words with incorrect substrings."
-                    )
-                    self.parameters.append(
-                        ("incorrect_substrings", incorrect_substrings)
-                    )
-
-                    checkbox = st.checkbox(
-                        "Diplay distribution",
-                        value=True,
-                        key="display_distribution_incorrect_substrings",
-                    )
-                    if checkbox:
-                        incor_sub = np.array(self.words["incorrect_substrings"]) * 1
-                        with_incor_sub = np.sum(incor_sub)
-                        without_incor_sub = len(incor_sub) - with_incor_sub
-                        st.markdown(
-                            f"Number of words with incorrect substrings: {with_incor_sub}"
-                        )
-                        st.markdown(
-                            f"Number of words without incorrect substrings: {without_incor_sub}"
-                        )
-
-                    if incorrect_substrings:
-                        cond_incorrect_substrings = np.invert(
-                            self.words["incorrect_substrings"]
-                        )
-                    else:
-                        cond_incorrect_substrings = np.array(
-                            [
-                                True
-                                for i in range(len(self.words["incorrect_substrings"]))
-                            ]
-                        )
-                    Visualization_for_lang.print_discarded_by_cond(
-                        cond_incorrect_substrings
-                    )
-                    conds_words["incorrect_substrings"] = cond_incorrect_substrings
-
-            all_conds_words = np.all(list(conds_words.values()), axis=0)
-
-            with st.expander(
-                f"Filtering on words, for {self.num_docs_for_words} {self.lang} documents"
-            ):
-                st.header(
-                    f"Filtering on words, for {self.num_docs_for_words} {self.lang} documents"
-                )
-
-                st.markdown(
-                    f"Since the number of words is way larger than the number of documents, "
-                    f"we consider in this section words for only {self.num_docs_for_words} documents."
-                )
-
-                Visualization_for_lang.display_dataset(
-                    self.words, np.invert(all_conds_words), "Discarded words", "words"
-                )
-
-                # st.subheader("Display discarded words by filter")
-                display_discarded_words_by_filter = st.checkbox(
-                    "Display discarded words by filter"
-                )
-
-                if display_discarded_words_by_filter:
-
-                    if "len_word" in columns:
-                        cond_filter = np.invert(conds_words["len_word"])
-                        Visualization_for_lang.display_dataset(
-                            self.words,
-                            cond_filter,
-                            "Discarded words for the filter on length",
-                            "words",
-                        )
-
-                    if "incorrect_substrings" in columns:
-                        cond_filter = np.invert(conds_words["incorrect_substrings"])
-                        Visualization_for_lang.display_dataset(
-                            self.words,
-                            cond_filter,
-                            "Discarded words for the filter on incorrect substrings",
-                            "words",
-                        )
-
-                Visualization_for_lang.display_dataset(
-                    self.words, all_conds_words, "Retained words", "words"
-                )
-
-    def download_parameters(self):
-        st.sidebar.subheader("Download parameters")
-        btn = st.sidebar.download_button(
-            label="Download current parameters as json",
-            data=json.dumps(self.parameters),
-            file_name=f"parameters_{self.lang_dataset_id}.json",
-        )
-
-    """
-    def plot_zipf_law(self):
-        if not (self.words is None):
-            st.header("Zipf's Law")
-
-            display_zipf_law = st.checkbox("Display Zipf's Law")
-
-            if display_zipf_law:
-
-                freq_words = {}
-                for _, row in self.words.iterrows():
-                    freq_words[row["word"]] = freq_words.get(row["word"], 0) + 1
-                freq_words = np.array(list(freq_words.values()))
-                freq_words = -np.sort(-freq_words)
-
-                fig, ax = plt.subplots()
-                ax.loglog(freq_words)
-                ax.set_title("Zipf's Law")
-                ax.set_xlabel("$i$-th most frequent word")
-                ax.set_ylabel("frequency in the documents")
-                st.pyplot(fig)
-    """
-
-    def analyse_personal_doc(self):
-        with st.expander("Analyse your own document"):
-            st.header("Analyse your own document")
-
-            personal_doc = st.text_area(
-                label="Paste here the document you want to analyse",
-                value="",
-                max_chars=10000,
-            )
-
-            is_discarded = False
-
-            def is_doc_discarded(key, score):
-                if key[2]:  # max cutoff
-                    return score > key[1]
-                else:
-                    return score < key[1]
-
-            if personal_doc:
-
-                st.markdown("Statistics of the document:")
-
-                for key in self.keys:
-                    if key[0] == "number_words":
-                        words = ModifyingDocuments.get_words_from_document(
-                            personal_doc,
-                            self.sentencepiece_model_tok,
-                            lower_case=False,
-                            strip_characters=self.param["strip_characters"],
-                        )
-                        if key[2]:
-                            st.markdown(f"Number of words: {len(words)}")
-                        if is_doc_discarded(key, len(words)):
-                            is_discarded = True
-
-                    elif key[0] == "character_repetition_ratio":
-                        character_repetition_ratio = (
-                            Filtering.compute_character_repetition_ratio(
-                                personal_doc, int(key[3])
-                            )
-                        )
-                        character_repetition_ratio = round(
-                            character_repetition_ratio, 3
-                        )
-                        st.markdown(
-                            f"Character repetition ratio: {character_repetition_ratio}"
-                        )
-                        if is_doc_discarded(key, character_repetition_ratio):
-                            is_discarded = True
-
-                    elif key[0] == "word_repetition_ratio":
-                        word_repetition_ratio = Filtering.compute_word_repetition_ratio(
-                            personal_doc,
-                            self.sentencepiece_model_tok,
-                            self.param["strip_characters"],
-                            int(key[3]),
-                        )
-                        word_repetition_ratio = round(word_repetition_ratio, 3)
-                        st.markdown(f"Word repetition ratio: {word_repetition_ratio}")
-                        if is_doc_discarded(key, word_repetition_ratio):
-                            is_discarded = True
-
-                    elif key[0] == "special_characters_ratio":
-                        special_characters_ratio = (
-                            Filtering.compute_special_characters_ratio(
-                                personal_doc, self.param["special_characters"]
-                            )
-                        )
-                        special_characters_ratio = round(special_characters_ratio, 3)
-                        st.markdown(
-                            f"Special characters ratio: {special_characters_ratio}"
-                        )
-                        if is_doc_discarded(key, special_characters_ratio):
-                            is_discarded = True
-
-                    elif key[0] == "stopwords_ratio":
-                        stopwords_ratio = Filtering.compute_stopwords_ratio(
-                            personal_doc,
-                            self.sentencepiece_model_tok,
-                            self.param["strip_characters"],
-                            self.param["cond_words_augmentation"],
-                            self.param["words_augmentation_group_sizes"],
-                            self.param["words_augmentation_join_char"],
-                            self.stopwords,
-                        )
-                        stopwords_ratio = round(stopwords_ratio, 3)
-                        st.markdown(f"Stop words ratio: {stopwords_ratio}")
-                        if is_doc_discarded(key, stopwords_ratio):
-                            is_discarded = True
-
-                    elif key[0] == "flagged_words_ratio":
-                        flagged_words_ratio = Filtering.compute_flagged_words_ratio(
-                            personal_doc,
-                            self.sentencepiece_model_tok,
-                            self.param["strip_characters"],
-                            self.param["cond_words_augmentation"],
-                            self.param["words_augmentation_group_sizes"],
-                            self.param["words_augmentation_join_char"],
-                            self.flagged_words,
-                        )
-                        flagged_words_ratio = round(flagged_words_ratio, 3)
-                        st.markdown(f"Flagged words ratio: {flagged_words_ratio}")
-                        if is_doc_discarded(key, flagged_words_ratio):
-                            is_discarded = True
-
-                    elif key[0] == "lang_id_score":
-                        (
-                            lang_pred_dataset_id,
-                            lang_id_score,
-                        ) = Filtering.compute_lang_id_pred_score(
-                            personal_doc, self.model_lang_id
-                        )
-                        lang_id_score = round(lang_id_score, 3)
-                        st.markdown(
-                            f"Language identification confidence score: {lang_id_score}"
-                        )
-                        if is_doc_discarded(key, flagged_words_ratio) or (
-                            self.lang_dataset_id != lang_pred_dataset_id
-                        ):
-                            is_discarded = True
-
-                    elif key[0] == "perplexity_score":
-                        perplexity_score = Filtering.compute_perplexity_score(
-                            personal_doc,
-                            self.sentencepiece_model,
-                            self.kenlm_model,
-                        )
-                        perplexity_score = round(perplexity_score, 3)
-                        st.markdown(f"Perplexity score: {perplexity_score}")
-                        if is_doc_discarded(key, perplexity_score):
-                            is_discarded = True
-
-                is_discarded = "" if is_discarded else "not "
-                st.markdown(
-                    f"With the current filtering parameters, this document **is {is_discarded}discarded**."
-                )
-
-    def visualization_for_lang(self):
-        self.set_title()
-        self.open_data()
-        self.filtering_of_docs()
-        self.filtering_of_words()
-        self.download_parameters()
-        self.analyse_personal_doc()
-
-
-class Visualization:
-    def __init__(self, path_instructions, param_visu_langs):
-        self.path_instructions = path_instructions
-        self.param_visu_langs = param_visu_langs
-
-    def preamble(self):
-        def get_binary_file_downloader_html(bin_file, file_label="File"):
-            with open(bin_file, "rb") as f:
-                data = f.read()
-            bin_str = base64.b64encode(data).decode()
-            href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">{file_label}</a>'
-            return href
-
-        st.markdown(
-            "Before diving into this demo, you might want to take a look at how the filtering pipeline looks like in more detail in this "
-            + get_binary_file_downloader_html(
-                self.path_instructions,
-                "pdf",
-            )
-            + ".",
-            unsafe_allow_html=True,
-        )
-
-    def warning_preamble(self):
-        st.markdown(
-            "This demo can be a little slow, and only allows you to process up to 5000 documents "
-            "for a decent speed. If you want to display up to three times more documents and have "
-            "a faster visualization, we invite you to run this "
-            "[code](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/filtering/visualization) "
-            "on your computer."
-        )
-
-    def choose_lang(self):
-        options = [
-            self.param_visu_langs[lang_dataset_id]["lang"]
-            for lang_dataset_id in self.param_visu_langs
-        ]
-        index = options.index("English") if ("English" in options) else 0
-        lang_chosen = st.selectbox(
-            label="Select the language for visualization",
-            options=options,
-            index=index,
-        )
-        if lang_chosen != "None":
-            lang_chosen_dataset_id = langs_id.loc[
-                langs_id["lang"] == lang_chosen, "dataset_id"
-            ].iloc[0]
-            visualization_for_lang = Visualization_for_lang(
-                path_data=self.param_visu_langs[lang_chosen_dataset_id]["path_data"],
-                lang=self.param_visu_langs[lang_chosen_dataset_id]["lang"],
-                num_docs=self.param_visu_langs[lang_chosen_dataset_id]["num_docs"],
-                num_docs_for_words=self.param_visu_langs[lang_chosen_dataset_id][
-                    "num_docs_for_words"
-                ],
-                max_len_text_display=self.param_visu_langs[lang_chosen_dataset_id][
-                    "max_len_text_display"
-                ],
-                lang_dataset_id=self.param_visu_langs[lang_chosen_dataset_id][
-                    "lang_dataset_id"
-                ],
-                path_fasttext_model=self.param_visu_langs[lang_chosen_dataset_id][
-                    "path_fasttext_model"
-                ],
-                path_sentencepiece_model=self.param_visu_langs[lang_chosen_dataset_id][
-                    "path_sentencepiece_model"
-                ],
-                path_kenlm_model=self.param_visu_langs[lang_chosen_dataset_id][
-                    "path_kenlm_model"
-                ],
-            )
-            visualization_for_lang.visualization_for_lang()
-
-    def visualization(self):
-        self.preamble()
-        self.warning_preamble()
-        self.choose_lang()
-
-
-path_instructions = "./explanation_filtering_pipeline.pdf"
-
-param_visu_langs = {
-    lang_dataset_id: {
-        "path_data": f"./{lang_dataset_id}_examples_with_stats.json",
-        "lang": langs_id.loc[langs_id["dataset_id"] == lang_dataset_id, "lang"].iloc[0],
-        "num_docs": 5000,
-        "num_docs_for_words": 500,
-        "max_len_text_display": 10000,
-        "lang_dataset_id": lang_dataset_id,
-        "path_fasttext_model": "./lid.176.bin",
-        "path_sentencepiece_model": f"./{lang_dataset_id}.sp.model",
-        "path_kenlm_model": f"./{lang_dataset_id}.arpa.bin",
-    }
-    for lang_dataset_id in ["eu", "ca", "zh", "en", "fr", "id", "pt", "es"]
-}
-
-visualization = Visualization(path_instructions, param_visu_langs)
-visualization.visualization()
diff --git a/spaces/huhlim/cg2all/README.md b/spaces/huhlim/cg2all/README.md
deleted file mode 100644
index ba772c26dbd82f25fc103d3253818da16e0e4b2c..0000000000000000000000000000000000000000
--- a/spaces/huhlim/cg2all/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: cg2all
-emoji: 🏃
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/losses.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/losses.py
deleted file mode 100644
index 7805d8f088e9b91f48b29d8304f87927ca65e0c4..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/losses.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import math
-
-import torch
-
-
-class CombinedMarginLoss(torch.nn.Module):
-    def __init__(self, s, m1, m2, m3, interclass_filtering_threshold=0):
-        super().__init__()
-        self.s = s
-        self.m1 = m1
-        self.m2 = m2
-        self.m3 = m3
-        self.interclass_filtering_threshold = interclass_filtering_threshold
-
-        # For ArcFace
-        self.cos_m = math.cos(self.m2)
-        self.sin_m = math.sin(self.m2)
-        self.theta = math.cos(math.pi - self.m2)
-        self.sinmm = math.sin(math.pi - self.m2) * self.m2
-        self.easy_margin = False
-
-    def forward(self, logits, labels):
-        index_positive = torch.where(labels != -1)[0]
-
-        if self.interclass_filtering_threshold > 0:
-            with torch.no_grad():
-                dirty = logits > self.interclass_filtering_threshold
-                dirty = dirty.float()
-                mask = torch.ones([index_positive.size(0), logits.size(1)], device=logits.device)
-                mask.scatter_(1, labels[index_positive], 0)
-                dirty[index_positive] *= mask
-                tensor_mul = 1 - dirty
-            logits = tensor_mul * logits
-
-        target_logit = logits[index_positive, labels[index_positive].view(-1)]
-
-        if self.m1 == 1.0 and self.m3 == 0.0:
-            with torch.no_grad():
-                target_logit.arccos_()
-                logits.arccos_()
-                final_target_logit = target_logit + self.m2
-                logits[index_positive, labels[index_positive].view(-1)] = final_target_logit
-                logits.cos_()
-            logits = logits * self.s
-
-        elif self.m3 > 0:
-            final_target_logit = target_logit - self.m3
-            logits[index_positive, labels[index_positive].view(-1)] = final_target_logit
-            logits = logits * self.s
-        else:
-            raise
-
-        return logits
-
-
-class ArcFace(torch.nn.Module):
-    """ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):"""
-
-    def __init__(self, s=64.0, margin=0.5):
-        super(ArcFace, self).__init__()
-        self.scale = s
-        self.margin = margin
-        self.cos_m = math.cos(margin)
-        self.sin_m = math.sin(margin)
-        self.theta = math.cos(math.pi - margin)
-        self.sinmm = math.sin(math.pi - margin) * margin
-        self.easy_margin = False
-
-    def forward(self, logits: torch.Tensor, labels: torch.Tensor):
-        index = torch.where(labels != -1)[0]
-        target_logit = logits[index, labels[index].view(-1)]
-
-        with torch.no_grad():
-            target_logit.arccos_()
-            logits.arccos_()
-            final_target_logit = target_logit + self.margin
-            logits[index, labels[index].view(-1)] = final_target_logit
-            logits.cos_()
-        logits = logits * self.s
-        return logits
-
-
-class CosFace(torch.nn.Module):
-    def __init__(self, s=64.0, m=0.40):
-        super(CosFace, self).__init__()
-        self.s = s
-        self.m = m
-
-    def forward(self, logits: torch.Tensor, labels: torch.Tensor):
-        index = torch.where(labels != -1)[0]
-        target_logit = logits[index, labels[index].view(-1)]
-        final_target_logit = target_logit - self.m
-        logits[index, labels[index].view(-1)] = final_target_logit
-        logits = logits * self.s
-        return logits
diff --git a/spaces/hzwluoye/gpt4/client/css/stop-generating.css b/spaces/hzwluoye/gpt4/client/css/stop-generating.css
deleted file mode 100644
index 3c2010d25065fbef63b104df743ef72c00259871..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/client/css/stop-generating.css
+++ /dev/null
@@ -1,38 +0,0 @@
-.stop-generating {
-	position: absolute;
-	bottom: 128px;
-	left: 50%;
-	transform: translateX(-50%);
-	z-index: 1000000;
-}
-
-.stop-generating button {
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	color: var(--colour-3);
-	cursor: pointer;
-	animation: show_popup 0.4s;
-}
-
-@keyframes show_popup {
-	from {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-@keyframes hide_popup {
-	to {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-.stop-generating-hiding button {
-	animation: hide_popup 0.4s;
-}
-
-.stop-generating-hidden button {
-	display: none;
-}
diff --git a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/components/output.py b/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/components/output.py
deleted file mode 100644
index f2f1736e9b6b6e9b394cbdfd635b87a570fa6f72..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/components/output.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Tuple, Optional
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.core import conditional_process
-from DeepFakeAI.uis.typing import Update
-from DeepFakeAI.utilities import is_image, is_video, normalize_output_path, clear_temp
-
-OUTPUT_START_BUTTON : Optional[gradio.Button] = None
-OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
-OUTPUT_IMAGE : Optional[gradio.Image] = None
-OUTPUT_VIDEO : Optional[gradio.Video] = None
-
-
-def render() -> None:
-	global OUTPUT_START_BUTTON
-	global OUTPUT_CLEAR_BUTTON
-	global OUTPUT_IMAGE
-	global OUTPUT_VIDEO
-
-	with gradio.Row():
-		with gradio.Box():
-			OUTPUT_IMAGE = gradio.Image(
-				label = wording.get('output_image_or_video_label'),
-				visible = False
-			)
-			OUTPUT_VIDEO = gradio.Video(
-				label = wording.get('output_image_or_video_label')
-			)
-	with gradio.Row():
-		OUTPUT_START_BUTTON = gradio.Button(wording.get('start_button_label'))
-		OUTPUT_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
-
-
-def listen() -> None:
-	OUTPUT_START_BUTTON.click(update, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-	OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-
-
-def update() -> Tuple[Update, Update]:
-	DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, '.')
-	if DeepFakeAI.globals.output_path:
-		conditional_process()
-		if is_image(DeepFakeAI.globals.output_path):
-			return gradio.update(value = DeepFakeAI.globals.output_path, visible = True), gradio.update(value = None, visible = False)
-		if is_video(DeepFakeAI.globals.output_path):
-			return gradio.update(value = None, visible = False), gradio.update(value = DeepFakeAI.globals.output_path, visible = True)
-	return gradio.update(value = None, visible = False), gradio.update(value = None, visible = False)
-
-
-def clear() -> Tuple[Update, Update]:
-	if DeepFakeAI.globals.target_path:
-		clear_temp(DeepFakeAI.globals.target_path)
-	return gradio.update(value = None), gradio.update(value = None)
diff --git a/spaces/innnky/nyaru4.0/resample.py b/spaces/innnky/nyaru4.0/resample.py
deleted file mode 100644
index 5e96106c9a066e6d73652c544322d029dd98f746..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru4.0/resample.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    # speaker 's5', 'p280', 'p315' are excluded,
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, None)
-        wav, _ = librosa.effects.trim(wav, top_db=20)
-        peak = np.abs(wav).max()
-        if peak > 1.0:
-            wav = 0.98 * wav / peak
-        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
-        wav2 /= max(wav2.max(), -wav2.min())
-        save_name = wav_name
-        save_path2 = os.path.join(args.out_dir2, speaker, save_name)
-        wavfile.write(
-            save_path2,
-            args.sr2,
-            (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-        )
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
-    parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
-    args = parser.parse_args()
-    processs = cpu_count()-2 if cpu_count() >4 else 1
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Acpi Genuineintel - X86 Family 6 Model 23 0 17.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Acpi Genuineintel - X86 Family 6 Model 23 0 17.md
deleted file mode 100644
index 65079ce267c9c4e2433e7d2024a2bbbc99bece46..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Acpi Genuineintel - X86 Family 6 Model 23 0 17.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
-<h1>What You Need to Know About ACPI GenuineIntel - x86 Family 6 Model 23 0 17</h1>
-
-<p>ACPI GenuineIntel - x86 Family 6 Model 23 0 17 is a CPUID code that identifies a specific type of Intel processor. CPUID codes are used by software and hardware to detect the features and capabilities of a CPU. In this article, we will explain what ACPI GenuineIntel - x86 Family 6 Model 23 0 17 means and how it affects your computer performance.</p>
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-
-<h2>What is ACPI?</h2>
-
-<p>ACPI stands for Advanced Configuration and Power Interface. It is a standard that defines how the operating system and the hardware communicate and cooperate to manage power and configuration settings. ACPI allows the operating system to control the power state of the CPU, such as putting it to sleep or waking it up, as well as adjusting its frequency and voltage according to the workload. ACPI also enables the operating system to identify and configure the devices connected to the CPU, such as memory, disk drives, graphics cards, etc.</p>
-
-<h2>What is GenuineIntel?</h2>
-
-<p>GenuineIntel is a brand string that indicates that the CPU is manufactured by Intel Corporation. It is part of the CPUID code that also contains other information about the CPU, such as its family, model, stepping, and features. The brand string can be read by software using a special instruction called CPUID. The brand string can also be displayed by some BIOS or UEFI firmware during boot-up.</p>
-
-<h2>What is x86 Family 6 Model 23?</h2>
-
-<p>x86 Family 6 Model 23 is a specific type of Intel processor that belongs to the Penryn microarchitecture. Penryn is the codename for the 45 nm process technology that was used to produce Intel Core 2 Duo, Core 2 Quad, Core 2 Extreme, Xeon, and Celeron processors. Penryn processors have improved performance, lower power consumption, and larger cache sizes than their predecessors. Some of the features of Penryn processors include:</p>
-
-<ul>
-<li>SSE4.1 instruction set extension that improves multimedia and vector processing</li>
-<li>Enhanced Intel SpeedStep Technology (EIST) that dynamically adjusts the CPU frequency and voltage according to the workload</li>
-<li>Enhanced Intel Thermal Monitor (TM2) that protects the CPU from overheating by reducing its frequency and voltage when necessary</li>
-<li>Intel Dynamic Acceleration (IDA) that boosts the performance of one core when the other core is idle</li>
-<li>Intel Virtualization Technology (VT-x) that enables hardware-assisted virtualization</li>
-<li>Intel Trusted Execution Technology (TXT) that enhances security by preventing unauthorized code execution</li>
-<li>Intel 64 Technology that supports 64-bit computing</li>
-<li>Execute Disable Bit (XD) that prevents buffer overflow attacks by marking some memory regions as non-executable</li>
-</ul>
-
-<h2>What is Stepping 0 17?</h2>
-
-<p>Stepping 0 17 is a revision number that indicates a minor update or bug fix for the x86 Family 6 Model 23 processor. Stepping numbers are assigned by Intel to identify different versions of the same processor model. Higher stepping numbers usually mean better performance, stability, or compatibility. Stepping numbers can be read by software using the CPUID instruction.</p>
-
-<h2>How Does ACPI GenuineIntel - x86 Family 6 Model 23 0 17 Affect Your Computer Performance?</h2>
-
-<p>ACPI GenuineIntel - x86 Family 6 Model 23 0 17 is a high-performance processor that can handle various tasks and applications with ease. It has a fast clock speed, large cache size, and advanced features that make it suitable for gaming, multimedia, multitasking, and virtualization. However, it also consumes more power and generates more heat than some newer processors. Therefore, it may require a good cooling system and a reliable power supply to operate properly. Moreover, it may not be compatible with some newer motherboards or operating systems that require newer processor models or features.</p>
-
-<h3>Conclusion</h3>
-
-<p>In conclusion, ACPI GenuineIntel - x86 Family 6 Model 23 0 17 is a CPUID code that identifies a specific type of Intel processor based on the Penryn microarchitecture. It has many features and capabilities that make it a powerful processor for various purposes. However, it also has some drawbacks and limitations that may affect its performance or compatibility with some hardware or software. Therefore, it is important to check the specifications and requirements of your computer system before buying or installing this processor.</p>
-<p></p>
-<h2>How to Update or Uninstall ACPI GenuineIntel - x86 Family 6 Model 23 0 17 Drivers?</h2>
-
-<p>ACPI GenuineIntel - x86 Family 6 Model 23 0 17 drivers are software components that enable the operating system and the hardware to communicate and function properly. Drivers are usually installed automatically by Windows Update or by the manufacturer's software. However, sometimes drivers may become outdated, corrupted, or incompatible with some hardware or software. This may cause performance issues, errors, or crashes. Therefore, it is important to keep your drivers updated or uninstall them if they are causing problems.</p>
-
-<p>To update or uninstall ACPI GenuineIntel - x86 Family 6 Model 23 0 17 drivers, you can follow these steps:</p>
-
-<ol>
-<li>Open Device Manager by pressing Windows key + X and selecting Device Manager from the menu.</li>
-<li>Expand the Processors category and right-click on the ACPI GenuineIntel - x86 Family 6 Model 23 0 17 processor.</li>
-<li>Select Update driver if you want to check for the latest driver updates online or from your computer. Follow the instructions on the screen to complete the update.</li>
-<li>Select Uninstall device if you want to remove the driver completely from your system. Check the box that says Delete the driver software for this device and click Uninstall. Restart your computer to apply the changes.</li>
-</ol>
-
-<p>Note: Updating or uninstalling drivers may affect your system stability or functionality. Make sure you have a backup of your important data before proceeding. If you encounter any problems after updating or uninstalling drivers, you can try to restore your system to a previous point using System Restore.</p>
-
-<h2>How to Find More Information About ACPI GenuineIntel - x86 Family 6 Model 23 0 17?</h2>
-
-<p>If you want to learn more about ACPI GenuineIntel - x86 Family 6 Model 23 0 17, you can visit some of the following websites:</p>
-
-<ul>
-<li><a href="https://www.intel.com/content/www/us/en/support/articles/000005533/software/chipset-software.html">Intel® Chipset Software and Drivers</a>: This website provides information and downloads for Intel chipset software and drivers, including Intel chipset device software, Intel graphics drivers, and Intel USB 3.0 eXtensible Host Controller driver.</li>
-<li><a href="https://community.intel.com/t5/Processors/Intel-driver-6-5-1-371-performance-issue/m-p/1286970">Intel driver 6.5.1.371- performance issue - Intel Community</a>: This forum thread discusses a performance issue caused by an Intel driver update for ACPI GenuineIntel - x86 Family 6 Model 142 processors. You can find possible solutions and feedback from other users who have experienced the same problem.</li>
-<li><a href="https://en.wikichip.org/wiki/intel/cpuid">CPUID - Intel - WikiChip</a>: This website provides a comprehensive list of Intel CPUID codes and their corresponding processor models and features. You can find detailed information about ACPI GenuineIntel - x86 Family 6 Model 23 processors and their microarchitecture, instruction set extensions, power management features, and more.</li>
-</ul>
-
-<h3>Conclusion</h3>
-
-<p>In conclusion, ACPI GenuineIntel - x86 Family 6 Model 23 0 17 is a CPUID code that identifies a specific type of Intel processor based on the Penryn microarchitecture. It has many features and capabilities that make it a powerful processor for various purposes. However, it also has some drawbacks and limitations that may affect its performance or compatibility with some hardware or software. Therefore, it is important to check the specifications and requirements of your computer system before buying or installing this processor. You can also update or uninstall its drivers if they are causing problems or find more information about it online.</p>
-<h2>How to Benchmark ACPI GenuineIntel - x86 Family 6 Model 23 0 17?</h2>
-
-<p>Benchmarking is a process of measuring and comparing the performance of a system or a component using standardized tests and metrics. Benchmarking can help you evaluate the capabilities and limitations of your processor and compare it with other processors in the market. Benchmarking can also help you identify potential bottlenecks or issues in your system and optimize it accordingly.</p>
-
-<p>To benchmark ACPI GenuineIntel - x86 Family 6 Model 23 0 17, you can use some of the following tools and methods:</p>
-
-<ul>
-<li>CPU-Z: CPU-Z is a freeware that gathers information about your processor, such as its name, model, core number, clock speed, cache size, voltage, instruction set, etc. CPU-Z also provides a simple benchmark test that measures the single-thread and multi-thread performance of your processor. You can download CPU-Z from <a href="https://www.cpuid.com/softwares/cpu-z.html">https://www.cpuid.com/softwares/cpu-z.html</a>.</li>
-<li>Cinebench: Cinebench is a benchmark tool that tests the performance of your processor and graphics card by rendering a complex 3D scene. Cinebench uses the Cinema 4D engine, which is widely used for animation and visual effects. Cinebench provides two tests: one for single-core performance and one for multi-core performance. You can download Cinebench from <a href="https://www.maxon.net/en/cinebench">https://www.maxon.net/en/cinebench</a>.</li>
-<li>Geekbench: Geekbench is a cross-platform benchmark tool that measures the performance of your processor and memory using various workloads that simulate real-world scenarios. Geekbench provides two modes: one for 32-bit performance and one for 64-bit performance. Geekbench also provides an online database where you can compare your results with other users. You can download Geekbench from <a href="https://www.geekbench.com/">https://www.geekbench.com/</a>.</li>
-<li>PassMark: PassMark is a comprehensive benchmark suite that tests various aspects of your system, such as CPU, GPU, RAM, disk, network, etc. PassMark provides several tests for each component and calculates an overall score based on the results. PassMark also provides an online database where you can compare your results with other users. You can download PassMark from <a href="https://www.passmark.com/products/performancetest/">https://www.passmark.com/products/performancetest/</a>.</li>
-</ul>
-
-<h2>How to Overclock ACPI GenuineIntel - x86 Family 6 Model 23 0 17?</h2>
-
-<p>Overclocking is a process of increasing the clock speed or frequency of a processor beyond its factory settings. Overclocking can improve the performance of your processor and allow it to run faster and handle more tasks. However, overclocking also increases the power consumption and heat generation of your processor, which may cause instability, damage, or reduced lifespan. Therefore, overclocking should be done with caution and proper cooling.</p>
-
-<p>To overclock ACPI GenuineIntel - x86 Family 6 Model 23 0 17, you can use some of the following tools and methods:</p>
-
-<ul>
-<li>BIOS or UEFI: BIOS or UEFI is the firmware that controls the basic functions and settings of your motherboard and processor. BIOS or UEFI usually provides options to adjust the clock speed, voltage, multiplier, and other parameters of your processor. To access BIOS or UEFI, you need to press a specific key (such as F2, F10, Del, etc.) during boot-up. However, not all BIOS or UEFI versions support overclocking or provide enough options to do so.</li>
-<li>Intel Extreme Tuning Utility (XTU): Intel XTU is a software that allows you to overclock your Intel processor from within Windows. Intel XTU provides various options to adjust the clock speed, voltage, multiplier, turbo boost, power limit, etc. of your processor. Intel XTU also provides monitoring tools to check the temperature, frequency, utilization, etc. of your processor. You can download Intel XTU from <a href="https://downloadcenter.intel.com/download/29183/Intel-Extreme-Tuning-Utility-Intel-XTU-">https://downloadcenter.intel.com/download/29183/Intel-Extreme-Tuning-Utility-Intel-XTU-</a>.</li>
-<li>ThrottleStop: ThrottleStop is a software that allows you to overclock your Intel processor by modifying its power management features. ThrottleStop provides options to disable or adjust the speed step, turbo boost, c-states, thermal throttling, etc. of your processor. ThrottleStop also provides monitoring tools to check the temperature, frequency, utilization, etc. of your processor. You can download ThrottleStop from <a href="https://www.techpowerup.com/download/techpowerup-throttlestop/">https://www.techpowerup.com/download/techpowerup-throttlestop/</a>.</li>
-</ul>
-
-<p>Note: Overclocking may void your warranty or damage your hardware. Make sure you have a backup of your important data before proceeding. If you encounter any problems after overclocking, you can try to restore your system to a previous point using System Restore.</p>
-<h3>Conclusion</h3>
-
-<p>In conclusion, ACPI GenuineIntel - x86 Family 6 Model 23 0 17 is a CPUID code that identifies a specific type of Intel processor based on the Penryn microarchitecture. It has many features and capabilities that make it a powerful processor for various purposes. However, it also has some drawbacks and limitations that may affect its performance or compatibility with some hardware or software. Therefore, it is important to check the specifications and requirements of your computer system before buying or installing this processor. You can also benchmark, update, uninstall, or overclock its drivers if you want to optimize its performance or functionality. You can find more information about this processor and its drivers online or by using some of the tools and methods mentioned in this article.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Azov Films Bf V2.0 Fkk Paul Calins Home Video (2011).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Azov Films Bf V2.0 Fkk Paul Calins Home Video (2011).md
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Enolsoft PDF To Word OCR 6.1.0 _VERIFIED_ Crack Mac Osx.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Enolsoft PDF To Word OCR 6.1.0 _VERIFIED_ Crack Mac Osx.md
deleted file mode 100644
index 1a3accff34d3c9f76860d46cb3f42b3c6edf712c..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Enolsoft PDF To Word OCR 6.1.0 _VERIFIED_ Crack Mac Osx.md	
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-
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-<h2>Features of Enolsoft PDF To Word OCR 6.1.0 Crack Mac Osx</h2>
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deleted file mode 100644
index 890bebf871308582786d72b0af307ebcbe04693c..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Generateur Cle Premium Uptobox.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
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diff --git a/spaces/inreVtussa/clothingai/Examples/Cubase LE AI Elements 8.0.35 Keygen.md b/spaces/inreVtussa/clothingai/Examples/Cubase LE AI Elements 8.0.35 Keygen.md
deleted file mode 100644
index 4096282bcf5e471166546fbeb7c2ff3f27b661d2..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Cubase LE AI Elements 8.0.35 Keygen.md	
+++ /dev/null
@@ -1,14 +0,0 @@
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diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/general_utils.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/general_utils.py
deleted file mode 100644
index 893a7ff2870a93c64822c3e1a9cbe8c51faca811..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/general_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import hashlib
-def checksum(filename, hash_factory=hashlib.blake2b, chunk_num_blocks=128):
-    h = hash_factory()
-    with open(filename,'rb') as f: 
-        while chunk := f.read(chunk_num_blocks*h.block_size): 
-            h.update(chunk)
-    return h.hexdigest()
-
-def get_os():
-    import platform
-    return {"Windows": "Windows", "Linux": "Linux", "Darwin": "Mac"}.get(platform.system(), "Unknown")
-
-# used in src/rife/inference_video.py and more, soon
-def duplicate_pngs_from_folder(from_folder, to_folder, img_batch_id, orig_vid_name):
-    import os, cv2, shutil #, subprocess
-    #TODO: don't copy-paste at all if the input is a video (now it copy-pastes, and if input is deforum run is also converts to make sure no errors rise cuz of 24-32 bit depth differences)
-    temp_convert_raw_png_path = os.path.join(from_folder, to_folder)
-    if not os.path.exists(temp_convert_raw_png_path):
-                os.makedirs(temp_convert_raw_png_path)
-                
-    frames_handled = 0
-    for f in os.listdir(from_folder):
-        if ('png' in f or 'jpg' in f) and '-' not in f and '_depth_' not in f and ((img_batch_id is not None and f.startswith(img_batch_id) or img_batch_id is None)):
-            frames_handled +=1
-            original_img_path = os.path.join(from_folder, f)
-            if orig_vid_name is not None:
-                shutil.copy(original_img_path, temp_convert_raw_png_path)
-            else:
-                image = cv2.imread(original_img_path)
-                new_path = os.path.join(temp_convert_raw_png_path, f)
-                cv2.imwrite(new_path, image, [cv2.IMWRITE_PNG_COMPRESSION, 0])
-    return frames_handled
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/Panoremix/src/components/ui/command.tsx b/spaces/jbilcke-hf/Panoremix/src/components/ui/command.tsx
deleted file mode 100644
index a4e602ef2508a071948aef7779023540c9f25381..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/components/ui/command.tsx
+++ /dev/null
@@ -1,155 +0,0 @@
-"use client"
-
-import * as React from "react"
-import { DialogProps } from "@radix-ui/react-dialog"
-import { Command as CommandPrimitive } from "cmdk"
-import { Search } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-import { Dialog, DialogContent } from "@/components/ui/dialog"
-
-const Command = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive
-    ref={ref}
-    className={cn(
-      "flex h-full w-full flex-col overflow-hidden rounded-md bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-Command.displayName = CommandPrimitive.displayName
-
-interface CommandDialogProps extends DialogProps {}
-
-const CommandDialog = ({ children, ...props }: CommandDialogProps) => {
-  return (
-    <Dialog {...props}>
-      <DialogContent className="overflow-hidden p-0 shadow-lg">
-        <Command className="[&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-stone-500 [&_[cmdk-group]:not([hidden])_~[cmdk-group]]:pt-0 [&_[cmdk-group]]:px-2 [&_[cmdk-input-wrapper]_svg]:h-5 [&_[cmdk-input-wrapper]_svg]:w-5 [&_[cmdk-input]]:h-12 [&_[cmdk-item]]:px-2 [&_[cmdk-item]]:py-3 [&_[cmdk-item]_svg]:h-5 [&_[cmdk-item]_svg]:w-5 dark:[&_[cmdk-group-heading]]:text-stone-400">
-          {children}
-        </Command>
-      </DialogContent>
-    </Dialog>
-  )
-}
-
-const CommandInput = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Input>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Input>
->(({ className, ...props }, ref) => (
-  <div className="flex items-center border-b px-3" cmdk-input-wrapper="">
-    <Search className="mr-2 h-4 w-4 shrink-0 opacity-50" />
-    <CommandPrimitive.Input
-      ref={ref}
-      className={cn(
-        "flex h-11 w-full rounded-md bg-transparent py-3 text-sm outline-none placeholder:text-stone-500 disabled:cursor-not-allowed disabled:opacity-50 dark:placeholder:text-stone-400",
-        className
-      )}
-      {...props}
-    />
-  </div>
-))
-
-CommandInput.displayName = CommandPrimitive.Input.displayName
-
-const CommandList = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.List>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.List>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.List
-    ref={ref}
-    className={cn("max-h-[300px] overflow-y-auto overflow-x-hidden", className)}
-    {...props}
-  />
-))
-
-CommandList.displayName = CommandPrimitive.List.displayName
-
-const CommandEmpty = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Empty>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Empty>
->((props, ref) => (
-  <CommandPrimitive.Empty
-    ref={ref}
-    className="py-6 text-center text-sm"
-    {...props}
-  />
-))
-
-CommandEmpty.displayName = CommandPrimitive.Empty.displayName
-
-const CommandGroup = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Group>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Group>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Group
-    ref={ref}
-    className={cn(
-      "overflow-hidden p-1 text-stone-950 [&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:py-1.5 [&_[cmdk-group-heading]]:text-xs [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-stone-500 dark:text-stone-50 dark:[&_[cmdk-group-heading]]:text-stone-400",
-      className
-    )}
-    {...props}
-  />
-))
-
-CommandGroup.displayName = CommandPrimitive.Group.displayName
-
-const CommandSeparator = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Separator
-    ref={ref}
-    className={cn("-mx-1 h-px bg-stone-200 dark:bg-stone-800", className)}
-    {...props}
-  />
-))
-CommandSeparator.displayName = CommandPrimitive.Separator.displayName
-
-const CommandItem = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Item>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Item
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none aria-selected:bg-stone-100 aria-selected:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:aria-selected:bg-stone-800 dark:aria-selected:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-
-CommandItem.displayName = CommandPrimitive.Item.displayName
-
-const CommandShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn(
-        "ml-auto text-xs tracking-widest text-stone-500 dark:text-stone-400",
-        className
-      )}
-      {...props}
-    />
-  )
-}
-CommandShortcut.displayName = "CommandShortcut"
-
-export {
-  Command,
-  CommandDialog,
-  CommandInput,
-  CommandList,
-  CommandEmpty,
-  CommandGroup,
-  CommandItem,
-  CommandShortcut,
-  CommandSeparator,
-}
diff --git a/spaces/jbilcke-hf/Panoremix/src/components/ui/label.tsx b/spaces/jbilcke-hf/Panoremix/src/components/ui/label.tsx
deleted file mode 100644
index 534182176bf87f9308355514adc884d2b69750a5..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/components/ui/label.tsx
+++ /dev/null
@@ -1,26 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as LabelPrimitive from "@radix-ui/react-label"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const labelVariants = cva(
-  "text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70"
-)
-
-const Label = React.forwardRef<
-  React.ElementRef<typeof LabelPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof LabelPrimitive.Root> &
-    VariantProps<typeof labelVariants>
->(({ className, ...props }, ref) => (
-  <LabelPrimitive.Root
-    ref={ref}
-    className={cn(labelVariants(), className)}
-    {...props}
-  />
-))
-Label.displayName = LabelPrimitive.Root.displayName
-
-export { Label }
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageTransform.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageTransform.py
deleted file mode 100644
index 7881f0d262b0db7ecaed224ee2268f3b69b836c9..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageTransform.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# transform wrappers
-#
-# History:
-# 2002-04-08 fl   Created
-#
-# Copyright (c) 2002 by Secret Labs AB
-# Copyright (c) 2002 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image
-
-
-class Transform(Image.ImageTransformHandler):
-    def __init__(self, data):
-        self.data = data
-
-    def getdata(self):
-        return self.method, self.data
-
-    def transform(self, size, image, **options):
-        # can be overridden
-        method, data = self.getdata()
-        return image.transform(size, method, data, **options)
-
-
-class AffineTransform(Transform):
-    """
-    Define an affine image transform.
-
-    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first
-    two rows from an affine transform matrix. For each pixel (x, y) in the
-    output image, the new value is taken from a position (a x + b y + c,
-    d x + e y + f) in the input image, rounded to nearest pixel.
-
-    This function can be used to scale, translate, rotate, and shear the
-    original image.
-
-    See :py:meth:`~PIL.Image.Image.transform`
-
-    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows
-        from an affine transform matrix.
-    """
-
-    method = Image.Transform.AFFINE
-
-
-class ExtentTransform(Transform):
-    """
-    Define a transform to extract a subregion from an image.
-
-    Maps a rectangle (defined by two corners) from the image to a rectangle of
-    the given size. The resulting image will contain data sampled from between
-    the corners, such that (x0, y0) in the input image will end up at (0,0) in
-    the output image, and (x1, y1) at size.
-
-    This method can be used to crop, stretch, shrink, or mirror an arbitrary
-    rectangle in the current image. It is slightly slower than crop, but about
-    as fast as a corresponding resize operation.
-
-    See :py:meth:`~PIL.Image.Image.transform`
-
-    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the
-        input image's coordinate system. See :ref:`coordinate-system`.
-    """
-
-    method = Image.Transform.EXTENT
-
-
-class QuadTransform(Transform):
-    """
-    Define a quad image transform.
-
-    Maps a quadrilateral (a region defined by four corners) from the image to a
-    rectangle of the given size.
-
-    See :py:meth:`~PIL.Image.Image.transform`
-
-    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the
-        upper left, lower left, lower right, and upper right corner of the
-        source quadrilateral.
-    """
-
-    method = Image.Transform.QUAD
-
-
-class MeshTransform(Transform):
-    """
-    Define a mesh image transform.  A mesh transform consists of one or more
-    individual quad transforms.
-
-    See :py:meth:`~PIL.Image.Image.transform`
-
-    :param data: A list of (bbox, quad) tuples.
-    """
-
-    method = Image.Transform.MESH
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiofiles/os.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiofiles/os.py
deleted file mode 100644
index 29bc748fa91a6d3de6ec42842416de6af7134f5c..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiofiles/os.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Async executor versions of file functions from the os module."""
-import os
-
-from . import ospath as path
-from .ospath import wrap
-
-__all__ = [
-    "path",
-    "stat",
-    "statvfs",
-    "rename",
-    "renames",
-    "replace",
-    "remove",
-    "unlink",
-    "mkdir",
-    "makedirs",
-    "rmdir",
-    "removedirs",
-    "link",
-    "symlink",
-    "readlink",
-    "listdir",
-    "scandir",
-    "access",
-    "sendfile",
-    "wrap",
-]
-
-
-stat = wrap(os.stat)
-rename = wrap(os.rename)
-renames = wrap(os.renames)
-replace = wrap(os.replace)
-remove = wrap(os.remove)
-unlink = wrap(os.unlink)
-mkdir = wrap(os.mkdir)
-makedirs = wrap(os.makedirs)
-rmdir = wrap(os.rmdir)
-removedirs = wrap(os.removedirs)
-link = wrap(os.link)
-symlink = wrap(os.symlink)
-readlink = wrap(os.readlink)
-listdir = wrap(os.listdir)
-scandir = wrap(os.scandir)
-access = wrap(os.access)
-
-if hasattr(os, "sendfile"):
-    sendfile = wrap(os.sendfile)
-if hasattr(os, "statvfs"):
-    statvfs = wrap(os.statvfs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/testing.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/testing.py
deleted file mode 100644
index e0df0d2a657fe19523957b85964b9956e5c78a30..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/testing.py
+++ /dev/null
@@ -1,479 +0,0 @@
-import contextlib
-import io
-import os
-import shlex
-import shutil
-import sys
-import tempfile
-import typing as t
-from types import TracebackType
-
-from . import formatting
-from . import termui
-from . import utils
-from ._compat import _find_binary_reader
-
-if t.TYPE_CHECKING:
-    from .core import BaseCommand
-
-
-class EchoingStdin:
-    def __init__(self, input: t.BinaryIO, output: t.BinaryIO) -> None:
-        self._input = input
-        self._output = output
-        self._paused = False
-
-    def __getattr__(self, x: str) -> t.Any:
-        return getattr(self._input, x)
-
-    def _echo(self, rv: bytes) -> bytes:
-        if not self._paused:
-            self._output.write(rv)
-
-        return rv
-
-    def read(self, n: int = -1) -> bytes:
-        return self._echo(self._input.read(n))
-
-    def read1(self, n: int = -1) -> bytes:
-        return self._echo(self._input.read1(n))  # type: ignore
-
-    def readline(self, n: int = -1) -> bytes:
-        return self._echo(self._input.readline(n))
-
-    def readlines(self) -> t.List[bytes]:
-        return [self._echo(x) for x in self._input.readlines()]
-
-    def __iter__(self) -> t.Iterator[bytes]:
-        return iter(self._echo(x) for x in self._input)
-
-    def __repr__(self) -> str:
-        return repr(self._input)
-
-
-@contextlib.contextmanager
-def _pause_echo(stream: t.Optional[EchoingStdin]) -> t.Iterator[None]:
-    if stream is None:
-        yield
-    else:
-        stream._paused = True
-        yield
-        stream._paused = False
-
-
-class _NamedTextIOWrapper(io.TextIOWrapper):
-    def __init__(
-        self, buffer: t.BinaryIO, name: str, mode: str, **kwargs: t.Any
-    ) -> None:
-        super().__init__(buffer, **kwargs)
-        self._name = name
-        self._mode = mode
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    @property
-    def mode(self) -> str:
-        return self._mode
-
-
-def make_input_stream(
-    input: t.Optional[t.Union[str, bytes, t.IO[t.Any]]], charset: str
-) -> t.BinaryIO:
-    # Is already an input stream.
-    if hasattr(input, "read"):
-        rv = _find_binary_reader(t.cast(t.IO[t.Any], input))
-
-        if rv is not None:
-            return rv
-
-        raise TypeError("Could not find binary reader for input stream.")
-
-    if input is None:
-        input = b""
-    elif isinstance(input, str):
-        input = input.encode(charset)
-
-    return io.BytesIO(input)
-
-
-class Result:
-    """Holds the captured result of an invoked CLI script."""
-
-    def __init__(
-        self,
-        runner: "CliRunner",
-        stdout_bytes: bytes,
-        stderr_bytes: t.Optional[bytes],
-        return_value: t.Any,
-        exit_code: int,
-        exception: t.Optional[BaseException],
-        exc_info: t.Optional[
-            t.Tuple[t.Type[BaseException], BaseException, TracebackType]
-        ] = None,
-    ):
-        #: The runner that created the result
-        self.runner = runner
-        #: The standard output as bytes.
-        self.stdout_bytes = stdout_bytes
-        #: The standard error as bytes, or None if not available
-        self.stderr_bytes = stderr_bytes
-        #: The value returned from the invoked command.
-        #:
-        #: .. versionadded:: 8.0
-        self.return_value = return_value
-        #: The exit code as integer.
-        self.exit_code = exit_code
-        #: The exception that happened if one did.
-        self.exception = exception
-        #: The traceback
-        self.exc_info = exc_info
-
-    @property
-    def output(self) -> str:
-        """The (standard) output as unicode string."""
-        return self.stdout
-
-    @property
-    def stdout(self) -> str:
-        """The standard output as unicode string."""
-        return self.stdout_bytes.decode(self.runner.charset, "replace").replace(
-            "\r\n", "\n"
-        )
-
-    @property
-    def stderr(self) -> str:
-        """The standard error as unicode string."""
-        if self.stderr_bytes is None:
-            raise ValueError("stderr not separately captured")
-        return self.stderr_bytes.decode(self.runner.charset, "replace").replace(
-            "\r\n", "\n"
-        )
-
-    def __repr__(self) -> str:
-        exc_str = repr(self.exception) if self.exception else "okay"
-        return f"<{type(self).__name__} {exc_str}>"
-
-
-class CliRunner:
-    """The CLI runner provides functionality to invoke a Click command line
-    script for unittesting purposes in a isolated environment.  This only
-    works in single-threaded systems without any concurrency as it changes the
-    global interpreter state.
-
-    :param charset: the character set for the input and output data.
-    :param env: a dictionary with environment variables for overriding.
-    :param echo_stdin: if this is set to `True`, then reading from stdin writes
-                       to stdout.  This is useful for showing examples in
-                       some circumstances.  Note that regular prompts
-                       will automatically echo the input.
-    :param mix_stderr: if this is set to `False`, then stdout and stderr are
-                       preserved as independent streams.  This is useful for
-                       Unix-philosophy apps that have predictable stdout and
-                       noisy stderr, such that each may be measured
-                       independently
-    """
-
-    def __init__(
-        self,
-        charset: str = "utf-8",
-        env: t.Optional[t.Mapping[str, t.Optional[str]]] = None,
-        echo_stdin: bool = False,
-        mix_stderr: bool = True,
-    ) -> None:
-        self.charset = charset
-        self.env: t.Mapping[str, t.Optional[str]] = env or {}
-        self.echo_stdin = echo_stdin
-        self.mix_stderr = mix_stderr
-
-    def get_default_prog_name(self, cli: "BaseCommand") -> str:
-        """Given a command object it will return the default program name
-        for it.  The default is the `name` attribute or ``"root"`` if not
-        set.
-        """
-        return cli.name or "root"
-
-    def make_env(
-        self, overrides: t.Optional[t.Mapping[str, t.Optional[str]]] = None
-    ) -> t.Mapping[str, t.Optional[str]]:
-        """Returns the environment overrides for invoking a script."""
-        rv = dict(self.env)
-        if overrides:
-            rv.update(overrides)
-        return rv
-
-    @contextlib.contextmanager
-    def isolation(
-        self,
-        input: t.Optional[t.Union[str, bytes, t.IO[t.Any]]] = None,
-        env: t.Optional[t.Mapping[str, t.Optional[str]]] = None,
-        color: bool = False,
-    ) -> t.Iterator[t.Tuple[io.BytesIO, t.Optional[io.BytesIO]]]:
-        """A context manager that sets up the isolation for invoking of a
-        command line tool.  This sets up stdin with the given input data
-        and `os.environ` with the overrides from the given dictionary.
-        This also rebinds some internals in Click to be mocked (like the
-        prompt functionality).
-
-        This is automatically done in the :meth:`invoke` method.
-
-        :param input: the input stream to put into sys.stdin.
-        :param env: the environment overrides as dictionary.
-        :param color: whether the output should contain color codes. The
-                      application can still override this explicitly.
-
-        .. versionchanged:: 8.0
-            ``stderr`` is opened with ``errors="backslashreplace"``
-            instead of the default ``"strict"``.
-
-        .. versionchanged:: 4.0
-            Added the ``color`` parameter.
-        """
-        bytes_input = make_input_stream(input, self.charset)
-        echo_input = None
-
-        old_stdin = sys.stdin
-        old_stdout = sys.stdout
-        old_stderr = sys.stderr
-        old_forced_width = formatting.FORCED_WIDTH
-        formatting.FORCED_WIDTH = 80
-
-        env = self.make_env(env)
-
-        bytes_output = io.BytesIO()
-
-        if self.echo_stdin:
-            bytes_input = echo_input = t.cast(
-                t.BinaryIO, EchoingStdin(bytes_input, bytes_output)
-            )
-
-        sys.stdin = text_input = _NamedTextIOWrapper(
-            bytes_input, encoding=self.charset, name="<stdin>", mode="r"
-        )
-
-        if self.echo_stdin:
-            # Force unbuffered reads, otherwise TextIOWrapper reads a
-            # large chunk which is echoed early.
-            text_input._CHUNK_SIZE = 1  # type: ignore
-
-        sys.stdout = _NamedTextIOWrapper(
-            bytes_output, encoding=self.charset, name="<stdout>", mode="w"
-        )
-
-        bytes_error = None
-        if self.mix_stderr:
-            sys.stderr = sys.stdout
-        else:
-            bytes_error = io.BytesIO()
-            sys.stderr = _NamedTextIOWrapper(
-                bytes_error,
-                encoding=self.charset,
-                name="<stderr>",
-                mode="w",
-                errors="backslashreplace",
-            )
-
-        @_pause_echo(echo_input)  # type: ignore
-        def visible_input(prompt: t.Optional[str] = None) -> str:
-            sys.stdout.write(prompt or "")
-            val = text_input.readline().rstrip("\r\n")
-            sys.stdout.write(f"{val}\n")
-            sys.stdout.flush()
-            return val
-
-        @_pause_echo(echo_input)  # type: ignore
-        def hidden_input(prompt: t.Optional[str] = None) -> str:
-            sys.stdout.write(f"{prompt or ''}\n")
-            sys.stdout.flush()
-            return text_input.readline().rstrip("\r\n")
-
-        @_pause_echo(echo_input)  # type: ignore
-        def _getchar(echo: bool) -> str:
-            char = sys.stdin.read(1)
-
-            if echo:
-                sys.stdout.write(char)
-
-            sys.stdout.flush()
-            return char
-
-        default_color = color
-
-        def should_strip_ansi(
-            stream: t.Optional[t.IO[t.Any]] = None, color: t.Optional[bool] = None
-        ) -> bool:
-            if color is None:
-                return not default_color
-            return not color
-
-        old_visible_prompt_func = termui.visible_prompt_func
-        old_hidden_prompt_func = termui.hidden_prompt_func
-        old__getchar_func = termui._getchar
-        old_should_strip_ansi = utils.should_strip_ansi  # type: ignore
-        termui.visible_prompt_func = visible_input
-        termui.hidden_prompt_func = hidden_input
-        termui._getchar = _getchar
-        utils.should_strip_ansi = should_strip_ansi  # type: ignore
-
-        old_env = {}
-        try:
-            for key, value in env.items():
-                old_env[key] = os.environ.get(key)
-                if value is None:
-                    try:
-                        del os.environ[key]
-                    except Exception:
-                        pass
-                else:
-                    os.environ[key] = value
-            yield (bytes_output, bytes_error)
-        finally:
-            for key, value in old_env.items():
-                if value is None:
-                    try:
-                        del os.environ[key]
-                    except Exception:
-                        pass
-                else:
-                    os.environ[key] = value
-            sys.stdout = old_stdout
-            sys.stderr = old_stderr
-            sys.stdin = old_stdin
-            termui.visible_prompt_func = old_visible_prompt_func
-            termui.hidden_prompt_func = old_hidden_prompt_func
-            termui._getchar = old__getchar_func
-            utils.should_strip_ansi = old_should_strip_ansi  # type: ignore
-            formatting.FORCED_WIDTH = old_forced_width
-
-    def invoke(
-        self,
-        cli: "BaseCommand",
-        args: t.Optional[t.Union[str, t.Sequence[str]]] = None,
-        input: t.Optional[t.Union[str, bytes, t.IO[t.Any]]] = None,
-        env: t.Optional[t.Mapping[str, t.Optional[str]]] = None,
-        catch_exceptions: bool = True,
-        color: bool = False,
-        **extra: t.Any,
-    ) -> Result:
-        """Invokes a command in an isolated environment.  The arguments are
-        forwarded directly to the command line script, the `extra` keyword
-        arguments are passed to the :meth:`~clickpkg.Command.main` function of
-        the command.
-
-        This returns a :class:`Result` object.
-
-        :param cli: the command to invoke
-        :param args: the arguments to invoke. It may be given as an iterable
-                     or a string. When given as string it will be interpreted
-                     as a Unix shell command. More details at
-                     :func:`shlex.split`.
-        :param input: the input data for `sys.stdin`.
-        :param env: the environment overrides.
-        :param catch_exceptions: Whether to catch any other exceptions than
-                                 ``SystemExit``.
-        :param extra: the keyword arguments to pass to :meth:`main`.
-        :param color: whether the output should contain color codes. The
-                      application can still override this explicitly.
-
-        .. versionchanged:: 8.0
-            The result object has the ``return_value`` attribute with
-            the value returned from the invoked command.
-
-        .. versionchanged:: 4.0
-            Added the ``color`` parameter.
-
-        .. versionchanged:: 3.0
-            Added the ``catch_exceptions`` parameter.
-
-        .. versionchanged:: 3.0
-            The result object has the ``exc_info`` attribute with the
-            traceback if available.
-        """
-        exc_info = None
-        with self.isolation(input=input, env=env, color=color) as outstreams:
-            return_value = None
-            exception: t.Optional[BaseException] = None
-            exit_code = 0
-
-            if isinstance(args, str):
-                args = shlex.split(args)
-
-            try:
-                prog_name = extra.pop("prog_name")
-            except KeyError:
-                prog_name = self.get_default_prog_name(cli)
-
-            try:
-                return_value = cli.main(args=args or (), prog_name=prog_name, **extra)
-            except SystemExit as e:
-                exc_info = sys.exc_info()
-                e_code = t.cast(t.Optional[t.Union[int, t.Any]], e.code)
-
-                if e_code is None:
-                    e_code = 0
-
-                if e_code != 0:
-                    exception = e
-
-                if not isinstance(e_code, int):
-                    sys.stdout.write(str(e_code))
-                    sys.stdout.write("\n")
-                    e_code = 1
-
-                exit_code = e_code
-
-            except Exception as e:
-                if not catch_exceptions:
-                    raise
-                exception = e
-                exit_code = 1
-                exc_info = sys.exc_info()
-            finally:
-                sys.stdout.flush()
-                stdout = outstreams[0].getvalue()
-                if self.mix_stderr:
-                    stderr = None
-                else:
-                    stderr = outstreams[1].getvalue()  # type: ignore
-
-        return Result(
-            runner=self,
-            stdout_bytes=stdout,
-            stderr_bytes=stderr,
-            return_value=return_value,
-            exit_code=exit_code,
-            exception=exception,
-            exc_info=exc_info,  # type: ignore
-        )
-
-    @contextlib.contextmanager
-    def isolated_filesystem(
-        self, temp_dir: t.Optional[t.Union[str, "os.PathLike[str]"]] = None
-    ) -> t.Iterator[str]:
-        """A context manager that creates a temporary directory and
-        changes the current working directory to it. This isolates tests
-        that affect the contents of the CWD to prevent them from
-        interfering with each other.
-
-        :param temp_dir: Create the temporary directory under this
-            directory. If given, the created directory is not removed
-            when exiting.
-
-        .. versionchanged:: 8.0
-            Added the ``temp_dir`` parameter.
-        """
-        cwd = os.getcwd()
-        dt = tempfile.mkdtemp(dir=temp_dir)
-        os.chdir(dt)
-
-        try:
-            yield dt
-        finally:
-            os.chdir(cwd)
-
-            if temp_dir is None:
-                try:
-                    shutil.rmtree(dt)
-                except OSError:  # noqa: B014
-                    pass
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/__init__.py
deleted file mode 100644
index a4249b9e7207639a0932109e5d38e7db0b732fca..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/__init__.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
-
-# Copyright (C) 2003-2007, 2009, 2011 Nominum, Inc.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose with or without fee is hereby granted,
-# provided that the above copyright notice and this permission notice
-# appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-"""dnspython DNS toolkit"""
-
-__all__ = [
-    "asyncbackend",
-    "asyncquery",
-    "asyncresolver",
-    "dnssec",
-    "dnssecalgs",
-    "dnssectypes",
-    "e164",
-    "edns",
-    "entropy",
-    "exception",
-    "flags",
-    "immutable",
-    "inet",
-    "ipv4",
-    "ipv6",
-    "message",
-    "name",
-    "namedict",
-    "node",
-    "opcode",
-    "query",
-    "quic",
-    "rcode",
-    "rdata",
-    "rdataclass",
-    "rdataset",
-    "rdatatype",
-    "renderer",
-    "resolver",
-    "reversename",
-    "rrset",
-    "serial",
-    "set",
-    "tokenizer",
-    "transaction",
-    "tsig",
-    "tsigkeyring",
-    "ttl",
-    "rdtypes",
-    "update",
-    "version",
-    "versioned",
-    "wire",
-    "xfr",
-    "zone",
-    "zonetypes",
-    "zonefile",
-]
-
-from dns.version import version as __version__  # noqa
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/oauth2.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/oauth2.py
deleted file mode 100644
index e4c4357e7303aad2bb7e4b86fb08ac34d37dbad2..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/security/oauth2.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from typing import Any, Dict, List, Optional, Union, cast
-
-from fastapi.exceptions import HTTPException
-from fastapi.openapi.models import OAuth2 as OAuth2Model
-from fastapi.openapi.models import OAuthFlows as OAuthFlowsModel
-from fastapi.param_functions import Form
-from fastapi.security.base import SecurityBase
-from fastapi.security.utils import get_authorization_scheme_param
-from starlette.requests import Request
-from starlette.status import HTTP_401_UNAUTHORIZED, HTTP_403_FORBIDDEN
-
-# TODO: import from typing when deprecating Python 3.9
-from typing_extensions import Annotated
-
-
-class OAuth2PasswordRequestForm:
-    """
-    This is a dependency class, use it like:
-
-        @app.post("/login")
-        def login(form_data: OAuth2PasswordRequestForm = Depends()):
-            data = form_data.parse()
-            print(data.username)
-            print(data.password)
-            for scope in data.scopes:
-                print(scope)
-            if data.client_id:
-                print(data.client_id)
-            if data.client_secret:
-                print(data.client_secret)
-            return data
-
-
-    It creates the following Form request parameters in your endpoint:
-
-    grant_type: the OAuth2 spec says it is required and MUST be the fixed string "password".
-        Nevertheless, this dependency class is permissive and allows not passing it. If you want to enforce it,
-        use instead the OAuth2PasswordRequestFormStrict dependency.
-    username: username string. The OAuth2 spec requires the exact field name "username".
-    password: password string. The OAuth2 spec requires the exact field name "password".
-    scope: Optional string. Several scopes (each one a string) separated by spaces. E.g.
-        "items:read items:write users:read profile openid"
-    client_id: optional string. OAuth2 recommends sending the client_id and client_secret (if any)
-        using HTTP Basic auth, as: client_id:client_secret
-    client_secret: optional string. OAuth2 recommends sending the client_id and client_secret (if any)
-        using HTTP Basic auth, as: client_id:client_secret
-    """
-
-    def __init__(
-        self,
-        *,
-        grant_type: Annotated[Union[str, None], Form(pattern="password")] = None,
-        username: Annotated[str, Form()],
-        password: Annotated[str, Form()],
-        scope: Annotated[str, Form()] = "",
-        client_id: Annotated[Union[str, None], Form()] = None,
-        client_secret: Annotated[Union[str, None], Form()] = None,
-    ):
-        self.grant_type = grant_type
-        self.username = username
-        self.password = password
-        self.scopes = scope.split()
-        self.client_id = client_id
-        self.client_secret = client_secret
-
-
-class OAuth2PasswordRequestFormStrict(OAuth2PasswordRequestForm):
-    """
-    This is a dependency class, use it like:
-
-        @app.post("/login")
-        def login(form_data: OAuth2PasswordRequestFormStrict = Depends()):
-            data = form_data.parse()
-            print(data.username)
-            print(data.password)
-            for scope in data.scopes:
-                print(scope)
-            if data.client_id:
-                print(data.client_id)
-            if data.client_secret:
-                print(data.client_secret)
-            return data
-
-
-    It creates the following Form request parameters in your endpoint:
-
-    grant_type: the OAuth2 spec says it is required and MUST be the fixed string "password".
-        This dependency is strict about it. If you want to be permissive, use instead the
-        OAuth2PasswordRequestForm dependency class.
-    username: username string. The OAuth2 spec requires the exact field name "username".
-    password: password string. The OAuth2 spec requires the exact field name "password".
-    scope: Optional string. Several scopes (each one a string) separated by spaces. E.g.
-        "items:read items:write users:read profile openid"
-    client_id: optional string. OAuth2 recommends sending the client_id and client_secret (if any)
-        using HTTP Basic auth, as: client_id:client_secret
-    client_secret: optional string. OAuth2 recommends sending the client_id and client_secret (if any)
-        using HTTP Basic auth, as: client_id:client_secret
-    """
-
-    def __init__(
-        self,
-        grant_type: Annotated[str, Form(pattern="password")],
-        username: Annotated[str, Form()],
-        password: Annotated[str, Form()],
-        scope: Annotated[str, Form()] = "",
-        client_id: Annotated[Union[str, None], Form()] = None,
-        client_secret: Annotated[Union[str, None], Form()] = None,
-    ):
-        super().__init__(
-            grant_type=grant_type,
-            username=username,
-            password=password,
-            scope=scope,
-            client_id=client_id,
-            client_secret=client_secret,
-        )
-
-
-class OAuth2(SecurityBase):
-    def __init__(
-        self,
-        *,
-        flows: Union[OAuthFlowsModel, Dict[str, Dict[str, Any]]] = OAuthFlowsModel(),
-        scheme_name: Optional[str] = None,
-        description: Optional[str] = None,
-        auto_error: bool = True,
-    ):
-        self.model = OAuth2Model(
-            flows=cast(OAuthFlowsModel, flows), description=description
-        )
-        self.scheme_name = scheme_name or self.__class__.__name__
-        self.auto_error = auto_error
-
-    async def __call__(self, request: Request) -> Optional[str]:
-        authorization = request.headers.get("Authorization")
-        if not authorization:
-            if self.auto_error:
-                raise HTTPException(
-                    status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"
-                )
-            else:
-                return None
-        return authorization
-
-
-class OAuth2PasswordBearer(OAuth2):
-    def __init__(
-        self,
-        tokenUrl: str,
-        scheme_name: Optional[str] = None,
-        scopes: Optional[Dict[str, str]] = None,
-        description: Optional[str] = None,
-        auto_error: bool = True,
-    ):
-        if not scopes:
-            scopes = {}
-        flows = OAuthFlowsModel(
-            password=cast(Any, {"tokenUrl": tokenUrl, "scopes": scopes})
-        )
-        super().__init__(
-            flows=flows,
-            scheme_name=scheme_name,
-            description=description,
-            auto_error=auto_error,
-        )
-
-    async def __call__(self, request: Request) -> Optional[str]:
-        authorization = request.headers.get("Authorization")
-        scheme, param = get_authorization_scheme_param(authorization)
-        if not authorization or scheme.lower() != "bearer":
-            if self.auto_error:
-                raise HTTPException(
-                    status_code=HTTP_401_UNAUTHORIZED,
-                    detail="Not authenticated",
-                    headers={"WWW-Authenticate": "Bearer"},
-                )
-            else:
-                return None
-        return param
-
-
-class OAuth2AuthorizationCodeBearer(OAuth2):
-    def __init__(
-        self,
-        authorizationUrl: str,
-        tokenUrl: str,
-        refreshUrl: Optional[str] = None,
-        scheme_name: Optional[str] = None,
-        scopes: Optional[Dict[str, str]] = None,
-        description: Optional[str] = None,
-        auto_error: bool = True,
-    ):
-        if not scopes:
-            scopes = {}
-        flows = OAuthFlowsModel(
-            authorizationCode=cast(
-                Any,
-                {
-                    "authorizationUrl": authorizationUrl,
-                    "tokenUrl": tokenUrl,
-                    "refreshUrl": refreshUrl,
-                    "scopes": scopes,
-                },
-            )
-        )
-        super().__init__(
-            flows=flows,
-            scheme_name=scheme_name,
-            description=description,
-            auto_error=auto_error,
-        )
-
-    async def __call__(self, request: Request) -> Optional[str]:
-        authorization = request.headers.get("Authorization")
-        scheme, param = get_authorization_scheme_param(authorization)
-        if not authorization or scheme.lower() != "bearer":
-            if self.auto_error:
-                raise HTTPException(
-                    status_code=HTTP_401_UNAUTHORIZED,
-                    detail="Not authenticated",
-                    headers={"WWW-Authenticate": "Bearer"},
-                )
-            else:
-                return None  # pragma: nocover
-        return param
-
-
-class SecurityScopes:
-    def __init__(self, scopes: Optional[List[str]] = None):
-        self.scopes = scopes or []
-        self.scope_str = " ".join(self.scopes)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/svgPathPen.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/svgPathPen.py
deleted file mode 100644
index ae6ebfbd5333a10f665d0b879d976294b2b9993e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/svgPathPen.py
+++ /dev/null
@@ -1,293 +0,0 @@
-from typing import Callable
-from fontTools.pens.basePen import BasePen
-
-
-def pointToString(pt, ntos=str):
-    return " ".join(ntos(i) for i in pt)
-
-
-class SVGPathPen(BasePen):
-    """Pen to draw SVG path d commands.
-
-    Example::
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((0, 0))
-        >>> pen.lineTo((1, 1))
-        >>> pen.curveTo((2, 2), (3, 3), (4, 4))
-        >>> pen.closePath()
-        >>> pen.getCommands()
-        'M0 0 1 1C2 2 3 3 4 4Z'
-
-    Args:
-        glyphSet: a dictionary of drawable glyph objects keyed by name
-            used to resolve component references in composite glyphs.
-        ntos: a callable that takes a number and returns a string, to
-            customize how numbers are formatted (default: str).
-
-    Note:
-        Fonts have a coordinate system where Y grows up, whereas in SVG,
-        Y grows down.  As such, rendering path data from this pen in
-        SVG typically results in upside-down glyphs.  You can fix this
-        by wrapping the data from this pen in an SVG group element with
-        transform, or wrap this pen in a transform pen.  For example:
-
-            spen = svgPathPen.SVGPathPen(glyphset)
-            pen= TransformPen(spen , (1, 0, 0, -1, 0, 0))
-            glyphset[glyphname].draw(pen)
-            print(tpen.getCommands())
-    """
-
-    def __init__(self, glyphSet, ntos: Callable[[float], str] = str):
-        BasePen.__init__(self, glyphSet)
-        self._commands = []
-        self._lastCommand = None
-        self._lastX = None
-        self._lastY = None
-        self._ntos = ntos
-
-    def _handleAnchor(self):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((0, 0))
-        >>> pen.moveTo((10, 10))
-        >>> pen._commands
-        ['M10 10']
-        """
-        if self._lastCommand == "M":
-            self._commands.pop(-1)
-
-    def _moveTo(self, pt):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((0, 0))
-        >>> pen._commands
-        ['M0 0']
-
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((10, 0))
-        >>> pen._commands
-        ['M10 0']
-
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((0, 10))
-        >>> pen._commands
-        ['M0 10']
-        """
-        self._handleAnchor()
-        t = "M%s" % (pointToString(pt, self._ntos))
-        self._commands.append(t)
-        self._lastCommand = "M"
-        self._lastX, self._lastY = pt
-
-    def _lineTo(self, pt):
-        """
-        # duplicate point
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((10, 10))
-        >>> pen.lineTo((10, 10))
-        >>> pen._commands
-        ['M10 10']
-
-        # vertical line
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((10, 10))
-        >>> pen.lineTo((10, 0))
-        >>> pen._commands
-        ['M10 10', 'V0']
-
-        # horizontal line
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((10, 10))
-        >>> pen.lineTo((0, 10))
-        >>> pen._commands
-        ['M10 10', 'H0']
-
-        # basic
-        >>> pen = SVGPathPen(None)
-        >>> pen.lineTo((70, 80))
-        >>> pen._commands
-        ['L70 80']
-
-        # basic following a moveto
-        >>> pen = SVGPathPen(None)
-        >>> pen.moveTo((0, 0))
-        >>> pen.lineTo((10, 10))
-        >>> pen._commands
-        ['M0 0', ' 10 10']
-        """
-        x, y = pt
-        # duplicate point
-        if x == self._lastX and y == self._lastY:
-            return
-        # vertical line
-        elif x == self._lastX:
-            cmd = "V"
-            pts = self._ntos(y)
-        # horizontal line
-        elif y == self._lastY:
-            cmd = "H"
-            pts = self._ntos(x)
-        # previous was a moveto
-        elif self._lastCommand == "M":
-            cmd = None
-            pts = " " + pointToString(pt, self._ntos)
-        # basic
-        else:
-            cmd = "L"
-            pts = pointToString(pt, self._ntos)
-        # write the string
-        t = ""
-        if cmd:
-            t += cmd
-            self._lastCommand = cmd
-        t += pts
-        self._commands.append(t)
-        # store for future reference
-        self._lastX, self._lastY = pt
-
-    def _curveToOne(self, pt1, pt2, pt3):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.curveTo((10, 20), (30, 40), (50, 60))
-        >>> pen._commands
-        ['C10 20 30 40 50 60']
-        """
-        t = "C"
-        t += pointToString(pt1, self._ntos) + " "
-        t += pointToString(pt2, self._ntos) + " "
-        t += pointToString(pt3, self._ntos)
-        self._commands.append(t)
-        self._lastCommand = "C"
-        self._lastX, self._lastY = pt3
-
-    def _qCurveToOne(self, pt1, pt2):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.qCurveTo((10, 20), (30, 40))
-        >>> pen._commands
-        ['Q10 20 30 40']
-        >>> from fontTools.misc.roundTools import otRound
-        >>> pen = SVGPathPen(None, ntos=lambda v: str(otRound(v)))
-        >>> pen.qCurveTo((3, 3), (7, 5), (11, 4))
-        >>> pen._commands
-        ['Q3 3 5 4', 'Q7 5 11 4']
-        """
-        assert pt2 is not None
-        t = "Q"
-        t += pointToString(pt1, self._ntos) + " "
-        t += pointToString(pt2, self._ntos)
-        self._commands.append(t)
-        self._lastCommand = "Q"
-        self._lastX, self._lastY = pt2
-
-    def _closePath(self):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.closePath()
-        >>> pen._commands
-        ['Z']
-        """
-        self._commands.append("Z")
-        self._lastCommand = "Z"
-        self._lastX = self._lastY = None
-
-    def _endPath(self):
-        """
-        >>> pen = SVGPathPen(None)
-        >>> pen.endPath()
-        >>> pen._commands
-        []
-        """
-        self._lastCommand = None
-        self._lastX = self._lastY = None
-
-    def getCommands(self):
-        return "".join(self._commands)
-
-
-def main(args=None):
-    """Generate per-character SVG from font and text"""
-
-    if args is None:
-        import sys
-
-        args = sys.argv[1:]
-
-    from fontTools.ttLib import TTFont
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        "fonttools pens.svgPathPen", description="Generate SVG from text"
-    )
-    parser.add_argument("font", metavar="font.ttf", help="Font file.")
-    parser.add_argument("text", metavar="text", help="Text string.")
-    parser.add_argument(
-        "-y",
-        metavar="<number>",
-        help="Face index into a collection to open. Zero based.",
-    )
-    parser.add_argument(
-        "--variations",
-        metavar="AXIS=LOC",
-        default="",
-        help="List of space separated locations. A location consist in "
-        "the name of a variation axis, followed by '=' and a number. E.g.: "
-        "wght=700 wdth=80. The default is the location of the base master.",
-    )
-
-    options = parser.parse_args(args)
-
-    fontNumber = int(options.y) if options.y is not None else 0
-
-    font = TTFont(options.font, fontNumber=fontNumber)
-    text = options.text
-
-    location = {}
-    for tag_v in options.variations.split():
-        fields = tag_v.split("=")
-        tag = fields[0].strip()
-        v = int(fields[1])
-        location[tag] = v
-
-    hhea = font["hhea"]
-    ascent, descent = hhea.ascent, hhea.descent
-
-    glyphset = font.getGlyphSet(location=location)
-    cmap = font["cmap"].getBestCmap()
-
-    s = ""
-    width = 0
-    for u in text:
-        g = cmap[ord(u)]
-        glyph = glyphset[g]
-
-        pen = SVGPathPen(glyphset)
-        glyph.draw(pen)
-        commands = pen.getCommands()
-
-        s += '<g transform="translate(%d %d) scale(1 -1)"><path d="%s"/></g>\n' % (
-            width,
-            ascent,
-            commands,
-        )
-
-        width += glyph.width
-
-    print('<?xml version="1.0" encoding="UTF-8"?>')
-    print(
-        '<svg width="%d" height="%d" xmlns="http://www.w3.org/2000/svg">'
-        % (width, ascent - descent)
-    )
-    print(s, end="")
-    print("</svg>")
-
-
-if __name__ == "__main__":
-    import sys
-
-    if len(sys.argv) == 1:
-        import doctest
-
-        sys.exit(doctest.testmod().failed)
-
-    sys.exit(main())
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_C_.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
deleted file mode 100644
index 573b3f9c3970766ea817994509f4939ef4f70f0c..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_T_S_I_C_(BaseTTXConverter):
-    pass
diff --git a/spaces/joe-aquino/keras_pretty_face/app.py b/spaces/joe-aquino/keras_pretty_face/app.py
deleted file mode 100644
index ed2cd0fd4bafff6f94695a45014a3cda6da3321d..0000000000000000000000000000000000000000
--- a/spaces/joe-aquino/keras_pretty_face/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-from keras.models import load_model
-from deepface import DeepFace
-
-learn = load_model("Attractive.hdf5") # may want to add weights instead
-
-def process_image(img_path):
-    face_objs = DeepFace.extract_faces(img_path, detector_backend = 'mtcnn')
-    arr = face_objs[0]['face']
-    features = arr.reshape(1, 224, 224, 3)
-    return features
-
-def predict_image(img_path):
-    s_min, s_max = 1.52, 5.48
-    features = process_image(img_path)
-    pred = learn.predict(features)
-    pred = (pred[0][0] - s_min) / (s_max - s_min) * 10
-    pred = min(pred, 10.0)
-    return round(pred, 1)
-
-# I could make it a classifier by making each integer a category
-
-image = gr.Image(type="filepath", label="Image")
-label = gr.Label(label="Rating")
-examples = ["olivia_dunne.jpg", "sydney_sweeney.jpg", "miss_piggy.jpg", "brad_pitt.jpg", "amber_heard.jpg", "nick_cage.jpg", "steve_buscemi.jpg"]
-title = "Attractive Model"
-description = "A regression model that rates the attractiveness of a human face on a scale from 0 to 10. VGG-Face is used as the \
-base model. It was trained on the Chicago Face Database which includes subjective scores from a sample of raters. The model works \
-best with photographs that contain front-facing faces without obtructions such as bangs or beards."
-
-intf = gr.Interface(fn=predict_image, inputs=image, outputs=label, examples=examples, title=title, description=description,
-                    article=None, allow_flagging="never")
-intf.launch()
diff --git a/spaces/johnslegers/bilingual_stable_diffusion/app.py b/spaces/johnslegers/bilingual_stable_diffusion/app.py
deleted file mode 100644
index ae5ad1724462d493cee57dccb4540633e4095299..0000000000000000000000000000000000000000
--- a/spaces/johnslegers/bilingual_stable_diffusion/app.py
+++ /dev/null
@@ -1,574 +0,0 @@
-import imp
-import gradio as gr
-import io 
-from PIL import Image, PngImagePlugin
-import base64
-import requests
-import json 
-import ui_functions as uifn
-from css_and_js import js, call_JS
-import re
-
-txt2img_defaults = {
-    'prompt': '',
-    'ddim_steps': 50,
-    'toggles': [1, 2, 3],
-    'sampler_name': 'k_lms',
-    'ddim_eta': 0.0,
-    'n_iter': 1,
-    'batch_size': 1,
-    'cfg_scale': 7.5,
-    'seed': '',
-    'height': 512,
-    'width': 512,
-    'fp': None,
-    'variant_amount': 0.0,
-    'variant_seed': '',
-    'submit_on_enter': 'Yes',
-}
-
-img2img_defaults = {
-    'prompt': '',
-    'ddim_steps': 50,
-    'toggles': [1, 4, 5],
-    'sampler_name': 'k_lms',
-    'ddim_eta': 0.0,
-    'n_iter': 1,
-    'batch_size': 1,
-    'cfg_scale': 5.0,
-    'denoising_strength': 0.75,
-    'mask_mode': 1,
-    'resize_mode': 0,
-    'seed': '',
-    'height': 512,
-    'width': 512,
-    'fp': None,
-}
-sample_img2img = None
-job_manager = None
-RealESRGAN = True
-show_embeddings = False
-
-img2img_resize_modes = [
-    "Just resize",
-    "Crop and resize",
-    "Resize and fill",
-]
-
-img2img_toggles = [
-    'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
-    'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
-    'Loopback (use images from previous batch when creating next batch)',
-    'Random loopback seed',
-    'Save individual images',
-    'Save grid',
-    'Sort samples by prompt',
-    'Write sample info files',
-    'Write sample info to one file',
-    'jpg samples',
-]
-
-img2img_toggle_defaults = [img2img_toggles[i] for i in img2img_defaults['toggles']]
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-def base2picture(resbase64):
-    res=resbase64.split(',')[1]
-    img_b64decode = base64.b64decode(res)
-    image = io.BytesIO(img_b64decode)
-    img = Image.open(image)
-    return img 
-
-def filter_content(raw_style: str):
-    if "(" in raw_style:
-        i = raw_style.index("(")
-    else :
-        i = -1
-
-    if i == -1:
-        return raw_style
-    else :
-        return raw_style[:i]
-
-def request_images(raw_text, class_draw, style_draw, batch_size, sr_option):
-    if filter_content(class_draw) != "国画":
-        if filter_content(class_draw) != "通用":
-            raw_text = raw_text + f"，{filter_content(class_draw)}"
-
-        for sty in style_draw:
-            raw_text = raw_text + f"，{filter_content(sty)}"
-        print(f"raw text is {raw_text}")
-        url = "http://flagart.baai.ac.cn/api/general/"
-    elif filter_content(class_draw) == "国画":
-        if raw_text.endswith("国画"):
-            pass 
-        else :
-            raw_text = raw_text + "，国画"
-        url = "http://flagart.baai.ac.cn/api/guohua/"
-
-    d = {"data":[raw_text, int(batch_size), sr_option]}
-    r = requests.post(url, json=d, headers={"Content-Type": "application/json", "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive"})
-    result_text = r.text
-    content = json.loads(result_text)["data"][0]
-    images = []
-    for i in range(int(batch_size)):
-        # print(content[i])
-        images.append(base2picture(content[i]))        
-
-    return images
-
-def sr_request_images(img_str, idx):
-    idx_map = {
-            "图片1(img1)":0,
-            "图片2(img2)":1,
-            "图片3(img3)":2,
-            "图片4(img4)":3,
-        }
-    idx = idx_map[idx]
-    # image_data = re.sub('^data:image/.+;base64,', '', img_str[idx])
-    image_data = img_str[idx]
-    # print(image_data)
-    d = {"data":[image_data, 0, False]} # batch_size设置为0，sr_opt设置为False
-    url = "http://flagart.baai.ac.cn/api/general/"
-    r = requests.post(url, json=d, headers={"Content-Type": "application/json", "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive"})
-    result_text = r.text
-    # print(result_text)
-    content = json.loads(result_text)["data"][0]
-    # print(content)
-    images = [base2picture(content[0])] 
-
-    return images
-
-
-def encode_pil_to_base64(pil_image):
-    with io.BytesIO() as output_bytes:
-
-        # Copy any text-only metadata
-        use_metadata = False
-        metadata = PngImagePlugin.PngInfo()
-        for key, value in pil_image.info.items():
-            if isinstance(key, str) and isinstance(value, str):
-                metadata.add_text(key, value)
-                use_metadata = True
-
-        pil_image.save(
-            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
-        )
-        bytes_data = output_bytes.getvalue()
-    base64_str = str(base64.b64encode(bytes_data), "utf-8")
-    return "data:image/png;base64," + base64_str
-
-def img2img(*args):
-    
-    # 处理image
-    for i, item in enumerate(args):
-        # print(type(item))
-        if type(item) == dict:
-            args[i]['image']  = encode_pil_to_base64(item['image'])
-            args[i]['mask'] = encode_pil_to_base64(item['mask'])
-        # else:
-        #     print(i,type(item))
-        #     print(item)
-    
-    batch_size = args[8]
-
-    url = "http://flagart.baai.ac.cn/api/img2img/"
-    d = {"data":args}
-    r = requests.post(url, json=d, headers={"Content-Type": "application/json", "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive"})
-    # print(r)
-    result_text = r.text
-    content = json.loads(result_text)["data"][0]
-    images = []
-    for i in range(batch_size):
-        # print(content[i])
-        images.append(base2picture(content[i]))   
-    # content = json.loads(result_text)
-    # print(result_text)
-    # print("服务器已经把东西返回来啦！！！！！！！乌拉乌拉！！！！！")
-    return images
-
-
-examples = [
-    '水墨蝴蝶和牡丹花，国画',
-    '苍劲有力的墨竹，国画',
-    '暴风雨中的灯塔',
-    '机械小松鼠，科学幻想',
-    '中国水墨山水画，国画',
-    "Lighthouse in the storm",
-    "A dog",
-    "Landscape by 张大千",
-    "A tiger 长了兔子耳朵",
-    "A baby bird 铅笔素描",
-
-]
-
-if __name__ == "__main__":
-    block = gr.Blocks(css=read_content('style.css'))
-
-    with block:
-        gr.HTML(read_content("header.html"))
-
-        with gr.Tabs(elem_id='tabss') as tabs:
-
-            with gr.TabItem("文生图(Text-to-img)", id='txt2img_tab'):
-
-                with gr.Group():
-                    with gr.Box():
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            text = gr.Textbox(
-                                label="Prompt",
-                                show_label=False,
-                                max_lines=1,
-                                placeholder="Input text(输入文字)",
-                                interactive=True,
-                            ).style(
-                                border=(True, False, True, True),
-                                rounded=(True, False, False, True),
-                                container=False,
-                            )
-                            
-                            btn = gr.Button("Generate image").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            class_draw = gr.Dropdown(["通用(general)", "国画(traditional Chinese painting)", 
-                                                        "照片，摄影(picture photography)", "油画(oil painting)", 
-                                                        "铅笔素描(pencil sketch)", "CG", 
-                                                        "水彩画(watercolor painting)", "水墨画(ink and wash)",
-                                                        "插画(illustrations)", "3D", "图生图(img2img)"],
-                                                    label="生成类型(type)",
-                                                    show_label=True,
-                                                    value="通用(general)")
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            style_draw = gr.CheckboxGroup(["蒸汽朋克(steampunk)", "电影摄影风格(film photography)", 
-                                                        "概念艺术(concept art)", "Warming lighting", 
-                                                        "Dramatic lighting", "Natural lighting", 
-                                                        "虚幻引擎(unreal engine)", "4k", "8k",
-                                                        "充满细节(full details)"],
-                                                    label="画面风格(style)",
-                                                    show_label=True,
-                                                    )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            # sample_size = gr.Slider(minimum=1,
-                            #                         maximum=4,
-                            #                         step=1,
-                            #                         label="生成数量(number)",
-                            #                         show_label=True,
-                            #                         interactive=True,
-                            #                         )
-                            sample_size = gr.Radio(choices=["1","2","3","4"], value="1", show_label=True, label='生成数量(number)')
-                        with gr.Row(visible=False).style(mobile_collapse=False, equal_height=True):
-                            sr_option = gr.Checkbox(value=False, label="是否使用超分(Whether to use super-resolution)")
-
-                    gallery = gr.Gallery(
-                        label="Generated images", show_label=False, elem_id="gallery"
-                    ).style(grid=[2,2])
-                    gr.Examples(examples=examples, fn=request_images, inputs=text, outputs=gallery, examples_per_page=100)
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        img_choices = gr.Dropdown(["图片1(img1)"],label='请选择一张图片发送到图生图或者超分',show_label=True,value="图片1(img1)")
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        output_txt2img_copy_to_input_btn = gr.Button("发送图片到图生图(Sent the image to img2img)").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-                        output_txt2img_sr_btn = gr.Button("将选择的图片进行超分(super-resolution)").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-
-                    sr_gallery = gr.Gallery(
-                        label="SR images", show_label=True, elem_id="sr_gallery"
-                    ).style(grid=[1,1])
-
-                    with gr.Row():
-                        prompt = gr.Markdown("提示(Prompt)：", visible=False)
-                    with gr.Row():
-                        move_prompt_zh = gr.Markdown("请移至图生图部分进行编辑(拉到顶部)", visible=False)
-                    with gr.Row():
-                        move_prompt_en = gr.Markdown("Please move to the img2img section for editing(Pull to the top)", visible=False)
-
-                        
-
-                    # 并没有什么软用，转跳还是很卡，而且只能转跳一次
-                    # go_to_img2img_btn = gr.Button("转至图生图(Go to img2img)").style(
-                    #             margin=False,
-                    #             rounded=(True, True, True, True),
-                    #         )
-
-                    text.submit(request_images, inputs=[text, class_draw, style_draw, sample_size, sr_option], outputs=gallery)
-                    btn.click(request_images, inputs=[text, class_draw, style_draw, sample_size, sr_option], outputs=gallery)
-                    # to do here
-                    output_txt2img_sr_btn.click(sr_request_images, inputs=[gallery, img_choices], outputs=[sr_gallery])
-
-                    sample_size.change(
-                        fn=uifn.change_img_choices,
-                        inputs=[sample_size],
-                        outputs=[img_choices]
-                    )
-
-            with gr.TabItem("图生图(Img-to-Img)", id="img2img_tab"):
-                with gr.Row(elem_id="prompt_row"):
-                    img2img_prompt = gr.Textbox(label="Prompt",
-                                                elem_id='img2img_prompt_input',
-                                                placeholder="神奇的森林，流淌的河流.",
-                                                lines=1,
-                                                max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
-                                                value=img2img_defaults['prompt'],
-                                                show_label=False).style()
-
-                    img2img_btn_mask = gr.Button("Generate", variant="primary", visible=False,
-                                                 elem_id="img2img_mask_btn")
-                    img2img_btn_editor = gr.Button("Generate", variant="primary", elem_id="img2img_edit_btn")
-                gr.Markdown('#### 输入图像')
-                with gr.Row().style(equal_height=False):
-                    #with gr.Column():
-                    img2img_image_mask = gr.Image(
-                        value=sample_img2img,
-                        source="upload",
-                        interactive=True,
-                        tool="sketch",
-                        type='pil',
-                        elem_id="img2img_mask",
-                        image_mode="RGBA"
-                    )
-                    img2img_image_editor = gr.Image(
-                        value=sample_img2img,
-                        source="upload",
-                        interactive=True,
-                        tool="select",
-                        type='pil',
-                        visible=False,
-                        image_mode="RGBA",
-                        elem_id="img2img_editor"
-                    )
-                    with gr.Tabs(visible=False):
-                        with gr.TabItem("编辑设置"):
-                            with gr.Row():
-                                # disable Uncrop for now
-                                choices=["Mask", "Crop", "Uncrop"]
-                                img2img_image_editor_mode = gr.Radio(choices=["Mask"],
-                                                                        label="编辑模式",
-                                                                        value="Mask", elem_id='edit_mode_select',
-                                                                        visible=True)
-                                img2img_mask = gr.Radio(choices=["保留mask区域", "生成mask区域"],
-                                                        label="Mask 方式",
-                                                        #value=img2img_mask_modes[img2img_defaults['mask_mode']],
-                                                        value = "生成mask区域",
-                                                        visible=True)
-
-                                img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1,
-                                                                        label="How much blurry should the mask be? (to avoid hard edges)",
-                                                                        value=3, visible=False)
-
-                                img2img_resize = gr.Radio(label="Resize mode",
-                                                            choices=["Just resize", "Crop and resize",
-                                                                    "Resize and fill"],
-                                                            value=img2img_resize_modes[
-                                                                img2img_defaults['resize_mode']], visible=False)
-
-                            img2img_painterro_btn = gr.Button("Advanced Editor",visible=False)
-                        # with gr.TabItem("Hints",visible=False):
-                        #     img2img_help = gr.Markdown(visible=False, value=uifn.help_text)
-                gr.Markdown('#### 编辑后的图片')
-                with gr.Row():
-                    output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(
-                        grid=[4,4,4] )
-                    img2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
-                    with gr.Column(visible=False):
-                        with gr.Tabs(visible=False):
-                            with gr.TabItem("", id="img2img_actions_tab",visible=False):
-                                gr.Markdown("Select an image, then press one of the buttons below")
-                                with gr.Row():
-                                    output_img2img_copy_to_clipboard_btn = gr.Button("Copy to clipboard")
-                                    output_img2img_copy_to_input_btn = gr.Button("Push to img2img input")
-                                    output_img2img_copy_to_mask_btn = gr.Button("Push to img2img input mask")
-
-                                gr.Markdown("Warning: This will clear your current image and mask settings!")
-                            with gr.TabItem("", id="img2img_output_info_tab",visible=False):
-                                output_img2img_params = gr.Textbox(label="Generation parameters")
-                                with gr.Row():
-                                    output_img2img_copy_params = gr.Button("Copy full parameters").click(
-                                        inputs=output_img2img_params, outputs=[],
-                                        _js='(x) => {navigator.clipboard.writeText(x.replace(": ",":"))}', fn=None,
-                                        show_progress=False)
-                                    output_img2img_seed = gr.Number(label='Seed', interactive=False, visible=False)
-                                    output_img2img_copy_seed = gr.Button("Copy only seed").click(
-                                        inputs=output_img2img_seed, outputs=[],
-                                        _js=call_JS("gradioInputToClipboard"), fn=None, show_progress=False)
-                                output_img2img_stats = gr.HTML(label='Stats')
-                with gr.Row():
-                    gr.Markdown('提示(prompt):')
-                with gr.Row():
-                    gr.Markdown('请选择一张图像掩盖掉一部分区域,并输入文本描述')
-                with gr.Row():
-                    gr.Markdown('Please select an image to cover up a part of the area and enter a text description.')
-                gr.Markdown('# 编辑设置',visible=False)
-
-                with gr.Row(visible=False):
-                    with gr.Column():
-                        img2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="图片宽度",
-                                                  value=img2img_defaults["width"])
-                        img2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="图片高度",
-                                                   value=img2img_defaults["height"])
-                        img2img_cfg = gr.Slider(minimum=-40.0, maximum=30.0, step=0.5,
-                                                label='文本引导强度',
-                                                value=img2img_defaults['cfg_scale'], elem_id='cfg_slider')
-                        img2img_seed = gr.Textbox(label="随机种子", lines=1, max_lines=1,
-                                                  value=img2img_defaults["seed"])
-                        img2img_batch_count = gr.Slider(minimum=1, maximum=50, step=1,
-                                                        label='生成数量',
-                                                        value=img2img_defaults['n_iter'])
-                        img2img_dimensions_info_text_box = gr.Textbox(
-                            label="长宽比设置")
-                    with gr.Column():
-                        img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="采样步数",
-                                                  value=img2img_defaults['ddim_steps'])
-
-                        img2img_sampling = gr.Dropdown(label='采样方式',
-                                                       choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler',
-                                                                'k_heun', 'k_lms'],
-                                                       value=img2img_defaults['sampler_name'])
-
-                        img2img_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength',
-                                                      value=img2img_defaults['denoising_strength'],visible=False)
-
-                        img2img_toggles = gr.CheckboxGroup(label='', choices=img2img_toggles,
-                                                           value=img2img_toggle_defaults,visible=False)
-
-                        img2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
-                                                                    choices=['RealESRGAN_x4plus',
-                                                                             'RealESRGAN_x4plus_anime_6B'],
-                                                                    value='RealESRGAN_x4plus',
-                                                                    visible=RealESRGAN is not None)  # TODO: Feels like I shouldnt slot it in here.
-
-                        img2img_embeddings = gr.File(label="Embeddings file for textual inversion",
-                                                     visible=show_embeddings)
-
-                img2img_image_editor_mode.change(
-                    uifn.change_image_editor_mode,
-                    [img2img_image_editor_mode,
-                     img2img_image_editor,
-                     img2img_image_mask,
-                     img2img_resize,
-                     img2img_width,
-                     img2img_height
-                     ],
-                    [img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask,
-                     img2img_painterro_btn, img2img_mask, img2img_mask_blur_strength]
-                )
-                
-                # 这个函数之前注释掉了，但是没有出现bug，看一下这个的作用；
-                img2img_image_editor_mode.change(
-                    uifn.update_image_mask,
-                    [img2img_image_editor, img2img_resize, img2img_width, img2img_height],
-                    img2img_image_mask
-                )
-
-                # 把上面这个注释掉下面就不管用了，很神奇无法理解... 后来又好了
-                # output_txt2img_copy_to_input_btn.click(
-                #     uifn.copy_img_to_input,
-                #     [gallery],
-                #     [tabs, img2img_image_editor, img2img_image_mask],
-                #     _js=call_JS("moveImageFromGallery",
-                #                 fromId="txt2img_gallery_output",
-                #                 toId="img2img_mask")
-                # )
-                output_txt2img_copy_to_input_btn.click(
-                    uifn.copy_img_to_input,
-                    [gallery, img_choices],
-                    [tabs, img2img_image_editor, img2img_image_mask, move_prompt_zh, move_prompt_en, prompt]
-                )
-
-                # go_to_img2img_btn.click(
-                #     uifn.go_to_img2img,
-                #     [],
-                #     [tabs],
-                # )
-                
-                # 下面这几个函数现在都没什么用
-                output_img2img_copy_to_input_btn.click(
-                    uifn.copy_img_to_edit,
-                    [output_img2img_gallery],
-                    [img2img_image_editor, tabs, img2img_image_editor_mode],
-                    _js=call_JS("moveImageFromGallery",
-                                fromId="gallery",
-                                toId="img2img_editor")
-                )
-                output_img2img_copy_to_mask_btn.click(
-                    uifn.copy_img_to_mask,
-                    [output_img2img_gallery],
-                    [img2img_image_mask, tabs, img2img_image_editor_mode],
-                    _js=call_JS("moveImageFromGallery",
-                                fromId="img2img_gallery_output",
-                                toId="img2img_editor")
-                )
-
-                output_img2img_copy_to_clipboard_btn.click(fn=None, inputs=output_img2img_gallery, outputs=[],
-                                                           _js=call_JS("copyImageFromGalleryToClipboard",
-                                                                       fromId="img2img_gallery_output")
-                                                           )
-
-                img2img_func = img2img
-                img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_mask,
-                                  img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
-                                  img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
-                                  img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
-                                  img2img_image_mask]
-                # img2img_outputs = [output_img2img_gallery, output_img2img_seed, output_img2img_params,
-                #                    output_img2img_stats]
-
-                img2img_outputs = [output_img2img_gallery]
-
-                # If a JobManager was passed in then wrap the Generate functions
-                if img2img_job_ui:
-                    img2img_func, img2img_inputs, img2img_outputs = img2img_job_ui.wrap_func(
-                        func=img2img_func,
-                        inputs=img2img_inputs,
-                        outputs=img2img_outputs,
-                    )
-
-                img2img_btn_mask.click(
-                    img2img_func,
-                    img2img_inputs,
-                    img2img_outputs
-                )
-
-                def img2img_submit_params():
-                    # print([img2img_prompt, img2img_image_editor_mode, img2img_mask,
-                    #              img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
-                    #              img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
-                    #              img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
-                    #              img2img_image_editor, img2img_image_mask, img2img_embeddings])
-                    return (img2img_func,
-                            img2img_inputs,
-                            img2img_outputs)
-
-                img2img_btn_editor.click(*img2img_submit_params())
-
-                # GENERATE ON ENTER
-                img2img_prompt.submit(None, None, None,
-                                      _js=call_JS("clickFirstVisibleButton",
-                                                  rowId="prompt_row"))
-
-                img2img_painterro_btn.click(None,
-                                            [img2img_image_editor, img2img_image_mask, img2img_image_editor_mode],
-                                            [img2img_image_editor, img2img_image_mask],
-                                            _js=call_JS("Painterro.init", toId="img2img_editor")
-                                            )
-
-                img2img_width.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height],
-                                     outputs=img2img_dimensions_info_text_box)
-                img2img_height.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height],
-                                      outputs=img2img_dimensions_info_text_box)
-
-        gr.HTML(read_content("footer.html"))
-        # gr.Image('./contributors.png')
-
-    block.queue(max_size=50, concurrency_count=20).launch()
\ No newline at end of file
diff --git a/spaces/jone/Music_Source_Separation/scripts/3_create_evaluation_audios/violin-piano/create_evaluation_audios.sh b/spaces/jone/Music_Source_Separation/scripts/3_create_evaluation_audios/violin-piano/create_evaluation_audios.sh
deleted file mode 100644
index 6ed036ff64b7d6d8cf5fbdaf67a493da64fb3fc5..0000000000000000000000000000000000000000
--- a/spaces/jone/Music_Source_Separation/scripts/3_create_evaluation_audios/violin-piano/create_evaluation_audios.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-VIOLIN_DATASET_DIR=${1:-"./datasets/instruments_solo/violin_solo/v0.1"}
-PIANO_DATASET_DIR=${2:-"./datasets/maestro"}
-WORKSPACE=${3:-"./workspaces/bytesep"}
-
-SAMPLE_RATE=44100
-CHANNELS=2
-EVALUATION_SEGMENTS_NUM=100
-
-EVLUATION_AUDIOS_DIR="${WORKSPACE}/evaluation_audios/violin-piano"
-
-python3 bytesep/dataset_creation/create_evaluation_audios/violin-piano.py \
-    --violin_dataset_dir=$VIOLIN_DATASET_DIR \
-    --piano_dataset_dir=$PIANO_DATASET_DIR \
-    --evaluation_audios_dir=$EVLUATION_AUDIOS_DIR \
-    --sample_rate=$SAMPLE_RATE \
-    --channels=$CHANNELS \
-    --evaluation_segments_num=$EVALUATION_SEGMENTS_NUM
-    
\ No newline at end of file
diff --git a/spaces/josedolot/HybridNet_Demo2/encoders/_base.py b/spaces/josedolot/HybridNet_Demo2/encoders/_base.py
deleted file mode 100644
index f4bca8b652e53b384e82a430ddea4494c0c3e848..0000000000000000000000000000000000000000
--- a/spaces/josedolot/HybridNet_Demo2/encoders/_base.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-import torch.nn as nn
-from typing import List
-from collections import OrderedDict
-
-from . import _utils as utils
-
-
-class EncoderMixin:
-    """Add encoder functionality such as:
-        - output channels specification of feature tensors (produced by encoder)
-        - patching first convolution for arbitrary input channels
-    """
-
-    @property
-    def out_channels(self):
-        """Return channels dimensions for each tensor of forward output of encoder"""
-        return self._out_channels[: self._depth + 1]
-
-    def set_in_channels(self, in_channels, pretrained=True):
-        """Change first convolution channels"""
-        if in_channels == 3:
-            return
-
-        self._in_channels = in_channels
-        if self._out_channels[0] == 3:
-            self._out_channels = tuple([in_channels] + list(self._out_channels)[1:])
-
-        utils.patch_first_conv(model=self, new_in_channels=in_channels, pretrained=pretrained)
-
-    def get_stages(self):
-        """Method should be overridden in encoder"""
-        raise NotImplementedError
-
-    def make_dilated(self, output_stride):
-
-        if output_stride == 16:
-            stage_list=[5,]
-            dilation_list=[2,]
-            
-        elif output_stride == 8:
-            stage_list=[4, 5]
-            dilation_list=[2, 4] 
-
-        else:
-            raise ValueError("Output stride should be 16 or 8, got {}.".format(output_stride))
-        
-        stages = self.get_stages()
-        for stage_indx, dilation_rate in zip(stage_list, dilation_list):
-            utils.replace_strides_with_dilation(
-                module=stages[stage_indx],
-                dilation_rate=dilation_rate,
-            )
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/partitioning.py b/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/partitioning.py
deleted file mode 100644
index f3da16dd653d5bf179fc0c566a663c691868cc7e..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/contrib/moe/partitioning.py
+++ /dev/null
@@ -1,446 +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.
-
-"""Pjit partitioner with Mixture of Experts overrides."""
-
-from typing import Any, Callable, Optional, Sequence, Union
-
-from absl import logging
-from flax import core as flax_core
-import jax
-import numpy as np
-
-from t5x import adafactor
-from t5x import partitioning as t5x_partitioning
-from t5x import train_state as train_state_lib
-
-from t5x.contrib.moe import training_utils
-
-DataLayout = t5x_partitioning.DataLayout
-FlaxOptimTrainState = train_state_lib.FlaxOptimTrainState
-HardwareMesh = t5x_partitioning.HardwareMesh
-InferenceState = train_state_lib.InferenceState
-LogicalAxisRules = t5x_partitioning.LogicalAxisRules
-PartitionSpec = t5x_partitioning.PartitionSpec
-Pytree = Any
-TrainState = train_state_lib.TrainState
-
-
-class MoePjitPartitioner(t5x_partitioning.PjitPartitioner):
-  """Pjit partitioner with overrides for Mixture of Experts support.
-
-  This MoE partitioner has two overrides relative to the default partitioner:
-  (1) It prepends an 'expert' axis to all MoE optimizer state terms, so that
-      they are sharded along the 'expert' axis; see get_logical_axes().
-  (2) In cases where model parallelism is used and the number of experts is less
-      than the number of devices, we treat the 'model' axis as a secondary data
-      axis. This allows us to decouple expert parallelism ('data' mesh axis)
-      from data parallelism ('data' and 'model' axes).
-  """
-
-  def __init__(self,
-               num_experts: int,
-               num_partitions: Optional[int] = None,
-               model_parallel_submesh: Optional[HardwareMesh] = None,
-               params_on_devices: bool = True,
-               logical_axis_rules: Optional[LogicalAxisRules] = None,
-               state_filter_fn: Optional[Callable[[str], bool]] = None):
-    """Configures the partitioner.
-
-    Args:
-      num_experts: Total number of experts across all devices.
-      num_partitions: Specifies the size of the model parallel submesh to be
-        automatically selected for the current topology. See
-        `model_parallel_submesh` for details on how this submesh is used.
-        Mutually exclusive with `model_parallel_submesh`.
-      model_parallel_submesh: 4-tuple that specifies the `(x, y, z, c)` submesh
-        model-parallel device tile -- an axis of accelerator parallelism
-        orthogonal to data parallelism. See t5x/partitioning.py for details.
-        This argument is mutually exclusive with `num_partitions`.
-      params_on_devices: Whether to keep the params on devices. If False, params
-        stay in the host memory.
-      logical_axis_rules: A priority-ordered sequence of KV tuples that maps
-        logical axis names to either `None` (not sharded), 'model' (to shard
-        across the model-parallel submesh), or 'data' (to shard across the
-        data-parallel submesh).
-      state_filter_fn: Function to identify which optimizer state axis rules
-        should be overridden to be sharded along the 'expert' axis. If None
-        (default), Adafactor expert sharding overrides are used.
-    """
-    # If True, treat 'model' axis as secondary data axis.
-    self.two_data_axes = _override_model_axis(num_experts, num_partitions,
-                                              model_parallel_submesh)
-    if self.two_data_axes:
-      # Override num_partitions to repurpose the 'model' axis as a secondary
-      # data axis, along which only the batch is sharded. Experts will be
-      # replicated along this secondary data axis.
-      num_partitions = jax.device_count() // num_experts
-
-      # Override user specified model parallel submesh. Rely on T5X partitioning
-      # to determine new submesh from updated `num_partitions`.
-      logging.info(
-          'Overriding user specified `model_parallel_submesh`=%s to support '
-          'expert parallelism for updated `num_partitions`=%d',
-          model_parallel_submesh, num_partitions)
-      model_parallel_submesh = None
-
-    super().__init__(
-        num_partitions=num_partitions,
-        model_parallel_submesh=model_parallel_submesh,
-        params_on_devices=params_on_devices,
-        logical_axis_rules=logical_axis_rules)
-
-    self._state_filter_fn = state_filter_fn
-
-  def get_data_layout(self,
-                      batch_size: Optional[int] = None,
-                      host_index: Optional[int] = None) -> DataLayout:
-    """Returns filled `DataLayout` based on the partitioned model layout.
-
-    Overrides default data layout in case were both mesh axes ('data' and
-    'model') are treated as data axes.
-
-    Args:
-      batch_size: If set, indicates the requested batch size. If not set, the
-        batch size is inferred from the layout.
-      host_index: Indicates the host index to use for the calculations, if not
-        set - use JAX-provided one. Should be in [0, num_hosts) interval and the
-        order should match the order of corresponding CPU devices in
-        `jax.devices()`.
-
-    Returns:
-      Filled `DataLayout` structure.
-    """
-    if self.two_data_axes:
-      if host_index is not None:
-        raise NotImplementedError('Explicit host_index is not yet implemented.')
-      mesh_size = self._local_chunker.global_mesh.shape[
-          'data'] * self._local_chunker.global_mesh.shape['model']
-      batch_size = batch_size or mesh_size
-      if batch_size % mesh_size:
-        raise ValueError(
-            f'Batch size ({batch_size}) must be divisible by corresponding '
-            f'mesh size ({mesh_size}).')
-      num_shards = self._local_chunker.num_chunks['data']
-      if batch_size % num_shards:
-        raise ValueError(
-            f'Batch size ({batch_size}) must be divisible by number of '
-            f'replicas ({num_shards}).')
-      replica_id = self._local_chunker.get_local_chunk_info(
-          (batch_size,), ('data', 'model')).replica_id
-      return DataLayout(
-          batch_size=batch_size,
-          shard_id=self._local_chunker.chunk_ids['data'],
-          num_shards=num_shards,
-          is_first_host_in_replica_set=(replica_id == 0))
-    else:
-      return super().get_data_layout(batch_size, host_index)
-
-  def get_logical_axes(
-      self, train_state: Union[FlaxOptimTrainState, InferenceState]
-  ) -> Union[FlaxOptimTrainState, InferenceState]:
-    """Returns a copy of TrainState with Optional[AxisNames] as leaves.
-
-    Overrides the default logical axes by prepending the 'expert' axis to any
-    MoE optimizer state terms (identified by self._state_filter_fn) so they are
-    correctly sharded along the 'expert' axis.
-
-    Args:
-      train_state: Object holding all relevant training of inference state.
-
-    Returns:
-      State object matching structure of input train_state but with axis names
-      as leaves.
-    """
-    logical_axes = train_state.as_logical_axes()
-
-    if isinstance(logical_axes, InferenceState):
-      # InferenceState does not contain any optimizer state, so we skip all
-      # expert partitioning overrides.
-      return logical_axes
-    else:
-      train_state: FlaxOptimTrainState
-
-    state_filter_fn = (
-        self._state_filter_fn or _infer_state_filter_fn(train_state))
-    if state_filter_fn is None:
-      # No state updates required.
-      return logical_axes
-
-    prepend_expert = lambda x: PartitionSpec(  # pylint: disable=g-long-lambda
-        'expert',) + x if x else PartitionSpec('expert',)
-    optimizer_axes = logical_axes._optimizer  # pylint: disable=protected-access
-    state_dict = flax_core.unfreeze(optimizer_axes.state_dict())
-    state_dict['state']['param_states'] = training_utils.tree_map_with_names(
-        prepend_expert, state_dict['state']['param_states'], state_filter_fn)
-
-    return train_state.restore_state(state_dict)
-
-  def partition(
-      self,
-      fn: Callable,  # pylint: disable=g-bare-generic
-      in_axis_resources: Pytree,
-      out_axis_resources: Pytree,
-      static_argnums: Union[int, Sequence[int]] = (),
-      donate_argnums: Union[int, Sequence[int]] = ()
-  ) -> t5x_partitioning.PjittedFnWithContext:
-    """Partitions the computation using pjit.
-
-    Overrides the default pjit partitioning in cases where expert and data axes
-    are decoupled -- wherein we treat the 'model' axis as a secondary data axis.
-
-    Args:
-      fn: Function to partition.
-      in_axis_resources: Pytree of structure matching that of arguments to `fn`,
-        with all actual arguments replaced by resource assignment
-        specifications.
-      out_axis_resources: Like `in_axis_resources`, but specifies resource
-        assignment for function outputs.
-      static_argnums: Specifies which positional arguments to treat as static
-        (compile-time constant) in the partitioned function.
-      donate_argnums: Specifies which argument buffers are "donated" to the
-        computation.
-
-    Returns:
-      A partitioned version of the input function.
-    """
-    if self.two_data_axes:
-      # Both axes are used for data parallelism in this case, so we override the
-      # partition specs.
-      in_axis_resources = _override_partition_specs(in_axis_resources)
-      out_axis_resources = _override_partition_specs(out_axis_resources)
-
-    pjitted = t5x_partitioning.pjit(
-        fn,
-        in_axis_resources=in_axis_resources,
-        out_axis_resources=out_axis_resources,
-        static_argnums=static_argnums,
-        donate_argnums=donate_argnums,
-        backend=self._backend)
-
-    return t5x_partitioning.PjittedFnWithContext(pjitted, self.mesh,
-                                                 self._logical_axis_rules)
-
-
-def standard_logical_axis_rules(
-    num_experts: int,
-    num_partitions: Optional[int] = None,
-    model_parallel_submesh: Optional[HardwareMesh] = None,
-    activation_partitioning_dims: int = 1,
-    parameter_partitioning_dims: int = 1,
-    additional_rules: Optional[LogicalAxisRules] = None):
-  """Returns partitioning rules for MoE models.
-
-  The partitioning rules vary based on whether the expert and data axes need to
-  be decoupled; see also MoePjitPartitioner for details of when expert and data
-  axes need to be decouple.
-
-  2D parameter sharding (`parameter_partitioning_dims=2`) is not supported.
-  Sharding parameters along the 'data' axis will interfere with expert
-  parallelism, because experts are also partitioned along the 'data' axis.
-
-  Args:
-    num_experts: Total number of experts across all devices.
-    num_partitions: Size of the model parallel submesh. Model parallelism is
-      only used if num_model_partitions > 1. Ignored if model_parallel_submesh
-      is specified.
-    model_parallel_submesh: 4-tuple that specifies the `(x, y, z, c)` submesh
-      model-parallel device tile -- an axis of accelerator parallelism
-      orthogonal to data parallelism. Model parallelism is only used if
-      np.prod(model_parallel_submesh) > 1. Mutually exclusive with
-      `num_partitions`.
-    activation_partitioning_dims: Enables 2-D activation sharding when set to 2.
-    parameter_partitioning_dims: Enables 2-D parameter sharding when set to 2.
-    additional_rules: Additional rules (a sequence of tuples) that will be
-      appended to the standard rules.
-
-  Returns:
-    Sequence of logical axis rules.
-
-  Raises:
-    ValueError if parameter_partitioning_dims=2.
-  """
-  if parameter_partitioning_dims == 2:
-    raise ValueError('2D parameter sharding (`parameter_partitioning_dims=2`) '
-                     'is not supported for MoE.')
-
-  default_rules = t5x_partitioning.standard_logical_axis_rules(
-      activation_partitioning_dims, parameter_partitioning_dims)
-  moe_rules = [
-      ('expert', 'data'),  # Shard experts along the data axis
-      ('expert_mlp', 'model'),  # Expert MLPs partitioned along model axis
-      ('expert_group', None),  # Replicated axis for all-to-all constraints
-      ('expert_replicas', None),  # Experts replicated along this axis
-      ('unmodeled', None),  # Replicated weights
-  ]
-  standard_rules = list(default_rules) + moe_rules
-  if additional_rules:
-    standard_rules.extend(additional_rules)
-
-  if _override_model_axis(num_experts, num_partitions, model_parallel_submesh):
-    overridden_rules = []
-    for logical_axis, mesh_axis in standard_rules:
-      if logical_axis == 'batch':
-        # Because we now treat the 'model' axis as a second data axis, we want
-        # to shard batches across both axes.
-        overridden_mesh_axis = ('data', 'model')
-      elif logical_axis == 'expert_replicas':
-        # "model" axis is repurposed as a second data axis, along which experts
-        # are replicated.
-        overridden_mesh_axis = 'model'
-      elif mesh_axis == 'model':
-        # Any weights ordinarily partitioned along the model axis, should be
-        # explicitly replicated.
-        overridden_mesh_axis = None
-      else:
-        overridden_mesh_axis = mesh_axis
-      overridden_rules.append((logical_axis, overridden_mesh_axis))
-
-    return overridden_rules
-
-  else:
-    return standard_rules
-
-
-def data_partition_spec(two_data_axes: bool) -> PartitionSpec:
-  """Returns data partitioning spec.
-
-  Args:
-    two_data_axes: If True, use 'model' axis as secondary data axis. Otherwise,
-      only use 'data' axis for data sharding.
-
-  Returns:
-    Mesh dependent partition spec.
-  """
-  if two_data_axes:
-    # Use 'model' axis as secondary data axis. Shard batches across both axes.
-    return PartitionSpec(('data', 'model'),)
-  else:
-    return PartitionSpec('data',)
-
-
-def _override_model_axis(
-    num_experts: int, num_partitions: Optional[int],
-    model_parallel_submesh: Optional[HardwareMesh]) -> bool:
-  """Returns true iff there is no model parallelism & num experts < num devices.
-
-  Args:
-    num_experts: Total number of experts across all devices.
-    num_partitions: Size of the model parallel submesh. Model parallelism is
-      only used if num_model_partitions > 1. Mutually exclusive with
-      `model_parallel_submesh`.
-    model_parallel_submesh: 4-tuple that specifies the `(x, y, z, c)` submesh
-      model-parallel device tile -- an axis of accelerator parallelism
-      orthogonal to data parallelism. Model parallelism is only used if
-      np.prod(model_parallel_submesh) > 1. Mutually exclusive with
-      `num_partitions`.
-
-  Returns:
-    True if there is no model parallelism & num experts < num devices; False
-    otherwise.
-  """
-  if (num_partitions is None) == (model_parallel_submesh is None):
-    raise ValueError(
-        'One, and only one, of {num_partitions, model_parallel_submesh} must '
-        'be specified. Received: %s and %s' %
-        (num_partitions, model_parallel_submesh))
-
-  if num_experts == 0 or jax.device_count() <= num_experts:
-    # No expert replication required. No need to override model mesh axis.
-    return False
-
-  return ((num_partitions is not None and num_partitions <= 1) or
-          (model_parallel_submesh is not None and
-           np.prod(model_parallel_submesh) <= 1))
-
-
-def _override_partition_specs(resources: Pytree):
-  """Override axis resources for two data axes setup.
-
-  In the two data axes setup, we treat the 'model' axis as a secondary data
-  axis. To this end, we override any hardcoded, raw partition specs:
-  - PartitionSpec('data',) -> PartitionSpec(('data', 'model'),)
-  - PartitionSpec('model',) -> None
-  There is no need to override any params or optimizer state as these will
-  inherit the correct specs from the logical axis rules; see
-  standard_logical_axis_rules().
-
-  Args:
-    resources: Axis resource assignment specifications.
-
-  Returns:
-    Axis resources with partition specs overridden to use 'model' as secondary
-    'data' axis.
-  """
-
-  def _maybe_overridde_spec(axis_resource: Pytree):
-    """Overrides "data" and "model" partition specs; leaves others unchanged."""
-    if axis_resource == PartitionSpec('data',):
-      # Shard all batches across both axes.
-      return PartitionSpec(('data', 'model'),)
-    elif axis_resource == PartitionSpec('model',):
-      # No model parallelism.
-      return None
-    else:
-      return axis_resource
-
-  if resources is None:
-    return resources
-  elif not isinstance(resources, Sequence):
-    return _maybe_overridde_spec(resources)
-  else:
-    overridden_resources = []
-    for resource in resources:
-      overridden_resources.append(_maybe_overridde_spec(resource))
-  return tuple(overridden_resources)
-
-
-def _infer_state_filter_fn(
-    train_state: FlaxOptimTrainState) -> Optional[Callable[[str], bool]]:
-  """Infers relevant regex matching sharded expert model state for optimizer.
-
-  Only the Adafactor optimizer is currently supported.
-
-  The model state generally inherits the correct partitioning specs from the
-  model parameters, except in cases where the kernel is factored (`v_col` and
-  `v_row` terms); see derive_logical_axes():
-  https://github.com/google-research/t5x/blob/main/t5x/adafactor.py#L591. For
-  those cases, we use the state_filter_fn to identify the factored kernel terms
-  that need to be partitioned along the expert axis.
-
-  Args:
-    train_state: Object holding optimizer and optimizer state (parameters).
-
-  Returns:
-    Function to identify which model state is sharded along 'expert' axis.
-
-  Raises:
-    ValueError if optimizer (on train state) is not an Adafactor optimizer.
-  """
-  optimizer = train_state._optimizer  # pylint: disable=protected-access
-  optimizer_def = optimizer.optimizer_def
-
-  # TODO(jamesleethorp): Revisit once other T5X optimizers are available.
-  if not isinstance(optimizer_def, adafactor.Adafactor):
-    raise ValueError('Inferred MoE overrides are currently only available for '
-                     f'the Adafactor optimizer. Received: {optimizer_def}')
-
-  if optimizer_def.hyper_params.factored:
-    # Factored kernel terms (`v_col` and `v_row`) need to be identified for
-    # expert sharding.
-    return training_utils.match_fn(r'.*expert.*/kernel/v_.*')
-  else:
-    # Non-factored kernel terms (`v`) inherit the correct specs, so no state
-    # updates will be required.
-    return None
diff --git a/spaces/jyseo/3DFuse/adapt_sd.py b/spaces/jyseo/3DFuse/adapt_sd.py
deleted file mode 100644
index 54e0b335bd4c0d87d71b08d365339f98c380f32a..0000000000000000000000000000000000000000
--- a/spaces/jyseo/3DFuse/adapt_sd.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-import numpy as np
-from einops import rearrange
-from torch import autocast
-from contextlib import nullcontext
-from math import sqrt
-from adapt import ScoreAdapter
-
-from cldm.model import create_model, load_state_dict
-
-from lora_util import *
-import warnings
-from transformers import logging
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-logging.set_verbosity_error()
-
-device = torch.device("cuda")
-
-
-def _sqrt(x):
-    if isinstance(x, float):
-        return sqrt(x)
-    else:
-        assert isinstance(x, torch.Tensor)
-        return torch.sqrt(x)
-
-def load_embedding(model,embedding):
-    length=len(embedding['string_to_param']['*'])
-    voc=[]
-    for i in range(length):
-        voc.append(f'<{str(i)}>')
-    print(f"Added Token: {voc}")
-    model.cond_stage_model.tokenizer._add_tokens(voc)
-
-    x=torch.nn.Embedding(model.cond_stage_model.tokenizer.__len__(),768)
-    
-    for params in x.parameters():
-        params.requires_grad=False
-
-    x.weight[:-length]=model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight
-    x.weight[-length:]=embedding['string_to_param']['*']
-    model.cond_stage_model.transformer.text_model.embeddings.token_embedding=x
-    
-def load_3DFuse(control,dir,alpha):
-    ######################LOADCONTROL###########################
-    model = create_model(control['control_yaml']).cpu()
-    model.load_state_dict(load_state_dict(control['control_weight'], location='cuda'))
-    state_dict, l = merge("runwayml/stable-diffusion-v1-5",dir,alpha)
-    
-    #######################OVERRIDE#############################
-    model.load_state_dict(state_dict,strict=False)
-    
-    #######################ADDEMBBEDDING########################
-    load_embedding(model,l)
-    ###############################################################
-    return model
-
-class StableDiffusion(ScoreAdapter):
-    def __init__(self, variant, v2_highres, prompt, scale, precision, dir, alpha=1.0):
-               
-        model=load_3DFuse(self.checkpoint_root(),dir,alpha)
-        self.model = model.cuda()
-        
-        H , W = (512, 512)
-
-        ae_resolution_f = 8
-
-        self._device = self.model._device
-
-        self.prompt = prompt
-        self.scale = scale
-        self.precision = precision
-        self.precision_scope = autocast if self.precision == "autocast" else nullcontext
-        self._data_shape = (4, H // ae_resolution_f, W // ae_resolution_f)
-
-        self.cond_func = self.model.get_learned_conditioning
-        self.M = 1000
-        noise_schedule = "linear"
-        self.noise_schedule = noise_schedule
-        self.us = self.linear_us(self.M)
-
-    def data_shape(self):
-        return self._data_shape
-
-    @property
-    def σ_max(self):
-        return self.us[0]
-
-    @property
-    def σ_min(self):
-        return self.us[-1]
-
-    @torch.no_grad()
-    def denoise(self, xs, σ,control, **model_kwargs):
-        with self.precision_scope("cuda"):
-            with self.model.ema_scope():
-                N = xs.shape[0]
-                c = model_kwargs.pop('c')
-                uc = model_kwargs.pop('uc')
-                conditional_conditioning = {"c_concat": [control], "c_crossattn": [c]}
-                unconditional_conditioning = {"c_concat": [control], "c_crossattn": [uc]}
-
-                cond_t, σ = self.time_cond_vec(N, σ)
-                unscaled_xs = xs
-                xs = xs / _sqrt(1 + σ**2)
-                if uc is None or self.scale == 1.:
-                    output = self.model.apply_model(xs, cond_t, c)
-                else:
-                    x_in = torch.cat([xs] * 2)
-                    t_in = torch.cat([cond_t] * 2)
-                    c_in = dict()
-                    for k in conditional_conditioning:
-                        if isinstance(conditional_conditioning[k], list):
-                            c_in[k] = [torch.cat([
-                                unconditional_conditioning[k][i],
-                                conditional_conditioning[k][i]]) for i in range(len(conditional_conditioning[k]))]
-                        else:
-                            c_in[k] = torch.cat([
-                                    unconditional_conditioning[k],
-                                    conditional_conditioning[k]])
-                    
-                    e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                    output = e_t_uncond + self.scale * (e_t - e_t_uncond)
-
-                if self.model.parameterization == "v":
-                    output = self.model.predict_eps_from_z_and_v(xs, cond_t, output)
-                else:
-
-                    output = output
-
-                Ds = unscaled_xs - σ * output
-                return Ds
-
-    def cond_info(self, batch_size):
-        prompts = batch_size * [self.prompt]
-        return self.prompts_emb(prompts)
-
-    @torch.no_grad()
-    def prompts_emb(self, prompts):
-        assert isinstance(prompts, list)
-        batch_size = len(prompts)
-        with self.precision_scope("cuda"):
-            with self.model.ema_scope():
-                cond = {}
-                c = self.cond_func(prompts)
-                cond['c'] = c
-                uc = None
-                if self.scale != 1.0:
-                    uc = self.cond_func(batch_size * [""])
-                cond['uc'] = uc
-                return cond
-
-    def unet_is_cond(self):
-        return True
-
-    def use_cls_guidance(self):
-        return False
-
-    def snap_t_to_nearest_tick(self, t):
-        j = np.abs(t - self.us).argmin()
-        return self.us[j], j
-
-    def time_cond_vec(self, N, σ):
-        if isinstance(σ, float):
-            σ, j = self.snap_t_to_nearest_tick(σ)  # σ might change due to snapping
-            cond_t = (self.M - 1) - j
-            cond_t = torch.tensor([cond_t] * N, device=self.device)
-            return cond_t, σ
-        else:
-            assert isinstance(σ, torch.Tensor)
-            σ = σ.reshape(-1).cpu().numpy()
-            σs = []
-            js = []
-            for elem in σ:
-                _σ, _j = self.snap_t_to_nearest_tick(elem)
-                σs.append(_σ)
-                js.append((self.M - 1) - _j)
-
-            cond_t = torch.tensor(js, device=self.device)
-            σs = torch.tensor(σs, device=self.device, dtype=torch.float32).reshape(-1, 1, 1, 1)
-            return cond_t, σs
-
-    @staticmethod
-    def linear_us(M=1000):
-        assert M == 1000
-        β_start = 0.00085
-        β_end = 0.0120
-        βs = np.linspace(β_start**0.5, β_end**0.5, M, dtype=np.float64)**2
-        αs = np.cumprod(1 - βs)
-        us = np.sqrt((1 - αs) / αs)
-        us = us[::-1]
-        return us
-
-    @torch.no_grad()
-    def encode(self, xs):
-        model = self.model
-        with self.precision_scope("cuda"):
-            with self.model.ema_scope():
-                zs = model.get_first_stage_encoding(
-                    model.encode_first_stage(xs)
-                )
-        return zs
-
-    @torch.no_grad()
-    def decode(self, xs):
-        with self.precision_scope("cuda"):
-            with self.model.ema_scope():
-                xs = self.model.decode_first_stage(xs)
-                return xs
diff --git a/spaces/kamranahmad92/gradialanchainChatBotOpenAi/README.md b/spaces/kamranahmad92/gradialanchainChatBotOpenAi/README.md
deleted file mode 100644
index 7890c9025b862388b9596d642aafdbcc97a1b495..0000000000000000000000000000000000000000
--- a/spaces/kamranahmad92/gradialanchainChatBotOpenAi/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GradialanchainChatBotOpenAi
-emoji: 🏆
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kcagle/AutoGPT/autogpt/agent/agent_manager.py b/spaces/kcagle/AutoGPT/autogpt/agent/agent_manager.py
deleted file mode 100644
index 898767a485e50b5e62625a7883edf1b30d5fddf9..0000000000000000000000000000000000000000
--- a/spaces/kcagle/AutoGPT/autogpt/agent/agent_manager.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""Agent manager for managing GPT agents"""
-from __future__ import annotations
-
-from typing import Union
-
-from autogpt.config.config import Singleton
-from autogpt.llm_utils import create_chat_completion
-
-
-class AgentManager(metaclass=Singleton):
-    """Agent manager for managing GPT agents"""
-
-    def __init__(self):
-        self.next_key = 0
-        self.agents = {}  # key, (task, full_message_history, model)
-
-    # Create new GPT agent
-    # TODO: Centralise use of create_chat_completion() to globally enforce token limit
-
-    def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
-        """Create a new agent and return its key
-
-        Args:
-            task: The task to perform
-            prompt: The prompt to use
-            model: The model to use
-
-        Returns:
-            The key of the new agent
-        """
-        messages = [
-            {"role": "user", "content": prompt},
-        ]
-
-        # Start GPT instance
-        agent_reply = create_chat_completion(
-            model=model,
-            messages=messages,
-        )
-
-        # Update full message history
-        messages.append({"role": "assistant", "content": agent_reply})
-
-        key = self.next_key
-        # This is done instead of len(agents) to make keys unique even if agents
-        # are deleted
-        self.next_key += 1
-
-        self.agents[key] = (task, messages, model)
-
-        return key, agent_reply
-
-    def message_agent(self, key: str | int, message: str) -> str:
-        """Send a message to an agent and return its response
-
-        Args:
-            key: The key of the agent to message
-            message: The message to send to the agent
-
-        Returns:
-            The agent's response
-        """
-        task, messages, model = self.agents[int(key)]
-
-        # Add user message to message history before sending to agent
-        messages.append({"role": "user", "content": message})
-
-        # Start GPT instance
-        agent_reply = create_chat_completion(
-            model=model,
-            messages=messages,
-        )
-
-        # Update full message history
-        messages.append({"role": "assistant", "content": agent_reply})
-
-        return agent_reply
-
-    def list_agents(self) -> list[tuple[str | int, str]]:
-        """Return a list of all agents
-
-        Returns:
-            A list of tuples of the form (key, task)
-        """
-
-        # Return a list of agent keys and their tasks
-        return [(key, task) for key, (task, _, _) in self.agents.items()]
-
-    def delete_agent(self, key: Union[str, int]) -> bool:
-        """Delete an agent from the agent manager
-
-        Args:
-            key: The key of the agent to delete
-
-        Returns:
-            True if successful, False otherwise
-        """
-
-        try:
-            del self.agents[int(key)]
-            return True
-        except KeyError:
-            return False
diff --git a/spaces/kepajide/keyiwei/transforms.py b/spaces/kepajide/keyiwei/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/kepajide/keyiwei/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/keras-io/supervised-contrastive-learning/app.py b/spaces/keras-io/supervised-contrastive-learning/app.py
deleted file mode 100644
index c01965f5857df98917e4d2f6829acdd6d946e151..0000000000000000000000000000000000000000
--- a/spaces/keras-io/supervised-contrastive-learning/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-from huggingface_hub import from_pretrained_keras
-
-model = from_pretrained_keras("keras-io/supervised-contrastive-learning-cifar10")
-
-labels = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
-
-
-def infer(test_image):
-    image = tf.constant(test_image)
-    image = tf.reshape(image, [-1, 32, 32, 3])
-    pred = model.predict(image)
-    pred_list = pred[0, :]
-    return {labels[i]: float(pred_list[i]) for i in range(10)}
-
-
-image = gr.inputs.Image(shape=(32, 32))
-label = gr.outputs.Label(num_top_classes=3)
-
-
-article = """<center>
-Authors: <a href='https://twitter.com/johko990' target='_blank'>Johannes Kolbe</a> after an example by Khalid Salama at 
-<a href='https://keras.io/examples/vision/supervised-contrastive-learning/' target='_blank'>keras.io</a> <br>
-<a href='https://arxiv.org/abs/2004.11362' target='_blank'>Original paper</a> by Prannay Khosla et al."""
-
-
-description = """Classification with a model trained via Supervised Contrastive Learning """
-
-
-Iface = gr.Interface(
-    fn=infer,
-    inputs=image,
-    outputs=label,
-    examples=[["examples/cat.jpg"], ["examples/ship.jpeg"]],
-    title="Supervised Contrastive Learning Classification",
-    article=article,
-    description=description,
-).launch()
diff --git a/spaces/kevinwang676/Bark-Voice-Cloning/cloning/clonevoice.py b/spaces/kevinwang676/Bark-Voice-Cloning/cloning/clonevoice.py
deleted file mode 100644
index fb38dc6df49195a5b6b052732fbfc35a7d69c55a..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-Voice-Cloning/cloning/clonevoice.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from bark.generation import load_codec_model, generate_text_semantic, grab_best_device
-from encodec.utils import convert_audio
-from bark.hubert.hubert_manager import HuBERTManager
-from bark.hubert.pre_kmeans_hubert import CustomHubert
-from bark.hubert.customtokenizer import CustomTokenizer
-
-import torchaudio
-import torch
-import os
-import gradio
-
-
-def clone_voice(audio_filepath, dest_filename, progress=gradio.Progress(track_tqdm=True)):
-    # if len(text) < 1:
-    #    raise gradio.Error('No transcription text entered!')
-
-    use_gpu = not os.environ.get("BARK_FORCE_CPU", False)
-    progress(0, desc="Loading Codec")
-    model = load_codec_model(use_gpu=use_gpu)
-    
-    # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
-    hubert_manager = HuBERTManager()
-    hubert_manager.make_sure_hubert_installed()
-    hubert_manager.make_sure_tokenizer_installed()
-
-    # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer 
-    # Load HuBERT for semantic tokens
-
-    # Load the HuBERT model
-    device = grab_best_device(use_gpu)
-    hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device)
-
-    # Load the CustomTokenizer model
-    tokenizer = CustomTokenizer.load_from_checkpoint('./models/hubert/en_tokenizer.pth').to(device)  # change to the correct path 
-
-    progress(0.25, desc="Converting WAV")
-
-    # Load and pre-process the audio waveform
-    wav, sr = torchaudio.load(audio_filepath)
-    if wav.shape[0] == 2:  # Stereo to mono if needed
-        wav = wav.mean(0, keepdim=True)
-
-    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
-    wav = wav.to(device)
-    progress(0.5, desc="Extracting codes")
-
-    semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate)
-    semantic_tokens = tokenizer.get_token(semantic_vectors)
-    
-    # Extract discrete codes from EnCodec
-    with torch.no_grad():
-        encoded_frames = model.encode(wav.unsqueeze(0))
-    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [n_q, T]
-
-    # get seconds of audio
-    # seconds = wav.shape[-1] / model.sample_rate
-    # generate semantic tokens
-    # semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds, top_k=50, top_p=.95, temp=0.7)
-
-    # move codes to cpu
-    codes = codes.cpu().numpy()
-    # move semantic tokens to cpu
-    semantic_tokens = semantic_tokens.cpu().numpy()
-
-    import numpy as np
-    output_path = dest_filename + '.npz'
-    np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)
-    return ["Finished", output_path]
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio_encoder.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio_encoder.py
deleted file mode 100644
index 6279d2014a2e786a6c549f084339e18d00e50331..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/audio2pose_models/audio_encoder.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        return self.act(out)
-
-class AudioEncoder(nn.Module):
-    def __init__(self, wav2lip_checkpoint, device):
-        super(AudioEncoder, self).__init__()
-
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
-
-        #### load the pre-trained audio_encoder, we do not need to load wav2lip model here.
-        # wav2lip_state_dict = torch.load(wav2lip_checkpoint, map_location=torch.device(device))['state_dict']
-        # state_dict = self.audio_encoder.state_dict()
-
-        # for k,v in wav2lip_state_dict.items():
-        #     if 'audio_encoder' in k:
-        #         state_dict[k.replace('module.audio_encoder.', '')] = v
-        # self.audio_encoder.load_state_dict(state_dict)
-
-
-    def forward(self, audio_sequences):
-        # audio_sequences = (B, T, 1, 80, 16)
-        B = audio_sequences.size(0)
-
-        audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
-
-        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
-        dim = audio_embedding.shape[1]
-        audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1))
-
-        return audio_embedding.squeeze(-1).squeeze(-1) #B seq_len+1 512 
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/facerecon_model.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/facerecon_model.py
deleted file mode 100644
index 7de8ca6eebc50ff1ed52c5ba37d31b43f977b5e1..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/facerecon_model.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This script defines the face reconstruction model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import torch
-from src.face3d.models.base_model import BaseModel
-from src.face3d.models import networks
-from src.face3d.models.bfm import ParametricFaceModel
-from src.face3d.models.losses import perceptual_loss, photo_loss, reg_loss, reflectance_loss, landmark_loss
-from src.face3d.util import util 
-from src.face3d.util.nvdiffrast import MeshRenderer
-# from src.face3d.util.preprocess import estimate_norm_torch
-
-import trimesh
-from scipy.io import savemat
-
-class FaceReconModel(BaseModel):
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train=False):
-        """  Configures options specific for CUT model
-        """
-        # net structure and parameters
-        parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='network structure')
-        parser.add_argument('--init_path', type=str, default='./checkpoints/init_model/resnet50-0676ba61.pth')
-        parser.add_argument('--use_last_fc', type=util.str2bool, nargs='?', const=True, default=False, help='zero initialize the last fc')
-        parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/')
-        parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model')
-
-        # renderer parameters
-        parser.add_argument('--focal', type=float, default=1015.)
-        parser.add_argument('--center', type=float, default=112.)
-        parser.add_argument('--camera_d', type=float, default=10.)
-        parser.add_argument('--z_near', type=float, default=5.)
-        parser.add_argument('--z_far', type=float, default=15.)
-
-        if is_train:
-            # training parameters
-            parser.add_argument('--net_recog', type=str, default='r50', choices=['r18', 'r43', 'r50'], help='face recog network structure')
-            parser.add_argument('--net_recog_path', type=str, default='checkpoints/recog_model/ms1mv3_arcface_r50_fp16/backbone.pth')
-            parser.add_argument('--use_crop_face', type=util.str2bool, nargs='?', const=True, default=False, help='use crop mask for photo loss')
-            parser.add_argument('--use_predef_M', type=util.str2bool, nargs='?', const=True, default=False, help='use predefined M for predicted face')
-
-            
-            # augmentation parameters
-            parser.add_argument('--shift_pixs', type=float, default=10., help='shift pixels')
-            parser.add_argument('--scale_delta', type=float, default=0.1, help='delta scale factor')
-            parser.add_argument('--rot_angle', type=float, default=10., help='rot angles, degree')
-
-            # loss weights
-            parser.add_argument('--w_feat', type=float, default=0.2, help='weight for feat loss')
-            parser.add_argument('--w_color', type=float, default=1.92, help='weight for loss loss')
-            parser.add_argument('--w_reg', type=float, default=3.0e-4, help='weight for reg loss')
-            parser.add_argument('--w_id', type=float, default=1.0, help='weight for id_reg loss')
-            parser.add_argument('--w_exp', type=float, default=0.8, help='weight for exp_reg loss')
-            parser.add_argument('--w_tex', type=float, default=1.7e-2, help='weight for tex_reg loss')
-            parser.add_argument('--w_gamma', type=float, default=10.0, help='weight for gamma loss')
-            parser.add_argument('--w_lm', type=float, default=1.6e-3, help='weight for lm loss')
-            parser.add_argument('--w_reflc', type=float, default=5.0, help='weight for reflc loss')
-
-        opt, _ = parser.parse_known_args()
-        parser.set_defaults(
-                focal=1015., center=112., camera_d=10., use_last_fc=False, z_near=5., z_far=15.
-            )
-        if is_train:
-            parser.set_defaults(
-                use_crop_face=True, use_predef_M=False
-            )
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this model class.
-
-        Parameters:
-            opt -- training/test options
-
-        A few things can be done here.
-        - (required) call the initialization function of BaseModel
-        - define loss function, visualization images, model names, and optimizers
-        """
-        BaseModel.__init__(self, opt)  # call the initialization method of BaseModel
-        
-        self.visual_names = ['output_vis']
-        self.model_names = ['net_recon']
-        self.parallel_names = self.model_names + ['renderer']
-
-        self.facemodel = ParametricFaceModel(
-            bfm_folder=opt.bfm_folder, camera_distance=opt.camera_d, focal=opt.focal, center=opt.center,
-            is_train=self.isTrain, default_name=opt.bfm_model
-        )
-        
-        fov = 2 * np.arctan(opt.center / opt.focal) * 180 / np.pi
-        self.renderer = MeshRenderer(
-            rasterize_fov=fov, znear=opt.z_near, zfar=opt.z_far, rasterize_size=int(2 * opt.center)
-        )
-
-        if self.isTrain:
-            self.loss_names = ['all', 'feat', 'color', 'lm', 'reg', 'gamma', 'reflc']
-
-            self.net_recog = networks.define_net_recog(
-                net_recog=opt.net_recog, pretrained_path=opt.net_recog_path
-                )
-            # loss func name: (compute_%s_loss) % loss_name
-            self.compute_feat_loss = perceptual_loss
-            self.comupte_color_loss = photo_loss
-            self.compute_lm_loss = landmark_loss
-            self.compute_reg_loss = reg_loss
-            self.compute_reflc_loss = reflectance_loss
-
-            self.optimizer = torch.optim.Adam(self.net_recon.parameters(), lr=opt.lr)
-            self.optimizers = [self.optimizer]
-            self.parallel_names += ['net_recog']
-        # Our program will automatically call <model.setup> to define schedulers, load networks, and print networks
-
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
-        Parameters:
-            input: a dictionary that contains the data itself and its metadata information.
-        """
-        self.input_img = input['imgs'].to(self.device) 
-        self.atten_mask = input['msks'].to(self.device) if 'msks' in input else None
-        self.gt_lm = input['lms'].to(self.device)  if 'lms' in input else None
-        self.trans_m = input['M'].to(self.device) if 'M' in input else None
-        self.image_paths = input['im_paths'] if 'im_paths' in input else None
-
-    def forward(self, output_coeff, device):
-        self.facemodel.to(device)
-        self.pred_vertex, self.pred_tex, self.pred_color, self.pred_lm = \
-            self.facemodel.compute_for_render(output_coeff)
-        self.pred_mask, _, self.pred_face = self.renderer(
-            self.pred_vertex, self.facemodel.face_buf, feat=self.pred_color)
-        
-        self.pred_coeffs_dict = self.facemodel.split_coeff(output_coeff)
-
-
-    def compute_losses(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-
-        assert self.net_recog.training == False
-        trans_m = self.trans_m
-        if not self.opt.use_predef_M:
-            trans_m = estimate_norm_torch(self.pred_lm, self.input_img.shape[-2])
-
-        pred_feat = self.net_recog(self.pred_face, trans_m)
-        gt_feat = self.net_recog(self.input_img, self.trans_m)
-        self.loss_feat = self.opt.w_feat * self.compute_feat_loss(pred_feat, gt_feat)
-
-        face_mask = self.pred_mask
-        if self.opt.use_crop_face:
-            face_mask, _, _ = self.renderer(self.pred_vertex, self.facemodel.front_face_buf)
-        
-        face_mask = face_mask.detach()
-        self.loss_color = self.opt.w_color * self.comupte_color_loss(
-            self.pred_face, self.input_img, self.atten_mask * face_mask)
-        
-        loss_reg, loss_gamma = self.compute_reg_loss(self.pred_coeffs_dict, self.opt)
-        self.loss_reg = self.opt.w_reg * loss_reg
-        self.loss_gamma = self.opt.w_gamma * loss_gamma
-
-        self.loss_lm = self.opt.w_lm * self.compute_lm_loss(self.pred_lm, self.gt_lm)
-
-        self.loss_reflc = self.opt.w_reflc * self.compute_reflc_loss(self.pred_tex, self.facemodel.skin_mask)
-
-        self.loss_all = self.loss_feat + self.loss_color + self.loss_reg + self.loss_gamma \
-                        + self.loss_lm + self.loss_reflc
-            
-
-    def optimize_parameters(self, isTrain=True):
-        self.forward()               
-        self.compute_losses()
-        """Update network weights; it will be called in every training iteration."""
-        if isTrain:
-            self.optimizer.zero_grad()  
-            self.loss_all.backward()         
-            self.optimizer.step()        
-
-    def compute_visuals(self):
-        with torch.no_grad():
-            input_img_numpy = 255. * self.input_img.detach().cpu().permute(0, 2, 3, 1).numpy()
-            output_vis = self.pred_face * self.pred_mask + (1 - self.pred_mask) * self.input_img
-            output_vis_numpy_raw = 255. * output_vis.detach().cpu().permute(0, 2, 3, 1).numpy()
-            
-            if self.gt_lm is not None:
-                gt_lm_numpy = self.gt_lm.cpu().numpy()
-                pred_lm_numpy = self.pred_lm.detach().cpu().numpy()
-                output_vis_numpy = util.draw_landmarks(output_vis_numpy_raw, gt_lm_numpy, 'b')
-                output_vis_numpy = util.draw_landmarks(output_vis_numpy, pred_lm_numpy, 'r')
-            
-                output_vis_numpy = np.concatenate((input_img_numpy, 
-                                    output_vis_numpy_raw, output_vis_numpy), axis=-2)
-            else:
-                output_vis_numpy = np.concatenate((input_img_numpy, 
-                                    output_vis_numpy_raw), axis=-2)
-
-            self.output_vis = torch.tensor(
-                    output_vis_numpy / 255., dtype=torch.float32
-                ).permute(0, 3, 1, 2).to(self.device)
-
-    def save_mesh(self, name):
-
-        recon_shape = self.pred_vertex  # get reconstructed shape
-        recon_shape[..., -1] = 10 - recon_shape[..., -1] # from camera space to world space
-        recon_shape = recon_shape.cpu().numpy()[0]
-        recon_color = self.pred_color
-        recon_color = recon_color.cpu().numpy()[0]
-        tri = self.facemodel.face_buf.cpu().numpy()
-        mesh = trimesh.Trimesh(vertices=recon_shape, faces=tri, vertex_colors=np.clip(255. * recon_color, 0, 255).astype(np.uint8))
-        mesh.export(name)
-
-    def save_coeff(self,name):
-
-        pred_coeffs = {key:self.pred_coeffs_dict[key].cpu().numpy() for key in self.pred_coeffs_dict}
-        pred_lm = self.pred_lm.cpu().numpy()
-        pred_lm = np.stack([pred_lm[:,:,0],self.input_img.shape[2]-1-pred_lm[:,:,1]],axis=2) # transfer to image coordinate
-        pred_coeffs['lm68'] = pred_lm
-        savemat(name,pred_coeffs)
-
-
-
diff --git a/spaces/kevinwang676/rvc-mlbb-v2/vc_infer_pipeline.py b/spaces/kevinwang676/rvc-mlbb-v2/vc_infer_pipeline.py
deleted file mode 100644
index 60d01a9647548debfb1d611fb34d8c0ced7e6c5c..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/rvc-mlbb-v2/vc_infer_pipeline.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-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
\ No newline at end of file
diff --git a/spaces/khizon/emotion-classifier-demo/download_dataset.py b/spaces/khizon/emotion-classifier-demo/download_dataset.py
deleted file mode 100644
index 0154e17d0eb67ac8b7746db63e1c55f7a3c446cc..0000000000000000000000000000000000000000
--- a/spaces/khizon/emotion-classifier-demo/download_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import pandas as pd
-import numpy as np
-
-import os
-import gdown
-
-from pathlib import Path
-from tqdm import tqdm
-from sklearn.model_selection import train_test_split
-
-import torchaudio
-
-if __name__ == '__main__':
-    
-    if not os.path.exists(os.path.join('data')):
-        os.makedirs(os.path.join('data'))
-        
-    os.system('gdown https://drive.google.com/uc?id=1_IAWexEWpH-ly_JaA5EGfZDp-_3flkN1')
-    os.system('unzip -q aesdd.zip -d data/')
-    os.rename(os.path.join('data', 'Acted Emotional Speech Dynamic Database'),
-              os.path.join('data', 'aesdd'))
-    
-    data = []
-    # Load the annotations file
-    for path in tqdm(Path("data/aesdd").glob("**/*.wav")):
-        name = str(path).split("/")[-1]
-        label = str(path).split('/')[-2]
-        path = os.path.join("data", "aesdd", label, name)
-
-        try:
-            # There are some broken files
-            s = torchaudio.load(path)
-            data.append({
-                "name": name,
-                "path": path,
-                "emotion": label
-            })
-        except Exception as e:
-            # print(str(path), e)
-            pass
-
-        
-        
-    df = pd.DataFrame(data)
-    print(df.head())
-    
-    # Filter broken and non-existed paths
-
-    print(f"Step 0: {len(df)}")
-
-    df["status"] = df["path"].apply(lambda path: True if os.path.exists(path) else None)
-    df = df.dropna(subset=["path"])
-    df = df.drop("status", 1)
-    print(f"Step 1: {len(df)}")
-
-    df = df.sample(frac=1)
-    df = df.reset_index(drop=True)
-    
-    # Train test split
-    save_path = "data"
-
-    train_df, test_df = train_test_split(df, test_size=0.2, random_state=101, stratify=df["emotion"])
-
-    train_df = train_df.reset_index(drop=True)
-    test_df = test_df.reset_index(drop=True)
-
-    train_df.to_csv(f"{save_path}/train.csv", sep="\t", encoding="utf-8", index=False)
-    test_df.to_csv(f"{save_path}/test.csv", sep="\t", encoding="utf-8", index=False)
-
-
-    print(train_df.shape)
-    print(test_df.shape)
\ No newline at end of file
diff --git a/spaces/kingabzpro/Urdu-ASR-SOTA/eval.py b/spaces/kingabzpro/Urdu-ASR-SOTA/eval.py
deleted file mode 100644
index 46be2af646b9df18a450c1336e0664103c7778c5..0000000000000000000000000000000000000000
--- a/spaces/kingabzpro/Urdu-ASR-SOTA/eval.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import re
-from typing import Dict
-
-import torch
-from datasets import Audio, Dataset, load_dataset, load_metric
-
-from transformers import AutoFeatureExtractor, pipeline
-
-
-def log_results(result: Dataset, args: Dict[str, str]):
-    """DO NOT CHANGE. This function computes and logs the result metrics."""
-
-    log_outputs = args.log_outputs
-    dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
-
-    # load metric
-    wer = load_metric("wer")
-    cer = load_metric("cer")
-
-    # compute metrics
-    wer_result = wer.compute(
-        references=result["target"], predictions=result["prediction"]
-    )
-    cer_result = cer.compute(
-        references=result["target"], predictions=result["prediction"]
-    )
-
-    # print & log results
-    result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
-    print(result_str)
-
-    with open(f"{dataset_id}_eval_results.txt", "w") as f:
-        f.write(result_str)
-
-    # log all results in text file. Possibly interesting for analysis
-    if log_outputs is not None:
-        pred_file = f"log_{dataset_id}_predictions.txt"
-        target_file = f"log_{dataset_id}_targets.txt"
-
-        with open(pred_file, "w") as p, open(target_file, "w") as t:
-
-            # mapping function to write output
-            def write_to_file(batch, i):
-                p.write(f"{i}" + "\n")
-                p.write(batch["prediction"] + "\n")
-                t.write(f"{i}" + "\n")
-                t.write(batch["target"] + "\n")
-
-            result.map(write_to_file, with_indices=True)
-
-
-def normalize_text(text: str) -> str:
-    """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
-
-    chars_to_ignore_regex = """[\!\؛\،\٫\؟\۔\٪\"\'\:\-\‘\’]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
-
-    text = re.sub(chars_to_ignore_regex, "", text.lower())
-    text = re.sub("[،]", "", text)
-    text = re.sub("[؟]", "", text)
-    text = re.sub("['َ]", "", text)
-    text = re.sub("['ُ]", "", text)
-    text = re.sub("['ِ]", "", text)
-    text = re.sub("['ّ]", "", text)
-    text = re.sub("['ٔ]", "", text)
-    text = re.sub("['ٰ]", "", text)
-    text = re.sub("[ۂ]", "ہ", text)
-    text = re.sub("[ي]", "ی", text)
-    text = re.sub("[ؤ]", "و", text)
-    # batch["sentence"] = re.sub("[ئ]", 'ى', batch["sentence"])
-    text = re.sub("[ى]", "ی", text)
-    text = re.sub("[۔]", "", text)
-
-    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
-    # note that order is important here!
-    token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]
-
-    for t in token_sequences_to_ignore:
-        text = " ".join(text.split(t))
-
-    return text
-
-
-def path_adjust(batch):
-    batch["path"] = "Data/ur/clips/" + str(batch["path"])
-    return batch
-
-
-def main(args):
-    # load dataset
-    dataset = load_dataset(args.dataset, args.config, delimiter="\t", split=args.split)
-
-    # for testing: only process the first two examples as a test
-    # dataset = dataset.select(range(10))
-
-    # load processor
-    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
-    sampling_rate = feature_extractor.sampling_rate
-
-    # resample audio
-    dataset = dataset.map(path_adjust)
-    dataset = dataset.cast_column("path", Audio(sampling_rate=sampling_rate))
-
-    # load eval pipeline
-    if args.device is None:
-        args.device = 0 if torch.cuda.is_available() else -1
-    asr = pipeline(
-        "automatic-speech-recognition", model=args.model_id, device=args.device
-    )
-
-    # map function to decode audio
-    def map_to_pred(batch):
-        prediction = asr(
-            batch["path"]["array"],
-            chunk_length_s=args.chunk_length_s,
-            stride_length_s=args.stride_length_s,
-        )
-
-        batch["prediction"] = prediction["text"]
-        batch["target"] = normalize_text(batch["sentence"])
-        return batch
-
-    # run inference on all examples
-    result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
-
-    # compute and log_results
-    # do not change function below
-    log_results(result, args)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_id",
-        type=str,
-        required=True,
-        help="Model identifier. Should be loadable with 🤗 Transformers",
-    )
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        required=True,
-        help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        required=True,
-        help="Config of the dataset. *E.g.* `'en'`  for Common Voice",
-    )
-    parser.add_argument(
-        "--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
-    )
-    parser.add_argument(
-        "--chunk_length_s",
-        type=float,
-        default=None,
-        help="Chunk length in seconds. Defaults to 5 seconds.",
-    )
-    parser.add_argument(
-        "--stride_length_s",
-        type=float,
-        default=None,
-        help="Stride of the audio chunks. Defaults to 1 second.",
-    )
-    parser.add_argument(
-        "--log_outputs",
-        action="store_true",
-        help="If defined, write outputs to log file for analysis.",
-    )
-    parser.add_argument(
-        "--device",
-        type=int,
-        default=None,
-        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
-    )
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py b/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py
deleted file mode 100644
index 8f3c8703ca37398b9d389ce5181bdfac2333cdf2..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py
+++ /dev/null
@@ -1,226 +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
-
-from fairseq import checkpoint_utils, tasks
-import sentencepiece as spm
-import torch
-
-try:
-    from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS
-    from simuleval.agents import TextAgent
-except ImportError:
-    print("Please install simuleval 'pip install simuleval'")
-
-
-BOS_PREFIX = "\u2581"
-
-
-class SimulTransTextAgentJA(TextAgent):
-    """
-    Simultaneous Translation
-    Text agent for Japanese
-    """
-    def __init__(self, args):
-
-        # Whether use gpu
-        self.gpu = getattr(args, "gpu", False)
-
-        # Max len
-        self.max_len = args.max_len
-
-        # Load Model
-        self.load_model_vocab(args)
-
-        # build word splitter
-        self.build_word_splitter(args)
-
-        self.eos = DEFAULT_EOS
-
-    def initialize_states(self, states):
-        states.incremental_states = dict()
-        states.incremental_states["online"] = dict()
-
-    def to_device(self, tensor):
-        if self.gpu:
-            return tensor.cuda()
-        else:
-            return tensor.cpu()
-
-    def load_model_vocab(self, args):
-
-        filename = args.model_path
-        if not os.path.exists(filename):
-            raise IOError("Model file not found: {}".format(filename))
-
-        state = checkpoint_utils.load_checkpoint_to_cpu(filename)
-
-        task_args = state["cfg"]["task"]
-        task_args.data = args.data_bin
-
-        task = tasks.setup_task(task_args)
-
-        # build model for ensemble
-        state["cfg"]["model"].load_pretrained_encoder_from = None
-        state["cfg"]["model"].load_pretrained_decoder_from = None
-
-        self.model = task.build_model(state["cfg"]["model"])
-        self.model.load_state_dict(state["model"], strict=True)
-        self.model.eval()
-        self.model.share_memory()
-
-        if self.gpu:
-            self.model.cuda()
-
-        # Set dictionary
-        self.dict = {}
-        self.dict["tgt"] = task.target_dictionary
-        self.dict["src"] = task.source_dictionary
-
-    @staticmethod
-    def add_args(parser):
-        # fmt: off
-        parser.add_argument('--model-path', type=str, required=True,
-                            help='path to your pretrained model.')
-        parser.add_argument("--data-bin", type=str, required=True,
-                            help="Path of data binary")
-        parser.add_argument("--max-len", type=int, default=100,
-                            help="Max length of translation")
-        parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece",
-                            help="Subword splitter type for target text.")
-        parser.add_argument("--tgt-splitter-path", type=str, default=None,
-                            help="Subword splitter model path for target text.")
-        parser.add_argument("--src-splitter-type", type=str, default="SentencePiece",
-                            help="Subword splitter type for source text.")
-        parser.add_argument("--src-splitter-path", type=str, default=None,
-                            help="Subword splitter model path for source text.")
-        # fmt: on
-        return parser
-
-    def build_word_splitter(self, args):
-        self.spm = {}
-        for lang in ['src', 'tgt']:
-            if getattr(args, f'{lang}_splitter_type', None):
-                path = getattr(args, f'{lang}_splitter_path', None)
-                if path:
-                    self.spm[lang] = spm.SentencePieceProcessor()
-                    self.spm[lang].Load(path)
-
-    def segment_to_units(self, segment, states):
-        # Split a full word (segment) into subwords (units)
-        return self.spm['src'].EncodeAsPieces(segment)
-
-    def update_model_encoder(self, states):
-        if len(states.units.source) == 0:
-            return
-
-        src_indices = [
-            self.dict['src'].index(x)
-            for x in states.units.source.value
-        ]
-
-        if states.finish_read():
-            # Append the eos index when the prediction is over
-            src_indices += [self.dict["tgt"].eos_index]
-
-        src_indices = self.to_device(
-            torch.LongTensor(src_indices).unsqueeze(0)
-        )
-        src_lengths = self.to_device(
-            torch.LongTensor([src_indices.size(1)])
-        )
-
-        states.encoder_states = self.model.encoder(src_indices, src_lengths)
-
-        torch.cuda.empty_cache()
-
-    def update_states_read(self, states):
-        # Happens after a read action.
-        self.update_model_encoder(states)
-
-    def units_to_segment(self, units, states):
-        # Merge sub words (units) to full word (segment).
-        # For Japanese, we can directly send
-        # the untokenized token to server except the BOS token
-        # with following option
-        # --sacrebleu-tokenizer MeCab
-        # --eval-latency-unit char
-        # --no-space
-        token = units.value.pop()
-
-        if (
-            token == self.dict["tgt"].eos_word
-            or len(states.segments.target) > self.max_len
-        ):
-            return DEFAULT_EOS
-
-        if BOS_PREFIX == token:
-            return None
-        if token[0] == BOS_PREFIX:
-            return token[1:]
-        else:
-            return token
-
-    def policy(self, states):
-
-        if not getattr(states, "encoder_states", None):
-            # No encoder states, read a token first
-            return READ_ACTION
-
-        # encode previous predicted target tokens
-        tgt_indices = self.to_device(
-            torch.LongTensor(
-                [self.model.decoder.dictionary.eos()]
-                + [
-                    self.dict['tgt'].index(x)
-                    for x in states.units.target.value
-                    if x is not None
-                ]
-            ).unsqueeze(0)
-        )
-
-        # Current steps
-        states.incremental_states["steps"] = {
-            "src": states.encoder_states["encoder_out"][0].size(0),
-            "tgt": 1 + len(states.units.target),
-        }
-
-        # Online only means the reading is not finished
-        states.incremental_states["online"]["only"] = (
-            torch.BoolTensor([not states.finish_read()])
-        )
-
-        x, outputs = self.model.decoder.forward(
-            prev_output_tokens=tgt_indices,
-            encoder_out=states.encoder_states,
-            incremental_state=states.incremental_states,
-        )
-
-        states.decoder_out = x
-
-        torch.cuda.empty_cache()
-
-        if outputs.action == 0:
-            return READ_ACTION
-        else:
-            return WRITE_ACTION
-
-    def predict(self, states):
-        # Predict target token from decoder states
-        decoder_states = states.decoder_out
-
-        lprobs = self.model.get_normalized_probs(
-            [decoder_states[:, -1:]], log_probs=True
-        )
-
-        index = lprobs.argmax(dim=-1)[0, 0].item()
-
-        if index != self.dict['tgt'].eos_index:
-            token = self.dict['tgt'].string([index])
-        else:
-            token = self.dict['tgt'].eos_word
-
-        return token
diff --git a/spaces/kokofixcomputers/chat-ui/src/lib/types/Message.ts b/spaces/kokofixcomputers/chat-ui/src/lib/types/Message.ts
deleted file mode 100644
index 4464e581b0742ad5733397252f3788e8dfcab7d0..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/lib/types/Message.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export interface Message {
-	from: "user" | "assistant";
-	id: ReturnType<typeof crypto.randomUUID>;
-	content: string;
-	score?: -1 | 0 | 1;
-}
diff --git a/spaces/kornia/kornia-image-filtering/app.py b/spaces/kornia/kornia-image-filtering/app.py
deleted file mode 100644
index 5f548da0f7d36638989d6b881f91ed4090258ce4..0000000000000000000000000000000000000000
--- a/spaces/kornia/kornia-image-filtering/app.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# created with great guidance from https://github.com/NimaBoscarino
-
-import gradio as gr
-
-import kornia as K
-from kornia.core import Tensor
-
-
-# Define Functions
-def box_blur_fn(file, box_blur):      
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-
-    x_out: Tensor = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
-    
-    return K.utils.tensor_to_image(x_out)
-
-
-def blur_pool2d_fn(file, blur_pool2d):      
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-
-    x_out: Tensor = K.filters.blur_pool2d(img, int(blur_pool2d))
-    
-    return K.utils.tensor_to_image(x_out)
-
-
-def gaussian_blur_fn(file, gaussian_blur2d):      
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-
-    x_out: Tensor = K.filters.gaussian_blur2d(img, 
-                                              (int(gaussian_blur2d), int(gaussian_blur2d)),
-                                              (float(gaussian_blur2d), float(gaussian_blur2d)))
-    
-    return K.utils.tensor_to_image(x_out)
-
-
-def max_blur_pool2d_fn(file, max_blur_pool2d):
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-
-    x_out: Tensor = K.filters.max_blur_pool2d(img, int(max_blur_pool2d))
-    
-    return K.utils.tensor_to_image(x_out)
-
-
-def median_blur_fn(file, median_blur):      
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-
-    x_out: Tensor = K.filters.median_blur(img, (int(median_blur), int(median_blur)))
-
-    return K.utils.tensor_to_image(x_out)
-
-
-# Define Examples
-examples = [
-    ["examples/monkey.jpg", 1, 1, 1, 1, 1],
-    ["examples/pikachu.jpg", 1, 1, 1, 1, 1]
-]
-
-
-# Define Demos
-box_blur_demo = gr.Interface(
-    box_blur_fn,
-    [
-        gr.inputs.Image(type="file"),
-        gr.inputs.Slider(minimum=1, maximum=20, step=1, default=10, label="Box Blur")
-    ],
-    "image",
-    examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
-    # live=True # --> 2022.09.08: Bug with click and dragging button. See: https://huggingface.co/spaces/kornia/kornia-image-filtering/discussions/2#63206d3b54a96c8e65c1fed2
-)
-
-
-blur_pool2d_demo = gr.Interface(
-    blur_pool2d_fn,
-    [
-        gr.inputs.Image(type="file"),
-        gr.inputs.Slider(minimum=1, maximum=40, step=1, default=20, label="Blur Pool")
-    ],
-    "image",
-    examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
-    # live=True # --> 2022.09.08: Bug with click and dragging button. See: https://huggingface.co/spaces/kornia/kornia-image-filtering/discussions/2#63206d3b54a96c8e65c1fed2
-)
-
-
-gaussian_blur_demo = gr.Interface(
-    gaussian_blur_fn,
-    [
-        gr.inputs.Image(type="file"),
-        gr.inputs.Slider(minimum=1, maximum=30, step=2, default=15, label="Gaussian Blur")
-    ],
-    "image",
-    examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
-    # live=True # --> 2022.09.08: Bug with click and dragging button. See: https://huggingface.co/spaces/kornia/kornia-image-filtering/discussions/2#63206d3b54a96c8e65c1fed2
-)
-
-
-max_blur_pool2d_demo = gr.Interface(
-    max_blur_pool2d_fn,
-    [
-        gr.inputs.Image(type="file"),
-        gr.inputs.Slider(minimum=1, maximum=40, step=1, default=20, label="Max Pool")
-    ],
-    "image",
-    examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
-    # live=True # --> 2022.09.08: Bug with click and dragging button. See: https://huggingface.co/spaces/kornia/kornia-image-filtering/discussions/2#63206d3b54a96c8e65c1fed2
-)
-
-median_blur_demo = gr.Interface(
-    median_blur_fn,
-    [
-        gr.inputs.Image(type="file"),
-        gr.inputs.Slider(minimum=1, maximum=30, step=2, default=15, label="Median Blur")
-    ],
-    "image",
-    examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
-    # live=True # --> 2022.09.08: Bug with click and dragging button. See: https://huggingface.co/spaces/kornia/kornia-image-filtering/discussions/2#63206d3b54a96c8e65c1fed2
-)
-
-
-# Create Interface
-demo = gr.TabbedInterface(
-    [
-      box_blur_demo,
-      blur_pool2d_demo,
-      gaussian_blur_demo,
-      max_blur_pool2d_demo,
-      median_blur_demo
-    ], 
-    [
-      "Box Blur", 
-      "Blur Pool",
-      "Gaussian Blur",
-      "Max Pool",
-      "Median Blur"
-    ]
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/__init__.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/__init__.py
deleted file mode 100644
index 82e1a9096a5bd8f3fb00e899d0239b078246cad4..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/__init__.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import logging
-
-from saicinpainting.training.modules.ffc import FFCResNetGenerator
-from saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
-    NLayerDiscriminator, MultidilatedNLayerDiscriminator
-
-def make_generator(config, kind, **kwargs):
-    logging.info(f'Make generator {kind}')
-
-    if kind == 'pix2pixhd_multidilated':
-        return MultiDilatedGlobalGenerator(**kwargs)
-    
-    if kind == 'pix2pixhd_global':
-        return GlobalGenerator(**kwargs)
-
-    if kind == 'ffc_resnet':
-        return FFCResNetGenerator(**kwargs)
-
-    raise ValueError(f'Unknown generator kind {kind}')
-
-
-def make_discriminator(kind, **kwargs):
-    logging.info(f'Make discriminator {kind}')
-
-    if kind == 'pix2pixhd_nlayer_multidilated':
-        return MultidilatedNLayerDiscriminator(**kwargs)
-
-    if kind == 'pix2pixhd_nlayer':
-        return NLayerDiscriminator(**kwargs)
-
-    raise ValueError(f'Unknown discriminator kind {kind}')
diff --git a/spaces/kukuhtw/AutoGPT/autogpt/logs.py b/spaces/kukuhtw/AutoGPT/autogpt/logs.py
deleted file mode 100644
index 35037404a98f7be9b7d577b625cc190ca27f4566..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/autogpt/logs.py
+++ /dev/null
@@ -1,332 +0,0 @@
-"""Logging module for Auto-GPT."""
-import json
-import logging
-import os
-import random
-import re
-import time
-import traceback
-from logging import LogRecord
-
-from colorama import Fore, Style
-
-from autogpt.config import Config, Singleton
-from autogpt.speech import say_text
-
-CFG = Config()
-
-
-class Logger(metaclass=Singleton):
-    """
-    Logger that handle titles in different colors.
-    Outputs logs in console, activity.log, and errors.log
-    For console handler: simulates typing
-    """
-
-    def __init__(self):
-        # create log directory if it doesn't exist
-        this_files_dir_path = os.path.dirname(__file__)
-        log_dir = os.path.join(this_files_dir_path, "../logs")
-        if not os.path.exists(log_dir):
-            os.makedirs(log_dir)
-
-        log_file = "activity.log"
-        error_file = "error.log"
-
-        console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
-
-        # Create a handler for console which simulate typing
-        self.typing_console_handler = TypingConsoleHandler()
-        self.typing_console_handler.setLevel(logging.INFO)
-        self.typing_console_handler.setFormatter(console_formatter)
-
-        # Create a handler for console without typing simulation
-        self.console_handler = ConsoleHandler()
-        self.console_handler.setLevel(logging.DEBUG)
-        self.console_handler.setFormatter(console_formatter)
-
-        # Info handler in activity.log
-        self.file_handler = logging.FileHandler(
-            os.path.join(log_dir, log_file), "a", "utf-8"
-        )
-        self.file_handler.setLevel(logging.DEBUG)
-        info_formatter = AutoGptFormatter(
-            "%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
-        )
-        self.file_handler.setFormatter(info_formatter)
-
-        # Error handler error.log
-        error_handler = logging.FileHandler(
-            os.path.join(log_dir, error_file), "a", "utf-8"
-        )
-        error_handler.setLevel(logging.ERROR)
-        error_formatter = AutoGptFormatter(
-            "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
-            " %(message_no_color)s"
-        )
-        error_handler.setFormatter(error_formatter)
-
-        self.typing_logger = logging.getLogger("TYPER")
-        self.typing_logger.addHandler(self.typing_console_handler)
-        self.typing_logger.addHandler(self.file_handler)
-        self.typing_logger.addHandler(error_handler)
-        self.typing_logger.setLevel(logging.DEBUG)
-
-        self.logger = logging.getLogger("LOGGER")
-        self.logger.addHandler(self.console_handler)
-        self.logger.addHandler(self.file_handler)
-        self.logger.addHandler(error_handler)
-        self.logger.setLevel(logging.DEBUG)
-
-    def typewriter_log(
-        self, title="", title_color="", content="", speak_text=False, level=logging.INFO
-    ):
-        if speak_text and CFG.speak_mode:
-            say_text(f"{title}. {content}")
-
-        if content:
-            if isinstance(content, list):
-                content = " ".join(content)
-        else:
-            content = ""
-
-        self.typing_logger.log(
-            level, content, extra={"title": title, "color": title_color}
-        )
-
-    def debug(
-        self,
-        message,
-        title="",
-        title_color="",
-    ):
-        self._log(title, title_color, message, logging.DEBUG)
-
-    def warn(
-        self,
-        message,
-        title="",
-        title_color="",
-    ):
-        self._log(title, title_color, message, logging.WARN)
-
-    def error(self, title, message=""):
-        self._log(title, Fore.RED, message, logging.ERROR)
-
-    def _log(self, title="", title_color="", message="", level=logging.INFO):
-        if message:
-            if isinstance(message, list):
-                message = " ".join(message)
-        self.logger.log(level, message, extra={"title": title, "color": title_color})
-
-    def set_level(self, level):
-        self.logger.setLevel(level)
-        self.typing_logger.setLevel(level)
-
-    def double_check(self, additionalText=None):
-        if not additionalText:
-            additionalText = (
-                "Please ensure you've setup and configured everything"
-                " correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
-                "double check. You can also create a github issue or join the discord"
-                " and ask there!"
-            )
-
-        self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
-
-
-"""
-Output stream to console using simulated typing
-"""
-
-
-class TypingConsoleHandler(logging.StreamHandler):
-    def emit(self, record):
-        min_typing_speed = 0.05
-        max_typing_speed = 0.01
-
-        msg = self.format(record)
-        try:
-            words = msg.split()
-            for i, word in enumerate(words):
-                print(word, end="", flush=True)
-                if i < len(words) - 1:
-                    print(" ", end="", flush=True)
-                typing_speed = random.uniform(min_typing_speed, max_typing_speed)
-                time.sleep(typing_speed)
-                # type faster after each word
-                min_typing_speed = min_typing_speed * 0.95
-                max_typing_speed = max_typing_speed * 0.95
-            print()
-        except Exception:
-            self.handleError(record)
-
-
-class ConsoleHandler(logging.StreamHandler):
-    def emit(self, record) -> None:
-        msg = self.format(record)
-        try:
-            print(msg)
-        except Exception:
-            self.handleError(record)
-
-
-class AutoGptFormatter(logging.Formatter):
-    """
-    Allows to handle custom placeholders 'title_color' and 'message_no_color'.
-    To use this formatter, make sure to pass 'color', 'title' as log extras.
-    """
-
-    def format(self, record: LogRecord) -> str:
-        if hasattr(record, "color"):
-            record.title_color = (
-                getattr(record, "color")
-                + getattr(record, "title")
-                + " "
-                + Style.RESET_ALL
-            )
-        else:
-            record.title_color = getattr(record, "title")
-        if hasattr(record, "msg"):
-            record.message_no_color = remove_color_codes(getattr(record, "msg"))
-        else:
-            record.message_no_color = ""
-        return super().format(record)
-
-
-def remove_color_codes(s: str) -> str:
-    ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
-    return ansi_escape.sub("", s)
-
-
-logger = Logger()
-
-
-def print_assistant_thoughts(ai_name, assistant_reply):
-    """Prints the assistant's thoughts to the console"""
-    from autogpt.json_utils.json_fix_llm import (
-        attempt_to_fix_json_by_finding_outermost_brackets,
-        fix_and_parse_json,
-    )
-
-    try:
-        try:
-            # Parse and print Assistant response
-            assistant_reply_json = fix_and_parse_json(assistant_reply)
-        except json.JSONDecodeError:
-            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
-            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-                assistant_reply
-            )
-            if isinstance(assistant_reply_json, str):
-                assistant_reply_json = fix_and_parse_json(assistant_reply_json)
-
-        # Check if assistant_reply_json is a string and attempt to parse
-        # it into a JSON object
-        if isinstance(assistant_reply_json, str):
-            try:
-                assistant_reply_json = json.loads(assistant_reply_json)
-            except json.JSONDecodeError:
-                logger.error("Error: Invalid JSON\n", assistant_reply)
-                assistant_reply_json = (
-                    attempt_to_fix_json_by_finding_outermost_brackets(
-                        assistant_reply_json
-                    )
-                )
-
-        assistant_thoughts_reasoning = None
-        assistant_thoughts_plan = None
-        assistant_thoughts_speak = None
-        assistant_thoughts_criticism = None
-        if not isinstance(assistant_reply_json, dict):
-            assistant_reply_json = {}
-        assistant_thoughts = assistant_reply_json.get("thoughts", {})
-        assistant_thoughts_text = assistant_thoughts.get("text")
-
-        if assistant_thoughts:
-            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
-            assistant_thoughts_plan = assistant_thoughts.get("plan")
-            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
-            assistant_thoughts_speak = assistant_thoughts.get("speak")
-
-        logger.typewriter_log(
-            f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
-        )
-        logger.typewriter_log(
-            "REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
-        )
-
-        if assistant_thoughts_plan:
-            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
-            # If it's a list, join it into a string
-            if isinstance(assistant_thoughts_plan, list):
-                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-            elif isinstance(assistant_thoughts_plan, dict):
-                assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-            # Split the input_string using the newline character and dashes
-            lines = assistant_thoughts_plan.split("\n")
-            for line in lines:
-                line = line.lstrip("- ")
-                logger.typewriter_log("- ", Fore.GREEN, line.strip())
-
-        logger.typewriter_log(
-            "CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
-        )
-        # Speak the assistant's thoughts
-        if CFG.speak_mode and assistant_thoughts_speak:
-            say_text(assistant_thoughts_speak)
-        else:
-            logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
-
-        return assistant_reply_json
-    except json.decoder.JSONDecodeError:
-        logger.error("Error: Invalid JSON\n", assistant_reply)
-        if CFG.speak_mode:
-            say_text(
-                "I have received an invalid JSON response from the OpenAI API."
-                " I cannot ignore this response."
-            )
-
-    # All other errors, return "Error: + error message"
-    except Exception:
-        call_stack = traceback.format_exc()
-        logger.error("Error: \n", call_stack)
-
-
-def print_assistant_thoughts(
-    ai_name: object, assistant_reply_json_valid: object
-) -> None:
-    assistant_thoughts_reasoning = None
-    assistant_thoughts_plan = None
-    assistant_thoughts_speak = None
-    assistant_thoughts_criticism = None
-
-    assistant_thoughts = assistant_reply_json_valid.get("thoughts", {})
-    assistant_thoughts_text = assistant_thoughts.get("text")
-    if assistant_thoughts:
-        assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
-        assistant_thoughts_plan = assistant_thoughts.get("plan")
-        assistant_thoughts_criticism = assistant_thoughts.get("criticism")
-        assistant_thoughts_speak = assistant_thoughts.get("speak")
-    logger.typewriter_log(
-        f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
-    )
-    logger.typewriter_log("REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}")
-    if assistant_thoughts_plan:
-        logger.typewriter_log("PLAN:", Fore.YELLOW, "")
-        # If it's a list, join it into a string
-        if isinstance(assistant_thoughts_plan, list):
-            assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-        elif isinstance(assistant_thoughts_plan, dict):
-            assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-        # Split the input_string using the newline character and dashes
-        lines = assistant_thoughts_plan.split("\n")
-        for line in lines:
-            line = line.lstrip("- ")
-            logger.typewriter_log("- ", Fore.GREEN, line.strip())
-    logger.typewriter_log("CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}")
-    # Speak the assistant's thoughts
-    if CFG.speak_mode and assistant_thoughts_speak:
-        say_text(assistant_thoughts_speak)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/v5/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/v5/__init__.py
deleted file mode 100644
index 38202d89c0d86a9be7a39d4b189781c43427983e..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/v5/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# ruff: noqa
-from .schema import *
-from .api import *
-
-from ...expr import datum, expr  # type: ignore[no-redef]
-
-from .display import VegaLite, renderers
-
-from .data import (
-    MaxRowsError,
-    pipe,
-    curry,
-    limit_rows,
-    sample,
-    to_json,
-    to_csv,
-    to_values,
-    default_data_transformer,
-    data_transformers,
-)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-54e1f6ba.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-54e1f6ba.css
deleted file mode 100644
index 586c94d187a710161f82861518f94ac5aa08a419..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-54e1f6ba.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_abnf.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_abnf.py
deleted file mode 100644
index 933587fba22290d7eb7df4c88e12f1e61702b8ce..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_abnf.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# We use native strings for all the re patterns, to take advantage of string
-# formatting, and then convert to bytestrings when compiling the final re
-# objects.
-
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#whitespace
-#  OWS            = *( SP / HTAB )
-#                 ; optional whitespace
-OWS = r"[ \t]*"
-
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#rule.token.separators
-#   token          = 1*tchar
-#
-#   tchar          = "!" / "#" / "$" / "%" / "&" / "'" / "*"
-#                  / "+" / "-" / "." / "^" / "_" / "`" / "|" / "~"
-#                  / DIGIT / ALPHA
-#                  ; any VCHAR, except delimiters
-token = r"[-!#$%&'*+.^_`|~0-9a-zA-Z]+"
-
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#header.fields
-#  field-name     = token
-field_name = token
-
-# The standard says:
-#
-#  field-value    = *( field-content / obs-fold )
-#  field-content  = field-vchar [ 1*( SP / HTAB ) field-vchar ]
-#  field-vchar    = VCHAR / obs-text
-#  obs-fold       = CRLF 1*( SP / HTAB )
-#                 ; obsolete line folding
-#                 ; see Section 3.2.4
-#
-# https://tools.ietf.org/html/rfc5234#appendix-B.1
-#
-#   VCHAR          =  %x21-7E
-#                  ; visible (printing) characters
-#
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#rule.quoted-string
-#   obs-text       = %x80-FF
-#
-# However, the standard definition of field-content is WRONG! It disallows
-# fields containing a single visible character surrounded by whitespace,
-# e.g. "foo a bar".
-#
-# See: https://www.rfc-editor.org/errata_search.php?rfc=7230&eid=4189
-#
-# So our definition of field_content attempts to fix it up...
-#
-# Also, we allow lots of control characters, because apparently people assume
-# that they're legal in practice (e.g., google analytics makes cookies with
-# \x01 in them!):
-#   https://github.com/python-hyper/h11/issues/57
-# We still don't allow NUL or whitespace, because those are often treated as
-# meta-characters and letting them through can lead to nasty issues like SSRF.
-vchar = r"[\x21-\x7e]"
-vchar_or_obs_text = r"[^\x00\s]"
-field_vchar = vchar_or_obs_text
-field_content = r"{field_vchar}+(?:[ \t]+{field_vchar}+)*".format(**globals())
-
-# We handle obs-fold at a different level, and our fixed-up field_content
-# already grows to swallow the whole value, so ? instead of *
-field_value = r"({field_content})?".format(**globals())
-
-#  header-field   = field-name ":" OWS field-value OWS
-header_field = (
-    r"(?P<field_name>{field_name})"
-    r":"
-    r"{OWS}"
-    r"(?P<field_value>{field_value})"
-    r"{OWS}".format(**globals())
-)
-
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#request.line
-#
-#   request-line   = method SP request-target SP HTTP-version CRLF
-#   method         = token
-#   HTTP-version   = HTTP-name "/" DIGIT "." DIGIT
-#   HTTP-name      = %x48.54.54.50 ; "HTTP", case-sensitive
-#
-# request-target is complicated (see RFC 7230 sec 5.3) -- could be path, full
-# URL, host+port (for connect), or even "*", but in any case we are guaranteed
-# that it contists of the visible printing characters.
-method = token
-request_target = r"{vchar}+".format(**globals())
-http_version = r"HTTP/(?P<http_version>[0-9]\.[0-9])"
-request_line = (
-    r"(?P<method>{method})"
-    r" "
-    r"(?P<target>{request_target})"
-    r" "
-    r"{http_version}".format(**globals())
-)
-
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#status.line
-#
-#   status-line = HTTP-version SP status-code SP reason-phrase CRLF
-#   status-code    = 3DIGIT
-#   reason-phrase  = *( HTAB / SP / VCHAR / obs-text )
-status_code = r"[0-9]{3}"
-reason_phrase = r"([ \t]|{vchar_or_obs_text})*".format(**globals())
-status_line = (
-    r"{http_version}"
-    r" "
-    r"(?P<status_code>{status_code})"
-    # However, there are apparently a few too many servers out there that just
-    # leave out the reason phrase:
-    #   https://github.com/scrapy/scrapy/issues/345#issuecomment-281756036
-    #   https://github.com/seanmonstar/httparse/issues/29
-    # so make it optional. ?: is a non-capturing group.
-    r"(?: (?P<reason>{reason_phrase}))?".format(**globals())
-)
-
-HEXDIG = r"[0-9A-Fa-f]"
-# Actually
-#
-#      chunk-size     = 1*HEXDIG
-#
-# but we impose an upper-limit to avoid ridiculosity. len(str(2**64)) == 20
-chunk_size = r"({HEXDIG}){{1,20}}".format(**globals())
-# Actually
-#
-#     chunk-ext      = *( ";" chunk-ext-name [ "=" chunk-ext-val ] )
-#
-# but we aren't parsing the things so we don't really care.
-chunk_ext = r";.*"
-chunk_header = (
-    r"(?P<chunk_size>{chunk_size})"
-    r"(?P<chunk_ext>{chunk_ext})?"
-    r"{OWS}\r\n".format(
-        **globals()
-    )  # Even though the specification does not allow for extra whitespaces,
-    # we are lenient with trailing whitespaces because some servers on the wild use it.
-)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/pyplot.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/pyplot.py
deleted file mode 100644
index 9a4cb27e30dc28c468c49e3a31dff377aa887279..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/pyplot.py
+++ /dev/null
@@ -1,3322 +0,0 @@
-# Note: The first part of this file can be modified in place, but the latter
-# part is autogenerated by the boilerplate.py script.
-
-"""
-`matplotlib.pyplot` is a state-based interface to matplotlib. It provides
-an implicit,  MATLAB-like, way of plotting.  It also opens figures on your
-screen, and acts as the figure GUI manager.
-
-pyplot is mainly intended for interactive plots and simple cases of
-programmatic plot generation::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    x = np.arange(0, 5, 0.1)
-    y = np.sin(x)
-    plt.plot(x, y)
-
-The explicit object-oriented API is recommended for complex plots, though
-pyplot is still usually used to create the figure and often the axes in the
-figure. See `.pyplot.figure`, `.pyplot.subplots`, and
-`.pyplot.subplot_mosaic` to create figures, and
-:doc:`Axes API </api/axes_api>` for the plotting methods on an Axes::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    x = np.arange(0, 5, 0.1)
-    y = np.sin(x)
-    fig, ax = plt.subplots()
-    ax.plot(x, y)
-
-
-See :ref:`api_interfaces` for an explanation of the tradeoffs between the
-implicit and explicit interfaces.
-"""
-
-from contextlib import ExitStack
-from enum import Enum
-import functools
-import importlib
-import inspect
-import logging
-from numbers import Number
-import re
-import sys
-import threading
-import time
-
-from cycler import cycler
-import matplotlib
-import matplotlib.colorbar
-import matplotlib.image
-from matplotlib import _api
-from matplotlib import rcsetup, style
-from matplotlib import _pylab_helpers, interactive
-from matplotlib import cbook
-from matplotlib import _docstring
-from matplotlib.backend_bases import (
-    FigureCanvasBase, FigureManagerBase, MouseButton)
-from matplotlib.figure import Figure, FigureBase, figaspect
-from matplotlib.gridspec import GridSpec, SubplotSpec
-from matplotlib import rcParams, rcParamsDefault, get_backend, rcParamsOrig
-from matplotlib.rcsetup import interactive_bk as _interactive_bk
-from matplotlib.artist import Artist
-from matplotlib.axes import Axes, Subplot
-from matplotlib.projections import PolarAxes
-from matplotlib import mlab  # for detrend_none, window_hanning
-from matplotlib.scale import get_scale_names
-
-from matplotlib import cm
-from matplotlib.cm import _colormaps as colormaps, register_cmap
-from matplotlib.colors import _color_sequences as color_sequences
-
-import numpy as np
-
-# We may not need the following imports here:
-from matplotlib.colors import Normalize
-from matplotlib.lines import Line2D
-from matplotlib.text import Text, Annotation
-from matplotlib.patches import Polygon, Rectangle, Circle, Arrow
-from matplotlib.widgets import Button, Slider, Widget
-
-from .ticker import (
-    TickHelper, Formatter, FixedFormatter, NullFormatter, FuncFormatter,
-    FormatStrFormatter, ScalarFormatter, LogFormatter, LogFormatterExponent,
-    LogFormatterMathtext, Locator, IndexLocator, FixedLocator, NullLocator,
-    LinearLocator, LogLocator, AutoLocator, MultipleLocator, MaxNLocator)
-
-_log = logging.getLogger(__name__)
-
-
-def _copy_docstring_and_deprecators(method, func=None):
-    if func is None:
-        return functools.partial(_copy_docstring_and_deprecators, method)
-    decorators = [_docstring.copy(method)]
-    # Check whether the definition of *method* includes @_api.rename_parameter
-    # or @_api.make_keyword_only decorators; if so, propagate them to the
-    # pyplot wrapper as well.
-    while getattr(method, "__wrapped__", None) is not None:
-        decorator = _api.deprecation.DECORATORS.get(method)
-        if decorator:
-            decorators.append(decorator)
-        method = method.__wrapped__
-    for decorator in decorators[::-1]:
-        func = decorator(func)
-    return func
-
-
-## Global ##
-
-
-# The state controlled by {,un}install_repl_displayhook().
-_ReplDisplayHook = Enum("_ReplDisplayHook", ["NONE", "PLAIN", "IPYTHON"])
-_REPL_DISPLAYHOOK = _ReplDisplayHook.NONE
-
-
-def _draw_all_if_interactive():
-    if matplotlib.is_interactive():
-        draw_all()
-
-
-def install_repl_displayhook():
-    """
-    Connect to the display hook of the current shell.
-
-    The display hook gets called when the read-evaluate-print-loop (REPL) of
-    the shell has finished the execution of a command. We use this callback
-    to be able to automatically update a figure in interactive mode.
-
-    This works both with IPython and with vanilla python shells.
-    """
-    global _REPL_DISPLAYHOOK
-
-    if _REPL_DISPLAYHOOK is _ReplDisplayHook.IPYTHON:
-        return
-
-    # See if we have IPython hooks around, if so use them.
-    # Use ``sys.modules.get(name)`` rather than ``name in sys.modules`` as
-    # entries can also have been explicitly set to None.
-    mod_ipython = sys.modules.get("IPython")
-    if not mod_ipython:
-        _REPL_DISPLAYHOOK = _ReplDisplayHook.PLAIN
-        return
-    ip = mod_ipython.get_ipython()
-    if not ip:
-        _REPL_DISPLAYHOOK = _ReplDisplayHook.PLAIN
-        return
-
-    ip.events.register("post_execute", _draw_all_if_interactive)
-    _REPL_DISPLAYHOOK = _ReplDisplayHook.IPYTHON
-
-    from IPython.core.pylabtools import backend2gui
-    # trigger IPython's eventloop integration, if available
-    ipython_gui_name = backend2gui.get(get_backend())
-    if ipython_gui_name:
-        ip.enable_gui(ipython_gui_name)
-
-
-def uninstall_repl_displayhook():
-    """Disconnect from the display hook of the current shell."""
-    global _REPL_DISPLAYHOOK
-    if _REPL_DISPLAYHOOK is _ReplDisplayHook.IPYTHON:
-        from IPython import get_ipython
-        ip = get_ipython()
-        ip.events.unregister("post_execute", _draw_all_if_interactive)
-    _REPL_DISPLAYHOOK = _ReplDisplayHook.NONE
-
-
-draw_all = _pylab_helpers.Gcf.draw_all
-
-
-@_copy_docstring_and_deprecators(matplotlib.set_loglevel)
-def set_loglevel(*args, **kwargs):  # Ensure this appears in the pyplot docs.
-    return matplotlib.set_loglevel(*args, **kwargs)
-
-
-@_copy_docstring_and_deprecators(Artist.findobj)
-def findobj(o=None, match=None, include_self=True):
-    if o is None:
-        o = gcf()
-    return o.findobj(match, include_self=include_self)
-
-
-def _get_required_interactive_framework(backend_mod):
-    if not hasattr(getattr(backend_mod, "FigureCanvas", None),
-                   "required_interactive_framework"):
-        _api.warn_deprecated(
-            "3.6", name="Support for FigureCanvases without a "
-            "required_interactive_framework attribute")
-        return None
-    # Inline this once the deprecation elapses.
-    return backend_mod.FigureCanvas.required_interactive_framework
-
-_backend_mod = None
-
-
-def _get_backend_mod():
-    """
-    Ensure that a backend is selected and return it.
-
-    This is currently private, but may be made public in the future.
-    """
-    if _backend_mod is None:
-        # Use rcParams._get("backend") to avoid going through the fallback
-        # logic (which will (re)import pyplot and then call switch_backend if
-        # we need to resolve the auto sentinel)
-        switch_backend(rcParams._get("backend"))
-    return _backend_mod
-
-
-def switch_backend(newbackend):
-    """
-    Set the pyplot backend.
-
-    Switching to an interactive backend is possible only if no event loop for
-    another interactive backend has started.  Switching to and from
-    non-interactive backends is always possible.
-
-    If the new backend is different than the current backend then all open
-    Figures will be closed via ``plt.close('all')``.
-
-    Parameters
-    ----------
-    newbackend : str
-        The case-insensitive name of the backend to use.
-
-    """
-    global _backend_mod
-    # make sure the init is pulled up so we can assign to it later
-    import matplotlib.backends
-
-    if newbackend is rcsetup._auto_backend_sentinel:
-        current_framework = cbook._get_running_interactive_framework()
-        mapping = {'qt': 'qtagg',
-                   'gtk3': 'gtk3agg',
-                   'gtk4': 'gtk4agg',
-                   'wx': 'wxagg',
-                   'tk': 'tkagg',
-                   'macosx': 'macosx',
-                   'headless': 'agg'}
-
-        best_guess = mapping.get(current_framework, None)
-        if best_guess is not None:
-            candidates = [best_guess]
-        else:
-            candidates = []
-        candidates += [
-            "macosx", "qtagg", "gtk4agg", "gtk3agg", "tkagg", "wxagg"]
-
-        # Don't try to fallback on the cairo-based backends as they each have
-        # an additional dependency (pycairo) over the agg-based backend, and
-        # are of worse quality.
-        for candidate in candidates:
-            try:
-                switch_backend(candidate)
-            except ImportError:
-                continue
-            else:
-                rcParamsOrig['backend'] = candidate
-                return
-        else:
-            # Switching to Agg should always succeed; if it doesn't, let the
-            # exception propagate out.
-            switch_backend("agg")
-            rcParamsOrig["backend"] = "agg"
-            return
-    # have to escape the switch on access logic
-    old_backend = dict.__getitem__(rcParams, 'backend')
-
-    backend_mod = importlib.import_module(
-        cbook._backend_module_name(newbackend))
-
-    required_framework = _get_required_interactive_framework(backend_mod)
-    if required_framework is not None:
-        current_framework = cbook._get_running_interactive_framework()
-        if (current_framework and required_framework
-                and current_framework != required_framework):
-            raise ImportError(
-                "Cannot load backend {!r} which requires the {!r} interactive "
-                "framework, as {!r} is currently running".format(
-                    newbackend, required_framework, current_framework))
-
-    # Load the new_figure_manager() and show() functions from the backend.
-
-    # Classically, backends can directly export these functions.  This should
-    # keep working for backcompat.
-    new_figure_manager = getattr(backend_mod, "new_figure_manager", None)
-    show = getattr(backend_mod, "show", None)
-
-    # In that classical approach, backends are implemented as modules, but
-    # "inherit" default method implementations from backend_bases._Backend.
-    # This is achieved by creating a "class" that inherits from
-    # backend_bases._Backend and whose body is filled with the module globals.
-    class backend_mod(matplotlib.backend_bases._Backend):
-        locals().update(vars(backend_mod))
-
-    # However, the newer approach for defining new_figure_manager and
-    # show is to derive them from canvas methods.  In that case, also
-    # update backend_mod accordingly; also, per-backend customization of
-    # draw_if_interactive is disabled.
-    if new_figure_manager is None:
-        # Only try to get the canvas class if have opted into the new scheme.
-        canvas_class = backend_mod.FigureCanvas
-
-        def new_figure_manager_given_figure(num, figure):
-            return canvas_class.new_manager(figure, num)
-
-        def new_figure_manager(num, *args, FigureClass=Figure, **kwargs):
-            fig = FigureClass(*args, **kwargs)
-            return new_figure_manager_given_figure(num, fig)
-
-        def draw_if_interactive():
-            if matplotlib.is_interactive():
-                manager = _pylab_helpers.Gcf.get_active()
-                if manager:
-                    manager.canvas.draw_idle()
-
-        backend_mod.new_figure_manager_given_figure = \
-            new_figure_manager_given_figure
-        backend_mod.new_figure_manager = new_figure_manager
-        backend_mod.draw_if_interactive = draw_if_interactive
-
-    # If the manager explicitly overrides pyplot_show, use it even if a global
-    # show is already present, as the latter may be here for backcompat.
-    manager_class = getattr(getattr(backend_mod, "FigureCanvas", None),
-                            "manager_class", None)
-    # We can't compare directly manager_class.pyplot_show and FMB.pyplot_show because
-    # pyplot_show is a classmethod so the above constructs are bound classmethods, and
-    # thus always different (being bound to different classes).  We also have to use
-    # getattr_static instead of vars as manager_class could have no __dict__.
-    manager_pyplot_show = inspect.getattr_static(manager_class, "pyplot_show", None)
-    base_pyplot_show = inspect.getattr_static(FigureManagerBase, "pyplot_show", None)
-    if (show is None
-            or (manager_pyplot_show is not None
-                and manager_pyplot_show != base_pyplot_show)):
-        backend_mod.show = manager_class.pyplot_show
-
-    _log.debug("Loaded backend %s version %s.",
-               newbackend, backend_mod.backend_version)
-
-    rcParams['backend'] = rcParamsDefault['backend'] = newbackend
-    _backend_mod = backend_mod
-    for func_name in ["new_figure_manager", "draw_if_interactive", "show"]:
-        globals()[func_name].__signature__ = inspect.signature(
-            getattr(backend_mod, func_name))
-
-    # Need to keep a global reference to the backend for compatibility reasons.
-    # See https://github.com/matplotlib/matplotlib/issues/6092
-    matplotlib.backends.backend = newbackend
-    if not cbook._str_equal(old_backend, newbackend):
-        close("all")
-
-    # make sure the repl display hook is installed in case we become
-    # interactive
-    install_repl_displayhook()
-
-
-def _warn_if_gui_out_of_main_thread():
-    warn = False
-    if _get_required_interactive_framework(_get_backend_mod()):
-        if hasattr(threading, 'get_native_id'):
-            # This compares native thread ids because even if Python-level
-            # Thread objects match, the underlying OS thread (which is what
-            # really matters) may be different on Python implementations with
-            # green threads.
-            if threading.get_native_id() != threading.main_thread().native_id:
-                warn = True
-        else:
-            # Fall back to Python-level Thread if native IDs are unavailable,
-            # mainly for PyPy.
-            if threading.current_thread() is not threading.main_thread():
-                warn = True
-    if warn:
-        _api.warn_external(
-            "Starting a Matplotlib GUI outside of the main thread will likely "
-            "fail.")
-
-
-# This function's signature is rewritten upon backend-load by switch_backend.
-def new_figure_manager(*args, **kwargs):
-    """Create a new figure manager instance."""
-    _warn_if_gui_out_of_main_thread()
-    return _get_backend_mod().new_figure_manager(*args, **kwargs)
-
-
-# This function's signature is rewritten upon backend-load by switch_backend.
-def draw_if_interactive(*args, **kwargs):
-    """
-    Redraw the current figure if in interactive mode.
-
-    .. warning::
-
-        End users will typically not have to call this function because the
-        the interactive mode takes care of this.
-    """
-    return _get_backend_mod().draw_if_interactive(*args, **kwargs)
-
-
-# This function's signature is rewritten upon backend-load by switch_backend.
-def show(*args, **kwargs):
-    """
-    Display all open figures.
-
-    Parameters
-    ----------
-    block : bool, optional
-        Whether to wait for all figures to be closed before returning.
-
-        If `True` block and run the GUI main loop until all figure windows
-        are closed.
-
-        If `False` ensure that all figure windows are displayed and return
-        immediately.  In this case, you are responsible for ensuring
-        that the event loop is running to have responsive figures.
-
-        Defaults to True in non-interactive mode and to False in interactive
-        mode (see `.pyplot.isinteractive`).
-
-    See Also
-    --------
-    ion : Enable interactive mode, which shows / updates the figure after
-          every plotting command, so that calling ``show()`` is not necessary.
-    ioff : Disable interactive mode.
-    savefig : Save the figure to an image file instead of showing it on screen.
-
-    Notes
-    -----
-    **Saving figures to file and showing a window at the same time**
-
-    If you want an image file as well as a user interface window, use
-    `.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)
-    ``show()`` the figure is closed and thus unregistered from pyplot. Calling
-    `.pyplot.savefig` afterwards would save a new and thus empty figure. This
-    limitation of command order does not apply if the show is non-blocking or
-    if you keep a reference to the figure and use `.Figure.savefig`.
-
-    **Auto-show in jupyter notebooks**
-
-    The jupyter backends (activated via ``%matplotlib inline``,
-    ``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at
-    the end of every cell by default. Thus, you usually don't have to call it
-    explicitly there.
-    """
-    _warn_if_gui_out_of_main_thread()
-    return _get_backend_mod().show(*args, **kwargs)
-
-
-def isinteractive():
-    """
-    Return whether plots are updated after every plotting command.
-
-    The interactive mode is mainly useful if you build plots from the command
-    line and want to see the effect of each command while you are building the
-    figure.
-
-    In interactive mode:
-
-    - newly created figures will be shown immediately;
-    - figures will automatically redraw on change;
-    - `.pyplot.show` will not block by default.
-
-    In non-interactive mode:
-
-    - newly created figures and changes to figures will not be reflected until
-      explicitly asked to be;
-    - `.pyplot.show` will block by default.
-
-    See Also
-    --------
-    ion : Enable interactive mode.
-    ioff : Disable interactive mode.
-    show : Show all figures (and maybe block).
-    pause : Show all figures, and block for a time.
-    """
-    return matplotlib.is_interactive()
-
-
-def ioff():
-    """
-    Disable interactive mode.
-
-    See `.pyplot.isinteractive` for more details.
-
-    See Also
-    --------
-    ion : Enable interactive mode.
-    isinteractive : Whether interactive mode is enabled.
-    show : Show all figures (and maybe block).
-    pause : Show all figures, and block for a time.
-
-    Notes
-    -----
-    For a temporary change, this can be used as a context manager::
-
-        # if interactive mode is on
-        # then figures will be shown on creation
-        plt.ion()
-        # This figure will be shown immediately
-        fig = plt.figure()
-
-        with plt.ioff():
-            # interactive mode will be off
-            # figures will not automatically be shown
-            fig2 = plt.figure()
-            # ...
-
-    To enable optional usage as a context manager, this function returns a
-    `~contextlib.ExitStack` object, which is not intended to be stored or
-    accessed by the user.
-    """
-    stack = ExitStack()
-    stack.callback(ion if isinteractive() else ioff)
-    matplotlib.interactive(False)
-    uninstall_repl_displayhook()
-    return stack
-
-
-def ion():
-    """
-    Enable interactive mode.
-
-    See `.pyplot.isinteractive` for more details.
-
-    See Also
-    --------
-    ioff : Disable interactive mode.
-    isinteractive : Whether interactive mode is enabled.
-    show : Show all figures (and maybe block).
-    pause : Show all figures, and block for a time.
-
-    Notes
-    -----
-    For a temporary change, this can be used as a context manager::
-
-        # if interactive mode is off
-        # then figures will not be shown on creation
-        plt.ioff()
-        # This figure will not be shown immediately
-        fig = plt.figure()
-
-        with plt.ion():
-            # interactive mode will be on
-            # figures will automatically be shown
-            fig2 = plt.figure()
-            # ...
-
-    To enable optional usage as a context manager, this function returns a
-    `~contextlib.ExitStack` object, which is not intended to be stored or
-    accessed by the user.
-    """
-    stack = ExitStack()
-    stack.callback(ion if isinteractive() else ioff)
-    matplotlib.interactive(True)
-    install_repl_displayhook()
-    return stack
-
-
-def pause(interval):
-    """
-    Run the GUI event loop for *interval* seconds.
-
-    If there is an active figure, it will be updated and displayed before the
-    pause, and the GUI event loop (if any) will run during the pause.
-
-    This can be used for crude animation.  For more complex animation use
-    :mod:`matplotlib.animation`.
-
-    If there is no active figure, sleep for *interval* seconds instead.
-
-    See Also
-    --------
-    matplotlib.animation : Proper animations
-    show : Show all figures and optional block until all figures are closed.
-    """
-    manager = _pylab_helpers.Gcf.get_active()
-    if manager is not None:
-        canvas = manager.canvas
-        if canvas.figure.stale:
-            canvas.draw_idle()
-        show(block=False)
-        canvas.start_event_loop(interval)
-    else:
-        time.sleep(interval)
-
-
-@_copy_docstring_and_deprecators(matplotlib.rc)
-def rc(group, **kwargs):
-    matplotlib.rc(group, **kwargs)
-
-
-@_copy_docstring_and_deprecators(matplotlib.rc_context)
-def rc_context(rc=None, fname=None):
-    return matplotlib.rc_context(rc, fname)
-
-
-@_copy_docstring_and_deprecators(matplotlib.rcdefaults)
-def rcdefaults():
-    matplotlib.rcdefaults()
-    if matplotlib.is_interactive():
-        draw_all()
-
-
-# getp/get/setp are explicitly reexported so that they show up in pyplot docs.
-
-
-@_copy_docstring_and_deprecators(matplotlib.artist.getp)
-def getp(obj, *args, **kwargs):
-    return matplotlib.artist.getp(obj, *args, **kwargs)
-
-
-@_copy_docstring_and_deprecators(matplotlib.artist.get)
-def get(obj, *args, **kwargs):
-    return matplotlib.artist.get(obj, *args, **kwargs)
-
-
-@_copy_docstring_and_deprecators(matplotlib.artist.setp)
-def setp(obj, *args, **kwargs):
-    return matplotlib.artist.setp(obj, *args, **kwargs)
-
-
-def xkcd(scale=1, length=100, randomness=2):
-    """
-    Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode.  This will
-    only have effect on things drawn after this function is called.
-
-    For best results, the "Humor Sans" font should be installed: it is
-    not included with Matplotlib.
-
-    Parameters
-    ----------
-    scale : float, optional
-        The amplitude of the wiggle perpendicular to the source line.
-    length : float, optional
-        The length of the wiggle along the line.
-    randomness : float, optional
-        The scale factor by which the length is shrunken or expanded.
-
-    Notes
-    -----
-    This function works by a number of rcParams, so it will probably
-    override others you have set before.
-
-    If you want the effects of this function to be temporary, it can
-    be used as a context manager, for example::
-
-        with plt.xkcd():
-            # This figure will be in XKCD-style
-            fig1 = plt.figure()
-            # ...
-
-        # This figure will be in regular style
-        fig2 = plt.figure()
-    """
-    # This cannot be implemented in terms of contextmanager() or rc_context()
-    # because this needs to work as a non-contextmanager too.
-
-    if rcParams['text.usetex']:
-        raise RuntimeError(
-            "xkcd mode is not compatible with text.usetex = True")
-
-    stack = ExitStack()
-    stack.callback(dict.update, rcParams, rcParams.copy())
-
-    from matplotlib import patheffects
-    rcParams.update({
-        'font.family': ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue',
-                        'Comic Sans MS'],
-        'font.size': 14.0,
-        'path.sketch': (scale, length, randomness),
-        'path.effects': [
-            patheffects.withStroke(linewidth=4, foreground="w")],
-        'axes.linewidth': 1.5,
-        'lines.linewidth': 2.0,
-        'figure.facecolor': 'white',
-        'grid.linewidth': 0.0,
-        'axes.grid': False,
-        'axes.unicode_minus': False,
-        'axes.edgecolor': 'black',
-        'xtick.major.size': 8,
-        'xtick.major.width': 3,
-        'ytick.major.size': 8,
-        'ytick.major.width': 3,
-    })
-
-    return stack
-
-
-## Figures ##
-
-@_api.make_keyword_only("3.6", "facecolor")
-def figure(num=None,  # autoincrement if None, else integer from 1-N
-           figsize=None,  # defaults to rc figure.figsize
-           dpi=None,  # defaults to rc figure.dpi
-           facecolor=None,  # defaults to rc figure.facecolor
-           edgecolor=None,  # defaults to rc figure.edgecolor
-           frameon=True,
-           FigureClass=Figure,
-           clear=False,
-           **kwargs
-           ):
-    """
-    Create a new figure, or activate an existing figure.
-
-    Parameters
-    ----------
-    num : int or str or `.Figure` or `.SubFigure`, optional
-        A unique identifier for the figure.
-
-        If a figure with that identifier already exists, this figure is made
-        active and returned. An integer refers to the ``Figure.number``
-        attribute, a string refers to the figure label.
-
-        If there is no figure with the identifier or *num* is not given, a new
-        figure is created, made active and returned.  If *num* is an int, it
-        will be used for the ``Figure.number`` attribute, otherwise, an
-        auto-generated integer value is used (starting at 1 and incremented
-        for each new figure). If *num* is a string, the figure label and the
-        window title is set to this value.  If num is a ``SubFigure``, its
-        parent ``Figure`` is activated.
-
-    figsize : (float, float), default: :rc:`figure.figsize`
-        Width, height in inches.
-
-    dpi : float, default: :rc:`figure.dpi`
-        The resolution of the figure in dots-per-inch.
-
-    facecolor : color, default: :rc:`figure.facecolor`
-        The background color.
-
-    edgecolor : color, default: :rc:`figure.edgecolor`
-        The border color.
-
-    frameon : bool, default: True
-        If False, suppress drawing the figure frame.
-
-    FigureClass : subclass of `~matplotlib.figure.Figure`
-        If set, an instance of this subclass will be created, rather than a
-        plain `.Figure`.
-
-    clear : bool, default: False
-        If True and the figure already exists, then it is cleared.
-
-    layout : {'constrained', 'compressed', 'tight', 'none', `.LayoutEngine`, None}, \
-default: None
-        The layout mechanism for positioning of plot elements to avoid
-        overlapping Axes decorations (labels, ticks, etc). Note that layout
-        managers can measurably slow down figure display.
-
-        - 'constrained': The constrained layout solver adjusts axes sizes
-          to avoid overlapping axes decorations.  Can handle complex plot
-          layouts and colorbars, and is thus recommended.
-
-          See :doc:`/tutorials/intermediate/constrainedlayout_guide`
-          for examples.
-
-        - 'compressed': uses the same algorithm as 'constrained', but
-          removes extra space between fixed-aspect-ratio Axes.  Best for
-          simple grids of axes.
-
-        - 'tight': Use the tight layout mechanism. This is a relatively
-          simple algorithm that adjusts the subplot parameters so that
-          decorations do not overlap. See `.Figure.set_tight_layout` for
-          further details.
-
-        - 'none': Do not use a layout engine.
-
-        - A `.LayoutEngine` instance. Builtin layout classes are
-          `.ConstrainedLayoutEngine` and `.TightLayoutEngine`, more easily
-          accessible by 'constrained' and 'tight'.  Passing an instance
-          allows third parties to provide their own layout engine.
-
-        If not given, fall back to using the parameters *tight_layout* and
-        *constrained_layout*, including their config defaults
-        :rc:`figure.autolayout` and :rc:`figure.constrained_layout.use`.
-
-    **kwargs
-        Additional keyword arguments are passed to the `.Figure` constructor.
-
-    Returns
-    -------
-    `~matplotlib.figure.Figure`
-
-    Notes
-    -----
-    A newly created figure is passed to the `~.FigureCanvasBase.new_manager`
-    method or the `new_figure_manager` function provided by the current
-    backend, which install a canvas and a manager on the figure.
-
-    Once this is done, :rc:`figure.hooks` are called, one at a time, on the
-    figure; these hooks allow arbitrary customization of the figure (e.g.,
-    attaching callbacks) or of associated elements (e.g., modifying the
-    toolbar).  See :doc:`/gallery/user_interfaces/mplcvd` for an example of
-    toolbar customization.
-
-    If you are creating many figures, make sure you explicitly call
-    `.pyplot.close` on the figures you are not using, because this will
-    enable pyplot to properly clean up the memory.
-
-    `~matplotlib.rcParams` defines the default values, which can be modified
-    in the matplotlibrc file.
-    """
-    if isinstance(num, FigureBase):
-        if num.canvas.manager is None:
-            raise ValueError("The passed figure is not managed by pyplot")
-        _pylab_helpers.Gcf.set_active(num.canvas.manager)
-        return num.figure
-
-    allnums = get_fignums()
-    next_num = max(allnums) + 1 if allnums else 1
-    fig_label = ''
-    if num is None:
-        num = next_num
-    elif isinstance(num, str):
-        fig_label = num
-        all_labels = get_figlabels()
-        if fig_label not in all_labels:
-            if fig_label == 'all':
-                _api.warn_external("close('all') closes all existing figures.")
-            num = next_num
-        else:
-            inum = all_labels.index(fig_label)
-            num = allnums[inum]
-    else:
-        num = int(num)  # crude validation of num argument
-
-    manager = _pylab_helpers.Gcf.get_fig_manager(num)
-    if manager is None:
-        max_open_warning = rcParams['figure.max_open_warning']
-        if len(allnums) == max_open_warning >= 1:
-            _api.warn_external(
-                f"More than {max_open_warning} figures have been opened. "
-                f"Figures created through the pyplot interface "
-                f"(`matplotlib.pyplot.figure`) are retained until explicitly "
-                f"closed and may consume too much memory. (To control this "
-                f"warning, see the rcParam `figure.max_open_warning`). "
-                f"Consider using `matplotlib.pyplot.close()`.",
-                RuntimeWarning)
-
-        manager = new_figure_manager(
-            num, figsize=figsize, dpi=dpi,
-            facecolor=facecolor, edgecolor=edgecolor, frameon=frameon,
-            FigureClass=FigureClass, **kwargs)
-        fig = manager.canvas.figure
-        if fig_label:
-            fig.set_label(fig_label)
-
-        for hookspecs in rcParams["figure.hooks"]:
-            module_name, dotted_name = hookspecs.split(":")
-            obj = importlib.import_module(module_name)
-            for part in dotted_name.split("."):
-                obj = getattr(obj, part)
-            obj(fig)
-
-        _pylab_helpers.Gcf._set_new_active_manager(manager)
-
-        # make sure backends (inline) that we don't ship that expect this
-        # to be called in plotting commands to make the figure call show
-        # still work.  There is probably a better way to do this in the
-        # FigureManager base class.
-        draw_if_interactive()
-
-        if _REPL_DISPLAYHOOK is _ReplDisplayHook.PLAIN:
-            fig.stale_callback = _auto_draw_if_interactive
-
-    if clear:
-        manager.canvas.figure.clear()
-
-    return manager.canvas.figure
-
-
-def _auto_draw_if_interactive(fig, val):
-    """
-    An internal helper function for making sure that auto-redrawing
-    works as intended in the plain python repl.
-
-    Parameters
-    ----------
-    fig : Figure
-        A figure object which is assumed to be associated with a canvas
-    """
-    if (val and matplotlib.is_interactive()
-            and not fig.canvas.is_saving()
-            and not fig.canvas._is_idle_drawing):
-        # Some artists can mark themselves as stale in the middle of drawing
-        # (e.g. axes position & tick labels being computed at draw time), but
-        # this shouldn't trigger a redraw because the current redraw will
-        # already take them into account.
-        with fig.canvas._idle_draw_cntx():
-            fig.canvas.draw_idle()
-
-
-def gcf():
-    """
-    Get the current figure.
-
-    If there is currently no figure on the pyplot figure stack, a new one is
-    created using `~.pyplot.figure()`.  (To test whether there is currently a
-    figure on the pyplot figure stack, check whether `~.pyplot.get_fignums()`
-    is empty.)
-    """
-    manager = _pylab_helpers.Gcf.get_active()
-    if manager is not None:
-        return manager.canvas.figure
-    else:
-        return figure()
-
-
-def fignum_exists(num):
-    """Return whether the figure with the given id exists."""
-    return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()
-
-
-def get_fignums():
-    """Return a list of existing figure numbers."""
-    return sorted(_pylab_helpers.Gcf.figs)
-
-
-def get_figlabels():
-    """Return a list of existing figure labels."""
-    managers = _pylab_helpers.Gcf.get_all_fig_managers()
-    managers.sort(key=lambda m: m.num)
-    return [m.canvas.figure.get_label() for m in managers]
-
-
-def get_current_fig_manager():
-    """
-    Return the figure manager of the current figure.
-
-    The figure manager is a container for the actual backend-depended window
-    that displays the figure on screen.
-
-    If no current figure exists, a new one is created, and its figure
-    manager is returned.
-
-    Returns
-    -------
-    `.FigureManagerBase` or backend-dependent subclass thereof
-    """
-    return gcf().canvas.manager
-
-
-@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_connect)
-def connect(s, func):
-    return gcf().canvas.mpl_connect(s, func)
-
-
-@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_disconnect)
-def disconnect(cid):
-    return gcf().canvas.mpl_disconnect(cid)
-
-
-def close(fig=None):
-    """
-    Close a figure window.
-
-    Parameters
-    ----------
-    fig : None or int or str or `.Figure`
-        The figure to close. There are a number of ways to specify this:
-
-        - *None*: the current figure
-        - `.Figure`: the given `.Figure` instance
-        - ``int``: a figure number
-        - ``str``: a figure name
-        - 'all': all figures
-
-    """
-    if fig is None:
-        manager = _pylab_helpers.Gcf.get_active()
-        if manager is None:
-            return
-        else:
-            _pylab_helpers.Gcf.destroy(manager)
-    elif fig == 'all':
-        _pylab_helpers.Gcf.destroy_all()
-    elif isinstance(fig, int):
-        _pylab_helpers.Gcf.destroy(fig)
-    elif hasattr(fig, 'int'):
-        # if we are dealing with a type UUID, we
-        # can use its integer representation
-        _pylab_helpers.Gcf.destroy(fig.int)
-    elif isinstance(fig, str):
-        all_labels = get_figlabels()
-        if fig in all_labels:
-            num = get_fignums()[all_labels.index(fig)]
-            _pylab_helpers.Gcf.destroy(num)
-    elif isinstance(fig, Figure):
-        _pylab_helpers.Gcf.destroy_fig(fig)
-    else:
-        raise TypeError("close() argument must be a Figure, an int, a string, "
-                        "or None, not %s" % type(fig))
-
-
-def clf():
-    """Clear the current figure."""
-    gcf().clear()
-
-
-def draw():
-    """
-    Redraw the current figure.
-
-    This is used to update a figure that has been altered, but not
-    automatically re-drawn.  If interactive mode is on (via `.ion()`), this
-    should be only rarely needed, but there may be ways to modify the state of
-    a figure without marking it as "stale".  Please report these cases as bugs.
-
-    This is equivalent to calling ``fig.canvas.draw_idle()``, where ``fig`` is
-    the current figure.
-
-    See Also
-    --------
-    .FigureCanvasBase.draw_idle
-    .FigureCanvasBase.draw
-    """
-    gcf().canvas.draw_idle()
-
-
-@_copy_docstring_and_deprecators(Figure.savefig)
-def savefig(*args, **kwargs):
-    fig = gcf()
-    res = fig.savefig(*args, **kwargs)
-    fig.canvas.draw_idle()  # Need this if 'transparent=True', to reset colors.
-    return res
-
-
-## Putting things in figures ##
-
-
-def figlegend(*args, **kwargs):
-    return gcf().legend(*args, **kwargs)
-if Figure.legend.__doc__:
-    figlegend.__doc__ = Figure.legend.__doc__ \
-        .replace(" legend(", " figlegend(") \
-        .replace("fig.legend(", "plt.figlegend(") \
-        .replace("ax.plot(", "plt.plot(")
-
-
-## Axes ##
-
-@_docstring.dedent_interpd
-def axes(arg=None, **kwargs):
-    """
-    Add an Axes to the current figure and make it the current Axes.
-
-    Call signatures::
-
-        plt.axes()
-        plt.axes(rect, projection=None, polar=False, **kwargs)
-        plt.axes(ax)
-
-    Parameters
-    ----------
-    arg : None or 4-tuple
-        The exact behavior of this function depends on the type:
-
-        - *None*: A new full window Axes is added using
-          ``subplot(**kwargs)``.
-        - 4-tuple of floats *rect* = ``[left, bottom, width, height]``.
-          A new Axes is added with dimensions *rect* in normalized
-          (0, 1) units using `~.Figure.add_axes` on the current figure.
-
-    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
-'polar', 'rectilinear', str}, optional
-        The projection type of the `~.axes.Axes`. *str* is the name of
-        a custom projection, see `~matplotlib.projections`. The default
-        None results in a 'rectilinear' projection.
-
-    polar : bool, default: False
-        If True, equivalent to projection='polar'.
-
-    sharex, sharey : `~.axes.Axes`, optional
-        Share the x or y `~matplotlib.axis` with sharex and/or sharey.
-        The axis will have the same limits, ticks, and scale as the axis
-        of the shared Axes.
-
-    label : str
-        A label for the returned Axes.
-
-    Returns
-    -------
-    `~.axes.Axes`, or a subclass of `~.axes.Axes`
-        The returned axes class depends on the projection used. It is
-        `~.axes.Axes` if rectilinear projection is used and
-        `.projections.polar.PolarAxes` if polar projection is used.
-
-    Other Parameters
-    ----------------
-    **kwargs
-        This method also takes the keyword arguments for
-        the returned Axes class. The keyword arguments for the
-        rectilinear Axes class `~.axes.Axes` can be found in
-        the following table but there might also be other keyword
-        arguments if another projection is used, see the actual Axes
-        class.
-
-        %(Axes:kwdoc)s
-
-    See Also
-    --------
-    .Figure.add_axes
-    .pyplot.subplot
-    .Figure.add_subplot
-    .Figure.subplots
-    .pyplot.subplots
-
-    Examples
-    --------
-    ::
-
-        # Creating a new full window Axes
-        plt.axes()
-
-        # Creating a new Axes with specified dimensions and a grey background
-        plt.axes((left, bottom, width, height), facecolor='grey')
-    """
-    fig = gcf()
-    pos = kwargs.pop('position', None)
-    if arg is None:
-        if pos is None:
-            return fig.add_subplot(**kwargs)
-        else:
-            return fig.add_axes(pos, **kwargs)
-    else:
-        return fig.add_axes(arg, **kwargs)
-
-
-def delaxes(ax=None):
-    """
-    Remove an `~.axes.Axes` (defaulting to the current axes) from its figure.
-    """
-    if ax is None:
-        ax = gca()
-    ax.remove()
-
-
-def sca(ax):
-    """
-    Set the current Axes to *ax* and the current Figure to the parent of *ax*.
-    """
-    figure(ax.figure)
-    ax.figure.sca(ax)
-
-
-def cla():
-    """Clear the current axes."""
-    # Not generated via boilerplate.py to allow a different docstring.
-    return gca().cla()
-
-
-## More ways of creating axes ##
-
-@_docstring.dedent_interpd
-def subplot(*args, **kwargs):
-    """
-    Add an Axes to the current figure or retrieve an existing Axes.
-
-    This is a wrapper of `.Figure.add_subplot` which provides additional
-    behavior when working with the implicit API (see the notes section).
-
-    Call signatures::
-
-       subplot(nrows, ncols, index, **kwargs)
-       subplot(pos, **kwargs)
-       subplot(**kwargs)
-       subplot(ax)
-
-    Parameters
-    ----------
-    *args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1)
-        The position of the subplot described by one of
-
-        - Three integers (*nrows*, *ncols*, *index*). The subplot will take the
-          *index* position on a grid with *nrows* rows and *ncols* columns.
-          *index* starts at 1 in the upper left corner and increases to the
-          right. *index* can also be a two-tuple specifying the (*first*,
-          *last*) indices (1-based, and including *last*) of the subplot, e.g.,
-          ``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the
-          upper 2/3 of the figure.
-        - A 3-digit integer. The digits are interpreted as if given separately
-          as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the
-          same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used
-          if there are no more than 9 subplots.
-        - A `.SubplotSpec`.
-
-    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
-'polar', 'rectilinear', str}, optional
-        The projection type of the subplot (`~.axes.Axes`). *str* is the name
-        of a custom projection, see `~matplotlib.projections`. The default
-        None results in a 'rectilinear' projection.
-
-    polar : bool, default: False
-        If True, equivalent to projection='polar'.
-
-    sharex, sharey : `~.axes.Axes`, optional
-        Share the x or y `~matplotlib.axis` with sharex and/or sharey. The
-        axis will have the same limits, ticks, and scale as the axis of the
-        shared axes.
-
-    label : str
-        A label for the returned axes.
-
-    Returns
-    -------
-    `~.axes.Axes`
-
-        The Axes of the subplot. The returned Axes can actually be an instance
-        of a subclass, such as `.projections.polar.PolarAxes` for polar
-        projections.
-
-    Other Parameters
-    ----------------
-    **kwargs
-        This method also takes the keyword arguments for the returned axes
-        base class; except for the *figure* argument. The keyword arguments
-        for the rectilinear base class `~.axes.Axes` can be found in
-        the following table but there might also be other keyword
-        arguments if another projection is used.
-
-        %(Axes:kwdoc)s
-
-    Notes
-    -----
-    Creating a new Axes will delete any preexisting Axes that
-    overlaps with it beyond sharing a boundary::
-
-        import matplotlib.pyplot as plt
-        # plot a line, implicitly creating a subplot(111)
-        plt.plot([1, 2, 3])
-        # now create a subplot which represents the top plot of a grid
-        # with 2 rows and 1 column. Since this subplot will overlap the
-        # first, the plot (and its axes) previously created, will be removed
-        plt.subplot(211)
-
-    If you do not want this behavior, use the `.Figure.add_subplot` method
-    or the `.pyplot.axes` function instead.
-
-    If no *kwargs* are passed and there exists an Axes in the location
-    specified by *args* then that Axes will be returned rather than a new
-    Axes being created.
-
-    If *kwargs* are passed and there exists an Axes in the location
-    specified by *args*, the projection type is the same, and the
-    *kwargs* match with the existing Axes, then the existing Axes is
-    returned.  Otherwise a new Axes is created with the specified
-    parameters.  We save a reference to the *kwargs* which we use
-    for this comparison.  If any of the values in *kwargs* are
-    mutable we will not detect the case where they are mutated.
-    In these cases we suggest using `.Figure.add_subplot` and the
-    explicit Axes API rather than the implicit pyplot API.
-
-    See Also
-    --------
-    .Figure.add_subplot
-    .pyplot.subplots
-    .pyplot.axes
-    .Figure.subplots
-
-    Examples
-    --------
-    ::
-
-        plt.subplot(221)
-
-        # equivalent but more general
-        ax1 = plt.subplot(2, 2, 1)
-
-        # add a subplot with no frame
-        ax2 = plt.subplot(222, frameon=False)
-
-        # add a polar subplot
-        plt.subplot(223, projection='polar')
-
-        # add a red subplot that shares the x-axis with ax1
-        plt.subplot(224, sharex=ax1, facecolor='red')
-
-        # delete ax2 from the figure
-        plt.delaxes(ax2)
-
-        # add ax2 to the figure again
-        plt.subplot(ax2)
-
-        # make the first axes "current" again
-        plt.subplot(221)
-
-    """
-    # Here we will only normalize `polar=True` vs `projection='polar'` and let
-    # downstream code deal with the rest.
-    unset = object()
-    projection = kwargs.get('projection', unset)
-    polar = kwargs.pop('polar', unset)
-    if polar is not unset and polar:
-        # if we got mixed messages from the user, raise
-        if projection is not unset and projection != 'polar':
-            raise ValueError(
-                f"polar={polar}, yet projection={projection!r}. "
-                "Only one of these arguments should be supplied."
-            )
-        kwargs['projection'] = projection = 'polar'
-
-    # if subplot called without arguments, create subplot(1, 1, 1)
-    if len(args) == 0:
-        args = (1, 1, 1)
-
-    # This check was added because it is very easy to type subplot(1, 2, False)
-    # when subplots(1, 2, False) was intended (sharex=False, that is). In most
-    # cases, no error will ever occur, but mysterious behavior can result
-    # because what was intended to be the sharex argument is instead treated as
-    # a subplot index for subplot()
-    if len(args) >= 3 and isinstance(args[2], bool):
-        _api.warn_external("The subplot index argument to subplot() appears "
-                           "to be a boolean. Did you intend to use "
-                           "subplots()?")
-    # Check for nrows and ncols, which are not valid subplot args:
-    if 'nrows' in kwargs or 'ncols' in kwargs:
-        raise TypeError("subplot() got an unexpected keyword argument 'ncols' "
-                        "and/or 'nrows'.  Did you intend to call subplots()?")
-
-    fig = gcf()
-
-    # First, search for an existing subplot with a matching spec.
-    key = SubplotSpec._from_subplot_args(fig, args)
-
-    for ax in fig.axes:
-        # if we found an Axes at the position sort out if we can re-use it
-        if ax.get_subplotspec() == key:
-            # if the user passed no kwargs, re-use
-            if kwargs == {}:
-                break
-            # if the axes class and kwargs are identical, reuse
-            elif ax._projection_init == fig._process_projection_requirements(
-                *args, **kwargs
-            ):
-                break
-    else:
-        # we have exhausted the known Axes and none match, make a new one!
-        ax = fig.add_subplot(*args, **kwargs)
-
-    fig.sca(ax)
-
-    axes_to_delete = [other for other in fig.axes
-                      if other != ax and ax.bbox.fully_overlaps(other.bbox)]
-    if axes_to_delete:
-        _api.warn_deprecated(
-            "3.6", message="Auto-removal of overlapping axes is deprecated "
-            "since %(since)s and will be removed %(removal)s; explicitly call "
-            "ax.remove() as needed.")
-    for ax_to_del in axes_to_delete:
-        delaxes(ax_to_del)
-
-    return ax
-
-
-def subplots(nrows=1, ncols=1, *, sharex=False, sharey=False, squeeze=True,
-             width_ratios=None, height_ratios=None,
-             subplot_kw=None, gridspec_kw=None, **fig_kw):
-    """
-    Create a figure and a set of subplots.
-
-    This utility wrapper makes it convenient to create common layouts of
-    subplots, including the enclosing figure object, in a single call.
-
-    Parameters
-    ----------
-    nrows, ncols : int, default: 1
-        Number of rows/columns of the subplot grid.
-
-    sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
-        Controls sharing of properties among x (*sharex*) or y (*sharey*)
-        axes:
-
-        - True or 'all': x- or y-axis will be shared among all subplots.
-        - False or 'none': each subplot x- or y-axis will be independent.
-        - 'row': each subplot row will share an x- or y-axis.
-        - 'col': each subplot column will share an x- or y-axis.
-
-        When subplots have a shared x-axis along a column, only the x tick
-        labels of the bottom subplot are created. Similarly, when subplots
-        have a shared y-axis along a row, only the y tick labels of the first
-        column subplot are created. To later turn other subplots' ticklabels
-        on, use `~matplotlib.axes.Axes.tick_params`.
-
-        When subplots have a shared axis that has units, calling
-        `~matplotlib.axis.Axis.set_units` will update each axis with the
-        new units.
-
-    squeeze : bool, default: True
-        - If True, extra dimensions are squeezed out from the returned
-          array of `~matplotlib.axes.Axes`:
-
-          - if only one subplot is constructed (nrows=ncols=1), the
-            resulting single Axes object is returned as a scalar.
-          - for Nx1 or 1xM subplots, the returned object is a 1D numpy
-            object array of Axes objects.
-          - for NxM, subplots with N>1 and M>1 are returned as a 2D array.
-
-        - If False, no squeezing at all is done: the returned Axes object is
-          always a 2D array containing Axes instances, even if it ends up
-          being 1x1.
-
-    width_ratios : array-like of length *ncols*, optional
-        Defines the relative widths of the columns. Each column gets a
-        relative width of ``width_ratios[i] / sum(width_ratios)``.
-        If not given, all columns will have the same width.  Equivalent
-        to ``gridspec_kw={'width_ratios': [...]}``.
-
-    height_ratios : array-like of length *nrows*, optional
-        Defines the relative heights of the rows. Each row gets a
-        relative height of ``height_ratios[i] / sum(height_ratios)``.
-        If not given, all rows will have the same height. Convenience
-        for ``gridspec_kw={'height_ratios': [...]}``.
-
-    subplot_kw : dict, optional
-        Dict with keywords passed to the
-        `~matplotlib.figure.Figure.add_subplot` call used to create each
-        subplot.
-
-    gridspec_kw : dict, optional
-        Dict with keywords passed to the `~matplotlib.gridspec.GridSpec`
-        constructor used to create the grid the subplots are placed on.
-
-    **fig_kw
-        All additional keyword arguments are passed to the
-        `.pyplot.figure` call.
-
-    Returns
-    -------
-    fig : `.Figure`
-
-    ax : `~.axes.Axes` or array of Axes
-        *ax* can be either a single `~.axes.Axes` object, or an array of Axes
-        objects if more than one subplot was created.  The dimensions of the
-        resulting array can be controlled with the squeeze keyword, see above.
-
-        Typical idioms for handling the return value are::
-
-            # using the variable ax for single a Axes
-            fig, ax = plt.subplots()
-
-            # using the variable axs for multiple Axes
-            fig, axs = plt.subplots(2, 2)
-
-            # using tuple unpacking for multiple Axes
-            fig, (ax1, ax2) = plt.subplots(1, 2)
-            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
-
-        The names ``ax`` and pluralized ``axs`` are preferred over ``axes``
-        because for the latter it's not clear if it refers to a single
-        `~.axes.Axes` instance or a collection of these.
-
-    See Also
-    --------
-    .pyplot.figure
-    .pyplot.subplot
-    .pyplot.axes
-    .Figure.subplots
-    .Figure.add_subplot
-
-    Examples
-    --------
-    ::
-
-        # First create some toy data:
-        x = np.linspace(0, 2*np.pi, 400)
-        y = np.sin(x**2)
-
-        # Create just a figure and only one subplot
-        fig, ax = plt.subplots()
-        ax.plot(x, y)
-        ax.set_title('Simple plot')
-
-        # Create two subplots and unpack the output array immediately
-        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
-        ax1.plot(x, y)
-        ax1.set_title('Sharing Y axis')
-        ax2.scatter(x, y)
-
-        # Create four polar axes and access them through the returned array
-        fig, axs = plt.subplots(2, 2, subplot_kw=dict(projection="polar"))
-        axs[0, 0].plot(x, y)
-        axs[1, 1].scatter(x, y)
-
-        # Share a X axis with each column of subplots
-        plt.subplots(2, 2, sharex='col')
-
-        # Share a Y axis with each row of subplots
-        plt.subplots(2, 2, sharey='row')
-
-        # Share both X and Y axes with all subplots
-        plt.subplots(2, 2, sharex='all', sharey='all')
-
-        # Note that this is the same as
-        plt.subplots(2, 2, sharex=True, sharey=True)
-
-        # Create figure number 10 with a single subplot
-        # and clears it if it already exists.
-        fig, ax = plt.subplots(num=10, clear=True)
-
-    """
-    fig = figure(**fig_kw)
-    axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,
-                       squeeze=squeeze, subplot_kw=subplot_kw,
-                       gridspec_kw=gridspec_kw, height_ratios=height_ratios,
-                       width_ratios=width_ratios)
-    return fig, axs
-
-
-def subplot_mosaic(mosaic, *, sharex=False, sharey=False,
-                   width_ratios=None, height_ratios=None, empty_sentinel='.',
-                   subplot_kw=None, gridspec_kw=None,
-                   per_subplot_kw=None, **fig_kw):
-    """
-    Build a layout of Axes based on ASCII art or nested lists.
-
-    This is a helper function to build complex GridSpec layouts visually.
-
-    See :doc:`/gallery/subplots_axes_and_figures/mosaic`
-    for an example and full API documentation
-
-    Parameters
-    ----------
-    mosaic : list of list of {hashable or nested} or str
-
-        A visual layout of how you want your Axes to be arranged
-        labeled as strings.  For example ::
-
-           x = [['A panel', 'A panel', 'edge'],
-                ['C panel', '.',       'edge']]
-
-        produces 4 axes:
-
-        - 'A panel' which is 1 row high and spans the first two columns
-        - 'edge' which is 2 rows high and is on the right edge
-        - 'C panel' which in 1 row and 1 column wide in the bottom left
-        - a blank space 1 row and 1 column wide in the bottom center
-
-        Any of the entries in the layout can be a list of lists
-        of the same form to create nested layouts.
-
-        If input is a str, then it must be of the form ::
-
-          '''
-          AAE
-          C.E
-          '''
-
-        where each character is a column and each line is a row.
-        This only allows only single character Axes labels and does
-        not allow nesting but is very terse.
-
-    sharex, sharey : bool, default: False
-        If True, the x-axis (*sharex*) or y-axis (*sharey*) will be shared
-        among all subplots.  In that case, tick label visibility and axis units
-        behave as for `subplots`.  If False, each subplot's x- or y-axis will
-        be independent.
-
-    width_ratios : array-like of length *ncols*, optional
-        Defines the relative widths of the columns. Each column gets a
-        relative width of ``width_ratios[i] / sum(width_ratios)``.
-        If not given, all columns will have the same width.  Convenience
-        for ``gridspec_kw={'width_ratios': [...]}``.
-
-    height_ratios : array-like of length *nrows*, optional
-        Defines the relative heights of the rows. Each row gets a
-        relative height of ``height_ratios[i] / sum(height_ratios)``.
-        If not given, all rows will have the same height. Convenience
-        for ``gridspec_kw={'height_ratios': [...]}``.
-
-    empty_sentinel : object, optional
-        Entry in the layout to mean "leave this space empty".  Defaults
-        to ``'.'``. Note, if *layout* is a string, it is processed via
-        `inspect.cleandoc` to remove leading white space, which may
-        interfere with using white-space as the empty sentinel.
-
-    subplot_kw : dict, optional
-        Dictionary with keywords passed to the `.Figure.add_subplot` call
-        used to create each subplot.  These values may be overridden by
-        values in *per_subplot_kw*.
-
-    per_subplot_kw : dict, optional
-        A dictionary mapping the Axes identifiers or tuples of identifiers
-        to a dictionary of keyword arguments to be passed to the
-        `.Figure.add_subplot` call used to create each subplot.  The values
-        in these dictionaries have precedence over the values in
-        *subplot_kw*.
-
-        If *mosaic* is a string, and thus all keys are single characters,
-        it is possible to use a single string instead of a tuple as keys;
-        i.e. ``"AB"`` is equivalent to ``("A", "B")``.
-
-        .. versionadded:: 3.7
-
-    gridspec_kw : dict, optional
-        Dictionary with keywords passed to the `.GridSpec` constructor used
-        to create the grid the subplots are placed on.
-
-    **fig_kw
-        All additional keyword arguments are passed to the
-        `.pyplot.figure` call.
-
-    Returns
-    -------
-    fig : `.Figure`
-       The new figure
-
-    dict[label, Axes]
-       A dictionary mapping the labels to the Axes objects.  The order of
-       the axes is left-to-right and top-to-bottom of their position in the
-       total layout.
-
-    """
-    fig = figure(**fig_kw)
-    ax_dict = fig.subplot_mosaic(
-        mosaic, sharex=sharex, sharey=sharey,
-        height_ratios=height_ratios, width_ratios=width_ratios,
-        subplot_kw=subplot_kw, gridspec_kw=gridspec_kw,
-        empty_sentinel=empty_sentinel,
-        per_subplot_kw=per_subplot_kw,
-    )
-    return fig, ax_dict
-
-
-def subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs):
-    """
-    Create a subplot at a specific location inside a regular grid.
-
-    Parameters
-    ----------
-    shape : (int, int)
-        Number of rows and of columns of the grid in which to place axis.
-    loc : (int, int)
-        Row number and column number of the axis location within the grid.
-    rowspan : int, default: 1
-        Number of rows for the axis to span downwards.
-    colspan : int, default: 1
-        Number of columns for the axis to span to the right.
-    fig : `.Figure`, optional
-        Figure to place the subplot in. Defaults to the current figure.
-    **kwargs
-        Additional keyword arguments are handed to `~.Figure.add_subplot`.
-
-    Returns
-    -------
-    `~.axes.Axes`
-
-        The Axes of the subplot. The returned Axes can actually be an instance
-        of a subclass, such as `.projections.polar.PolarAxes` for polar
-        projections.
-
-    Notes
-    -----
-    The following call ::
-
-        ax = subplot2grid((nrows, ncols), (row, col), rowspan, colspan)
-
-    is identical to ::
-
-        fig = gcf()
-        gs = fig.add_gridspec(nrows, ncols)
-        ax = fig.add_subplot(gs[row:row+rowspan, col:col+colspan])
-    """
-
-    if fig is None:
-        fig = gcf()
-
-    rows, cols = shape
-    gs = GridSpec._check_gridspec_exists(fig, rows, cols)
-
-    subplotspec = gs.new_subplotspec(loc, rowspan=rowspan, colspan=colspan)
-    ax = fig.add_subplot(subplotspec, **kwargs)
-
-    axes_to_delete = [other for other in fig.axes
-                      if other != ax and ax.bbox.fully_overlaps(other.bbox)]
-    if axes_to_delete:
-        _api.warn_deprecated(
-            "3.6", message="Auto-removal of overlapping axes is deprecated "
-            "since %(since)s and will be removed %(removal)s; explicitly call "
-            "ax.remove() as needed.")
-    for ax_to_del in axes_to_delete:
-        delaxes(ax_to_del)
-
-    return ax
-
-
-def twinx(ax=None):
-    """
-    Make and return a second axes that shares the *x*-axis.  The new axes will
-    overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be
-    on the right.
-
-    Examples
-    --------
-    :doc:`/gallery/subplots_axes_and_figures/two_scales`
-    """
-    if ax is None:
-        ax = gca()
-    ax1 = ax.twinx()
-    return ax1
-
-
-def twiny(ax=None):
-    """
-    Make and return a second axes that shares the *y*-axis.  The new axes will
-    overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be
-    on the top.
-
-    Examples
-    --------
-    :doc:`/gallery/subplots_axes_and_figures/two_scales`
-    """
-    if ax is None:
-        ax = gca()
-    ax1 = ax.twiny()
-    return ax1
-
-
-def subplot_tool(targetfig=None):
-    """
-    Launch a subplot tool window for a figure.
-
-    Returns
-    -------
-    `matplotlib.widgets.SubplotTool`
-    """
-    if targetfig is None:
-        targetfig = gcf()
-    tb = targetfig.canvas.manager.toolbar
-    if hasattr(tb, "configure_subplots"):  # toolbar2
-        return tb.configure_subplots()
-    elif hasattr(tb, "trigger_tool"):  # toolmanager
-        return tb.trigger_tool("subplots")
-    else:
-        raise ValueError("subplot_tool can only be launched for figures with "
-                         "an associated toolbar")
-
-
-def box(on=None):
-    """
-    Turn the axes box on or off on the current axes.
-
-    Parameters
-    ----------
-    on : bool or None
-        The new `~matplotlib.axes.Axes` box state. If ``None``, toggle
-        the state.
-
-    See Also
-    --------
-    :meth:`matplotlib.axes.Axes.set_frame_on`
-    :meth:`matplotlib.axes.Axes.get_frame_on`
-    """
-    ax = gca()
-    if on is None:
-        on = not ax.get_frame_on()
-    ax.set_frame_on(on)
-
-## Axis ##
-
-
-def xlim(*args, **kwargs):
-    """
-    Get or set the x limits of the current axes.
-
-    Call signatures::
-
-        left, right = xlim()  # return the current xlim
-        xlim((left, right))   # set the xlim to left, right
-        xlim(left, right)     # set the xlim to left, right
-
-    If you do not specify args, you can pass *left* or *right* as kwargs,
-    i.e.::
-
-        xlim(right=3)  # adjust the right leaving left unchanged
-        xlim(left=1)  # adjust the left leaving right unchanged
-
-    Setting limits turns autoscaling off for the x-axis.
-
-    Returns
-    -------
-    left, right
-        A tuple of the new x-axis limits.
-
-    Notes
-    -----
-    Calling this function with no arguments (e.g. ``xlim()``) is the pyplot
-    equivalent of calling `~.Axes.get_xlim` on the current axes.
-    Calling this function with arguments is the pyplot equivalent of calling
-    `~.Axes.set_xlim` on the current axes. All arguments are passed though.
-    """
-    ax = gca()
-    if not args and not kwargs:
-        return ax.get_xlim()
-    ret = ax.set_xlim(*args, **kwargs)
-    return ret
-
-
-def ylim(*args, **kwargs):
-    """
-    Get or set the y-limits of the current axes.
-
-    Call signatures::
-
-        bottom, top = ylim()  # return the current ylim
-        ylim((bottom, top))   # set the ylim to bottom, top
-        ylim(bottom, top)     # set the ylim to bottom, top
-
-    If you do not specify args, you can alternatively pass *bottom* or
-    *top* as kwargs, i.e.::
-
-        ylim(top=3)  # adjust the top leaving bottom unchanged
-        ylim(bottom=1)  # adjust the bottom leaving top unchanged
-
-    Setting limits turns autoscaling off for the y-axis.
-
-    Returns
-    -------
-    bottom, top
-        A tuple of the new y-axis limits.
-
-    Notes
-    -----
-    Calling this function with no arguments (e.g. ``ylim()``) is the pyplot
-    equivalent of calling `~.Axes.get_ylim` on the current axes.
-    Calling this function with arguments is the pyplot equivalent of calling
-    `~.Axes.set_ylim` on the current axes. All arguments are passed though.
-    """
-    ax = gca()
-    if not args and not kwargs:
-        return ax.get_ylim()
-    ret = ax.set_ylim(*args, **kwargs)
-    return ret
-
-
-def xticks(ticks=None, labels=None, *, minor=False, **kwargs):
-    """
-    Get or set the current tick locations and labels of the x-axis.
-
-    Pass no arguments to return the current values without modifying them.
-
-    Parameters
-    ----------
-    ticks : array-like, optional
-        The list of xtick locations.  Passing an empty list removes all xticks.
-    labels : array-like, optional
-        The labels to place at the given *ticks* locations.  This argument can
-        only be passed if *ticks* is passed as well.
-    minor : bool, default: False
-        If ``False``, get/set the major ticks/labels; if ``True``, the minor
-        ticks/labels.
-    **kwargs
-        `.Text` properties can be used to control the appearance of the labels.
-
-    Returns
-    -------
-    locs
-        The list of xtick locations.
-    labels
-        The list of xlabel `.Text` objects.
-
-    Notes
-    -----
-    Calling this function with no arguments (e.g. ``xticks()``) is the pyplot
-    equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on
-    the current axes.
-    Calling this function with arguments is the pyplot equivalent of calling
-    `~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes.
-
-    Examples
-    --------
-    >>> locs, labels = xticks()  # Get the current locations and labels.
-    >>> xticks(np.arange(0, 1, step=0.2))  # Set label locations.
-    >>> xticks(np.arange(3), ['Tom', 'Dick', 'Sue'])  # Set text labels.
-    >>> xticks([0, 1, 2], ['January', 'February', 'March'],
-    ...        rotation=20)  # Set text labels and properties.
-    >>> xticks([])  # Disable xticks.
-    """
-    ax = gca()
-
-    if ticks is None:
-        locs = ax.get_xticks(minor=minor)
-        if labels is not None:
-            raise TypeError("xticks(): Parameter 'labels' can't be set "
-                            "without setting 'ticks'")
-    else:
-        locs = ax.set_xticks(ticks, minor=minor)
-
-    if labels is None:
-        labels = ax.get_xticklabels(minor=minor)
-        for l in labels:
-            l._internal_update(kwargs)
-    else:
-        labels = ax.set_xticklabels(labels, minor=minor, **kwargs)
-
-    return locs, labels
-
-
-def yticks(ticks=None, labels=None, *, minor=False, **kwargs):
-    """
-    Get or set the current tick locations and labels of the y-axis.
-
-    Pass no arguments to return the current values without modifying them.
-
-    Parameters
-    ----------
-    ticks : array-like, optional
-        The list of ytick locations.  Passing an empty list removes all yticks.
-    labels : array-like, optional
-        The labels to place at the given *ticks* locations.  This argument can
-        only be passed if *ticks* is passed as well.
-    minor : bool, default: False
-        If ``False``, get/set the major ticks/labels; if ``True``, the minor
-        ticks/labels.
-    **kwargs
-        `.Text` properties can be used to control the appearance of the labels.
-
-    Returns
-    -------
-    locs
-        The list of ytick locations.
-    labels
-        The list of ylabel `.Text` objects.
-
-    Notes
-    -----
-    Calling this function with no arguments (e.g. ``yticks()``) is the pyplot
-    equivalent of calling `~.Axes.get_yticks` and `~.Axes.get_yticklabels` on
-    the current axes.
-    Calling this function with arguments is the pyplot equivalent of calling
-    `~.Axes.set_yticks` and `~.Axes.set_yticklabels` on the current axes.
-
-    Examples
-    --------
-    >>> locs, labels = yticks()  # Get the current locations and labels.
-    >>> yticks(np.arange(0, 1, step=0.2))  # Set label locations.
-    >>> yticks(np.arange(3), ['Tom', 'Dick', 'Sue'])  # Set text labels.
-    >>> yticks([0, 1, 2], ['January', 'February', 'March'],
-    ...        rotation=45)  # Set text labels and properties.
-    >>> yticks([])  # Disable yticks.
-    """
-    ax = gca()
-
-    if ticks is None:
-        locs = ax.get_yticks(minor=minor)
-        if labels is not None:
-            raise TypeError("yticks(): Parameter 'labels' can't be set "
-                            "without setting 'ticks'")
-    else:
-        locs = ax.set_yticks(ticks, minor=minor)
-
-    if labels is None:
-        labels = ax.get_yticklabels(minor=minor)
-        for l in labels:
-            l._internal_update(kwargs)
-    else:
-        labels = ax.set_yticklabels(labels, minor=minor, **kwargs)
-
-    return locs, labels
-
-
-def rgrids(radii=None, labels=None, angle=None, fmt=None, **kwargs):
-    """
-    Get or set the radial gridlines on the current polar plot.
-
-    Call signatures::
-
-     lines, labels = rgrids()
-     lines, labels = rgrids(radii, labels=None, angle=22.5, fmt=None, **kwargs)
-
-    When called with no arguments, `.rgrids` simply returns the tuple
-    (*lines*, *labels*). When called with arguments, the labels will
-    appear at the specified radial distances and angle.
-
-    Parameters
-    ----------
-    radii : tuple with floats
-        The radii for the radial gridlines
-
-    labels : tuple with strings or None
-        The labels to use at each radial gridline. The
-        `matplotlib.ticker.ScalarFormatter` will be used if None.
-
-    angle : float
-        The angular position of the radius labels in degrees.
-
-    fmt : str or None
-        Format string used in `matplotlib.ticker.FormatStrFormatter`.
-        For example '%f'.
-
-    Returns
-    -------
-    lines : list of `.lines.Line2D`
-        The radial gridlines.
-
-    labels : list of `.text.Text`
-        The tick labels.
-
-    Other Parameters
-    ----------------
-    **kwargs
-        *kwargs* are optional `.Text` properties for the labels.
-
-    See Also
-    --------
-    .pyplot.thetagrids
-    .projections.polar.PolarAxes.set_rgrids
-    .Axis.get_gridlines
-    .Axis.get_ticklabels
-
-    Examples
-    --------
-    ::
-
-      # set the locations of the radial gridlines
-      lines, labels = rgrids( (0.25, 0.5, 1.0) )
-
-      # set the locations and labels of the radial gridlines
-      lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ))
-    """
-    ax = gca()
-    if not isinstance(ax, PolarAxes):
-        raise RuntimeError('rgrids only defined for polar axes')
-    if all(p is None for p in [radii, labels, angle, fmt]) and not kwargs:
-        lines = ax.yaxis.get_gridlines()
-        labels = ax.yaxis.get_ticklabels()
-    else:
-        lines, labels = ax.set_rgrids(
-            radii, labels=labels, angle=angle, fmt=fmt, **kwargs)
-    return lines, labels
-
-
-def thetagrids(angles=None, labels=None, fmt=None, **kwargs):
-    """
-    Get or set the theta gridlines on the current polar plot.
-
-    Call signatures::
-
-     lines, labels = thetagrids()
-     lines, labels = thetagrids(angles, labels=None, fmt=None, **kwargs)
-
-    When called with no arguments, `.thetagrids` simply returns the tuple
-    (*lines*, *labels*). When called with arguments, the labels will
-    appear at the specified angles.
-
-    Parameters
-    ----------
-    angles : tuple with floats, degrees
-        The angles of the theta gridlines.
-
-    labels : tuple with strings or None
-        The labels to use at each radial gridline. The
-        `.projections.polar.ThetaFormatter` will be used if None.
-
-    fmt : str or None
-        Format string used in `matplotlib.ticker.FormatStrFormatter`.
-        For example '%f'. Note that the angle in radians will be used.
-
-    Returns
-    -------
-    lines : list of `.lines.Line2D`
-        The theta gridlines.
-
-    labels : list of `.text.Text`
-        The tick labels.
-
-    Other Parameters
-    ----------------
-    **kwargs
-        *kwargs* are optional `.Text` properties for the labels.
-
-    See Also
-    --------
-    .pyplot.rgrids
-    .projections.polar.PolarAxes.set_thetagrids
-    .Axis.get_gridlines
-    .Axis.get_ticklabels
-
-    Examples
-    --------
-    ::
-
-      # set the locations of the angular gridlines
-      lines, labels = thetagrids(range(45, 360, 90))
-
-      # set the locations and labels of the angular gridlines
-      lines, labels = thetagrids(range(45, 360, 90), ('NE', 'NW', 'SW', 'SE'))
-    """
-    ax = gca()
-    if not isinstance(ax, PolarAxes):
-        raise RuntimeError('thetagrids only defined for polar axes')
-    if all(param is None for param in [angles, labels, fmt]) and not kwargs:
-        lines = ax.xaxis.get_ticklines()
-        labels = ax.xaxis.get_ticklabels()
-    else:
-        lines, labels = ax.set_thetagrids(angles,
-                                          labels=labels, fmt=fmt, **kwargs)
-    return lines, labels
-
-
-@_api.deprecated("3.7", pending=True)
-def get_plot_commands():
-    """
-    Get a sorted list of all of the plotting commands.
-    """
-    NON_PLOT_COMMANDS = {
-        'connect', 'disconnect', 'get_current_fig_manager', 'ginput',
-        'new_figure_manager', 'waitforbuttonpress'}
-    return (name for name in _get_pyplot_commands()
-            if name not in NON_PLOT_COMMANDS)
-
-
-def _get_pyplot_commands():
-    # This works by searching for all functions in this module and removing
-    # a few hard-coded exclusions, as well as all of the colormap-setting
-    # functions, and anything marked as private with a preceding underscore.
-    exclude = {'colormaps', 'colors', 'get_plot_commands', *colormaps}
-    this_module = inspect.getmodule(get_plot_commands)
-    return sorted(
-        name for name, obj in globals().items()
-        if not name.startswith('_') and name not in exclude
-           and inspect.isfunction(obj)
-           and inspect.getmodule(obj) is this_module)
-
-
-## Plotting part 1: manually generated functions and wrappers ##
-
-
-@_copy_docstring_and_deprecators(Figure.colorbar)
-def colorbar(mappable=None, cax=None, ax=None, **kwargs):
-    if mappable is None:
-        mappable = gci()
-        if mappable is None:
-            raise RuntimeError('No mappable was found to use for colorbar '
-                               'creation. First define a mappable such as '
-                               'an image (with imshow) or a contour set ('
-                               'with contourf).')
-    ret = gcf().colorbar(mappable, cax=cax, ax=ax, **kwargs)
-    return ret
-
-
-def clim(vmin=None, vmax=None):
-    """
-    Set the color limits of the current image.
-
-    If either *vmin* or *vmax* is None, the image min/max respectively
-    will be used for color scaling.
-
-    If you want to set the clim of multiple images, use
-    `~.ScalarMappable.set_clim` on every image, for example::
-
-      for im in gca().get_images():
-          im.set_clim(0, 0.5)
-
-    """
-    im = gci()
-    if im is None:
-        raise RuntimeError('You must first define an image, e.g., with imshow')
-
-    im.set_clim(vmin, vmax)
-
-
-# eventually this implementation should move here, use indirection for now to
-# avoid having two copies of the code floating around.
-def get_cmap(name=None, lut=None):
-    return cm._get_cmap(name=name, lut=lut)
-get_cmap.__doc__ = cm._get_cmap.__doc__
-
-
-def set_cmap(cmap):
-    """
-    Set the default colormap, and applies it to the current image if any.
-
-    Parameters
-    ----------
-    cmap : `~matplotlib.colors.Colormap` or str
-        A colormap instance or the name of a registered colormap.
-
-    See Also
-    --------
-    colormaps
-    matplotlib.cm.register_cmap
-    matplotlib.cm.get_cmap
-    """
-    cmap = get_cmap(cmap)
-
-    rc('image', cmap=cmap.name)
-    im = gci()
-
-    if im is not None:
-        im.set_cmap(cmap)
-
-
-@_copy_docstring_and_deprecators(matplotlib.image.imread)
-def imread(fname, format=None):
-    return matplotlib.image.imread(fname, format)
-
-
-@_copy_docstring_and_deprecators(matplotlib.image.imsave)
-def imsave(fname, arr, **kwargs):
-    return matplotlib.image.imsave(fname, arr, **kwargs)
-
-
-def matshow(A, fignum=None, **kwargs):
-    """
-    Display an array as a matrix in a new figure window.
-
-    The origin is set at the upper left hand corner and rows (first
-    dimension of the array) are displayed horizontally.  The aspect
-    ratio of the figure window is that of the array, unless this would
-    make an excessively short or narrow figure.
-
-    Tick labels for the xaxis are placed on top.
-
-    Parameters
-    ----------
-    A : 2D array-like
-        The matrix to be displayed.
-
-    fignum : None or int or False
-        If *None*, create a new figure window with automatic numbering.
-
-        If a nonzero integer, draw into the figure with the given number
-        (create it if it does not exist).
-
-        If 0, use the current axes (or create one if it does not exist).
-
-        .. note::
-
-           Because of how `.Axes.matshow` tries to set the figure aspect
-           ratio to be the one of the array, strange things may happen if you
-           reuse an existing figure.
-
-    Returns
-    -------
-    `~matplotlib.image.AxesImage`
-
-    Other Parameters
-    ----------------
-    **kwargs : `~matplotlib.axes.Axes.imshow` arguments
-
-    """
-    A = np.asanyarray(A)
-    if fignum == 0:
-        ax = gca()
-    else:
-        # Extract actual aspect ratio of array and make appropriately sized
-        # figure.
-        fig = figure(fignum, figsize=figaspect(A))
-        ax = fig.add_axes([0.15, 0.09, 0.775, 0.775])
-    im = ax.matshow(A, **kwargs)
-    sci(im)
-    return im
-
-
-def polar(*args, **kwargs):
-    """
-    Make a polar plot.
-
-    call signature::
-
-      polar(theta, r, **kwargs)
-
-    Multiple *theta*, *r* arguments are supported, with format strings, as in
-    `plot`.
-    """
-    # If an axis already exists, check if it has a polar projection
-    if gcf().get_axes():
-        ax = gca()
-        if not isinstance(ax, PolarAxes):
-            _api.warn_external('Trying to create polar plot on an Axes '
-                               'that does not have a polar projection.')
-    else:
-        ax = axes(projection="polar")
-    return ax.plot(*args, **kwargs)
-
-
-# If rcParams['backend_fallback'] is true, and an interactive backend is
-# requested, ignore rcParams['backend'] and force selection of a backend that
-# is compatible with the current running interactive framework.
-if (rcParams["backend_fallback"]
-        and rcParams._get_backend_or_none() in (
-            set(_interactive_bk) - {'WebAgg', 'nbAgg'})
-        and cbook._get_running_interactive_framework()):
-    rcParams._set("backend", rcsetup._auto_backend_sentinel)
-
-
-################# REMAINING CONTENT GENERATED BY boilerplate.py ##############
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.figimage)
-def figimage(
-        X, xo=0, yo=0, alpha=None, norm=None, cmap=None, vmin=None,
-        vmax=None, origin=None, resize=False, **kwargs):
-    return gcf().figimage(
-        X, xo=xo, yo=yo, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
-        vmax=vmax, origin=origin, resize=resize, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.text)
-def figtext(x, y, s, fontdict=None, **kwargs):
-    return gcf().text(x, y, s, fontdict=fontdict, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.gca)
-def gca():
-    return gcf().gca()
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure._gci)
-def gci():
-    return gcf()._gci()
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.ginput)
-def ginput(
-        n=1, timeout=30, show_clicks=True,
-        mouse_add=MouseButton.LEFT, mouse_pop=MouseButton.RIGHT,
-        mouse_stop=MouseButton.MIDDLE):
-    return gcf().ginput(
-        n=n, timeout=timeout, show_clicks=show_clicks,
-        mouse_add=mouse_add, mouse_pop=mouse_pop,
-        mouse_stop=mouse_stop)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.subplots_adjust)
-def subplots_adjust(
-        left=None, bottom=None, right=None, top=None, wspace=None,
-        hspace=None):
-    return gcf().subplots_adjust(
-        left=left, bottom=bottom, right=right, top=top, wspace=wspace,
-        hspace=hspace)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.suptitle)
-def suptitle(t, **kwargs):
-    return gcf().suptitle(t, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.tight_layout)
-def tight_layout(*, pad=1.08, h_pad=None, w_pad=None, rect=None):
-    return gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Figure.waitforbuttonpress)
-def waitforbuttonpress(timeout=-1):
-    return gcf().waitforbuttonpress(timeout=timeout)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.acorr)
-def acorr(x, *, data=None, **kwargs):
-    return gca().acorr(
-        x, **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.angle_spectrum)
-def angle_spectrum(
-        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *,
-        data=None, **kwargs):
-    return gca().angle_spectrum(
-        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.annotate)
-def annotate(
-        text, xy, xytext=None, xycoords='data', textcoords=None,
-        arrowprops=None, annotation_clip=None, **kwargs):
-    return gca().annotate(
-        text, xy, xytext=xytext, xycoords=xycoords,
-        textcoords=textcoords, arrowprops=arrowprops,
-        annotation_clip=annotation_clip, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.arrow)
-def arrow(x, y, dx, dy, **kwargs):
-    return gca().arrow(x, y, dx, dy, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.autoscale)
-def autoscale(enable=True, axis='both', tight=None):
-    return gca().autoscale(enable=enable, axis=axis, tight=tight)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axhline)
-def axhline(y=0, xmin=0, xmax=1, **kwargs):
-    return gca().axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axhspan)
-def axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs):
-    return gca().axhspan(ymin, ymax, xmin=xmin, xmax=xmax, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axis)
-def axis(arg=None, /, *, emit=True, **kwargs):
-    return gca().axis(arg, emit=emit, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axline)
-def axline(xy1, xy2=None, *, slope=None, **kwargs):
-    return gca().axline(xy1, xy2=xy2, slope=slope, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axvline)
-def axvline(x=0, ymin=0, ymax=1, **kwargs):
-    return gca().axvline(x=x, ymin=ymin, ymax=ymax, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.axvspan)
-def axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs):
-    return gca().axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.bar)
-def bar(
-        x, height, width=0.8, bottom=None, *, align='center',
-        data=None, **kwargs):
-    return gca().bar(
-        x, height, width=width, bottom=bottom, align=align,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.barbs)
-def barbs(*args, data=None, **kwargs):
-    return gca().barbs(
-        *args, **({"data": data} if data is not None else {}),
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.barh)
-def barh(
-        y, width, height=0.8, left=None, *, align='center',
-        data=None, **kwargs):
-    return gca().barh(
-        y, width, height=height, left=left, align=align,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.bar_label)
-def bar_label(
-        container, labels=None, *, fmt='%g', label_type='edge',
-        padding=0, **kwargs):
-    return gca().bar_label(
-        container, labels=labels, fmt=fmt, label_type=label_type,
-        padding=padding, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.boxplot)
-def boxplot(
-        x, notch=None, sym=None, vert=None, whis=None,
-        positions=None, widths=None, patch_artist=None,
-        bootstrap=None, usermedians=None, conf_intervals=None,
-        meanline=None, showmeans=None, showcaps=None, showbox=None,
-        showfliers=None, boxprops=None, labels=None, flierprops=None,
-        medianprops=None, meanprops=None, capprops=None,
-        whiskerprops=None, manage_ticks=True, autorange=False,
-        zorder=None, capwidths=None, *, data=None):
-    return gca().boxplot(
-        x, notch=notch, sym=sym, vert=vert, whis=whis,
-        positions=positions, widths=widths, patch_artist=patch_artist,
-        bootstrap=bootstrap, usermedians=usermedians,
-        conf_intervals=conf_intervals, meanline=meanline,
-        showmeans=showmeans, showcaps=showcaps, showbox=showbox,
-        showfliers=showfliers, boxprops=boxprops, labels=labels,
-        flierprops=flierprops, medianprops=medianprops,
-        meanprops=meanprops, capprops=capprops,
-        whiskerprops=whiskerprops, manage_ticks=manage_ticks,
-        autorange=autorange, zorder=zorder, capwidths=capwidths,
-        **({"data": data} if data is not None else {}))
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.broken_barh)
-def broken_barh(xranges, yrange, *, data=None, **kwargs):
-    return gca().broken_barh(
-        xranges, yrange,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.clabel)
-def clabel(CS, levels=None, **kwargs):
-    return gca().clabel(CS, levels=levels, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.cohere)
-def cohere(
-        x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
-        window=mlab.window_hanning, noverlap=0, pad_to=None,
-        sides='default', scale_by_freq=None, *, data=None, **kwargs):
-    return gca().cohere(
-        x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
-        noverlap=noverlap, pad_to=pad_to, sides=sides,
-        scale_by_freq=scale_by_freq,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.contour)
-def contour(*args, data=None, **kwargs):
-    __ret = gca().contour(
-        *args, **({"data": data} if data is not None else {}),
-        **kwargs)
-    if __ret._A is not None: sci(__ret)  # noqa
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.contourf)
-def contourf(*args, data=None, **kwargs):
-    __ret = gca().contourf(
-        *args, **({"data": data} if data is not None else {}),
-        **kwargs)
-    if __ret._A is not None: sci(__ret)  # noqa
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.csd)
-def csd(
-        x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
-        noverlap=None, pad_to=None, sides=None, scale_by_freq=None,
-        return_line=None, *, data=None, **kwargs):
-    return gca().csd(
-        x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
-        noverlap=noverlap, pad_to=pad_to, sides=sides,
-        scale_by_freq=scale_by_freq, return_line=return_line,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.errorbar)
-def errorbar(
-        x, y, yerr=None, xerr=None, fmt='', ecolor=None,
-        elinewidth=None, capsize=None, barsabove=False, lolims=False,
-        uplims=False, xlolims=False, xuplims=False, errorevery=1,
-        capthick=None, *, data=None, **kwargs):
-    return gca().errorbar(
-        x, y, yerr=yerr, xerr=xerr, fmt=fmt, ecolor=ecolor,
-        elinewidth=elinewidth, capsize=capsize, barsabove=barsabove,
-        lolims=lolims, uplims=uplims, xlolims=xlolims,
-        xuplims=xuplims, errorevery=errorevery, capthick=capthick,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.eventplot)
-def eventplot(
-        positions, orientation='horizontal', lineoffsets=1,
-        linelengths=1, linewidths=None, colors=None, alpha=None,
-        linestyles='solid', *, data=None, **kwargs):
-    return gca().eventplot(
-        positions, orientation=orientation, lineoffsets=lineoffsets,
-        linelengths=linelengths, linewidths=linewidths, colors=colors,
-        alpha=alpha, linestyles=linestyles,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.fill)
-def fill(*args, data=None, **kwargs):
-    return gca().fill(
-        *args, **({"data": data} if data is not None else {}),
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.fill_between)
-def fill_between(
-        x, y1, y2=0, where=None, interpolate=False, step=None, *,
-        data=None, **kwargs):
-    return gca().fill_between(
-        x, y1, y2=y2, where=where, interpolate=interpolate, step=step,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.fill_betweenx)
-def fill_betweenx(
-        y, x1, x2=0, where=None, step=None, interpolate=False, *,
-        data=None, **kwargs):
-    return gca().fill_betweenx(
-        y, x1, x2=x2, where=where, step=step, interpolate=interpolate,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.grid)
-def grid(visible=None, which='major', axis='both', **kwargs):
-    return gca().grid(visible=visible, which=which, axis=axis, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.hexbin)
-def hexbin(
-        x, y, C=None, gridsize=100, bins=None, xscale='linear',
-        yscale='linear', extent=None, cmap=None, norm=None, vmin=None,
-        vmax=None, alpha=None, linewidths=None, edgecolors='face',
-        reduce_C_function=np.mean, mincnt=None, marginals=False, *,
-        data=None, **kwargs):
-    __ret = gca().hexbin(
-        x, y, C=C, gridsize=gridsize, bins=bins, xscale=xscale,
-        yscale=yscale, extent=extent, cmap=cmap, norm=norm, vmin=vmin,
-        vmax=vmax, alpha=alpha, linewidths=linewidths,
-        edgecolors=edgecolors, reduce_C_function=reduce_C_function,
-        mincnt=mincnt, marginals=marginals,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.hist)
-def hist(
-        x, bins=None, range=None, density=False, weights=None,
-        cumulative=False, bottom=None, histtype='bar', align='mid',
-        orientation='vertical', rwidth=None, log=False, color=None,
-        label=None, stacked=False, *, data=None, **kwargs):
-    return gca().hist(
-        x, bins=bins, range=range, density=density, weights=weights,
-        cumulative=cumulative, bottom=bottom, histtype=histtype,
-        align=align, orientation=orientation, rwidth=rwidth, log=log,
-        color=color, label=label, stacked=stacked,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.stairs)
-def stairs(
-        values, edges=None, *, orientation='vertical', baseline=0,
-        fill=False, data=None, **kwargs):
-    return gca().stairs(
-        values, edges=edges, orientation=orientation,
-        baseline=baseline, fill=fill,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.hist2d)
-def hist2d(
-        x, y, bins=10, range=None, density=False, weights=None,
-        cmin=None, cmax=None, *, data=None, **kwargs):
-    __ret = gca().hist2d(
-        x, y, bins=bins, range=range, density=density,
-        weights=weights, cmin=cmin, cmax=cmax,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret[-1])
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.hlines)
-def hlines(
-        y, xmin, xmax, colors=None, linestyles='solid', label='', *,
-        data=None, **kwargs):
-    return gca().hlines(
-        y, xmin, xmax, colors=colors, linestyles=linestyles,
-        label=label, **({"data": data} if data is not None else {}),
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.imshow)
-def imshow(
-        X, cmap=None, norm=None, *, aspect=None, interpolation=None,
-        alpha=None, vmin=None, vmax=None, origin=None, extent=None,
-        interpolation_stage=None, filternorm=True, filterrad=4.0,
-        resample=None, url=None, data=None, **kwargs):
-    __ret = gca().imshow(
-        X, cmap=cmap, norm=norm, aspect=aspect,
-        interpolation=interpolation, alpha=alpha, vmin=vmin,
-        vmax=vmax, origin=origin, extent=extent,
-        interpolation_stage=interpolation_stage,
-        filternorm=filternorm, filterrad=filterrad, resample=resample,
-        url=url, **({"data": data} if data is not None else {}),
-        **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.legend)
-def legend(*args, **kwargs):
-    return gca().legend(*args, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.locator_params)
-def locator_params(axis='both', tight=None, **kwargs):
-    return gca().locator_params(axis=axis, tight=tight, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.loglog)
-def loglog(*args, **kwargs):
-    return gca().loglog(*args, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.magnitude_spectrum)
-def magnitude_spectrum(
-        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None,
-        scale=None, *, data=None, **kwargs):
-    return gca().magnitude_spectrum(
-        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
-        scale=scale, **({"data": data} if data is not None else {}),
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.margins)
-def margins(*margins, x=None, y=None, tight=True):
-    return gca().margins(*margins, x=x, y=y, tight=tight)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.minorticks_off)
-def minorticks_off():
-    return gca().minorticks_off()
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.minorticks_on)
-def minorticks_on():
-    return gca().minorticks_on()
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.pcolor)
-def pcolor(
-        *args, shading=None, alpha=None, norm=None, cmap=None,
-        vmin=None, vmax=None, data=None, **kwargs):
-    __ret = gca().pcolor(
-        *args, shading=shading, alpha=alpha, norm=norm, cmap=cmap,
-        vmin=vmin, vmax=vmax,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.pcolormesh)
-def pcolormesh(
-        *args, alpha=None, norm=None, cmap=None, vmin=None,
-        vmax=None, shading=None, antialiased=False, data=None,
-        **kwargs):
-    __ret = gca().pcolormesh(
-        *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
-        vmax=vmax, shading=shading, antialiased=antialiased,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.phase_spectrum)
-def phase_spectrum(
-        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *,
-        data=None, **kwargs):
-    return gca().phase_spectrum(
-        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.pie)
-def pie(
-        x, explode=None, labels=None, colors=None, autopct=None,
-        pctdistance=0.6, shadow=False, labeldistance=1.1,
-        startangle=0, radius=1, counterclock=True, wedgeprops=None,
-        textprops=None, center=(0, 0), frame=False,
-        rotatelabels=False, *, normalize=True, hatch=None, data=None):
-    return gca().pie(
-        x, explode=explode, labels=labels, colors=colors,
-        autopct=autopct, pctdistance=pctdistance, shadow=shadow,
-        labeldistance=labeldistance, startangle=startangle,
-        radius=radius, counterclock=counterclock,
-        wedgeprops=wedgeprops, textprops=textprops, center=center,
-        frame=frame, rotatelabels=rotatelabels, normalize=normalize,
-        hatch=hatch, **({"data": data} if data is not None else {}))
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.plot)
-def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-    return gca().plot(
-        *args, scalex=scalex, scaley=scaley,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.plot_date)
-def plot_date(
-        x, y, fmt='o', tz=None, xdate=True, ydate=False, *,
-        data=None, **kwargs):
-    return gca().plot_date(
-        x, y, fmt=fmt, tz=tz, xdate=xdate, ydate=ydate,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.psd)
-def psd(
-        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
-        noverlap=None, pad_to=None, sides=None, scale_by_freq=None,
-        return_line=None, *, data=None, **kwargs):
-    return gca().psd(
-        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
-        noverlap=noverlap, pad_to=pad_to, sides=sides,
-        scale_by_freq=scale_by_freq, return_line=return_line,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.quiver)
-def quiver(*args, data=None, **kwargs):
-    __ret = gca().quiver(
-        *args, **({"data": data} if data is not None else {}),
-        **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.quiverkey)
-def quiverkey(Q, X, Y, U, label, **kwargs):
-    return gca().quiverkey(Q, X, Y, U, label, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.scatter)
-def scatter(
-        x, y, s=None, c=None, marker=None, cmap=None, norm=None,
-        vmin=None, vmax=None, alpha=None, linewidths=None, *,
-        edgecolors=None, plotnonfinite=False, data=None, **kwargs):
-    __ret = gca().scatter(
-        x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,
-        vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths,
-        edgecolors=edgecolors, plotnonfinite=plotnonfinite,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.semilogx)
-def semilogx(*args, **kwargs):
-    return gca().semilogx(*args, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.semilogy)
-def semilogy(*args, **kwargs):
-    return gca().semilogy(*args, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.specgram)
-def specgram(
-        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
-        noverlap=None, cmap=None, xextent=None, pad_to=None,
-        sides=None, scale_by_freq=None, mode=None, scale=None,
-        vmin=None, vmax=None, *, data=None, **kwargs):
-    __ret = gca().specgram(
-        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
-        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
-        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
-        scale=scale, vmin=vmin, vmax=vmax,
-        **({"data": data} if data is not None else {}), **kwargs)
-    sci(__ret[-1])
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.spy)
-def spy(
-        Z, precision=0, marker=None, markersize=None, aspect='equal',
-        origin='upper', **kwargs):
-    __ret = gca().spy(
-        Z, precision=precision, marker=marker, markersize=markersize,
-        aspect=aspect, origin=origin, **kwargs)
-    if isinstance(__ret, cm.ScalarMappable): sci(__ret)  # noqa
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.stackplot)
-def stackplot(
-        x, *args, labels=(), colors=None, baseline='zero', data=None,
-        **kwargs):
-    return gca().stackplot(
-        x, *args, labels=labels, colors=colors, baseline=baseline,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.stem)
-def stem(
-        *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0,
-        label=None,
-        use_line_collection=_api.deprecation._deprecated_parameter,
-        orientation='vertical', data=None):
-    return gca().stem(
-        *args, linefmt=linefmt, markerfmt=markerfmt, basefmt=basefmt,
-        bottom=bottom, label=label,
-        use_line_collection=use_line_collection,
-        orientation=orientation,
-        **({"data": data} if data is not None else {}))
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.step)
-def step(x, y, *args, where='pre', data=None, **kwargs):
-    return gca().step(
-        x, y, *args, where=where,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.streamplot)
-def streamplot(
-        x, y, u, v, density=1, linewidth=None, color=None, cmap=None,
-        norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1,
-        transform=None, zorder=None, start_points=None, maxlength=4.0,
-        integration_direction='both', broken_streamlines=True, *,
-        data=None):
-    __ret = gca().streamplot(
-        x, y, u, v, density=density, linewidth=linewidth, color=color,
-        cmap=cmap, norm=norm, arrowsize=arrowsize,
-        arrowstyle=arrowstyle, minlength=minlength,
-        transform=transform, zorder=zorder, start_points=start_points,
-        maxlength=maxlength,
-        integration_direction=integration_direction,
-        broken_streamlines=broken_streamlines,
-        **({"data": data} if data is not None else {}))
-    sci(__ret.lines)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.table)
-def table(
-        cellText=None, cellColours=None, cellLoc='right',
-        colWidths=None, rowLabels=None, rowColours=None,
-        rowLoc='left', colLabels=None, colColours=None,
-        colLoc='center', loc='bottom', bbox=None, edges='closed',
-        **kwargs):
-    return gca().table(
-        cellText=cellText, cellColours=cellColours, cellLoc=cellLoc,
-        colWidths=colWidths, rowLabels=rowLabels,
-        rowColours=rowColours, rowLoc=rowLoc, colLabels=colLabels,
-        colColours=colColours, colLoc=colLoc, loc=loc, bbox=bbox,
-        edges=edges, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.text)
-def text(x, y, s, fontdict=None, **kwargs):
-    return gca().text(x, y, s, fontdict=fontdict, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.tick_params)
-def tick_params(axis='both', **kwargs):
-    return gca().tick_params(axis=axis, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.ticklabel_format)
-def ticklabel_format(
-        *, axis='both', style='', scilimits=None, useOffset=None,
-        useLocale=None, useMathText=None):
-    return gca().ticklabel_format(
-        axis=axis, style=style, scilimits=scilimits,
-        useOffset=useOffset, useLocale=useLocale,
-        useMathText=useMathText)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.tricontour)
-def tricontour(*args, **kwargs):
-    __ret = gca().tricontour(*args, **kwargs)
-    if __ret._A is not None: sci(__ret)  # noqa
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.tricontourf)
-def tricontourf(*args, **kwargs):
-    __ret = gca().tricontourf(*args, **kwargs)
-    if __ret._A is not None: sci(__ret)  # noqa
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.tripcolor)
-def tripcolor(
-        *args, alpha=1.0, norm=None, cmap=None, vmin=None, vmax=None,
-        shading='flat', facecolors=None, **kwargs):
-    __ret = gca().tripcolor(
-        *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
-        vmax=vmax, shading=shading, facecolors=facecolors, **kwargs)
-    sci(__ret)
-    return __ret
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.triplot)
-def triplot(*args, **kwargs):
-    return gca().triplot(*args, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.violinplot)
-def violinplot(
-        dataset, positions=None, vert=True, widths=0.5,
-        showmeans=False, showextrema=True, showmedians=False,
-        quantiles=None, points=100, bw_method=None, *, data=None):
-    return gca().violinplot(
-        dataset, positions=positions, vert=vert, widths=widths,
-        showmeans=showmeans, showextrema=showextrema,
-        showmedians=showmedians, quantiles=quantiles, points=points,
-        bw_method=bw_method,
-        **({"data": data} if data is not None else {}))
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.vlines)
-def vlines(
-        x, ymin, ymax, colors=None, linestyles='solid', label='', *,
-        data=None, **kwargs):
-    return gca().vlines(
-        x, ymin, ymax, colors=colors, linestyles=linestyles,
-        label=label, **({"data": data} if data is not None else {}),
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.xcorr)
-def xcorr(
-        x, y, normed=True, detrend=mlab.detrend_none, usevlines=True,
-        maxlags=10, *, data=None, **kwargs):
-    return gca().xcorr(
-        x, y, normed=normed, detrend=detrend, usevlines=usevlines,
-        maxlags=maxlags,
-        **({"data": data} if data is not None else {}), **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes._sci)
-def sci(im):
-    return gca()._sci(im)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.set_title)
-def title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs):
-    return gca().set_title(
-        label, fontdict=fontdict, loc=loc, pad=pad, y=y, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.set_xlabel)
-def xlabel(xlabel, fontdict=None, labelpad=None, *, loc=None, **kwargs):
-    return gca().set_xlabel(
-        xlabel, fontdict=fontdict, labelpad=labelpad, loc=loc,
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.set_ylabel)
-def ylabel(ylabel, fontdict=None, labelpad=None, *, loc=None, **kwargs):
-    return gca().set_ylabel(
-        ylabel, fontdict=fontdict, labelpad=labelpad, loc=loc,
-        **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.set_xscale)
-def xscale(value, **kwargs):
-    return gca().set_xscale(value, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-@_copy_docstring_and_deprecators(Axes.set_yscale)
-def yscale(value, **kwargs):
-    return gca().set_yscale(value, **kwargs)
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def autumn():
-    """
-    Set the colormap to 'autumn'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('autumn')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def bone():
-    """
-    Set the colormap to 'bone'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('bone')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def cool():
-    """
-    Set the colormap to 'cool'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('cool')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def copper():
-    """
-    Set the colormap to 'copper'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('copper')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def flag():
-    """
-    Set the colormap to 'flag'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('flag')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def gray():
-    """
-    Set the colormap to 'gray'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('gray')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def hot():
-    """
-    Set the colormap to 'hot'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('hot')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def hsv():
-    """
-    Set the colormap to 'hsv'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('hsv')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def jet():
-    """
-    Set the colormap to 'jet'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('jet')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def pink():
-    """
-    Set the colormap to 'pink'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('pink')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def prism():
-    """
-    Set the colormap to 'prism'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('prism')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def spring():
-    """
-    Set the colormap to 'spring'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('spring')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def summer():
-    """
-    Set the colormap to 'summer'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('summer')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def winter():
-    """
-    Set the colormap to 'winter'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('winter')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def magma():
-    """
-    Set the colormap to 'magma'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('magma')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def inferno():
-    """
-    Set the colormap to 'inferno'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('inferno')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def plasma():
-    """
-    Set the colormap to 'plasma'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('plasma')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def viridis():
-    """
-    Set the colormap to 'viridis'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('viridis')
-
-
-# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
-def nipy_spectral():
-    """
-    Set the colormap to 'nipy_spectral'.
-
-    This changes the default colormap as well as the colormap of the current
-    image if there is one. See ``help(colormaps)`` for more information.
-    """
-    set_cmap('nipy_spectral')
diff --git a/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/onnx_inference.py b/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/lcipolina/Print_Gallery/glide_text2im/text2im_model.py b/spaces/lcipolina/Print_Gallery/glide_text2im/text2im_model.py
deleted file mode 100644
index c74394090a1bd61054f9aeabf15075e701d81601..0000000000000000000000000000000000000000
--- a/spaces/lcipolina/Print_Gallery/glide_text2im/text2im_model.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .nn import timestep_embedding
-from .unet import UNetModel
-from .xf import LayerNorm, Transformer, convert_module_to_f16
-
-
-class Text2ImUNet(UNetModel):
-    """
-    A UNetModel that conditions on text with an encoding transformer.
-
-    Expects an extra kwarg `tokens` of text.
-
-    :param text_ctx: number of text tokens to expect.
-    :param xf_width: width of the transformer.
-    :param xf_layers: depth of the transformer.
-    :param xf_heads: heads in the transformer.
-    :param xf_final_ln: use a LayerNorm after the output layer.
-    :param tokenizer: the text tokenizer for sampling/vocab size.
-    """
-
-    def __init__(
-        self,
-        text_ctx,
-        xf_width,
-        xf_layers,
-        xf_heads,
-        xf_final_ln,
-        tokenizer,
-        *args,
-        cache_text_emb=False,
-        xf_ar=0.0,
-        xf_padding=False,
-        share_unemb=False,
-        **kwargs,
-    ):
-        self.text_ctx = text_ctx
-        self.xf_width = xf_width
-        self.xf_ar = xf_ar
-        self.xf_padding = xf_padding
-        self.tokenizer = tokenizer
-
-        if not xf_width:
-            super().__init__(*args, **kwargs, encoder_channels=None)
-        else:
-            super().__init__(*args, **kwargs, encoder_channels=xf_width)
-        if self.xf_width:
-            self.transformer = Transformer(
-                text_ctx,
-                xf_width,
-                xf_layers,
-                xf_heads,
-            )
-            if xf_final_ln:
-                self.final_ln = LayerNorm(xf_width)
-            else:
-                self.final_ln = None
-
-            self.token_embedding = nn.Embedding(self.tokenizer.n_vocab, xf_width)
-            self.positional_embedding = nn.Parameter(th.empty(text_ctx, xf_width, dtype=th.float32))
-            self.transformer_proj = nn.Linear(xf_width, self.model_channels * 4)
-
-            if self.xf_padding:
-                self.padding_embedding = nn.Parameter(
-                    th.empty(text_ctx, xf_width, dtype=th.float32)
-                )
-            if self.xf_ar:
-                self.unemb = nn.Linear(xf_width, self.tokenizer.n_vocab)
-                if share_unemb:
-                    self.unemb.weight = self.token_embedding.weight
-
-        self.cache_text_emb = cache_text_emb
-        self.cache = None
-
-    def convert_to_fp16(self):
-        super().convert_to_fp16()
-        if self.xf_width:
-            self.transformer.apply(convert_module_to_f16)
-            self.transformer_proj.to(th.float16)
-            self.token_embedding.to(th.float16)
-            self.positional_embedding.to(th.float16)
-            if self.xf_padding:
-                self.padding_embedding.to(th.float16)
-            if self.xf_ar:
-                self.unemb.to(th.float16)
-
-    def get_text_emb(self, tokens, mask):
-        assert tokens is not None
-
-        if self.cache_text_emb and self.cache is not None:
-            assert (
-                tokens == self.cache["tokens"]
-            ).all(), f"Tokens {tokens.cpu().numpy().tolist()} do not match cache {self.cache['tokens'].cpu().numpy().tolist()}"
-            return self.cache
-
-        xf_in = self.token_embedding(tokens.long())
-        xf_in = xf_in + self.positional_embedding[None]
-        if self.xf_padding:
-            assert mask is not None
-            xf_in = th.where(mask[..., None], xf_in, self.padding_embedding[None])
-        xf_out = self.transformer(xf_in.to(self.dtype))
-        if self.final_ln is not None:
-            xf_out = self.final_ln(xf_out)
-        xf_proj = self.transformer_proj(xf_out[:, -1])
-        xf_out = xf_out.permute(0, 2, 1)  # NLC -> NCL
-
-        outputs = dict(xf_proj=xf_proj, xf_out=xf_out)
-
-        if self.cache_text_emb:
-            self.cache = dict(
-                tokens=tokens,
-                xf_proj=xf_proj.detach(),
-                xf_out=xf_out.detach() if xf_out is not None else None,
-            )
-
-        return outputs
-
-    def del_cache(self):
-        self.cache = None
-
-    def forward(self, x, timesteps, tokens=None, mask=None):
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-        if self.xf_width:
-            text_outputs = self.get_text_emb(tokens, mask)
-            xf_proj, xf_out = text_outputs["xf_proj"], text_outputs["xf_out"]
-            emb = emb + xf_proj.to(emb)
-        else:
-            xf_out = None
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, xf_out)
-            hs.append(h)
-        h = self.middle_block(h, emb, xf_out)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, xf_out)
-        h = h.type(x.dtype)
-        h = self.out(h)
-        return h
-
-
-class SuperResText2ImUNet(Text2ImUNet):
-    """
-    A text2im model that performs super-resolution.
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 2
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 2
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(
-            low_res, (new_height, new_width), mode="bilinear", align_corners=False
-        )
-        x = th.cat([x, upsampled], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class InpaintText2ImUNet(Text2ImUNet):
-    """
-    A text2im model which can perform inpainting.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 2 + 1
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 2 + 1
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x, timesteps, inpaint_image=None, inpaint_mask=None, **kwargs):
-        if inpaint_image is None:
-            inpaint_image = th.zeros_like(x)
-        if inpaint_mask is None:
-            inpaint_mask = th.zeros_like(x[:, :1])
-        return super().forward(
-            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask], dim=1),
-            timesteps,
-            **kwargs,
-        )
-
-
-class SuperResInpaintText2ImUnet(Text2ImUNet):
-    """
-    A text2im model which can perform both upsampling and inpainting.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 3 + 1
-        super().__init__(*args, **kwargs)
-
-    def forward(
-        self,
-        x,
-        timesteps,
-        inpaint_image=None,
-        inpaint_mask=None,
-        low_res=None,
-        **kwargs,
-    ):
-        if inpaint_image is None:
-            inpaint_image = th.zeros_like(x)
-        if inpaint_mask is None:
-            inpaint_mask = th.zeros_like(x[:, :1])
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(
-            low_res, (new_height, new_width), mode="bilinear", align_corners=False
-        )
-        return super().forward(
-            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask, upsampled], dim=1),
-            timesteps,
-            **kwargs,
-        )
diff --git a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/doc/TODO.md b/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/doc/TODO.md
deleted file mode 100644
index cb71be2ba899b09747933af336cce5f4f3d76193..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/doc/TODO.md
+++ /dev/null
@@ -1,46 +0,0 @@
-## Model compatibility
-
-- [ ] Verify compatibility with Llama-2 34B once released
-
-## GPU compatibility (etc.)
-
-- [ ] Optimizations for ROCm
-- [ ] Optimizations for RTX 20-series maybe
-- [ ] Look into improving P40 performance
-
-## Testing
-
-- [ ] More testing on Llama 2 models
-
-## Optimization
-
-- [ ] Flash Attention 2.0 (?)
-- [ ] Find a way to eliminate `ExLlamaAttention.repeat_kv` (custom attention kernel?)
-- [ ] C++ implementations of sampler functions
-
-## Generation
-
-- [ ] Optimized/batched beam search
-- [ ] Allow stackable LoRAs
-- [ ] Guidance or equivalent
-
-## Interface
-
-- [ ] Comprehensive API server (more than `example_flask.py`
-
-## Web UI
-
-- [ ] Controls to enable beam search
-- [ ] Rewrite/refactor all the JavaScript and CSS
-- [ ] Make it a little prettier
-- [ ] Better error handling
-- [ ] LoRA controls
-- [ ] Multiple chat modes with prompt templates (instruct, etc.)
-
-## ??
-
-- [ ] Support for other quantization methods
-- [ ] Support for other LLM architectures
-- [ ] Allow for backpropagation
-- [ ] LoRA training features
-- [ ] Soft prompt training
\ No newline at end of file
diff --git a/spaces/lewiswu1209/MockingBird/synthesizer/models/tacotron.py b/spaces/lewiswu1209/MockingBird/synthesizer/models/tacotron.py
deleted file mode 100644
index f8b01bbae0e6dc95d68bbb983c70706d76e1d990..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/synthesizer/models/tacotron.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import torch
-import torch.nn as nn
-from .sublayer.global_style_token import GlobalStyleToken
-from .sublayer.pre_net import PreNet
-from .sublayer.cbhg import CBHG
-from .sublayer.lsa import LSA
-from .base import Base
-from synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp
-from synthesizer.hparams import hparams
-
-class Encoder(nn.Module):
-    def __init__(self, num_chars, embed_dims=512, encoder_dims=256, K=5, num_highways=4, dropout=0.5):
-        """ Encoder for SV2TTS
-
-        Args:
-            num_chars (int): length of symbols
-            embed_dims (int, optional): embedding dim for input texts. Defaults to 512.
-            encoder_dims (int, optional): output dim for encoder. Defaults to 256.
-            K (int, optional): _description_. Defaults to 5.
-            num_highways (int, optional): _description_. Defaults to 4.
-            dropout (float, optional): _description_. Defaults to 0.5.
-        """             
-        super().__init__()
-        self.embedding = nn.Embedding(num_chars, embed_dims)
-        self.pre_net = PreNet(embed_dims, fc1_dims=encoder_dims, fc2_dims=encoder_dims,
-                              dropout=dropout)
-        self.cbhg = CBHG(K=K, in_channels=encoder_dims, channels=encoder_dims,
-                         proj_channels=[encoder_dims, encoder_dims],
-                         num_highways=num_highways)
-
-    def forward(self, x):
-        """forward pass for encoder
-
-        Args:
-            x (2D tensor with size `[batch_size, text_num_chars]`): input texts list
-
-        Returns:
-            3D tensor with size `[batch_size, text_num_chars, encoder_dims]`
-            
-        """
-        x = self.embedding(x) # return: [batch_size, text_num_chars, tts_embed_dims]
-        x = self.pre_net(x) # return: [batch_size, text_num_chars, encoder_dims]
-        x.transpose_(1, 2)  # return: [batch_size, encoder_dims, text_num_chars]
-        return self.cbhg(x) # return: [batch_size, text_num_chars, encoder_dims]
-
-class Decoder(nn.Module):
-    # Class variable because its value doesn't change between classes
-    # yet ought to be scoped by class because its a property of a Decoder
-    max_r = 20
-    def __init__(self, n_mels, input_dims, decoder_dims, lstm_dims,
-                 dropout, speaker_embedding_size):
-        super().__init__()
-        self.register_buffer("r", torch.tensor(1, dtype=torch.int))
-        self.n_mels = n_mels
-        self.prenet = PreNet(n_mels, fc1_dims=decoder_dims * 2, fc2_dims=decoder_dims * 2,
-                             dropout=dropout)
-        self.attn_net = LSA(decoder_dims)
-        if hparams.use_gst:
-            speaker_embedding_size += gst_hp.E
-        self.attn_rnn = nn.GRUCell(input_dims + decoder_dims * 2, decoder_dims)
-        self.rnn_input = nn.Linear(input_dims  + decoder_dims, lstm_dims)
-        self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims)
-        self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims)
-        self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False)
-        self.stop_proj = nn.Linear(input_dims + lstm_dims, 1)
-
-    def zoneout(self, prev, current, device, p=0.1):
-        mask = torch.zeros(prev.size(),device=device).bernoulli_(p)
-        return prev * mask + current * (1 - mask)
-
-    def forward(self, encoder_seq, encoder_seq_proj, prenet_in,
-                hidden_states, cell_states, context_vec, times, chars):
-        """_summary_
-
-        Args:
-            encoder_seq (3D tensor `[batch_size, text_num_chars, project_dim(default to 512)]`): _description_
-            encoder_seq_proj (3D tensor `[batch_size, text_num_chars, decoder_dims(default to 128)]`): _description_
-            prenet_in (2D tensor `[batch_size, n_mels]`): _description_
-            hidden_states (_type_): _description_
-            cell_states (_type_): _description_
-            context_vec (2D tensor `[batch_size, project_dim(default to 512)]`): _description_
-            times (int): the number of times runned
-            chars (2D tensor with size `[batch_size, text_num_chars]`): original texts list input
-
-        """
-        # Need this for reshaping mels
-        batch_size = encoder_seq.size(0)
-        device = encoder_seq.device
-        # Unpack the hidden and cell states
-        attn_hidden, rnn1_hidden, rnn2_hidden = hidden_states
-        rnn1_cell, rnn2_cell = cell_states
-
-        # PreNet for the Attention RNN
-        prenet_out = self.prenet(prenet_in) # return: `[batch_size, decoder_dims * 2(256)]`
-
-        # Compute the Attention RNN hidden state
-        attn_rnn_in = torch.cat([context_vec, prenet_out], dim=-1) # `[batch_size, project_dim + decoder_dims * 2 (768)]`
-        attn_hidden = self.attn_rnn(attn_rnn_in.squeeze(1), attn_hidden) #  `[batch_size, decoder_dims (128)]`
-
-        # Compute the attention scores
-        scores = self.attn_net(encoder_seq_proj, attn_hidden, times, chars)
-
-        # Dot product to create the context vector
-        context_vec = scores @ encoder_seq
-        context_vec = context_vec.squeeze(1)
-
-        # Concat Attention RNN output w. Context Vector & project
-        x = torch.cat([context_vec, attn_hidden], dim=1) # `[batch_size, project_dim + decoder_dims (630)]`
-        x = self.rnn_input(x) # `[batch_size, lstm_dims(1024)]`
-
-        # Compute first Residual RNN, training with fixed zoneout rate 0.1
-        rnn1_hidden_next, rnn1_cell = self.res_rnn1(x, (rnn1_hidden, rnn1_cell)) # `[batch_size, lstm_dims(1024)]`
-        if self.training:
-            rnn1_hidden = self.zoneout(rnn1_hidden, rnn1_hidden_next,device=device)
-        else:
-            rnn1_hidden = rnn1_hidden_next
-        x = x + rnn1_hidden
-
-        # Compute second Residual RNN
-        rnn2_hidden_next, rnn2_cell = self.res_rnn2(x, (rnn2_hidden, rnn2_cell)) # `[batch_size, lstm_dims(1024)]`
-        if self.training:
-            rnn2_hidden = self.zoneout(rnn2_hidden, rnn2_hidden_next, device=device)
-        else:
-            rnn2_hidden = rnn2_hidden_next
-        x = x + rnn2_hidden
-
-        # Project Mels
-        mels = self.mel_proj(x) # `[batch_size, 1600]`
-        mels = mels.view(batch_size, self.n_mels, self.max_r)[:, :, :self.r] # `[batch_size, n_mels, r]`
-        hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden)
-        cell_states = (rnn1_cell, rnn2_cell)
-
-        # Stop token prediction
-        s = torch.cat((x, context_vec), dim=1)
-        s = self.stop_proj(s)
-        stop_tokens = torch.sigmoid(s)
-
-        return mels, scores, hidden_states, cell_states, context_vec, stop_tokens
-
-class Tacotron(Base):
-    def __init__(self, embed_dims, num_chars, encoder_dims, decoder_dims, n_mels, 
-                 fft_bins, postnet_dims, encoder_K, lstm_dims, postnet_K, num_highways,
-                 dropout, stop_threshold, speaker_embedding_size):
-        super().__init__(stop_threshold)
-        self.n_mels = n_mels
-        self.lstm_dims = lstm_dims
-        self.encoder_dims = encoder_dims
-        self.decoder_dims = decoder_dims
-        self.speaker_embedding_size = speaker_embedding_size
-        self.encoder = Encoder(num_chars, embed_dims, encoder_dims,
-                               encoder_K, num_highways, dropout)
-        self.project_dims = encoder_dims + speaker_embedding_size
-        if hparams.use_gst: 
-            self.project_dims += gst_hp.E
-        self.encoder_proj = nn.Linear(self.project_dims, decoder_dims, bias=False)
-        if hparams.use_gst: 
-            self.gst = GlobalStyleToken(speaker_embedding_size)
-        self.decoder = Decoder(n_mels, self.project_dims, decoder_dims, lstm_dims,
-                               dropout, speaker_embedding_size)
-        self.postnet = CBHG(postnet_K, n_mels, postnet_dims,
-                            [postnet_dims, fft_bins], num_highways)
-        self.post_proj = nn.Linear(postnet_dims, fft_bins, bias=False)
-
-    @staticmethod
-    def _concat_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
-        return outputs
-
-    @staticmethod
-    def _add_speaker_embedding(x, speaker_embedding):
-        """Add speaker embedding
-            This concats the speaker embedding for each char in the encoder output
-        Args:
-            x (3D tensor with size `[batch_size, text_num_chars, encoder_dims]`): the encoder output
-            speaker_embedding (2D tensor `[batch_size, speaker_embedding_size]`): the speaker embedding
-
-        Returns:
-            3D tensor with size `[batch_size, text_num_chars, encoder_dims+speaker_embedding_size]`
-        """        
-        # Save the dimensions as human-readable names
-        batch_size = x.size()[0]
-        text_num_chars = x.size()[1]
-
-        # Start by making a copy of each speaker embedding to match the input text length
-        # The output of this has size (batch_size, text_num_chars * speaker_embedding_size)
-        speaker_embedding_size = speaker_embedding.size()[1]
-        e = speaker_embedding.repeat_interleave(text_num_chars, dim=1)
-
-        # Reshape it and transpose
-        e = e.reshape(batch_size, speaker_embedding_size, text_num_chars)
-        e = e.transpose(1, 2)
-
-        # Concatenate the tiled speaker embedding with the encoder output
-        x = torch.cat((x, e), 2)
-        return x
-
-    def forward(self, texts, mels, speaker_embedding, steps=2000, style_idx=0, min_stop_token=5):
-        """Forward pass for Tacotron
-
-        Args:
-            texts (`[batch_size, text_num_chars]`): input texts list
-            mels (`[batch_size, varied_mel_lengths, steps]`): mels for comparison (training only)
-            speaker_embedding (`[batch_size, speaker_embedding_size(default to 256)]`): referring embedding.
-            steps (int, optional): . Defaults to 2000.
-            style_idx (int, optional): GST style selected. Defaults to 0.
-            min_stop_token (int, optional): decoder min_stop_token. Defaults to 5.
-        """
-        device = texts.device  # use same device as parameters
-
-        if self.training:
-            self.step += 1
-            batch_size, _, steps  = mels.size()
-        else:
-            batch_size, _  = texts.size()
-
-        # Initialise all hidden states and pack into tuple
-        attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device)
-        rnn1_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
-        rnn2_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
-        hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden)
-
-        # Initialise all lstm cell states and pack into tuple
-        rnn1_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
-        rnn2_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
-        cell_states = (rnn1_cell, rnn2_cell)
-
-        # <GO> Frame for start of decoder loop
-        go_frame = torch.zeros(batch_size, self.n_mels, device=device)
-
-        # SV2TTS: Run the encoder with the speaker embedding
-        # The projection avoids unnecessary matmuls in the decoder loop
-        encoder_seq = self.encoder(texts)
-        
-        encoder_seq = self._add_speaker_embedding(encoder_seq, speaker_embedding)
-
-        if hparams.use_gst and self.gst is not None:
-            if self.training:
-                style_embed = self.gst(speaker_embedding, speaker_embedding) # for training, speaker embedding can represent both style inputs and referenced
-                # style_embed = style_embed.expand_as(encoder_seq)
-                # encoder_seq = torch.cat((encoder_seq, style_embed), 2)
-            elif style_idx >= 0 and style_idx < 10:
-                query = torch.zeros(1, 1, self.gst.stl.attention.num_units)
-                if device.type == 'cuda':
-                    query = query.cuda()
-                gst_embed = torch.tanh(self.gst.stl.embed)
-                key = gst_embed[style_idx].unsqueeze(0).expand(1, -1, -1)
-                style_embed = self.gst.stl.attention(query, key)
-            else:
-                speaker_embedding_style = torch.zeros(speaker_embedding.size()[0], 1, self.speaker_embedding_size).to(device)
-                style_embed = self.gst(speaker_embedding_style, speaker_embedding)
-            encoder_seq = self._concat_speaker_embedding(encoder_seq, style_embed) # return: [batch_size, text_num_chars, project_dims]
-        
-        encoder_seq_proj = self.encoder_proj(encoder_seq) # return: [batch_size, text_num_chars, decoder_dims]
-
-        # Need a couple of lists for outputs
-        mel_outputs, attn_scores, stop_outputs = [], [], []
-
-        # Need an initial context vector
-        context_vec = torch.zeros(batch_size, self.project_dims, device=device)
-
-        # Run the decoder loop
-        for t in range(0, steps, self.r):
-            if self.training:
-                prenet_in = mels[:, :, t -1] if t > 0 else go_frame
-            else:
-                prenet_in = mel_outputs[-1][:, :, -1] if t > 0 else go_frame
-            mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \
-                self.decoder(encoder_seq, encoder_seq_proj, prenet_in,
-                             hidden_states, cell_states, context_vec, t, texts)
-            mel_outputs.append(mel_frames)
-            attn_scores.append(scores)
-            stop_outputs.extend([stop_tokens] * self.r)
-            if not self.training and (stop_tokens * 10 > min_stop_token).all() and t > 10: break
-
-        # Concat the mel outputs into sequence
-        mel_outputs = torch.cat(mel_outputs, dim=2)
-
-        # Post-Process for Linear Spectrograms
-        postnet_out = self.postnet(mel_outputs)
-        linear = self.post_proj(postnet_out)
-        linear = linear.transpose(1, 2)
-
-        # For easy visualisation
-        attn_scores = torch.cat(attn_scores, 1)
-        # attn_scores = attn_scores.cpu().data.numpy()
-        stop_outputs = torch.cat(stop_outputs, 1)
-
-        if self.training:
-            self.train()
-            
-        return mel_outputs, linear, attn_scores, stop_outputs
-
-    def generate(self, x, speaker_embedding, steps=2000, style_idx=0, min_stop_token=5):
-        self.eval()
-        mel_outputs, linear, attn_scores, _ =  self.forward(x, None, speaker_embedding, steps, style_idx, min_stop_token)
-        return mel_outputs, linear, attn_scores
diff --git a/spaces/libhost/tech/README.md b/spaces/libhost/tech/README.md
deleted file mode 100644
index 8bd31760437f7ed06c54b552993f8a8893ab7232..0000000000000000000000000000000000000000
--- a/spaces/libhost/tech/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: 💻 TechLib
-emoji: 🧑‍💻
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: true
-license: mit
-app_port: 3000
----
-
-![TechLib](https://huggingface.co/spaces/libhost/tech/resolve/main/public/favicon.ico)
-<h1>Search any AI!</h1>
-<h1>by Evgeniy Hristoforu</h1>
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat XI Pro 11.0.31 FINAL Crack !!LINK!!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat XI Pro 11.0.31 FINAL Crack !!LINK!!.md
deleted file mode 100644
index 324b9a918d819b8218f2ee84b6b48440e87708a7..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat XI Pro 11.0.31 FINAL Crack !!LINK!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Adobe Acrobat XI Pro 11.0.31 FINAL Crack</h2><br /><p><b><b>DOWNLOAD</b> &#10022; <a href="https://bytlly.com/2uGvN1">https://bytlly.com/2uGvN1</a></b></p><br /><br />
-
-For an even more extensive list of what we offer and where we are go to see ... Adobe Illustrator CC 2018 V21.0.2.242 Incl Crack Download Pc ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kobaspeech 2 Activation Version 2.1.0 Keygenl.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kobaspeech 2 Activation Version 2.1.0 Keygenl.md
deleted file mode 100644
index 868c5fc70c2bee601607af870b29c1e838bc8c9a..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kobaspeech 2 Activation Version 2.1.0 Keygenl.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>Kobaspeech 2 Activation Version 2.1.0 Keygenl: A Tool to Crack the Speech Synthesis Software</h1>
-<p>Kobaspeech 2 is a speech synthesis software that allows users to convert text to speech using various voices and languages. It is designed for people who have difficulties with reading, writing, or speaking, such as dyslexia, aphasia, or vocal cord disorders. Kobaspeech 2 also offers features such as pronunciation correction, voice speed adjustment, and text highlighting.</p>
-<p>However, Kobaspeech 2 is not a free software. Users need to purchase a license key to activate the software and use it without limitations. The license key costs $99 for one voice and $199 for three voices. Some users may find this price too expensive or unfair, especially if they need the software for personal or educational purposes.</p>
-<h2>Kobaspeech 2 Activation Version 2.1.0 Keygenl</h2><br /><p><b><b>DOWNLOAD</b> ---> <a href="https://bytlly.com/2uGvUy">https://bytlly.com/2uGvUy</a></b></p><br /><br />
-<p>That is why some users resort to using Kobaspeech 2 Activation Version 2.1.0 Keygenl, a tool that claims to generate valid license keys for Kobaspeech 2. By using this tool, users can bypass the activation process and use Kobaspeech 2 for free. However, this tool is not authorized by the developers of Kobaspeech 2 and may pose some risks and disadvantages.</p>
-<p>First of all, using Kobaspeech 2 Activation Version 2.1.0 Keygenl is illegal and unethical. It violates the terms and conditions of Kobaspeech 2 and infringes the intellectual property rights of the developers. Users who use this tool may face legal consequences or penalties if they are caught.</p>
-<p>Secondly, using Kobaspeech 2 Activation Version 2.1.0 Keygenl may compromise the quality and security of the software. The tool may not generate valid license keys or may generate keys that have already been used by other users. This may result in errors, glitches, or malfunctions in the software. Moreover, the tool may contain viruses, malware, or spyware that can harm the user's computer or steal their personal information.</p>
-<p>Therefore, it is not advisable to use Kobaspeech 2 Activation Version 2.1.0 Keygenl to crack Kobaspeech 2. Users who need the software should purchase a legitimate license key from the official website of Kobaspeech 2 or look for alternative speech synthesis software that are free or affordable.</p>
-
-<p>In conclusion, Kobaspeech 2 Activation Version 2.1.0 Keygenl is a tool that tries to crack Kobaspeech 2, a speech synthesis software that converts text to speech. However, using this tool is not recommended as it is illegal, unethical, and risky. Users who need Kobaspeech 2 should buy a license key from the official website or look for other speech synthesis software that are free or affordable.</p>
-
-<p>How does Kobaspeech 2 work? Kobaspeech 2 uses text-to-speech (TTS) technology to convert written text into spoken words. TTS technology is based on two main components: a text analysis component and a speech synthesis component. The text analysis component analyzes the text and converts it into a phonetic representation that contains information about the pronunciation, intonation, and stress of each word. The speech synthesis component then uses this phonetic representation to generate synthetic speech using a voice database that contains samples of human speech.</p>
-<p></p>
-<p>What are the benefits of Kobaspeech 2? Kobaspeech 2 can help users who have difficulties with reading, writing, or speaking to access and produce information more easily and effectively. For example, users who have dyslexia can use Kobaspeech 2 to listen to text instead of reading it, which can improve their comprehension and retention. Users who have aphasia can use Kobaspeech 2 to write text using speech recognition or keyboard input, which can enhance their communication and expression. Users who have vocal cord disorders can use Kobaspeech 2 to speak using a synthetic voice, which can restore their voice and confidence.</p>
-<p>What are the features of Kobaspeech 2? Kobaspeech 2 offers various features that make it user-friendly and customizable. For instance, users can choose from different voices and languages, such as English, French, German, Spanish, Italian, Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Portuguese, Russian, Turkish, Arabic, Mandarin Chinese, Japanese, and Korean. Users can also adjust the voice speed, pitch, and volume according to their preferences. Moreover, users can use pronunciation correction to modify the pronunciation of specific words or phrases. Additionally, users can use text highlighting to synchronize the text and speech output.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lixq/bingo61/src/lib/hooks/use-bing.ts b/spaces/lixq/bingo61/src/lib/hooks/use-bing.ts
deleted file mode 100644
index dcdb1667ced0cba299b0825c0e91c4732411308c..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/src/lib/hooks/use-bing.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-'use client'
-
-import { useState, useCallback, useEffect, useMemo } from 'react'
-import { useAtom, useAtomValue } from 'jotai'
-import { chatFamily, bingConversationStyleAtom, GreetMessages, hashAtom, voiceAtom } from '@/state'
-import { setConversationMessages } from './chat-history'
-import { ChatMessageModel, BotId, FileItem } from '@/lib/bots/bing/types'
-import { nanoid } from '../utils'
-import { TTS } from '../bots/bing/tts'
-
-export function useBing(botId: BotId = 'bing') {
-  const chatAtom = useMemo(() => chatFamily({ botId, page: 'singleton' }), [botId])
-  const [enableTTS] = useAtom(voiceAtom)
-  const speaker = useMemo(() => new TTS(), [])
-  const [hash, setHash] = useAtom(hashAtom)
-  const bingConversationStyle = useAtomValue(bingConversationStyleAtom)
-  const [chatState, setChatState] = useAtom(chatAtom)
-  const [input, setInput] = useState('')
-  const [attachmentList, setAttachmentList] = useState<FileItem[]>([])
-
-  const updateMessage = useCallback(
-    (messageId: string, updater: (message: ChatMessageModel) => void) => {
-      setChatState((draft) => {
-        const message = draft.messages.find((m) => m.id === messageId)
-        if (message) {
-          updater(message)
-        }
-      })
-    },
-    [setChatState],
-  )
-
-  const sendMessage = useCallback(
-    async (input: string, options = {}) => {
-      const botMessageId = nanoid()
-      const imageUrl = attachmentList?.[0]?.status === 'loaded' ? attachmentList[0].url : undefined
-      setChatState((draft) => {
-        const text = imageUrl ? `${input}\n\n![image](${imageUrl})` : input
-        draft.messages.push({ id: nanoid(), text, author: 'user' }, { id: botMessageId, text: '', author: 'bot' })
-        setAttachmentList([])
-      })
-      const abortController = new AbortController()
-      setChatState((draft) => {
-        draft.generatingMessageId = botMessageId
-        draft.abortController = abortController
-      })
-      speaker.reset()
-      await chatState.bot.sendMessage({
-        prompt: input,
-        imageUrl: /\?bcid=([^&]+)/.test(imageUrl ?? '') ? `https://www.bing.com/images/blob?bcid=${RegExp.$1}` : imageUrl,
-        options: {
-          ...options,
-          bingConversationStyle,
-        },
-        signal: abortController.signal,
-        onEvent(event) {
-          if (event.type === 'UPDATE_ANSWER') {
-            updateMessage(botMessageId, (message) => {
-              if (event.data.text.length > message.text.length) {
-                message.text = event.data.text
-              }
-
-              if (event.data.spokenText && enableTTS) {
-                speaker.speak(event.data.spokenText)
-              }
-
-              message.throttling = event.data.throttling || message.throttling
-              message.sourceAttributions = event.data.sourceAttributions || message.sourceAttributions
-              message.suggestedResponses = event.data.suggestedResponses || message.suggestedResponses
-            })
-          } else if (event.type === 'ERROR') {
-            updateMessage(botMessageId, (message) => {
-              message.error = event.error
-            })
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          } else if (event.type === 'DONE') {
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          }
-        },
-      })
-    },
-    [botId, attachmentList, chatState.bot, setChatState, updateMessage],
-  )
-
-  const uploadImage = useCallback(async (imgUrl: string) => {
-    setAttachmentList([{ url: imgUrl, status: 'loading' }])
-    const response = await chatState.bot.uploadImage(imgUrl, bingConversationStyle)
-    if (response?.blobId) {
-      setAttachmentList([{ url: `/api/blob?bcid=${response.blobId}`, status: 'loaded' }])
-    } else {
-      setAttachmentList([{ url: imgUrl, status: 'error' }])
-    }
-  }, [chatState.bot])
-
-  const resetConversation = useCallback(() => {
-    chatState.bot.resetConversation()
-    speaker.abort()
-    setChatState((draft) => {
-      draft.abortController = undefined
-      draft.generatingMessageId = ''
-      draft.messages = [{ author: 'bot', text: GreetMessages[Math.floor(GreetMessages.length * Math.random())], id: nanoid() }]
-      draft.conversationId = nanoid()
-    })
-  }, [chatState.bot, setChatState])
-
-  const stopGenerating = useCallback(() => {
-    chatState.abortController?.abort()
-    if (chatState.generatingMessageId) {
-      updateMessage(chatState.generatingMessageId, (message) => {
-        if (!message.text && !message.error) {
-          message.text = 'Cancelled'
-        }
-      })
-    }
-    setChatState((draft) => {
-      draft.generatingMessageId = ''
-    })
-  }, [chatState.abortController, chatState.generatingMessageId, setChatState, updateMessage])
-
-  useEffect(() => {
-    if (chatState.messages.length) {
-      setConversationMessages(botId, chatState.conversationId, chatState.messages)
-    }
-  }, [botId, chatState.conversationId, chatState.messages])
-
-  useEffect(() => {
-    if (hash === 'reset') {
-      resetConversation()
-      setHash('')
-    }
-  }, [hash, setHash])
-
-  const chat = useMemo(
-    () => ({
-      botId,
-      bot: chatState.bot,
-      isSpeaking: speaker.isSpeaking,
-      messages: chatState.messages,
-      sendMessage,
-      setInput,
-      input,
-      resetConversation,
-      generating: !!chatState.generatingMessageId,
-      stopGenerating,
-      uploadImage,
-      setAttachmentList,
-      attachmentList,
-    }),
-    [
-      botId,
-      bingConversationStyle,
-      chatState.bot,
-      chatState.generatingMessageId,
-      chatState.messages,
-      speaker.isSpeaking,
-      setInput,
-      input,
-      setAttachmentList,
-      attachmentList,
-      resetConversation,
-      sendMessage,
-      stopGenerating,
-    ],
-  )
-
-  return chat
-}
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/trivial_sequence.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/trivial_sequence.h
deleted file mode 100644
index b6c3ed9ebb3cd9022368644edb77ca101ec133e3..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/trivial_sequence.h
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file trivial_sequence.h
- *  \brief Container-like class for wrapping sequences.  The wrapped
- *         sequence always has trivial iterators, even when the input
- *         sequence does not.
- */
-
-
-#pragma once
-
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/detail/temporary_array.h>
-#include <thrust/type_traits/is_contiguous_iterator.h>
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// never instantiated
-template<typename Iterator, typename DerivedPolicy, typename is_trivial> struct _trivial_sequence { };
-
-// trivial case
-template<typename Iterator, typename DerivedPolicy>
-struct _trivial_sequence<Iterator, DerivedPolicy, thrust::detail::true_type>
-{
-    typedef Iterator iterator_type;
-    Iterator first, last;
-
-    __host__ __device__
-    _trivial_sequence(thrust::execution_policy<DerivedPolicy> &, Iterator _first, Iterator _last) : first(_first), last(_last)
-    {
-    }
-
-    __host__ __device__
-    iterator_type begin() { return first; }
-
-    __host__ __device__
-    iterator_type end()   { return last; }
-};
-
-// non-trivial case
-template<typename Iterator, typename DerivedPolicy>
-struct _trivial_sequence<Iterator, DerivedPolicy, thrust::detail::false_type>
-{
-    typedef typename thrust::iterator_value<Iterator>::type iterator_value;
-    typedef typename thrust::detail::temporary_array<iterator_value, DerivedPolicy>::iterator iterator_type;
-    
-    thrust::detail::temporary_array<iterator_value, DerivedPolicy> buffer;
-
-    __host__ __device__
-    _trivial_sequence(thrust::execution_policy<DerivedPolicy> &exec, Iterator first, Iterator last)
-      : buffer(exec, first, last)
-    {
-    }
-
-    __host__ __device__
-    iterator_type begin() { return buffer.begin(); }
-
-    __host__ __device__
-    iterator_type end()   { return buffer.end(); }
-};
-
-template <typename Iterator, typename DerivedPolicy>
-struct trivial_sequence
-  : detail::_trivial_sequence<Iterator, DerivedPolicy, typename thrust::is_contiguous_iterator<Iterator>::type>
-{
-    typedef _trivial_sequence<Iterator, DerivedPolicy, typename thrust::is_contiguous_iterator<Iterator>::type> super_t;
-
-    __host__ __device__
-    trivial_sequence(thrust::execution_policy<DerivedPolicy> &exec, Iterator first, Iterator last) : super_t(exec, first, last) { }
-};
-
-} // end namespace detail
-
-} // end namespace thrust
-
diff --git a/spaces/maker57sk/linkedin_analysis/app.py b/spaces/maker57sk/linkedin_analysis/app.py
deleted file mode 100644
index 12dc94c9100c63dc5aa544010434321d6d9550e3..0000000000000000000000000000000000000000
--- a/spaces/maker57sk/linkedin_analysis/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import streamlit as st
-from googlesearch import search
-import re
-
-st.header("LinkedIn Company Search")
-
-company  = st.text_input("Search a Company here")
-
-def get_linkedin_url(company):
-    # Init linkedinbio
-    linkedinbio = 'No Search Found'
-    
-    # Create query
-    query = f"{company}+Linkedin"
-    print("Google query: ", query)
-    
-    # Search in Google
-    for i in search(query, tld="com", num=10, stop=10, pause=2):
-        pattern = "https:\/\/.+.linkedin.com\/company\/.([^?])+"
-        result = re.search(pattern, i)
-
-        # Return value if result is not None
-        if result != None:
-            linkedinbio = result.group(0).replace(" ", "")
-        return linkedinbio
-    
-
-linkedin_url = get_linkedin_url(company)
-st.write(linkedin_url)
-
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/__init__.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/__init__.py
deleted file mode 100644
index ae19f3a8c51abf469697d6affa91449d668716ba..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/__init__.py
+++ /dev/null
@@ -1,17 +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.
-"""
-Solvers. A Solver is a training recipe, combining the dataloaders, models,
-optimizer, losses etc into a single convenient object.
-"""
-
-# flake8: noqa
-from .audiogen import AudioGenSolver
-from .builders import get_solver
-from .base import StandardSolver
-from .compression import CompressionSolver
-from .musicgen import MusicGenSolver
-from .diffusion import DiffusionSolver
diff --git a/spaces/merve/anonymization/public/data-leak/index.html b/spaces/merve/anonymization/public/data-leak/index.html
deleted file mode 100644
index 48382c629935410818fbefd120b3f743019c4f40..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/data-leak/index.html
+++ /dev/null
@@ -1,170 +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>Why Some Models Leak Data</title>
-  <meta property="og:title" content="Why Some Models Leak Data">
-  <meta property="og:url" content="https://pair.withgoogle.com/explorables/data-leak/">
-
-  <meta name="og:description" content="Machine learning models use large amounts of data, some of which can be sensitive. If they're not trained correctly, sometimes that data is inadvertently revealed.">
-  <meta property="og:image" content="https://pair.withgoogle.com/explorables/images/model-inversion.png">
-  <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'>Why Some Models Leak Data</h1>
-  <div class="post-summary">Machine learning models use large amounts of data, some of which can be sensitive. If they're not trained correctly, sometimes that data is inadvertently revealed.</div>
-  <link rel="stylesheet" href="style.css">
-
-
-<p>Let’s take a look at a game of soccer. </p>
-<link rel="stylesheet" href="style.css">
-
-<div id='field-grass' class='field'></div>
-
-<p><br></br> </p>
-<p>Using the position of each player as training data, we can teach a model to predict which team would get to a loose ball first at each spot on the field, indicated by the color of the pixel.</p>
-<div id='field-prediction' class='field'></div>
-
-<p>It updates in real-time—drag the players around to see the model change.</p>
-<p><br></br> </p>
-<p>This model reveals quite a lot about the data used to train it. Even without the actual positions of the players, it is simple to see where players might be.   </p>
-<div id='field-playerless' class='field'></div>
-
-<p>Click this button to <span class="button" id="player-button">move the players</span> </p>
-<p>Take a guess at where the yellow team’s goalie is now, then check their actual position. How close were you?</p>
-<h3>Sensitive Salary Data</h3>
-
-<p>In this specific soccer example, being able to make educated guesses about the data a model was trained on doesn’t matter too much. But what if our data points represent something more sensitive?</p>
-<div id='field-scatter' class='field'></div>
-
-<p>We’ve fed the same numbers into the model, but now they represent salary data instead of soccer data. Building models like this is a common technique to <a href="https://www.eeoc.gov/laws/guidance/section-10-compensation-discrimination#c.%20Using%20More%20Sophisticated%20Statistical%20Techniques%20to%20Evaluate">detect discrimination</a>. A union might test if a company is paying men and women fairly by building a salary model that takes into account years of experience. They can then <a href="https://postguild.org/2019-pay-study/">publish</a> the results to bring pressure for change or show improvement.</p>
-<p>In this hypothetical salary study, even though no individual salaries have been published, it is easy to infer the salary of the newest male hire. And carefully cross referencing public start dates on LinkedIn with the model could almost perfectly reveal everyone’s salary.</p>
-<p>Because the model here is so flexible (there are hundreds of square patches with independently calculated predictions) and we have so few data points (just 22 people), it is able to “memorize” individual data points. If we’re looking to share information about patterns in salaries, a simpler and more constrained model like a linear regression might be more appropriate. </p>
-<div id='field-regression' class='field'></div>
-
-<p>By boiling down the 22 data points to two lines we’re able to see broad trends without being able to guess anyone’s salary.</p>
-<h3>Subtle Leaks</h3>
-
-<p>Removing complexity isn’t a complete solution though. Depending on how the data is distributed, even a simple line can inadvertently reveal information.</p>
-<div id='field-regression-leak' class='field'></div>
-
-<p>In this company, almost all the men started several years ago, so the slope of the line is especially sensitive to the salary of the new hire. </p>
-<p>Is their salary <span class="button" id="high-button">higher or lower</span> than average? Based on the line, we can make a pretty good guess.</p>
-<p>Notice that changing the salary of someone with a more common tenure barely moves the line. In general, more typical data points are less susceptible to being leaked. This sets up a tricky trade off: we want models to learn about edge cases while being sure they haven’t memorized individual data points.</p>
-<h3>Real World Data</h3>
-
-<p>Models of real world data are often quite complex—this can improve accuracy, but makes them <a href="https://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library.html">more susceptible</a> to unexpectedly leaking information. Medical models have inadvertently revealed <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827719/">patients’ genetic markers</a>. Language models have memorized <a href="https://bair.berkeley.edu/blog/2019/08/13/memorization/">credit card numbers</a>. Faces can even be <a href="https://rist.tech.cornell.edu/papers/mi-ccs.pdf">reconstructed</a> from image models: </p>
-<div class='face-container'><img src='face.png'></div>
-
-<p><a href="https://rist.tech.cornell.edu/papers/mi-ccs.pdf">Fredrikson et al</a> were able to extract the image on the left by repeatedly querying a facial recognition API. It isn’t an exact match with the individual’s actual face (on the right), but this attack only required access to the model’s predictions, not its internal state.  </p>
-<h3>Protecting Private Data</h3>
-
-<p>Training models with <a href="http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html">differential privacy</a> stops the training data from leaking by limiting how much the model can learn from any one data point. Differentially private models are still at the cutting edge of research, but they’re being packaged into <a href="https://blog.tensorflow.org/2019/03/introducing-tensorflow-privacy-learning.html">machine learning frameworks</a>, making them much easier to use. When it isn’t possible to train differentially private models, there are also tools that can <a href="https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/membership_inference_attack">measure</a> how much data is the model memorizing. Also, standard techniques such as aggregation and limiting how much data a single source can contribute are still useful and usually improve the privacy of the model.</p>
-<p>As we saw in the <a href="https://pair.withgoogle.com/explorables/anonymization/">Collecting Sensitive Information Explorable</a>, adding enough random noise with differential privacy to protect outliers like the new hire  can increase the amount of data required to reach a good level of accuracy. Depending on the application, the constraints of differential privacy could even improve the model—for instance, not learning too much from one data point can help prevent <a href="https://openreview.net/forum?id=r1xyx3R9tQ">overfitting</a>. </p>
-<p>Given the increasing utility of machine learning models for many real-world tasks, it’s clear that more and more systems, devices and apps will be powered, to some extent, by machine learning in the future. While <a href="https://owasp.org/www-project-top-ten/">standard privacy best practices</a> developed for non-machine learning systems still apply to those with machine learning, the introduction of machine learning introduces new challenges, including the ability of the model to memorize some specific training data points and thus be vulnerable to privacy attacks that seek to extract this data from the model. Fortunately, techniques such as differential privacy exist that can be helpful in overcoming this specific challenge. Just as with other areas of <a href="https://ai.google/responsibilities/responsible-ai-practices/">Responsible AI</a>, it’s important to be aware of these new challenges that come along with machine learning and what steps can be taken to mitigate them. </p>
-<h3>Credits</h3>
-
-<p>Adam Pearce and Ellen Jiang // December 2020</p>
-<p>Thanks to Andreas Terzis, Ben Wedin, Carey Radebaugh, David Weinberger, Emily Reif, Fernanda Viégas, Hal Abelson, Kristen Olson, Martin Wattenberg, Michael Terry, Miguel Guevara, Thomas Steinke, Yannick Assogba, Zan Armstrong and our other colleagues at Google for their help with this piece.</p>
-<h3>More Explorables</h3>
-
-<p id='recirc'></p>
-
-
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/simple-statistics.min.js'></script>
-<script src='players0.js'></script>
-<script src='script.js'></script>
-
-
-<script src='../third_party/recirc.js'></script>
-</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/measuring-fairness/source/fill-in-the-blank/tokenizer.js b/spaces/merve/measuring-fairness/source/fill-in-the-blank/tokenizer.js
deleted file mode 100644
index 47c8bee46f60ed69ce963ee36af39bacacd9a095..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/source/fill-in-the-blank/tokenizer.js
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-
-// https://github.com/tensorflow/tfjs-models/blob/master/universal-sentence-encoder/src/tokenizer/trie.ts
-
-class TrieNode {
-  constructor(key) {
-    this.key = key;
-    this.parent = null;
-    this.children = {};
-    this.end = false;
-  }
-
-  getWord() {
-    const output = [];
-    let node = this;
-
-    while (node !== null) {
-      if (node.key !== null) {
-        output.unshift(node.key);
-      }
-      node = node.parent;
-    }
-
-    return [output, this.score, this.index];
-  }
-}
-
-class Trie {
-  constructor() {
-    this.root = new TrieNode(null);
-  }
-
-  insert(word, score, index) {
-    let node = this.root;
-
-    const symbols = [];
-    for (const symbol of word) {
-      symbols.push(symbol);
-    }
-
-    for (let i = 0; i < symbols.length; i++) {
-      if (!node.children[symbols[i]]) {
-        node.children[symbols[i]] = new TrieNode(symbols[i]);
-        node.children[symbols[i]].parent = node;
-      }
-
-      node = node.children[symbols[i]];
-
-      if (i === symbols.length - 1) {
-        node.end = true;
-        node.score = score;
-        node.index = index;
-      }
-    }
-  }
-
-  find(ss) {
-    let node = this.root;
-    let iter = 0;
-
-    while (iter < ss.length && node != null) {
-      node = node.children[ss[iter]];
-      iter++;
-    }
-
-    return node;
-  }
-}
-
-const bert = {
-  loadTokenizer: async () => {
-    const tokenizer = new BertTokenizer();
-    await tokenizer.load();
-
-    return tokenizer;
-  }
-};
-
-class BertTokenizer {
-  constructor() {
-    this.separator = '\u2581';
-    this.UNK_INDEX = 100;
-  }
-
-  async load() {
-    this.vocab = await this.loadVocab();
-
-    this.trie = new Trie();
-    // Actual tokens start at 999.
-    for (let i = 999; i < this.vocab.length; i++) {
-      const word = this.vocab[i];
-      this.trie.insert(word, 1, i);
-    }
-
-    this.token2Id = {}
-    this.vocab.forEach((d, i) => {
-      this.token2Id[d] = i
-    })
-
-    this.decode = a => a.map(d => this.vocab[d].replace('▁', ' ')).join('')
-    // Adds [CLS] and [SEP]
-    this.tokenizeCLS = str => [101, ...this.tokenize(str), 102]
-  }
-
-  async loadVocab() {
-    if (!window.bertProcessedVocab){
-      window.bertProcessedVocab = await (await fetch('data/processed_vocab.json')).json()
-    }
-    return window.bertProcessedVocab
-  }
-
-  processInput(text) {
-    const words = text.split(' ');
-    return words.map(word => {
-      if (word !== '[CLS]' && word !== '[SEP]') {
-        return this.separator + word.toLowerCase().normalize('NFKC');
-      }
-      return word;
-    });
-  }
-
-  tokenize(text) {
-    // Source:
-    // https://github.com/google-research/bert/blob/88a817c37f788702a363ff935fd173b6dc6ac0d6/tokenization.py#L311
-
-    let outputTokens = [];
-
-    const words = this.processInput(text);
-
-    for (let i = 0; i < words.length; i++) {
-      const chars = [];
-      for (const symbol of words[i]) {
-        chars.push(symbol);
-      }
-
-      let isUnknown = false;
-      let start = 0;
-      const subTokens = [];
-
-      const charsLength = chars.length;
-
-      while (start < charsLength) {
-        let end = charsLength;
-        let currIndex;
-
-        while (start < end) {
-          let substr = chars.slice(start, end).join('');
-
-          const match = this.trie.find(substr);
-
-          if (match != null && match.end) {
-            currIndex = match.getWord()[2];
-            break;
-          }
-
-          end = end - 1;
-        }
-
-        if (currIndex == null) {
-          isUnknown = true;
-          break;
-        }
-
-        subTokens.push(currIndex);
-        start = end;
-      }
-
-      if (isUnknown) {
-        outputTokens.push(this.UNK_INDEX);
-      } else {
-        outputTokens = outputTokens.concat(subTokens);
-      }
-    }
-
-    return outputTokens;
-  }
-}
\ No newline at end of file
diff --git a/spaces/merve/measuring-fairness/source/measuring-fairness/students.js b/spaces/merve/measuring-fairness/source/measuring-fairness/students.js
deleted file mode 100644
index 4af55cba8cc763d96aa478be96a785048d9edc42..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/source/measuring-fairness/students.js
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-window.makeStudents = function(){
-  var seed = new Math.seedrandom('he4a15')
-  var rand = d3.randomUniform.source(seed)(0, 1)
-  var letters = 'abcdefgijlmnopqrsuvwxyz'
-  letters = (letters + letters.toUpperCase()).split('')
-
-  var nSickCols = 6
-  var mSickCols = 8
-  var fSickCols = nSickCols*2 - mSickCols
-
-  var students = d3.range(nCols*nCols).map(i => {
-    var letter = letters[~~d3.randomUniform.source(seed)(0, letters.length)()]
-
-    var isMale = i % 2 == 0
-    var isSick = i < (isMale ? mSickCols : fSickCols)*nCols
-    var grade = isSick*.5 + rand()
-    var pos = {}
-
-    return {letter, isSick, isMale, grade, pos}
-  })
-
-  students = _.sortBy(students, d => -d.grade)
-  d3.nestBy(students, d => d.isSick).forEach(group => {
-    var isSick = group[0].isSick
-
-    var sickCols = nSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-
-    group.forEach((d, i) => {
-      d.pos.allIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.allIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.all = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  d3.nestBy(students, d => d.isSick + '-' + d.isMale).forEach(group => {
-    var isSick = group[0].isSick
-    var isMale = group[0].isMale
-
-    var sickCols = isMale ? mSickCols : fSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-    var yOffset = isMale ? nCols/2 + 2 : 0
-
-    group.forEach((d, i) => {
-      d.pos.sexIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols) + yOffset]
-      d.pos.sexGroupIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.sexIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.sex = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  students.maleOffsetJ = nCols/2 + 2
-  students.maleOffsetPx= students.maleOffsetJ*c.width/10
-
-  students.fSickCols = fSickCols
-  students.mSickCols = mSickCols
-
-  students.colWidth = c.width/10
-
-  students.rand = rand
-  return students
-}
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/mfkeles/Track-Anything/template.html b/spaces/mfkeles/Track-Anything/template.html
deleted file mode 100644
index c476716dc1e6d258db094bb0d79a6a232cb986b0..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/template.html
+++ /dev/null
@@ -1,27 +0,0 @@
-<!-- template.html -->
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Gradio Video Pause Time</title>
-</head>
-<body>
-    <video id="video" controls>
-        <source src="{{VIDEO_URL}}" type="video/mp4">
-        Your browser does not support the video tag.
-    </video>
-    <script>
-        const video = document.getElementById("video");
-        let pauseTime = null;
-
-        video.addEventListener("pause", () => {
-            pauseTime = video.currentTime;
-        });
-
-        function getPauseTime() {
-            return pauseTime;
-        }
-    </script>
-</body>
-</html>
diff --git a/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/fused_act.py b/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/fused_act.py
deleted file mode 100644
index 7e3d464ae656920c6875bc877281cadb2eaa4105..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/fused_act.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import platform
-
-import torch
-from torch import nn
-from torch.autograd import Function
-import torch.nn.functional as F
-from torch.utils.cpp_extension import load
-
-use_fallback = False
-
-# Try loading precompiled, otherwise use native fallback
-try:
-    import fused
-except ModuleNotFoundError as e:
-    print('StyleGAN2: Optimized CUDA op FusedLeakyReLU not available, using native PyTorch fallback.')
-    use_fallback = True
-
-
-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):
-    if use_fallback or input.device.type == 'cpu':
-        return scale * F.leaky_relu(
-            input + bias.view((1, -1)+(1,)*(input.ndim-2)), negative_slope=negative_slope
-        )
-    else:
-        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/mindtube/Diffusion50XX/app.py b/spaces/mindtube/Diffusion50XX/app.py
deleted file mode 100644
index dc4df0e4156518480719c90a1b45d8d6b999ee78..0000000000000000000000000000000000000000
--- a/spaces/mindtube/Diffusion50XX/app.py
+++ /dev/null
@@ -1,311 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-
-models = [
-    {"name": "7 Pa", "url": "AIARTCHAN/7pa"},
-    {"name": "A Certain Model", "url": "JosephusCheung/ACertainModel"},
-    {"name": "A Certain Thing", "url": "JosephusCheung/ACertainThing"},
-    {"name": "A Certainity", "url": "JosephusCheung/ACertainty"},
-    {"name": "Abyss Hell Hero", "url": "AIARTCHAN/AbyssHellHero"},
-    {"name": "Abyss Maple 3", "url": "AIARTCHAN/AbyssMapleVer3"},
-    {"name": "Abyss Orange Mix 2", "url": "WarriorMama777/AbyssOrangeMix2"},
-    {"name": "Abyss Orange Mix", "url": "WarriorMama777/AbyssOrangeMix"},
-    {"name": "AbyssHell 3", "url": "AIARTCHAN/AbyssHellVer3"},
-    {"name": "Alice in Diffusion Land", "url": "Guizmus/SDArt_AliceInDiffusionLand"},
-    {"name": "All 526 Animated", "url": "stablediffusionapi/all-526-animated"},
-    {"name": "All 526", "url": "stablediffusionapi/all-526"},
-    {"name": "Alloy Mix", "url": "p1atdev/alloy-mix"},
-    {"name": "Alt Clip", "url": "BAAI/AltCLIP"},
-    {"name": "Ambient Mix", "url": "OedoSoldier/ambientmix"},
-    {"name": "AmiIReal", "url": "stablediffusionapi/amireal"},
-    {"name": "Analog Diffusion", "url": "wavymulder/Analog-Diffusion"},
-    {"name": "Anidosmix 3", "url": "AIARTCHAN/anidosmixV2"},
-    {"name": "Anime Kawai Diffusion", "url": "Ojimi/anime-kawai-diffusion"},
-    {"name": "Anime Screencap", "url": "Conflictx/AnimeScreencap"},
-    {"name": "Animix", "url": "OedoSoldier/animix"},
-    {"name": "Anireal 3D V2", "url": "circulus/sd-anireal-3d-v2"},
-    {"name": "Any Pastel", "url": "m4gnett/any-pastel"},
-    {"name": "AnyLORA", "url": "kubanemil/AnyLORA"},
-    {"name": "Anything 2.1", "url": "swl-models/anything-v2.1"},
-    {"name": "Anything 3.0 Light", "url": "mm00/anything-v3.0-light"},
-    {"name": "Anything 3.0", "url": "Linaqruf/anything-v3.0"},
-    {"name": "Anything 3.1", "url": "cag/anything-v3-1"},
-    {"name": "Anything 3X", "url": "iZELX1/Anything-V3-X"},
-    {"name": "Anything 5.0", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Anything Else 4", "url": "stablediffusionapi/anythingelse-v4"},
-    {"name": "Anything Else 5", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Anything Midjourney 4.1", "url": "Joeythemonster/anything-midjourney-v-4-1"},
-    {"name": "Arcane Diffusion", "url": "nitrosocke/Arcane-Diffusion"},
-    {"name": "Archer Diffusion", "url": "nitrosocke/archer-diffusion"},
-    {"name": "Asian Mix", "url": "D1b4l4p/AsianMix"},
-    {"name": "Aurora", "url": "SweetLuna/Aurora"},
-    {"name": "Balloon Art", "url": "Fictiverse/Stable_Diffusion_BalloonArt_Model"},
-    {"name": "Blood Orange Mix", "url": "WarriorMama777/BloodOrangeMix"},
-    {"name": "Blue pencil", "url": "bluepen5805/blue_pencil"},
-    {"name": "CamelliaMix 2.5D","url": "stablediffusionapi/camelliamix25d"},
-    {"name": "CamelliaMix Line","url": "stablediffusionapi/camelliamixline"},
-    {"name": "CamelliaMix","url": "Powidl43/CamelliaMix"},
-    {"name": "Candy Punk", "url": "Conflictx/CandyPunk"},
-    {"name": "Cetusmix", "url": "stablediffusionapi/cetusmix"},
-    {"name": "CGI Animation", "url": "Conflictx/CGI_Animation"},
-    {"name": "Chaos and Order", "url": "Guizmus/SDArt_ChaosAndOrder768"},
-    {"name": "Chem Punk", "url": "Conflictx/Chempunk"},
-    {"name": "Chikmix", "url": "stablediffusionapi/chikmix"},
-    {"name": "Chillout App Factory","url": "stablediffusionapi/chillout-app-factory"},
-    {"name": "Chilloutclara", "url": "Fred99774/chilloutvlara"},
-    {"name": "Circulus 2.8", "url": "circulus/sd-photoreal-v2.8"},
-    {"name": "Circulus Photoreal V2", "url": "circulus/sd-photoreal-real-v2"},
-    {"name": "Circulus Semi Real 2", "url": "circulus/sd-photoreal-semi-v2"},
-    {"name": "Clam", "url": "Johnhex/Clam"},
-    {"name": "Clarity", "url": "Schisim/Clarity"},
-    {"name": "Classic Anime", "url": "nitrosocke/classic-anim-diffusion"},
-    {"name": "Claudfuen 1", "url": "claudfuen/photorealistic-fuen-v1"},
-    {"name": "Collage Diffusion", "url": "wavymulder/collage-diffusion"},
-    {"name": "Colorful 4.5", "url": "Manseo/Colorful-v4.5-Plus"},
-    {"name": "Comic Diffusion", "url": "ogkalu/Comic-Diffusion"},
-    {"name": "Complex-Lineart", "url": "Conflictx/Complex-Lineart"},
-    {"name": "Cool Japan Diffusion 2.1.2", "url": "aipicasso/cool-japan-diffusion-2-1-2"},
-    {"name": "Cool Japan Diffusion", "url": "aipicasso/cool-japan-diffusion-2-1-2"},
-    {"name": "CoreMix", "url": "corechan/CoreMix"},
-    {"name": "Cosmic Babes", "url": "stablediffusionapi/cosmic-babes"},
-    {"name": "Cosmic Horros 768", "url": "Guizmus/SDArt_cosmichorrors768"},
-    {"name": "Cosmic Horros", "url": "Guizmus/SDArt_cosmichorrors"},
-    {"name": "Counterfeit 1.0", "url": "gsdf/counterfeit-v1.0"},
-    {"name": "Counterfeit 2", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 2.0", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 3.0", "url": "stablediffusionapi/counterfeit-v30"},
-    {"name": "CuteSexyRobutts", "url": "andite/cutesexyrobutts-diffusion"},
-    {"name": "CyberPunk Anime", "url": "DGSpitzer/Cyberpunk-Anime-Diffusion"},
-    {"name": "Cyberrealistic", "url": "stablediffusionapi/cyberrealistic"},
-    {"name": "Dark Sushi Mix", "url": "stablediffusionapi/dark-sushi-mix"},
-    {"name": "Dash Sushi 25d", "url": "stablediffusionapi/dark-sushi-25d"},
-    {"name": "Deliberate", "url": "Masagin/Deliberate"},
-    {"name": "DGSpitzer", "url": "DGSpitzer/DGSpitzer-Art-Diffusion"},
-    {"name": "Double Exposure Diffusion", "url": "joachimsallstrom/Double-Exposure-Diffusion"},
-    {"name": "Dreamful 2", "url": "Hius/DreamFul-V2"},
-    {"name": "Dreamlike Anime", "url": "dreamlike-art/dreamlike-anime-1.0"},
-    {"name": "Dreamlike Art", "url": "dreamlike-art/dreamlike-diffusion-1.0"},
-    {"name": "Dreamlike Diffusion", "url": "dreamlike-art/dreamlike-diffusion-1.0"},
-    {"name": "Dreamlike Photoreal", "url": "dreamlike-art/dreamlike-photoreal-2.0"},
-    {"name": "Dreamshaper", "url": "Lykon/DreamShaper"},
-    {"name": "DucHaiten Anime", "url": "DucHaiten/DucHaitenAnime"},
-    {"name": "DucHaiten Art", "url": "DucHaiten/DucHaitenAIart"},
-    {"name": "DucHaiten ClassicAnime", "url": "DucHaiten/DH_ClassicAnime"},
-    {"name": "DucHaiten DreamWorld", "url": "DucHaiten/DucHaitenDreamWorld"},
-    {"name": "DucHaiten Journey", "url": "DucHaiten/DucHaitenJourney"},
-    {"name": "DucHaiten StyleLikeMe", "url": "DucHaiten/DucHaiten-StyleLikeMe"},
-    {"name": "DucHaiten SuperCute", "url": "DucHaiten/DucHaitenSuperCute"},
-    {"name": "Dungeons and Diffusion", "url": "0xJustin/Dungeons-and-Diffusion"},
-    {"name": "Eerie Orange Mix", "url": "WarriorMama777/EerieOrangeMix"},
-    {"name": "Eimis Anime Diffusion", "url": "eimiss/EimisAnimeDiffusion_1.0v"},
-    {"name": "Elden Ring", "url": "nitrosocke/elden-ring-diffusion"},
-    {"name": "EmotionalBackLightsMix", "url": "Kuyoh/EmotionalBackLightsMix"},
-    {"name": "Epi PhaV", "url": "EmiPha/EmiPhaV"},
-    {"name": "Epic Diffusion 1.1", "url": "johnslegers/epic-diffusion-v1.1"},
-    {"name": "Epic Diffusion", "url": "johnslegers/epic-diffusion"},
-    {"name": "EpicMix Realism", "url": "Duskfallcrew/EpicMix_Realism"},
-    {"name": "Estrilda Mix", "url": "BIGMON/EstrildaMix"},
-    {"name": "FantaStel", "url": "0RisingStar0/FantaStel"},
-    {"name": "Fantasy Mix", "url": "theintuitiveye/FantasyMix"},
-    {"name": "FeverDream", "url": "Cosmo-Hug/FeverDream"},
-    {"name": "Fluid Art", "url": "Fictiverse/Stable_Diffusion_FluidArt_Model"},
-    {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
-    {"name": "Gakki Mix 768", "url": "Sa1i/gakki-mix-768"},
-    {"name": "Ghibli Diffusion", "url": "nitrosocke/Ghibli-Diffusion"},
-    {"name": "Girl New 1", "url": "Fred99774/girlnew1"},
-    {"name": "Gorynich Mix", "url": "JackAnon/GorynichMix"},
-    {"name": "GorynichMix", "url": "JackAnon/GorynichMix"},
-    {"name": "GrapeFruit", "url": "iZELX1/Grapefruit"},
-    {"name": "Grimoeresigils", "url": "ECarbenia/grimoiresigils"},
-    {"name": "GTA5 Artwork Diffusion", "url": "ItsJayQz/GTA5_Artwork_Diffusion"},
-    {"name": "GuoFeng 3", "url": "xiaolxl/GuoFeng3"},
-    {"name": "Guweiz Diffusion", "url": "andite/guweiz-diffusion"},
-    {"name": "Hanamix", "url": "Hanabiaiart/Hanamix"},
-    {"name": "HARDBlend", "url": "theintuitiveye/HARDblend"},
-    {"name": "HassanBlend 1.4", "url": "hassanblend/hassanblend1.4"},
-    {"name": "HassanBlend 1.5.1.2", "url": "hassanblend/HassanBlend1.5.1.2"},
-    {"name": "High Rise Mix 1", "url": "0RisingStar0/HighRiseMixV1"},
-    {"name": "High Rise Mix 2", "url": "0RisingStar0/HighRiseMixV2"},
-    {"name": "HimawariMixs", "url": "natsusakiyomi/HimawariMixs"},
-    {"name": "Hiten Diffusion", "url": "andite/hiten-diffusion"},
-    {"name": "Hrrzg Style 768px", "url": "TheLastBen/hrrzg-style-768px"},
-    {"name": "Icomix 2", "url": "stablediffusionapi/icomix-2"},
-    {"name": "InkPunk Diffusion", "url": "Envvi/Inkpunk-Diffusion"},
-    {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
-    {"name": "Kipaki Egyptian Sci Fi", "url": "Conflictx/Kipaki-EgyptianSciFi"},
-    {"name": "Lit 6B", "url": "hakurei/lit-6B"},
-    {"name": "LiveArca Mix", "url": "0RisingStar0/LiveArcaMix"},
-    {"name": "Lomo Diffusion", "url": "wavymulder/lomo-diffusion"},
-    {"name": "Luna Diffusion", "url": "proximasanfinetuning/luna-diffusion"},
-    {"name": "Majic Mix Fantasy", "url": "stablediffusionapi/majicmixfantasy"},
-    {"name": "Majic Mix Realistic", "url": "stablediffusionapi/majicmixrealistic"},
-    {"name": "Majic Mix Sombre", "url": "stablediffusionapi/majicmixsombre"},
-    {"name": "Mama Orange Mixs", "url": "WarriorMama777/OrangeMixs"},
-    {"name": "Marvel WhatIf Diffusion", "url": "ItsJayQz/Marvel_WhatIf_Diffusion"},
-    {"name": "Mashuu Diffusion", "url": "andite/mashuu-diffusion"},
-    {"name": "Meina Alter", "url": "stablediffusionapi/meinaalter"},
-    {"name": "Meina Pastel", "url": "stablediffusionapi/meinapastel"},
-    {"name": "MeinaMix 7", "url": "Nacholmo/meinamixv7-diffusers"},
-    {"name": "MF-Base", "url": "MyneFactory/MF-Base"},    
-    {"name": "Microscopic Model", "url": "Fictiverse/Stable_Diffusion_Microscopic_model"},
-    {"name": "Midjourney 4.0", "url": "flax/midjourney-v4-diffusion"},
-    {"name": "Midjourney 4.1", "url": "Joeythemonster/anything-midjourney-v-4-1"},
-    {"name": "Mignon Diffusion", "url": "andite/mignon-diffusion"},
-    {"name": "MikaPikazo Diffusion", "url": "andite/mikapikazo-diffusion"},
-    {"name": "Mikapikazo", "url": "andite/mikapikazo-diffusion"},
-    {"name": "Mix Pro V4", "url": "AIARTCHAN/MIX-Pro-V4"},
-    {"name": "Model Shoot", "url": "wavymulder/modelshoot"},
-    {"name": "Mo-Di Diffusion", "url": "nitrosocke/mo-di-diffusion"},
-    {"name": "MoistMix 1", "url": "MoistMix/MoistMixV1"},
-    {"name": "Naruto Diffuser", "url": "lambdalabs/sd-naruto-diffusers"},
-    {"name": "Never Ending Dream 2", "url": "luongphamit/NeverEnding-Dream2"},
-    {"name": "NeverEnding-Dream", "url": "Lykon/NeverEnding-Dream"},
-    {"name": "Nitro Diffusion", "url": "nitrosocke/Nitro-Diffusion"},
-    {"name": "NotSoXJB", "url": "AerinK/NotSoXJB-Mix-1"},    
-    {"name": "Openjourney 4", "url": "prompthero/openjourney-v4"},
-    {"name": "Openjourney V2", "url": "prompthero/openjourney-v2"},
-    {"name": "Openjourney", "url": "prompthero/openjourney"},
-    {"name": "OpenNiji", "url": "Korakoe/OpenNiji"},
-    {"name": "Papercut", "url": "Fictiverse/Stable_Diffusion_PaperCut_Model"},
-    {"name": "Pastel Mix", "url": "andite/pastel-mix"},
-    {"name": "Picasso Diffusion 1.1", "url": "aipicasso/picasso-diffusion-1-1"},
-    {"name": "Piromizu Diffusion", "url": "andite/piromizu-diffusion"},
-    {"name": "Plat Diffusion", "url": "p1atdev/plat-diffusion"},
-    {"name": "Pokemon Diffuser", "url": "lambdalabs/sd-pokemon-diffusers"},
-    {"name": "Portrait Plus", "url": "wavymulder/portraitplus"},
-    {"name": "Protogen 2.2", "url": "darkstorm2150/Protogen_v2.2_Official_Release"},
-    {"name": "Protogen Infinity", "url": "darkstorm2150/Protogen_Infinity_Official_Release"},
-    {"name": "Protogen X 3.4", "url": "darkstorm2150/Protogen_x3.4_Official_Release"},
-    {"name": "Protogen X 5.8", "url": "darkstorm2150/Protogen_x5.8_Official_Release"},
-    {"name": "QuinceMix", "url": "Hemlok/QuinceMix"},
-    {"name": "Realistic Vision 1.4", "url": "SG161222/Realistic_Vision_V1.4"},
-    {"name": "Redshift 768", "url": "nitrosocke/redshift-diffusion-768"},
-    {"name": "Redshift Diffusion 768", "url": "nitrosocke/redshift-diffusion-768"},
-    {"name": "Redshift Diffusion", "url": "nitrosocke/redshift-diffusion"},
-    {"name": "Replicant V2", "url": "gsdf/Replicant-V2.0"},
-    {"name": "Replicant-V1.0", "url": "gsdf/Replicant-V1.0"},
-    {"name": "Rev Anim", "url": "stablediffusionapi/rev-anim"},
-    {"name": "Rev Animated", "url": "coreml/coreml-ReV-Animated"},
-    {"name": "Rev Animated", "url": "LottePeisch/RevAnimated-Diffusers"},
-    {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
-    {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
-    {"name": "Salt_mix", "url": "AsAHuman/Salt_mix"},
-    {"name": "Seek Art Mega", "url": "coreco/seek.art_MEGA"},
-    {"name": "Semi Real Mix", "url": "robotjung/SemiRealMix"},
-    {"name": "SEmix", "url": "Deyo/SEmix"},
-    {"name": "Something V 2.2","url": "NoCrypt/SomethingV2_2"},
-    {"name": "Something V2","url": "NoCrypt/SomethingV2"},
-    {"name": "Something", "url": "Guizmus/SDArt_something"},
-    {"name": "Spider Verse diffusion", "url": "nitrosocke/spider-verse-diffusion"},
-    {"name": "SpyBG", "url": "stablediffusionapi/spybg"},
-    {"name": "Stable Diffusion 1.4","url": "CompVis/stable-diffusion-v1-4"},
-    {"name": "Stable Diffusion 1.5","url": "runwayml/stable-diffusion-v1-5"},
-    {"name": "Stable Diffusion 2.1 Base","url": "stabilityai/stable-diffusion-2-1-base"},
-    {"name": "Stable Diffusion 2.1 Unclip","url": "stabilityai/stable-diffusion-2-1-unclip"},
-    {"name": "Stable Diffusion 2.1","url": "stabilityai/stable-diffusion-2-1"},
-    {"name": "Subtly", "url": "ddPn08/subtly"},
-    {"name": "SukiyakiMix", "url": "Vsukiyaki/SukiyakiMix-v1.0"},
-    {"name": "Synthwave Punk 2", "url": "ItsJayQz/SynthwavePunk-v2"},
-    {"name": "The Ally", "url": "stablediffusionapi/the-ally"},
-    {"name": "Three Delicacy wonto", "url": "stablediffusionapi/three-delicacy-wonto"},
-    {"name": "Three Delicacy", "url": "stablediffusionapi/three-delicacy"},
-    {"name": "Timeless Diffusion", "url": "wavymulder/timeless-diffusion"},
-    {"name": "TMND mix", "url": "stablediffusionapi/tmnd-mix"},
-    {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
-    {"name": "UltraSkin", "url": "VegaKH/Ultraskin"},
-    {"name": "Valorant Diffusion", "url": "ItsJayQz/Valorant_Diffusion"},
-    {"name": "Van Gogh Diffusion", "url": "dallinmackay/Van-Gogh-diffusion"},
-    {"name": "Vectorartz Diffusion", "url": "coder119/Vectorartz_Diffusion"},
-    {"name": "Vintedois 1.0", "url": "22h/vintedois-diffusion-v0-1"},
-    {"name": "Vintedois 2.0", "url": "22h/vintedois-diffusion-v0-2"},
-    {"name": "Vivid Watercolors", "url": "Evel/VividWatercolors"},
-    {"name": "VoxelArt", "url": "Fictiverse/Stable_Diffusion_VoxelArt_Model"},
-    {"name": "Waifu Diffusion", "url": "hakurei/waifu-diffusion"},
-    {"name": "Wavyfusion", "url": "wavymulder/wavyfusion"},
-    {"name": "West Magic", "url": "xiaolxl/WestMagic"},
-    {"name": "X Mix", "url": "les-chien/X-mix"},
-]
-
-current_model = models[0]
-
-text_gen = gr.Interface.load("spaces/daspartho/prompt-extend") 
-
-models2 = []
-for model in models:
-    model_url = f"models/{model['url']}"
-    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
-    models2.append(loaded_model)
-
-
-def text_it(inputs, text_gen=text_gen):
-    return text_gen(inputs)
-
-
-def set_model(current_model_index):
-    global current_model
-    current_model = models[current_model_index]
-    return gr.update(value=f"{current_model['name']}")
-
-
-def send_it(inputs, model_choice):
-    proc = models2[model_choice]
-    return proc(inputs)
-
-
-with gr.Blocks() as myface:
-    gr.HTML(
-
-    )
-
-    with gr.Row():
-        with gr.Row():
-            input_text = gr.Textbox(label="Prompt idea",  placeholder="", lines=1)
-            # Model selection dropdown
-            model_name1 = gr.Dropdown(
-                label="Choose Model",
-                choices=[m["name"] for m in models],
-                type="index",
-                value=current_model["name"],
-                interactive=True,
-            )
-        with gr.Row():
-            see_prompts = gr.Button("Generate Prompts")
-            run = gr.Button("Generate Images", variant="primary")
-    
-    with gr.Row():
-        output1 = gr.Image(label="")
-        output2 = gr.Image(label="")
-        output3 = gr.Image(label="")
-    with gr.Row():
-        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
-    with gr.Row():
-        output4 = gr.Image(label="")
-        output5 = gr.Image(label="")
-        output6 = gr.Image(label="")
-    with gr.Row():
-        magic4 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic5 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic6 = gr.Textbox(label="Generated Prompt", lines=2)
-
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3, output4, output5, output6])
-
-    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
-    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
-    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
-    run.click(send_it, inputs=[magic4, model_name1], outputs=[output4])
-    run.click(send_it, inputs=[magic5, model_name1], outputs=[output5])
-    run.click(send_it, inputs=[magic6, model_name1], outputs=[output6])
-
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic4])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic5])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic6])
-
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)
\ No newline at end of file
diff --git a/spaces/mithril-security/blind_chat/src/lib/server/modelEndpoint.ts b/spaces/mithril-security/blind_chat/src/lib/server/modelEndpoint.ts
deleted file mode 100644
index f824b5fe43921dccbbcc524c272d5c71ad880e25..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/server/modelEndpoint.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-import {
-	HF_ACCESS_TOKEN,
-	HF_API_ROOT,
-	USE_CLIENT_CERTIFICATE,
-	CERT_PATH,
-	KEY_PATH,
-	CA_PATH,
-	CLIENT_KEY_PASSWORD,
-	REJECT_UNAUTHORIZED,
-} from "$env/static/private";
-import { sum } from "$lib/utils/sum";
-import type { BackendModel, Endpoint } from "./models";
-
-import { loadClientCertificates } from "$lib/utils/loadClientCerts";
-
-if (USE_CLIENT_CERTIFICATE === "true") {
-	loadClientCertificates(
-		CERT_PATH,
-		KEY_PATH,
-		CA_PATH,
-		CLIENT_KEY_PASSWORD,
-		REJECT_UNAUTHORIZED === "true"
-	);
-}
-
-/**
- * Find a random load-balanced endpoint
- */
-export function modelEndpoint(model: BackendModel): Endpoint {
-	if (model.is_local ?? false) {
-		return {
-			host: "local",
-			model: model.name,
-			weight: 1,
-			url: `${HF_API_ROOT}/${model.name}`,
-			authorization: `Bearer ${HF_ACCESS_TOKEN}`,
-		};
-	} else if (!model.endpoints) {
-		return {
-			host: "tgi",
-			url: `${HF_API_ROOT}/${model.name}`,
-			authorization: `Bearer ${HF_ACCESS_TOKEN}`,
-			weight: 1,
-		};
-	}
-	const endpoints = model.endpoints;
-	const totalWeight = sum(endpoints.map((e) => e.weight));
-
-	let random = Math.random() * totalWeight;
-	for (const endpoint of endpoints) {
-		if (random < endpoint.weight) {
-			console.log(endpoint);
-			return endpoint;
-		}
-		random -= endpoint.weight;
-	}
-
-	throw new Error("Invalid config, no endpoint found");
-}
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py b/spaces/mshukor/UnIVAL/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
deleted file mode 100644
index 062bb82f669f63a537b6ee8df4d42d292eb2575e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import nltk
-from misc.bleu_utils import sentence_bleu
-import warnings
-
-
-def get_target_sequences(manifest, ground_truth, to_take=1000):
-    import json
-    import pathlib
-
-    with open(ground_truth, 'r') as fin:
-        original_continuations = json.loads(fin.read())
-
-    sequence2length = [(k, v[0]) for k, v in original_continuations.items()]
-    assert all(float(v) >= 6.0 for (_, v) in sequence2length)  # 6 seconds
-
-    sequence2length.sort(key=lambda x: x[1])
-    to_take_sequences = set(v[0] for v in sequence2length[:to_take])
-    to_take_ids = []
-
-    with open(manifest, 'r') as f:
-        f.readline()
-
-        for i, line in enumerate(f.readlines()):
-            seq_id = line.split()[0]
-            seq_id = pathlib.Path(seq_id).name.split('__')[0]
-
-            if seq_id in to_take_sequences:
-                to_take_ids.append(i)
-
-    print(f'Took {len(to_take_ids)} ids')
-    return set(to_take_ids)
-
-
-def get_args():
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--asr-transcript', type=str,
-                        help='Path to the transcript file.')
-
-    parser.add_argument('--manifest', required=True)
-    parser.add_argument('--prompts-description', required=True)
-
-    parser.add_argument('--cut-id', action='store_true',
-                        help='Whether cut the first token (typically a seq id)')
-    parser.add_argument('--cut-tail', action='store_true',
-                        help='Whether cut the last token (typically a speaker id)')
-    parser.add_argument('--debug', action='store_true')
-
-    args = parser.parse_args()
-
-    return args
-
-
-def get_self_bleu(utterances, averaging_mode, weights):
-    self_bleu = []
-
-    for i in range(len(utterances)):
-        hypo = utterances[i]
-        rest = utterances[:i] + utterances[i+1:]
-
-        self_bleu.append(sentence_bleu(rest, hypo, weights,
-                         no_length_penalty=True, averaging_mode=averaging_mode))
-
-    return self_bleu
-
-
-def get_self_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)  # equal weight for unigrams and bigrams
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def get_auto_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_auto_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_self_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def auto_bleu(sentence, weights, mean_mode='arithmetic'):
-    if len(sentence) <= 1:
-        return 0
-
-    N = len(weights)
-
-    bleu_n = np.zeros([N])
-    for n in range(N):
-        targ_ngrams = list(nltk.ngrams(sentence, n+1))
-        for p in range(len(targ_ngrams)):
-            left = sentence[:p]
-            right = sentence[(p+n+1):]
-            rest_ngrams = list(nltk.ngrams(left, n+1)) + \
-                list(nltk.ngrams(right, n+1))
-            # compute the nb of matching ngrams
-            bleu_n[n] += targ_ngrams[p] in rest_ngrams
-        bleu_n[n] /= len(targ_ngrams)  # average them to get a proportion
-
-    weights = np.array(weights)
-    if mean_mode == 'arithmetic':
-        return (bleu_n * weights).sum()
-    elif mean_mode == 'geometric':
-        return (bleu_n ** weights).prod()
-    else:
-        raise ValueError(f'Unknown agggregation mode {mean_mode}')
-
-
-def main():
-    from multiprocessing import Pool
-
-    args = get_args()
-    target_ids = get_target_sequences(args.manifest, args.prompts_description)
-
-    with open(args.asr_transcript, 'r') as fin:
-        lines = fin.readlines()
-
-    terms = [x.strip().split() for x in lines]
-    filtered = []
-    for term in terms:
-        line_id = int(term[-1].split('-')[1][:-1])
-        if line_id in target_ids:
-            filtered.append(term)
-    terms = filtered
-
-    if args.cut_id:
-        terms = [x[1:] for x in terms]
-    if args.cut_tail:
-        terms = [x[:-1] for x in terms]
-
-    if args.debug:
-        terms = terms[:10]
-
-    tasks = [
-        ('Self-BLEU2-arithmetic', get_self_bleu2_arithmetic),
-        ('Self-BLEU2-geometric', get_self_bleu2_geometric),
-        ('Auto-BLEU2-arithmetic', get_auto_bleu2_arithmetic),
-        ('Auto-BLEU2-geometric', get_auto_bleu2_geometric),
-
-        ('Self-BLEU3-arithmetic', get_self_bleu3_arithmetic),
-        ('Self-BLEU3-geometric', get_self_bleu3_geometric),
-        ('Auto-BLEU3-arithmetic', get_auto_bleu3_arithmetic),
-        ('Auto-BLEU3-geometric', get_auto_bleu3_geometric),
-    ]
-
-    n_processes = min(16, len(tasks))
-    with Pool(n_processes) as pool:
-        metrics = pool.map(run_f, [(t[1], terms) for t in tasks])
-
-    for (metric_name, _), metric in zip(tasks, metrics):
-        metric, sem = np.mean(metric), np.std(metric) / np.sqrt(len(metric))
-
-        metric, sem = [
-            round(100 * x, 2) for x in [metric, sem]
-        ]
-
-        print(f'{metric_name} {metric} +- {sem}')
-
-
-def run_f(task_params):
-    f, terms = task_params
-    return f(terms)
-
-
-if __name__ == '__main__':
-    # NLTK produces warnings
-    warnings.filterwarnings("ignore")
-
-    main()
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/data/augmentation/__init__.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/data/augmentation/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mueller-franzes/medfusion-app/setup.py b/spaces/mueller-franzes/medfusion-app/setup.py
deleted file mode 100644
index 2d659285650d9875515d363064a06904a72e2590..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/setup.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from setuptools import setup, find_packages
-
-with open('README.md', encoding='utf-8') as f:
-    long_description = f.read()
-
-with open('requirements.txt', encoding='utf-8') as f:
-    install_requires = f.read()
-
-
-
-setup(
-    name='Medical Diffusion',
-    author="",
-    version="1.0",
-    description="Diffusion model for medical images", 
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
-    install_requires=install_requires,
-)
\ No newline at end of file
diff --git a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/popper.min.js b/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/popper.min.js
deleted file mode 100644
index 2269d66967635e9bde6edb948cd93b0ae1978dfc..0000000000000000000000000000000000000000
--- a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/popper.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/**
- * @popperjs/core v2.11.4 - MIT License
- */
-
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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-
diff --git a/spaces/mygyasir/remove-photo-object/README.md b/spaces/mygyasir/remove-photo-object/README.md
deleted file mode 100644
index df1ca393f7c486dd2260c23c754d2028a8856659..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/remove-photo-object/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Remove Photo Object
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-python_version: 3.9.5
-app_file: app.py
-pinned: false
-duplicated_from: aryadytm/remove-photo-object
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/neko321/Voice-Changer1/util.py b/spaces/neko321/Voice-Changer1/util.py
deleted file mode 100644
index 8d6bcff1135c2d97e4caad7922f03f05c98484da..0000000000000000000000000000000000000000
--- a/spaces/neko321/Voice-Changer1/util.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import sys
-import asyncio
-from io import BytesIO
-
-from fairseq import checkpoint_utils
-
-import torch
-
-import edge_tts
-import librosa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/config.py#L43-L55  # noqa
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, 'has_mps', False):
-            return False
-
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-def is_half(device: str) -> bool:
-    if not device.startswith('cuda'):
-        return False
-    else:
-        gpu_name = torch.cuda.get_device_name(
-            int(device.split(':')[-1])
-        ).upper()
-
-        # ...regex?
-        if (
-            ('16' in gpu_name and 'V100' not in gpu_name)
-            or 'P40' in gpu_name
-            or '1060' in gpu_name
-            or '1070' in gpu_name
-            or '1080' in gpu_name
-        ):
-            return False
-
-    return True
-
-
-def load_hubert_model(device: str, model_path: str = 'hubert_base.pt'):
-    model = checkpoint_utils.load_model_ensemble_and_task(
-        [model_path]
-    )[0][0].to(device)
-
-    if is_half(device):
-        return model.half()
-    else:
-        return model.float()
-
-
-async def call_edge_tts(speaker_name: str, text: str):
-    tts_com = edge_tts.Communicate(text, speaker_name)
-    tts_raw = b''
-
-    # Stream TTS audio to bytes
-    async for chunk in tts_com.stream():
-        if chunk['type'] == 'audio':
-            tts_raw += chunk['data']
-
-    # Convert mp3 stream to wav
-    ffmpeg_proc = await asyncio.create_subprocess_exec(
-        'ffmpeg',
-        '-f', 'mp3',
-        '-i', '-',
-        '-f', 'wav',
-        '-',
-        stdin=asyncio.subprocess.PIPE,
-        stdout=asyncio.subprocess.PIPE
-    )
-    (tts_wav, _) = await ffmpeg_proc.communicate(tts_raw)
-
-    return librosa.load(BytesIO(tts_wav))
diff --git a/spaces/nesticot/pp_roundup/app.py b/spaces/nesticot/pp_roundup/app.py
deleted file mode 100644
index 71bb13ba2e30f5e84725bfad21910dd1f1cc7ce1..0000000000000000000000000000000000000000
--- a/spaces/nesticot/pp_roundup/app.py
+++ /dev/null
@@ -1,462 +0,0 @@
-print('Running')
-import time
-import requests
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-from matplotlib.pyplot import figure
-from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-from scipy import stats
-import matplotlib.lines as mlines
-import matplotlib.transforms as mtransforms
-import numpy as np
-import time
-#import plotly.express as px
-#!pip install chart_studio
-#import chart_studio.tools as tls
-from bs4 import BeautifulSoup
-import matplotlib.pyplot as plt
-import numpy as np
-import matplotlib.font_manager as font_manager
-from datetime import datetime
-import pytz
-from matplotlib.ticker import MaxNLocator
-from matplotlib.patches import Ellipse
-import matplotlib.transforms as transforms
-from matplotlib.gridspec import GridSpec
-datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
-# Configure Notebook
-#%matplotlib inline
-plt.style.use('fivethirtyeight')
-sns.set_context("notebook")
-import warnings
-warnings.filterwarnings('ignore')
-# import yfpy
-# from yfpy.query import YahooFantasySportsQuery
-# import yahoo_oauth
-import json
-#import openpyxl
-# from sklearn import preprocessing
-from datetime import timedelta
-# import dataframe_image as dfi
-# from google.colab import drive
-def percentile(n):
-    def percentile_(x):
-        return np.percentile(x, n)
-    percentile_.__name__ = 'percentile_%s' % n
-    return percentile_
-
-# import os
-# import praw
-# import matplotlib.pyplot as plt
-# import matplotlib.colors
-# import matplotlib.colors as mcolors
-# cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285f4","#FFFFFF","#F0E442"])
-#import pybaseball
-import math
-# import matplotlib.ticker as mtick
-# import matplotlib.ticker as ticker
-
-# colour_palette = ['#FFB000','#648FFF','#785EF0',
-                #   '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-# import matplotlib.colors as mcolors
-# from  matplotlib.ticker import FuncFormatter
-# from matplotlib.font_manager import FontProperties
-
-import numpy as np
-# import matplotlib.pyplot as plt
-import matplotlib.colors
-
-# import undetected_chromedriver as uc
-# from selenium import webdriver
-# from seleniumbase import Driver
-
-# driver = Driver(uc=True)
-# driver.get('https://www.naturalstattrick.com')
-
-#x,y,c = zip(*np.random.rand(30,3)*4-2)
-
-#norm=plt.Normalize(-2,2)
-#co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#ffffff","#F0E442"])
-co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"])
-#co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"])
-
-
-
-
-try: 
-    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=3000;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-    key_check = data_r['fantasy_content']['league']['players']
-
-except KeyError:
-    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.l.public;out=settings/players;position=ALL;start=0;count=1151;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-    print('key_checked')
-
-total_list = []
-
-for x in data_r['fantasy_content']['league']['players']:
-    single_list = []
-
-    single_list.append(int(x['player']['player_id']))
-    single_list.append(int(x['player']['player_ranks'][0]['player_rank']['rank_value']))
-    single_list.append(x['player']['name']['full'])
-    single_list.append(x['player']['name']['first'])
-    single_list.append(x['player']['name']['last'])
-    single_list.append(x['player']['draft_analysis']['average_pick'])
-    single_list.append(x['player']['average_auction_cost'])
-    single_list.append(x['player']['display_position'])
-    single_list.append(x['player']['editorial_team_abbr'])
-    if 'value' in x['player']['percent_owned']:
-        single_list.append(x['player']['percent_owned']['value']/100)
-    else:
-        single_list.append(0)
-    total_list.append(single_list)
-
-
-yahoo_df = pd.DataFrame(total_list,columns = ['player_id','rank_value','full','first','last','average_pick','average_auction_cost','display_position','editorial_team_abbr','percent_owned'])
-yahoo_df_2 = yahoo_df.copy()
-
-# # Write your code here.
-# response = requests.get("https://www.naturalstattrick.com/playerlist.php?fromseason=20232024&thruseason=20232024&stype=2&sit=all&stdoi=oi&rate=n")
-# soup = BeautifulSoup(response.text, 'html.parser')
-# table_rows = soup.findAll('tr')
-# table_rows = table_rows[1:-1]
-# table_rows[0].findAll('td')
-
-# player_name = []
-# player_position = []
-# player_team = []
-# player_id = []
-
-# for i in range(0,len(table_rows)-1):
-#     player_name.append(str(table_rows[i].findAll('td')[0].contents[0]))
-#     player_position.append(table_rows[i].findAll('td')[1].contents[0])
-#     player_team.append(table_rows[i].findAll('td')[2].contents[0])
-#     player_id.append(str(table_rows[i].findAll('td')[3].contents[0])[-76:][:7])
-
-# player_id_df = pd.DataFrame({'Player':player_name,'Player ID':player_id,'Position':player_position,'Team':player_team})
-# #player_id_df.index.name = 'Player Name'
-# player_id_df.head()
-
-# skater_df = player_id_df[player_id_df['Position'] != 'G']
-# goalie_df = player_id_df[player_id_df['Position'] == 'G']
-
-season = 20232024
-seasontype = 2
-
-def nat_stat_trick_range_pp_gp(rookie='n',start_date='2022-10-01',end_date=str(pd.to_datetime(datetime.now(pytz.timezone('US/Pacific')).strftime('%Y-%m-%d')).date()),sit='all',gp=1):
-    time.sleep(2)
-    url = f'https://www.naturalstattrick.com/playerteams.php?fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&stdoi=std&rate=y&team=ALL&pos=S&loc=B&toi=0&gpfilt=gpteam&fd=&td=&tgp='+str(gp)+'&lines=single&draftteam=ALL'
-
-    player_list_all = []
-    response = requests.get(url)
-    soup = BeautifulSoup(response.text, 'html.parser')
-    table_rows = soup.findAll('tr')
-    table_rows = table_rows[1:]
-
-    for j in range(0,len(table_rows)):
-      p_string = [str(x).strip('<td>').strip('</') for x  in list(table_rows[j].findAll('td')) if "<td>" in str(x)]
-      player_list_all.append([p_string[0]]+[str(table_rows[j].findAll('td')[1]).split('>')[2].split('<')[0]]+p_string[1:]+[str(table_rows[j].findAll('td')[1])[98:105].strip('</a></td>')])
-    #table_rows[0].findAll('td')
-
-    if soup != "":
-      columns_list = [str(x).split('>')[1].split('<')[0] for x in soup.findAll('th')]+['player_id']
-      df_url = pd.DataFrame(data=player_list_all,columns=columns_list)
-
-    df_url = df_url.fillna(0)
-    df_url['Shots+Hits+Blocks/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)+df_url['Shots Blocked/60'].astype(float)
-    df_url['Shots+Hits/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)
-    #print(url)
-    return df_url
-
-team_abv = pd.read_csv('team_abv.csv')
-team_dict = team_abv.set_index('team_abv').to_dict()['team_name']
-
-yahoo_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')
-
-
-player_games_df_old = pd.read_csv('player_games_cards.csv',index_col=[0]).sort_values(by='date',ascending=False)
-team_games_df = pd.read_csv('team_games.csv',index_col=[0])#.sort_values(by='date',ascending=False)
-
-player_games_df_2 = player_games_df_old.copy()
-
-team_games_df['game_count'] = team_games_df.groupby('team')['team'].cumcount()+1
-team_games_df['max_games'] = team_games_df.groupby('team').game_count.transform('max')
-team_games_df['abv'] = team_games_df.team.map(team_abv.set_index('team_name')['team_abv'].to_dict())
-team_games_df = team_games_df.sort_values(by='game_count',ascending=False)
-#team_abv = pd.read_csv('team_abv.csv')
-
-
-
-def nat_stat_convert(df):
-    for i in range(0,len(df.columns)):
-        if df.columns[i][-3:]=='/60':
-            if 'ix' not in df.columns[i]:
-                df[df.columns[i]] = np.round(df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60,0)
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
-            else:
-                df[df.columns[i]] = df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
-
-    df['Faceoffs %'] = df['Faceoffs Won']/(df['Faceoffs Won']+df['Faceoffs Lost'])
-
-    return df
-
-from shiny import ui, render, App
-import matplotlib.image as mpimg
-app_ui = ui.page_fluid(
-    #ui.panel_title("Simulate a normal distribution"),
-
-    ui.layout_sidebar(
-
-      ui.panel_sidebar(
-        #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
-        ui.input_select("team_id", "Select Team",team_dict,width=1,size=1,selected='ANA',),
-        ui.input_numeric("n_1", "Last Games x", value=1),
-        ui.input_numeric("n_2", "Last Games y", value=0),
-        ui.input_numeric("n_3", "Last Games z", value=0),
-        ui.input_numeric("top_n", "Show top 'n'", value=10),
-        ui.input_switch("x", "Drop N/A"),
-        #ui.input_select("ignore_id", "Remove Columns",['Position','Roster%'],multiple=True,selectize=True),
-      ),
-
-        ui.panel_main(ui.tags.h3(""),
-                      ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title")),
-                      #ui.tags.h2("Fantasy Hockey Schedule Summary"),
-                      ui.tags.h5("Created By: @TJStats,     Data: Natural Stat Trick, Yahoo Fantasy"),
-                      ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),
-                      ui.output_table("pp_roundup"),
-                      #ui.tags.h5('Legend'),
-                     #ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
-                      #ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
-        )
-    ),
-)
-
-
-
-
-from urllib.request import Request, urlopen
-from shiny import App, reactive, ui
-from shiny.ui import h2, tags
-# importing OpenCV(cv2) module
-
-
-
-
-#print(app_ui)
-def server(input, output, session):
-
-
-    @output
-    @render.text
-    def txt():
-        return   f'{team_dict[input.team_id()]} Last Games PP Summary'
-      
-    @output
-    @render.text
-    def txt_title():
-    
-        return f'Team Last Games PP% Leaders'
-
-
-
-
-
-    @output
-    @render.table
-    def pp_roundup():
-
-        top_n = input.top_n()
-        n_1 = input.n_1()
-        n_2 = input.n_2()
-        n_3 = input.n_3()
-
-        list_of_columns =  ['Player', 'Team', 'display_position','percent_owned','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
-
-        list_of_columns_name =  ['Player', 'Team', 'Position','Roster%','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
-
-        if type(n_1) is not int:
-            n_1 = 1
-            
-        if (n_2 == 0) or (n_2 == n_1) or (n_2 == None):
-            list_of_columns.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns.remove(f'L{str(n_2)} PP%')
-            list_of_columns_name.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_2)} PP%')
-
-        if (n_3 == 0) or (n_3 == n_1) or (n_3 == n_2) or (n_3 == None):
-            list_of_columns.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns.remove(f'L{str(n_3)} PP%')
-            list_of_columns_name.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_3)} PP%')
-
-            
-        
-        start_date ='2023-09-01'
-        end_date = '2024-05-01'
-        player_games_df = player_games_df_2.copy()
-        player_games_df = player_games_df.merge(right=team_games_df,left_on=['Team','date'],right_on=['abv','date'])
-
-        df_pp_1 = player_games_df.groupby('player_id').head(n_1)
-        df_pp_1 = df_pp_1[df_pp_1.game_count > df_pp_1.max_games-n_1]
-
-        df_pp_2 = df_pp_2 = player_games_df.groupby('player_id').head(n_2)
-        df_pp_2[df_pp_2.game_count > df_pp_2.max_games-n_2]
-
-        df_pp_3 = df_pp_3 = player_games_df.groupby('player_id').head(n_3)
-        df_pp_3[df_pp_3.game_count > df_pp_3.max_games-n_3]
-
-        team_games_df_1 = team_games_df.groupby('team').head(n_1)
-        team_games_df_2 = team_games_df.groupby('team').head(n_2)
-        team_games_df_3 = team_games_df.groupby('team').head(n_3)
-
-        df_all_pp_1 = df_pp_1.copy()
-        df_all_pp_2 = df_pp_2.copy()
-        df_all_pp_3 = df_pp_3.copy()
-
-        df_all_pp_1_final = df_all_pp_1.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
-        df_all_pp_2_final = df_all_pp_2.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
-        df_all_pp_3_final = df_all_pp_3.groupby(['player_id','Player','Team','Position']).sum()[['TOI_pp']].reset_index()
-
-        team_games_df_1_final = team_games_df_1.groupby(['abv']).sum()[['pp_toi']].reset_index()
-        team_games_df_2_final = team_games_df_2.groupby(['abv']).sum()[['pp_toi']].reset_index()
-        team_games_df_3_final = team_games_df_3.groupby(['abv']).sum()[['pp_toi']].reset_index()
-
-        df_final =  df_all_pp_1_final.merge( df_all_pp_2_final,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("","_2"))
-        df_final =  df_final.merge( df_all_pp_3_final,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("_1","_3"))
-
-        team_final =  team_games_df_1_final.merge( team_games_df_2_final,how='outer',left_on=['abv'],right_on=['abv'],suffixes=("","_2"))
-        team_final =  team_final.merge( team_games_df_3_final,how='outer',left_on=['abv'],right_on=['abv'],suffixes=("_1","_3"))
-
-
-        df_final = df_final.merge(team_final,left_on='Team_1',right_on='abv')
-
-        test = df_final[['player_id','Player_1','Team_1','Position_1','TOI_pp_1','TOI_pp_2','TOI_pp_3','pp_toi_1','pp_toi_2','pp_toi_3']]
-        test.columns = ['player_id','Player','Team','Position','TOI_1','TOI_2','TOI_3','pp_toi_1','pp_toi_2','pp_toi_3']
-        test = test.fillna('0')
-
-
-        test['PP%_1'] = test['TOI_1'].astype(float)/ test['pp_toi_1'].astype(float)
-        test['PP%_2'] = test['TOI_2'].astype(float)/ test['pp_toi_2'].astype(float)
-        test['PP%_3'] = test['TOI_3'].astype(float)/ test['pp_toi_3'].astype(float)
-        # test = test.fillna(0)
-        test['TOI_1'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_1'].astype(float)]
-        test['TOI_2'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_2'].astype(float)]
-        test['TOI_3'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_3'].astype(float)]
-        test = test.drop(['pp_toi_1','pp_toi_2','pp_toi_3'],axis=1)
-        test.columns = ['player_id','Player','Team','Position','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI','L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
-
-
-        yahoo_df = yahoo_df_2.merge(yahoo_nhl_df,left_on = 'player_id',right_on='player_id_yahoo',suffixes=['','_y'])
-        yahoo_df.nhl_id = yahoo_df.nhl_id.astype(float)
-        test.player_id = test.player_id.astype(float)
-
-
-        test = test.merge(right=yahoo_df,left_on='player_id',right_on='nhl_id',suffixes=['','_y'],how='left')
-        # if len(test[test.player_id == 8478427]) > 0:
-        #     return test.style
-            
-            #print(test[test.nhl_id == 8478427])
-
-        test.loc[test.display_position.isna(),'display_position'] = test.loc[test.display_position.isna(),'Position']
-        test.display_position = test.display_position.replace({'L':'LW','R':'RW'})
-        test.percent_owned = test.percent_owned.fillna(0)
-
-        
-
-
-        print('Column List')
-        print(test.columns)
-
-
-        print(list_of_columns)
-        test = test[list_of_columns]
-        test = test.rename(columns={'percent_owned':'Roster%'})
-        test = test.rename(columns={'display_position':'Position'})
-        
-
-        top_d_score = test.copy()[(test.Team==input.team_id())].sort_values(by=['L'+str(n_1)+' PP%'],ascending=False).reset_index(drop=True)
-        print(top_d_score)
-        if input.x():
-            top_d_score = top_d_score.dropna(axis='columns')
-        
-        top_d_score = top_d_score.head(min(len(top_d_score),top_n))
-
-
-        #top_d_score.columns = list_of_columns_name
-
-        
-
-        top_d_score['Deployment'] = "PP2"
-        top_d_score['Deployment'][0:5] = "PP1"
-
-        
-        cols = top_d_score.columns.tolist();
-        print('we made it here',cols)
-
-        # for i in list(input.ignore_id()):
-        #     print('we made it here')
-        #     print(i)
-        #     cols.remove(i)
-
-        # df_style_bang = top_d_score.head(10).style.background_gradient(cmap=co, subset=['L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%','Roster%'],vmin=0,vmax=1).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'}).set_table_styles([{
-        #         'selector': 'caption',
-        #         'props': [
-        #             ('color', ''),
-        #             ('fontname', 'Century Gothic'),
-        #             ('font-size', '20px'),
-        #             ('font-style', 'italic'),
-        #             ('font-weight', ''),
-        #             ('text-align', 'centre'),
-        #             ]
-
-        #             },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('fontname', 'Century Gothic')]}]).format(
-        #                 {'L'+str(n_1)+' PP%': '{:.0%}',
-        #                 'L'+str(n_2)+' PP%': '{:.0%}',
-        #                 'L'+str(n_3)+' PP%': '{:.0%}',
-        #                 'Roster%': '{:.0%}',
-        #                 },)
-
-        df_style_bang = top_d_score[cols].head(input.top_n()).style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
-            'selector': 'caption',
-            'props': [
-                ('color', ''),
-                ('fontname', 'Century Gothic'),
-                ('font-size', '20px'),
-                ('font-style', 'italic'),
-                ('font-weight', ''),
-                ('text-align', 'centre'),
-                ]
-
-                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
-                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
-                    **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False).set_properties(
-                    **{'background-color':'White','index':'White','min-width':'200px'},overwrite=False,subset=cols[0]).set_table_styles(
-            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
-            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
-            [{'selector': 'tr', 'props': [('line-height', '35px')]}],overwrite=False).set_properties(
-            **{'Height': '35px'},**{'text-align': 'center'},overwrite=False).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(top_d_score.index[:]),top_d_score.columns[:]))).set_properties(**{
-                                'color': 'black'},overwrite=False).set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(top_d_score.index[:]),top_d_score.columns[:]))).format(
-            {
-            'L'+str(n_1)+' PP%': '{:.0%}',
-            'L'+str(n_2)+' PP%': '{:.0%}',
-            'L'+str(n_3)+' PP%': '{:.0%}',
-            'Roster%': '{:.0%}',
-            },).background_gradient(cmap=co, subset=[x for x in cols if x.endswith('PP%')]).hide_index().background_gradient(cmap=co,vmin=0.0,vmax=0.7, subset=[x for x in cols if x.endswith('PP%')])
-
-        return df_style_bang
-
-
-
-# test = test.fillna(0)
-#test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']]
-
-
-
-app = App(app_ui, server)
-#time.sleep(60)
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Premiere Cs6 Mac Osx Torrent.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Premiere Cs6 Mac Osx Torrent.md
deleted file mode 100644
index 5ea979d1a6f232022c32a588fc2ae3866a4fad69..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Premiere Cs6 Mac Osx Torrent.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-
-<h1>Adobe Premiere CS6 Mac OSX Torrent: How to Download and Install</h1>
-<p>Adobe Premiere CS6 is one of the most popular and powerful video editing software for Mac users. It allows you to create stunning videos with professional effects, transitions, titles, and audio. However, Adobe Premiere CS6 is not a free software, and you need to pay a monthly or yearly subscription fee to use it. If you want to save some money and still enjoy the features of Adobe Premiere CS6, you might be interested in downloading and installing it from a torrent.</p>
-<p>A torrent is a file-sharing method that uses peer-to-peer (P2P) technology to distribute large files over the internet. By using a torrent, you can download Adobe Premiere CS6 Mac OSX from other users who have already downloaded it, without relying on a central server. However, downloading and installing Adobe Premiere CS6 Mac OSX from a torrent also involves some risks and challenges, such as finding a reliable torrent site, avoiding malware and viruses, and bypassing the activation process.</p>
-<h2>Adobe Premiere Cs6 Mac Osx Torrent</h2><br /><p><b><b>Download Zip</b> &#11088; <a href="https://urlcod.com/2uIaDW">https://urlcod.com/2uIaDW</a></b></p><br /><br />
-<p>In this article, we will guide you through the steps of downloading and installing Adobe Premiere CS6 Mac OSX from a torrent, as well as provide you with some tips and precautions to ensure a safe and successful experience. Let's get started!</p>
- <h2>What is Adobe Premiere CS6?</h2>
-<p>Adobe Premiere CS6 is the sixth major version of Adobe Premiere Pro, which is a video editing software developed by Adobe Systems. It was released in May 2012 as part of the Adobe Creative Suite 6 (CS6) package, along with other applications such as Photoshop, Illustrator, After Effects, and more.</p>
-<h3>Features of Adobe Premiere CS6</h3>
-<p>Adobe Premiere CS6 offers many features that make it an ideal choice for video editors of all levels. Some of these features are:</p>
-<ul>
-<li><b>Improved performance:</b> Adobe Premiere CS6 uses the Mercury Playback Engine, which leverages the power of your GPU (graphics processing unit) to provide faster and smoother playback, rendering, and exporting of your videos. You can also adjust the resolution and quality of your playback to suit your preferences.</li>
-<li><b>Enhanced user interface:</b> Adobe Premiere CS6 has a sleek and intuitive user interface that allows you to customize your workspace according to your needs. You can easily access the tools and panels you need, as well as use keyboard shortcuts and gestures to speed up your workflow.</li>
-<li><b>New editing tools:</b> Adobe Premiere CS6 introduces new editing tools that help you create more professional and creative videos. For example, you can use the Warp Stabilizer effect to stabilize shaky footage, the Rolling Shutter Repair effect to fix distortion caused by CMOS sensors, the Adjustment Layer feature to apply effects across multiple clips, and more.</li>
-<li><b>Integration with other Adobe applications:</b> Adobe Premiere CS6 works seamlessly with other Adobe applications, such as Photoshop, After Effects, Audition, Media Encoder, and more. You can import and export files between these applications without losing quality or metadata. You can also use the Dynamic Link feature to edit your videos in After Effects without rendering or exporting them first.</li>
-<li><b>Support for various formats and devices:</b> Adobe Premiere CS6 supports a wide range of video formats and codecs, such as AVCHD, H.264, MPEG-2, ProRes, RED, XDCAM, and more. You can also edit footage from various devices, such as DSLR cameras, smartphones, tablets, and more. You can also export your videos to different formats and platforms, such as YouTube, Vimeo, Facebook, DVD, Blu-ray, and more.</li>
-</ul>
-<h3>System requirements for Adobe Premiere CS6</h3>
-<p>To run Adobe Premiere CS6 on your Mac, you need to meet the following system requirements:</p>
-<p></p>
-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>Mac OS X v10.6.8 or v10.7</td>
-<td>Mac OS X v10.7 or later</td>
-</tr>
-<tr>
-<td>Intel Core 2 Duo or AMD Phenom II processor; 64-bit support required</td>
-<td>Intel Core i7 or faster processor; 64-bit support required</td>
-</tr>
-<tr>
-<td>4 GB of RAM (8 GB recommended)</td>
-<td>8 GB of RAM or more</td>
-</tr>
-<tr>
-<td>4 GB of available hard-disk space for installation; additional free space required during installation (cannot install on a volume that uses a case-sensitive file system or on removable flash storage devices)</td>
-<td>10 GB of available hard-disk space for installation; additional free space required during installation (cannot install on a volume that uses a case-sensitive file system or on removable flash storage devices)</td>
-</tr>
-<tr>
-<td>Additional disk space required for preview files and other working files (10 GB recommended)</td>
-<td>Additional disk space required for preview files and other working files (20 GB recommended)</td>
-</tr>
-<tr>
-<td>1280 x 900 display with 16-bit color and 512 MB of VRAM; 1680 x 1050 display required and second professionally calibrated viewing display recommended for Adobe SpeedGrade</td>
-<td>1920 x 1080 display with 16-bit color and 1 GB of VRAM; second professionally calibrated viewing display recommended for Adobe SpeedGrade</td>
-</tr>
-<tr>
-<td>OpenGL 2.0–capable system</td>
-<td>OpenGL 2.0–capable system with dedicated GPU</td>
-</tr>
-<tr>
-<td>DVD-ROM drive compatible with dual-layer DVDs (SuperDrive for burning DVDs; external Blu-ray burner for creating Blu-ray Disc media)</td>
-<td>DVD-ROM drive compatible with dual-layer DVDs (SuperDrive for burning DVDs; external Blu-ray burner for creating Blu-ray Disc media)</td>
-</tr>
-<tr>
-<td>Java Runtime Environment 1.6</td>
-<td>Java Runtime Environment 1.6 or later</td>
-</tr>
-<tr>
-<td>QuickTime 7.6.6 software required for QuickTime features</td>
-<td>QuickTime 7.6.6 software required for QuickTime features</td>
-</tr> <h2>What is a torrent?</h2>
-<p>A torrent is a file that contains information about a larger file or a group of files that you want to download from the internet. The torrent file does not contain the actual data of the files, but rather the metadata, such as the file names, sizes, checksums, and the locations of the peers who have the files or parts of them. A peer is a user who is connected to a P2P network and shares files with other users.</p>
-<p>To download a file from a torrent, you need a software program called a torrent client, such as uTorrent, BitTorrent, Transmission, or Vuze. The torrent client reads the torrent file and connects you to other peers who have the file or parts of it. The torrent client then downloads the file or files from these peers in small pieces, and reassembles them on your computer. This way, you can download large files faster and more efficiently than from a single source.</p>
-<h3>Advantages and disadvantages of using torrents</h3>
-<p>Using torrents has some advantages and disadvantages that you should be aware of before downloading and installing Adobe Premiere CS6 Mac OSX from a torrent. Here are some of them:</p>
-<ul>
-<li><b>Advantages:</b>
-    <ul>
-    <li>You can download large files faster and more efficiently than from a single source, as you can benefit from the bandwidth and resources of multiple peers.</li>
-    <li>You can download multiple files at once, as well as pause and resume your downloads at any time.</li>
-    <li>You can access rare or unavailable files that are not hosted on any server, as long as there are peers who have them and are willing to share them.</li>
-    <li>You can save money by downloading free or cheaper alternatives to paid software, such as Adobe Premiere CS6.</li>
-    </ul>
-</li>
-<li><b>Disadvantages:</b>
-    <ul>
-    <li>You may encounter legal issues if you download or share copyrighted or pirated files, such as Adobe Premiere CS6. You may face fines, lawsuits, or even criminal charges depending on the laws of your country.</li>
-    <li>You may expose your computer to malware and viruses that may be hidden in the torrent files or the crack or patch files that are needed to activate Adobe Premiere CS6.</li>
-    <li>You may compromise your privacy and security by exposing your IP address and other personal information to other peers or third parties who may monitor your torrent activity.</li>
-    <li>You may experience slow or unstable downloads if there are not enough peers who have the file or parts of it, or if they have low upload speeds or bandwidth limits.</li>
-    </ul>
-</li>
-</ul> <h3>Risks and precautions of using torrents</h3>
-<p>As you can see, using torrents has some risks that you should not ignore. If you decide to download and install Adobe Premiere CS6 Mac OSX from a torrent, you should take some precautions to minimize these risks and protect yourself from potential problems. Here are some tips that you should follow:</p>
-<ul>
-<li><b>Use a VPN:</b> A VPN (virtual private network) is a service that encrypts your internet traffic and hides your IP address and location from other peers and third parties. By using a VPN, you can avoid legal issues, malware, and privacy breaches that may result from using torrents. You can choose from many VPN providers, such as NordVPN, ExpressVPN, Surfshark, or CyberGhost.</li>
-<li><b>Use a reputable torrent site:</b> A torrent site is a website that hosts and indexes torrent files for various files and categories. You should use a reputable torrent site that has a large and active community, a good reputation, and a high level of security. You should also avoid torrent sites that have pop-ups, ads, or redirects that may contain malware or viruses. Some of the best torrent sites for Mac users are The Pirate Bay, RARBG, 1337x, or Torrentz2.</li>
-<li><b>Use a reliable torrent client:</b> A torrent client is a software program that allows you to download and upload files from torrents. You should use a reliable torrent client that has a good reputation, a user-friendly interface, and advanced features. You should also update your torrent client regularly to fix any bugs or vulnerabilities. Some of the best torrent clients for Mac users are uTorrent, BitTorrent, Transmission, or Vuze.</li>
-<li><b>Check the comments and ratings:</b> Before downloading any torrent file, you should check the comments and ratings of other users who have downloaded it before. This way, you can get an idea of the quality, authenticity, and safety of the file. You can also ask questions or report any issues that you encounter with the file. You should avoid downloading any torrent file that has negative comments, low ratings, or no feedback at all.</li>
-<li><b>Scan the files for malware and viruses:</b> After downloading the torrent file or the magnet link, you should scan the files for malware and viruses using a reputable antivirus software, such as Malwarebytes, Avast, or Norton. You should also scan the crack or patch files that are needed to activate Adobe Premiere CS6. You should delete any file that is detected as malicious or suspicious by your antivirus software.</li>
-</ul>
- <h2>How to download Adobe Premiere CS6 Mac OSX torrent?</h2>
-<p>Now that you know what Adobe Premiere CS6 is, what a torrent is, and what precautions you should take when using torrents, you are ready to download Adobe Premiere CS6 Mac OSX from a torrent. Here are the steps that you need to follow:</p>
-<h3>Step 1: Find a reliable torrent site</h3>
-<p>The first step is to find a reliable torrent site that hosts Adobe Premiere CS6 Mac OSX torrent files. As we mentioned before, some of the best torrent sites for Mac users are The Pirate Bay, RARBG, 1337x, or Torrentz2. You can access these sites using your web browser, such as Safari, Chrome, or Firefox.</p>
-<p>However, some of these sites may be blocked or restricted in your country due to legal issues or censorship. If this is the case, you can use a VPN to bypass these blocks and access these sites anonymously. Alternatively, you can use proxy sites or mirror sites that are clones of the original sites but have different domain names or IP addresses.</p>
-<h3>Step 2: Search for Adobe Premiere CS6 Mac OSX torrent</h3>
-<p>The next step is to search for Adobe Premiere CS6 Mac OSX torrent on the torrent site of your choice. You can use the search bar on the site to type in keywords such as "Adobe Premiere CS6 Mac OSX", "Adobe Premiere CS6 Mac", "Adobe Premiere CS6 OSX", or "Adobe Premiere CS6". You can also use filters or categories to narrow down your search results by file type, size, date, seeders, leechers, or quality.</p>
-<p>A seeder is a peer who has the complete file and is uploading it to other peers. A leecher is a peer who is downloading the file but has not completed it yet. The more seeders and leechers a torrent file has, the faster and more reliable your download will be.</p>
-<p>You should look for an Adobe Premiere CS6 Mac OSX torrent file that has a high number of seeders and leechers, a good rating and feedback from other users, and a clear description of the file contents and instructions.</p>
-<h3>Step 3: Download the torrent file or magnet link</h3>
-<p>The third step is to download the torrent file or the magnet link of the Adobe Premiere CS6 Mac OSX torrent that you have chosen. A torrent file is a small file that contains the metadata of the larger file or files that you want to download. A magnet link is a URL that contains the same information as a torrent file, but without requiring a separate file.</p>
-<p>To download the torrent file or the magnet link, you need to click on the download button or the magnet icon on the torrent site. This will open a dialog box that will ask you to choose a location to save the torrent file or to confirm the magnet link. You can also right-click on the download button or the magnet icon and choose "Save Link As" or "Copy Link Address" to save the torrent file or copy the magnet link.</p>
-<p>Once you have downloaded the torrent file or copied the magnet link, you are ready to open it with a torrent client.</p>
-<h3>Step 4: Open the torrent file or magnet link with a torrent client</h3>
-<p>The fourth and final step is to open the torrent file or the magnet link with a torrent client, such as uTorrent, BitTorrent, Transmission, or Vuze. A torrent client is a software program that allows you to download and upload files from torrents.</p>
-<p>To open the torrent file or the magnet link with a torrent client, you need to double-click on the torrent file or paste the magnet link into the torrent client. This will launch the torrent client and add the Adobe Premiere CS6 Mac OSX torrent to your download list. You can also drag and drop the torrent file or the magnet link into the torrent client.</p>
-<p>Once you have added the Adobe Premiere CS6 Mac OSX torrent to your download list, you can see various information about it, such as the file name, size, status, progress, speed, peers, and more. You can also adjust some settings, such as the download location, priority, bandwidth limit, and more.</p>
-<p>The torrent client will then start downloading Adobe Premiere CS6 Mac OSX from other peers who have it or parts of it. The download time will depend on various factors, such as your internet speed, the number of seeders and leechers, and the size of the file. You can monitor your download progress and speed on the torrent client.</p>
-<p>When your download is complete, you will see a notification on your torrent client. You can then proceed to install Adobe Premiere CS6 Mac OSX on your Mac.</p>
- <h2>How to install Adobe Premiere CS6 Mac OSX torrent?</h2>
-<p>After downloading Adobe Premiere CS6 Mac OSX from a torrent, you need to install it on your Mac. Here are the steps that you need to follow:</p>
-<h3>Step 1: Mount the disk image file</h3>
-<p>The first step is to mount the disk image file that contains Adobe Premiere CS6 Mac OSX. A disk image file is a file that acts like a virtual disk that contains other files and folders. The disk image file for Adobe Premiere CS6 Mac OSX usually has a .dmg extension.</p>
-<p>To mount the disk image file, you need to double-click on it. This will open a new window that shows the contents of the disk image file. You can also right-click on it and choose "Open With" and then "DiskImageMounter".</p>
-<p>Once you have mounted the disk image file, you will see a new icon on your desktop that represents it. You can then access its contents by clicking on it.</p>
-<h3>Step 2: Run p>To apply the crack or patch, you need to locate it in the torrent file or the disk image file that you downloaded. You may need to extract it from a compressed file, such as a .zip or .rar file, using a software program such as WinZip, WinRAR, or The Unarchiver. You may also need to scan it for malware and viruses using your antivirus software.</p>
-<p>Once you have located the crack or patch, you need to follow the instructions that come with it. You may need to copy and paste it into the installation folder of Adobe Premiere CS6 Mac OSX, replace an existing file, run it as an administrator, or enter a serial number or a license key. The instructions may vary depending on the crack or patch that you use.</p>
-<p>Once you have applied the crack or patch, you should be able to launch Adobe Premiere CS6 Mac OSX without any activation or registration issues.</p>
-<h3>Step 4: Launch Adobe Premiere CS6 and enjoy</h3>
-<p>The fourth and final step is to launch Adobe Premiere CS6 and enjoy its features and functions. To launch Adobe Premiere CS6, you need to click on its icon on your desktop, dock, or applications folder. You can also use Spotlight to search for it.</p>
-<p>Once you have launched Adobe Premiere CS6, you can start creating and editing your videos with professional effects, transitions, titles, and audio. You can also explore its various tools and settings to customize your workspace and workflow. You can also integrate it with other Adobe applications, such as Photoshop, After Effects, Audition, Media Encoder, and more.</p>
-<p>Congratulations! You have successfully downloaded and installed Adobe Premiere CS6 Mac OSX from a torrent. Enjoy!</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and install Adobe Premiere CS6 Mac OSX from a torrent. We have explained what Adobe Premiere CS6 is, what a torrent is, and what precautions you should take when using torrents. We have also provided you with a step-by-step guide on how to download and install Adobe Premiere CS6 Mac OSX from a torrent, as well as how to apply the crack or patch if needed.</p>
-<p>We hope that this article has been helpful and informative for you. However, we would like to remind you that downloading and installing Adobe Premiere CS6 Mac OSX from a torrent is not legal or ethical, as it violates the terms and conditions of Adobe Systems. You may face legal issues, malware infections, privacy breaches, or other problems if you use torrents. Therefore, we do not recommend or endorse this method of obtaining Adobe Premiere CS6 Mac OSX.</p>
-<p>If you want to use Adobe Premiere CS6 Mac OSX legally and safely, you should purchase it from the official website of Adobe Systems or from an authorized reseller. You can also try other free or cheaper alternatives to Adobe Premiere CS6 Mac OSX, such as iMovie, Final Cut Pro X, DaVinci Resolve, or Shotcut.</p>
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to leave them in the comments section below. We would love to hear from you!</p>
- <h4>FAQs</h4>
-<ul>
-<li><b>Q: Is Adobe Premiere CS6 Mac OSX compatible with macOS Catalina or Big Sur?</b></li>
-<li>A: No, Adobe Premiere CS6 Mac OSX is not compatible with macOS Catalina or Big Sur. This is because these versions of macOS only support 64-bit applications, while Adobe Premiere CS6 Mac OSX is a 32-bit application. If you want to use Adobe Premiere CS6 Mac OSX on macOS Catalina or Big Sur, you need to downgrade your macOS version or use a virtual machine.</li>
-<li><b>Q: How can I update Adobe Premiere CS6 Mac OSX?</b></li>
-<li>A: You cannot update Adobe Premiere CS6 Mac OSX if you have downloaded and installed it from a torrent. This is because the update process will detect that your copy of Adobe Premiere CS6 Mac OSX is not genuine and will deactivate it. If you want to update Adobe Premiere CS6 Mac OSX, you need to purchase it from the official website of Adobe Systems or from an authorized reseller.</li>
-<li><b>Q: How can I uninstall Adobe Premiere CS6 Mac OSX?</b></li>
-<li>A: To uninstall Adobe Premiere CS6 Mac OSX from your Mac, you need to follow these steps:</li>
-<ol>
-<li>Quit Adobe Premiere CS6 if it is running.</li>
-<li>Open the Finder and go to the Applications folder.</li>
-<li>Drag the Adobe Premiere Pro CS6 folder to the Trash icon.</li>
-<li>Empty the Trash icon.</li>
-</ol>
-<li><b>Q: What are some alternatives to Adobe Premiere CS6 Mac OSX?</b></li>
-<li>A: Some of the alternatives to Adobe Premiere CS6 Mac OSX are:</li>
-<ul>
-<li><b>iMovie:</b> iMovie is a free video editing software that comes pre-installed on Mac computers. It has a simple and intuitive interface that allows you to create and edit videos with basic features, such as trimming, cropping, adding transitions, titles, music, and effects. You can also share your videos to YouTube, Facebook, or other platforms directly from iMovie.</li>
-<li><b>Final Cut Pro X:</b> Final Cut Pro X is a professional video editing software that is developed by Apple. It has a powerful and advanced interface that allows you to create and edit videos with features, such as multicam editing, 360-degree video editing, motion graphics, color grading, audio mixing, and more. You can also use plugins and extensions to enhance your video editing experience. Final Cut Pro X costs $299.99 and is available on the Mac App Store.</li>
-<li><b>DaVinci Resolve:</b> DaVinci Resolve is a professional video editing software that is developed by Blackmagic Design. It has a comprehensive and sophisticated interface that allows you to create and edit videos with features, such as nonlinear editing, visual effects, color correction, audio post-production, and more. You can also use its collaboration tools to work with other editors, directors, or clients. DaVinci Resolve has a free version and a paid version that costs $299.</li>
-<li><b>Shotcut:</b> Shotcut is a free and open-source video editing software that is compatible with Mac, Windows, and Linux. It has a user-friendly and customizable interface that allows you to create and edit videos with features, such as trimming, cutting, filtering, encoding, exporting, and more. You can also use its timeline and multitrack editing features to create complex videos.</li>
-</ul>
-</ul></p> b2dd77e56b<br />
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-<h1>How to Crack Windows Password with Daossoft Windows Password Rescuer Personal</h1>
-<p>If you forgot your Windows password and can't log in to your computer, don't worry. There is a simple and effective way to crack your Windows password without losing any data or reinstalling the system. All you need is a tool called Daossoft Windows Password Rescuer Personal, which can help you reset or remove any Windows password in minutes.</p>
-<p>Daossoft Windows Password Rescuer Personal is a powerful and easy-to-use software that can recover forgotten or lost Windows password for Windows 10, 8.1, 8, 7, Vista, XP, 2000 and NT. It can also create a new administrator account without logging in. It supports all types of hard disks and RAID, and works with both CD/DVD and USB flash drive.</p>
-<h2>daossoft windows password rescuer personal crack 12</h2><br /><p><b><b>DOWNLOAD</b> &raquo; <a href="https://urlcod.com/2uIcnz">https://urlcod.com/2uIcnz</a></b></p><br /><br />
-<p>In this article, we will show you how to crack Windows password with Daossoft Windows Password Rescuer Personal step by step.</p>
-<h2>Step 1: Download and install Daossoft Windows Password Rescuer Personal</h2>
-<p>You can download Daossoft Windows Password Rescuer Personal from its official website: https://www.daossoft.com/products/windows-password-rescuer-personal.html. It is a small file of about 30 MB. After downloading, install it on an accessible computer that you can use to create a password recovery disk.</p>
-<h2>Step 2: Create a password recovery disk</h2>
-<p>You can use either a CD/DVD or a USB flash drive to create a password recovery disk. Here we will take USB flash drive as an example.</p>
-<ul>
-<li>Insert a blank USB flash drive into the computer where you installed Daossoft Windows Password Rescuer Personal.</li>
-<li>Run the software and select "USB Device" as the media type.</li>
-<li>Select the USB drive from the drop-down list and click "Begin Burning" to start creating the password recovery disk.</li>
-<li>Wait for a few seconds until the burning process is completed. Then click "OK" and eject the USB flash drive.</li>
-</ul>
-<h2>Step 3: Boot your locked computer from the password recovery disk</h2>
-<p>Now you need to insert the password recovery disk into your locked computer and boot it from the disk.</p>
-<ul>
-<li>Turn on your locked computer and press the key (such as F12, F9, F2, ESC, etc.) that can enter the Boot Menu or BIOS Setup according to your computer model.</li>
-<li>Select the USB flash drive as the first boot device and save the changes.</li>
-<li>Restart your computer and it will boot from the password recovery disk automatically.</li>
-</ul>
-<h2>Step 4: Crack Windows password with Daossoft Windows Password Rescuer Personal</h2>
-<p>After booting from the password recovery disk, you will see the Daossoft Windows Password Rescuer Personal interface on your screen.</p>
-<ul>
-<li>Select the Windows system that you want to crack the password for.</li>
-<li>Select the user account that you want to reset or remove the password for.</li>
-<li>Click "Reset Password" to reset the password to blank, or click "Remove Password" to remove the password completely.</li>
-<li>Click "Reboot" to restart your computer and remove the password recovery disk.</li>
-</ul>
-<p>Congratulations! You have successfully cracked your Windows password with Daossoft Windows Password Rescuer Personal. Now you can log in to your computer without any password or with a new password if you created one.</p>
-<p></p> 81aa517590<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FULL Maxon Cinema 4D R12 Portable [gRabb9] !EXCLUSIVE!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FULL Maxon Cinema 4D R12 Portable [gRabb9] !EXCLUSIVE!.md
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-<br />
-<h1>FULL Maxon Cinema 4D R12 Portable [gRabb9]: A Comprehensive Review</h1> | <p>If you are looking for a 3D modeling, animation, and rendering software that is easy to use, powerful, and portable, then you might want to check out FULL Maxon Cinema 4D R12 Portable [gRabb9]. This is a version of the popular Cinema 4D software that can run on any Windows computer without installation or activation. In this article, we will review what Maxon Cinema 4D R12 Portable is, what features and benefits it offers, how to download and install it, why you should choose it over other 3D software, how to use it for your projects, and some FAQs that you might have.</p>
- <h2>What is Maxon Cinema 4D R12 Portable?</h2>
- <p>Maxon Cinema 4D R12 Portable is a 3D modeling, animation, and rendering software developed by MAXON. It is a portable version of the Cinema 4D Studio R12 software, which means that it can run on any Windows computer without installation or activation. You can simply download it from the internet, unzip it to a folder of your choice, and launch it from there. You can also copy it to a USB drive or an external hard drive and use it on any computer you want.</p>
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- <p>Cinema 4D is one of the most popular 3D software in the market today. It is used by professionals and hobbyists alike for creating stunning 3D graphics for movies, games, TV shows, commercials, websites, and more. It has a user-friendly interface that makes it easy to learn and use. It also has a wide range of features and tools that allow you to create anything you can imagine in 3D.</p>
- <h3>Features and Benefits of Maxon Cinema 4D R12 Portable</h3>
- <p>Maxon Cinema 4D R12 Portable has all the features and benefits of the original Cinema 4D Studio R12 software. Some of them are:</p>
- <ul>
-<li>Polygon modeling: You can create complex 3D models using polygons, splines, nurbs, deformers, generators, modifiers, and more.</li>
-<li>Physical rendering: You can render realistic images with accurate lighting, shadows, reflections, refractions, depth of field, motion blur, and more. You can also use global illumination, ambient occlusion, caustics, and other advanced techniques.</li>
-<li>Animation: You can animate your 3D models using keyframes, curves, expressions, drivers, constraints, and more. You can also use the powerful character animation tools, such as the character object, the CMotion system, the pose morph tag, and the mocap data.</li>
-<li>Dynamics: You can simulate realistic physical effects, such as collisions, gravity, friction, wind, fire, smoke, cloth, hair, fur, and more. You can also use the rigid body, soft body, cloth, hair, and particle systems.</li>
-<li>MoGraph: You can create stunning motion graphics using the MoGraph module, which includes the cloner object, the fracture object, the matrix object, the tracer object, the spline wrap object, the random effector, the delay effector, and more.</li>
-<li>Materials and Textures: You can create realistic materials and textures using the material editor, the layer shader, the noise shader, the gradient shader, the projection shader, the UV editor, the bodypaint 3D module, and more.</li>
-<li>Plugins and Scripts: You can extend the functionality of Cinema 4D using plugins and scripts that are available online. Some of them are free and some of them are paid. You can also create your own plugins and scripts using the C++ SDK or the Python API.</li>
-</ul>
- <h3>How to Download and Install Maxon Cinema 4D R12 Portable</h3>
- <p>To download and install Maxon Cinema 4D R12 Portable, you need to follow these steps:</p>
- <ol>
-<li>Go to this link: [FULL Maxon Cinema 4D R12 Portable [gRabb9]]</li>
-<li>Click on the download button and wait for the file to be downloaded. The file size is about 1.2 GB.</li>
-<li>Extract the file using a program like WinRAR or 7-Zip. You will get a folder named "Cinema 4D R12 Portable".</li>
-<li>Open the folder and double-click on the "Cinema 4D.exe" file to launch the software.</li>
-<li>Enjoy using Maxon Cinema 4D R12 Portable without installation or activation.</li>
-</ol>
- <p>Note: You can also copy the folder to a USB drive or an external hard drive and use it on any computer you want. Just make sure that you have enough space on your device.</p>
- <h2>Why Choose Maxon Cinema 4D R12 Portable Over Other 3D Software?</h2>
- <p>There are many reasons why you should choose Maxon Cinema 4D R12 Portable over other 3D software. Here are some of them:</p>
- <h3>Comparison with Other 3D Software</h3>
- <p>Cinema 4D is one of the most popular 3D software in the market today. It competes with other 3D software such as Blender, Maya, and 3ds Max. Let's see how Cinema 4D compares with them in terms of features, performance, price, and user-friendliness.</p>
-<p></p>
- <h4>Cinema 4D vs Blender</h4>
- <p>Blender is a free and open-source 3D software that has a large and active community of users and developers. It has a lot of features and tools that can rival those of Cinema 4D. However,</p> <p>But what makes Cinema 4D stand out from other 3D software is the MoGraph module, which is a set of tools that allow you to create complex and dynamic motion graphics with ease. You can clone objects, fracture them, animate them with effectors, and apply fields to control their behavior. You can also use the Volume Builder and Volume Mesher to create organic shapes from any object. MoGraph is a powerful and versatile toolset that can help you create stunning animations and visual effects.</p>
- <h4>Cinema 4D vs Maya</h4>
- <p>Maya is another 3D software that is widely used in the film and video game industry. It has a reputation for being a powerful and complex software that can handle any kind of 3D project. Maya has a lot of features and tools that can rival those of Cinema 4D, such as polygon modeling, physical rendering, animation, dynamics, materials and textures, plugins and scripts, and more.</p>
- <p>However, Maya also has a steep learning curve and a cluttered interface that can be intimidating for beginners. Maya also requires a lot of hardware resources and can be slow and unstable at times. Maya is more suited for professionals who need a high level of control and customization over their 3D work.</p>
- <p>Cinema 4D, on the other hand, is more user-friendly and intuitive, making it easier to learn and use. Cinema 4D also has a faster and smoother performance, especially with the MoGraph module. Cinema 4D is more suited for motion graphics artists who need a quick and easy way to create stunning animations and visual effects.</p>
- <h4>Cinema 4D vs 3ds Max</h4>
- <p>3ds Max is another 3D software that is popular among architects, engineers, designers, and game developers. It has a lot of features and tools that can compete with those of Cinema 4D, such as polygon modeling, physical rendering, animation, dynamics, materials and textures, plugins and scripts, and more.</p>
- <p>However, 3ds Max also has a complicated interface and a difficult workflow that can be frustrating for beginners. 3ds Max also has some limitations and bugs that can affect the quality and efficiency of your 3D work. 3ds Max is more suited for technical and precise 3D work that requires a lot of details and accuracy.</p>
- <p>Cinema 4D, on the other hand, is more simple and elegant, making it more enjoyable to use. Cinema 4D also has more creative and artistic features, especially with the MoGraph module. Cinema 4D is more suited for artistic and expressive 3D work that requires a lot of flexibility and experimentation.</p>
- <h3>Advantages of Maxon Cinema 4D R12 Portable</h3>
- <p>As you can see, Cinema 4D has a lot of advantages over other 3D software in terms of features, performance, usability, and creativity. But what are the advantages of using the portable version of Cinema 4D R12?</p>
- <p>Here are some of them:</p>
- <ul>
-<li>No installation or activation required: You can run Cinema 4D R12 Portable on any Windows computer without installing or activating it. This means you don't have to worry about license issues or compatibility problems.</li>
-<li>No internet connection required: You can use Cinema 4D R12 Portable offline without needing an internet connection. This means you don't have to worry about updates or online services.</li>
-<li>No disk space required: You can store Cinema 4D R12 Portable on a USB drive or an external hard drive without taking up any disk space on your computer. This means you don't have to worry about storage or performance issues.</li>
-<li>No system requirements required: You can run Cinema 4D R12 Portable on any Windows computer regardless of its system specifications. This means you don't have to worry about hardware or software limitations.</li>
-<li>No cost required: You can download Cinema 4D R12 Portable for free from the internet without paying anything. This means you don't have to worry about budget or value issues.</li>
-</ul>
- <p>As you can see, Cinema 4D R12 Portable has a lot of advantages over other versions of Cinema 4D in terms of convenience, accessibility, portability, and affordability.</p>
- <h2>How to Use Maxon Cinema 4D R12 Portable for Your Projects?</h2>
- <p>Now that you know what Maxon Cinema 4D R12 Portable is and why you should use it over other 3D software, you might be wondering how to use it for your projects.</p>
- <p>Here are some tips and tricks for beginners, tutorials and resources for advanced users, and examples and inspiration from professional artists.</p>
- <h3>Tips and Tricks for Beginners</h3> <p>If you are new to Cinema 4D, here are some tips and tricks that can help you get started with the software:</p>
- <ul>
-<li>Learn the basics of the interface: Cinema 4D has a simple and intuitive interface that consists of four main parts: the menu bar, the toolbar, the viewport, and the manager. The menu bar contains various menus that give you access to different commands and options. The toolbar contains various tools that let you create and manipulate objects in the viewport. The viewport is where you can see and interact with your 3D scene. The manager is where you can see and adjust the properties and settings of your objects, materials, cameras, lights, etc.</li>
-<li>Use the help system: Cinema 4D has a comprehensive and interactive help system that can answer any question you might have about the software. You can access it by pressing F1 on your keyboard or by clicking on the help icon on the top right corner of the interface. You can also right-click on any tool, object, or parameter and choose "Show Help" to get more information about it.</li>
-<li>Use the presets: Cinema 4D has a lot of presets that can save you time and effort when creating your 3D models, materials, animations, and more. You can access them by clicking on the content browser icon on the right side of the interface or by pressing Shift+F8 on your keyboard. You can browse through different categories and subcategories of presets and drag and drop them into your scene.</li>
-<li>Use the modifiers: Cinema 4D has a lot of modifiers that can help you modify and enhance your 3D models without affecting their original geometry. You can access them by clicking on the modifier menu on the top left corner of the interface or by pressing M on your keyboard. You can apply different modifiers to your objects, such as bend, twist, taper, bulge, extrude, bevel, etc.</li>
-<li>Use the render settings: Cinema 4D has a lot of render settings that can help you control how your 3D scene will look when rendered. You can access them by clicking on the render settings icon on the bottom right corner of the interface or by pressing Ctrl+B on your keyboard. You can adjust different parameters, such as resolution, anti-aliasing, output format, global illumination, ambient occlusion, etc.</li>
-</ul>
- <h3>Tutorials and Resources for Advanced Users</h3>
- <p>If you are already familiar with Cinema 4D and want to learn more advanced techniques and skills, here are some tutorials and resources that can help you improve your 3D work:</p>
- <ul>
-<li>[Cineversity]: This is an online learning platform that offers hundreds of video tutorials, courses, plugins, scripts, presets, and more for Cinema 4D users. You can learn from expert instructors and artists who share their tips and tricks on various topics, such as modeling, animation, rendering, motion graphics, character rigging, scripting, etc.</li>
-<li>[Greyscalegorilla]: This is a website that provides high-quality training, tools, and resources for Cinema 4D users. You can find free and paid tutorials, podcasts, blogs, products, and more that cover different aspects of Cinema 4D, such as lighting, texturing, dynamics, MoGraph, etc.</li>
-<li>[CG Shortcuts]: This is a YouTube channel that features short and easy tutorials for Cinema 4D users. You can learn how to create various effects and animations using Cinema 4D and other software, such as After Effects, Photoshop, Illustrator, etc.</li>
-<li>[Eyedesyn]: This is a website that showcases the work and tutorials of EJ Hassenfratz, a motion graphics designer and Cinema 4D expert. You can find free and paid tutorials that teach you how to create fun and creative projects using Cinema 4D and other software.</li>
-<li>[Cinema 4D Tutorials]: This is a website that collects and curates the best tutorials for Cinema 4D users from different sources. You can find tutorials for beginners to advanced users on various topics, such as modeling, animation, rendering, motion graphics, etc.</li>
-</ul>
- <h3>Examples and Inspiration from Professional Artists</h3>
- <p>If you want to see what other Cinema 4D users are creating and get inspired by their work, here are some examples and sources of inspiration from professional artists:</p>
- <ul>
-<li>[Behance]: This is an online platform that showcases the creative work of artists and designers from different fields and industries. You can find amazing 3D projects created using Cinema 4D and other software by browsing through different categories, such as illustration, animation, branding, advertising, etc.</li>
-<li>[Vimeo]: This is an online video platform that hosts and streams high-quality videos from filmmakers and animators. You can find stunning 3D animations and visual effects created using Cinema 4D and other software by searching for keywords, tags, channels, groups, etc.</li>
-<li>[ArtStation]: This is an online community and marketplace for artists and designers who work in the entertainment industry. You can find incredible 3D artwork created using Cinema 4D and other software by exploring different genres, such as sci-fi, fantasy, horror, etc.</li>
-<li>[Instagram]: This is a social media platform that allows users to share photos and videos with their followers. You can find inspiring 3D posts created using Cinema 4D and other software by following hashtags, accounts, stories, reels, etc.</li>
-<li>[Pinterest]: This is a visual discovery engine that helps users find and save ideas for different projects and interests. You can find beautiful 3D images created using Cinema 4D and other software by browsing through different boards, pins, topics, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, FULL Maxon Cinema 4D R12 Portable [gRabb9] is a 3D modeling, animation, and rendering software that is easy to use, powerful, and portable. It has a lot of features and benefits that make it one of the best 3D software in the market today. It also has a lot of advantages over other 3D software in terms of convenience, accessibility, portability, and affordability. It is a great tool for creating stunning 3D graphics for movies, games, TV shows, commercials, websites, and more.</p>
- <p>If you want to learn more about Maxon Cinema 4D R12 Portable or download it for free, you can visit this link: [FULL Maxon Cinema 4D R12 Portable [gRabb9]]. You can also check out the tutorials and resources we mentioned above to improve your skills and knowledge of Cinema 4D. And if you need some inspiration or examples of what you can create with Cinema 4D, you can look at the work of professional artists we listed above.</p>
- <p>We hope you enjoyed this article and found it useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Maxon Cinema 4D R12 Portable:</p>
- <ol>
-<li>Is Maxon Cinema 4D R12 Portable legal?</li>
-<p>Maxon Cinema 4D R12 Portable is not an official product of MAXON. It is a modified version of the original Cinema 4D Studio R12 software that bypasses the installation and activation process. Therefore, it is not legal to use or distribute Maxon Cinema 4D R12 Portable without the permission of MAXON. We do not endorse or support the use of Maxon Cinema 4D R12 Portable for any commercial or personal purposes. We only provide this information for educational purposes only.</p>
-<li>Is Maxon Cinema 4D R12 Portable safe?</li>
-<p>Maxon Cinema 4D R12 Portable is not guaranteed to be safe or virus-free. It is a third-party product that may contain malware or spyware that can harm your computer or compromise your privacy. We do not take any responsibility for any damage or loss caused by the use of Maxon Cinema 4D R12 Portable. We advise you to scan the file with an antivirus program before opening it or use it at your own risk.</p>
-<li>Is Maxon Cinema 4D R12 Portable compatible with other versions of Cinema 4D?</li>
-<p>Maxon Cinema 4D R12 Portable is compatible with other versions of Cinema 4D up to R15. However, it may not be compatible with newer versions of Cinema 4D or other software that require installation or activation. It may also have some bugs or errors that can affect the quality or functionality of your 3D work. We recommend you to use the latest version of Cinema 4D from the official website of MAXON if you want to have the best experience and results.</p>
-<li>Is Max on Cinema 4D R12 Portable updated?</li>
-<p>Maxon Cinema 4D R12 Portable is not updated by MAXON or any other official source. It is a static version of the Cinema 4D Studio R12 software that was released in 2010. Therefore, it does not have any of the new features, improvements, or fixes that have been added to the newer versions of Cinema 4D. It may also have some outdated or obsolete features or tools that are no longer supported or compatible with the current standards and technologies. We suggest you to use the latest version of Cinema 4D from the official website of MAXON if you want to have the most up-to-date and reliable version of the software.</p>
-<li>How can I learn more about Maxon Cinema 4D R12 Portable?</li>
-<p>If you want to learn more about Maxon Cinema 4D R12 Portable, you can visit this link: [FULL Maxon Cinema 4D R12 Portable [gRabb9]]. You can also check out the tutorials and resources we mentioned above to improve your skills and knowledge of Cinema 4D. And if you need some inspiration or examples of what you can create with Cinema 4D, you can look at the work of professional artists we listed above.</p>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solarwinds Network Topology Mapper Serial Number REPACK.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solarwinds Network Topology Mapper Serial Number REPACK.md
deleted file mode 100644
index 402056d1256de9cab6a4846b26250397961c2f12..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solarwinds Network Topology Mapper Serial Number REPACK.md	
+++ /dev/null
@@ -1,57 +0,0 @@
-
-<h1>How to Find and Manage Your Solarwinds Network Topology Mapper Serial Number</h1>
-<p>Solarwinds Network Topology Mapper (NTM) is a powerful tool that helps you scan and map your IT infrastructure, including network devices, servers, virtualization hosts, and more. NTM also helps you automate device discovery and mapping, export network diagrams to various formats, monitor for changes and unknown systems, and maintain compliance requirements.</p>
-<h2>Solarwinds Network Topology Mapper Serial Number</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://urlcod.com/2uI9SM">https://urlcod.com/2uI9SM</a></b></p><br /><br />
-<p>To use NTM, you need to activate a license with a serial number that you can find in your Customer Portal. In this article, we will show you how to find and manage your NTM serial number, as well as how to create and export network maps with NTM.</p>
-<h2>How to Find Your NTM Serial Number</h2>
-<p>To find your NTM serial number, follow these steps:</p>
-<ol>
-<li>Log in to your <a href="https://customerportal.solarwinds.com/">Customer Portal</a> with your email address and password.</li>
-<li>Select <strong>Licenses</strong> > <strong>Manage Licenses</strong>.</li>
-<li>Find the <strong>Network Topology Mapper</strong> product in the list and click the <strong>+</strong> sign to expand it.</li>
-<li>You will see your serial number under the <strong>Activation Key</strong> column. You can copy it to your clipboard by clicking the <strong>Copy</strong> icon.</li>
-</ol>
-<h2>How to Activate Your NTM License</h2>
-<p>To activate your NTM license, follow these steps:</p>
-<ol>
-<li>Run the NTM installer on the computer where you want to install NTM.</li>
-<li>Follow the instructions in the installation wizard until you reach the <strong>Licensing Options</strong> screen.</li>
-<li>Select <strong>I have a license key</strong> and paste your serial number in the text box.</li>
-<li>Click <strong>Next</strong> and complete the installation process.</li>
-<li>Launch NTM and start scanning and mapping your network.</li>
-</ol>
-<h2>How to Create and Export Network Maps with NTM</h2>
-<p>To create and export network maps with NTM, follow these steps:</p>
-<ol>
-<li>In NTM, click <strong>New Scan</strong>.</li>
-<li>Add credentials for the devices you want to scan.</li>
-<li>Identify the network segment (subnet, IP ranges) you want to scan. You can also ignore devices that you don't want to include in the map.</li>
-<li>Kick off the scan and wait for it to finish.</li>
-<li>A network map will be created based on the scan results. You can rearrange objects on the map, add or delete devices, and change which links are displayed.</li>
-<li>Save the map with a name and description.</li>
-<li>To export the map, click <strong>Export Map</strong> and choose a format (Visio, Network Atlas, PDF, etc.). You can also create reports from the map data (such as inventory reports) and export them to CSV or other formats.</li>
-</ol>
-
-<h2>Benefits of Network Topology Mapper</h2>
-<p>Network topology mapper software can provide several benefits for network administrators, engineers, and managers. Some of the benefits are:</p>
-<ul>
-<li><strong>Increased visibility:</strong> You can quickly and accurately identify device issues and their effects on the rest of the network. You can also see the physical and logical relationships between devices, such as switch ports, VLANs, subnets, and Etherchannel.</li>
-<li><strong>Improved troubleshooting:</strong> You can easily navigate to any device and obtain vital data and establish a remote connection. You can also use network maps to diagnose network connectivity issues, evaluate network slowdowns, and troubleshoot issues in general.</li>
-<li><strong>Network changes simplified:</strong> You can monitor for new devices, changes, and unknown systems to ensure an accurate and up-to-date record of your network. You can also edit node details and connect devices manually to reflect your network configuration.</li>
-<li><strong>Streamlined client onboarding:</strong> You can scan and map new client networks quickly and efficiently. You can also create multiple maps from a single scan to show different aspects of the network.</li>
-<li><strong>Real-time updates:</strong> You can schedule device and topology discovery to run automatically at regular intervals. You can also receive alerts when changes are detected in your network topology.</li>
-<li><strong>Increased security:</strong> You can protect your stored and exported maps with a password and use FIPS-compliant encryption to secure map data. You can also identify unauthorized or rogue devices on your network and isolate them.</li>
-<li><strong>Increased efficiency:</strong> You can save time and resources by automating network mapping tasks. You can also export network diagrams to various formats, such as Visio, Network Atlas, PDF, etc. You can also generate reports on various network parameters, such as switch ports, VLANs, subnets, and device inventory.</li>
-</ul>
-<h2>How to Choose the Best Network Topology Mapper Software</h2>
-<p>There are many network topology mapper software tools available in the market, but not all of them are created equal. Some of the factors you should consider when choosing the best network topology mapper software for your needs are:</p>
-<ul>
-<li><strong>Discovery methods:</strong> The software should support multiple discovery methods, such as ICMP, SNMP, WMI, CDP, VMware, Microsoft Hyper-V, etc. This will ensure that you can scan and map all types of devices and their interconnections on your network.</li>
-<li><strong>Mapping features:</strong> The software should allow you to create multiple maps from a single scan and show both physical and logical connections between devices. It should also allow you to edit node details, arrange nodes according to predefined or custom layouts, add or delete devices, change link display options, etc.</li>
-<li><strong>Export and reporting features:</strong> The software should allow you to export network diagrams to various formats that are compatible with other tools or applications. It should also allow you to generate reports on various network parameters that can help you with inventory management, compliance requirements, performance analysis, etc.</li>
-<li><strong>Scheduling and alerting features:</strong> The software should allow you to schedule device and topology discovery to run automatically at regular intervals. It should also alert you when changes are detected in your network topology or when devices go down or become unreachable.</li>
-<li><strong>Security features:</strong> The software should allow you to protect your stored and exported maps with a password and use FIPS-compliant encryption to secure map data. It should also help you identify unauthorized or rogue devices on your network and isolate them.</li>
-<li><strong>Pricing and support:</strong> The software should offer a reasonable pricing model that fits your budget and needs. It should also offer a free trial version that you can test before buying. It should also provide reliable customer support and documentation that can help you with installation, configuration, troubleshooting, etc.</li>
-</ul></p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/nicole-ocampo/digimap-mp/train.py b/spaces/nicole-ocampo/digimap-mp/train.py
deleted file mode 100644
index 9d054ad4e420ee439eb7918ff95fea6749f0c27b..0000000000000000000000000000000000000000
--- a/spaces/nicole-ocampo/digimap-mp/train.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-
-import torch
-import torch.backends.cudnn as cudnn
-import torch.utils.data as data
-
-from tensorboardX import SummaryWriter
-from tqdm import tqdm
-from pathlib import Path
-from sampler import InfiniteSamplerWrapper
-
-from net import Net
-from datasets import TrainDataset
-from util import adjust_learning_rate
-
-cudnn.benchmark = True
-
-def train(args):
-
-    # Device, save and log configuration
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    save_dir = Path(os.path.join(args.save_dir, args.name))
-    save_dir.mkdir(exist_ok=True, parents=True)
-    log_dir = Path(os.path.join(args.log_dir, args.name))
-    log_dir.mkdir(exist_ok=True, parents=True)
-    writer = SummaryWriter(log_dir=str(log_dir))
-
-    # Prepare datasets
-
-    content_dataset = TrainDataset(args.content_dir, args.img_size)
-    texture_dataset = TrainDataset(args.texture_dir, args.img_size, gray_only=True)
-    color_dataset = TrainDataset(args.color_dir, args.img_size)
-
-    content_iter = iter(data.DataLoader(
-        content_dataset, batch_size=args.batch_size,
-        sampler=InfiniteSamplerWrapper(content_dataset),
-        num_workers=args.n_threads))
-    texture_iter = iter(data.DataLoader(
-        texture_dataset, batch_size=args.batch_size,
-        sampler=InfiniteSamplerWrapper(texture_dataset),
-        num_workers=args.n_threads))
-    color_iter = iter(data.DataLoader(
-        color_dataset, batch_size=args.batch_size,
-        sampler=InfiniteSamplerWrapper(color_dataset),
-        num_workers=args.n_threads))
-
-    # Prepare network
-
-    network = Net(args)
-    network.train()
-    network.to(device)
-
-    # Training options
-
-    opt_L = torch.optim.Adam(network.L_path.parameters(), lr=args.lr)
-    opt_AB = torch.optim.Adam(network.AB_path.parameters(), lr=args.lr)
-
-    opts = [opt_L, opt_AB]
-
-    # Start Training
-
-    for i in tqdm(range(args.max_iter)):
-        # S1: Adjust lr and prepare data
-
-        adjust_learning_rate(opts, iteration_count=i, args=args)
-
-        content_l, content_ab = [x.to(device) for x in next(content_iter)]
-        texture_l = next(texture_iter).to(device)
-        color_l, color_ab = [x.to(device) for x in next(color_iter)]
-
-        # S2: Forward
-
-        l_pred, ab_pred = network(content_l, content_ab, texture_l, color_ab)
-
-        # S3: Calculate loss
-
-        loss_ct, loss_t = network.ct_t_loss(l_pred, content_l, texture_l)
-        loss_cr = network.cr_loss(ab_pred, color_ab)
-
-        loss_ctw = args.content_weight * loss_ct
-        loss_tw = args.texture_weight * loss_t
-        loss_crw = args.color_weight * loss_cr
-
-        loss = loss_ctw + loss_tw + loss_crw
-
-        # S4: Backward
-
-        for opt in opts:
-            opt.zero_grad()
-        loss.backward()
-        for opt in opts:
-            opt.step()
-
-        # S5: Summary loss and save subnets
-
-        writer.add_scalar('loss_content', loss_ct.item(), i + 1)
-        writer.add_scalar('loss_texture', loss_t.item(), i + 1)
-        writer.add_scalar('loss_color', loss_cr.item(), i + 1)
-
-        if (i + 1) % args.save_model_interval == 0 or (i + 1) == args.max_iter:
-            state_dict = network.state_dict()
-            for key in state_dict.keys():
-                state_dict[key] = state_dict[key].to(torch.device('cpu'))
-            torch.save(state_dict, save_dir /
-                       'network_iter_{:d}.pth.tar'.format(i + 1))
-    writer.close()
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/install.md b/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/install.md
deleted file mode 100644
index 5f52b2be3c9650cfc3e16ffb8fa374d3fcbad371..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/install.md
+++ /dev/null
@@ -1 +0,0 @@
-../../INSTALL.md
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/training.md b/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/training.md
deleted file mode 100644
index 83a6cb0a8e38ca06bbf96201ac2595d2116523c3..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/training.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Training
-
-From the previous tutorials, you may now have a custom model and a data loader.
-To run training, users typically have a preference in one of the following two styles:
-
-### Custom Training Loop
-
-With a model and a data loader ready, everything else needed to write a training loop can
-be found in PyTorch, and you are free to write the training loop yourself.
-This style allows researchers to manage the entire training logic more clearly and have full control.
-One such example is provided in [tools/plain_train_net.py](../../tools/plain_train_net.py).
-
-Any customization on the training logic is then easily controlled by the user.
-
-### Trainer Abstraction
-
-We also provide a standardized "trainer" abstraction with a
-hook system that helps simplify the standard training behavior.
-It includes the following two instantiations:
-
-* [SimpleTrainer](../modules/engine.html#detectron2.engine.SimpleTrainer)
-  provides a minimal training loop for single-cost single-optimizer single-data-source training, with nothing else.
-  Other tasks (checkpointing, logging, etc) can be implemented using
-  [the hook system](../modules/engine.html#detectron2.engine.HookBase).
-* [DefaultTrainer](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) is a `SimpleTrainer` initialized from a
-  yacs config, used by
-  [tools/train_net.py](../../tools/train_net.py) and many scripts.
-  It includes more standard default behaviors that one might want to opt in,
-  including default configurations for optimizer, learning rate schedule,
-  logging, evaluation, checkpointing etc.
-
-To customize a `DefaultTrainer`:
-
-1. For simple customizations (e.g. change optimizer, evaluator, LR scheduler, data loader, etc.), overwrite [its methods](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) in a subclass, just like [tools/train_net.py](../../tools/train_net.py).
-2. For extra tasks during training, check the
-   [hook system](../modules/engine.html#detectron2.engine.HookBase) to see if it's supported.
-
-   As an example, to print hello during training:
-   ```python
-   class HelloHook(HookBase):
-     def after_step(self):
-       if self.trainer.iter % 100 == 0:
-         print(f"Hello at iteration {self.trainer.iter}!")
-   ```
-3. Using a trainer+hook system means there will always be some non-standard behaviors that cannot be supported, especially in research.
-   For this reason, we intentionally keep the trainer & hook system minimal, rather than powerful.
-   If anything cannot be achieved by such a system, it's easier to start from [tools/plain_train_net.py](../../tools/plain_train_net.py) to implement custom training logic manually.
-
-### Logging of Metrics
-
-During training, detectron2 models and trainer put metrics to a centralized [EventStorage](../modules/utils.html#detectron2.utils.events.EventStorage).
-You can use the following code to access it and log metrics to it:
-```python
-from detectron2.utils.events import get_event_storage
-
-# inside the model:
-if self.training:
-  value = # compute the value from inputs
-  storage = get_event_storage()
-  storage.put_scalar("some_accuracy", value)
-```
-
-Refer to its documentation for more details.
-
-Metrics are then written to various destinations with [EventWriter](../modules/utils.html#module-detectron2.utils.events).
-DefaultTrainer enables a few `EventWriter` with default configurations.
-See above for how to customize them.
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/engine/trainer.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/engine/trainer.py
deleted file mode 100644
index a8ffe82c3d64d01ae36bb3c07cc6d75950937389..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/engine/trainer.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import logging
-import os
-from collections import OrderedDict
-from typing import List, Optional, Union
-import torch
-from torch import nn
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import CfgNode
-from detectron2.engine import DefaultTrainer
-from detectron2.evaluation import (
-    DatasetEvaluator,
-    DatasetEvaluators,
-    inference_on_dataset,
-    print_csv_format,
-)
-from detectron2.solver.build import get_default_optimizer_params, maybe_add_gradient_clipping
-from detectron2.utils import comm
-from detectron2.utils.events import EventWriter, get_event_storage
-
-from densepose import DensePoseDatasetMapperTTA, DensePoseGeneralizedRCNNWithTTA, load_from_cfg
-from densepose.data import (
-    DatasetMapper,
-    build_combined_loader,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    build_inference_based_loaders,
-    has_inference_based_loaders,
-)
-from densepose.evaluation.d2_evaluator_adapter import Detectron2COCOEvaluatorAdapter
-from densepose.evaluation.evaluator import DensePoseCOCOEvaluator, build_densepose_evaluator_storage
-from densepose.modeling.cse import Embedder
-
-
-class SampleCountingLoader:
-    def __init__(self, loader):
-        self.loader = loader
-
-    def __iter__(self):
-        it = iter(self.loader)
-        storage = get_event_storage()
-        while True:
-            try:
-                batch = next(it)
-                num_inst_per_dataset = {}
-                for data in batch:
-                    dataset_name = data["dataset"]
-                    if dataset_name not in num_inst_per_dataset:
-                        num_inst_per_dataset[dataset_name] = 0
-                    num_inst = len(data["instances"])
-                    num_inst_per_dataset[dataset_name] += num_inst
-                for dataset_name in num_inst_per_dataset:
-                    storage.put_scalar(f"batch/{dataset_name}", num_inst_per_dataset[dataset_name])
-                yield batch
-            except StopIteration:
-                break
-
-
-class SampleCountMetricPrinter(EventWriter):
-    def __init__(self):
-        self.logger = logging.getLogger(__name__)
-
-    def write(self):
-        storage = get_event_storage()
-        batch_stats_strs = []
-        for key, buf in storage.histories().items():
-            if key.startswith("batch/"):
-                batch_stats_strs.append(f"{key} {buf.avg(20)}")
-        self.logger.info(", ".join(batch_stats_strs))
-
-
-class Trainer(DefaultTrainer):
-    @classmethod
-    def extract_embedder_from_model(cls, model: nn.Module) -> Optional[Embedder]:
-        if isinstance(model, nn.parallel.DistributedDataParallel):
-            model = model.module
-        if hasattr(model, "roi_heads") and hasattr(model.roi_heads, "embedder"):
-            return model.roi_heads.embedder
-        return None
-
-    # TODO: the only reason to copy the base class code here is to pass the embedder from
-    # the model to the evaluator; that should be refactored to avoid unnecessary copy-pasting
-    @classmethod
-    def test(
-        cls,
-        cfg: CfgNode,
-        model: nn.Module,
-        evaluators: Optional[Union[DatasetEvaluator, List[DatasetEvaluator]]] = None,
-    ):
-        """
-        Args:
-            cfg (CfgNode):
-            model (nn.Module):
-            evaluators (DatasetEvaluator, list[DatasetEvaluator] or None): if None, will call
-                :meth:`build_evaluator`. Otherwise, must have the same length as
-                ``cfg.DATASETS.TEST``.
-
-        Returns:
-            dict: a dict of result metrics
-        """
-        logger = logging.getLogger(__name__)
-        if isinstance(evaluators, DatasetEvaluator):
-            evaluators = [evaluators]
-        if evaluators is not None:
-            assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
-                len(cfg.DATASETS.TEST), len(evaluators)
-            )
-
-        results = OrderedDict()
-        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
-            data_loader = cls.build_test_loader(cfg, dataset_name)
-            # When evaluators are passed in as arguments,
-            # implicitly assume that evaluators can be created before data_loader.
-            if evaluators is not None:
-                evaluator = evaluators[idx]
-            else:
-                try:
-                    embedder = cls.extract_embedder_from_model(model)
-                    evaluator = cls.build_evaluator(cfg, dataset_name, embedder=embedder)
-                except NotImplementedError:
-                    logger.warn(
-                        "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
-                        "or implement its `build_evaluator` method."
-                    )
-                    results[dataset_name] = {}
-                    continue
-            if cfg.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE or comm.is_main_process():
-                results_i = inference_on_dataset(model, data_loader, evaluator)
-            else:
-                results_i = {}
-            results[dataset_name] = results_i
-            if comm.is_main_process():
-                assert isinstance(
-                    results_i, dict
-                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
-                    results_i
-                )
-                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
-                print_csv_format(results_i)
-
-        if len(results) == 1:
-            results = list(results.values())[0]
-        return results
-
-    @classmethod
-    def build_evaluator(
-        cls,
-        cfg: CfgNode,
-        dataset_name: str,
-        output_folder: Optional[str] = None,
-        embedder: Optional[Embedder] = None,
-    ) -> DatasetEvaluators:
-        if output_folder is None:
-            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-        evaluators = []
-        distributed = cfg.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE
-        # Note: we currently use COCO evaluator for both COCO and LVIS datasets
-        # to have compatible metrics. LVIS bbox evaluator could also be used
-        # with an adapter to properly handle filtered / mapped categories
-        # evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-        # if evaluator_type == "coco":
-        #     evaluators.append(COCOEvaluator(dataset_name, output_dir=output_folder))
-        # elif evaluator_type == "lvis":
-        #     evaluators.append(LVISEvaluator(dataset_name, output_dir=output_folder))
-        evaluators.append(
-            Detectron2COCOEvaluatorAdapter(
-                dataset_name, output_dir=output_folder, distributed=distributed
-            )
-        )
-        if cfg.MODEL.DENSEPOSE_ON:
-            storage = build_densepose_evaluator_storage(cfg, output_folder)
-            evaluators.append(
-                DensePoseCOCOEvaluator(
-                    dataset_name,
-                    distributed,
-                    output_folder,
-                    evaluator_type=cfg.DENSEPOSE_EVALUATION.TYPE,
-                    min_iou_threshold=cfg.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD,
-                    storage=storage,
-                    embedder=embedder,
-                    should_evaluate_mesh_alignment=cfg.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT,
-                    mesh_alignment_mesh_names=cfg.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES,
-                )
-            )
-        return DatasetEvaluators(evaluators)
-
-    @classmethod
-    def build_optimizer(cls, cfg: CfgNode, model: nn.Module):
-        params = get_default_optimizer_params(
-            model,
-            base_lr=cfg.SOLVER.BASE_LR,
-            weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
-            bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
-            weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
-            overrides={
-                "features": {
-                    "lr": cfg.SOLVER.BASE_LR * cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR,
-                },
-                "embeddings": {
-                    "lr": cfg.SOLVER.BASE_LR * cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR,
-                },
-            },
-        )
-        optimizer = torch.optim.SGD(
-            params,
-            cfg.SOLVER.BASE_LR,
-            momentum=cfg.SOLVER.MOMENTUM,
-            nesterov=cfg.SOLVER.NESTEROV,
-            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
-        )
-        # pyre-fixme[6]: For 2nd param expected `Type[Optimizer]` but got `SGD`.
-        return maybe_add_gradient_clipping(cfg, optimizer)
-
-    @classmethod
-    def build_test_loader(cls, cfg: CfgNode, dataset_name):
-        return build_detection_test_loader(cfg, dataset_name, mapper=DatasetMapper(cfg, False))
-
-    @classmethod
-    def build_train_loader(cls, cfg: CfgNode):
-        data_loader = build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True))
-        if not has_inference_based_loaders(cfg):
-            return data_loader
-        model = cls.build_model(cfg)
-        model.to(cfg.BOOTSTRAP_MODEL.DEVICE)
-        DetectionCheckpointer(model).resume_or_load(cfg.BOOTSTRAP_MODEL.WEIGHTS, resume=False)
-        inference_based_loaders, ratios = build_inference_based_loaders(cfg, model)
-        loaders = [data_loader] + inference_based_loaders
-        ratios = [1.0] + ratios
-        combined_data_loader = build_combined_loader(cfg, loaders, ratios)
-        sample_counting_loader = SampleCountingLoader(combined_data_loader)
-        return sample_counting_loader
-
-    def build_writers(self):
-        writers = super().build_writers()
-        writers.append(SampleCountMetricPrinter())
-        return writers
-
-    @classmethod
-    def test_with_TTA(cls, cfg: CfgNode, 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 ...")
-        transform_data = load_from_cfg(cfg)
-        model = DensePoseGeneralizedRCNNWithTTA(
-            cfg, model, transform_data, DensePoseDatasetMapperTTA(cfg)
-        )
-        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)  # pyre-ignore[6]
-        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
-        return res
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/roi_heads.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/roi_heads.py
deleted file mode 100644
index 74ccc34a1193c604fcc34b8deed5ece53fee3f19..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/roi_heads.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-
-from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
-
-
-@ROI_HEADS_REGISTRY.register()
-class PointRendROIHeads(StandardROIHeads):
-    """
-    Identical to StandardROIHeads, except for some weights conversion code to
-    handle old models.
-    """
-
-    _version = 2
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        version = local_metadata.get("version", None)
-        if version is None or version < 2:
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                "Weight format of PointRend models have changed! "
-                "Please upgrade your models. Applying automatic conversion now ..."
-            )
-            for k in list(state_dict.keys()):
-                newk = k
-                if k.startswith(prefix + "mask_point_head"):
-                    newk = k.replace(prefix + "mask_point_head", prefix + "mask_head.point_head")
-                if k.startswith(prefix + "mask_coarse_head"):
-                    newk = k.replace(prefix + "mask_coarse_head", prefix + "mask_head.coarse_head")
-                if newk != k:
-                    state_dict[newk] = state_dict[k]
-                    del state_dict[k]
-
-    @classmethod
-    def _init_mask_head(cls, cfg, input_shape):
-        if cfg.MODEL.MASK_ON and cfg.MODEL.ROI_MASK_HEAD.NAME != "PointRendMaskHead":
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                "Config of PointRend models have changed! "
-                "Please upgrade your models. Applying automatic conversion now ..."
-            )
-            assert cfg.MODEL.ROI_MASK_HEAD.NAME == "CoarseMaskHead"
-            cfg.defrost()
-            cfg.MODEL.ROI_MASK_HEAD.NAME = "PointRendMaskHead"
-            cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE = ""
-            cfg.freeze()
-        return super()._init_mask_head(cfg, input_shape)
diff --git a/spaces/nikravan/Text2Sql/README.md b/spaces/nikravan/Text2Sql/README.md
deleted file mode 100644
index 0769e391b2a172be63cd5afaf3b9e14353b9a0d9..0000000000000000000000000000000000000000
--- a/spaces/nikravan/Text2Sql/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text2Sql
-emoji: 🌖
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nivalk/dermAI/app.py b/spaces/nivalk/dermAI/app.py
deleted file mode 100644
index 1c5341d7e2536e4e42b6c5822749472e036783c2..0000000000000000000000000000000000000000
--- a/spaces/nivalk/dermAI/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-
-learn = load_learner('resnet.pkl')
-
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Skin Disease Classifier"
-description = "A prototype app for classifying common skin diseases, developed with fastai. Created as a demo for Gradio and HuggingFace Spaces."
-
-examples = ['acne.jpg']
-interpretation='default'
-enable_queue=True
-
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),
-title=title,description=description, examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
\ No newline at end of file
diff --git a/spaces/nomic-ai/EleutherAI_lambada_openai/style.css b/spaces/nomic-ai/EleutherAI_lambada_openai/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/EleutherAI_lambada_openai/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nomic-ai/blended_skill_talk/README.md b/spaces/nomic-ai/blended_skill_talk/README.md
deleted file mode 100644
index 1669dfd0dd4bae51ca0eb0404e2cb8776d738836..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/blended_skill_talk/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: blended_skill_talk
-emoji: 🗺️
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
-duplicated_from: nomic-ai/gsm8k
----
diff --git a/spaces/nus-cs5647-team-5/Mandarin_Tone_Evaluation/evaluate_speech_model.py b/spaces/nus-cs5647-team-5/Mandarin_Tone_Evaluation/evaluate_speech_model.py
deleted file mode 100644
index 87e0d01e75029a9f7a5eb6da494733fb55bbd7d7..0000000000000000000000000000000000000000
--- a/spaces/nus-cs5647-team-5/Mandarin_Tone_Evaluation/evaluate_speech_model.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-
-from speech_model import ModelSpeech
-from speech_model_zoo import SpeechModel251BN
-from data_loader import DataLoader
-from speech_features import Spectrogram
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-
-AUDIO_LENGTH = 1600
-AUDIO_FEATURE_LENGTH = 200
-CHANNELS = 1
-# 默认输出的拼音的表示大小是1428，即1427个拼音+1个空白块
-OUTPUT_SIZE = 1428
-sm251bn = SpeechModel251BN(
-    input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
-    output_size=OUTPUT_SIZE
-)
-feat = Spectrogram()
-evalue_data = DataLoader('dev')
-ms = ModelSpeech(sm251bn, feat, max_label_length=64)
-
-ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5')
-ms.evaluate_model(data_loader=evalue_data, data_count=-1,
-                  out_report=True, show_ratio=True, show_per_step=100)
diff --git a/spaces/perilli/tortoise-tts-v2/eval_multiple.py b/spaces/perilli/tortoise-tts-v2/eval_multiple.py
deleted file mode 100644
index 9defa525e790a0a53ceff9940ffe5a6cda228d79..0000000000000000000000000000000000000000
--- a/spaces/perilli/tortoise-tts-v2/eval_multiple.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-
-import torchaudio
-
-from api import TextToSpeech
-from utils.audio import load_audio
-
-if __name__ == '__main__':
-    fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
-    stop_after = 128
-    outpath_base = 'D:\\tmp\\tortoise-tts-eval\\audiobooks'
-    outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
-
-    os.makedirs(outpath_real, exist_ok=True)
-    with open(fname, 'r', encoding='utf-8') as f:
-        lines = [l.strip().split('\t') for l in f.readlines()]
-
-    tts = TextToSpeech()
-    for k in range(3):
-        outpath = f'{outpath_base}_{k}'
-        os.makedirs(outpath, exist_ok=True)
-        recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
-        for e, line in enumerate(lines):
-            if e >= stop_after:
-                break
-            transcript = line[0]
-            path = os.path.join(os.path.dirname(fname), line[1])
-            cond_audio = load_audio(path, 22050)
-            torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
-            sample = tts.tts_with_preset(transcript, [cond_audio, cond_audio], preset='standard')
-
-            down = torchaudio.functional.resample(sample, 24000, 22050)
-            fout_path = os.path.join(outpath, os.path.basename(line[1]))
-            torchaudio.save(fout_path, down.squeeze(0), 22050)
-
-            recorder.write(f'{transcript}\t{fout_path}\n')
-            recorder.flush()
-        recorder.close()
\ No newline at end of file
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py
deleted file mode 100644
index d0955f9e608377940f0d548576964f2fcf3caf48..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main, skipUnless
-
-try:
-    from unittest.mock import Mock, patch
-except ImportError:
-    from mock import Mock, patch
-
-from ..winterm import WinColor, WinStyle, WinTerm
-
-
-class WinTermTest(TestCase):
-
-    @patch('colorama.winterm.win32')
-    def testInit(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 7 + 6 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        self.assertEqual(term._fore, 7)
-        self.assertEqual(term._back, 6)
-        self.assertEqual(term._style, 8)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testGetAttrs(self):
-        term = WinTerm()
-
-        term._fore = 0
-        term._back = 0
-        term._style = 0
-        self.assertEqual(term.get_attrs(), 0)
-
-        term._fore = WinColor.YELLOW
-        self.assertEqual(term.get_attrs(), WinColor.YELLOW)
-
-        term._back = WinColor.MAGENTA
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16)
-
-        term._style = WinStyle.BRIGHT
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16 + WinStyle.BRIGHT)
-
-    @patch('colorama.winterm.win32')
-    def testResetAll(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 1 + 2 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-
-        term.set_console = Mock()
-        term._fore = -1
-        term._back = -1
-        term._style = -1
-
-        term.reset_all()
-
-        self.assertEqual(term._fore, 1)
-        self.assertEqual(term._back, 2)
-        self.assertEqual(term._style, 8)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testFore(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._fore = 0
-
-        term.fore(5)
-
-        self.assertEqual(term._fore, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testBack(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._back = 0
-
-        term.back(5)
-
-        self.assertEqual(term._back, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testStyle(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._style = 0
-
-        term.style(22)
-
-        self.assertEqual(term._style, 22)
-        self.assertEqual(term.set_console.called, True)
-
-    @patch('colorama.winterm.win32')
-    def testSetConsole(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console()
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDOUT, term.get_attrs()), {})
-        )
-
-    @patch('colorama.winterm.win32')
-    def testSetConsoleOnStderr(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console(on_stderr=True)
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDERR, term.get_attrs()), {})
-        )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/README.md b/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/README.md
deleted file mode 100644
index 568cb5a03717e1f4f2fd2b1292c74a5ae0c790ee..0000000000000000000000000000000000000000
--- a/spaces/poetrychor/Gustavosta-MagicPrompt-Stable-Diffusion/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AutoTrain Advanced
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: autotrain-projects/autotrain-advanced
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/probing-vits/class-saliency/utils.py b/spaces/probing-vits/class-saliency/utils.py
deleted file mode 100644
index b14208d5e825bb5a40efe5560859e6e528ed839c..0000000000000000000000000000000000000000
--- a/spaces/probing-vits/class-saliency/utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from typing import Dict
-
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-from tensorflow import keras
-
-RESOLUTION = 224
-PATCH_SIZE = 16
-
-
-crop_layer = keras.layers.CenterCrop(RESOLUTION, RESOLUTION)
-norm_layer = keras.layers.Normalization(
-    mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
-    variance=[(0.229 * 255) ** 2, (0.224 * 255) ** 2, (0.225 * 255) ** 2],
-)
-
-
-def preprocess_image(orig_image: Image, size: int):
-    """Image preprocessing utility."""
-    image = np.array(orig_image)
-    image_resized = tf.expand_dims(image, 0)
-    resize_size = int((256 / 224) * size)
-    image_resized = tf.image.resize(
-        image_resized, (resize_size, resize_size), method="bicubic"
-    )
-    image_resized = crop_layer(image_resized)
-    return image_resized.numpy().squeeze(), norm_layer(image_resized).numpy()
-
-
-# Reference:
-# https://github.com/facebookresearch/dino/blob/main/visualize_attention.py
-
-
-def get_cls_attention_map(
-    preprocessed_image: np.ndarray,
-    attn_score_dict: Dict[str, np.ndarray],
-    block_key="ca_ffn_block_0_att",
-):
-    """Utility to generate class saliency map modeling spatial-class relationships."""
-    w_featmap = preprocessed_image.shape[2] // PATCH_SIZE
-    h_featmap = preprocessed_image.shape[1] // PATCH_SIZE
-
-    attention_scores = attn_score_dict[block_key]
-    nh = attention_scores.shape[1]  # Number of attention heads.
-
-    # Taking the representations from CLS token.
-    attentions = attention_scores[0, :, 0, 1:].reshape(nh, -1)
-
-    # Reshape the attention scores to resemble mini patches.
-    attentions = attentions.reshape(nh, w_featmap, h_featmap)
-
-    attentions = np.mean(attentions, axis=0)
-    attentions = (attentions - attentions.min()) / (
-        attentions.max() - attentions.min()
-    )
-    attentions = np.expand_dims(attentions, -1)
-
-    # Resize the attention patches to 224x224 (224: 14x16)
-    attentions = tf.image.resize(
-        attentions,
-        size=(h_featmap * PATCH_SIZE, w_featmap * PATCH_SIZE),
-        method="bicubic",
-    )
-
-    return attentions.numpy()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/statisticsPen.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/statisticsPen.py
deleted file mode 100644
index 8e00aa44da6a005f9c246f8c8e752a95ee601e0d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/pens/statisticsPen.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""Pen calculating area, center of mass, variance and standard-deviation,
-covariance and correlation, and slant, of glyph shapes."""
-import math
-from fontTools.pens.momentsPen import MomentsPen
-
-__all__ = ["StatisticsPen"]
-
-
-class StatisticsPen(MomentsPen):
-
-    """Pen calculating area, center of mass, variance and
-    standard-deviation, covariance and correlation, and slant,
-    of glyph shapes.
-
-    Note that all the calculated values are 'signed'. Ie. if the
-    glyph shape is self-intersecting, the values are not correct
-    (but well-defined). As such, area will be negative if contour
-    directions are clockwise.  Moreover, variance might be negative
-    if the shapes are self-intersecting in certain ways."""
-
-    def __init__(self, glyphset=None):
-        MomentsPen.__init__(self, glyphset=glyphset)
-        self.__zero()
-
-    def _closePath(self):
-        MomentsPen._closePath(self)
-        self.__update()
-
-    def __zero(self):
-        self.meanX = 0
-        self.meanY = 0
-        self.varianceX = 0
-        self.varianceY = 0
-        self.stddevX = 0
-        self.stddevY = 0
-        self.covariance = 0
-        self.correlation = 0
-        self.slant = 0
-
-    def __update(self):
-        area = self.area
-        if not area:
-            self.__zero()
-            return
-
-        # Center of mass
-        # https://en.wikipedia.org/wiki/Center_of_mass#A_continuous_volume
-        self.meanX = meanX = self.momentX / area
-        self.meanY = meanY = self.momentY / area
-
-        #  Var(X) = E[X^2] - E[X]^2
-        self.varianceX = varianceX = self.momentXX / area - meanX**2
-        self.varianceY = varianceY = self.momentYY / area - meanY**2
-
-        self.stddevX = stddevX = math.copysign(abs(varianceX) ** 0.5, varianceX)
-        self.stddevY = stddevY = math.copysign(abs(varianceY) ** 0.5, varianceY)
-
-        #  Covariance(X,Y) = ( E[X.Y] - E[X]E[Y] )
-        self.covariance = covariance = self.momentXY / area - meanX * meanY
-
-        #  Correlation(X,Y) = Covariance(X,Y) / ( stddev(X) * stddev(Y) )
-        # https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
-        if stddevX * stddevY == 0:
-            correlation = float("NaN")
-        else:
-            correlation = covariance / (stddevX * stddevY)
-        self.correlation = correlation if abs(correlation) > 1e-3 else 0
-
-        slant = covariance / varianceY if varianceY != 0 else float("NaN")
-        self.slant = slant if abs(slant) > 1e-3 else 0
-
-
-def _test(glyphset, upem, glyphs, quiet=False):
-    from fontTools.pens.transformPen import TransformPen
-    from fontTools.misc.transform import Scale
-
-    wght_sum = 0
-    wght_sum_perceptual = 0
-    wdth_sum = 0
-    slnt_sum = 0
-    slnt_sum_perceptual = 0
-    for glyph_name in glyphs:
-        glyph = glyphset[glyph_name]
-        pen = StatisticsPen(glyphset=glyphset)
-        transformer = TransformPen(pen, Scale(1.0 / upem))
-        glyph.draw(transformer)
-
-        wght_sum += abs(pen.area)
-        wght_sum_perceptual += abs(pen.area) * glyph.width
-        wdth_sum += glyph.width
-        slnt_sum += pen.slant
-        slnt_sum_perceptual += pen.slant * glyph.width
-
-        if quiet:
-            continue
-
-        print()
-        print("glyph:", glyph_name)
-
-        for item in [
-            "area",
-            "momentX",
-            "momentY",
-            "momentXX",
-            "momentYY",
-            "momentXY",
-            "meanX",
-            "meanY",
-            "varianceX",
-            "varianceY",
-            "stddevX",
-            "stddevY",
-            "covariance",
-            "correlation",
-            "slant",
-        ]:
-            print("%s: %g" % (item, getattr(pen, item)))
-
-    if not quiet:
-        print()
-        print("font:")
-
-    print("weight: %g" % (wght_sum * upem / wdth_sum))
-    print("weight (perceptual): %g" % (wght_sum_perceptual / wdth_sum))
-    print("width:  %g" % (wdth_sum / upem / len(glyphs)))
-    slant = slnt_sum / len(glyphs)
-    print("slant:  %g" % slant)
-    print("slant angle:  %g" % -math.degrees(math.atan(slant)))
-    slant_perceptual = slnt_sum_perceptual / wdth_sum
-    print("slant (perceptual):  %g" % slant_perceptual)
-    print("slant (perceptual) angle:  %g" % -math.degrees(math.atan(slant_perceptual)))
-
-
-def main(args):
-    """Report font glyph shape geometricsl statistics"""
-
-    if args is None:
-        import sys
-
-        args = sys.argv[1:]
-
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        "fonttools pens.statisticsPen",
-        description="Report font glyph shape geometricsl statistics",
-    )
-    parser.add_argument("font", metavar="font.ttf", help="Font file.")
-    parser.add_argument("glyphs", metavar="glyph-name", help="Glyph names.", nargs="*")
-    parser.add_argument(
-        "-y",
-        metavar="<number>",
-        help="Face index into a collection to open. Zero based.",
-    )
-    parser.add_argument(
-        "-q", "--quiet", action="store_true", help="Only report font-wide statistics."
-    )
-    parser.add_argument(
-        "--variations",
-        metavar="AXIS=LOC",
-        default="",
-        help="List of space separated locations. A location consist in "
-        "the name of a variation axis, followed by '=' and a number. E.g.: "
-        "wght=700 wdth=80. The default is the location of the base master.",
-    )
-
-    options = parser.parse_args(args)
-
-    glyphs = options.glyphs
-    fontNumber = int(options.y) if options.y is not None else 0
-
-    location = {}
-    for tag_v in options.variations.split():
-        fields = tag_v.split("=")
-        tag = fields[0].strip()
-        v = int(fields[1])
-        location[tag] = v
-
-    from fontTools.ttLib import TTFont
-
-    font = TTFont(options.font, fontNumber=fontNumber)
-    if not glyphs:
-        glyphs = font.getGlyphOrder()
-    _test(
-        font.getGlyphSet(location=location),
-        font["head"].unitsPerEm,
-        glyphs,
-        quiet=options.quiet,
-    )
-
-
-if __name__ == "__main__":
-    import sys
-
-    main(sys.argv[1:])
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/wasm/src/worker-proxy.ts b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/wasm/src/worker-proxy.ts
deleted file mode 100644
index 1af1581b09af527ff3c7bce1c15506c09093fdbf..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/wasm/src/worker-proxy.ts
+++ /dev/null
@@ -1,223 +0,0 @@
-import { CrossOriginWorkerMaker as Worker } from "./cross-origin-worker";
-import type {
-	EmscriptenFile,
-	EmscriptenFileUrl,
-	HttpRequest,
-	HttpResponse,
-	InMessage,
-	InMessageWebSocket,
-	OutMessage,
-	ReplyMessage
-} from "./message-types";
-import { MessagePortWebSocket } from "./messageportwebsocket";
-import { PromiseDelegate } from "./promise-delegate";
-
-export interface WorkerProxyOptions {
-	gradioWheelUrl: string;
-	gradioClientWheelUrl: string;
-	files: Record<string, EmscriptenFile | EmscriptenFileUrl>;
-	requirements: string[];
-}
-
-export class WorkerProxy extends EventTarget {
-	private worker: globalThis.Worker;
-
-	private firstRunPromiseDelegate = new PromiseDelegate<void>();
-
-	constructor(options: WorkerProxyOptions) {
-		super();
-
-		console.debug("WorkerProxy.constructor(): Create a new worker.");
-		// Loading a worker here relies on Vite's support for WebWorkers (https://vitejs.dev/guide/features.html#web-workers),
-		// assuming that this module is imported from the Gradio frontend (`@gradio/app`), which is bundled with Vite.
-		// HACK: Use `CrossOriginWorkerMaker` imported as `Worker` here.
-		// Read the comment in `cross-origin-worker.ts` for the detail.
-		const workerMaker = new Worker(new URL("./webworker.js", import.meta.url));
-		this.worker = workerMaker.worker;
-
-		this.worker.onmessage = (e) => {
-			this._processWorkerMessage(e.data);
-		};
-
-		this.postMessageAsync({
-			type: "init",
-			data: {
-				gradioWheelUrl: options.gradioWheelUrl,
-				gradioClientWheelUrl: options.gradioClientWheelUrl,
-				files: options.files,
-				requirements: options.requirements
-			}
-		})
-			.then(() => {
-				console.debug("WorkerProxy.constructor(): Initialization is done.");
-			})
-			.catch((error) => {
-				console.error(
-					"WorkerProxy.constructor(): Initialization failed.",
-					error
-				);
-				this.dispatchEvent(
-					new CustomEvent("initialization-error", {
-						detail: error
-					})
-				);
-			});
-	}
-
-	public async runPythonCode(code: string): Promise<void> {
-		await this.postMessageAsync({
-			type: "run-python-code",
-			data: {
-				code
-			}
-		});
-		this.firstRunPromiseDelegate.resolve();
-	}
-
-	public async runPythonFile(path: string): Promise<void> {
-		await this.postMessageAsync({
-			type: "run-python-file",
-			data: {
-				path
-			}
-		});
-		this.firstRunPromiseDelegate.resolve();
-	}
-
-	// A wrapper for this.worker.postMessage(). Unlike that function, which
-	// returns void immediately, this function returns a promise, which resolves
-	// when a ReplyMessage is received from the worker.
-	// The original implementation is in https://github.com/rstudio/shinylive/blob/v0.1.2/src/pyodide-proxy.ts#L404-L418
-	private postMessageAsync(msg: InMessage): Promise<unknown> {
-		return new Promise((resolve, reject) => {
-			const channel = new MessageChannel();
-
-			channel.port1.onmessage = (e) => {
-				channel.port1.close();
-				const msg = e.data as ReplyMessage;
-				if (msg.type === "reply:error") {
-					reject(msg.error);
-					return;
-				}
-
-				resolve(msg.data);
-			};
-
-			this.worker.postMessage(msg, [channel.port2]);
-		});
-	}
-
-	private _processWorkerMessage(msg: OutMessage): void {
-		switch (msg.type) {
-			case "progress-update": {
-				this.dispatchEvent(
-					new CustomEvent("progress-update", {
-						detail: msg.data.log
-					})
-				);
-				break;
-			}
-		}
-	}
-
-	public async httpRequest(request: HttpRequest): Promise<HttpResponse> {
-		// Wait for the first run to be done
-		// to avoid the "Gradio app has not been launched." error
-		// in case running the code takes long time.
-		// Ref: https://github.com/gradio-app/gradio/issues/5957
-		await this.firstRunPromiseDelegate.promise;
-
-		console.debug("WorkerProxy.httpRequest()", request);
-		const result = await this.postMessageAsync({
-			type: "http-request",
-			data: {
-				request
-			}
-		});
-		const response = (result as { response: HttpResponse }).response;
-
-		if (Math.floor(response.status / 100) !== 2) {
-			let bodyText: string;
-			let bodyJson: unknown;
-			try {
-				bodyText = new TextDecoder().decode(response.body);
-			} catch (e) {
-				bodyText = "(failed to decode body)";
-			}
-			try {
-				bodyJson = JSON.parse(bodyText);
-			} catch (e) {
-				bodyJson = "(failed to parse body as JSON)";
-			}
-			console.error("Wasm HTTP error", {
-				request,
-				response,
-				bodyText,
-				bodyJson
-			});
-		}
-
-		return response;
-	}
-
-	public openWebSocket(path: string): MessagePortWebSocket {
-		const channel = new MessageChannel();
-
-		const msg: InMessageWebSocket = {
-			type: "websocket",
-			data: {
-				path
-			}
-		};
-		this.worker.postMessage(msg, [channel.port2]);
-
-		return new MessagePortWebSocket(channel.port1);
-	}
-
-	public writeFile(
-		path: string,
-		data: string | ArrayBufferView,
-		opts?: Record<string, unknown>
-	): Promise<void> {
-		return this.postMessageAsync({
-			type: "file:write",
-			data: {
-				path,
-				data,
-				opts
-			}
-		}) as Promise<void>;
-	}
-
-	public renameFile(oldPath: string, newPath: string): Promise<void> {
-		return this.postMessageAsync({
-			type: "file:rename",
-			data: {
-				oldPath,
-				newPath
-			}
-		}) as Promise<void>;
-	}
-
-	public unlink(path: string): Promise<void> {
-		return this.postMessageAsync({
-			type: "file:unlink",
-			data: {
-				path
-			}
-		}) as Promise<void>;
-	}
-
-	public install(requirements: string[]): Promise<void> {
-		return this.postMessageAsync({
-			type: "install",
-			data: {
-				requirements
-			}
-		}) as Promise<void>;
-	}
-
-	public terminate(): void {
-		this.worker.terminate();
-	}
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/array_api/_searching_functions.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/array_api/_searching_functions.py
deleted file mode 100644
index a1f4b0c904c11834b4ccbcf202b639628c8aaf5c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/array_api/_searching_functions.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from __future__ import annotations
-
-from ._array_object import Array
-from ._dtypes import _result_type, _real_numeric_dtypes
-
-from typing import Optional, Tuple
-
-import numpy as np
-
-
-def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
-    """
-    Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
-
-    See its docstring for more information.
-    """
-    if x.dtype not in _real_numeric_dtypes:
-        raise TypeError("Only real numeric dtypes are allowed in argmax")
-    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
-
-
-def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
-    """
-    Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
-
-    See its docstring for more information.
-    """
-    if x.dtype not in _real_numeric_dtypes:
-        raise TypeError("Only real numeric dtypes are allowed in argmin")
-    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
-
-
-def nonzero(x: Array, /) -> Tuple[Array, ...]:
-    """
-    Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
-
-    See its docstring for more information.
-    """
-    return tuple(Array._new(i) for i in np.nonzero(x._array))
-
-
-def where(condition: Array, x1: Array, x2: Array, /) -> Array:
-    """
-    Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
-
-    See its docstring for more information.
-    """
-    # Call result type here just to raise on disallowed type combinations
-    _result_type(x1.dtype, x2.dtype)
-    x1, x2 = Array._normalize_two_args(x1, x2)
-    return Array._new(np.where(condition._array, x1._array, x2._array))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/random/_examples/cffi/parse.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/random/_examples/cffi/parse.py
deleted file mode 100644
index d41c4c2db23df58f2ee8755e2088b1e7b4aba8e1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/random/_examples/cffi/parse.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-
-
-def parse_distributions_h(ffi, inc_dir):
-    """
-    Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
-
-    Read the function declarations without the "#define ..." macros that will
-    be filled in when loading the library.
-    """
-
-    with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:
-        s = []
-        for line in fid:
-            # massage the include file
-            if line.strip().startswith('#'):
-                continue
-            s.append(line)
-        ffi.cdef('\n'.join(s))
-
-    with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:
-        s = []
-        in_skip = 0
-        ignoring = False
-        for line in fid:
-            # check for and remove extern "C" guards
-            if ignoring:
-                if line.strip().startswith('#endif'):
-                    ignoring = False
-                continue
-            if line.strip().startswith('#ifdef __cplusplus'):
-                ignoring = True
-            
-            # massage the include file
-            if line.strip().startswith('#'):
-                continue
-    
-            # skip any inlined function definition
-            # which starts with 'static inline xxx(...) {'
-            # and ends with a closing '}'
-            if line.strip().startswith('static inline'):
-                in_skip += line.count('{')
-                continue
-            elif in_skip > 0:
-                in_skip += line.count('{')
-                in_skip -= line.count('}')
-                continue
-    
-            # replace defines with their value or remove them
-            line = line.replace('DECLDIR', '')
-            line = line.replace('RAND_INT_TYPE', 'int64_t')
-            s.append(line)
-        ffi.cdef('\n'.join(s))
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py
deleted file mode 100644
index c62f73271577d5a75da3010e29a0d72851df99ac..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py
+++ /dev/null
@@ -1,1884 +0,0 @@
-from __future__ import annotations
-
-from abc import (
-    ABC,
-    abstractmethod,
-)
-from collections.abc import (
-    Hashable,
-    Iterable,
-    Sequence,
-)
-from typing import (
-    TYPE_CHECKING,
-    Literal,
-)
-import warnings
-
-import matplotlib as mpl
-import numpy as np
-
-from pandas.errors import AbstractMethodError
-from pandas.util._decorators import cache_readonly
-from pandas.util._exceptions import find_stack_level
-
-from pandas.core.dtypes.common import (
-    is_any_real_numeric_dtype,
-    is_float,
-    is_float_dtype,
-    is_hashable,
-    is_integer,
-    is_integer_dtype,
-    is_iterator,
-    is_list_like,
-    is_number,
-    is_numeric_dtype,
-)
-from pandas.core.dtypes.dtypes import (
-    CategoricalDtype,
-    ExtensionDtype,
-)
-from pandas.core.dtypes.generic import (
-    ABCDataFrame,
-    ABCIndex,
-    ABCMultiIndex,
-    ABCPeriodIndex,
-    ABCSeries,
-)
-from pandas.core.dtypes.missing import (
-    isna,
-    notna,
-)
-
-import pandas.core.common as com
-from pandas.core.frame import DataFrame
-from pandas.util.version import Version
-
-from pandas.io.formats.printing import pprint_thing
-from pandas.plotting._matplotlib import tools
-from pandas.plotting._matplotlib.converter import register_pandas_matplotlib_converters
-from pandas.plotting._matplotlib.groupby import reconstruct_data_with_by
-from pandas.plotting._matplotlib.misc import unpack_single_str_list
-from pandas.plotting._matplotlib.style import get_standard_colors
-from pandas.plotting._matplotlib.timeseries import (
-    decorate_axes,
-    format_dateaxis,
-    maybe_convert_index,
-    maybe_resample,
-    use_dynamic_x,
-)
-from pandas.plotting._matplotlib.tools import (
-    create_subplots,
-    flatten_axes,
-    format_date_labels,
-    get_all_lines,
-    get_xlim,
-    handle_shared_axes,
-)
-
-if TYPE_CHECKING:
-    from matplotlib.artist import Artist
-    from matplotlib.axes import Axes
-    from matplotlib.axis import Axis
-
-    from pandas._typing import (
-        IndexLabel,
-        PlottingOrientation,
-        npt,
-    )
-
-
-def _color_in_style(style: str) -> bool:
-    """
-    Check if there is a color letter in the style string.
-    """
-    from matplotlib.colors import BASE_COLORS
-
-    return not set(BASE_COLORS).isdisjoint(style)
-
-
-class MPLPlot(ABC):
-    """
-    Base class for assembling a pandas plot using matplotlib
-
-    Parameters
-    ----------
-    data :
-
-    """
-
-    @property
-    @abstractmethod
-    def _kind(self) -> str:
-        """Specify kind str. Must be overridden in child class"""
-        raise NotImplementedError
-
-    _layout_type = "vertical"
-    _default_rot = 0
-
-    @property
-    def orientation(self) -> str | None:
-        return None
-
-    axes: np.ndarray  # of Axes objects
-
-    def __init__(
-        self,
-        data,
-        kind=None,
-        by: IndexLabel | None = None,
-        subplots: bool | Sequence[Sequence[str]] = False,
-        sharex=None,
-        sharey: bool = False,
-        use_index: bool = True,
-        figsize: tuple[float, float] | None = None,
-        grid=None,
-        legend: bool | str = True,
-        rot=None,
-        ax=None,
-        fig=None,
-        title=None,
-        xlim=None,
-        ylim=None,
-        xticks=None,
-        yticks=None,
-        xlabel: Hashable | None = None,
-        ylabel: Hashable | None = None,
-        fontsize: int | None = None,
-        secondary_y: bool | tuple | list | np.ndarray = False,
-        colormap=None,
-        table: bool = False,
-        layout=None,
-        include_bool: bool = False,
-        column: IndexLabel | None = None,
-        **kwds,
-    ) -> None:
-        import matplotlib.pyplot as plt
-
-        self.data = data
-
-        # if users assign an empty list or tuple, raise `ValueError`
-        # similar to current `df.box` and `df.hist` APIs.
-        if by in ([], ()):
-            raise ValueError("No group keys passed!")
-        self.by = com.maybe_make_list(by)
-
-        # Assign the rest of columns into self.columns if by is explicitly defined
-        # while column is not, only need `columns` in hist/box plot when it's DF
-        # TODO: Might deprecate `column` argument in future PR (#28373)
-        if isinstance(data, DataFrame):
-            if column:
-                self.columns = com.maybe_make_list(column)
-            elif self.by is None:
-                self.columns = [
-                    col for col in data.columns if is_numeric_dtype(data[col])
-                ]
-            else:
-                self.columns = [
-                    col
-                    for col in data.columns
-                    if col not in self.by and is_numeric_dtype(data[col])
-                ]
-
-        # For `hist` plot, need to get grouped original data before `self.data` is
-        # updated later
-        if self.by is not None and self._kind == "hist":
-            self._grouped = data.groupby(unpack_single_str_list(self.by))
-
-        self.kind = kind
-
-        self.subplots = self._validate_subplots_kwarg(subplots)
-
-        if sharex is None:
-            # if by is defined, subplots are used and sharex should be False
-            if ax is None and by is None:
-                self.sharex = True
-            else:
-                # if we get an axis, the users should do the visibility
-                # setting...
-                self.sharex = False
-        else:
-            self.sharex = sharex
-
-        self.sharey = sharey
-        self.figsize = figsize
-        self.layout = layout
-
-        self.xticks = xticks
-        self.yticks = yticks
-        self.xlim = xlim
-        self.ylim = ylim
-        self.title = title
-        self.use_index = use_index
-        self.xlabel = xlabel
-        self.ylabel = ylabel
-
-        self.fontsize = fontsize
-
-        if rot is not None:
-            self.rot = rot
-            # need to know for format_date_labels since it's rotated to 30 by
-            # default
-            self._rot_set = True
-        else:
-            self._rot_set = False
-            self.rot = self._default_rot
-
-        if grid is None:
-            grid = False if secondary_y else plt.rcParams["axes.grid"]
-
-        self.grid = grid
-        self.legend = legend
-        self.legend_handles: list[Artist] = []
-        self.legend_labels: list[Hashable] = []
-
-        self.logx = kwds.pop("logx", False)
-        self.logy = kwds.pop("logy", False)
-        self.loglog = kwds.pop("loglog", False)
-        self.label = kwds.pop("label", None)
-        self.style = kwds.pop("style", None)
-        self.mark_right = kwds.pop("mark_right", True)
-        self.stacked = kwds.pop("stacked", False)
-
-        self.ax = ax
-        self.fig = fig
-        self.axes = np.array([], dtype=object)  # "real" version get set in `generate`
-
-        # parse errorbar input if given
-        xerr = kwds.pop("xerr", None)
-        yerr = kwds.pop("yerr", None)
-        self.errors = {
-            kw: self._parse_errorbars(kw, err)
-            for kw, err in zip(["xerr", "yerr"], [xerr, yerr])
-        }
-
-        if not isinstance(secondary_y, (bool, tuple, list, np.ndarray, ABCIndex)):
-            secondary_y = [secondary_y]
-        self.secondary_y = secondary_y
-
-        # ugly TypeError if user passes matplotlib's `cmap` name.
-        # Probably better to accept either.
-        if "cmap" in kwds and colormap:
-            raise TypeError("Only specify one of `cmap` and `colormap`.")
-        if "cmap" in kwds:
-            self.colormap = kwds.pop("cmap")
-        else:
-            self.colormap = colormap
-
-        self.table = table
-        self.include_bool = include_bool
-
-        self.kwds = kwds
-
-        self._validate_color_args()
-
-    def _validate_subplots_kwarg(
-        self, subplots: bool | Sequence[Sequence[str]]
-    ) -> bool | list[tuple[int, ...]]:
-        """
-        Validate the subplots parameter
-
-        - check type and content
-        - check for duplicate columns
-        - check for invalid column names
-        - convert column names into indices
-        - add missing columns in a group of their own
-        See comments in code below for more details.
-
-        Parameters
-        ----------
-        subplots : subplots parameters as passed to PlotAccessor
-
-        Returns
-        -------
-        validated subplots : a bool or a list of tuples of column indices. Columns
-        in the same tuple will be grouped together in the resulting plot.
-        """
-
-        if isinstance(subplots, bool):
-            return subplots
-        elif not isinstance(subplots, Iterable):
-            raise ValueError("subplots should be a bool or an iterable")
-
-        supported_kinds = (
-            "line",
-            "bar",
-            "barh",
-            "hist",
-            "kde",
-            "density",
-            "area",
-            "pie",
-        )
-        if self._kind not in supported_kinds:
-            raise ValueError(
-                "When subplots is an iterable, kind must be "
-                f"one of {', '.join(supported_kinds)}. Got {self._kind}."
-            )
-
-        if isinstance(self.data, ABCSeries):
-            raise NotImplementedError(
-                "An iterable subplots for a Series is not supported."
-            )
-
-        columns = self.data.columns
-        if isinstance(columns, ABCMultiIndex):
-            raise NotImplementedError(
-                "An iterable subplots for a DataFrame with a MultiIndex column "
-                "is not supported."
-            )
-
-        if columns.nunique() != len(columns):
-            raise NotImplementedError(
-                "An iterable subplots for a DataFrame with non-unique column "
-                "labels is not supported."
-            )
-
-        # subplots is a list of tuples where each tuple is a group of
-        # columns to be grouped together (one ax per group).
-        # we consolidate the subplots list such that:
-        # - the tuples contain indices instead of column names
-        # - the columns that aren't yet in the list are added in a group
-        #   of their own.
-        # For example with columns from a to g, and
-        # subplots = [(a, c), (b, f, e)],
-        # we end up with [(ai, ci), (bi, fi, ei), (di,), (gi,)]
-        # This way, we can handle self.subplots in a homogeneous manner
-        # later.
-        # TODO: also accept indices instead of just names?
-
-        out = []
-        seen_columns: set[Hashable] = set()
-        for group in subplots:
-            if not is_list_like(group):
-                raise ValueError(
-                    "When subplots is an iterable, each entry "
-                    "should be a list/tuple of column names."
-                )
-            idx_locs = columns.get_indexer_for(group)
-            if (idx_locs == -1).any():
-                bad_labels = np.extract(idx_locs == -1, group)
-                raise ValueError(
-                    f"Column label(s) {list(bad_labels)} not found in the DataFrame."
-                )
-            unique_columns = set(group)
-            duplicates = seen_columns.intersection(unique_columns)
-            if duplicates:
-                raise ValueError(
-                    "Each column should be in only one subplot. "
-                    f"Columns {duplicates} were found in multiple subplots."
-                )
-            seen_columns = seen_columns.union(unique_columns)
-            out.append(tuple(idx_locs))
-
-        unseen_columns = columns.difference(seen_columns)
-        for column in unseen_columns:
-            idx_loc = columns.get_loc(column)
-            out.append((idx_loc,))
-        return out
-
-    def _validate_color_args(self):
-        if (
-            "color" in self.kwds
-            and self.nseries == 1
-            and self.kwds["color"] is not None
-            and not is_list_like(self.kwds["color"])
-        ):
-            # support series.plot(color='green')
-            self.kwds["color"] = [self.kwds["color"]]
-
-        if (
-            "color" in self.kwds
-            and isinstance(self.kwds["color"], tuple)
-            and self.nseries == 1
-            and len(self.kwds["color"]) in (3, 4)
-        ):
-            # support RGB and RGBA tuples in series plot
-            self.kwds["color"] = [self.kwds["color"]]
-
-        if (
-            "color" in self.kwds or "colors" in self.kwds
-        ) and self.colormap is not None:
-            warnings.warn(
-                "'color' and 'colormap' cannot be used simultaneously. Using 'color'",
-                stacklevel=find_stack_level(),
-            )
-
-        if "color" in self.kwds and self.style is not None:
-            if is_list_like(self.style):
-                styles = self.style
-            else:
-                styles = [self.style]
-            # need only a single match
-            for s in styles:
-                if _color_in_style(s):
-                    raise ValueError(
-                        "Cannot pass 'style' string with a color symbol and "
-                        "'color' keyword argument. Please use one or the "
-                        "other or pass 'style' without a color symbol"
-                    )
-
-    def _iter_data(self, data=None, keep_index: bool = False, fillna=None):
-        if data is None:
-            data = self.data
-        if fillna is not None:
-            data = data.fillna(fillna)
-
-        for col, values in data.items():
-            if keep_index is True:
-                yield col, values
-            else:
-                yield col, values.values
-
-    @property
-    def nseries(self) -> int:
-        # When `by` is explicitly assigned, grouped data size will be defined, and
-        # this will determine number of subplots to have, aka `self.nseries`
-        if self.data.ndim == 1:
-            return 1
-        elif self.by is not None and self._kind == "hist":
-            return len(self._grouped)
-        elif self.by is not None and self._kind == "box":
-            return len(self.columns)
-        else:
-            return self.data.shape[1]
-
-    def draw(self) -> None:
-        self.plt.draw_if_interactive()
-
-    def generate(self) -> None:
-        self._args_adjust()
-        self._compute_plot_data()
-        self._setup_subplots()
-        self._make_plot()
-        self._add_table()
-        self._make_legend()
-        self._adorn_subplots()
-
-        for ax in self.axes:
-            self._post_plot_logic_common(ax, self.data)
-            self._post_plot_logic(ax, self.data)
-
-    @abstractmethod
-    def _args_adjust(self) -> None:
-        pass
-
-    def _has_plotted_object(self, ax: Axes) -> bool:
-        """check whether ax has data"""
-        return len(ax.lines) != 0 or len(ax.artists) != 0 or len(ax.containers) != 0
-
-    def _maybe_right_yaxis(self, ax: Axes, axes_num):
-        if not self.on_right(axes_num):
-            # secondary axes may be passed via ax kw
-            return self._get_ax_layer(ax)
-
-        if hasattr(ax, "right_ax"):
-            # if it has right_ax property, ``ax`` must be left axes
-            return ax.right_ax
-        elif hasattr(ax, "left_ax"):
-            # if it has left_ax property, ``ax`` must be right axes
-            return ax
-        else:
-            # otherwise, create twin axes
-            orig_ax, new_ax = ax, ax.twinx()
-            # TODO: use Matplotlib public API when available
-            new_ax._get_lines = orig_ax._get_lines
-            new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill
-            orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax
-
-            if not self._has_plotted_object(orig_ax):  # no data on left y
-                orig_ax.get_yaxis().set_visible(False)
-
-            if self.logy is True or self.loglog is True:
-                new_ax.set_yscale("log")
-            elif self.logy == "sym" or self.loglog == "sym":
-                new_ax.set_yscale("symlog")
-            return new_ax
-
-    def _setup_subplots(self):
-        if self.subplots:
-            naxes = (
-                self.nseries if isinstance(self.subplots, bool) else len(self.subplots)
-            )
-            fig, axes = create_subplots(
-                naxes=naxes,
-                sharex=self.sharex,
-                sharey=self.sharey,
-                figsize=self.figsize,
-                ax=self.ax,
-                layout=self.layout,
-                layout_type=self._layout_type,
-            )
-        elif self.ax is None:
-            fig = self.plt.figure(figsize=self.figsize)
-            axes = fig.add_subplot(111)
-        else:
-            fig = self.ax.get_figure()
-            if self.figsize is not None:
-                fig.set_size_inches(self.figsize)
-            axes = self.ax
-
-        axes = flatten_axes(axes)
-
-        valid_log = {False, True, "sym", None}
-        input_log = {self.logx, self.logy, self.loglog}
-        if input_log - valid_log:
-            invalid_log = next(iter(input_log - valid_log))
-            raise ValueError(
-                f"Boolean, None and 'sym' are valid options, '{invalid_log}' is given."
-            )
-
-        if self.logx is True or self.loglog is True:
-            [a.set_xscale("log") for a in axes]
-        elif self.logx == "sym" or self.loglog == "sym":
-            [a.set_xscale("symlog") for a in axes]
-
-        if self.logy is True or self.loglog is True:
-            [a.set_yscale("log") for a in axes]
-        elif self.logy == "sym" or self.loglog == "sym":
-            [a.set_yscale("symlog") for a in axes]
-
-        self.fig = fig
-        self.axes = axes
-
-    @property
-    def result(self):
-        """
-        Return result axes
-        """
-        if self.subplots:
-            if self.layout is not None and not is_list_like(self.ax):
-                return self.axes.reshape(*self.layout)
-            else:
-                return self.axes
-        else:
-            sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
-            # error: Argument 1 to "len" has incompatible type "Union[bool,
-            # Tuple[Any, ...], List[Any], ndarray[Any, Any]]"; expected "Sized"
-            all_sec = (
-                is_list_like(self.secondary_y)
-                and len(self.secondary_y) == self.nseries  # type: ignore[arg-type]
-            )
-            if sec_true or all_sec:
-                # if all data is plotted on secondary, return right axes
-                return self._get_ax_layer(self.axes[0], primary=False)
-            else:
-                return self.axes[0]
-
-    def _convert_to_ndarray(self, data):
-        # GH31357: categorical columns are processed separately
-        if isinstance(data.dtype, CategoricalDtype):
-            return data
-
-        # GH32073: cast to float if values contain nulled integers
-        if (is_integer_dtype(data.dtype) or is_float_dtype(data.dtype)) and isinstance(
-            data.dtype, ExtensionDtype
-        ):
-            return data.to_numpy(dtype="float", na_value=np.nan)
-
-        # GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to
-        # np.ndarray before plot.
-        if len(data) > 0:
-            return np.asarray(data)
-
-        return data
-
-    def _compute_plot_data(self):
-        data = self.data
-
-        if isinstance(data, ABCSeries):
-            label = self.label
-            if label is None and data.name is None:
-                label = ""
-            if label is None:
-                # We'll end up with columns of [0] instead of [None]
-                data = data.to_frame()
-            else:
-                data = data.to_frame(name=label)
-        elif self._kind in ("hist", "box"):
-            cols = self.columns if self.by is None else self.columns + self.by
-            data = data.loc[:, cols]
-
-        # GH15079 reconstruct data if by is defined
-        if self.by is not None:
-            self.subplots = True
-            data = reconstruct_data_with_by(self.data, by=self.by, cols=self.columns)
-
-        # GH16953, infer_objects is needed as fallback, for ``Series``
-        # with ``dtype == object``
-        data = data.infer_objects(copy=False)
-        include_type = [np.number, "datetime", "datetimetz", "timedelta"]
-
-        # GH23719, allow plotting boolean
-        if self.include_bool is True:
-            include_type.append(np.bool_)
-
-        # GH22799, exclude datetime-like type for boxplot
-        exclude_type = None
-        if self._kind == "box":
-            # TODO: change after solving issue 27881
-            include_type = [np.number]
-            exclude_type = ["timedelta"]
-
-        # GH 18755, include object and category type for scatter plot
-        if self._kind == "scatter":
-            include_type.extend(["object", "category"])
-
-        numeric_data = data.select_dtypes(include=include_type, exclude=exclude_type)
-
-        try:
-            is_empty = numeric_data.columns.empty
-        except AttributeError:
-            is_empty = not len(numeric_data)
-
-        # no non-numeric frames or series allowed
-        if is_empty:
-            raise TypeError("no numeric data to plot")
-
-        self.data = numeric_data.apply(self._convert_to_ndarray)
-
-    def _make_plot(self):
-        raise AbstractMethodError(self)
-
-    def _add_table(self) -> None:
-        if self.table is False:
-            return
-        elif self.table is True:
-            data = self.data.transpose()
-        else:
-            data = self.table
-        ax = self._get_ax(0)
-        tools.table(ax, data)
-
-    def _post_plot_logic_common(self, ax, data):
-        """Common post process for each axes"""
-        if self.orientation == "vertical" or self.orientation is None:
-            self._apply_axis_properties(ax.xaxis, rot=self.rot, fontsize=self.fontsize)
-            self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize)
-
-            if hasattr(ax, "right_ax"):
-                self._apply_axis_properties(ax.right_ax.yaxis, fontsize=self.fontsize)
-
-        elif self.orientation == "horizontal":
-            self._apply_axis_properties(ax.yaxis, rot=self.rot, fontsize=self.fontsize)
-            self._apply_axis_properties(ax.xaxis, fontsize=self.fontsize)
-
-            if hasattr(ax, "right_ax"):
-                self._apply_axis_properties(ax.right_ax.yaxis, fontsize=self.fontsize)
-        else:  # pragma no cover
-            raise ValueError
-
-    @abstractmethod
-    def _post_plot_logic(self, ax, data) -> None:
-        """Post process for each axes. Overridden in child classes"""
-
-    def _adorn_subplots(self):
-        """Common post process unrelated to data"""
-        if len(self.axes) > 0:
-            all_axes = self._get_subplots()
-            nrows, ncols = self._get_axes_layout()
-            handle_shared_axes(
-                axarr=all_axes,
-                nplots=len(all_axes),
-                naxes=nrows * ncols,
-                nrows=nrows,
-                ncols=ncols,
-                sharex=self.sharex,
-                sharey=self.sharey,
-            )
-
-        for ax in self.axes:
-            ax = getattr(ax, "right_ax", ax)
-            if self.yticks is not None:
-                ax.set_yticks(self.yticks)
-
-            if self.xticks is not None:
-                ax.set_xticks(self.xticks)
-
-            if self.ylim is not None:
-                ax.set_ylim(self.ylim)
-
-            if self.xlim is not None:
-                ax.set_xlim(self.xlim)
-
-            # GH9093, currently Pandas does not show ylabel, so if users provide
-            # ylabel will set it as ylabel in the plot.
-            if self.ylabel is not None:
-                ax.set_ylabel(pprint_thing(self.ylabel))
-
-            ax.grid(self.grid)
-
-        if self.title:
-            if self.subplots:
-                if is_list_like(self.title):
-                    if len(self.title) != self.nseries:
-                        raise ValueError(
-                            "The length of `title` must equal the number "
-                            "of columns if using `title` of type `list` "
-                            "and `subplots=True`.\n"
-                            f"length of title = {len(self.title)}\n"
-                            f"number of columns = {self.nseries}"
-                        )
-
-                    for ax, title in zip(self.axes, self.title):
-                        ax.set_title(title)
-                else:
-                    self.fig.suptitle(self.title)
-            else:
-                if is_list_like(self.title):
-                    msg = (
-                        "Using `title` of type `list` is not supported "
-                        "unless `subplots=True` is passed"
-                    )
-                    raise ValueError(msg)
-                self.axes[0].set_title(self.title)
-
-    def _apply_axis_properties(
-        self, axis: Axis, rot=None, fontsize: int | None = None
-    ) -> None:
-        """
-        Tick creation within matplotlib is reasonably expensive and is
-        internally deferred until accessed as Ticks are created/destroyed
-        multiple times per draw. It's therefore beneficial for us to avoid
-        accessing unless we will act on the Tick.
-        """
-        if rot is not None or fontsize is not None:
-            # rot=0 is a valid setting, hence the explicit None check
-            labels = axis.get_majorticklabels() + axis.get_minorticklabels()
-            for label in labels:
-                if rot is not None:
-                    label.set_rotation(rot)
-                if fontsize is not None:
-                    label.set_fontsize(fontsize)
-
-    @property
-    def legend_title(self) -> str | None:
-        if not isinstance(self.data.columns, ABCMultiIndex):
-            name = self.data.columns.name
-            if name is not None:
-                name = pprint_thing(name)
-            return name
-        else:
-            stringified = map(pprint_thing, self.data.columns.names)
-            return ",".join(stringified)
-
-    def _mark_right_label(self, label: str, index: int) -> str:
-        """
-        Append ``(right)`` to the label of a line if it's plotted on the right axis.
-
-        Note that ``(right)`` is only appended when ``subplots=False``.
-        """
-        if not self.subplots and self.mark_right and self.on_right(index):
-            label += " (right)"
-        return label
-
-    def _append_legend_handles_labels(self, handle: Artist, label: str) -> None:
-        """
-        Append current handle and label to ``legend_handles`` and ``legend_labels``.
-
-        These will be used to make the legend.
-        """
-        self.legend_handles.append(handle)
-        self.legend_labels.append(label)
-
-    def _make_legend(self) -> None:
-        ax, leg = self._get_ax_legend(self.axes[0])
-
-        handles = []
-        labels = []
-        title = ""
-
-        if not self.subplots:
-            if leg is not None:
-                title = leg.get_title().get_text()
-                # Replace leg.legend_handles because it misses marker info
-                if Version(mpl.__version__) < Version("3.7"):
-                    handles = leg.legendHandles
-                else:
-                    handles = leg.legend_handles
-                labels = [x.get_text() for x in leg.get_texts()]
-
-            if self.legend:
-                if self.legend == "reverse":
-                    handles += reversed(self.legend_handles)
-                    labels += reversed(self.legend_labels)
-                else:
-                    handles += self.legend_handles
-                    labels += self.legend_labels
-
-                if self.legend_title is not None:
-                    title = self.legend_title
-
-            if len(handles) > 0:
-                ax.legend(handles, labels, loc="best", title=title)
-
-        elif self.subplots and self.legend:
-            for ax in self.axes:
-                if ax.get_visible():
-                    ax.legend(loc="best")
-
-    def _get_ax_legend(self, ax: Axes):
-        """
-        Take in axes and return ax and legend under different scenarios
-        """
-        leg = ax.get_legend()
-
-        other_ax = getattr(ax, "left_ax", None) or getattr(ax, "right_ax", None)
-        other_leg = None
-        if other_ax is not None:
-            other_leg = other_ax.get_legend()
-        if leg is None and other_leg is not None:
-            leg = other_leg
-            ax = other_ax
-        return ax, leg
-
-    @cache_readonly
-    def plt(self):
-        import matplotlib.pyplot as plt
-
-        return plt
-
-    _need_to_set_index = False
-
-    def _get_xticks(self, convert_period: bool = False):
-        index = self.data.index
-        is_datetype = index.inferred_type in ("datetime", "date", "datetime64", "time")
-
-        if self.use_index:
-            if convert_period and isinstance(index, ABCPeriodIndex):
-                self.data = self.data.reindex(index=index.sort_values())
-                x = self.data.index.to_timestamp()._mpl_repr()
-            elif is_any_real_numeric_dtype(index.dtype):
-                # Matplotlib supports numeric values or datetime objects as
-                # xaxis values. Taking LBYL approach here, by the time
-                # matplotlib raises exception when using non numeric/datetime
-                # values for xaxis, several actions are already taken by plt.
-                x = index._mpl_repr()
-            elif is_datetype:
-                self.data = self.data[notna(self.data.index)]
-                self.data = self.data.sort_index()
-                x = self.data.index._mpl_repr()
-            else:
-                self._need_to_set_index = True
-                x = list(range(len(index)))
-        else:
-            x = list(range(len(index)))
-
-        return x
-
-    @classmethod
-    @register_pandas_matplotlib_converters
-    def _plot(
-        cls, ax: Axes, x, y: np.ndarray, style=None, is_errorbar: bool = False, **kwds
-    ):
-        mask = isna(y)
-        if mask.any():
-            y = np.ma.array(y)
-            y = np.ma.masked_where(mask, y)
-
-        if isinstance(x, ABCIndex):
-            x = x._mpl_repr()
-
-        if is_errorbar:
-            if "xerr" in kwds:
-                kwds["xerr"] = np.array(kwds.get("xerr"))
-            if "yerr" in kwds:
-                kwds["yerr"] = np.array(kwds.get("yerr"))
-            return ax.errorbar(x, y, **kwds)
-        else:
-            # prevent style kwarg from going to errorbar, where it is unsupported
-            args = (x, y, style) if style is not None else (x, y)
-            return ax.plot(*args, **kwds)
-
-    def _get_custom_index_name(self):
-        """Specify whether xlabel/ylabel should be used to override index name"""
-        return self.xlabel
-
-    def _get_index_name(self) -> str | None:
-        if isinstance(self.data.index, ABCMultiIndex):
-            name = self.data.index.names
-            if com.any_not_none(*name):
-                name = ",".join([pprint_thing(x) for x in name])
-            else:
-                name = None
-        else:
-            name = self.data.index.name
-            if name is not None:
-                name = pprint_thing(name)
-
-        # GH 45145, override the default axis label if one is provided.
-        index_name = self._get_custom_index_name()
-        if index_name is not None:
-            name = pprint_thing(index_name)
-
-        return name
-
-    @classmethod
-    def _get_ax_layer(cls, ax, primary: bool = True):
-        """get left (primary) or right (secondary) axes"""
-        if primary:
-            return getattr(ax, "left_ax", ax)
-        else:
-            return getattr(ax, "right_ax", ax)
-
-    def _col_idx_to_axis_idx(self, col_idx: int) -> int:
-        """Return the index of the axis where the column at col_idx should be plotted"""
-        if isinstance(self.subplots, list):
-            # Subplots is a list: some columns will be grouped together in the same ax
-            return next(
-                group_idx
-                for (group_idx, group) in enumerate(self.subplots)
-                if col_idx in group
-            )
-        else:
-            # subplots is True: one ax per column
-            return col_idx
-
-    def _get_ax(self, i: int):
-        # get the twinx ax if appropriate
-        if self.subplots:
-            i = self._col_idx_to_axis_idx(i)
-            ax = self.axes[i]
-            ax = self._maybe_right_yaxis(ax, i)
-            self.axes[i] = ax
-        else:
-            ax = self.axes[0]
-            ax = self._maybe_right_yaxis(ax, i)
-
-        ax.get_yaxis().set_visible(True)
-        return ax
-
-    @classmethod
-    def get_default_ax(cls, ax) -> None:
-        import matplotlib.pyplot as plt
-
-        if ax is None and len(plt.get_fignums()) > 0:
-            with plt.rc_context():
-                ax = plt.gca()
-            ax = cls._get_ax_layer(ax)
-
-    def on_right(self, i):
-        if isinstance(self.secondary_y, bool):
-            return self.secondary_y
-
-        if isinstance(self.secondary_y, (tuple, list, np.ndarray, ABCIndex)):
-            return self.data.columns[i] in self.secondary_y
-
-    def _apply_style_colors(self, colors, kwds, col_num, label: str):
-        """
-        Manage style and color based on column number and its label.
-        Returns tuple of appropriate style and kwds which "color" may be added.
-        """
-        style = None
-        if self.style is not None:
-            if isinstance(self.style, list):
-                try:
-                    style = self.style[col_num]
-                except IndexError:
-                    pass
-            elif isinstance(self.style, dict):
-                style = self.style.get(label, style)
-            else:
-                style = self.style
-
-        has_color = "color" in kwds or self.colormap is not None
-        nocolor_style = style is None or not _color_in_style(style)
-        if (has_color or self.subplots) and nocolor_style:
-            if isinstance(colors, dict):
-                kwds["color"] = colors[label]
-            else:
-                kwds["color"] = colors[col_num % len(colors)]
-        return style, kwds
-
-    def _get_colors(
-        self,
-        num_colors: int | None = None,
-        color_kwds: str = "color",
-    ):
-        if num_colors is None:
-            num_colors = self.nseries
-
-        return get_standard_colors(
-            num_colors=num_colors,
-            colormap=self.colormap,
-            color=self.kwds.get(color_kwds),
-        )
-
-    def _parse_errorbars(self, label, err):
-        """
-        Look for error keyword arguments and return the actual errorbar data
-        or return the error DataFrame/dict
-
-        Error bars can be specified in several ways:
-            Series: the user provides a pandas.Series object of the same
-                    length as the data
-            ndarray: provides a np.ndarray of the same length as the data
-            DataFrame/dict: error values are paired with keys matching the
-                    key in the plotted DataFrame
-            str: the name of the column within the plotted DataFrame
-
-        Asymmetrical error bars are also supported, however raw error values
-        must be provided in this case. For a ``N`` length :class:`Series`, a
-        ``2xN`` array should be provided indicating lower and upper (or left
-        and right) errors. For a ``MxN`` :class:`DataFrame`, asymmetrical errors
-        should be in a ``Mx2xN`` array.
-        """
-        if err is None:
-            return None
-
-        def match_labels(data, e):
-            e = e.reindex(data.index)
-            return e
-
-        # key-matched DataFrame
-        if isinstance(err, ABCDataFrame):
-            err = match_labels(self.data, err)
-        # key-matched dict
-        elif isinstance(err, dict):
-            pass
-
-        # Series of error values
-        elif isinstance(err, ABCSeries):
-            # broadcast error series across data
-            err = match_labels(self.data, err)
-            err = np.atleast_2d(err)
-            err = np.tile(err, (self.nseries, 1))
-
-        # errors are a column in the dataframe
-        elif isinstance(err, str):
-            evalues = self.data[err].values
-            self.data = self.data[self.data.columns.drop(err)]
-            err = np.atleast_2d(evalues)
-            err = np.tile(err, (self.nseries, 1))
-
-        elif is_list_like(err):
-            if is_iterator(err):
-                err = np.atleast_2d(list(err))
-            else:
-                # raw error values
-                err = np.atleast_2d(err)
-
-            err_shape = err.shape
-
-            # asymmetrical error bars
-            if isinstance(self.data, ABCSeries) and err_shape[0] == 2:
-                err = np.expand_dims(err, 0)
-                err_shape = err.shape
-                if err_shape[2] != len(self.data):
-                    raise ValueError(
-                        "Asymmetrical error bars should be provided "
-                        f"with the shape (2, {len(self.data)})"
-                    )
-            elif isinstance(self.data, ABCDataFrame) and err.ndim == 3:
-                if (
-                    (err_shape[0] != self.nseries)
-                    or (err_shape[1] != 2)
-                    or (err_shape[2] != len(self.data))
-                ):
-                    raise ValueError(
-                        "Asymmetrical error bars should be provided "
-                        f"with the shape ({self.nseries}, 2, {len(self.data)})"
-                    )
-
-            # broadcast errors to each data series
-            if len(err) == 1:
-                err = np.tile(err, (self.nseries, 1))
-
-        elif is_number(err):
-            err = np.tile([err], (self.nseries, len(self.data)))
-
-        else:
-            msg = f"No valid {label} detected"
-            raise ValueError(msg)
-
-        return err
-
-    def _get_errorbars(
-        self, label=None, index=None, xerr: bool = True, yerr: bool = True
-    ):
-        errors = {}
-
-        for kw, flag in zip(["xerr", "yerr"], [xerr, yerr]):
-            if flag:
-                err = self.errors[kw]
-                # user provided label-matched dataframe of errors
-                if isinstance(err, (ABCDataFrame, dict)):
-                    if label is not None and label in err.keys():
-                        err = err[label]
-                    else:
-                        err = None
-                elif index is not None and err is not None:
-                    err = err[index]
-
-                if err is not None:
-                    errors[kw] = err
-        return errors
-
-    def _get_subplots(self):
-        from matplotlib.axes import Subplot
-
-        return [
-            ax
-            for ax in self.fig.get_axes()
-            if (isinstance(ax, Subplot) and ax.get_subplotspec() is not None)
-        ]
-
-    def _get_axes_layout(self) -> tuple[int, int]:
-        axes = self._get_subplots()
-        x_set = set()
-        y_set = set()
-        for ax in axes:
-            # check axes coordinates to estimate layout
-            points = ax.get_position().get_points()
-            x_set.add(points[0][0])
-            y_set.add(points[0][1])
-        return (len(y_set), len(x_set))
-
-
-class PlanePlot(MPLPlot, ABC):
-    """
-    Abstract class for plotting on plane, currently scatter and hexbin.
-    """
-
-    _layout_type = "single"
-
-    def __init__(self, data, x, y, **kwargs) -> None:
-        MPLPlot.__init__(self, data, **kwargs)
-        if x is None or y is None:
-            raise ValueError(self._kind + " requires an x and y column")
-        if is_integer(x) and not self.data.columns._holds_integer():
-            x = self.data.columns[x]
-        if is_integer(y) and not self.data.columns._holds_integer():
-            y = self.data.columns[y]
-
-        # Scatter plot allows to plot objects data
-        if self._kind == "hexbin":
-            if len(self.data[x]._get_numeric_data()) == 0:
-                raise ValueError(self._kind + " requires x column to be numeric")
-            if len(self.data[y]._get_numeric_data()) == 0:
-                raise ValueError(self._kind + " requires y column to be numeric")
-
-        self.x = x
-        self.y = y
-
-    @property
-    def nseries(self) -> int:
-        return 1
-
-    def _post_plot_logic(self, ax: Axes, data) -> None:
-        x, y = self.x, self.y
-        xlabel = self.xlabel if self.xlabel is not None else pprint_thing(x)
-        ylabel = self.ylabel if self.ylabel is not None else pprint_thing(y)
-        ax.set_xlabel(xlabel)
-        ax.set_ylabel(ylabel)
-
-    def _plot_colorbar(self, ax: Axes, **kwds):
-        # Addresses issues #10611 and #10678:
-        # When plotting scatterplots and hexbinplots in IPython
-        # inline backend the colorbar axis height tends not to
-        # exactly match the parent axis height.
-        # The difference is due to small fractional differences
-        # in floating points with similar representation.
-        # To deal with this, this method forces the colorbar
-        # height to take the height of the parent axes.
-        # For a more detailed description of the issue
-        # see the following link:
-        # https://github.com/ipython/ipython/issues/11215
-
-        # GH33389, if ax is used multiple times, we should always
-        # use the last one which contains the latest information
-        # about the ax
-        img = ax.collections[-1]
-        return self.fig.colorbar(img, ax=ax, **kwds)
-
-
-class ScatterPlot(PlanePlot):
-    @property
-    def _kind(self) -> Literal["scatter"]:
-        return "scatter"
-
-    def __init__(self, data, x, y, s=None, c=None, **kwargs) -> None:
-        if s is None:
-            # hide the matplotlib default for size, in case we want to change
-            # the handling of this argument later
-            s = 20
-        elif is_hashable(s) and s in data.columns:
-            s = data[s]
-        super().__init__(data, x, y, s=s, **kwargs)
-        if is_integer(c) and not self.data.columns._holds_integer():
-            c = self.data.columns[c]
-        self.c = c
-
-    def _make_plot(self):
-        x, y, c, data = self.x, self.y, self.c, self.data
-        ax = self.axes[0]
-
-        c_is_column = is_hashable(c) and c in self.data.columns
-
-        color_by_categorical = c_is_column and isinstance(
-            self.data[c].dtype, CategoricalDtype
-        )
-
-        color = self.kwds.pop("color", None)
-        if c is not None and color is not None:
-            raise TypeError("Specify exactly one of `c` and `color`")
-        if c is None and color is None:
-            c_values = self.plt.rcParams["patch.facecolor"]
-        elif color is not None:
-            c_values = color
-        elif color_by_categorical:
-            c_values = self.data[c].cat.codes
-        elif c_is_column:
-            c_values = self.data[c].values
-        else:
-            c_values = c
-
-        if self.colormap is not None:
-            cmap = mpl.colormaps.get_cmap(self.colormap)
-        # cmap is only used if c_values are integers, otherwise UserWarning.
-        # GH-53908: additionally call isinstance() because is_integer_dtype
-        # returns True for "b" (meaning "blue" and not int8 in this context)
-        elif not isinstance(c_values, str) and is_integer_dtype(c_values):
-            # pandas uses colormap, matplotlib uses cmap.
-            cmap = mpl.colormaps["Greys"]
-        else:
-            cmap = None
-
-        if color_by_categorical:
-            from matplotlib import colors
-
-            n_cats = len(self.data[c].cat.categories)
-            cmap = colors.ListedColormap([cmap(i) for i in range(cmap.N)])
-            bounds = np.linspace(0, n_cats, n_cats + 1)
-            norm = colors.BoundaryNorm(bounds, cmap.N)
-        else:
-            norm = self.kwds.pop("norm", None)
-        # plot colorbar if
-        # 1. colormap is assigned, and
-        # 2.`c` is a column containing only numeric values
-        plot_colorbar = self.colormap or c_is_column
-        cb = self.kwds.pop("colorbar", is_numeric_dtype(c_values) and plot_colorbar)
-
-        if self.legend and hasattr(self, "label"):
-            label = self.label
-        else:
-            label = None
-        scatter = ax.scatter(
-            data[x].values,
-            data[y].values,
-            c=c_values,
-            label=label,
-            cmap=cmap,
-            norm=norm,
-            **self.kwds,
-        )
-        if cb:
-            cbar_label = c if c_is_column else ""
-            cbar = self._plot_colorbar(ax, label=cbar_label)
-            if color_by_categorical:
-                cbar.set_ticks(np.linspace(0.5, n_cats - 0.5, n_cats))
-                cbar.ax.set_yticklabels(self.data[c].cat.categories)
-
-        if label is not None:
-            self._append_legend_handles_labels(scatter, label)
-        else:
-            self.legend = False
-
-        errors_x = self._get_errorbars(label=x, index=0, yerr=False)
-        errors_y = self._get_errorbars(label=y, index=0, xerr=False)
-        if len(errors_x) > 0 or len(errors_y) > 0:
-            err_kwds = dict(errors_x, **errors_y)
-            err_kwds["ecolor"] = scatter.get_facecolor()[0]
-            ax.errorbar(data[x].values, data[y].values, linestyle="none", **err_kwds)
-
-    def _args_adjust(self) -> None:
-        pass
-
-
-class HexBinPlot(PlanePlot):
-    @property
-    def _kind(self) -> Literal["hexbin"]:
-        return "hexbin"
-
-    def __init__(self, data, x, y, C=None, **kwargs) -> None:
-        super().__init__(data, x, y, **kwargs)
-        if is_integer(C) and not self.data.columns._holds_integer():
-            C = self.data.columns[C]
-        self.C = C
-
-    def _make_plot(self) -> None:
-        x, y, data, C = self.x, self.y, self.data, self.C
-        ax = self.axes[0]
-        # pandas uses colormap, matplotlib uses cmap.
-        cmap = self.colormap or "BuGn"
-        cmap = mpl.colormaps.get_cmap(cmap)
-        cb = self.kwds.pop("colorbar", True)
-
-        if C is None:
-            c_values = None
-        else:
-            c_values = data[C].values
-
-        ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap, **self.kwds)
-        if cb:
-            self._plot_colorbar(ax)
-
-    def _make_legend(self) -> None:
-        pass
-
-    def _args_adjust(self) -> None:
-        pass
-
-
-class LinePlot(MPLPlot):
-    _default_rot = 0
-
-    @property
-    def orientation(self) -> PlottingOrientation:
-        return "vertical"
-
-    @property
-    def _kind(self) -> Literal["line", "area", "hist", "kde", "box"]:
-        return "line"
-
-    def __init__(self, data, **kwargs) -> None:
-        from pandas.plotting import plot_params
-
-        MPLPlot.__init__(self, data, **kwargs)
-        if self.stacked:
-            self.data = self.data.fillna(value=0)
-        self.x_compat = plot_params["x_compat"]
-        if "x_compat" in self.kwds:
-            self.x_compat = bool(self.kwds.pop("x_compat"))
-
-    def _is_ts_plot(self) -> bool:
-        # this is slightly deceptive
-        return not self.x_compat and self.use_index and self._use_dynamic_x()
-
-    def _use_dynamic_x(self):
-        return use_dynamic_x(self._get_ax(0), self.data)
-
-    def _make_plot(self) -> None:
-        if self._is_ts_plot():
-            data = maybe_convert_index(self._get_ax(0), self.data)
-
-            x = data.index  # dummy, not used
-            plotf = self._ts_plot
-            it = self._iter_data(data=data, keep_index=True)
-        else:
-            x = self._get_xticks(convert_period=True)
-            # error: Incompatible types in assignment (expression has type
-            # "Callable[[Any, Any, Any, Any, Any, Any, KwArg(Any)], Any]", variable has
-            # type "Callable[[Any, Any, Any, Any, KwArg(Any)], Any]")
-            plotf = self._plot  # type: ignore[assignment]
-            it = self._iter_data()
-
-        stacking_id = self._get_stacking_id()
-        is_errorbar = com.any_not_none(*self.errors.values())
-
-        colors = self._get_colors()
-        for i, (label, y) in enumerate(it):
-            ax = self._get_ax(i)
-            kwds = self.kwds.copy()
-            style, kwds = self._apply_style_colors(colors, kwds, i, label)
-
-            errors = self._get_errorbars(label=label, index=i)
-            kwds = dict(kwds, **errors)
-
-            label = pprint_thing(label)  # .encode('utf-8')
-            label = self._mark_right_label(label, index=i)
-            kwds["label"] = label
-
-            newlines = plotf(
-                ax,
-                x,
-                y,
-                style=style,
-                column_num=i,
-                stacking_id=stacking_id,
-                is_errorbar=is_errorbar,
-                **kwds,
-            )
-            self._append_legend_handles_labels(newlines[0], label)
-
-            if self._is_ts_plot():
-                # reset of xlim should be used for ts data
-                # TODO: GH28021, should find a way to change view limit on xaxis
-                lines = get_all_lines(ax)
-                left, right = get_xlim(lines)
-                ax.set_xlim(left, right)
-
-    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
-    @classmethod
-    def _plot(  # type: ignore[override]
-        cls, ax: Axes, x, y, style=None, column_num=None, stacking_id=None, **kwds
-    ):
-        # column_num is used to get the target column from plotf in line and
-        # area plots
-        if column_num == 0:
-            cls._initialize_stacker(ax, stacking_id, len(y))
-        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds["label"])
-        lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
-        cls._update_stacker(ax, stacking_id, y)
-        return lines
-
-    def _ts_plot(self, ax: Axes, x, data, style=None, **kwds):
-        # accept x to be consistent with normal plot func,
-        # x is not passed to tsplot as it uses data.index as x coordinate
-        # column_num must be in kwds for stacking purpose
-        freq, data = maybe_resample(data, ax, kwds)
-
-        # Set ax with freq info
-        decorate_axes(ax, freq, kwds)
-        # digging deeper
-        if hasattr(ax, "left_ax"):
-            decorate_axes(ax.left_ax, freq, kwds)
-        if hasattr(ax, "right_ax"):
-            decorate_axes(ax.right_ax, freq, kwds)
-        ax._plot_data.append((data, self._kind, kwds))
-
-        lines = self._plot(ax, data.index, data.values, style=style, **kwds)
-        # set date formatter, locators and rescale limits
-        format_dateaxis(ax, ax.freq, data.index)
-        return lines
-
-    def _get_stacking_id(self):
-        if self.stacked:
-            return id(self.data)
-        else:
-            return None
-
-    @classmethod
-    def _initialize_stacker(cls, ax: Axes, stacking_id, n: int) -> None:
-        if stacking_id is None:
-            return
-        if not hasattr(ax, "_stacker_pos_prior"):
-            ax._stacker_pos_prior = {}
-        if not hasattr(ax, "_stacker_neg_prior"):
-            ax._stacker_neg_prior = {}
-        ax._stacker_pos_prior[stacking_id] = np.zeros(n)
-        ax._stacker_neg_prior[stacking_id] = np.zeros(n)
-
-    @classmethod
-    def _get_stacked_values(cls, ax: Axes, stacking_id, values, label):
-        if stacking_id is None:
-            return values
-        if not hasattr(ax, "_stacker_pos_prior"):
-            # stacker may not be initialized for subplots
-            cls._initialize_stacker(ax, stacking_id, len(values))
-
-        if (values >= 0).all():
-            return ax._stacker_pos_prior[stacking_id] + values
-        elif (values <= 0).all():
-            return ax._stacker_neg_prior[stacking_id] + values
-
-        raise ValueError(
-            "When stacked is True, each column must be either "
-            "all positive or all negative. "
-            f"Column '{label}' contains both positive and negative values"
-        )
-
-    @classmethod
-    def _update_stacker(cls, ax: Axes, stacking_id, values) -> None:
-        if stacking_id is None:
-            return
-        if (values >= 0).all():
-            ax._stacker_pos_prior[stacking_id] += values
-        elif (values <= 0).all():
-            ax._stacker_neg_prior[stacking_id] += values
-
-    def _args_adjust(self) -> None:
-        pass
-
-    def _post_plot_logic(self, ax: Axes, data) -> None:
-        from matplotlib.ticker import FixedLocator
-
-        def get_label(i):
-            if is_float(i) and i.is_integer():
-                i = int(i)
-            try:
-                return pprint_thing(data.index[i])
-            except Exception:
-                return ""
-
-        if self._need_to_set_index:
-            xticks = ax.get_xticks()
-            xticklabels = [get_label(x) for x in xticks]
-            ax.xaxis.set_major_locator(FixedLocator(xticks))
-            ax.set_xticklabels(xticklabels)
-
-        # If the index is an irregular time series, then by default
-        # we rotate the tick labels. The exception is if there are
-        # subplots which don't share their x-axes, in which we case
-        # we don't rotate the ticklabels as by default the subplots
-        # would be too close together.
-        condition = (
-            not self._use_dynamic_x()
-            and (data.index._is_all_dates and self.use_index)
-            and (not self.subplots or (self.subplots and self.sharex))
-        )
-
-        index_name = self._get_index_name()
-
-        if condition:
-            # irregular TS rotated 30 deg. by default
-            # probably a better place to check / set this.
-            if not self._rot_set:
-                self.rot = 30
-            format_date_labels(ax, rot=self.rot)
-
-        if index_name is not None and self.use_index:
-            ax.set_xlabel(index_name)
-
-
-class AreaPlot(LinePlot):
-    @property
-    def _kind(self) -> Literal["area"]:
-        return "area"
-
-    def __init__(self, data, **kwargs) -> None:
-        kwargs.setdefault("stacked", True)
-        data = data.fillna(value=0)
-        LinePlot.__init__(self, data, **kwargs)
-
-        if not self.stacked:
-            # use smaller alpha to distinguish overlap
-            self.kwds.setdefault("alpha", 0.5)
-
-        if self.logy or self.loglog:
-            raise ValueError("Log-y scales are not supported in area plot")
-
-    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
-    @classmethod
-    def _plot(  # type: ignore[override]
-        cls,
-        ax: Axes,
-        x,
-        y,
-        style=None,
-        column_num=None,
-        stacking_id=None,
-        is_errorbar: bool = False,
-        **kwds,
-    ):
-        if column_num == 0:
-            cls._initialize_stacker(ax, stacking_id, len(y))
-        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds["label"])
-
-        # need to remove label, because subplots uses mpl legend as it is
-        line_kwds = kwds.copy()
-        line_kwds.pop("label")
-        lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds)
-
-        # get data from the line to get coordinates for fill_between
-        xdata, y_values = lines[0].get_data(orig=False)
-
-        # unable to use ``_get_stacked_values`` here to get starting point
-        if stacking_id is None:
-            start = np.zeros(len(y))
-        elif (y >= 0).all():
-            start = ax._stacker_pos_prior[stacking_id]
-        elif (y <= 0).all():
-            start = ax._stacker_neg_prior[stacking_id]
-        else:
-            start = np.zeros(len(y))
-
-        if "color" not in kwds:
-            kwds["color"] = lines[0].get_color()
-
-        rect = ax.fill_between(xdata, start, y_values, **kwds)
-        cls._update_stacker(ax, stacking_id, y)
-
-        # LinePlot expects list of artists
-        res = [rect]
-        return res
-
-    def _args_adjust(self) -> None:
-        pass
-
-    def _post_plot_logic(self, ax: Axes, data) -> None:
-        LinePlot._post_plot_logic(self, ax, data)
-
-        is_shared_y = len(list(ax.get_shared_y_axes())) > 0
-        # do not override the default axis behaviour in case of shared y axes
-        if self.ylim is None and not is_shared_y:
-            if (data >= 0).all().all():
-                ax.set_ylim(0, None)
-            elif (data <= 0).all().all():
-                ax.set_ylim(None, 0)
-
-
-class BarPlot(MPLPlot):
-    @property
-    def _kind(self) -> Literal["bar", "barh"]:
-        return "bar"
-
-    _default_rot = 90
-
-    @property
-    def orientation(self) -> PlottingOrientation:
-        return "vertical"
-
-    def __init__(self, data, **kwargs) -> None:
-        # we have to treat a series differently than a
-        # 1-column DataFrame w.r.t. color handling
-        self._is_series = isinstance(data, ABCSeries)
-        self.bar_width = kwargs.pop("width", 0.5)
-        pos = kwargs.pop("position", 0.5)
-        kwargs.setdefault("align", "center")
-        self.tick_pos = np.arange(len(data))
-
-        self.bottom = kwargs.pop("bottom", 0)
-        self.left = kwargs.pop("left", 0)
-
-        self.log = kwargs.pop("log", False)
-        MPLPlot.__init__(self, data, **kwargs)
-
-        if self.stacked or self.subplots:
-            self.tickoffset = self.bar_width * pos
-            if kwargs["align"] == "edge":
-                self.lim_offset = self.bar_width / 2
-            else:
-                self.lim_offset = 0
-        elif kwargs["align"] == "edge":
-            w = self.bar_width / self.nseries
-            self.tickoffset = self.bar_width * (pos - 0.5) + w * 0.5
-            self.lim_offset = w * 0.5
-        else:
-            self.tickoffset = self.bar_width * pos
-            self.lim_offset = 0
-
-        self.ax_pos = self.tick_pos - self.tickoffset
-
-    def _args_adjust(self) -> None:
-        if is_list_like(self.bottom):
-            self.bottom = np.array(self.bottom)
-        if is_list_like(self.left):
-            self.left = np.array(self.left)
-
-    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
-    @classmethod
-    def _plot(  # type: ignore[override]
-        cls,
-        ax: Axes,
-        x,
-        y,
-        w,
-        start: int | npt.NDArray[np.intp] = 0,
-        log: bool = False,
-        **kwds,
-    ):
-        return ax.bar(x, y, w, bottom=start, log=log, **kwds)
-
-    @property
-    def _start_base(self):
-        return self.bottom
-
-    def _make_plot(self) -> None:
-        colors = self._get_colors()
-        ncolors = len(colors)
-
-        pos_prior = neg_prior = np.zeros(len(self.data))
-        K = self.nseries
-
-        for i, (label, y) in enumerate(self._iter_data(fillna=0)):
-            ax = self._get_ax(i)
-            kwds = self.kwds.copy()
-            if self._is_series:
-                kwds["color"] = colors
-            elif isinstance(colors, dict):
-                kwds["color"] = colors[label]
-            else:
-                kwds["color"] = colors[i % ncolors]
-
-            errors = self._get_errorbars(label=label, index=i)
-            kwds = dict(kwds, **errors)
-
-            label = pprint_thing(label)
-            label = self._mark_right_label(label, index=i)
-
-            if (("yerr" in kwds) or ("xerr" in kwds)) and (kwds.get("ecolor") is None):
-                kwds["ecolor"] = mpl.rcParams["xtick.color"]
-
-            start = 0
-            if self.log and (y >= 1).all():
-                start = 1
-            start = start + self._start_base
-
-            if self.subplots:
-                w = self.bar_width / 2
-                rect = self._plot(
-                    ax,
-                    self.ax_pos + w,
-                    y,
-                    self.bar_width,
-                    start=start,
-                    label=label,
-                    log=self.log,
-                    **kwds,
-                )
-                ax.set_title(label)
-            elif self.stacked:
-                mask = y > 0
-                start = np.where(mask, pos_prior, neg_prior) + self._start_base
-                w = self.bar_width / 2
-                rect = self._plot(
-                    ax,
-                    self.ax_pos + w,
-                    y,
-                    self.bar_width,
-                    start=start,
-                    label=label,
-                    log=self.log,
-                    **kwds,
-                )
-                pos_prior = pos_prior + np.where(mask, y, 0)
-                neg_prior = neg_prior + np.where(mask, 0, y)
-            else:
-                w = self.bar_width / K
-                rect = self._plot(
-                    ax,
-                    self.ax_pos + (i + 0.5) * w,
-                    y,
-                    w,
-                    start=start,
-                    label=label,
-                    log=self.log,
-                    **kwds,
-                )
-            self._append_legend_handles_labels(rect, label)
-
-    def _post_plot_logic(self, ax: Axes, data) -> None:
-        if self.use_index:
-            str_index = [pprint_thing(key) for key in data.index]
-        else:
-            str_index = [pprint_thing(key) for key in range(data.shape[0])]
-
-        s_edge = self.ax_pos[0] - 0.25 + self.lim_offset
-        e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset
-
-        self._decorate_ticks(ax, self._get_index_name(), str_index, s_edge, e_edge)
-
-    def _decorate_ticks(self, ax: Axes, name, ticklabels, start_edge, end_edge) -> None:
-        ax.set_xlim((start_edge, end_edge))
-
-        if self.xticks is not None:
-            ax.set_xticks(np.array(self.xticks))
-        else:
-            ax.set_xticks(self.tick_pos)
-            ax.set_xticklabels(ticklabels)
-
-        if name is not None and self.use_index:
-            ax.set_xlabel(name)
-
-
-class BarhPlot(BarPlot):
-    @property
-    def _kind(self) -> Literal["barh"]:
-        return "barh"
-
-    _default_rot = 0
-
-    @property
-    def orientation(self) -> Literal["horizontal"]:
-        return "horizontal"
-
-    @property
-    def _start_base(self):
-        return self.left
-
-    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
-    @classmethod
-    def _plot(  # type: ignore[override]
-        cls,
-        ax: Axes,
-        x,
-        y,
-        w,
-        start: int | npt.NDArray[np.intp] = 0,
-        log: bool = False,
-        **kwds,
-    ):
-        return ax.barh(x, y, w, left=start, log=log, **kwds)
-
-    def _get_custom_index_name(self):
-        return self.ylabel
-
-    def _decorate_ticks(self, ax: Axes, name, ticklabels, start_edge, end_edge) -> None:
-        # horizontal bars
-        ax.set_ylim((start_edge, end_edge))
-        ax.set_yticks(self.tick_pos)
-        ax.set_yticklabels(ticklabels)
-        if name is not None and self.use_index:
-            ax.set_ylabel(name)
-        ax.set_xlabel(self.xlabel)
-
-
-class PiePlot(MPLPlot):
-    @property
-    def _kind(self) -> Literal["pie"]:
-        return "pie"
-
-    _layout_type = "horizontal"
-
-    def __init__(self, data, kind=None, **kwargs) -> None:
-        data = data.fillna(value=0)
-        if (data < 0).any().any():
-            raise ValueError(f"{self._kind} plot doesn't allow negative values")
-        MPLPlot.__init__(self, data, kind=kind, **kwargs)
-
-    def _args_adjust(self) -> None:
-        self.grid = False
-        self.logy = False
-        self.logx = False
-        self.loglog = False
-
-    def _validate_color_args(self) -> None:
-        pass
-
-    def _make_plot(self) -> None:
-        colors = self._get_colors(num_colors=len(self.data), color_kwds="colors")
-        self.kwds.setdefault("colors", colors)
-
-        for i, (label, y) in enumerate(self._iter_data()):
-            ax = self._get_ax(i)
-            if label is not None:
-                label = pprint_thing(label)
-                ax.set_ylabel(label)
-
-            kwds = self.kwds.copy()
-
-            def blank_labeler(label, value):
-                if value == 0:
-                    return ""
-                else:
-                    return label
-
-            idx = [pprint_thing(v) for v in self.data.index]
-            labels = kwds.pop("labels", idx)
-            # labels is used for each wedge's labels
-            # Blank out labels for values of 0 so they don't overlap
-            # with nonzero wedges
-            if labels is not None:
-                blabels = [blank_labeler(left, value) for left, value in zip(labels, y)]
-            else:
-                blabels = None
-            results = ax.pie(y, labels=blabels, **kwds)
-
-            if kwds.get("autopct", None) is not None:
-                patches, texts, autotexts = results
-            else:
-                patches, texts = results
-                autotexts = []
-
-            if self.fontsize is not None:
-                for t in texts + autotexts:
-                    t.set_fontsize(self.fontsize)
-
-            # leglabels is used for legend labels
-            leglabels = labels if labels is not None else idx
-            for _patch, _leglabel in zip(patches, leglabels):
-                self._append_legend_handles_labels(_patch, _leglabel)
-
-    def _post_plot_logic(self, ax: Axes, data) -> None:
-        pass
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_apply.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_apply.py
deleted file mode 100644
index 3a3f73a68374bf96960b5cc8125dcd7effc4d147..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_frame_apply.py
+++ /dev/null
@@ -1,1634 +0,0 @@
-from datetime import datetime
-import warnings
-
-import numpy as np
-import pytest
-
-from pandas.core.dtypes.dtypes import CategoricalDtype
-
-import pandas as pd
-from pandas import (
-    DataFrame,
-    MultiIndex,
-    Series,
-    Timestamp,
-    date_range,
-)
-import pandas._testing as tm
-from pandas.tests.frame.common import zip_frames
-
-
-def test_apply(float_frame):
-    with np.errstate(all="ignore"):
-        # ufunc
-        result = np.sqrt(float_frame["A"])
-        expected = float_frame.apply(np.sqrt)["A"]
-        tm.assert_series_equal(result, expected)
-
-        # aggregator
-        result = float_frame.apply(np.mean)["A"]
-        expected = np.mean(float_frame["A"])
-        assert result == expected
-
-        d = float_frame.index[0]
-        result = float_frame.apply(np.mean, axis=1)
-        expected = np.mean(float_frame.xs(d))
-        assert result[d] == expected
-        assert result.index is float_frame.index
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_args(float_frame, axis):
-    result = float_frame.apply(lambda x, y: x + y, axis, args=(1,))
-    expected = float_frame + 1
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_categorical_func():
-    # GH 9573
-    df = DataFrame({"c0": ["A", "A", "B", "B"], "c1": ["C", "C", "D", "D"]})
-    result = df.apply(lambda ts: ts.astype("category"))
-
-    assert result.shape == (4, 2)
-    assert isinstance(result["c0"].dtype, CategoricalDtype)
-    assert isinstance(result["c1"].dtype, CategoricalDtype)
-
-
-def test_apply_axis1_with_ea():
-    # GH#36785
-    expected = DataFrame({"A": [Timestamp("2013-01-01", tz="UTC")]})
-    result = expected.apply(lambda x: x, axis=1)
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "data, dtype",
-    [(1, None), (1, CategoricalDtype([1])), (Timestamp("2013-01-01", tz="UTC"), None)],
-)
-def test_agg_axis1_duplicate_index(data, dtype):
-    # GH 42380
-    expected = DataFrame([[data], [data]], index=["a", "a"], dtype=dtype)
-    result = expected.agg(lambda x: x, axis=1)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_mixed_datetimelike():
-    # mixed datetimelike
-    # GH 7778
-    expected = DataFrame(
-        {
-            "A": date_range("20130101", periods=3),
-            "B": pd.to_timedelta(np.arange(3), unit="s"),
-        }
-    )
-    result = expected.apply(lambda x: x, axis=1)
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("func", [np.sqrt, np.mean])
-def test_apply_empty(func):
-    # empty
-    empty_frame = DataFrame()
-
-    result = empty_frame.apply(func)
-    assert result.empty
-
-
-def test_apply_float_frame(float_frame):
-    no_rows = float_frame[:0]
-    result = no_rows.apply(lambda x: x.mean())
-    expected = Series(np.nan, index=float_frame.columns)
-    tm.assert_series_equal(result, expected)
-
-    no_cols = float_frame.loc[:, []]
-    result = no_cols.apply(lambda x: x.mean(), axis=1)
-    expected = Series(np.nan, index=float_frame.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_empty_except_index():
-    # GH 2476
-    expected = DataFrame(index=["a"])
-    result = expected.apply(lambda x: x["a"], axis=1)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_with_reduce_empty():
-    # reduce with an empty DataFrame
-    empty_frame = DataFrame()
-
-    x = []
-    result = empty_frame.apply(x.append, axis=1, result_type="expand")
-    tm.assert_frame_equal(result, empty_frame)
-    result = empty_frame.apply(x.append, axis=1, result_type="reduce")
-    expected = Series([], dtype=np.float64)
-    tm.assert_series_equal(result, expected)
-
-    empty_with_cols = DataFrame(columns=["a", "b", "c"])
-    result = empty_with_cols.apply(x.append, axis=1, result_type="expand")
-    tm.assert_frame_equal(result, empty_with_cols)
-    result = empty_with_cols.apply(x.append, axis=1, result_type="reduce")
-    expected = Series([], dtype=np.float64)
-    tm.assert_series_equal(result, expected)
-
-    # Ensure that x.append hasn't been called
-    assert x == []
-
-
-@pytest.mark.parametrize("func", ["sum", "prod", "any", "all"])
-def test_apply_funcs_over_empty(func):
-    # GH 28213
-    df = DataFrame(columns=["a", "b", "c"])
-
-    result = df.apply(getattr(np, func))
-    expected = getattr(df, func)()
-    if func in ("sum", "prod"):
-        expected = expected.astype(float)
-    tm.assert_series_equal(result, expected)
-
-
-def test_nunique_empty():
-    # GH 28213
-    df = DataFrame(columns=["a", "b", "c"])
-
-    result = df.nunique()
-    expected = Series(0, index=df.columns)
-    tm.assert_series_equal(result, expected)
-
-    result = df.T.nunique()
-    expected = Series([], dtype=np.float64)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_standard_nonunique():
-    df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=["a", "a", "c"])
-
-    result = df.apply(lambda s: s[0], axis=1)
-    expected = Series([1, 4, 7], ["a", "a", "c"])
-    tm.assert_series_equal(result, expected)
-
-    result = df.T.apply(lambda s: s[0], axis=0)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_broadcast_scalars(float_frame):
-    # scalars
-    result = float_frame.apply(np.mean, result_type="broadcast")
-    expected = DataFrame([float_frame.mean()], index=float_frame.index)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_broadcast_scalars_axis1(float_frame):
-    result = float_frame.apply(np.mean, axis=1, result_type="broadcast")
-    m = float_frame.mean(axis=1)
-    expected = DataFrame({c: m for c in float_frame.columns})
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_broadcast_lists_columns(float_frame):
-    # lists
-    result = float_frame.apply(
-        lambda x: list(range(len(float_frame.columns))),
-        axis=1,
-        result_type="broadcast",
-    )
-    m = list(range(len(float_frame.columns)))
-    expected = DataFrame(
-        [m] * len(float_frame.index),
-        dtype="float64",
-        index=float_frame.index,
-        columns=float_frame.columns,
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_broadcast_lists_index(float_frame):
-    result = float_frame.apply(
-        lambda x: list(range(len(float_frame.index))), result_type="broadcast"
-    )
-    m = list(range(len(float_frame.index)))
-    expected = DataFrame(
-        {c: m for c in float_frame.columns},
-        dtype="float64",
-        index=float_frame.index,
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_broadcast_list_lambda_func(int_frame_const_col):
-    # preserve columns
-    df = int_frame_const_col
-    result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="broadcast")
-    tm.assert_frame_equal(result, df)
-
-
-def test_apply_broadcast_series_lambda_func(int_frame_const_col):
-    df = int_frame_const_col
-    result = df.apply(
-        lambda x: Series([1, 2, 3], index=list("abc")),
-        axis=1,
-        result_type="broadcast",
-    )
-    expected = df.copy()
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_raw_float_frame(float_frame, axis):
-    def _assert_raw(x):
-        assert isinstance(x, np.ndarray)
-        assert x.ndim == 1
-
-    float_frame.apply(_assert_raw, axis=axis, raw=True)
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_raw_float_frame_lambda(float_frame, axis):
-    result = float_frame.apply(np.mean, axis=axis, raw=True)
-    expected = float_frame.apply(lambda x: x.values.mean(), axis=axis)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_raw_float_frame_no_reduction(float_frame):
-    # no reduction
-    result = float_frame.apply(lambda x: x * 2, raw=True)
-    expected = float_frame * 2
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_raw_mixed_type_frame(mixed_type_frame, axis):
-    def _assert_raw(x):
-        assert isinstance(x, np.ndarray)
-        assert x.ndim == 1
-
-    # Mixed dtype (GH-32423)
-    mixed_type_frame.apply(_assert_raw, axis=axis, raw=True)
-
-
-def test_apply_axis1(float_frame):
-    d = float_frame.index[0]
-    result = float_frame.apply(np.mean, axis=1)[d]
-    expected = np.mean(float_frame.xs(d))
-    assert result == expected
-
-
-def test_apply_mixed_dtype_corner():
-    df = DataFrame({"A": ["foo"], "B": [1.0]})
-    result = df[:0].apply(np.mean, axis=1)
-    # the result here is actually kind of ambiguous, should it be a Series
-    # or a DataFrame?
-    expected = Series(np.nan, index=pd.Index([], dtype="int64"))
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_mixed_dtype_corner_indexing():
-    df = DataFrame({"A": ["foo"], "B": [1.0]})
-    result = df.apply(lambda x: x["A"], axis=1)
-    expected = Series(["foo"], index=[0])
-    tm.assert_series_equal(result, expected)
-
-    result = df.apply(lambda x: x["B"], axis=1)
-    expected = Series([1.0], index=[0])
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.filterwarnings("ignore::RuntimeWarning")
-@pytest.mark.parametrize("ax", ["index", "columns"])
-@pytest.mark.parametrize(
-    "func", [lambda x: x, lambda x: x.mean()], ids=["identity", "mean"]
-)
-@pytest.mark.parametrize("raw", [True, False])
-@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_empty_infer_type(ax, func, raw, axis):
-    df = DataFrame(**{ax: ["a", "b", "c"]})
-
-    with np.errstate(all="ignore"):
-        test_res = func(np.array([], dtype="f8"))
-        is_reduction = not isinstance(test_res, np.ndarray)
-
-        result = df.apply(func, axis=axis, raw=raw)
-        if is_reduction:
-            agg_axis = df._get_agg_axis(axis)
-            assert isinstance(result, Series)
-            assert result.index is agg_axis
-        else:
-            assert isinstance(result, DataFrame)
-
-
-def test_apply_empty_infer_type_broadcast():
-    no_cols = DataFrame(index=["a", "b", "c"])
-    result = no_cols.apply(lambda x: x.mean(), result_type="broadcast")
-    assert isinstance(result, DataFrame)
-
-
-def test_apply_with_args_kwds_add_some(float_frame):
-    def add_some(x, howmuch=0):
-        return x + howmuch
-
-    result = float_frame.apply(add_some, howmuch=2)
-    expected = float_frame.apply(lambda x: x + 2)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_with_args_kwds_agg_and_add(float_frame):
-    def agg_and_add(x, howmuch=0):
-        return x.mean() + howmuch
-
-    result = float_frame.apply(agg_and_add, howmuch=2)
-    expected = float_frame.apply(lambda x: x.mean() + 2)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_with_args_kwds_subtract_and_divide(float_frame):
-    def subtract_and_divide(x, sub, divide=1):
-        return (x - sub) / divide
-
-    result = float_frame.apply(subtract_and_divide, args=(2,), divide=2)
-    expected = float_frame.apply(lambda x: (x - 2.0) / 2.0)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_yield_list(float_frame):
-    result = float_frame.apply(list)
-    tm.assert_frame_equal(result, float_frame)
-
-
-def test_apply_reduce_Series(float_frame):
-    float_frame.iloc[::2, float_frame.columns.get_loc("A")] = np.nan
-    expected = float_frame.mean(1)
-    result = float_frame.apply(np.mean, axis=1)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_reduce_to_dict():
-    # GH 25196 37544
-    data = DataFrame([[1, 2], [3, 4]], columns=["c0", "c1"], index=["i0", "i1"])
-
-    result = data.apply(dict, axis=0)
-    expected = Series([{"i0": 1, "i1": 3}, {"i0": 2, "i1": 4}], index=data.columns)
-    tm.assert_series_equal(result, expected)
-
-    result = data.apply(dict, axis=1)
-    expected = Series([{"c0": 1, "c1": 2}, {"c0": 3, "c1": 4}], index=data.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_differently_indexed():
-    df = DataFrame(np.random.default_rng(2).standard_normal((20, 10)))
-
-    result = df.apply(Series.describe, axis=0)
-    expected = DataFrame({i: v.describe() for i, v in df.items()}, columns=df.columns)
-    tm.assert_frame_equal(result, expected)
-
-    result = df.apply(Series.describe, axis=1)
-    expected = DataFrame({i: v.describe() for i, v in df.T.items()}, columns=df.index).T
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_bug():
-    # GH 6125
-    positions = DataFrame(
-        [
-            [1, "ABC0", 50],
-            [1, "YUM0", 20],
-            [1, "DEF0", 20],
-            [2, "ABC1", 50],
-            [2, "YUM1", 20],
-            [2, "DEF1", 20],
-        ],
-        columns=["a", "market", "position"],
-    )
-
-    def f(r):
-        return r["market"]
-
-    expected = positions.apply(f, axis=1)
-
-    positions = DataFrame(
-        [
-            [datetime(2013, 1, 1), "ABC0", 50],
-            [datetime(2013, 1, 2), "YUM0", 20],
-            [datetime(2013, 1, 3), "DEF0", 20],
-            [datetime(2013, 1, 4), "ABC1", 50],
-            [datetime(2013, 1, 5), "YUM1", 20],
-            [datetime(2013, 1, 6), "DEF1", 20],
-        ],
-        columns=["a", "market", "position"],
-    )
-    result = positions.apply(f, axis=1)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_convert_objects():
-    expected = DataFrame(
-        {
-            "A": [
-                "foo",
-                "foo",
-                "foo",
-                "foo",
-                "bar",
-                "bar",
-                "bar",
-                "bar",
-                "foo",
-                "foo",
-                "foo",
-            ],
-            "B": [
-                "one",
-                "one",
-                "one",
-                "two",
-                "one",
-                "one",
-                "one",
-                "two",
-                "two",
-                "two",
-                "one",
-            ],
-            "C": [
-                "dull",
-                "dull",
-                "shiny",
-                "dull",
-                "dull",
-                "shiny",
-                "shiny",
-                "dull",
-                "shiny",
-                "shiny",
-                "shiny",
-            ],
-            "D": np.random.default_rng(2).standard_normal(11),
-            "E": np.random.default_rng(2).standard_normal(11),
-            "F": np.random.default_rng(2).standard_normal(11),
-        }
-    )
-
-    result = expected.apply(lambda x: x, axis=1)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_attach_name(float_frame):
-    result = float_frame.apply(lambda x: x.name)
-    expected = Series(float_frame.columns, index=float_frame.columns)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_attach_name_axis1(float_frame):
-    result = float_frame.apply(lambda x: x.name, axis=1)
-    expected = Series(float_frame.index, index=float_frame.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_attach_name_non_reduction(float_frame):
-    # non-reductions
-    result = float_frame.apply(lambda x: np.repeat(x.name, len(x)))
-    expected = DataFrame(
-        np.tile(float_frame.columns, (len(float_frame.index), 1)),
-        index=float_frame.index,
-        columns=float_frame.columns,
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_attach_name_non_reduction_axis1(float_frame):
-    result = float_frame.apply(lambda x: np.repeat(x.name, len(x)), axis=1)
-    expected = Series(
-        np.repeat(t[0], len(float_frame.columns)) for t in float_frame.itertuples()
-    )
-    expected.index = float_frame.index
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_multi_index():
-    index = MultiIndex.from_arrays([["a", "a", "b"], ["c", "d", "d"]])
-    s = DataFrame([[1, 2], [3, 4], [5, 6]], index=index, columns=["col1", "col2"])
-    result = s.apply(lambda x: Series({"min": min(x), "max": max(x)}), 1)
-    expected = DataFrame([[1, 2], [3, 4], [5, 6]], index=index, columns=["min", "max"])
-    tm.assert_frame_equal(result, expected, check_like=True)
-
-
-@pytest.mark.parametrize(
-    "df, dicts",
-    [
-        [
-            DataFrame([["foo", "bar"], ["spam", "eggs"]]),
-            Series([{0: "foo", 1: "spam"}, {0: "bar", 1: "eggs"}]),
-        ],
-        [DataFrame([[0, 1], [2, 3]]), Series([{0: 0, 1: 2}, {0: 1, 1: 3}])],
-    ],
-)
-def test_apply_dict(df, dicts):
-    # GH 8735
-    fn = lambda x: x.to_dict()
-    reduce_true = df.apply(fn, result_type="reduce")
-    reduce_false = df.apply(fn, result_type="expand")
-    reduce_none = df.apply(fn)
-
-    tm.assert_series_equal(reduce_true, dicts)
-    tm.assert_frame_equal(reduce_false, df)
-    tm.assert_series_equal(reduce_none, dicts)
-
-
-def test_apply_non_numpy_dtype():
-    # GH 12244
-    df = DataFrame({"dt": date_range("2015-01-01", periods=3, tz="Europe/Brussels")})
-    result = df.apply(lambda x: x)
-    tm.assert_frame_equal(result, df)
-
-    result = df.apply(lambda x: x + pd.Timedelta("1day"))
-    expected = DataFrame(
-        {"dt": date_range("2015-01-02", periods=3, tz="Europe/Brussels")}
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_non_numpy_dtype_category():
-    df = DataFrame({"dt": ["a", "b", "c", "a"]}, dtype="category")
-    result = df.apply(lambda x: x)
-    tm.assert_frame_equal(result, df)
-
-
-def test_apply_dup_names_multi_agg():
-    # GH 21063
-    df = DataFrame([[0, 1], [2, 3]], columns=["a", "a"])
-    expected = DataFrame([[0, 1]], columns=["a", "a"], index=["min"])
-    result = df.agg(["min"])
-
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("op", ["apply", "agg"])
-def test_apply_nested_result_axis_1(op):
-    # GH 13820
-    def apply_list(row):
-        return [2 * row["A"], 2 * row["C"], 2 * row["B"]]
-
-    df = DataFrame(np.zeros((4, 4)), columns=list("ABCD"))
-    result = getattr(df, op)(apply_list, axis=1)
-    expected = Series(
-        [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
-    )
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_noreduction_tzaware_object():
-    # https://github.com/pandas-dev/pandas/issues/31505
-    expected = DataFrame(
-        {"foo": [Timestamp("2020", tz="UTC")]}, dtype="datetime64[ns, UTC]"
-    )
-    result = expected.apply(lambda x: x)
-    tm.assert_frame_equal(result, expected)
-    result = expected.apply(lambda x: x.copy())
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_function_runs_once():
-    # https://github.com/pandas-dev/pandas/issues/30815
-
-    df = DataFrame({"a": [1, 2, 3]})
-    names = []  # Save row names function is applied to
-
-    def reducing_function(row):
-        names.append(row.name)
-
-    def non_reducing_function(row):
-        names.append(row.name)
-        return row
-
-    for func in [reducing_function, non_reducing_function]:
-        del names[:]
-
-        df.apply(func, axis=1)
-        assert names == list(df.index)
-
-
-def test_apply_raw_function_runs_once():
-    # https://github.com/pandas-dev/pandas/issues/34506
-
-    df = DataFrame({"a": [1, 2, 3]})
-    values = []  # Save row values function is applied to
-
-    def reducing_function(row):
-        values.extend(row)
-
-    def non_reducing_function(row):
-        values.extend(row)
-        return row
-
-    for func in [reducing_function, non_reducing_function]:
-        del values[:]
-
-        df.apply(func, raw=True, axis=1)
-        assert values == list(df.a.to_list())
-
-
-def test_apply_with_byte_string():
-    # GH 34529
-    df = DataFrame(np.array([b"abcd", b"efgh"]), columns=["col"])
-    expected = DataFrame(np.array([b"abcd", b"efgh"]), columns=["col"], dtype=object)
-    # After we make the apply we expect a dataframe just
-    # like the original but with the object datatype
-    result = df.apply(lambda x: x.astype("object"))
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("val", ["asd", 12, None, np.nan])
-def test_apply_category_equalness(val):
-    # Check if categorical comparisons on apply, GH 21239
-    df_values = ["asd", None, 12, "asd", "cde", np.nan]
-    df = DataFrame({"a": df_values}, dtype="category")
-
-    result = df.a.apply(lambda x: x == val)
-    expected = Series(
-        [np.nan if pd.isnull(x) else x == val for x in df_values], name="a"
-    )
-    tm.assert_series_equal(result, expected)
-
-
-# the user has supplied an opaque UDF where
-# they are transforming the input that requires
-# us to infer the output
-
-
-def test_infer_row_shape():
-    # GH 17437
-    # if row shape is changing, infer it
-    df = DataFrame(np.random.default_rng(2).random((10, 2)))
-    result = df.apply(np.fft.fft, axis=0).shape
-    assert result == (10, 2)
-
-    result = df.apply(np.fft.rfft, axis=0).shape
-    assert result == (6, 2)
-
-
-@pytest.mark.parametrize(
-    "ops, by_row, expected",
-    [
-        ({"a": lambda x: x + 1}, "compat", DataFrame({"a": [2, 3]})),
-        ({"a": lambda x: x + 1}, False, DataFrame({"a": [2, 3]})),
-        ({"a": lambda x: x.sum()}, "compat", Series({"a": 3})),
-        ({"a": lambda x: x.sum()}, False, Series({"a": 3})),
-        (
-            {"a": ["sum", np.sum, lambda x: x.sum()]},
-            "compat",
-            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", "<lambda>"]),
-        ),
-        (
-            {"a": ["sum", np.sum, lambda x: x.sum()]},
-            False,
-            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", "<lambda>"]),
-        ),
-        ({"a": lambda x: 1}, "compat", DataFrame({"a": [1, 1]})),
-        ({"a": lambda x: 1}, False, Series({"a": 1})),
-    ],
-)
-def test_dictlike_lambda(ops, by_row, expected):
-    # GH53601
-    df = DataFrame({"a": [1, 2]})
-    result = df.apply(ops, by_row=by_row)
-    tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops",
-    [
-        {"a": lambda x: x + 1},
-        {"a": lambda x: x.sum()},
-        {"a": ["sum", np.sum, lambda x: x.sum()]},
-        {"a": lambda x: 1},
-    ],
-)
-def test_dictlike_lambda_raises(ops):
-    # GH53601
-    df = DataFrame({"a": [1, 2]})
-    with pytest.raises(ValueError, match="by_row=True not allowed"):
-        df.apply(ops, by_row=True)
-
-
-def test_with_dictlike_columns():
-    # GH 17602
-    df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
-    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1)
-    expected = Series([{"s": 3} for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-    df["tm"] = [
-        Timestamp("2017-05-01 00:00:00"),
-        Timestamp("2017-05-02 00:00:00"),
-    ]
-    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1)
-    tm.assert_series_equal(result, expected)
-
-    # compose a series
-    result = (df["a"] + df["b"]).apply(lambda x: {"s": x})
-    expected = Series([{"s": 3}, {"s": 3}])
-    tm.assert_series_equal(result, expected)
-
-
-def test_with_dictlike_columns_with_datetime():
-    # GH 18775
-    df = DataFrame()
-    df["author"] = ["X", "Y", "Z"]
-    df["publisher"] = ["BBC", "NBC", "N24"]
-    df["date"] = pd.to_datetime(
-        ["17-10-2010 07:15:30", "13-05-2011 08:20:35", "15-01-2013 09:09:09"],
-        dayfirst=True,
-    )
-    result = df.apply(lambda x: {}, axis=1)
-    expected = Series([{}, {}, {}])
-    tm.assert_series_equal(result, expected)
-
-
-def test_with_dictlike_columns_with_infer():
-    # GH 17602
-    df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
-    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1, result_type="expand")
-    expected = DataFrame({"s": [3, 3]})
-    tm.assert_frame_equal(result, expected)
-
-    df["tm"] = [
-        Timestamp("2017-05-01 00:00:00"),
-        Timestamp("2017-05-02 00:00:00"),
-    ]
-    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1, result_type="expand")
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops, by_row, expected",
-    [
-        ([lambda x: x + 1], "compat", DataFrame({("a", "<lambda>"): [2, 3]})),
-        ([lambda x: x + 1], False, DataFrame({("a", "<lambda>"): [2, 3]})),
-        ([lambda x: x.sum()], "compat", DataFrame({"a": [3]}, index=["<lambda>"])),
-        ([lambda x: x.sum()], False, DataFrame({"a": [3]}, index=["<lambda>"])),
-        (
-            ["sum", np.sum, lambda x: x.sum()],
-            "compat",
-            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", "<lambda>"]),
-        ),
-        (
-            ["sum", np.sum, lambda x: x.sum()],
-            False,
-            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", "<lambda>"]),
-        ),
-        (
-            [lambda x: x + 1, lambda x: 3],
-            "compat",
-            DataFrame([[2, 3], [3, 3]], columns=[["a", "a"], ["<lambda>", "<lambda>"]]),
-        ),
-        (
-            [lambda x: 2, lambda x: 3],
-            False,
-            DataFrame({"a": [2, 3]}, ["<lambda>", "<lambda>"]),
-        ),
-    ],
-)
-def test_listlike_lambda(ops, by_row, expected):
-    # GH53601
-    df = DataFrame({"a": [1, 2]})
-    result = df.apply(ops, by_row=by_row)
-    tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops",
-    [
-        [lambda x: x + 1],
-        [lambda x: x.sum()],
-        ["sum", np.sum, lambda x: x.sum()],
-        [lambda x: x + 1, lambda x: 3],
-    ],
-)
-def test_listlike_lambda_raises(ops):
-    # GH53601
-    df = DataFrame({"a": [1, 2]})
-    with pytest.raises(ValueError, match="by_row=True not allowed"):
-        df.apply(ops, by_row=True)
-
-
-def test_with_listlike_columns():
-    # GH 17348
-    df = DataFrame(
-        {
-            "a": Series(np.random.default_rng(2).standard_normal(4)),
-            "b": ["a", "list", "of", "words"],
-            "ts": date_range("2016-10-01", periods=4, freq="H"),
-        }
-    )
-
-    result = df[["a", "b"]].apply(tuple, axis=1)
-    expected = Series([t[1:] for t in df[["a", "b"]].itertuples()])
-    tm.assert_series_equal(result, expected)
-
-    result = df[["a", "ts"]].apply(tuple, axis=1)
-    expected = Series([t[1:] for t in df[["a", "ts"]].itertuples()])
-    tm.assert_series_equal(result, expected)
-
-
-def test_with_listlike_columns_returning_list():
-    # GH 18919
-    df = DataFrame({"x": Series([["a", "b"], ["q"]]), "y": Series([["z"], ["q", "t"]])})
-    df.index = MultiIndex.from_tuples([("i0", "j0"), ("i1", "j1")])
-
-    result = df.apply(lambda row: [el for el in row["x"] if el in row["y"]], axis=1)
-    expected = Series([[], ["q"]], index=df.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_infer_output_shape_columns():
-    # GH 18573
-
-    df = DataFrame(
-        {
-            "number": [1.0, 2.0],
-            "string": ["foo", "bar"],
-            "datetime": [
-                Timestamp("2017-11-29 03:30:00"),
-                Timestamp("2017-11-29 03:45:00"),
-            ],
-        }
-    )
-    result = df.apply(lambda row: (row.number, row.string), axis=1)
-    expected = Series([(t.number, t.string) for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-
-def test_infer_output_shape_listlike_columns():
-    # GH 16353
-
-    df = DataFrame(
-        np.random.default_rng(2).standard_normal((6, 3)), columns=["A", "B", "C"]
-    )
-
-    result = df.apply(lambda x: [1, 2, 3], axis=1)
-    expected = Series([[1, 2, 3] for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-    result = df.apply(lambda x: [1, 2], axis=1)
-    expected = Series([[1, 2] for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("val", [1, 2])
-def test_infer_output_shape_listlike_columns_np_func(val):
-    # GH 17970
-    df = DataFrame({"a": [1, 2, 3]}, index=list("abc"))
-
-    result = df.apply(lambda row: np.ones(val), axis=1)
-    expected = Series([np.ones(val) for t in df.itertuples()], index=df.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_infer_output_shape_listlike_columns_with_timestamp():
-    # GH 17892
-    df = DataFrame(
-        {
-            "a": [
-                Timestamp("2010-02-01"),
-                Timestamp("2010-02-04"),
-                Timestamp("2010-02-05"),
-                Timestamp("2010-02-06"),
-            ],
-            "b": [9, 5, 4, 3],
-            "c": [5, 3, 4, 2],
-            "d": [1, 2, 3, 4],
-        }
-    )
-
-    def fun(x):
-        return (1, 2)
-
-    result = df.apply(fun, axis=1)
-    expected = Series([(1, 2) for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("lst", [[1, 2, 3], [1, 2]])
-def test_consistent_coerce_for_shapes(lst):
-    # we want column names to NOT be propagated
-    # just because the shape matches the input shape
-    df = DataFrame(
-        np.random.default_rng(2).standard_normal((4, 3)), columns=["A", "B", "C"]
-    )
-
-    result = df.apply(lambda x: lst, axis=1)
-    expected = Series([lst for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-
-def test_consistent_names(int_frame_const_col):
-    # if a Series is returned, we should use the resulting index names
-    df = int_frame_const_col
-
-    result = df.apply(
-        lambda x: Series([1, 2, 3], index=["test", "other", "cols"]), axis=1
-    )
-    expected = int_frame_const_col.rename(
-        columns={"A": "test", "B": "other", "C": "cols"}
-    )
-    tm.assert_frame_equal(result, expected)
-
-    result = df.apply(lambda x: Series([1, 2], index=["test", "other"]), axis=1)
-    expected = expected[["test", "other"]]
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-
-    result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="expand")
-    expected = df.copy()
-    expected.columns = [0, 1, 2]
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type_shorter_list(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-    result = df.apply(lambda x: [1, 2], axis=1, result_type="expand")
-    expected = df[["A", "B"]].copy()
-    expected.columns = [0, 1]
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type_broadcast(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-    # broadcast result
-    result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="broadcast")
-    expected = df.copy()
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type_broadcast_series_func(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-    columns = ["other", "col", "names"]
-    result = df.apply(
-        lambda x: Series([1, 2, 3], index=columns), axis=1, result_type="broadcast"
-    )
-    expected = df.copy()
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type_series_result(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-    # series result
-    result = df.apply(lambda x: Series([1, 2, 3], index=x.index), axis=1)
-    expected = df.copy()
-    tm.assert_frame_equal(result, expected)
-
-
-def test_result_type_series_result_other_index(int_frame_const_col):
-    # result_type should be consistent no matter which
-    # path we take in the code
-    df = int_frame_const_col
-    # series result with other index
-    columns = ["other", "col", "names"]
-    result = df.apply(lambda x: Series([1, 2, 3], index=columns), axis=1)
-    expected = df.copy()
-    expected.columns = columns
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "box",
-    [lambda x: list(x), lambda x: tuple(x), lambda x: np.array(x, dtype="int64")],
-    ids=["list", "tuple", "array"],
-)
-def test_consistency_for_boxed(box, int_frame_const_col):
-    # passing an array or list should not affect the output shape
-    df = int_frame_const_col
-
-    result = df.apply(lambda x: box([1, 2]), axis=1)
-    expected = Series([box([1, 2]) for t in df.itertuples()])
-    tm.assert_series_equal(result, expected)
-
-    result = df.apply(lambda x: box([1, 2]), axis=1, result_type="expand")
-    expected = int_frame_const_col[["A", "B"]].rename(columns={"A": 0, "B": 1})
-    tm.assert_frame_equal(result, expected)
-
-
-def test_agg_transform(axis, float_frame):
-    other_axis = 1 if axis in {0, "index"} else 0
-
-    with np.errstate(all="ignore"):
-        f_abs = np.abs(float_frame)
-        f_sqrt = np.sqrt(float_frame)
-
-        # ufunc
-        expected = f_sqrt.copy()
-        result = float_frame.apply(np.sqrt, axis=axis)
-        tm.assert_frame_equal(result, expected)
-
-        # list-like
-        result = float_frame.apply([np.sqrt], axis=axis)
-        expected = f_sqrt.copy()
-        if axis in {0, "index"}:
-            expected.columns = MultiIndex.from_product([float_frame.columns, ["sqrt"]])
-        else:
-            expected.index = MultiIndex.from_product([float_frame.index, ["sqrt"]])
-        tm.assert_frame_equal(result, expected)
-
-        # multiple items in list
-        # these are in the order as if we are applying both
-        # functions per series and then concatting
-        result = float_frame.apply([np.abs, np.sqrt], axis=axis)
-        expected = zip_frames([f_abs, f_sqrt], axis=other_axis)
-        if axis in {0, "index"}:
-            expected.columns = MultiIndex.from_product(
-                [float_frame.columns, ["absolute", "sqrt"]]
-            )
-        else:
-            expected.index = MultiIndex.from_product(
-                [float_frame.index, ["absolute", "sqrt"]]
-            )
-        tm.assert_frame_equal(result, expected)
-
-
-def test_demo():
-    # demonstration tests
-    df = DataFrame({"A": range(5), "B": 5})
-
-    result = df.agg(["min", "max"])
-    expected = DataFrame(
-        {"A": [0, 4], "B": [5, 5]}, columns=["A", "B"], index=["min", "max"]
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_demo_dict_agg():
-    # demonstration tests
-    df = DataFrame({"A": range(5), "B": 5})
-    result = df.agg({"A": ["min", "max"], "B": ["sum", "max"]})
-    expected = DataFrame(
-        {"A": [4.0, 0.0, np.nan], "B": [5.0, np.nan, 25.0]},
-        columns=["A", "B"],
-        index=["max", "min", "sum"],
-    )
-    tm.assert_frame_equal(result.reindex_like(expected), expected)
-
-
-def test_agg_with_name_as_column_name():
-    # GH 36212 - Column name is "name"
-    data = {"name": ["foo", "bar"]}
-    df = DataFrame(data)
-
-    # result's name should be None
-    result = df.agg({"name": "count"})
-    expected = Series({"name": 2})
-    tm.assert_series_equal(result, expected)
-
-    # Check if name is still preserved when aggregating series instead
-    result = df["name"].agg({"name": "count"})
-    expected = Series({"name": 2}, name="name")
-    tm.assert_series_equal(result, expected)
-
-
-def test_agg_multiple_mixed():
-    # GH 20909
-    mdf = DataFrame(
-        {
-            "A": [1, 2, 3],
-            "B": [1.0, 2.0, 3.0],
-            "C": ["foo", "bar", "baz"],
-        }
-    )
-    expected = DataFrame(
-        {
-            "A": [1, 6],
-            "B": [1.0, 6.0],
-            "C": ["bar", "foobarbaz"],
-        },
-        index=["min", "sum"],
-    )
-    # sorted index
-    result = mdf.agg(["min", "sum"])
-    tm.assert_frame_equal(result, expected)
-
-    result = mdf[["C", "B", "A"]].agg(["sum", "min"])
-    # GH40420: the result of .agg should have an index that is sorted
-    # according to the arguments provided to agg.
-    expected = expected[["C", "B", "A"]].reindex(["sum", "min"])
-    tm.assert_frame_equal(result, expected)
-
-
-def test_agg_multiple_mixed_raises():
-    # GH 20909
-    mdf = DataFrame(
-        {
-            "A": [1, 2, 3],
-            "B": [1.0, 2.0, 3.0],
-            "C": ["foo", "bar", "baz"],
-            "D": date_range("20130101", periods=3),
-        }
-    )
-
-    # sorted index
-    msg = "does not support reduction"
-    with pytest.raises(TypeError, match=msg):
-        mdf.agg(["min", "sum"])
-
-    with pytest.raises(TypeError, match=msg):
-        mdf[["D", "C", "B", "A"]].agg(["sum", "min"])
-
-
-def test_agg_reduce(axis, float_frame):
-    other_axis = 1 if axis in {0, "index"} else 0
-    name1, name2 = float_frame.axes[other_axis].unique()[:2].sort_values()
-
-    # all reducers
-    expected = pd.concat(
-        [
-            float_frame.mean(axis=axis),
-            float_frame.max(axis=axis),
-            float_frame.sum(axis=axis),
-        ],
-        axis=1,
-    )
-    expected.columns = ["mean", "max", "sum"]
-    expected = expected.T if axis in {0, "index"} else expected
-
-    result = float_frame.agg(["mean", "max", "sum"], axis=axis)
-    tm.assert_frame_equal(result, expected)
-
-    # dict input with scalars
-    func = {name1: "mean", name2: "sum"}
-    result = float_frame.agg(func, axis=axis)
-    expected = Series(
-        [
-            float_frame.loc(other_axis)[name1].mean(),
-            float_frame.loc(other_axis)[name2].sum(),
-        ],
-        index=[name1, name2],
-    )
-    tm.assert_series_equal(result, expected)
-
-    # dict input with lists
-    func = {name1: ["mean"], name2: ["sum"]}
-    result = float_frame.agg(func, axis=axis)
-    expected = DataFrame(
-        {
-            name1: Series([float_frame.loc(other_axis)[name1].mean()], index=["mean"]),
-            name2: Series([float_frame.loc(other_axis)[name2].sum()], index=["sum"]),
-        }
-    )
-    expected = expected.T if axis in {1, "columns"} else expected
-    tm.assert_frame_equal(result, expected)
-
-    # dict input with lists with multiple
-    func = {name1: ["mean", "sum"], name2: ["sum", "max"]}
-    result = float_frame.agg(func, axis=axis)
-    expected = pd.concat(
-        {
-            name1: Series(
-                [
-                    float_frame.loc(other_axis)[name1].mean(),
-                    float_frame.loc(other_axis)[name1].sum(),
-                ],
-                index=["mean", "sum"],
-            ),
-            name2: Series(
-                [
-                    float_frame.loc(other_axis)[name2].sum(),
-                    float_frame.loc(other_axis)[name2].max(),
-                ],
-                index=["sum", "max"],
-            ),
-        },
-        axis=1,
-    )
-    expected = expected.T if axis in {1, "columns"} else expected
-    tm.assert_frame_equal(result, expected)
-
-
-def test_nuiscance_columns():
-    # GH 15015
-    df = DataFrame(
-        {
-            "A": [1, 2, 3],
-            "B": [1.0, 2.0, 3.0],
-            "C": ["foo", "bar", "baz"],
-            "D": date_range("20130101", periods=3),
-        }
-    )
-
-    result = df.agg("min")
-    expected = Series([1, 1.0, "bar", Timestamp("20130101")], index=df.columns)
-    tm.assert_series_equal(result, expected)
-
-    result = df.agg(["min"])
-    expected = DataFrame(
-        [[1, 1.0, "bar", Timestamp("20130101")]],
-        index=["min"],
-        columns=df.columns,
-    )
-    tm.assert_frame_equal(result, expected)
-
-    msg = "does not support reduction"
-    with pytest.raises(TypeError, match=msg):
-        df.agg("sum")
-
-    result = df[["A", "B", "C"]].agg("sum")
-    expected = Series([6, 6.0, "foobarbaz"], index=["A", "B", "C"])
-    tm.assert_series_equal(result, expected)
-
-    msg = "does not support reduction"
-    with pytest.raises(TypeError, match=msg):
-        df.agg(["sum"])
-
-
-@pytest.mark.parametrize("how", ["agg", "apply"])
-def test_non_callable_aggregates(how):
-    # GH 16405
-    # 'size' is a property of frame/series
-    # validate that this is working
-    # GH 39116 - expand to apply
-    df = DataFrame(
-        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
-    )
-
-    # Function aggregate
-    result = getattr(df, how)({"A": "count"})
-    expected = Series({"A": 2})
-
-    tm.assert_series_equal(result, expected)
-
-    # Non-function aggregate
-    result = getattr(df, how)({"A": "size"})
-    expected = Series({"A": 3})
-
-    tm.assert_series_equal(result, expected)
-
-    # Mix function and non-function aggs
-    result1 = getattr(df, how)(["count", "size"])
-    result2 = getattr(df, how)(
-        {"A": ["count", "size"], "B": ["count", "size"], "C": ["count", "size"]}
-    )
-    expected = DataFrame(
-        {
-            "A": {"count": 2, "size": 3},
-            "B": {"count": 2, "size": 3},
-            "C": {"count": 2, "size": 3},
-        }
-    )
-
-    tm.assert_frame_equal(result1, result2, check_like=True)
-    tm.assert_frame_equal(result2, expected, check_like=True)
-
-    # Just functional string arg is same as calling df.arg()
-    result = getattr(df, how)("count")
-    expected = df.count()
-
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("how", ["agg", "apply"])
-def test_size_as_str(how, axis):
-    # GH 39934
-    df = DataFrame(
-        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
-    )
-    # Just a string attribute arg same as calling df.arg
-    # on the columns
-    result = getattr(df, how)("size", axis=axis)
-    if axis in (0, "index"):
-        expected = Series(df.shape[0], index=df.columns)
-    else:
-        expected = Series(df.shape[1], index=df.index)
-    tm.assert_series_equal(result, expected)
-
-
-def test_agg_listlike_result():
-    # GH-29587 user defined function returning list-likes
-    df = DataFrame({"A": [2, 2, 3], "B": [1.5, np.nan, 1.5], "C": ["foo", None, "bar"]})
-
-    def func(group_col):
-        return list(group_col.dropna().unique())
-
-    result = df.agg(func)
-    expected = Series([[2, 3], [1.5], ["foo", "bar"]], index=["A", "B", "C"])
-    tm.assert_series_equal(result, expected)
-
-    result = df.agg([func])
-    expected = expected.to_frame("func").T
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-@pytest.mark.parametrize(
-    "args, kwargs",
-    [
-        ((1, 2, 3), {}),
-        ((8, 7, 15), {}),
-        ((1, 2), {}),
-        ((1,), {"b": 2}),
-        ((), {"a": 1, "b": 2}),
-        ((), {"a": 2, "b": 1}),
-        ((), {"a": 1, "b": 2, "c": 3}),
-    ],
-)
-def test_agg_args_kwargs(axis, args, kwargs):
-    def f(x, a, b, c=3):
-        return x.sum() + (a + b) / c
-
-    df = DataFrame([[1, 2], [3, 4]])
-
-    if axis == 0:
-        expected = Series([5.0, 7.0])
-    else:
-        expected = Series([4.0, 8.0])
-
-    result = df.agg(f, axis, *args, **kwargs)
-
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("num_cols", [2, 3, 5])
-def test_frequency_is_original(num_cols):
-    # GH 22150
-    index = pd.DatetimeIndex(["1950-06-30", "1952-10-24", "1953-05-29"])
-    original = index.copy()
-    df = DataFrame(1, index=index, columns=range(num_cols))
-    df.apply(lambda x: x)
-    assert index.freq == original.freq
-
-
-def test_apply_datetime_tz_issue():
-    # GH 29052
-
-    timestamps = [
-        Timestamp("2019-03-15 12:34:31.909000+0000", tz="UTC"),
-        Timestamp("2019-03-15 12:34:34.359000+0000", tz="UTC"),
-        Timestamp("2019-03-15 12:34:34.660000+0000", tz="UTC"),
-    ]
-    df = DataFrame(data=[0, 1, 2], index=timestamps)
-    result = df.apply(lambda x: x.name, axis=1)
-    expected = Series(index=timestamps, data=timestamps)
-
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("df", [DataFrame({"A": ["a", None], "B": ["c", "d"]})])
-@pytest.mark.parametrize("method", ["min", "max", "sum"])
-def test_mixed_column_raises(df, method):
-    # GH 16832
-    if method == "sum":
-        msg = r'can only concatenate str \(not "int"\) to str'
-    else:
-        msg = "not supported between instances of 'str' and 'float'"
-    with pytest.raises(TypeError, match=msg):
-        getattr(df, method)()
-
-
-@pytest.mark.parametrize("col", [1, 1.0, True, "a", np.nan])
-def test_apply_dtype(col):
-    # GH 31466
-    df = DataFrame([[1.0, col]], columns=["a", "b"])
-    result = df.apply(lambda x: x.dtype)
-    expected = df.dtypes
-
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_mutating(using_array_manager, using_copy_on_write):
-    # GH#35462 case where applied func pins a new BlockManager to a row
-    df = DataFrame({"a": range(100), "b": range(100, 200)})
-    df_orig = df.copy()
-
-    def func(row):
-        mgr = row._mgr
-        row.loc["a"] += 1
-        assert row._mgr is not mgr
-        return row
-
-    expected = df.copy()
-    expected["a"] += 1
-
-    result = df.apply(func, axis=1)
-
-    tm.assert_frame_equal(result, expected)
-    if using_copy_on_write or using_array_manager:
-        # INFO(CoW) With copy on write, mutating a viewing row doesn't mutate the parent
-        # INFO(ArrayManager) With BlockManager, the row is a view and mutated in place,
-        # with ArrayManager the row is not a view, and thus not mutated in place
-        tm.assert_frame_equal(df, df_orig)
-    else:
-        tm.assert_frame_equal(df, result)
-
-
-def test_apply_empty_list_reduce():
-    # GH#35683 get columns correct
-    df = DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=["a", "b"])
-
-    result = df.apply(lambda x: [], result_type="reduce")
-    expected = Series({"a": [], "b": []}, dtype=object)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_no_suffix_index():
-    # GH36189
-    pdf = DataFrame([[4, 9]] * 3, columns=["A", "B"])
-    result = pdf.apply(["sum", lambda x: x.sum(), lambda x: x.sum()])
-    expected = DataFrame(
-        {"A": [12, 12, 12], "B": [27, 27, 27]}, index=["sum", "<lambda>", "<lambda>"]
-    )
-
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_raw_returns_string():
-    # https://github.com/pandas-dev/pandas/issues/35940
-    df = DataFrame({"A": ["aa", "bbb"]})
-    result = df.apply(lambda x: x[0], axis=1, raw=True)
-    expected = Series(["aa", "bbb"])
-    tm.assert_series_equal(result, expected)
-
-
-def test_aggregation_func_column_order():
-    # GH40420: the result of .agg should have an index that is sorted
-    # according to the arguments provided to agg.
-    df = DataFrame(
-        [
-            (1, 0, 0),
-            (2, 0, 0),
-            (3, 0, 0),
-            (4, 5, 4),
-            (5, 6, 6),
-            (6, 7, 7),
-        ],
-        columns=("att1", "att2", "att3"),
-    )
-
-    def sum_div2(s):
-        return s.sum() / 2
-
-    aggs = ["sum", sum_div2, "count", "min"]
-    result = df.agg(aggs)
-    expected = DataFrame(
-        {
-            "att1": [21.0, 10.5, 6.0, 1.0],
-            "att2": [18.0, 9.0, 6.0, 0.0],
-            "att3": [17.0, 8.5, 6.0, 0.0],
-        },
-        index=["sum", "sum_div2", "count", "min"],
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_apply_getitem_axis_1():
-    # GH 13427
-    df = DataFrame({"a": [0, 1, 2], "b": [1, 2, 3]})
-    result = df[["a", "a"]].apply(lambda x: x.iloc[0] + x.iloc[1], axis=1)
-    expected = Series([0, 2, 4])
-    tm.assert_series_equal(result, expected)
-
-
-def test_nuisance_depr_passes_through_warnings():
-    # GH 43740
-    # DataFrame.agg with list-likes may emit warnings for both individual
-    # args and for entire columns, but we only want to emit once. We
-    # catch and suppress the warnings for individual args, but need to make
-    # sure if some other warnings were raised, they get passed through to
-    # the user.
-
-    def expected_warning(x):
-        warnings.warn("Hello, World!")
-        return x.sum()
-
-    df = DataFrame({"a": [1, 2, 3]})
-    with tm.assert_produces_warning(UserWarning, match="Hello, World!"):
-        df.agg([expected_warning])
-
-
-def test_apply_type():
-    # GH 46719
-    df = DataFrame(
-        {"col1": [3, "string", float], "col2": [0.25, datetime(2020, 1, 1), np.nan]},
-        index=["a", "b", "c"],
-    )
-
-    # axis=0
-    result = df.apply(type, axis=0)
-    expected = Series({"col1": Series, "col2": Series})
-    tm.assert_series_equal(result, expected)
-
-    # axis=1
-    result = df.apply(type, axis=1)
-    expected = Series({"a": Series, "b": Series, "c": Series})
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_on_empty_dataframe():
-    # GH 39111
-    df = DataFrame({"a": [1, 2], "b": [3, 0]})
-    result = df.head(0).apply(lambda x: max(x["a"], x["b"]), axis=1)
-    expected = Series([], dtype=np.float64)
-    tm.assert_series_equal(result, expected)
-
-
-def test_apply_return_list():
-    df = DataFrame({"a": [1, 2], "b": [2, 3]})
-    result = df.apply(lambda x: [x.values])
-    expected = DataFrame({"a": [[1, 2]], "b": [[2, 3]]})
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "test, constant",
-    [
-        ({"a": [1, 2, 3], "b": [1, 1, 1]}, {"a": [1, 2, 3], "b": [1]}),
-        ({"a": [2, 2, 2], "b": [1, 1, 1]}, {"a": [2], "b": [1]}),
-    ],
-)
-def test_unique_agg_type_is_series(test, constant):
-    # GH#22558
-    df1 = DataFrame(test)
-    expected = Series(data=constant, index=["a", "b"], dtype="object")
-    aggregation = {"a": "unique", "b": "unique"}
-
-    result = df1.agg(aggregation)
-
-    tm.assert_series_equal(result, expected)
-
-
-def test_any_apply_keyword_non_zero_axis_regression():
-    # https://github.com/pandas-dev/pandas/issues/48656
-    df = DataFrame({"A": [1, 2, 0], "B": [0, 2, 0], "C": [0, 0, 0]})
-    expected = Series([True, True, False])
-    tm.assert_series_equal(df.any(axis=1), expected)
-
-    result = df.apply("any", axis=1)
-    tm.assert_series_equal(result, expected)
-
-    result = df.apply("any", 1)
-    tm.assert_series_equal(result, expected)
-
-
-def test_agg_mapping_func_deprecated():
-    # GH 53325
-    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
-
-    # single func already takes the vectorized path
-    result = df.agg(foo1, 0, 3, c=4)
-    expected = df + 7
-    tm.assert_frame_equal(result, expected)
-
-    msg = "using .+ in Series.agg cannot aggregate and"
-
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.agg([foo1, foo2], 0, 3, c=4)
-    expected = DataFrame(
-        [[8, 8], [9, 9], [10, 10]], columns=[["x", "x"], ["foo1", "foo2"]]
-    )
-    tm.assert_frame_equal(result, expected)
-
-    # TODO: the result below is wrong, should be fixed (GH53325)
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.agg({"x": foo1}, 0, 3, c=4)
-    expected = DataFrame([2, 3, 4], columns=["x"])
-    tm.assert_frame_equal(result, expected)
-
-
-def test_agg_std():
-    df = DataFrame(np.arange(6).reshape(3, 2), columns=["A", "B"])
-
-    with tm.assert_produces_warning(FutureWarning, match="using DataFrame.std"):
-        result = df.agg(np.std)
-    expected = Series({"A": 2.0, "B": 2.0}, dtype=float)
-    tm.assert_series_equal(result, expected)
-
-    with tm.assert_produces_warning(FutureWarning, match="using Series.std"):
-        result = df.agg([np.std])
-    expected = DataFrame({"A": 2.0, "B": 2.0}, index=["std"])
-    tm.assert_frame_equal(result, expected)
-
-
-def test_agg_dist_like_and_nonunique_columns():
-    # GH#51099
-    df = DataFrame(
-        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
-    )
-    df.columns = ["A", "A", "C"]
-
-    result = df.agg({"A": "count"})
-    expected = df["A"].count()
-    tm.assert_series_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_na_scalar.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_na_scalar.py
deleted file mode 100644
index 287b7557f50f9f6a81763f86d0eb616cfd730f8c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_na_scalar.py
+++ /dev/null
@@ -1,316 +0,0 @@
-from datetime import (
-    date,
-    time,
-    timedelta,
-)
-import pickle
-
-import numpy as np
-import pytest
-
-from pandas._libs.missing import NA
-
-from pandas.core.dtypes.common import is_scalar
-
-import pandas as pd
-import pandas._testing as tm
-
-
-def test_singleton():
-    assert NA is NA
-    new_NA = type(NA)()
-    assert new_NA is NA
-
-
-def test_repr():
-    assert repr(NA) == "<NA>"
-    assert str(NA) == "<NA>"
-
-
-def test_format():
-    # GH-34740
-    assert format(NA) == "<NA>"
-    assert format(NA, ">10") == "      <NA>"
-    assert format(NA, "xxx") == "<NA>"  # NA is flexible, accept any format spec
-
-    assert f"{NA}" == "<NA>"
-    assert f"{NA:>10}" == "      <NA>"
-    assert f"{NA:xxx}" == "<NA>"
-
-
-def test_truthiness():
-    msg = "boolean value of NA is ambiguous"
-
-    with pytest.raises(TypeError, match=msg):
-        bool(NA)
-
-    with pytest.raises(TypeError, match=msg):
-        not NA
-
-
-def test_hashable():
-    assert hash(NA) == hash(NA)
-    d = {NA: "test"}
-    assert d[NA] == "test"
-
-
-@pytest.mark.parametrize(
-    "other", [NA, 1, 1.0, "a", b"a", np.int64(1), np.nan], ids=repr
-)
-def test_arithmetic_ops(all_arithmetic_functions, other):
-    op = all_arithmetic_functions
-
-    if op.__name__ in ("pow", "rpow", "rmod") and isinstance(other, (str, bytes)):
-        pytest.skip(reason=f"{op.__name__} with NA and {other} not defined.")
-    if op.__name__ in ("divmod", "rdivmod"):
-        assert op(NA, other) is (NA, NA)
-    else:
-        if op.__name__ == "rpow":
-            # avoid special case
-            other += 1
-        assert op(NA, other) is NA
-
-
-@pytest.mark.parametrize(
-    "other",
-    [
-        NA,
-        1,
-        1.0,
-        "a",
-        b"a",
-        np.int64(1),
-        np.nan,
-        np.bool_(True),
-        time(0),
-        date(1, 2, 3),
-        timedelta(1),
-        pd.NaT,
-    ],
-)
-def test_comparison_ops(comparison_op, other):
-    assert comparison_op(NA, other) is NA
-    assert comparison_op(other, NA) is NA
-
-
-@pytest.mark.parametrize(
-    "value",
-    [
-        0,
-        0.0,
-        -0,
-        -0.0,
-        False,
-        np.bool_(False),
-        np.int_(0),
-        np.float64(0),
-        np.int_(-0),
-        np.float64(-0),
-    ],
-)
-@pytest.mark.parametrize("asarray", [True, False])
-def test_pow_special(value, asarray):
-    if asarray:
-        value = np.array([value])
-    result = NA**value
-
-    if asarray:
-        result = result[0]
-    else:
-        # this assertion isn't possible for ndarray.
-        assert isinstance(result, type(value))
-    assert result == 1
-
-
-@pytest.mark.parametrize(
-    "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float64(1)]
-)
-@pytest.mark.parametrize("asarray", [True, False])
-def test_rpow_special(value, asarray):
-    if asarray:
-        value = np.array([value])
-    result = value**NA
-
-    if asarray:
-        result = result[0]
-    elif not isinstance(value, (np.float64, np.bool_, np.int_)):
-        # this assertion isn't possible with asarray=True
-        assert isinstance(result, type(value))
-
-    assert result == value
-
-
-@pytest.mark.parametrize("value", [-1, -1.0, np.int_(-1), np.float64(-1)])
-@pytest.mark.parametrize("asarray", [True, False])
-def test_rpow_minus_one(value, asarray):
-    if asarray:
-        value = np.array([value])
-    result = value**NA
-
-    if asarray:
-        result = result[0]
-
-    assert pd.isna(result)
-
-
-def test_unary_ops():
-    assert +NA is NA
-    assert -NA is NA
-    assert abs(NA) is NA
-    assert ~NA is NA
-
-
-def test_logical_and():
-    assert NA & True is NA
-    assert True & NA is NA
-    assert NA & False is False
-    assert False & NA is False
-    assert NA & NA is NA
-
-    msg = "unsupported operand type"
-    with pytest.raises(TypeError, match=msg):
-        NA & 5
-
-
-def test_logical_or():
-    assert NA | True is True
-    assert True | NA is True
-    assert NA | False is NA
-    assert False | NA is NA
-    assert NA | NA is NA
-
-    msg = "unsupported operand type"
-    with pytest.raises(TypeError, match=msg):
-        NA | 5
-
-
-def test_logical_xor():
-    assert NA ^ True is NA
-    assert True ^ NA is NA
-    assert NA ^ False is NA
-    assert False ^ NA is NA
-    assert NA ^ NA is NA
-
-    msg = "unsupported operand type"
-    with pytest.raises(TypeError, match=msg):
-        NA ^ 5
-
-
-def test_logical_not():
-    assert ~NA is NA
-
-
-@pytest.mark.parametrize("shape", [(3,), (3, 3), (1, 2, 3)])
-def test_arithmetic_ndarray(shape, all_arithmetic_functions):
-    op = all_arithmetic_functions
-    a = np.zeros(shape)
-    if op.__name__ == "pow":
-        a += 5
-    result = op(NA, a)
-    expected = np.full(a.shape, NA, dtype=object)
-    tm.assert_numpy_array_equal(result, expected)
-
-
-def test_is_scalar():
-    assert is_scalar(NA) is True
-
-
-def test_isna():
-    assert pd.isna(NA) is True
-    assert pd.notna(NA) is False
-
-
-def test_series_isna():
-    s = pd.Series([1, NA], dtype=object)
-    expected = pd.Series([False, True])
-    tm.assert_series_equal(s.isna(), expected)
-
-
-def test_ufunc():
-    assert np.log(NA) is NA
-    assert np.add(NA, 1) is NA
-    result = np.divmod(NA, 1)
-    assert result[0] is NA and result[1] is NA
-
-    result = np.frexp(NA)
-    assert result[0] is NA and result[1] is NA
-
-
-def test_ufunc_raises():
-    msg = "ufunc method 'at'"
-    with pytest.raises(ValueError, match=msg):
-        np.log.at(NA, 0)
-
-
-def test_binary_input_not_dunder():
-    a = np.array([1, 2, 3])
-    expected = np.array([NA, NA, NA], dtype=object)
-    result = np.logaddexp(a, NA)
-    tm.assert_numpy_array_equal(result, expected)
-
-    result = np.logaddexp(NA, a)
-    tm.assert_numpy_array_equal(result, expected)
-
-    # all NA, multiple inputs
-    assert np.logaddexp(NA, NA) is NA
-
-    result = np.modf(NA, NA)
-    assert len(result) == 2
-    assert all(x is NA for x in result)
-
-
-def test_divmod_ufunc():
-    # binary in, binary out.
-    a = np.array([1, 2, 3])
-    expected = np.array([NA, NA, NA], dtype=object)
-
-    result = np.divmod(a, NA)
-    assert isinstance(result, tuple)
-    for arr in result:
-        tm.assert_numpy_array_equal(arr, expected)
-        tm.assert_numpy_array_equal(arr, expected)
-
-    result = np.divmod(NA, a)
-    for arr in result:
-        tm.assert_numpy_array_equal(arr, expected)
-        tm.assert_numpy_array_equal(arr, expected)
-
-
-def test_integer_hash_collision_dict():
-    # GH 30013
-    result = {NA: "foo", hash(NA): "bar"}
-
-    assert result[NA] == "foo"
-    assert result[hash(NA)] == "bar"
-
-
-def test_integer_hash_collision_set():
-    # GH 30013
-    result = {NA, hash(NA)}
-
-    assert len(result) == 2
-    assert NA in result
-    assert hash(NA) in result
-
-
-def test_pickle_roundtrip():
-    # https://github.com/pandas-dev/pandas/issues/31847
-    result = pickle.loads(pickle.dumps(NA))
-    assert result is NA
-
-
-def test_pickle_roundtrip_pandas():
-    result = tm.round_trip_pickle(NA)
-    assert result is NA
-
-
-@pytest.mark.parametrize(
-    "values, dtype", [([1, 2, NA], "Int64"), (["A", "B", NA], "string")]
-)
-@pytest.mark.parametrize("as_frame", [True, False])
-def test_pickle_roundtrip_containers(as_frame, values, dtype):
-    s = pd.Series(pd.array(values, dtype=dtype))
-    if as_frame:
-        s = s.to_frame(name="A")
-    result = tm.round_trip_pickle(s)
-    tm.assert_equal(result, s)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timedelta/test_formats.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timedelta/test_formats.py
deleted file mode 100644
index 753186ee4b7382fec7081435c8826bb5cd0d35ec..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/timedelta/test_formats.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pytest
-
-from pandas import Timedelta
-
-
-@pytest.mark.parametrize(
-    "td, expected_repr",
-    [
-        (Timedelta(10, unit="d"), "Timedelta('10 days 00:00:00')"),
-        (Timedelta(10, unit="s"), "Timedelta('0 days 00:00:10')"),
-        (Timedelta(10, unit="ms"), "Timedelta('0 days 00:00:00.010000')"),
-        (Timedelta(-10, unit="ms"), "Timedelta('-1 days +23:59:59.990000')"),
-    ],
-)
-def test_repr(td, expected_repr):
-    assert repr(td) == expected_repr
-
-
-@pytest.mark.parametrize(
-    "td, expected_iso",
-    [
-        (
-            Timedelta(
-                days=6,
-                minutes=50,
-                seconds=3,
-                milliseconds=10,
-                microseconds=10,
-                nanoseconds=12,
-            ),
-            "P6DT0H50M3.010010012S",
-        ),
-        (Timedelta(days=4, hours=12, minutes=30, seconds=5), "P4DT12H30M5S"),
-        (Timedelta(nanoseconds=123), "P0DT0H0M0.000000123S"),
-        # trim nano
-        (Timedelta(microseconds=10), "P0DT0H0M0.00001S"),
-        # trim micro
-        (Timedelta(milliseconds=1), "P0DT0H0M0.001S"),
-        # don't strip every 0
-        (Timedelta(minutes=1), "P0DT0H1M0S"),
-    ],
-)
-def test_isoformat(td, expected_iso):
-    assert td.isoformat() == expected_iso
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/vcs/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/vcs/__init__.py
deleted file mode 100644
index b6beddbe6d24d2949dc89ed07abfebd59d8b63b9..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/vcs/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Expose a limited set of classes and functions so callers outside of
-# the vcs package don't need to import deeper than `pip._internal.vcs`.
-# (The test directory may still need to import from a vcs sub-package.)
-# Import all vcs modules to register each VCS in the VcsSupport object.
-import pip._internal.vcs.bazaar
-import pip._internal.vcs.git
-import pip._internal.vcs.mercurial
-import pip._internal.vcs.subversion  # noqa: F401
-from pip._internal.vcs.versioncontrol import (  # noqa: F401
-    RemoteNotFoundError,
-    RemoteNotValidError,
-    is_url,
-    make_vcs_requirement_url,
-    vcs,
-)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_tsql_builtins.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_tsql_builtins.py
deleted file mode 100644
index 96b16bf4a4ea4f58683f4800f3e1f666f8504f29..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_tsql_builtins.py
+++ /dev/null
@@ -1,1003 +0,0 @@
-"""
-    pygments.lexers._tsql_builtins
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    These are manually translated lists from https://msdn.microsoft.com.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-# See https://msdn.microsoft.com/en-us/library/ms174986.aspx.
-OPERATORS = (
-    '!<',
-    '!=',
-    '!>',
-    '<',
-    '<=',
-    '<>',
-    '=',
-    '>',
-    '>=',
-    '+',
-    '+=',
-    '-',
-    '-=',
-    '*',
-    '*=',
-    '/',
-    '/=',
-    '%',
-    '%=',
-    '&',
-    '&=',
-    '|',
-    '|=',
-    '^',
-    '^=',
-    '~',
-    '::',
-)
-
-OPERATOR_WORDS = (
-    'all',
-    'and',
-    'any',
-    'between',
-    'except',
-    'exists',
-    'in',
-    'intersect',
-    'like',
-    'not',
-    'or',
-    'some',
-    'union',
-)
-
-_KEYWORDS_SERVER = (
-    'add',
-    'all',
-    'alter',
-    'and',
-    'any',
-    'as',
-    'asc',
-    'authorization',
-    'backup',
-    'begin',
-    'between',
-    'break',
-    'browse',
-    'bulk',
-    'by',
-    'cascade',
-    'case',
-    'catch',
-    'check',
-    'checkpoint',
-    'close',
-    'clustered',
-    'coalesce',
-    'collate',
-    'column',
-    'commit',
-    'compute',
-    'constraint',
-    'contains',
-    'containstable',
-    'continue',
-    'convert',
-    'create',
-    'cross',
-    'current',
-    'current_date',
-    'current_time',
-    'current_timestamp',
-    'current_user',
-    'cursor',
-    'database',
-    'dbcc',
-    'deallocate',
-    'declare',
-    'default',
-    'delete',
-    'deny',
-    'desc',
-    'disk',
-    'distinct',
-    'distributed',
-    'double',
-    'drop',
-    'dump',
-    'else',
-    'end',
-    'errlvl',
-    'escape',
-    'except',
-    'exec',
-    'execute',
-    'exists',
-    'exit',
-    'external',
-    'fetch',
-    'file',
-    'fillfactor',
-    'for',
-    'foreign',
-    'freetext',
-    'freetexttable',
-    'from',
-    'full',
-    'function',
-    'goto',
-    'grant',
-    'group',
-    'having',
-    'holdlock',
-    'identity',
-    'identity_insert',
-    'identitycol',
-    'if',
-    'in',
-    'index',
-    'inner',
-    'insert',
-    'intersect',
-    'into',
-    'is',
-    'join',
-    'key',
-    'kill',
-    'left',
-    'like',
-    'lineno',
-    'load',
-    'merge',
-    'national',
-    'nocheck',
-    'nonclustered',
-    'not',
-    'null',
-    'nullif',
-    'of',
-    'off',
-    'offsets',
-    'on',
-    'open',
-    'opendatasource',
-    'openquery',
-    'openrowset',
-    'openxml',
-    'option',
-    'or',
-    'order',
-    'outer',
-    'over',
-    'percent',
-    'pivot',
-    'plan',
-    'precision',
-    'primary',
-    'print',
-    'proc',
-    'procedure',
-    'public',
-    'raiserror',
-    'read',
-    'readtext',
-    'reconfigure',
-    'references',
-    'replication',
-    'restore',
-    'restrict',
-    'return',
-    'revert',
-    'revoke',
-    'right',
-    'rollback',
-    'rowcount',
-    'rowguidcol',
-    'rule',
-    'save',
-    'schema',
-    'securityaudit',
-    'select',
-    'semantickeyphrasetable',
-    'semanticsimilaritydetailstable',
-    'semanticsimilaritytable',
-    'session_user',
-    'set',
-    'setuser',
-    'shutdown',
-    'some',
-    'statistics',
-    'system_user',
-    'table',
-    'tablesample',
-    'textsize',
-    'then',
-    'throw',
-    'to',
-    'top',
-    'tran',
-    'transaction',
-    'trigger',
-    'truncate',
-    'try',
-    'try_convert',
-    'tsequal',
-    'union',
-    'unique',
-    'unpivot',
-    'update',
-    'updatetext',
-    'use',
-    'user',
-    'values',
-    'varying',
-    'view',
-    'waitfor',
-    'when',
-    'where',
-    'while',
-    'with',
-    'within',
-    'writetext',
-)
-
-_KEYWORDS_FUTURE = (
-    'absolute',
-    'action',
-    'admin',
-    'after',
-    'aggregate',
-    'alias',
-    'allocate',
-    'are',
-    'array',
-    'asensitive',
-    'assertion',
-    'asymmetric',
-    'at',
-    'atomic',
-    'before',
-    'binary',
-    'bit',
-    'blob',
-    'boolean',
-    'both',
-    'breadth',
-    'call',
-    'called',
-    'cardinality',
-    'cascaded',
-    'cast',
-    'catalog',
-    'char',
-    'character',
-    'class',
-    'clob',
-    'collation',
-    'collect',
-    'completion',
-    'condition',
-    'connect',
-    'connection',
-    'constraints',
-    'constructor',
-    'corr',
-    'corresponding',
-    'covar_pop',
-    'covar_samp',
-    'cube',
-    'cume_dist',
-    'current_catalog',
-    'current_default_transform_group',
-    'current_path',
-    'current_role',
-    'current_schema',
-    'current_transform_group_for_type',
-    'cycle',
-    'data',
-    'date',
-    'day',
-    'dec',
-    'decimal',
-    'deferrable',
-    'deferred',
-    'depth',
-    'deref',
-    'describe',
-    'descriptor',
-    'destroy',
-    'destructor',
-    'deterministic',
-    'diagnostics',
-    'dictionary',
-    'disconnect',
-    'domain',
-    'dynamic',
-    'each',
-    'element',
-    'end-exec',
-    'equals',
-    'every',
-    'exception',
-    'false',
-    'filter',
-    'first',
-    'float',
-    'found',
-    'free',
-    'fulltexttable',
-    'fusion',
-    'general',
-    'get',
-    'global',
-    'go',
-    'grouping',
-    'hold',
-    'host',
-    'hour',
-    'ignore',
-    'immediate',
-    'indicator',
-    'initialize',
-    'initially',
-    'inout',
-    'input',
-    'int',
-    'integer',
-    'intersection',
-    'interval',
-    'isolation',
-    'iterate',
-    'language',
-    'large',
-    'last',
-    'lateral',
-    'leading',
-    'less',
-    'level',
-    'like_regex',
-    'limit',
-    'ln',
-    'local',
-    'localtime',
-    'localtimestamp',
-    'locator',
-    'map',
-    'match',
-    'member',
-    'method',
-    'minute',
-    'mod',
-    'modifies',
-    'modify',
-    'module',
-    'month',
-    'multiset',
-    'names',
-    'natural',
-    'nchar',
-    'nclob',
-    'new',
-    'next',
-    'no',
-    'none',
-    'normalize',
-    'numeric',
-    'object',
-    'occurrences_regex',
-    'old',
-    'only',
-    'operation',
-    'ordinality',
-    'out',
-    'output',
-    'overlay',
-    'pad',
-    'parameter',
-    'parameters',
-    'partial',
-    'partition',
-    'path',
-    'percent_rank',
-    'percentile_cont',
-    'percentile_disc',
-    'position_regex',
-    'postfix',
-    'prefix',
-    'preorder',
-    'prepare',
-    'preserve',
-    'prior',
-    'privileges',
-    'range',
-    'reads',
-    'real',
-    'recursive',
-    'ref',
-    'referencing',
-    'regr_avgx',
-    'regr_avgy',
-    'regr_count',
-    'regr_intercept',
-    'regr_r2',
-    'regr_slope',
-    'regr_sxx',
-    'regr_sxy',
-    'regr_syy',
-    'relative',
-    'release',
-    'result',
-    'returns',
-    'role',
-    'rollup',
-    'routine',
-    'row',
-    'rows',
-    'savepoint',
-    'scope',
-    'scroll',
-    'search',
-    'second',
-    'section',
-    'sensitive',
-    'sequence',
-    'session',
-    'sets',
-    'similar',
-    'size',
-    'smallint',
-    'space',
-    'specific',
-    'specifictype',
-    'sql',
-    'sqlexception',
-    'sqlstate',
-    'sqlwarning',
-    'start',
-    'state',
-    'statement',
-    'static',
-    'stddev_pop',
-    'stddev_samp',
-    'structure',
-    'submultiset',
-    'substring_regex',
-    'symmetric',
-    'system',
-    'temporary',
-    'terminate',
-    'than',
-    'time',
-    'timestamp',
-    'timezone_hour',
-    'timezone_minute',
-    'trailing',
-    'translate_regex',
-    'translation',
-    'treat',
-    'true',
-    'uescape',
-    'under',
-    'unknown',
-    'unnest',
-    'usage',
-    'using',
-    'value',
-    'var_pop',
-    'var_samp',
-    'varchar',
-    'variable',
-    'whenever',
-    'width_bucket',
-    'window',
-    'within',
-    'without',
-    'work',
-    'write',
-    'xmlagg',
-    'xmlattributes',
-    'xmlbinary',
-    'xmlcast',
-    'xmlcomment',
-    'xmlconcat',
-    'xmldocument',
-    'xmlelement',
-    'xmlexists',
-    'xmlforest',
-    'xmliterate',
-    'xmlnamespaces',
-    'xmlparse',
-    'xmlpi',
-    'xmlquery',
-    'xmlserialize',
-    'xmltable',
-    'xmltext',
-    'xmlvalidate',
-    'year',
-    'zone',
-)
-
-_KEYWORDS_ODBC = (
-    'absolute',
-    'action',
-    'ada',
-    'add',
-    'all',
-    'allocate',
-    'alter',
-    'and',
-    'any',
-    'are',
-    'as',
-    'asc',
-    'assertion',
-    'at',
-    'authorization',
-    'avg',
-    'begin',
-    'between',
-    'bit',
-    'bit_length',
-    'both',
-    'by',
-    'cascade',
-    'cascaded',
-    'case',
-    'cast',
-    'catalog',
-    'char',
-    'char_length',
-    'character',
-    'character_length',
-    'check',
-    'close',
-    'coalesce',
-    'collate',
-    'collation',
-    'column',
-    'commit',
-    'connect',
-    'connection',
-    'constraint',
-    'constraints',
-    'continue',
-    'convert',
-    'corresponding',
-    'count',
-    'create',
-    'cross',
-    'current',
-    'current_date',
-    'current_time',
-    'current_timestamp',
-    'current_user',
-    'cursor',
-    'date',
-    'day',
-    'deallocate',
-    'dec',
-    'decimal',
-    'declare',
-    'default',
-    'deferrable',
-    'deferred',
-    'delete',
-    'desc',
-    'describe',
-    'descriptor',
-    'diagnostics',
-    'disconnect',
-    'distinct',
-    'domain',
-    'double',
-    'drop',
-    'else',
-    'end',
-    'end-exec',
-    'escape',
-    'except',
-    'exception',
-    'exec',
-    'execute',
-    'exists',
-    'external',
-    'extract',
-    'false',
-    'fetch',
-    'first',
-    'float',
-    'for',
-    'foreign',
-    'fortran',
-    'found',
-    'from',
-    'full',
-    'get',
-    'global',
-    'go',
-    'goto',
-    'grant',
-    'group',
-    'having',
-    'hour',
-    'identity',
-    'immediate',
-    'in',
-    'include',
-    'index',
-    'indicator',
-    'initially',
-    'inner',
-    'input',
-    'insensitive',
-    'insert',
-    'int',
-    'integer',
-    'intersect',
-    'interval',
-    'into',
-    'is',
-    'isolation',
-    'join',
-    'key',
-    'language',
-    'last',
-    'leading',
-    'left',
-    'level',
-    'like',
-    'local',
-    'lower',
-    'match',
-    'max',
-    'min',
-    'minute',
-    'module',
-    'month',
-    'names',
-    'national',
-    'natural',
-    'nchar',
-    'next',
-    'no',
-    'none',
-    'not',
-    'null',
-    'nullif',
-    'numeric',
-    'octet_length',
-    'of',
-    'on',
-    'only',
-    'open',
-    'option',
-    'or',
-    'order',
-    'outer',
-    'output',
-    'overlaps',
-    'pad',
-    'partial',
-    'pascal',
-    'position',
-    'precision',
-    'prepare',
-    'preserve',
-    'primary',
-    'prior',
-    'privileges',
-    'procedure',
-    'public',
-    'read',
-    'real',
-    'references',
-    'relative',
-    'restrict',
-    'revoke',
-    'right',
-    'rollback',
-    'rows',
-    'schema',
-    'scroll',
-    'second',
-    'section',
-    'select',
-    'session',
-    'session_user',
-    'set',
-    'size',
-    'smallint',
-    'some',
-    'space',
-    'sql',
-    'sqlca',
-    'sqlcode',
-    'sqlerror',
-    'sqlstate',
-    'sqlwarning',
-    'substring',
-    'sum',
-    'system_user',
-    'table',
-    'temporary',
-    'then',
-    'time',
-    'timestamp',
-    'timezone_hour',
-    'timezone_minute',
-    'to',
-    'trailing',
-    'transaction',
-    'translate',
-    'translation',
-    'trim',
-    'true',
-    'union',
-    'unique',
-    'unknown',
-    'update',
-    'upper',
-    'usage',
-    'user',
-    'using',
-    'value',
-    'values',
-    'varchar',
-    'varying',
-    'view',
-    'when',
-    'whenever',
-    'where',
-    'with',
-    'work',
-    'write',
-    'year',
-    'zone',
-)
-
-# See https://msdn.microsoft.com/en-us/library/ms189822.aspx.
-KEYWORDS = sorted(set(_KEYWORDS_FUTURE + _KEYWORDS_ODBC + _KEYWORDS_SERVER))
-
-# See https://msdn.microsoft.com/en-us/library/ms187752.aspx.
-TYPES = (
-    'bigint',
-    'binary',
-    'bit',
-    'char',
-    'cursor',
-    'date',
-    'datetime',
-    'datetime2',
-    'datetimeoffset',
-    'decimal',
-    'float',
-    'hierarchyid',
-    'image',
-    'int',
-    'money',
-    'nchar',
-    'ntext',
-    'numeric',
-    'nvarchar',
-    'real',
-    'smalldatetime',
-    'smallint',
-    'smallmoney',
-    'sql_variant',
-    'table',
-    'text',
-    'time',
-    'timestamp',
-    'tinyint',
-    'uniqueidentifier',
-    'varbinary',
-    'varchar',
-    'xml',
-)
-
-# See https://msdn.microsoft.com/en-us/library/ms174318.aspx.
-FUNCTIONS = (
-    '$partition',
-    'abs',
-    'acos',
-    'app_name',
-    'applock_mode',
-    'applock_test',
-    'ascii',
-    'asin',
-    'assemblyproperty',
-    'atan',
-    'atn2',
-    'avg',
-    'binary_checksum',
-    'cast',
-    'ceiling',
-    'certencoded',
-    'certprivatekey',
-    'char',
-    'charindex',
-    'checksum',
-    'checksum_agg',
-    'choose',
-    'col_length',
-    'col_name',
-    'columnproperty',
-    'compress',
-    'concat',
-    'connectionproperty',
-    'context_info',
-    'convert',
-    'cos',
-    'cot',
-    'count',
-    'count_big',
-    'current_request_id',
-    'current_timestamp',
-    'current_transaction_id',
-    'current_user',
-    'cursor_status',
-    'database_principal_id',
-    'databasepropertyex',
-    'dateadd',
-    'datediff',
-    'datediff_big',
-    'datefromparts',
-    'datename',
-    'datepart',
-    'datetime2fromparts',
-    'datetimefromparts',
-    'datetimeoffsetfromparts',
-    'day',
-    'db_id',
-    'db_name',
-    'decompress',
-    'degrees',
-    'dense_rank',
-    'difference',
-    'eomonth',
-    'error_line',
-    'error_message',
-    'error_number',
-    'error_procedure',
-    'error_severity',
-    'error_state',
-    'exp',
-    'file_id',
-    'file_idex',
-    'file_name',
-    'filegroup_id',
-    'filegroup_name',
-    'filegroupproperty',
-    'fileproperty',
-    'floor',
-    'format',
-    'formatmessage',
-    'fulltextcatalogproperty',
-    'fulltextserviceproperty',
-    'get_filestream_transaction_context',
-    'getansinull',
-    'getdate',
-    'getutcdate',
-    'grouping',
-    'grouping_id',
-    'has_perms_by_name',
-    'host_id',
-    'host_name',
-    'iif',
-    'index_col',
-    'indexkey_property',
-    'indexproperty',
-    'is_member',
-    'is_rolemember',
-    'is_srvrolemember',
-    'isdate',
-    'isjson',
-    'isnull',
-    'isnumeric',
-    'json_modify',
-    'json_query',
-    'json_value',
-    'left',
-    'len',
-    'log',
-    'log10',
-    'lower',
-    'ltrim',
-    'max',
-    'min',
-    'min_active_rowversion',
-    'month',
-    'nchar',
-    'newid',
-    'newsequentialid',
-    'ntile',
-    'object_definition',
-    'object_id',
-    'object_name',
-    'object_schema_name',
-    'objectproperty',
-    'objectpropertyex',
-    'opendatasource',
-    'openjson',
-    'openquery',
-    'openrowset',
-    'openxml',
-    'original_db_name',
-    'original_login',
-    'parse',
-    'parsename',
-    'patindex',
-    'permissions',
-    'pi',
-    'power',
-    'pwdcompare',
-    'pwdencrypt',
-    'quotename',
-    'radians',
-    'rand',
-    'rank',
-    'replace',
-    'replicate',
-    'reverse',
-    'right',
-    'round',
-    'row_number',
-    'rowcount_big',
-    'rtrim',
-    'schema_id',
-    'schema_name',
-    'scope_identity',
-    'serverproperty',
-    'session_context',
-    'session_user',
-    'sign',
-    'sin',
-    'smalldatetimefromparts',
-    'soundex',
-    'sp_helplanguage',
-    'space',
-    'sqrt',
-    'square',
-    'stats_date',
-    'stdev',
-    'stdevp',
-    'str',
-    'string_escape',
-    'string_split',
-    'stuff',
-    'substring',
-    'sum',
-    'suser_id',
-    'suser_name',
-    'suser_sid',
-    'suser_sname',
-    'switchoffset',
-    'sysdatetime',
-    'sysdatetimeoffset',
-    'system_user',
-    'sysutcdatetime',
-    'tan',
-    'textptr',
-    'textvalid',
-    'timefromparts',
-    'todatetimeoffset',
-    'try_cast',
-    'try_convert',
-    'try_parse',
-    'type_id',
-    'type_name',
-    'typeproperty',
-    'unicode',
-    'upper',
-    'user_id',
-    'user_name',
-    'var',
-    'varp',
-    'xact_state',
-    'year',
-)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/wait.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/wait.py
deleted file mode 100644
index aeca0c7ad5b232eeb1ad9c43d315bd1d74eaed9a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/wait.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from __future__ import annotations
-
-import select
-import socket
-from functools import partial
-
-__all__ = ["wait_for_read", "wait_for_write"]
-
-
-# How should we wait on sockets?
-#
-# There are two types of APIs you can use for waiting on sockets: the fancy
-# modern stateful APIs like epoll/kqueue, and the older stateless APIs like
-# select/poll. The stateful APIs are more efficient when you have a lots of
-# sockets to keep track of, because you can set them up once and then use them
-# lots of times. But we only ever want to wait on a single socket at a time
-# and don't want to keep track of state, so the stateless APIs are actually
-# more efficient. So we want to use select() or poll().
-#
-# Now, how do we choose between select() and poll()? On traditional Unixes,
-# select() has a strange calling convention that makes it slow, or fail
-# altogether, for high-numbered file descriptors. The point of poll() is to fix
-# that, so on Unixes, we prefer poll().
-#
-# On Windows, there is no poll() (or at least Python doesn't provide a wrapper
-# for it), but that's OK, because on Windows, select() doesn't have this
-# strange calling convention; plain select() works fine.
-#
-# So: on Windows we use select(), and everywhere else we use poll(). We also
-# fall back to select() in case poll() is somehow broken or missing.
-
-
-def select_wait_for_socket(
-    sock: socket.socket,
-    read: bool = False,
-    write: bool = False,
-    timeout: float | None = None,
-) -> bool:
-    if not read and not write:
-        raise RuntimeError("must specify at least one of read=True, write=True")
-    rcheck = []
-    wcheck = []
-    if read:
-        rcheck.append(sock)
-    if write:
-        wcheck.append(sock)
-    # When doing a non-blocking connect, most systems signal success by
-    # marking the socket writable. Windows, though, signals success by marked
-    # it as "exceptional". We paper over the difference by checking the write
-    # sockets for both conditions. (The stdlib selectors module does the same
-    # thing.)
-    fn = partial(select.select, rcheck, wcheck, wcheck)
-    rready, wready, xready = fn(timeout)
-    return bool(rready or wready or xready)
-
-
-def poll_wait_for_socket(
-    sock: socket.socket,
-    read: bool = False,
-    write: bool = False,
-    timeout: float | None = None,
-) -> bool:
-    if not read and not write:
-        raise RuntimeError("must specify at least one of read=True, write=True")
-    mask = 0
-    if read:
-        mask |= select.POLLIN
-    if write:
-        mask |= select.POLLOUT
-    poll_obj = select.poll()
-    poll_obj.register(sock, mask)
-
-    # For some reason, poll() takes timeout in milliseconds
-    def do_poll(t: float | None) -> list[tuple[int, int]]:
-        if t is not None:
-            t *= 1000
-        return poll_obj.poll(t)
-
-    return bool(do_poll(timeout))
-
-
-def _have_working_poll() -> bool:
-    # Apparently some systems have a select.poll that fails as soon as you try
-    # to use it, either due to strange configuration or broken monkeypatching
-    # from libraries like eventlet/greenlet.
-    try:
-        poll_obj = select.poll()
-        poll_obj.poll(0)
-    except (AttributeError, OSError):
-        return False
-    else:
-        return True
-
-
-def wait_for_socket(
-    sock: socket.socket,
-    read: bool = False,
-    write: bool = False,
-    timeout: float | None = None,
-) -> bool:
-    # We delay choosing which implementation to use until the first time we're
-    # called. We could do it at import time, but then we might make the wrong
-    # decision if someone goes wild with monkeypatching select.poll after
-    # we're imported.
-    global wait_for_socket
-    if _have_working_poll():
-        wait_for_socket = poll_wait_for_socket
-    elif hasattr(select, "select"):
-        wait_for_socket = select_wait_for_socket
-    return wait_for_socket(sock, read, write, timeout)
-
-
-def wait_for_read(sock: socket.socket, timeout: float | None = None) -> bool:
-    """Waits for reading to be available on a given socket.
-    Returns True if the socket is readable, or False if the timeout expired.
-    """
-    return wait_for_socket(sock, read=True, timeout=timeout)
-
-
-def wait_for_write(sock: socket.socket, timeout: float | None = None) -> bool:
-    """Waits for writing to be available on a given socket.
-    Returns True if the socket is readable, or False if the timeout expired.
-    """
-    return wait_for_socket(sock, write=True, timeout=timeout)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/reader.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/reader.py
deleted file mode 100644
index 774b0219b5932a0ee1c27e637371de5ba8d9cb16..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/reader.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# This module contains abstractions for the input stream. You don't have to
-# looks further, there are no pretty code.
-#
-# We define two classes here.
-#
-#   Mark(source, line, column)
-# It's just a record and its only use is producing nice error messages.
-# Parser does not use it for any other purposes.
-#
-#   Reader(source, data)
-# Reader determines the encoding of `data` and converts it to unicode.
-# Reader provides the following methods and attributes:
-#   reader.peek(length=1) - return the next `length` characters
-#   reader.forward(length=1) - move the current position to `length` characters.
-#   reader.index - the number of the current character.
-#   reader.line, stream.column - the line and the column of the current character.
-
-__all__ = ['Reader', 'ReaderError']
-
-from .error import YAMLError, Mark
-
-import codecs, re
-
-class ReaderError(YAMLError):
-
-    def __init__(self, name, position, character, encoding, reason):
-        self.name = name
-        self.character = character
-        self.position = position
-        self.encoding = encoding
-        self.reason = reason
-
-    def __str__(self):
-        if isinstance(self.character, bytes):
-            return "'%s' codec can't decode byte #x%02x: %s\n"  \
-                    "  in \"%s\", position %d"    \
-                    % (self.encoding, ord(self.character), self.reason,
-                            self.name, self.position)
-        else:
-            return "unacceptable character #x%04x: %s\n"    \
-                    "  in \"%s\", position %d"    \
-                    % (self.character, self.reason,
-                            self.name, self.position)
-
-class Reader(object):
-    # Reader:
-    # - determines the data encoding and converts it to a unicode string,
-    # - checks if characters are in allowed range,
-    # - adds '\0' to the end.
-
-    # Reader accepts
-    #  - a `bytes` object,
-    #  - a `str` object,
-    #  - a file-like object with its `read` method returning `str`,
-    #  - a file-like object with its `read` method returning `unicode`.
-
-    # Yeah, it's ugly and slow.
-
-    def __init__(self, stream):
-        self.name = None
-        self.stream = None
-        self.stream_pointer = 0
-        self.eof = True
-        self.buffer = ''
-        self.pointer = 0
-        self.raw_buffer = None
-        self.raw_decode = None
-        self.encoding = None
-        self.index = 0
-        self.line = 0
-        self.column = 0
-        if isinstance(stream, str):
-            self.name = "<unicode string>"
-            self.check_printable(stream)
-            self.buffer = stream+'\0'
-        elif isinstance(stream, bytes):
-            self.name = "<byte string>"
-            self.raw_buffer = stream
-            self.determine_encoding()
-        else:
-            self.stream = stream
-            self.name = getattr(stream, 'name', "<file>")
-            self.eof = False
-            self.raw_buffer = None
-            self.determine_encoding()
-
-    def peek(self, index=0):
-        try:
-            return self.buffer[self.pointer+index]
-        except IndexError:
-            self.update(index+1)
-            return self.buffer[self.pointer+index]
-
-    def prefix(self, length=1):
-        if self.pointer+length >= len(self.buffer):
-            self.update(length)
-        return self.buffer[self.pointer:self.pointer+length]
-
-    def forward(self, length=1):
-        if self.pointer+length+1 >= len(self.buffer):
-            self.update(length+1)
-        while length:
-            ch = self.buffer[self.pointer]
-            self.pointer += 1
-            self.index += 1
-            if ch in '\n\x85\u2028\u2029'  \
-                    or (ch == '\r' and self.buffer[self.pointer] != '\n'):
-                self.line += 1
-                self.column = 0
-            elif ch != '\uFEFF':
-                self.column += 1
-            length -= 1
-
-    def get_mark(self):
-        if self.stream is None:
-            return Mark(self.name, self.index, self.line, self.column,
-                    self.buffer, self.pointer)
-        else:
-            return Mark(self.name, self.index, self.line, self.column,
-                    None, None)
-
-    def determine_encoding(self):
-        while not self.eof and (self.raw_buffer is None or len(self.raw_buffer) < 2):
-            self.update_raw()
-        if isinstance(self.raw_buffer, bytes):
-            if self.raw_buffer.startswith(codecs.BOM_UTF16_LE):
-                self.raw_decode = codecs.utf_16_le_decode
-                self.encoding = 'utf-16-le'
-            elif self.raw_buffer.startswith(codecs.BOM_UTF16_BE):
-                self.raw_decode = codecs.utf_16_be_decode
-                self.encoding = 'utf-16-be'
-            else:
-                self.raw_decode = codecs.utf_8_decode
-                self.encoding = 'utf-8'
-        self.update(1)
-
-    NON_PRINTABLE = re.compile('[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]')
-    def check_printable(self, data):
-        match = self.NON_PRINTABLE.search(data)
-        if match:
-            character = match.group()
-            position = self.index+(len(self.buffer)-self.pointer)+match.start()
-            raise ReaderError(self.name, position, ord(character),
-                    'unicode', "special characters are not allowed")
-
-    def update(self, length):
-        if self.raw_buffer is None:
-            return
-        self.buffer = self.buffer[self.pointer:]
-        self.pointer = 0
-        while len(self.buffer) < length:
-            if not self.eof:
-                self.update_raw()
-            if self.raw_decode is not None:
-                try:
-                    data, converted = self.raw_decode(self.raw_buffer,
-                            'strict', self.eof)
-                except UnicodeDecodeError as exc:
-                    character = self.raw_buffer[exc.start]
-                    if self.stream is not None:
-                        position = self.stream_pointer-len(self.raw_buffer)+exc.start
-                    else:
-                        position = exc.start
-                    raise ReaderError(self.name, position, character,
-                            exc.encoding, exc.reason)
-            else:
-                data = self.raw_buffer
-                converted = len(data)
-            self.check_printable(data)
-            self.buffer += data
-            self.raw_buffer = self.raw_buffer[converted:]
-            if self.eof:
-                self.buffer += '\0'
-                self.raw_buffer = None
-                break
-
-    def update_raw(self, size=4096):
-        data = self.stream.read(size)
-        if self.raw_buffer is None:
-            self.raw_buffer = data
-        else:
-            self.raw_buffer += data
-        self.stream_pointer += len(data)
-        if not data:
-            self.eof = True
diff --git a/spaces/pyInter/Liyuu_sovits4/models.py b/spaces/pyInter/Liyuu_sovits4/models.py
deleted file mode 100644
index 13278d680493970f5a670cf3fc955a6e9b7ab1d5..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/models.py
+++ /dev/null
@@ -1,420 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.attentions as attentions
-import modules.commons as commons
-import modules.modules as modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import utils
-from modules.commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    # print(x.shape,x_lengths.shape)
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      gin_channels=0,
-      filter_channels=None,
-      n_heads=None,
-      p_dropout=None):
-    super().__init__()
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.f0_emb = nn.Embedding(256, hidden_channels)
-
-    self.enc_ =  attentions.Encoder(
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-
-  def forward(self, x, x_mask, f0=None, noice_scale=1):
-    x = x + self.f0_emb(f0).transpose(1,2)
-    x = self.enc_(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
-
-    return z, m, logs, x_mask
-
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
-    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
-        super(SpeakerEncoder, self).__init__()
-        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
-        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, mels):
-        self.lstm.flatten_parameters()
-        _, (hidden, _) = self.lstm(mels)
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
-    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
-        mel_slices = []
-        for i in range(0, total_frames-partial_frames, partial_hop):
-            mel_range = torch.arange(i, i+partial_frames)
-            mel_slices.append(mel_range)
-
-        return mel_slices
-
-    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
-        mel_len = mel.size(1)
-        last_mel = mel[:,-partial_frames:]
-
-        if mel_len > partial_frames:
-            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
-            mels = list(mel[:,s] for s in mel_slices)
-            mels.append(last_mel)
-            mels = torch.stack(tuple(mels), 0).squeeze(1)
-
-            with torch.no_grad():
-                partial_embeds = self(mels)
-            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
-            #embed = embed / torch.linalg.norm(embed, 2)
-        else:
-            with torch.no_grad():
-                embed = self(last_mel)
-
-        return embed
-
-class F0Decoder(nn.Module):
-    def __init__(self,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 spk_channels=0):
-        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.spk_channels = spk_channels
-
-        self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
-        self.decoder = attentions.FFT(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout)
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.f0_prenet = nn.Conv1d(1, hidden_channels , 3, padding=1)
-        self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
-
-    def forward(self, x, norm_f0, x_mask, spk_emb=None):
-        x = torch.detach(x)
-        if (spk_emb is not None):
-            x = x + self.cond(spk_emb)
-        x += self.f0_prenet(norm_f0)
-        x = self.prenet(x) * x_mask
-        x = self.decoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock,
-    resblock_kernel_sizes,
-    resblock_dilation_sizes,
-    upsample_rates,
-    upsample_initial_channel,
-    upsample_kernel_sizes,
-    gin_channels,
-    ssl_dim,
-    n_speakers,
-    sampling_rate=44100,
-    **kwargs):
-
-    super().__init__()
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.gin_channels = gin_channels
-    self.ssl_dim = ssl_dim
-    self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
-
-    self.enc_p = TextEncoder(
-        inter_channels,
-        hidden_channels,
-        filter_channels=filter_channels,
-        n_heads=n_heads,
-        n_layers=n_layers,
-        kernel_size=kernel_size,
-        p_dropout=p_dropout
-    )
-    hps = {
-        "sampling_rate": sampling_rate,
-        "inter_channels": inter_channels,
-        "resblock": resblock,
-        "resblock_kernel_sizes": resblock_kernel_sizes,
-        "resblock_dilation_sizes": resblock_dilation_sizes,
-        "upsample_rates": upsample_rates,
-        "upsample_initial_channel": upsample_initial_channel,
-        "upsample_kernel_sizes": upsample_kernel_sizes,
-        "gin_channels": gin_channels,
-    }
-    self.dec = Generator(h=hps)
-    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-    self.f0_decoder = F0Decoder(
-        1,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        spk_channels=gin_channels
-    )
-    self.emb_uv = nn.Embedding(2, hidden_channels)
-
-  def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
-    g = self.emb_g(g).transpose(1,2)
-    # ssl prenet
-    x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
-    x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
-
-    # f0 predict
-    lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
-    norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
-    pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
-
-    # encoder
-    z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
-    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
-
-    # flow
-    z_p = self.flow(z, spec_mask, g=g)
-    z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
-
-    # nsf decoder
-    o = self.dec(z_slice, g=g, f0=pitch_slice)
-
-    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
-
-  def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
-    c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    g = self.emb_g(g).transpose(1,2)
-    x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
-    x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
-
-    if predict_f0:
-        lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
-        norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
-        pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
-        f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
-
-    z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
-    z = self.flow(z_p, c_mask, g=g, reverse=True)
-    o = self.dec(z * c_mask, g=g, f0=f0)
-    return o
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diff --git a/spaces/rachana219/MODT2/trackers/bytetrack/kalman_filter.py b/spaces/rachana219/MODT2/trackers/bytetrack/kalman_filter.py
deleted file mode 100644
index deda8a26292b81bc6512a8f6145afabde6c16d7a..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/bytetrack/kalman_filter.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-import scipy.linalg
-
-
-"""
-Table for the 0.95 quantile of the chi-square distribution with N degrees of
-freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
-function and used as Mahalanobis gating threshold.
-"""
-chi2inv95 = {
-    1: 3.8415,
-    2: 5.9915,
-    3: 7.8147,
-    4: 9.4877,
-    5: 11.070,
-    6: 12.592,
-    7: 14.067,
-    8: 15.507,
-    9: 16.919}
-
-
-class KalmanFilter(object):
-    """
-    A simple Kalman filter for tracking bounding boxes in image space.
-
-    The 8-dimensional state space
-
-        x, y, a, h, vx, vy, va, vh
-
-    contains the bounding box center position (x, y), aspect ratio a, height h,
-    and their respective velocities.
-
-    Object motion follows a constant velocity model. The bounding box location
-    (x, y, a, h) is taken as direct observation of the state space (linear
-    observation model).
-
-    """
-
-    def __init__(self):
-        ndim, dt = 4, 1.
-
-        # Create Kalman filter model matrices.
-        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
-        for i in range(ndim):
-            self._motion_mat[i, ndim + i] = dt
-        self._update_mat = np.eye(ndim, 2 * ndim)
-
-        # Motion and observation uncertainty are chosen relative to the current
-        # state estimate. These weights control the amount of uncertainty in
-        # the model. This is a bit hacky.
-        self._std_weight_position = 1. / 20
-        self._std_weight_velocity = 1. / 160
-
-    def initiate(self, measurement):
-        """Create track from unassociated measurement.
-
-        Parameters
-        ----------
-        measurement : ndarray
-            Bounding box coordinates (x, y, a, h) with center position (x, y),
-            aspect ratio a, and height h.
-
-        Returns
-        -------
-        (ndarray, ndarray)
-            Returns the mean vector (8 dimensional) and covariance matrix (8x8
-            dimensional) of the new track. Unobserved velocities are initialized
-            to 0 mean.
-
-        """
-        mean_pos = measurement
-        mean_vel = np.zeros_like(mean_pos)
-        mean = np.r_[mean_pos, mean_vel]
-
-        std = [
-            2 * self._std_weight_position * measurement[3],
-            2 * self._std_weight_position * measurement[3],
-            1e-2,
-            2 * self._std_weight_position * measurement[3],
-            10 * self._std_weight_velocity * measurement[3],
-            10 * self._std_weight_velocity * measurement[3],
-            1e-5,
-            10 * self._std_weight_velocity * measurement[3]]
-        covariance = np.diag(np.square(std))
-        return mean, covariance
-
-    def predict(self, mean, covariance):
-        """Run Kalman filter prediction step.
-
-        Parameters
-        ----------
-        mean : ndarray
-            The 8 dimensional mean vector of the object state at the previous
-            time step.
-        covariance : ndarray
-            The 8x8 dimensional covariance matrix of the object state at the
-            previous time step.
-
-        Returns
-        -------
-        (ndarray, ndarray)
-            Returns the mean vector and covariance matrix of the predicted
-            state. Unobserved velocities are initialized to 0 mean.
-
-        """
-        std_pos = [
-            self._std_weight_position * mean[3],
-            self._std_weight_position * mean[3],
-            1e-2,
-            self._std_weight_position * mean[3]]
-        std_vel = [
-            self._std_weight_velocity * mean[3],
-            self._std_weight_velocity * mean[3],
-            1e-5,
-            self._std_weight_velocity * mean[3]]
-        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
-
-        #mean = np.dot(self._motion_mat, mean)
-        mean = np.dot(mean, self._motion_mat.T)
-        covariance = np.linalg.multi_dot((
-            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
-
-        return mean, covariance
-
-    def project(self, mean, covariance):
-        """Project state distribution to measurement space.
-
-        Parameters
-        ----------
-        mean : ndarray
-            The state's mean vector (8 dimensional array).
-        covariance : ndarray
-            The state's covariance matrix (8x8 dimensional).
-
-        Returns
-        -------
-        (ndarray, ndarray)
-            Returns the projected mean and covariance matrix of the given state
-            estimate.
-
-        """
-        std = [
-            self._std_weight_position * mean[3],
-            self._std_weight_position * mean[3],
-            1e-1,
-            self._std_weight_position * mean[3]]
-        innovation_cov = np.diag(np.square(std))
-
-        mean = np.dot(self._update_mat, mean)
-        covariance = np.linalg.multi_dot((
-            self._update_mat, covariance, self._update_mat.T))
-        return mean, covariance + innovation_cov
-
-    def multi_predict(self, mean, covariance):
-        """Run Kalman filter prediction step (Vectorized version).
-        Parameters
-        ----------
-        mean : ndarray
-            The Nx8 dimensional mean matrix of the object states at the previous
-            time step.
-        covariance : ndarray
-            The Nx8x8 dimensional covariance matrics of the object states at the
-            previous time step.
-        Returns
-        -------
-        (ndarray, ndarray)
-            Returns the mean vector and covariance matrix of the predicted
-            state. Unobserved velocities are initialized to 0 mean.
-        """
-        std_pos = [
-            self._std_weight_position * mean[:, 3],
-            self._std_weight_position * mean[:, 3],
-            1e-2 * np.ones_like(mean[:, 3]),
-            self._std_weight_position * mean[:, 3]]
-        std_vel = [
-            self._std_weight_velocity * mean[:, 3],
-            self._std_weight_velocity * mean[:, 3],
-            1e-5 * np.ones_like(mean[:, 3]),
-            self._std_weight_velocity * mean[:, 3]]
-        sqr = np.square(np.r_[std_pos, std_vel]).T
-
-        motion_cov = []
-        for i in range(len(mean)):
-            motion_cov.append(np.diag(sqr[i]))
-        motion_cov = np.asarray(motion_cov)
-
-        mean = np.dot(mean, self._motion_mat.T)
-        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
-        covariance = np.dot(left, self._motion_mat.T) + motion_cov
-
-        return mean, covariance
-
-    def update(self, mean, covariance, measurement):
-        """Run Kalman filter correction step.
-
-        Parameters
-        ----------
-        mean : ndarray
-            The predicted state's mean vector (8 dimensional).
-        covariance : ndarray
-            The state's covariance matrix (8x8 dimensional).
-        measurement : ndarray
-            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
-            is the center position, a the aspect ratio, and h the height of the
-            bounding box.
-
-        Returns
-        -------
-        (ndarray, ndarray)
-            Returns the measurement-corrected state distribution.
-
-        """
-        projected_mean, projected_cov = self.project(mean, covariance)
-
-        chol_factor, lower = scipy.linalg.cho_factor(
-            projected_cov, lower=True, check_finite=False)
-        kalman_gain = scipy.linalg.cho_solve(
-            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
-            check_finite=False).T
-        innovation = measurement - projected_mean
-
-        new_mean = mean + np.dot(innovation, kalman_gain.T)
-        new_covariance = covariance - np.linalg.multi_dot((
-            kalman_gain, projected_cov, kalman_gain.T))
-        return new_mean, new_covariance
-
-    def gating_distance(self, mean, covariance, measurements,
-                        only_position=False, metric='maha'):
-        """Compute gating distance between state distribution and measurements.
-        A suitable distance threshold can be obtained from `chi2inv95`. If
-        `only_position` is False, the chi-square distribution has 4 degrees of
-        freedom, otherwise 2.
-        Parameters
-        ----------
-        mean : ndarray
-            Mean vector over the state distribution (8 dimensional).
-        covariance : ndarray
-            Covariance of the state distribution (8x8 dimensional).
-        measurements : ndarray
-            An Nx4 dimensional matrix of N measurements, each in
-            format (x, y, a, h) where (x, y) is the bounding box center
-            position, a the aspect ratio, and h the height.
-        only_position : Optional[bool]
-            If True, distance computation is done with respect to the bounding
-            box center position only.
-        Returns
-        -------
-        ndarray
-            Returns an array of length N, where the i-th element contains the
-            squared Mahalanobis distance between (mean, covariance) and
-            `measurements[i]`.
-        """
-        mean, covariance = self.project(mean, covariance)
-        if only_position:
-            mean, covariance = mean[:2], covariance[:2, :2]
-            measurements = measurements[:, :2]
-
-        d = measurements - mean
-        if metric == 'gaussian':
-            return np.sum(d * d, axis=1)
-        elif metric == 'maha':
-            cholesky_factor = np.linalg.cholesky(covariance)
-            z = scipy.linalg.solve_triangular(
-                cholesky_factor, d.T, lower=True, check_finite=False,
-                overwrite_b=True)
-            squared_maha = np.sum(z * z, axis=0)
-            return squared_maha
-        else:
-            raise ValueError('invalid distance metric')
\ No newline at end of file
diff --git a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-img2img.py b/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-img2img.py
deleted file mode 100644
index b9021816b95429a81545c7ccb6b26f9d056b72d5..0000000000000000000000000000000000000000
--- a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-img2img.py
+++ /dev/null
@@ -1,262 +0,0 @@
-import asyncio
-import json
-import logging
-import traceback
-from pydantic import BaseModel
-
-from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-
-from diffusers import AutoPipelineForImage2Image, AutoencoderTiny
-from compel import Compel
-import torch
-
-try:
-    import intel_extension_for_pytorch as ipex
-except:
-    pass
-from PIL import Image
-import numpy as np
-import gradio as gr
-import io
-import uuid
-import os
-import time
-import psutil
-
-MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
-TIMEOUT = float(os.environ.get("TIMEOUT", 0))
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)   
-
-WIDTH = 512
-HEIGHT = 512
-# disable tiny autoencoder for better quality speed tradeoff
-USE_TINY_AUTOENCODER = True
-
-# check if MPS is available OSX only M1/M2/M3 chips
-mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
-device = torch.device(
-    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
-)
-torch_device = device
-
-# change to torch.float16 to save GPU memory
-torch_dtype = torch.float32
-
-print(f"TIMEOUT: {TIMEOUT}")
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
-print(f"device: {device}")
-
-if mps_available:
-    device = torch.device("mps")
-    torch_device = "cpu"
-    torch_dtype = torch.float32
-
-if SAFETY_CHECKER == "True":
-    pipe = AutoPipelineForImage2Image.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-    )
-else:
-    pipe = AutoPipelineForImage2Image.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-        safety_checker=None,
-    )
-
-if USE_TINY_AUTOENCODER:
-    pipe.vae = AutoencoderTiny.from_pretrained(
-        "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
-    )
-pipe.set_progress_bar_config(disable=True)
-pipe.to(device=torch_device, dtype=torch_dtype).to(device)
-pipe.unet.to(memory_format=torch.channels_last)
-
-if psutil.virtual_memory().total < 64 * 1024**3:
-    pipe.enable_attention_slicing()
-
-if TORCH_COMPILE:
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
-    pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
-
-compel_proc = Compel(
-    tokenizer=pipe.tokenizer,
-    text_encoder=pipe.text_encoder,
-    truncate_long_prompts=False,
-)
-user_queue_map = {}
-
-
-class InputParams(BaseModel):
-    seed: int = 2159232
-    prompt: str
-    guidance_scale: float = 8.0
-    strength: float = 0.5
-    steps: int = 4
-    lcm_steps: int = 50
-    width: int = WIDTH
-    height: int = HEIGHT
-
-def predict(input_image: Image.Image, params: InputParams, prompt_embeds: torch.Tensor = None):
-    generator = torch.manual_seed(params.seed)
-    results = pipe(
-        prompt_embeds=prompt_embeds,
-        generator=generator,
-        image=input_image,
-        strength=params.strength,
-        num_inference_steps=params.steps,
-        guidance_scale=params.guidance_scale,
-        width=params.width,
-        height=params.height,
-        original_inference_steps=params.lcm_steps,
-        output_type="pil",
-    )
-    nsfw_content_detected = (
-        results.nsfw_content_detected[0]
-        if "nsfw_content_detected" in results
-        else False
-    )
-    if nsfw_content_detected:
-        return None
-    return results.images[0]
-
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.websocket("/ws")
-async def websocket_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
-        print("Server is full")
-        await websocket.send_json({"status": "error", "message": "Server is full"})
-        await websocket.close()
-        return
-
-    try:
-        uid = str(uuid.uuid4())
-        print(f"New user connected: {uid}")
-        await websocket.send_json(
-            {"status": "success", "message": "Connected", "userId": uid}
-        )
-        user_queue_map[uid] = {"queue": asyncio.Queue()}
-        await websocket.send_json(
-            {"status": "start", "message": "Start Streaming", "userId": uid}
-        )
-        await handle_websocket_data(websocket, uid)
-    except WebSocketDisconnect as e:
-        logging.error(f"WebSocket Error: {e}, {uid}")
-        traceback.print_exc()
-    finally:
-        print(f"User disconnected: {uid}")
-        queue_value = user_queue_map.pop(uid, None)
-        queue = queue_value.get("queue", None)
-        if queue:
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-
-
-@app.get("/queue_size")
-async def get_queue_size():
-    queue_size = len(user_queue_map)
-    return JSONResponse({"queue_size": queue_size})
-
-
-@app.get("/stream/{user_id}")
-async def stream(user_id: uuid.UUID):
-    uid = str(user_id)
-    try:
-        user_queue = user_queue_map[uid]
-        queue = user_queue["queue"]
-
-        async def generate():
-            last_prompt: str = None
-            prompt_embeds: torch.Tensor = None
-            while True:
-                data = await queue.get()
-                input_image = data["image"]
-                params = data["params"]
-                if input_image is None:
-                    continue
-                # avoid recalculate prompt embeds
-                if last_prompt != params.prompt:
-                    print("new prompt")
-                    prompt_embeds = compel_proc(params.prompt)
-                    last_prompt = params.prompt
-
-                image = predict(
-                    input_image,
-                    params,
-                    prompt_embeds,
-                )
-                if image is None:
-                    continue
-                frame_data = io.BytesIO()
-                image.save(frame_data, format="JPEG")
-                frame_data = frame_data.getvalue()
-                if frame_data is not None and len(frame_data) > 0:
-                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
-
-                await asyncio.sleep(1.0 / 120.0)
-
-        return StreamingResponse(
-            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
-        )
-    except Exception as e:
-        logging.error(f"Streaming Error: {e}, {user_queue_map}")
-        traceback.print_exc()
-        return HTTPException(status_code=404, detail="User not found")
-
-
-async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
-    uid = str(user_id)
-    user_queue = user_queue_map[uid]
-    queue = user_queue["queue"]
-    if not queue:
-        return HTTPException(status_code=404, detail="User not found")
-    last_time = time.time()
-    try:
-        while True:
-            data = await websocket.receive_bytes()
-            params = await websocket.receive_json()
-            params = InputParams(**params)
-            pil_image = Image.open(io.BytesIO(data))
-
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-            await queue.put({"image": pil_image, "params": params})
-            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
-                await websocket.send_json(
-                    {
-                        "status": "timeout",
-                        "message": "Your session has ended",
-                        "userId": uid,
-                    }
-                )
-                await websocket.close()
-                return
-
-    except Exception as e:
-        logging.error(f"Error: {e}")
-        traceback.print_exc()
-
-
-app.mount("/", StaticFiles(directory="img2img", html=True), name="public")
diff --git a/spaces/radames/TinyStories-Candle-Wasm-Magic/README.md b/spaces/radames/TinyStories-Candle-Wasm-Magic/README.md
deleted file mode 100644
index be37f1ee84a4e3ea76984b49d26982a8b5c72249..0000000000000000000000000000000000000000
--- a/spaces/radames/TinyStories-Candle-Wasm-Magic/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: TinyStories Candle Wasm Magic
-emoji: 🕯️📖
-colorFrom: yellow
-colorTo: purple
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/007 Thunderball Operazione Tuono Download Torrent Ita Why You Should Watch This Bond Movie Again.md b/spaces/raedeXanto/academic-chatgpt-beta/007 Thunderball Operazione Tuono Download Torrent Ita Why You Should Watch This Bond Movie Again.md
deleted file mode 100644
index 204fa76b64a09251376ba31b3155862c11e5905a..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/007 Thunderball Operazione Tuono Download Torrent Ita Why You Should Watch This Bond Movie Again.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-
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-<p>Kaspersky is one of the most popular and trusted antivirus software in the market. It offers comprehensive protection against all kinds of malware, ransomware, phishing, spyware, and other online threats. It also provides advanced features such as parental control, password manager, VPN, firewall, and more.</p>
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-</ol>
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- <ol>
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- </ol>
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- </ol>
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- <h3>Frequently Asked Questions</h3>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Kaspersky Trial Resetter legal?</td>
-<td>Kaspersky Trial Resetter is not legal or authorized by Kaspersky Lab. It is a third-party tool that violates the terms and conditions of Kaspersky products. Use it at your own risk.</td>
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- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Airport Madness 4 Full Version Crack The Latest and Greatest in the Airport Madness Series.md b/spaces/raedeXanto/academic-chatgpt-beta/Airport Madness 4 Full Version Crack The Latest and Greatest in the Airport Madness Series.md
deleted file mode 100644
index 87657d53538f7a783896601a96b723aa7e11e87b..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Airport Madness 4 Full Version Crack The Latest and Greatest in the Airport Madness Series.md	
+++ /dev/null
@@ -1,140 +0,0 @@
-<br />
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- <p>To play Airport Madness 4 effectively and avoid collisions, here are some of the best tips and tricks that you can use:</p>
- <ul>
-<li>Use your mouse or keyboard shortcuts to control the planes. You can click on a plane to select it, then click on another point on the screen to give it a new heading. You can also use your mouse wheel or keyboard arrows to adjust its altitude. You can right-click on a plane to access more options, such as holding pattern, go around, expedite departure, emergency landing, etc.</li>
-<li>Use your radar scope wisely. You can zoom in or out using your mouse wheel or keyboard arrows. You can also drag your mouse or use your keyboard arrows to move around the screen. You can toggle between normal view and radar view using your spacebar. You can also toggle between day mode and night mode using your N key.</li>
-<li>Prioritize safety over efficiency. Don't try to squeeze too many planes into a small space or rush them through a busy airspace. Give them enough space and time to maneuver safely. Avoid crossing paths or creating conflicts with other planes. Use holding patterns or go arounds if necessary.</li>
-<li>Communicate with your planes clearly and timely. Listen to their requests and respond accordingly. Give them clearances when they are ready and when it is safe. Don't keep them waiting too long or ignore them completely. Use your radio panel or keyboard numbers to communicate with them.</li>
-<li>Plan ahead and anticipate problems. Look at your radar screen regularly and identify potential hazards or conflicts before they happen. Use your information panel or keyboard I key to see more details about each plane. Use your vertical speed indicator or keyboard V key to see their altitudes relative to each other. Use your pause button or keyboard P key to pause the game if you need more time to think.</li>
-<li>Leverage your airport facilities efficiently. Use all available runways for takeoffs and landings if possible. Use different runways for different types of planes if possible. Use taxiways wisely to avoid congestion on the ground. Use gates appropriately for parking planes.</li>
-<li>Leverage your special abilities wisely. You have three special abilities that you can use once per level: fast forward (F key), emergency stop (E key), and radar scan (R key). Fast forward speeds up time by two times for ten seconds. Emergency stop stops all planes on screen for five seconds. Radar scan reveals all hidden planes on screen for ten seconds.</li>
-<li>Have fun and learn from your mistakes. Don't get frustrated if you cause a collision or fail a level. Learn from your mistakes and try again. Experiment with different strategies and techniques. Enjoy the challenge and satisfaction of being an air traffic controller!</li>
-</ul>
- <h4>Conclusion</h4>
- <p>Airport Madness 4 is a fun and challenging air traffic control game that you can play online or download for free. It offers you realistic graphics, smooth gameplay, and addictive challenge. It also offers you various features, such as different airports, aircraft, modes, difficulty levels, achievements, and surprises. It is a game that tests your concentration, coordination, multitasking, and quick thinking skills.</p>
- <p>If you want to download and install Airport Madness 4 full version crack for free, you can follow the steps we provided in this article. However, we warn you about the risks and legal issues of doing so, and we advise you to buy the official version of the game from Steam or other authorized platforms.</p>
- <p>If you want to play Airport Madness 4 effectively and avoid collisions, you can use the tips and tricks we provided in this article. You can also experiment with different strategies and techniques, and learn from your mistakes. You can also have fun and enjoy the challenge and satisfaction of being an air traffic controller!</p>
- <p>We hope you enjoyed this article and learned something new. If you did, please share it with your friends and family who might be interested in Airport Madness 4. If you have any questions or feedback, please leave a comment below. Thank you for reading!</p>
- <p>Now, are you ready to try Airport Madness 4? Click on the link below to play it online or download it for free!</p>
- <h5>FAQs</h5>
- <ul>
-<li>Q: What is the difference between Airport Madness 4 and Airport Madness 4 Lite?</li>
-<li>A: Airport Madness 4 Lite is a free online version of Airport Madness 4 that offers only two airports and two modes. Airport Madness 4 is the full version of the game that offers six airports and four modes.</li>
-<li>Q: How many levels are there in Airport Madness 4?</li>
-<li>A: There are 12 levels in Airport Madness 4, each with a different airport, scenario, and challenge.</li>
-<li>Q: How can I unlock more airports and modes in Airport Madness 4?</li>
-<li>A: You can unlock more airports and modes by completing the previous levels with a good score and without causing any collisions.</li>
-<li>Q: How can I get more points and stars in Airport Madness 4?</li>
-<li>A: You can get more points and stars by managing more planes safely and efficiently, without causing any delays or conflicts.</li>
-<li>Q: How can I get more help or support for Airport Madness 4?</li>
-<li>A: You can get more help or support for Airport Madness 4 by visiting the official website of Big Fat Simulations or contacting them via email or social media.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Crack Revit Architecture 2009 Free 12 [HOT].md b/spaces/raedeXanto/academic-chatgpt-beta/Crack Revit Architecture 2009 Free 12 [HOT].md
deleted file mode 100644
index 0b46a0dc81be423a1a51ca8efb9bc4a968938bae..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Crack Revit Architecture 2009 Free 12 [HOT].md	
+++ /dev/null
@@ -1,33 +0,0 @@
-<br />
-```html
-<h1>How to Download and Install Revit Architecture 2009 for Free</h1>
-<p>Revit Architecture 2009 is a powerful software that allows you to design 2D and 3D models of buildings with ventilation, plumbing, and circuit boards. It is one of the most popular tools for building information modeling (BIM) and can help you create stunning architectural projects.</p>
-<p>However, Revit Architecture 2009 is not a cheap software. It costs around $5,000 for a single license and requires a high-end computer to run smoothly. If you are looking for a way to download and install Revit Architecture 2009 for free, you might be disappointed to find out that there is no official or legal way to do so.</p>
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-<p>But don't worry, there are some alternatives that can help you get Revit Architecture 2009 for free or at a low cost. Here are some of them:</p>
-<ul>
-<li><strong>Use a trial version.</strong> Autodesk, the developer of Revit Architecture 2009, offers a 30-day free trial of the software on its website[^1^]. You can download and install the trial version on your computer and use it for a limited time. This is a good option if you want to test the software before buying it or if you only need it for a short-term project.</li>
-<li><strong>Use an educational version.</strong> If you are a student or an educator, you can get access to Revit Architecture 2009 for free through Autodesk's Education Community[^2^]. You will need to register with your academic email address and verify your eligibility. You can then download and install the educational version of the software on your computer and use it for non-commercial purposes.</li>
-<li><strong>Use an older version.</strong> If you don't need the latest features and updates of Revit Architecture 2009, you can try to find an older version of the software that is compatible with your computer and operating system. For example, you can look for Revit Architecture 2008 or 2007 on online platforms like eBay or Amazon. You might be able to get them at a lower price or even for free from sellers who no longer need them.</li>
-<li><strong>Use an alternative software.</strong> If none of the above options work for you, you can consider using another software that has similar functions and features as Revit Architecture 2009. For example, you can try SketchUp, AutoCAD, ArchiCAD, or Blender. Some of these software are free or have low-cost versions that you can download and install on your computer. They might not have all the capabilities of Revit Architecture 2009, but they can still help you create impressive architectural designs.</li>
-</ul>
-<p>In conclusion, Revit Architecture 2009 is a great software for BIM and architectural design, but it is not easy to get it for free. However, you can try some of the alternatives mentioned above to download and install it on your computer without spending too much money. Remember to always check the legality and reliability of the sources before downloading any software.</p>
-```
-
-```html
-<h2>What is Revit Architecture 2009?</h2>
-<p>Revit Architecture 2009 is a software that belongs to the Revit family of products, which also includes Revit Structure and Revit MEP. Revit Architecture 2009 is specifically designed for architects and building designers who want to create realistic and accurate models of their projects.</p>
-<p>Revit Architecture 2009 uses a parametric modeling approach, which means that every element in the model is connected to a set of parameters that define its properties and behavior. For example, if you change the height of a wall, the windows and doors on that wall will automatically adjust to the new dimension. This allows you to make changes easily and quickly without affecting the integrity of the model.</p>
-<p>Revit Architecture 2009 also supports BIM, which stands for building information modeling. BIM is a process that involves creating and managing digital representations of physical and functional characteristics of buildings. BIM enables you to store and share information about the design, construction, and operation of a building throughout its lifecycle. BIM can help you improve collaboration, efficiency, quality, and sustainability of your projects.</p>
-<h2>What are the benefits of using Revit Architecture 2009?</h2>
-<p>Revit Architecture 2009 has many benefits that can help you enhance your architectural design workflow. Some of them are:</p>
-<p></p>
-<ul>
-<li><strong>It is easy to use.</strong> Revit Architecture 2009 has a user-friendly interface that guides you through the modeling process. It also has a comprehensive help system that provides tutorials, tips, and examples. You can also customize the interface to suit your preferences and needs.</li>
-<li><strong>It is flexible and versatile.</strong> Revit Architecture 2009 can handle various types of projects, from small-scale residential buildings to large-scale commercial complexes. It can also accommodate different design styles, from traditional to contemporary. You can create complex shapes and forms with ease using the powerful modeling tools.</li>
-<li><strong>It is accurate and reliable.</strong> Revit Architecture 2009 ensures that your model is consistent and coherent at all times. It automatically updates all the views and drawings whenever you make a change in the model. It also performs error-checking and conflict-resolution to avoid mistakes and clashes. You can also generate detailed documentation and reports from your model with accuracy and clarity.</li>
-<li><strong>It is collaborative and integrative.</strong> Revit Architecture 2009 enables you to work with other disciplines and stakeholders involved in your project. You can import and export data from other software, such as AutoCAD, SketchUp, or Excel. You can also link your model with other Revit models, such as Revit Structure or Revit MEP. You can also share your model with clients and contractors using various formats, such as PDF, DWG, DWF, or IFC.</li>
-</ul>
-```</p> 7b8c122e87<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Economia Mcgraw-hill 1 Bachillerato Pdf 31 EXCLUSIVE.md b/spaces/raedeXanto/academic-chatgpt-beta/Economia Mcgraw-hill 1 Bachillerato Pdf 31 EXCLUSIVE.md
deleted file mode 100644
index afaac6b59f47b25b9b0381af3f1ab2488973a662..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Economia Mcgraw-hill 1 Bachillerato Pdf 31 EXCLUSIVE.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>Economia McGraw-Hill 1 Bachillerato PDF: A Comprehensive Guide for Students</h1>
-<p>If you are looking for a reliable and updated textbook for your economics course in the first year of high school, you might want to consider Economia McGraw-Hill 1 Bachillerato PDF. This is a digital version of the popular book by Jose Luis Cardenas and Antonio Pulido, published by McGraw-Hill Education.</p>
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-<h2>What is Economia McGraw-Hill 1 Bachillerato PDF?</h2>
-<p>Economia McGraw-Hill 1 Bachillerato PDF is a textbook that covers the basic concepts and principles of economics, such as scarcity, opportunity cost, supply and demand, market structures, macroeconomic indicators, fiscal and monetary policy, international trade, and economic development.</p>
-<p>The book is divided into 10 units, each with a clear and concise introduction, objectives, key terms, diagrams, tables, graphs, exercises, summaries, and self-evaluation tests. The book also includes a glossary of economic terms and an index of concepts.</p>
-<p></p>
-<p>The book is written in Spanish and follows the official curriculum of the Spanish Ministry of Education for the first year of high school (Bachillerato).</p>
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-<ul>
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-</ul>
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-<ul>
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diff --git a/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/cpp/cppipc/policy.h b/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/cpp/cppipc/policy.h
deleted file mode 100644
index f88ab5d8cb343f97026966b402eaeed8831e356a..0000000000000000000000000000000000000000
--- a/spaces/rainy3/chatgpt_academic/crazy_functions/test_project/cpp/cppipc/policy.h
+++ /dev/null
@@ -1,25 +0,0 @@
-#pragma once
-
-#include <type_traits>
-
-#include "libipc/def.h"
-#include "libipc/prod_cons.h"
-
-#include "libipc/circ/elem_array.h"
-
-namespace ipc {
-namespace policy {
-
-template <template <typename, std::size_t...> class Elems, typename Flag>
-struct choose;
-
-template <typename Flag>
-struct choose<circ::elem_array, Flag> {
-    using flag_t = Flag;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    using elems_t = circ::elem_array<ipc::prod_cons_impl<flag_t>, DataSize, AlignSize>;
-};
-
-} // namespace policy
-} // namespace ipc
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Dynasty Warrior 5 Extreme Legend Pc Download BEST.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Dynasty Warrior 5 Extreme Legend Pc Download BEST.md
deleted file mode 100644
index 143d1bba2df76039268cdb0be5cba3377d6f6fc1..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Dynasty Warrior 5 Extreme Legend Pc Download BEST.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-
-<p>the visuals in dynasty warriors 5 xtreme legends are fairly impressive. the environments are evocative and lush, and it is easy to get lost in the various settings as you progress through the many story-driven missions.</p>
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-<p>right at the start of the game you can chose what you're going to play. later in the game the paths you take are mirrored. there are some very clever things you can do too. for example, you can mimic a path another character is taking by beating them up before you finish your own path. </p>
-<p>the dynasty warriors games have a very detailed and interesting narrative. when choosing between the battles you can also create a battlefield that the characters of both sides fight on. you do this by going to a town and selecting a map area you want to play on. <br> the story revolves around the three kingdoms of china, wa, wu and shu. the son of the last of these three kingdoms is the main playable character. his father died because of the son and took his position instead. his "adopted" son is the protagonist of the game. the story revolves around the battles between the opposing armies on different battlefields. the reason for this is that you can't just choose any battlefield at the start of the game. you have to start your own story. in the earlier games you fought from the perspective of the war lords on different battlefields. in the game you play as the main character. </p> 899543212b<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/rkrstacic/Chatbot-integration-built-on-processes/app.py b/spaces/rkrstacic/Chatbot-integration-built-on-processes/app.py
deleted file mode 100644
index 372401454d8dd6edccd96ec9b965a45eea3ca85b..0000000000000000000000000000000000000000
--- a/spaces/rkrstacic/Chatbot-integration-built-on-processes/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from api_call_module import get_answer
-
-processes = ["Praksa", "Izrada završnog rada"]
-answers_key = "answers"
-
-# Session init
-if answers_key not in st.session_state:
-    st.session_state[answers_key] = []
-
-
-# Save the answer to session
-def save_answer(answer: str) -> None:
-    st.session_state[answers_key].append(answer)
-
-
-# Clears the session
-def clear_session() -> None:
-    st.session_state[answers_key] = []
-
-
-# Submits the query and saves the answer
-def submit_query() -> None:
-    if query == "":
-        return
-
-    ans = get_answer(query, process)
-
-    save_answer("A: " + ans)
-    save_answer("Q: " + query)
-    save_answer("**Question {}**")
-    save_answer("")
-
-
-# Sidebar
-with st.sidebar:
-    st.title("Set up your chatbot!")
-    process = st.selectbox("Select a process", processes)
-    st.caption("You can find this chatbot implementation [here](https://huggingface.co/spaces/rkrstacic/"
-               "Software-module-for-answering-questions-on-processes/tree/main)")
-
-# Main
-st.title("Welcome to the process answering chatbot!")
-query = st.text_input("Ask me a question", placeholder="e.g. Koliko traje proces prakse?")
-
-# Buttons
-col1, col2 = st.columns([6, 1])
-col1.button("Send", on_click=submit_query)
-col2.button("Clear chat", on_click=clear_session)
-
-# Chat history
-with st.expander("Chat History", expanded=True):
-    for index, ans in enumerate(st.session_state[answers_key][::-1]):
-        st.write(ans if index % 4 != 1 else ans.format((len(st.session_state[answers_key]) - index) // 4 + 1))
diff --git a/spaces/robinhad/qirimtatar-tts/crh_transliterator/transliterator.py b/spaces/robinhad/qirimtatar-tts/crh_transliterator/transliterator.py
deleted file mode 100644
index 1c48d195a06e853c00b0400b977625ed586fc416..0000000000000000000000000000000000000000
--- a/spaces/robinhad/qirimtatar-tts/crh_transliterator/transliterator.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .cyr_to_lat import mappings
-import re
-
-
-def transliterate(text: str):
-    text = text.lower()  # always treat lowercase
-    text = " " + text + " "
-
-    for mapping in mappings:
-        text = re.sub(mapping[0], mapping[1], text)
-
-    return text[1:-1]
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_retina_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_retina_head.py
deleted file mode 100644
index 6d9e874c2bfdd07b408d148110eb4dd85c3a9069..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_retina_head.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-from mmcv.cnn import ConvModule
-from mmcv.ops import MaskedConv2d
-
-from ..builder import HEADS
-from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
-
-
-@HEADS.register_module()
-class GARetinaHead(GuidedAnchorHead):
-    """Guided-Anchor-based RetinaNet head."""
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 init_cfg=None,
-                 **kwargs):
-        if init_cfg is None:
-            init_cfg = dict(
-                type='Normal',
-                layer='Conv2d',
-                std=0.01,
-                override=[
-                    dict(
-                        type='Normal',
-                        name='conv_loc',
-                        std=0.01,
-                        bias_prob=0.01),
-                    dict(
-                        type='Normal',
-                        name='retina_cls',
-                        std=0.01,
-                        bias_prob=0.01)
-                ])
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(GARetinaHead, self).__init__(
-            num_classes, in_channels, init_cfg=init_cfg, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-
-        self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
-        self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
-                                    1)
-        self.feature_adaption_cls = FeatureAdaption(
-            self.feat_channels,
-            self.feat_channels,
-            kernel_size=3,
-            deform_groups=self.deform_groups)
-        self.feature_adaption_reg = FeatureAdaption(
-            self.feat_channels,
-            self.feat_channels,
-            kernel_size=3,
-            deform_groups=self.deform_groups)
-        self.retina_cls = MaskedConv2d(
-            self.feat_channels,
-            self.num_base_priors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.retina_reg = MaskedConv2d(
-            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
-
-    def forward_single(self, x):
-        """Forward feature map of a single scale level."""
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-
-        loc_pred = self.conv_loc(cls_feat)
-        shape_pred = self.conv_shape(reg_feat)
-
-        cls_feat = self.feature_adaption_cls(cls_feat, shape_pred)
-        reg_feat = self.feature_adaption_reg(reg_feat, shape_pred)
-
-        if not self.training:
-            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
-        else:
-            mask = None
-        cls_score = self.retina_cls(cls_feat, mask)
-        bbox_pred = self.retina_reg(reg_feat, mask)
-        return cls_score, bbox_pred, shape_pred, loc_pred
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Fifa Street 4 PC Download Full MF How to Get the Best Street Soccer Game for Free.md b/spaces/rorallitri/biomedical-language-models/logs/Fifa Street 4 PC Download Full MF How to Get the Best Street Soccer Game for Free.md
deleted file mode 100644
index 59937ccc488a9d6b6b3577d71de67ecc8e2f9c49..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Fifa Street 4 PC Download Full MF How to Get the Best Street Soccer Game for Free.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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index 0887e22f3ea8a16112e64474532af6abf557dd86..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/How to Play Free Super Bounce Out Online and Create Explosive Matches.md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Left 4 Dead 2 Crack Launcher Cheats No Verification Tips and Tricks for the Best Experience.md b/spaces/rorallitri/biomedical-language-models/logs/Left 4 Dead 2 Crack Launcher Cheats No Verification Tips and Tricks for the Best Experience.md
deleted file mode 100644
index a213a727d45c6eaabab5a82e9cb14f6897abe602..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Left 4 Dead 2 Crack Launcher Cheats No Verification Tips and Tricks for the Best Experience.md	
+++ /dev/null
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-<p><b>A RIDE DENIED (15)</b> - Kill a Jockey within 2 seconds of it jumping on a Survivor.<br><b>A SPITTLE HELP FROM MY FRIENDS (15)</b> - As the Spitter, spit on a Survivor being choked by a Smoker.<br><b>ACID REFLEX (15)</b> - Kill a Spitter before she is able to spit.<br><b>ARMORY OF ONE (15)</b> - Deploy an ammo upgrade and have your team use it.<br><b>BACK IN THE SADDLE (15)</b> - As the Jockey, ride the Survivors twice in a single life.<br><b>BEAT THE RUSH (15)</b> - In a Survival round, get a medal only using melee weapons.<br><b>BRIDGE BURNER (20)</b> - Survive the Parish campaign.<br><b>BRIDGE OVER TREBLED SLAUGHTER (30)</b> - Cross the bridge finale in less than three minutes.<br><b>BURNING SENSATION (15)</b> - Ignite 50 Common Infected with incendiary ammo.<br><b>CACHE AND CARRY (20)</b> - Collect 15 gas cans in a single Scavenge round.<br><b>CHAIN OF COMMAND (15)</b> - Kill 100 Common Infected with the chainsaw.<br><b>CL0WND (15)</b> - Honk the noses of 10 Clowns.<br><b>CLUB DEAD (15)</b> - Use every melee weapon to kill Common Infected.<br><b>CONFEDERACY OF CRUNCHES (30)</b> - Finish a campaign using only melee weapons.<br><b>CRASS MENAGERIE (20)</b> - Kill one of each Uncommon Infected.<br><b>DEAD IN THE WATER (20)</b> - Kill 10 swampy Mudmen while they are in the water.<br><b>DISMEMBERMENT PLAN (20)</b> - Kill 15 Infected with a single grenade launcher blast.<br><b>FRIED PIPER (15)</b> - Using a Molotov, burn a Clown leading at least 10 Common Infected.<br><b>FUEL CRISIS (15)</b> - Make a Survivor drop a gas can during overtime.<br><b>GAS GUZZLER (20)</b> - Collect 100 gas cans in Scavenge.<br><b>GAS SHORTAGE (20)</b> - Cause 25 gas can drops as a Special Infected.<br><b>GONG SHOW (15)</b> - Prove you are stronger than Moustachio.<br><b>GREAT EXPECTORATIONS (15)</b> - As the Spitter, hit every Survivor with a single acid patch.<br><b>GUARDIN' GNOME (30)</b> - Rescue Gnome Chompski from the Carnival.<br><b>HEAD HONCHO (15)</b> - Decapitate 200 Infected with a melee weapon.<br><b>HEARTWARMER (20)</b> - In a Versus round, leave the saferoom to defibrillate a dead teammate.<br><b>HUNTING PARTY (15)</b> - Win a game of Scavenge.<br><b>LEVEL A CHARGE (15)</b> - Kill a Charger with a melee weapon while they are charging.<br><b>LONG DISTANCE CARRIER (15)</b> - As the Charger, grab a Survivor and carry them over 80 feet.<br><b>MEAT TENDERIZER (20)</b> - As the Charger, grab a Survivor and smash them into the ground for a solid 15 seconds.<br><b>MIDNIGHT RIDER (20)</b> - Survive the Dark Carnival campaign.<br><b>PRICE CHOPPER (20)</b> - Survive the Dead Center campaign.<br><b>QUALIFIED RIDE (15)</b> - As the Jockey, ride a Survivor for more than 12 seconds.<br><b>RAGIN' CAJUN (20)</b> - Survive the Swamp Fever campaign.<br><b>ROBBED ZOMBIE (15)</b> - Collect 10 vials of Boomer vomit from infected CEDA agents you have killed.<br><b>RODE HARD, PUT AWAY WET (20)</b> - As the Jockey, ride a Survivor and steer them into a Spitter's acid patch.<br><b>SCATTERING RAM (20)</b> - As the Charger, bowl through the entire enemy team in a single charge.<br><b>SCAVENGE HUNT (15)</b> - Stop the enemy team from collecting any gas cans during a Scavenge round.<br><b>SEPTIC TANK (15)</b> - Use a bile bomb on a Tank.<br><b>SHOCK JOCK (30)</b> - Revive 10 dead Survivors with the defibrillator.<br><b>SOB STORY (30)</b> - Navigate the sugar mill and reach the safe room without killing any Witches.<br><b>STACHE WHACKER (15)</b> - Prove you are faster than Moustachio.<br><b>STILL SOMETHING TO PROVE (35)</b> - Survive all campaigns on Expert.<br><b>STRENGTH IN NUMBERS (15)</b> - Form a team and beat an enemy team in 4v4 Versus or Scavenge.<br><b>TANK BURGER (30)</b> - Kill a Tank with melee weapons.<br><b>THE QUICK AND THE DEAD (30)</b> - Revive 10 incapacitated Survivors while under the speed-boosting effects of adrenaline.<br><b>THE REAL DEAL (35)</b> - Survive a campaign on Expert skill with Realism mode enabled.<br><b>VIOLENCE IN SILENCE (30)</b> - Navigate the impound lot and reach the cemetary safe room without tripping any alarms.<br><b>WEATHERMAN (20)</b> - Survive the Hard Rain campaign.<br><b>WING AND A PRAYER (30)</b> - Defend yourself at the crashed airliner without taking damage.</p>
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-<p><b>A ride denied </b> - Kill a jockey within 2 seconds of it jumping on a survivor.<br><b>A spittle help from my friends </b> - As the spitter, spit on a survivor being choked by a smoker.<br><b>Acid reflex </b> - Kill a spitter before she is able to spit.<br><b>Armory of one </b> - Deploy an ammo upgrade and have your team use it.<br><b>Back in the saddle </b> - As the jockey, ride the survivors twice in a single life.<br><b>Beat the rush </b> - In a survival round, get a medal only using melee weapons.<br><b>Bridge burner </b> - Survive the parish campaign.<br><b>Bridge over trebled slaughter </b> - Cross the bridge finale in less than three minutes.<br><b>Burning sensation </b> - Ignite 50 common infected with incendiary ammo.<br><b>Cache and carry </b> - Collect 15 gas cans in a single scavenge round.<br><b>Chain of command </b> - Kill 100 common infected with the chainsaw.<br><b>Cl0wnd </b> - Honk the noses of 10 clowns.<br><b>Club dead </b> - Use every melee weapon to kill common infected.<br><b>Confederacy of crunches </b> - Finish a campaign using only melee weapons.<br><b>Crass menagerie </b> - Kill one of each uncommon infected.<br><b>Dead in the water </b> - Kill 10 swampy mudmen while they are in the water.<br><b>Dismemberment plan </b> - Kill 15 infected with a single grenade launcher blast.<br><b>Fried piper </b> - Using a molotov, burn a clown leading at least 10 common infected.<br><b>Fuel crisis </b> - Make a survivor drop a gas can during overtime.<br><b>Gas guzzler </b> - Collect 100 gas cans in scavenge.<br><b>Gas shortage </b> - Cause 25 gas can drops as a special infected.<br><b>Gong show </b> - Prove you are stronger than moustachio.<br><b>Great expectorations </b> - As the spitter, hit every survivor with a single acid patch.<br><b>Guardin' gnome </b> - Rescue gnome chompski from the carnival.<br><b>Head honcho </b> - Decapitate 200 infected with a melee weapon.<br><b>Heartwarmer </b> - In a versus round, leave the saferoom to defibrillate a dead teammate.<br><b>Hunting party </b> - Win a game of scavenge.<br><b>Level a charge </b> - Kill a charger with a melee weapon while they are charging.<br><b>Long distance carrier </b> - As the charger, grab a survivor and carry them over 80 feet.<br><b>Meat tenderizer </b> - As the charger, grab a survivor and smash them into the ground for a solid 15 seconds.<br><b>Midnight rider </b> - Survive the dark carnival campaign.<br><b>Price chopper </b> - Survive the dead center campaign.<br><b>Qualified ride </b> - As the jockey, ride a survivor for more than 12 seconds.<br><b>Ragin' cajun </b> - Survive the swamp fever campaign.<br><b>Robbed zombie </b> - Collect 10 vials of boomer vomit from infected ceda agents you have killed.<br><b>Rode hard, put away wet </b> - As the jockey, ride a survivor and steer them into a spitter's acid patch.<br><b>Scattering ram </b> - As the charger, bowl through the entire enemy team in a single charge.<br><b>Scavenge hunt </b> - Stop the enemy team from collecting any gas cans during a scavenge round.<br><b>Septic tank </b> - Use a bile bomb on a tank.<br><b>Shock jock </b> - Revive 10 dead survivors with the defibrillator.<br><b>Sob story </b> - Navigate the sugar mill and reach the safe room without killing any witches.<br><b>Stache whacker </b> - Prove you are faster than moustachio.<br><b>Still something to prove </b> - Survive all campaigns on expert.<br><b>Strength in numbers </b> - Form a team and beat an enemy team in 4V4 versus or scavenge.<br><b>Tank burger </b> - Kill a tank with melee weapons.<br><b>The quick and the dead </b> - Revive 10 incapacitated survivors while under the speed-boosting effects of adrenaline.<br><b>The real deal </b> - Survive a campaign on expert skill with realism mode enabled.<br><b>Violence in silence </b> - Navigate the impound lot and reach the cemetary safe room without tripping any alarms.<br><b>Weatherman </b> - Survive the hard rain campaign.<br><b>Wing and a prayer </b> - Defend yourself at the crashed airliner without taking damage.</p>
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diff --git a/spaces/saad-k7/Jewelli-Chatbot/README.md b/spaces/saad-k7/Jewelli-Chatbot/README.md
deleted file mode 100644
index 154d588cd6decfb3f3498a7d763957514b231a49..0000000000000000000000000000000000000000
--- a/spaces/saad-k7/Jewelli-Chatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Jewelli Chatbot
-emoji: 💻
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.0.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/common.py b/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/common.py
deleted file mode 100644
index 2bf15236a3eb24d8526073bc4fa2b274cccb3f96..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/common.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-
-from typing import Type
-
-
-class MLPBlock(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        mlp_dim: int,
-        act: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        super().__init__()
-        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
-        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
-        self.act = act()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
-class LayerNorm2d(nn.Module):
-    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        u = x.mean(1, keepdim=True)
-        s = (x - u).pow(2).mean(1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.eps)
-        x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
diff --git a/spaces/sbhatti2009/stock-analysis/README.md b/spaces/sbhatti2009/stock-analysis/README.md
deleted file mode 100644
index fc5a39f75d6c618e0332f5f10c434118f1e78878..0000000000000000000000000000000000000000
--- a/spaces/sbhatti2009/stock-analysis/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stock Analysis
-emoji: ⚡
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-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/sccstandardteam/ChuanhuChatGPT/modules/models/StableLM.py b/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/StableLM.py
deleted file mode 100644
index f4affc3699e335f1e42bf5fc8c93e92a41d027fe..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/StableLM.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from .base_model import BaseLLMModel
-from threading import Thread
-
-STABLELM_MODEL = None
-STABLELM_TOKENIZER = None
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-class StableLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        global STABLELM_MODEL, STABLELM_TOKENIZER
-        print(f"Starting to load StableLM to memory")
-        if model_name == "StableLM":
-            model_name = "stabilityai/stablelm-tuned-alpha-7b"
-        else:
-            model_name = f"models/{model_name}"
-        if STABLELM_MODEL is None:
-            STABLELM_MODEL = AutoModelForCausalLM.from_pretrained(
-                model_name, torch_dtype=torch.float16).cuda()
-        if STABLELM_TOKENIZER is None:
-            STABLELM_TOKENIZER = AutoTokenizer.from_pretrained(model_name)
-        self.generator = pipeline(
-            'text-generation', model=STABLELM_MODEL, tokenizer=STABLELM_TOKENIZER, device=0)
-        print(f"Sucessfully loaded StableLM to the memory")
-        self.system_prompt = """StableAssistant
-- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
-- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
-- StableAssistant will refuse to participate in anything that could harm a human."""
-        self.max_generation_token = 1024
-        self.top_p = 0.95
-        self.temperature = 1.0
-
-    def _get_stablelm_style_input(self):
-        history = self.history + [{"role": "assistant", "content": ""}]
-        print(history)
-        messages = self.system_prompt + \
-            "".join(["".join(["<|USER|>"+history[i]["content"], "<|ASSISTANT|>"+history[i + 1]["content"]])
-                    for i in range(0, len(history), 2)])
-        return messages
-
-    def _generate(self, text, bad_text=None):
-        stop = StopOnTokens()
-        result = self.generator(text, max_new_tokens=self.max_generation_token, num_return_sequences=1, num_beams=1, do_sample=True,
-                                temperature=self.temperature, top_p=self.top_p, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
-        return result[0]["generated_text"].replace(text, "")
-
-    def get_answer_at_once(self):
-        messages = self._get_stablelm_style_input()
-        return self._generate(messages), len(messages)
-
-    def get_answer_stream_iter(self):
-        stop = StopOnTokens()
-        messages = self._get_stablelm_style_input()
-
-        # model_inputs = tok([messages], return_tensors="pt")['input_ids'].cuda()[:, :4096-1024]
-        model_inputs = STABLELM_TOKENIZER(
-            [messages], return_tensors="pt").to("cuda")
-        streamer = TextIteratorStreamer(
-            STABLELM_TOKENIZER, timeout=10., skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            model_inputs,
-            streamer=streamer,
-            max_new_tokens=self.max_generation_token,
-            do_sample=True,
-            top_p=self.top_p,
-            top_k=1000,
-            temperature=self.temperature,
-            num_beams=1,
-            stopping_criteria=StoppingCriteriaList([stop])
-        )
-        t = Thread(target=STABLELM_MODEL.generate, kwargs=generate_kwargs)
-        t.start()
-
-        partial_text = ""
-        for new_text in streamer:
-            partial_text += new_text
-            yield partial_text
diff --git a/spaces/scedlatioru/img-to-music/example/Dosprn 1.79 ((NEW)) Keygen.md b/spaces/scedlatioru/img-to-music/example/Dosprn 1.79 ((NEW)) Keygen.md
deleted file mode 100644
index b8a257c04e4e7077d9b839340859948cb37ca063..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Dosprn 1.79 ((NEW)) Keygen.md	
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deleted file mode 100644
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--- a/spaces/scedlatioru/img-to-music/example/Iw5mp Cegexe Null 0x80041002.md	
+++ /dev/null
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- . .
-
-‎07-18-2014, 05:06 PM
-
-gamemusic
-
-Quote:
-
-Originally Posted by shawn
-
-How to fix iw5mp ceg.exe to be able...
-
-That's a game problem, not a CW problem.
-
-You need to find a thread that's actually running the game to ask for help with that.
-
-‎07-18-2014, 06:47 PM
-
-Rykel
-
-First you will need to identify if it is the game's problem, or a problem with the game's hardware, or a problem with your system.
-
-Next, you will need to find out if this error has ever happened in the past with this game.
-
-Next, you will need to find out if other people with this game have reported this issue.
-
-Next, you will need to find out if this is something that happens when the game is started, or something that happens only when multiplayer is started.
-
-Next, you will need to find out what kind of video card is in your system.
-
-Next, you will need to find out if other people with this game have reported this issue with this video card.
-
-It seems like you are trying to find a solution that could be specific to your system, and your particular video card.
-
-If that's not the case, then it would be useful to others to know if this is something that happens when the game is started, or something that happens only when multiplayer is started.
-
-‎07-18-2014, 07:38 PM
-
-RaketRapier
-
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-
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diff --git a/spaces/seduerr/text_analytics/text_analytics/indices/readability_indices.py b/spaces/seduerr/text_analytics/text_analytics/indices/readability_indices.py
deleted file mode 100644
index 75167a7f0cb9c2503884445f29ec7314599306cc..0000000000000000000000000000000000000000
--- a/spaces/seduerr/text_analytics/text_analytics/indices/readability_indices.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import multiprocessing
-
-import spacy
-
-from text_analytics.indices.descriptive_indices import DescriptiveIndices
-from text_analytics.constants import ACCEPTED_LANGUAGES
-
-
-class ReadabilityIndices:
-    '''
-    This class will handle all operations to find the readability indices of a text according to Coh-Metrix.
-    '''
-
-    def __init__(self, nlp, language: str='en', descriptive_indices: DescriptiveIndices=None) -> None:
-        '''
-        The constructor will initialize this object that calculates the readability indices for a specific language of those that are available.
-
-        Parameters:
-        nlp: The spacy model that corresponds to a language.
-        language(str): The language that the texts to process will have.
-        descriptive_indices(DescriptiveIndices): The class that calculates the descriptive indices of a text in a certain language.
-
-        Returns:
-        None.
-        '''
-        if not language in ACCEPTED_LANGUAGES:
-            raise ValueError(f'Language {language} is not supported yet')
-        elif descriptive_indices is not None and descriptive_indices.language != language:
-            raise ValueError(f'The descriptive indices analyzer must be of the same language as the word information analyzer.')
-        
-        self.language = language
-        self._nlp = nlp
-
-        if descriptive_indices is None: # Assign the descriptive indices to an attribute
-            self._di = DescriptiveIndices(language=language, nlp=nlp)
-        else:
-            self._di = descriptive_indices
-
-    def calculate_fernandez_huertas_grade_level(self, text: str=None, mean_syllables_per_word: int=None, mean_words_per_sentence: int=None, workers: int=-1) -> float:
-        '''
-        This function obtains the Fernández-Huertas readability index for a text.
-
-        Parameters:
-        text(str): The text to be analized.
-        word_count(int): The amount of words in the text.
-        workers(int): Amount of threads that will complete this operation. If it's -1 then all cpu cores will be used.
-        mean_syllables_per_word(int): The mean of syllables per word in the text.
-        mean_words_per_sentence(int): The mean amount of words per sentences in the text.
-
-        Returns:
-        float: The Fernández-Huertas readability index for a text.
-        '''
-        if self.language != 'en':
-            raise ValueError('This readability index is for spanish.')
-        elif text is not None and len(text) == 0:
-            raise ValueError('The word is empty.')
-        elif text is None and (mean_syllables_per_word is None or mean_words_per_sentence is None):
-            raise ValueError('If there\'s no text, then you must pass mean_syllables_per_word and mean_words_per_sentence at the same time.')
-        elif workers == 0 or workers < -1:
-            raise ValueError('Workers must be -1 or any positive number greater than 0')
-        else:
-            threads = multiprocessing.cpu_count() if workers == -1 else workers
-            mspw = mean_syllables_per_word if mean_syllables_per_word is not None else self._di.get_mean_of_syllables_per_word(text=text, workers=threads).mean
-            mwps = mean_words_per_sentence if mean_words_per_sentence is not None else self._di.get_mean_of_length_of_sentences(text=text, workers=threads).mean
-            return 206.84 - 0.6 * mspw - 1.02 * mwps
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder_layer.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder_layer.py
deleted file mode 100644
index 98e5e011dfb2c12bf487d3a82ae5cd579e09d602..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder_layer.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Decoder self-attention layer definition."""
-
-import torch
-from torch import nn
-
-from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
-
-
-class DecoderLayer(nn.Module):
-    """Single decoder layer module.
-
-    Args:
-        size (int): Input dimension.
-        self_attn (torch.nn.Module): Self-attention module instance.
-            `MultiHeadedAttention` instance can be used as the argument.
-        src_attn (torch.nn.Module): Self-attention module instance.
-            `MultiHeadedAttention` instance can be used as the argument.
-        feed_forward (torch.nn.Module): Feed-forward module instance.
-            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
-            can be used as the argument.
-        dropout_rate (float): Dropout rate.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
-            if True, additional linear will be applied.
-            i.e. x -> x + linear(concat(x, att(x)))
-            if False, no additional linear will be applied. i.e. x -> x + att(x)
-
-
-    """
-
-    def __init__(
-        self,
-        size,
-        self_attn,
-        src_attn,
-        feed_forward,
-        dropout_rate,
-        normalize_before=True,
-        concat_after=False,
-    ):
-        """Construct an DecoderLayer object."""
-        super(DecoderLayer, self).__init__()
-        self.size = size
-        self.self_attn = self_attn
-        self.src_attn = src_attn
-        self.feed_forward = feed_forward
-        self.norm1 = LayerNorm(size)
-        self.norm2 = LayerNorm(size)
-        self.norm3 = LayerNorm(size)
-        self.dropout = nn.Dropout(dropout_rate)
-        self.normalize_before = normalize_before
-        self.concat_after = concat_after
-        if self.concat_after:
-            self.concat_linear1 = nn.Linear(size + size, size)
-            self.concat_linear2 = nn.Linear(size + size, size)
-
-    def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None):
-        """Compute decoded features.
-
-        Args:
-            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
-            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
-            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
-            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
-            cache (List[torch.Tensor]): List of cached tensors.
-                Each tensor shape should be (#batch, maxlen_out - 1, size).
-
-        Returns:
-            torch.Tensor: Output tensor(#batch, maxlen_out, size).
-            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
-            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
-            torch.Tensor: Encoded memory mask (#batch, maxlen_in).
-
-        """
-        residual = tgt
-        if self.normalize_before:
-            tgt = self.norm1(tgt)
-
-        if cache is None:
-            tgt_q = tgt
-            tgt_q_mask = tgt_mask
-        else:
-            # compute only the last frame query keeping dim: max_time_out -> 1
-            assert cache.shape == (
-                tgt.shape[0],
-                tgt.shape[1] - 1,
-                self.size,
-            ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
-            tgt_q = tgt[:, -1:, :]
-            residual = residual[:, -1:, :]
-            tgt_q_mask = None
-            if tgt_mask is not None:
-                tgt_q_mask = tgt_mask[:, -1:, :]
-
-        if self.concat_after:
-            tgt_concat = torch.cat(
-                (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1
-            )
-            x = residual + self.concat_linear1(tgt_concat)
-        else:
-            x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
-        if not self.normalize_before:
-            x = self.norm1(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.norm2(x)
-        if self.concat_after:
-            x_concat = torch.cat(
-                (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1
-            )
-            x = residual + self.concat_linear2(x_concat)
-        else:
-            x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask))
-        if not self.normalize_before:
-            x = self.norm2(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.norm3(x)
-        x = residual + self.dropout(self.feed_forward(x))
-        if not self.normalize_before:
-            x = self.norm3(x)
-
-        if cache is not None:
-            x = torch.cat([cache, x], dim=1)
-
-        return x, tgt_mask, memory, memory_mask
diff --git a/spaces/shatrunjai/FutureMeMotivator/docs/custom.css b/spaces/shatrunjai/FutureMeMotivator/docs/custom.css
deleted file mode 100644
index 86246c71166b8757d78f397f3682c044d72ea570..0000000000000000000000000000000000000000
--- a/spaces/shatrunjai/FutureMeMotivator/docs/custom.css
+++ /dev/null
@@ -1,14 +0,0 @@
-body {
-    font-family: Arial;
-    font-size: 13px;
-    color: #F6F4EE;
-}
-
-#header {
-    float: left;
-    height: 75px;
-    width: 1000px;
-    background: rgb(251,81,59);
-}
-
-
diff --git a/spaces/shengyi-qian/3DOI/monoarti/detr/segmentation.py b/spaces/shengyi-qian/3DOI/monoarti/detr/segmentation.py
deleted file mode 100644
index 81d68fa9b6e2271792cded9022c76d5d612df2ef..0000000000000000000000000000000000000000
--- a/spaces/shengyi-qian/3DOI/monoarti/detr/segmentation.py
+++ /dev/null
@@ -1,365 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-This file provides the definition of the convolutional heads used to predict masks, as well as the losses
-"""
-import io
-from collections import defaultdict
-from typing import List, Optional
-import pdb
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from PIL import Image
-
-from . import box_ops
-from .misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
-
-try:
-    from panopticapi.utils import id2rgb, rgb2id
-except ImportError:
-    pass
-
-
-class DETRsegm(nn.Module):
-    def __init__(self, detr, freeze_detr=False):
-        super().__init__()
-        self.detr = detr
-
-        if freeze_detr:
-            for p in self.parameters():
-                p.requires_grad_(False)
-
-        hidden_dim, nheads = detr.transformer.d_model, detr.transformer.nhead
-        self.bbox_attention = MHAttentionMap(hidden_dim, hidden_dim, nheads, dropout=0.0)
-        self.mask_head = MaskHeadSmallConv(hidden_dim + nheads, [1024, 512, 256], hidden_dim)
-
-    def forward(self, samples: NestedTensor):
-        if isinstance(samples, (list, torch.Tensor)):
-            samples = nested_tensor_from_tensor_list(samples)
-        features, pos = self.detr.backbone(samples)
-
-        bs = features[-1].tensors.shape[0]
-
-        src, mask = features[-1].decompose()
-        assert mask is not None
-        src_proj = self.detr.input_proj(src)
-        hs, memory = self.detr.transformer(src_proj, mask, self.detr.query_embed.weight, pos[-1])
-
-        outputs_class = self.detr.class_embed(hs)
-        outputs_coord = self.detr.bbox_embed(hs).sigmoid()
-        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]}
-        if self.detr.aux_loss:
-            out['aux_outputs'] = self.detr._set_aux_loss(outputs_class, outputs_coord)
-
-        # FIXME h_boxes takes the last one computed, keep this in mind
-        bbox_mask = self.bbox_attention(hs[-1], memory, mask=mask)
-
-        seg_masks = self.mask_head(src_proj, bbox_mask, [features[2].tensors, features[1].tensors, features[0].tensors])
-        outputs_seg_masks = seg_masks.view(bs, self.detr.num_queries, seg_masks.shape[-2], seg_masks.shape[-1])
-
-        out["pred_masks"] = outputs_seg_masks
-        return out
-
-
-def _expand(tensor, length: int):
-    return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1)
-
-
-class MaskHeadSmallConv(nn.Module):
-    """
-    Simple convolutional head, using group norm.
-    Upsampling is done using a FPN approach
-    """
-
-    def __init__(self, dim, fpn_dims, context_dim, nheads = 8):
-        super().__init__()
-
-        inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64]
-        self.lay1 = torch.nn.Conv2d(dim, dim, 3, padding=1)
-        self.gn1 = torch.nn.GroupNorm(nheads, dim)
-        self.lay2 = torch.nn.Conv2d(dim, inter_dims[1], 3, padding=1)
-        self.gn2 = torch.nn.GroupNorm(nheads, inter_dims[1])
-        self.lay3 = torch.nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1)
-        self.gn3 = torch.nn.GroupNorm(nheads, inter_dims[2])
-        self.lay4 = torch.nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1)
-        self.gn4 = torch.nn.GroupNorm(nheads, inter_dims[3])
-        self.lay5 = torch.nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1)
-        self.gn5 = torch.nn.GroupNorm(nheads, inter_dims[4])
-        self.out_lay = torch.nn.Conv2d(inter_dims[4], 1, 3, padding=1)
-
-        self.dim = dim
-
-        self.adapter1 = torch.nn.Conv2d(fpn_dims[0], inter_dims[1], 1)
-        self.adapter2 = torch.nn.Conv2d(fpn_dims[1], inter_dims[2], 1)
-        self.adapter3 = torch.nn.Conv2d(fpn_dims[2], inter_dims[3], 1)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_uniform_(m.weight, a=1)
-                nn.init.constant_(m.bias, 0)
-
-    def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]):
-        x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1)
-        x = self.lay1(x)
-        x = self.gn1(x)
-        x = F.relu(x)
-        x = self.lay2(x)
-        x = self.gn2(x)
-        x = F.relu(x)
-
-        cur_fpn = self.adapter1(fpns[0])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay3(x)
-        x = self.gn3(x)
-        x = F.relu(x)
-
-        cur_fpn = self.adapter2(fpns[1])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay4(x)
-        x = self.gn4(x)
-        x = F.relu(x)
-
-        cur_fpn = self.adapter3(fpns[2])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay5(x)
-        x = self.gn5(x)
-        x = F.relu(x)
-
-        x = self.out_lay(x)
-        return x
-
-
-class MHAttentionMap(nn.Module):
-    """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
-
-    def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True):
-        super().__init__()
-        self.num_heads = num_heads
-        self.hidden_dim = hidden_dim
-        self.dropout = nn.Dropout(dropout)
-
-        self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
-        self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
-
-        nn.init.zeros_(self.k_linear.bias)
-        nn.init.zeros_(self.q_linear.bias)
-        nn.init.xavier_uniform_(self.k_linear.weight)
-        nn.init.xavier_uniform_(self.q_linear.weight)
-        self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5
-
-    def forward(self, q, k, mask: Optional[Tensor] = None):
-        q = self.q_linear(q)
-        k = F.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias)
-        qh = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads)
-        kh = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1])
-        weights = torch.einsum("bqnc,bnchw->bqnhw", qh * self.normalize_fact, kh)
-
-        if mask is not None:
-            weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), float("-inf"))
-        weights = F.softmax(weights.flatten(2), dim=-1).view(weights.size())
-        weights = self.dropout(weights)
-        return weights
-
-
-def dice_loss(inputs, targets, num_boxes):
-    """
-    Compute the DICE loss, similar to generalized IOU for masks
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-    """
-    inputs = inputs.sigmoid()
-    inputs = inputs.flatten(1)
-    numerator = 2 * (inputs * targets).sum(1)
-    denominator = inputs.sum(-1) + targets.sum(-1)
-    loss = 1 - (numerator + 1) / (denominator + 1)
-    return loss.sum() / num_boxes
-
-
-def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-        alpha: (optional) Weighting factor in range (0,1) to balance
-                positive vs negative examples. Default = -1 (no weighting).
-        gamma: Exponent of the modulating factor (1 - p_t) to
-               balance easy vs hard examples.
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    #pdb.set_trace()
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class PostProcessSegm(nn.Module):
-    def __init__(self, threshold=0.5):
-        super().__init__()
-        self.threshold = threshold
-
-    @torch.no_grad()
-    def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
-        assert len(orig_target_sizes) == len(max_target_sizes)
-        max_h, max_w = max_target_sizes.max(0)[0].tolist()
-        outputs_masks = outputs["pred_masks"].squeeze(2)
-        outputs_masks = F.interpolate(outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False)
-        outputs_masks = (outputs_masks.sigmoid() > self.threshold).cpu()
-
-        for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):
-            img_h, img_w = t[0], t[1]
-            results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1)
-            results[i]["masks"] = F.interpolate(
-                results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest"
-            ).byte()
-
-        return results
-
-
-class PostProcessPanoptic(nn.Module):
-    """This class converts the output of the model to the final panoptic result, in the format expected by the
-    coco panoptic API """
-
-    def __init__(self, is_thing_map, threshold=0.85):
-        """
-        Parameters:
-           is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether
-                          the class is  a thing (True) or a stuff (False) class
-           threshold: confidence threshold: segments with confidence lower than this will be deleted
-        """
-        super().__init__()
-        self.threshold = threshold
-        self.is_thing_map = is_thing_map
-
-    def forward(self, outputs, processed_sizes, target_sizes=None):
-        """ This function computes the panoptic prediction from the model's predictions.
-        Parameters:
-            outputs: This is a dict coming directly from the model. See the model doc for the content.
-            processed_sizes: This is a list of tuples (or torch tensors) of sizes of the images that were passed to the
-                             model, ie the size after data augmentation but before batching.
-            target_sizes: This is a list of tuples (or torch tensors) corresponding to the requested final size
-                          of each prediction. If left to None, it will default to the processed_sizes
-            """
-        if target_sizes is None:
-            target_sizes = processed_sizes
-        assert len(processed_sizes) == len(target_sizes)
-        out_logits, raw_masks, raw_boxes = outputs["pred_logits"], outputs["pred_masks"], outputs["pred_boxes"]
-        assert len(out_logits) == len(raw_masks) == len(target_sizes)
-        preds = []
-
-        def to_tuple(tup):
-            if isinstance(tup, tuple):
-                return tup
-            return tuple(tup.cpu().tolist())
-
-        for cur_logits, cur_masks, cur_boxes, size, target_size in zip(
-            out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes
-        ):
-            # we filter empty queries and detection below threshold
-            scores, labels = cur_logits.softmax(-1).max(-1)
-            keep = labels.ne(outputs["pred_logits"].shape[-1] - 1) & (scores > self.threshold)
-            cur_scores, cur_classes = cur_logits.softmax(-1).max(-1)
-            cur_scores = cur_scores[keep]
-            cur_classes = cur_classes[keep]
-            cur_masks = cur_masks[keep]
-            cur_masks = interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
-            cur_boxes = box_ops.box_cxcywh_to_xyxy(cur_boxes[keep])
-
-            h, w = cur_masks.shape[-2:]
-            assert len(cur_boxes) == len(cur_classes)
-
-            # It may be that we have several predicted masks for the same stuff class.
-            # In the following, we track the list of masks ids for each stuff class (they are merged later on)
-            cur_masks = cur_masks.flatten(1)
-            stuff_equiv_classes = defaultdict(lambda: [])
-            for k, label in enumerate(cur_classes):
-                if not self.is_thing_map[label.item()]:
-                    stuff_equiv_classes[label.item()].append(k)
-
-            def get_ids_area(masks, scores, dedup=False):
-                # This helper function creates the final panoptic segmentation image
-                # It also returns the area of the masks that appears on the image
-
-                m_id = masks.transpose(0, 1).softmax(-1)
-
-                if m_id.shape[-1] == 0:
-                    # We didn't detect any mask :(
-                    m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)
-                else:
-                    m_id = m_id.argmax(-1).view(h, w)
-
-                if dedup:
-                    # Merge the masks corresponding to the same stuff class
-                    for equiv in stuff_equiv_classes.values():
-                        if len(equiv) > 1:
-                            for eq_id in equiv:
-                                m_id.masked_fill_(m_id.eq(eq_id), equiv[0])
-
-                final_h, final_w = to_tuple(target_size)
-
-                seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy()))
-                seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST)
-
-                np_seg_img = (
-                    torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())).view(final_h, final_w, 3).numpy()
-                )
-                m_id = torch.from_numpy(rgb2id(np_seg_img))
-
-                area = []
-                for i in range(len(scores)):
-                    area.append(m_id.eq(i).sum().item())
-                return area, seg_img
-
-            area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)
-            if cur_classes.numel() > 0:
-                # We know filter empty masks as long as we find some
-                while True:
-                    filtered_small = torch.as_tensor(
-                        [area[i] <= 4 for i, c in enumerate(cur_classes)], dtype=torch.bool, device=keep.device
-                    )
-                    if filtered_small.any().item():
-                        cur_scores = cur_scores[~filtered_small]
-                        cur_classes = cur_classes[~filtered_small]
-                        cur_masks = cur_masks[~filtered_small]
-                        area, seg_img = get_ids_area(cur_masks, cur_scores)
-                    else:
-                        break
-
-            else:
-                cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device)
-
-            segments_info = []
-            for i, a in enumerate(area):
-                cat = cur_classes[i].item()
-                segments_info.append({"id": i, "isthing": self.is_thing_map[cat], "category_id": cat, "area": a})
-            del cur_classes
-
-            with io.BytesIO() as out:
-                seg_img.save(out, format="PNG")
-                predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
-            preds.append(predictions)
-        return preds
diff --git a/spaces/shi-labs/OneFormer/oneformer/evaluation/detection_coco_evaluator.py b/spaces/shi-labs/OneFormer/oneformer/evaluation/detection_coco_evaluator.py
deleted file mode 100644
index b3834ecbca97d01262edd13c9939874c07613428..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/OneFormer/oneformer/evaluation/detection_coco_evaluator.py
+++ /dev/null
@@ -1,723 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import contextlib
-import copy
-import io
-import itertools
-import json
-import logging
-import numpy as np
-import os
-import pickle
-from collections import OrderedDict
-import pycocotools.mask as mask_util
-import torch
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-from tabulate import tabulate
-
-import detectron2.utils.comm as comm
-from detectron2.config import CfgNode
-from detectron2.data import MetadataCatalog
-from detectron2.data.datasets.coco import convert_to_coco_json
-from detectron2.structures import Boxes, BoxMode, pairwise_iou
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import create_small_table
-
-from .evaluator import DatasetEvaluator
-
-try:
-    from detectron2.evaluation.fast_eval_api import COCOeval_opt
-except ImportError:
-    COCOeval_opt = COCOeval
-
-
-class DetectionCOCOEvaluator(DatasetEvaluator):
-    """
-    Evaluate AR for object proposals, AP for instance detection/segmentation, AP
-    for keypoint detection outputs using COCO's metrics.
-    See http://cocodataset.org/#detection-eval and
-    http://cocodataset.org/#keypoints-eval to understand its metrics.
-    The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
-    the metric cannot be computed (e.g. due to no predictions made).
-
-    In addition to COCO, this evaluator is able to support any bounding box detection,
-    instance segmentation, or keypoint detection dataset.
-    """
-
-    def __init__(
-        self,
-        dataset_name,
-        tasks=None,
-        distributed=True,
-        output_dir=None,
-        *,
-        max_dets_per_image=None,
-        use_fast_impl=True,
-        kpt_oks_sigmas=(),
-        allow_cached_coco=True,
-    ):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-                It must have either the following corresponding metadata:
-
-                    "json_file": the path to the COCO format annotation
-
-                Or it must be in detectron2's standard dataset format
-                so it can be converted to COCO format automatically.
-            tasks (tuple[str]): tasks that can be evaluated under the given
-                configuration. A task is one of "bbox", "segm", "keypoints".
-                By default, will infer this automatically from predictions.
-            distributed (True): if True, will collect results from all ranks and run evaluation
-                in the main process.
-                Otherwise, will only evaluate the results in the current process.
-            output_dir (str): optional, an output directory to dump all
-                results predicted on the dataset. The dump contains two files:
-
-                1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
-                   contains all the results in the format they are produced by the model.
-                2. "coco_instances_results.json" a json file in COCO's result format.
-            max_dets_per_image (int): limit on the maximum number of detections per image.
-                By default in COCO, this limit is to 100, but this can be customized
-                to be greater, as is needed in evaluation metrics AP fixed and AP pool
-                (see https://arxiv.org/pdf/2102.01066.pdf)
-                This doesn't affect keypoint evaluation.
-            use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
-                Although the results should be very close to the official implementation in COCO
-                API, it is still recommended to compute results with the official API for use in
-                papers. The faster implementation also uses more RAM.
-            kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
-                See http://cocodataset.org/#keypoints-eval
-                When empty, it will use the defaults in COCO.
-                Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
-            allow_cached_coco (bool): Whether to use cached coco json from previous validation
-                runs. You should set this to False if you need to use different validation data.
-                Defaults to True.
-        """
-        self._logger = logging.getLogger(__name__)
-        self._distributed = distributed
-        self._output_dir = output_dir
-
-        if use_fast_impl and (COCOeval_opt is COCOeval):
-            self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
-            use_fast_impl = False
-        self._use_fast_impl = use_fast_impl
-
-        # COCOeval requires the limit on the number of detections per image (maxDets) to be a list
-        # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
-        # 3rd element (100) is used as the limit on the number of detections per image when
-        # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
-        # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
-        if max_dets_per_image is None:
-            max_dets_per_image = [1, 10, 100]
-        else:
-            max_dets_per_image = [1, 10, max_dets_per_image]
-        self._max_dets_per_image = max_dets_per_image
-
-        if tasks is not None and isinstance(tasks, CfgNode):
-            kpt_oks_sigmas = (
-                tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
-            )
-            self._logger.warn(
-                "COCO Evaluator instantiated using config, this is deprecated behavior."
-                " Please pass in explicit arguments instead."
-            )
-            self._tasks = None  # Infering it from predictions should be better
-        else:
-            self._tasks = tasks
-
-        self._cpu_device = torch.device("cpu")
-
-        self._metadata = MetadataCatalog.get(dataset_name)
-        if not hasattr(self._metadata, "json_file"):
-            if output_dir is None:
-                raise ValueError(
-                    "output_dir must be provided to COCOEvaluator "
-                    "for datasets not in COCO format."
-                )
-            self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
-
-            cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
-            self._metadata.json_file = cache_path
-            convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)
-
-        json_file = PathManager.get_local_path(self._metadata.json_file)
-        with contextlib.redirect_stdout(io.StringIO()):
-            self._coco_api = COCO(json_file)
-
-        # Test set json files do not contain annotations (evaluation must be
-        # performed using the COCO evaluation server).
-        self._do_evaluation = "annotations" in self._coco_api.dataset
-        if self._do_evaluation:
-            self._kpt_oks_sigmas = kpt_oks_sigmas
-
-    def reset(self):
-        self._predictions = []
-
-    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
-                "box_instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "box_instances" in output:
-                instances = output["box_instances"].to(self._cpu_device)
-                prediction["box_instances"] = instances_to_coco_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            if len(prediction) > 1:
-                self._predictions.append(prediction)
-
-    def evaluate(self, img_ids=None):
-        """
-        Args:
-            img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
-        """
-        if self._distributed:
-            comm.synchronize()
-            predictions = comm.gather(self._predictions, dst=0)
-            predictions = list(itertools.chain(*predictions))
-
-            if not comm.is_main_process():
-                return {}
-        else:
-            predictions = self._predictions
-
-        if len(predictions) == 0:
-            self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
-            return {}
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "instances_predictions.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(predictions, f)
-
-        self._results = OrderedDict()
-        if "proposals" in predictions[0]:
-            self._eval_box_proposals(predictions)
-        if "box_instances" in predictions[0]:
-            self._eval_predictions(predictions, img_ids=img_ids)
-        # Copy so the caller can do whatever with results
-        return copy.deepcopy(self._results)
-
-    def _tasks_from_predictions(self, predictions):
-        """
-        Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.
-        """
-        tasks = {"bbox"}
-        for pred in predictions:
-            if "keypoints" in pred:
-                tasks.add("keypoints")
-        return sorted(tasks)
-
-    def _eval_predictions(self, predictions, img_ids=None):
-        """
-        Evaluate predictions. Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["box_instances"] for x in predictions]))
-        tasks = self._tasks or self._tasks_from_predictions(coco_results)
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
-            all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
-            num_classes = len(all_contiguous_ids)
-            assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
-
-            reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
-            for result in coco_results:
-                category_id = result["category_id"]
-                assert category_id < num_classes, (
-                    f"A prediction has class={category_id}, "
-                    f"but the dataset only has {num_classes} classes and "
-                    f"predicted class id should be in [0, {num_classes - 1}]."
-                )
-                result["category_id"] = reverse_id_mapping[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 with {} COCO API...".format(
-                "unofficial" if self._use_fast_impl else "official"
-            )
-        )
-        for task in sorted(tasks):
-            assert task in {"bbox", "keypoints"}, f"Got unknown task: {task}!"
-            coco_eval = (
-                _evaluate_predictions_on_coco(
-                    self._coco_api,
-                    coco_results,
-                    task,
-                    kpt_oks_sigmas=self._kpt_oks_sigmas,
-                    use_fast_impl=self._use_fast_impl,
-                    img_ids=img_ids,
-                    max_dets_per_image=self._max_dets_per_image,
-                )
-                if len(coco_results) > 0
-                else None  # cocoapi does not handle empty results very well
-            )
-
-            res = self._derive_coco_results(
-                coco_eval, task, class_names=self._metadata.get("thing_classes")
-            )
-            self._results[task] = res
-
-    def _eval_box_proposals(self, predictions):
-        """
-        Evaluate the box proposals in predictions.
-        Fill self._results with the metrics for "box_proposals" task.
-        """
-        if self._output_dir:
-            # Saving generated box proposals to file.
-            # Predicted box_proposals are in XYXY_ABS mode.
-            bbox_mode = BoxMode.XYXY_ABS.value
-            ids, boxes, objectness_logits = [], [], []
-            for prediction in predictions:
-                ids.append(prediction["image_id"])
-                boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
-                objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
-
-            proposal_data = {
-                "boxes": boxes,
-                "objectness_logits": objectness_logits,
-                "ids": ids,
-                "bbox_mode": bbox_mode,
-            }
-            with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
-                pickle.dump(proposal_data, f)
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating bbox proposals ...")
-        res = {}
-        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
-        for limit in [100, 1000]:
-            for area, suffix in areas.items():
-                stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)
-                key = "AR{}@{:d}".format(suffix, limit)
-                res[key] = float(stats["ar"].item() * 100)
-        self._logger.info("Proposal metrics: \n" + create_small_table(res))
-        self._results["box_proposals"] = res
-
-    def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
-        """
-        Derive the desired score numbers from summarized COCOeval.
-
-        Args:
-            coco_eval (None or COCOEval): None represents no predictions from model.
-            iou_type (str):
-            class_names (None or list[str]): if provided, will use it to predict
-                per-category AP.
-
-        Returns:
-            a dict of {metric name: score}
-        """
-
-        metrics = {
-            "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
-            "keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
-        }[iou_type]
-
-        if coco_eval is None:
-            self._logger.warn("No predictions from the model!")
-            return {metric: float("nan") for metric in metrics}
-
-        # the standard metrics
-        results = {
-            metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
-            for idx, metric in enumerate(metrics)
-        }
-        self._logger.info(
-            "Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
-        )
-        if not np.isfinite(sum(results.values())):
-            self._logger.info("Some metrics cannot be computed and is shown as NaN.")
-
-        if class_names is None or len(class_names) <= 1:
-            return results
-        # Compute per-category AP
-        # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
-        precisions = coco_eval.eval["precision"]
-        # precision has dims (iou, recall, cls, area range, max dets)
-        assert len(class_names) == precisions.shape[2]
-
-        results_per_category = []
-        for idx, name in enumerate(class_names):
-            # area range index 0: all area ranges
-            # max dets index -1: typically 100 per image
-            precision = precisions[:, :, idx, 0, -1]
-            precision = precision[precision > -1]
-            ap = np.mean(precision) if precision.size else float("nan")
-            results_per_category.append(("{}".format(name), float(ap * 100)))
-
-        # tabulate it
-        N_COLS = min(6, len(results_per_category) * 2)
-        results_flatten = list(itertools.chain(*results_per_category))
-        results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
-        table = tabulate(
-            results_2d,
-            tablefmt="pipe",
-            floatfmt=".3f",
-            headers=["category", "AP"] * (N_COLS // 2),
-            numalign="left",
-        )
-        self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
-
-        results.update({"AP-" + name: ap for name, ap in results_per_category})
-        return results
-
-
-def instances_to_coco_json(instances, img_id):
-    """
-    Dump an "Instances" object to a COCO-format json that's used for evaluation.
-
-    Args:
-        instances (Instances):
-        img_id (int): the image id
-
-    Returns:
-        list[dict]: list of json annotations in COCO format.
-    """
-    num_instance = len(instances)
-    if num_instance == 0:
-        return []
-
-    boxes = instances.pred_boxes.tensor.numpy()
-    boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    boxes = boxes.tolist()
-    scores = instances.scores.tolist()
-    classes = instances.pred_classes.tolist()
-
-    has_mask = instances.has("pred_masks")
-    if has_mask:
-        # use RLE to encode the masks, because they are too large and takes memory
-        # since this evaluator stores outputs of the entire dataset
-        rles = [
-            mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
-            for mask in instances.pred_masks
-        ]
-        for rle in rles:
-            # "counts" is an array encoded by mask_util as a byte-stream. Python3's
-            # json writer which always produces strings cannot serialize a bytestream
-            # unless you decode it. Thankfully, utf-8 works out (which is also what
-            # the pycocotools/_mask.pyx does).
-            rle["counts"] = rle["counts"].decode("utf-8")
-
-    has_keypoints = instances.has("pred_keypoints")
-    if has_keypoints:
-        keypoints = instances.pred_keypoints
-
-    results = []
-    for k in range(num_instance):
-        result = {
-            "image_id": img_id,
-            "category_id": classes[k],
-            "bbox": boxes[k],
-            "score": scores[k],
-        }
-        if has_mask:
-            result["segmentation"] = rles[k]
-        if has_keypoints:
-            # In COCO annotations,
-            # keypoints coordinates are pixel indices.
-            # However our predictions are floating point coordinates.
-            # Therefore we subtract 0.5 to be consistent with the annotation format.
-            # This is the inverse of data loading logic in `datasets/coco.py`.
-            keypoints[k][:, :2] -= 0.5
-            result["keypoints"] = keypoints[k].flatten().tolist()
-        results.append(result)
-    return results
-
-
-# inspired from Detectron:
-# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
-def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None):
-    """
-    Evaluate detection proposal recall metrics. This function is a much
-    faster alternative to the official COCO API recall evaluation code. However,
-    it produces slightly different results.
-    """
-    # Record max overlap value for each gt box
-    # Return vector of overlap values
-    areas = {
-        "all": 0,
-        "small": 1,
-        "medium": 2,
-        "large": 3,
-        "96-128": 4,
-        "128-256": 5,
-        "256-512": 6,
-        "512-inf": 7,
-    }
-    area_ranges = [
-        [0**2, 1e5**2],  # all
-        [0**2, 32**2],  # small
-        [32**2, 96**2],  # medium
-        [96**2, 1e5**2],  # large
-        [96**2, 128**2],  # 96-128
-        [128**2, 256**2],  # 128-256
-        [256**2, 512**2],  # 256-512
-        [512**2, 1e5**2],
-    ]  # 512-inf
-    assert area in areas, "Unknown area range: {}".format(area)
-    area_range = area_ranges[areas[area]]
-    gt_overlaps = []
-    num_pos = 0
-
-    for prediction_dict in dataset_predictions:
-        predictions = prediction_dict["proposals"]
-
-        # sort predictions in descending order
-        # TODO maybe remove this and make it explicit in the documentation
-        inds = predictions.objectness_logits.sort(descending=True)[1]
-        predictions = predictions[inds]
-
-        ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"])
-        anno = coco_api.loadAnns(ann_ids)
-        gt_boxes = [
-            BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-            for obj in anno
-            if obj["iscrowd"] == 0
-        ]
-        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
-        gt_boxes = Boxes(gt_boxes)
-        gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
-
-        if len(gt_boxes) == 0 or len(predictions) == 0:
-            continue
-
-        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
-        gt_boxes = gt_boxes[valid_gt_inds]
-
-        num_pos += len(gt_boxes)
-
-        if len(gt_boxes) == 0:
-            continue
-
-        if limit is not None and len(predictions) > limit:
-            predictions = predictions[:limit]
-
-        overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
-
-        _gt_overlaps = torch.zeros(len(gt_boxes))
-        for j in range(min(len(predictions), len(gt_boxes))):
-            # find which proposal box maximally covers each gt box
-            # and get the iou amount of coverage for each gt box
-            max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-            # find which gt box is 'best' covered (i.e. 'best' = most iou)
-            gt_ovr, gt_ind = max_overlaps.max(dim=0)
-            assert gt_ovr >= 0
-            # find the proposal box that covers the best covered gt box
-            box_ind = argmax_overlaps[gt_ind]
-            # record the iou coverage of this gt box
-            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
-            assert _gt_overlaps[j] == gt_ovr
-            # mark the proposal box and the gt box as used
-            overlaps[box_ind, :] = -1
-            overlaps[:, gt_ind] = -1
-
-        # append recorded iou coverage level
-        gt_overlaps.append(_gt_overlaps)
-    gt_overlaps = (
-        torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
-    )
-    gt_overlaps, _ = torch.sort(gt_overlaps)
-
-    if thresholds is None:
-        step = 0.05
-        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
-    recalls = torch.zeros_like(thresholds)
-    # compute recall for each iou threshold
-    for i, t in enumerate(thresholds):
-        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
-    # ar = 2 * np.trapz(recalls, thresholds)
-    ar = recalls.mean()
-    return {
-        "ar": ar,
-        "recalls": recalls,
-        "thresholds": thresholds,
-        "gt_overlaps": gt_overlaps,
-        "num_pos": num_pos,
-    }
-
-
-def _evaluate_predictions_on_coco(
-    coco_gt,
-    coco_results,
-    iou_type,
-    kpt_oks_sigmas=None,
-    use_fast_impl=True,
-    img_ids=None,
-    max_dets_per_image=None,
-):
-    """
-    Evaluate the coco results using COCOEval API.
-    """
-    assert len(coco_results) > 0
-
-    if iou_type == "segm":
-        coco_results = copy.deepcopy(coco_results)
-        # When evaluating mask AP, if the results contain bbox, cocoapi will
-        # use the box area as the area of the instance, instead of the mask area.
-        # This leads to a different definition of small/medium/large.
-        # We remove the bbox field to let mask AP use mask area.
-        for c in coco_results:
-            c.pop("bbox", None)
-
-    coco_dt = coco_gt.loadRes(coco_results)
-    coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)
-    # For COCO, the default max_dets_per_image is [1, 10, 100].
-    if max_dets_per_image is None:
-        max_dets_per_image = [1, 10, 100]  # Default from COCOEval
-    else:
-        assert (
-            len(max_dets_per_image) >= 3
-        ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
-        # In the case that user supplies a custom input for max_dets_per_image,
-        # apply COCOevalMaxDets to evaluate AP with the custom input.
-        if max_dets_per_image[2] != 100:
-            coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)
-    if iou_type != "keypoints":
-        coco_eval.params.maxDets = max_dets_per_image
-
-    if img_ids is not None:
-        coco_eval.params.imgIds = img_ids
-
-    if iou_type == "keypoints":
-        # Use the COCO default keypoint OKS sigmas unless overrides are specified
-        if kpt_oks_sigmas:
-            assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!"
-            coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
-        # COCOAPI requires every detection and every gt to have keypoints, so
-        # we just take the first entry from both
-        num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
-        num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3
-        num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
-        assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
-            f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
-            f"Ground truth contains {num_keypoints_gt} keypoints. "
-            f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
-            "They have to agree with each other. For meaning of OKS, please refer to "
-            "http://cocodataset.org/#keypoints-eval."
-        )
-
-    coco_eval.evaluate()
-    coco_eval.accumulate()
-    coco_eval.summarize()
-
-    return coco_eval
-
-
-class COCOevalMaxDets(COCOeval):
-    """
-    Modified version of COCOeval for evaluating AP with a custom
-    maxDets (by default for COCO, maxDets is 100)
-    """
-
-    def summarize(self):
-        """
-        Compute and display summary metrics for evaluation results given
-        a custom value for  max_dets_per_image
-        """
-
-        def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
-            p = self.params
-            iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
-            titleStr = "Average Precision" if ap == 1 else "Average Recall"
-            typeStr = "(AP)" if ap == 1 else "(AR)"
-            iouStr = (
-                "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
-                if iouThr is None
-                else "{:0.2f}".format(iouThr)
-            )
-
-            aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
-            mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
-            if ap == 1:
-                # dimension of precision: [TxRxKxAxM]
-                s = self.eval["precision"]
-                # IoU
-                if iouThr is not None:
-                    t = np.where(iouThr == p.iouThrs)[0]
-                    s = s[t]
-                s = s[:, :, :, aind, mind]
-            else:
-                # dimension of recall: [TxKxAxM]
-                s = self.eval["recall"]
-                if iouThr is not None:
-                    t = np.where(iouThr == p.iouThrs)[0]
-                    s = s[t]
-                s = s[:, :, aind, mind]
-            if len(s[s > -1]) == 0:
-                mean_s = -1
-            else:
-                mean_s = np.mean(s[s > -1])
-            print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
-            return mean_s
-
-        def _summarizeDets():
-            stats = np.zeros((12,))
-            # Evaluate AP using the custom limit on maximum detections per image
-            stats[0] = _summarize(1, maxDets=self.params.maxDets[2])
-            stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
-            stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
-            stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
-            stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
-            stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
-            stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
-            stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
-            stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
-            stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
-            stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
-            stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
-            return stats
-
-        def _summarizeKps():
-            stats = np.zeros((10,))
-            stats[0] = _summarize(1, maxDets=20)
-            stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
-            stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
-            stats[3] = _summarize(1, maxDets=20, areaRng="medium")
-            stats[4] = _summarize(1, maxDets=20, areaRng="large")
-            stats[5] = _summarize(0, maxDets=20)
-            stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
-            stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
-            stats[8] = _summarize(0, maxDets=20, areaRng="medium")
-            stats[9] = _summarize(0, maxDets=20, areaRng="large")
-            return stats
-
-        if not self.eval:
-            raise Exception("Please run accumulate() first")
-        iouType = self.params.iouType
-        if iouType == "segm" or iouType == "bbox":
-            summarize = _summarizeDets
-        elif iouType == "keypoints":
-            summarize = _summarizeKps
-        self.stats = summarize()
-
-    def __str__(self):
-        self.summarize()
\ No newline at end of file
diff --git a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
deleted file mode 100644
index 55436f358ae708827d40a49b7d991da60bb89b39..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import warnings
-import math
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.init import xavier_uniform_, constant_
-
-if torch.cuda.is_available():
-    from ..functions import MSDeformAttnFunction
-else:
-    MSDeformAttnFunction = None
-from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch
-
-
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n-1) == 0) and n != 0
-
-
-class MSDeformAttn(nn.Module):
-    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
-        """
-        Multi-Scale Deformable Attention Module
-        :param d_model      hidden dimension
-        :param n_levels     number of feature levels
-        :param n_heads      number of attention heads
-        :param n_points     number of sampling points per attention head per feature level
-        """
-        super().__init__()
-        if d_model % n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
-        _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
-        if not _is_power_of_2(_d_per_head):
-            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
-                          "which is more efficient in our CUDA implementation.")
-
-        self.im2col_step = 128
-
-        self.d_model = d_model
-        self.n_levels = n_levels
-        self.n_heads = n_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
-        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(d_model, d_model)
-        self.output_proj = nn.Linear(d_model, d_model)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        constant_(self.sampling_offsets.weight.data, 0.)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)
-        for i in range(self.n_points):
-            grid_init[:, :, i, :] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.)
-        constant_(self.attention_weights.bias.data, 0.)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.)
-
-    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
-        """
-        :param query                       (N, Length_{query}, C)
-        :param reference_points            (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
-                                        or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
-        :param input_flatten               (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
-        :param input_spatial_shapes        (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
-        :param input_level_start_index     (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
-        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
-        :return output                     (N, Length_{query}, C)
-        """
-        N, Len_q, _ = query.shape
-        N, Len_in, _ = input_flatten.shape
-        assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
-
-        value = self.value_proj(input_flatten)
-        if input_padding_mask is not None:
-            value = value.masked_fill(input_padding_mask[..., None], float(0))
-        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
-        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
-        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        # N, Len_q, n_heads, n_levels, n_points, 2
-        if reference_points.shape[-1] == 2:
-            offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
-            sampling_locations = reference_points[:, :, None, :, None, :] \
-                                 + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
-        elif reference_points.shape[-1] == 4:
-            sampling_locations = reference_points[:, :, None, :, None, :2] \
-                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
-        else:
-            raise ValueError(
-                'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))
-        # try:
-        if torch.cuda.is_available():  
-            output = MSDeformAttnFunction.apply(
-                value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)
-        # except:
-        else:
-            # CPU
-            output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
-        # # For FLOPs calculation only
-        # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
-        output = self.output_proj(output)
-        return output
\ No newline at end of file
diff --git a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/attention.py b/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/attention.py
deleted file mode 100644
index ac1ad1a1ce83ea9d4dbb4d597b39b2f4ae6dbe1f..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/attention.py
+++ /dev/null
@@ -1,435 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-from .diffusion_utils import checkpoint
-
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-class SpatialSelfAttention(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 = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        attn = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', attn, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False):
-        super().__init__()
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                                    context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False):
-        super().__init__()
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim,
-                                   disable_self_attn=disable_self_attn)
-                for d in range(depth)]
-        )
-
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        for block in self.transformer_blocks:
-            x = block(x, context=context)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        x = self.proj_out(x)
-        return x + x_in
-
-
-##########################
-# transformer no context #
-##########################
-
-class BasicTransformerBlockNoContext(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., gated_ff=True, checkpoint=True):
-        super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, 
-                                    dropout=dropout, context_dim=None)
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, 
-                                    dropout=dropout, context_dim=None)
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), self.checkpoint)
-
-    def _forward(self, x):
-        x = self.attn1(self.norm1(x)) + x
-        x = self.attn2(self.norm2(x)) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-class SpatialTransformerNoContext(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0.,):
-        super().__init__()
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlockNoContext(inner_dim, n_heads, d_head, dropout=dropout)
-                for d in range(depth)]
-        )
-
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
-
-    def forward(self, x):
-        # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        for block in self.transformer_blocks:
-            x = block(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        x = self.proj_out(x)
-        return x + x_in
-
-
-#######################################
-# Spatial Transformer with Two Branch #
-#######################################
-
-class DualSpatialTransformer(nn.Module):
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False):
-        super().__init__()
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-
-        # First crossattn
-        self.norm_0 = Normalize(in_channels)
-        self.proj_in_0 = nn.Conv2d(
-            in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-        self.transformer_blocks_0 = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim,
-                                   disable_self_attn=disable_self_attn)
-                for d in range(depth)]
-        )
-        self.proj_out_0 = zero_module(nn.Conv2d(
-            inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
-
-        # Second crossattn
-        self.norm_1 = Normalize(in_channels)
-        self.proj_in_1 = nn.Conv2d(
-            in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-        self.transformer_blocks_1 = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim,
-                                   disable_self_attn=disable_self_attn)
-                for d in range(depth)]
-        )
-        self.proj_out_1 = zero_module(nn.Conv2d(
-            inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
-
-    def forward(self, x, context=None, which=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        if which==0:
-            norm, proj_in, blocks, proj_out = \
-                self.norm_0, self.proj_in_0, self.transformer_blocks_0, self.proj_out_0
-        elif which==1:
-            norm, proj_in, blocks, proj_out = \
-                self.norm_1, self.proj_in_1, self.transformer_blocks_1, self.proj_out_1
-        else:
-            # assert False, 'DualSpatialTransformer forward with a invalid which branch!'
-            # import numpy.random as npr
-            # rwhich = 0 if npr.rand() < which else 1
-            # context = context[rwhich]
-            # if rwhich==0:
-            #     norm, proj_in, blocks, proj_out = \
-            #         self.norm_0, self.proj_in_0, self.transformer_blocks_0, self.proj_out_0
-            # elif rwhich==1:
-            #     norm, proj_in, blocks, proj_out = \
-            #         self.norm_1, self.proj_in_1, self.transformer_blocks_1, self.proj_out_1
-
-            # import numpy.random as npr
-            # rwhich = 0 if npr.rand() < 0.33 else 1
-            # if rwhich==0:
-            #     context = context[rwhich]
-            #     norm, proj_in, blocks, proj_out = \
-            #         self.norm_0, self.proj_in_0, self.transformer_blocks_0, self.proj_out_0
-            # else:
-
-            norm, proj_in, blocks, proj_out = \
-                self.norm_0, self.proj_in_0, self.transformer_blocks_0, self.proj_out_0
-            x0 = norm(x)
-            x0 = proj_in(x0)
-            x0 = rearrange(x0, 'b c h w -> b (h w) c').contiguous()
-            for block in blocks:
-                x0 = block(x0, context=context[0])
-            x0 = rearrange(x0, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-            x0 = proj_out(x0)
-
-            norm, proj_in, blocks, proj_out = \
-                self.norm_1, self.proj_in_1, self.transformer_blocks_1, self.proj_out_1
-            x1 = norm(x)
-            x1 = proj_in(x1)
-            x1 = rearrange(x1, 'b c h w -> b (h w) c').contiguous()
-            for block in blocks:
-                x1 = block(x1, context=context[1])
-            x1 = rearrange(x1, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-            x1 = proj_out(x1)
-            return x0*which + x1*(1-which) + x_in
-
-        x = norm(x)
-        x = proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        for block in blocks:
-            x = block(x, context=context)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        x = proj_out(x)
-        return x + x_in
diff --git a/spaces/shivambhosale/spacenet3-unet-1024-1024/app.py b/spaces/shivambhosale/spacenet3-unet-1024-1024/app.py
deleted file mode 100644
index 59095790b4db16831c37fcfc678fff0931771fc4..0000000000000000000000000000000000000000
--- a/spaces/shivambhosale/spacenet3-unet-1024-1024/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from UNet import *
-from make_predictions import *
-import torch
-import gradio as gr
-
-def getoutput(threshold,input_img):
-    unet = UNet().to('cpu')
-    unet = torch.load("unet_06_07_2022_22_43_42_1024_1024.pth", map_location='cpu').to('cpu')
-    output_img = make_predictions(unet, input_img, threshold=threshold)
-    return output_img
-
-demo = gr.Interface(
-    fn=getoutput, 
-    inputs=["number", gr.Image(shape=(200, 200))], 
-    outputs=["image"])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/text/cleaners.py b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/text/cleaners.py
deleted file mode 100644
index de17a8b952f733daf464548de8a332cb4c89766d..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/text/cleaners.py
+++ /dev/null
@@ -1,73 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import re
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-
-_whitespace_re = re.compile(r'\s+')
-
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def expand_numbers(text):
-    return normalize_numbers(text)
-
-
-def lowercase(text):
-    return text.lower()
-
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-
-def basic_cleaners(text):
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def transliteration_cleaners(text):
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaners(text):
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = collapse_whitespace(text)
-    return text
diff --git a/spaces/simonraj/ELOralCoachHONGWEN/HongWenData.py b/spaces/simonraj/ELOralCoachHONGWEN/HongWenData.py
deleted file mode 100644
index bbb0833d9a5b2c8ece4124f04f613540194784e0..0000000000000000000000000000000000000000
--- a/spaces/simonraj/ELOralCoachHONGWEN/HongWenData.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#HongWenData.py
-strategy_text = {
-    "TREES": (
-        "TREES strategy - Thought, Reasons, Elaboration, Experiences, Summary or Suggestions", 
-        "Structure your feedback using the TREES strategy. Begin with a Thought, provide Reasons, Elaborate using the 5Ws1H method: Who, What, Where, When, Why, and How, share Experiences, and finally offer a Summary or Suggestions."
-    )
-}
-
-description = (
-    "The image showcases a promotional advertisement banner titled \"Bestsellers for the month of October!\" Underneath the title, there's a sub-caption that reads, \"Do not miss out! Now at a special discount for 3 days only!\""
-    "\n\nThe items displayed in the advertisement are:"
-    "\n\nBestseller #1 - A board game. It is illustrated as a rectangular game box with question marks on its cover, suggesting the content or theme of the game might be a surprise or mystery."
-    "\n\nBestseller #2 - A gaming console. The illustration shows a sleek, flat gaming console device next to a controller with buttons and a joystick."
-    "\n\nBestseller #3 - Titled \"Smash It\", this item is depicted as a badminton racket paired with a shuttlecock."
-    "\n\nThe products are clearly labeled and emphasized to showcase their popularity and relevance for the month of October. The entire design aims to attract customers and prompt them to avail of the special discount."
-)
-
-questions = [
-    f"1. Look at the picture. Would you be interested in playing with these toys and games? Why / Why not? ",
-    f"2. Do you spend a lot of time playing with toys and games? Why / Why not? ",
-    f"3. What do you think are some benefits of leisure activities? "
-]
-
-def generate_system_message():
-    strategy, explanation = strategy_text["TREES"]
-    
-    system_message = f"""
-    As your English Oral Coach, my role is to guide you as you prepare to answer the oral questions. I'll be asking thought-provoking questions to help you develop your own answers. 
-
-    Now, let's focus on the {strategy}. {explanation}
-
-    Along the way, I'll prompt you to clarify your thoughts, explore key terms, challenge your reasoning, and reflect on the discussion. 
-
-    Once we've thoroughly explored each part of the strategy, I'll assist you in assembling your thoughts into a comprehensive and eloquent response using the insights we've gathered.
-
-    Remember, our ultimate goal is to enhance your critical thinking skills and independence. Try to use sophisticated vocabulary and expressions, and refer to the picture where relevant to support your response.
-
-    Please ensure your response is in English.
-    """
-    return system_message
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Crafting and Building 1.17 on PC and Experience the Fun of Creative Construction.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Crafting and Building 1.17 on PC and Experience the Fun of Creative Construction.md
deleted file mode 100644
index ec517590ed6f6391ec467efa1fadbcbf87934dbf..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Crafting and Building 1.17 on PC and Experience the Fun of Creative Construction.md	
+++ /dev/null
@@ -1,235 +0,0 @@
-
-<h1>Crafting and Building 1.17: A Free Alternative to Minecraft</h1> | <p>If you like building games, you might have heard of Minecraft, the best-selling game of all time that lets you create anything you can imagine in a pixelated world. But did you know that there is another game that offers a similar experience for free? It's called Crafting and Building 1.17, and it's a new free building game for Android devices that lets you explore, craft, and build in a sandbox world.</p>
- <p>In this article, we will tell you everything you need to know about Crafting and Building 1.17, how it compares with Minecraft, and some tips and tricks to help you play the game better.</p>
-<h2>crafting and building 1.17 download apk for pc</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://ssurll.com/2uNUET">https://ssurll.com/2uNUET</a></b></p><br /><br />
- <h2>What is Crafting and Building 1.17?</h2>
- <p>Crafting and Building 1.17 is an adventure game developed by GeneRe, a mobile game studio based in Turkey. It was released on April 17, 2023, as an update to the previous version of the game, which had over 50 million downloads on Google Play.</p>
- <p>Crafting and Building 1.17 is a free game for the whole family, from kids to adults, that lets you build anything you want in a sandbox world. You can choose your character, customize your skin, decorate your house, play with pets, meet new neighbors, explore hidden caves, create massive castles and temples, and play with your friends online.</p>
- <h3>The gameplay of Crafting and Building 1.17</h3>
- <p>The gameplay of Crafting and Building 1.17 is similar to Minecraft's survival mode, where you have to gather resources, craft tools and weapons, fight enemies, and survive in a hostile environment.</p>
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- <p>You start the game by choosing a world type (flat or normal) and a difficulty level (peaceful or normal). Then, you spawn in a random location in the world, where you have to find a suitable place to build your house.</p>
- <p>You can use your fists or tools to break blocks of different materials, such as wood, stone, dirt, sand, etc., and collect them in your inventory. You can also find chests with loot, animals with meat and leather, plants with seeds and flowers, etc.</p>
- <p>You can use a crafting table to combine different items into new ones, such as planks from wood, torches from coal and sticks, doors from planks, etc. You can also use a furnace to smelt ores into ingots or cook food.</p>
- <p>You can place blocks anywhere you want to create structures or decorations for your house or realm. You can also use different items to interact with the world or other players, such as beds to sleep at night or skip rainstorms, signs to write messages or labels, paintings to add some art to your walls, etc.</p>
- <p>You have to watch out for your health and hunger bars at the bottom of the screen. If your health drops to zero due to fall damage or enemy attacks, you will die and lose all your items (unless you enable keep inventory option). If your hunger drops too low due to not eating enough food or doing too much physical activity, you will lose health <p>gradually and eventually die. You can restore your health by eating food or using potions, and restore your hunger by eating food or drinking water.</p>
- <p>You can also encounter various enemies in the world, such as zombies, skeletons, spiders, creepers, etc., that will try to kill you or destroy your buildings. You can fight them with your weapons or traps, or avoid them by hiding or running away. You can also tame some animals, such as dogs, cats, horses, etc., that will help you in combat or transportation.</p>
- <p>You can also play the game in creative mode, where you have unlimited resources and no enemies, and you can fly around the world and build anything you want without any restrictions.</p>
- <h3>The features of Crafting and Building 1.17</h3>
- <p>Crafting and Building 1.17 has many features that make it a fun and exciting game to play. Some of the main features are:</p>
- <ul>
-<li>Multiplayer mode: You can play the game with your friends online, either in the same world or in different worlds. You can chat with them, trade with them, cooperate with them, or compete with them. You can also join public servers with other players from around the world.</li>
-<li>Skins editor: You can customize your character's appearance by choosing from hundreds of skins or creating your own skin with the skin editor. You can also change your clothes, hair, eyes, etc.</li>
-<li>Pets: You can adopt various pets in the game, such as dogs, cats, birds, fish, etc., that will follow you around and provide you with companionship and entertainment. You can also feed them, play with them, and train them.</li>
-<li>Worlds: You can create your own worlds or join existing worlds created by other players. You can choose from different world types, such as flat, normal, city, village, etc., and different biomes, such as forest, desert, snow, etc. You can also explore different dimensions, such as the nether and the end.</li>
-<li>Blocks: You can use over 1000 different blocks to build anything you want in the game. You can choose from different categories of blocks, such as natural blocks (wood, stone, dirt, etc.), building blocks (bricks, glass, concrete, etc.), decoration blocks (furniture, paintings, flowers, etc.), functional blocks (doors, chests, furnaces, etc.), and special blocks (glowstone, TNT, command blocks, etc.).</li>
-<li>Items: You can use over 500 different items to interact with the game world or other players. You can choose from different categories of items, such as tools (pickaxes, axes, shovels, etc.), weapons (swords, bows, arrows, etc.), armor (helmets, chestplates, leggings, etc.), food (bread, apples, cake, etc.), potions (healing, speed, invisibility, etc.), and miscellaneous items (books, maps, compasses, etc.).</li>
-<li>Mods: You can download and install various mods that add new features or change the game mechanics. You can choose from different types of mods, such as adventure mods (quests, dungeons, bosses, etc.), aesthetic mods (shaders, textures, sounds, etc.), utility mods (minimap, inventory tweaks, waypoints, etc.), and cheat mods (fly mode, god mode, unlimited resources, etc.).</li>
-</ul>
- <h2>How does Crafting and Building 1.17 compare with Minecraft?</h2>
- <p>Crafting and Building 1.17 is clearly inspired by Minecraft, and shares many similarities with it. However, it also has some differences that make it a unique game in its own right. Here are some of the main similarities and differences between Crafting and Building 1.17 and Minecraft:</p>
- <h3>The similarities between Crafting and Building 1.17 and Minecraft</h3>
- <ul>
-<li>Both games are sandbox games that let you build anything you want in a pixelated world.</li>
-<li>Both games have survival and creative modes that offer different challenges and possibilities.</li>
-<li>Both games have multiplayer modes that let you play with your friends online or join public servers.</li>
-<li>Both games have similar gameplay mechanics, such as breaking and placing blocks, crafting and smelting items, fighting enemies and animals, exploring caves and dimensions, etc.</li>
-<li>Both games have similar graphics and sound effects that create a retro and nostalgic atmosphere.</li>
-</ul>
- <h3>The differences between Crafting and Building 1.17 and Minecraft</h3>
- <ul>
-<li>Crafting and Building 1.17 is a free game for Android devices only, while Minecraft is a paid game for multiple platforms.</li>
-<li>Crafting and Building 1.17 has more blocks and items than Minecraft.</li>
-<li>Crafting and Building 1.17 has more pets than Minecraft.</li>
-<li>Crafting and Building 1.17 has more skins than Minecraft.</li>
-<li>Crafting and Building 1.17 has more worlds than Minecraft.</li>
-<li>Crafting and Building 1.17 has more mods than Minecraft.</li>
-</ul>
- <h2>Tips and tricks for playing Crafting and Building 1.17</h2>
- <p>If you want to enjoy Crafting and Building 1.17 to the fullest, here are some tips and tricks that will help you play the game better:</p>
- <h3>How to download and install Crafting and Building 1.17 on PC</h3>
- <p>If you want to play Crafting and Building 1.17 on your PC instead of your Android device, <p>you can use one of the following methods:</p>
- <ul>
-<li>Windows 11 and native Android emulation: If you have a Windows 11 PC, you can use the native Android emulation feature to run Android apps, including games, on your PC. You can access the Amazon Appstore from the Microsoft Store and download the games you want. You can also install the Google Play Store on Windows 11 and get more games from there. This method does not require any third-party emulator, but it is only available in some regions for now. You can learn more about this method from [this article](^2^).</li>
-<li>Android emulator: If you have a Windows 10 or older PC, or if you want more control and customization over your Android gaming experience, you can use an Android emulator to run Android games on your PC. An Android emulator is a software that simulates an Android device on your PC, allowing you to install and run Android apps. There are many Android emulators available, such as Bluestacks 5, Nox Player, Gameloop, etc. You can download and install any of them from their official websites and sign in to your Google account to access the Google Play Store. You can also install APK files from other sources if you want. You can learn more about these emulators from [this article](^1^).</li>
-</ul>
- <p>Once you have downloaded and installed Crafting and Building 1.17 on your PC using either of these methods, you can launch the game and start playing.</p>
- <h3>How to build your house in a castle or in a mine</h3>
- <p>One of the most fun aspects of Crafting and Building 1.17 is building your own house in the game world. You can build your house anywhere you want, but two of the most popular locations are castles and mines.</p>
- <p>A castle is a large and impressive structure that can protect you from enemies and show off your creativity. A mine is a deep and dark place that can provide you with valuable resources and hidden secrets.</p>
- <p>To build your house in a castle or in a mine, you need to follow these steps:</p>
- <ol>
-<li>Find a suitable location: You need to find a place where you can build your castle or mine without any obstacles or dangers. You can either use an existing structure or create your own from scratch. For a castle, you can look for a flat land near a mountain or a lake, or use an existing village or temple as a base. For a mine, you can look for a cave entrance or dig your own tunnel underground.</li>
-<li>Gather the materials: You need to collect the blocks and items that you will use to build your castle or mine. You can use any blocks that you like, but some of the most common ones are stone, wood, bricks, glass, etc. You can also use some special blocks, such as glowstone, TNT, command blocks, etc., to add some functionality or flair to your building. You can also use some items, such as doors, chests, furnaces, beds, etc., to furnish your castle or mine.</li>
-<li>Design your layout: You need to plan how you want your castle or mine to look like before you start building. You can sketch your design on paper or use an online tool like [this one] to help you visualize your building. You should consider the size, shape, style, color, and theme of your castle or mine. You should also think about the rooms, corridors, stairs, windows, etc.), or use an online tool like [this one](^2^) to help you visualize your building. You should consider the size, shape, style, color, and theme of your castle or mine. You should also think about the rooms, corridors, stairs, windows, etc.</li>
-<li>Build your structure: You need to place the blocks and items according to your design and create your castle or mine. You can use different techniques to make your building more realistic and attractive, such as adding details, textures, patterns, lighting, etc. You can also use some redstone devices to add some functionality or automation to your building, such as doors, traps, elevators, etc.</li>
-</ol>
- <p>Here are some examples of castles and mines that you can take inspiration from or copy:</p>
- <table>
-<tr>
-<th>Castles</th>
-<th>Mines</th>
-</tr>
-<tr>
-<td><img src="^1^" alt="A medieval castle with towers and walls"></td>
-<td><img src="^4^" alt="A mine entrance with a crane and a cart"></td>
-</tr>
-<tr>
-<td><img src="^3^" alt="A Japanese castle with pagodas and gardens"></td>
-<td><img src="^5^" alt="A mine shaft with rails and torches"></td>
-</tr>
-<tr>
-<td><img src="^6^" alt="A fantasy castle with a dragon and a bridge"></td>
-<td><img src="^7^" alt="A mine camp with tents and a furnace"></td>
-</tr>
-</table>
- <h3>How to play with your friends online</h3>
- <p>One of the best things about Crafting and Building 1.17 is that you can play with your friends online and share your creations with them. You can either join their worlds or invite them to yours. Here's how to do it:</p>
- <ul>
-<li>Joining a friend's world: If you want to join a friend's world, you need to know their username and the name of their world. Then, you can follow these steps:     <ol>
-    <li>Open the game and tap the "Multiplayer" button on the main menu.</li>
-    <li>Tap the "Friends" tab on the top of the screen.</li>
-    <li>Tap the "Add Friend" button on the bottom of the screen.</li>
-    <li>Type your friend's username in the text box and tap "OK".</li>
-    <li>Wait for your friend to accept your request. You will see their name appear on your friends list.</li>
-    <li>Tap your friend's name and select the world you want to join from the list.</li>
-    <li>Wait for the world to load and enjoy playing with your friend.</li>
-    </ol></li>
-<li>Inviting a friend to your world: If you want to invite a friend to your world, you need to make sure that they have added you as a friend first. Then, you can follow these steps:     <ol>
-    <li>Open the game and tap the "Play" button on the main menu.</li>
-    <li>Select the world you want to invite your friend to from the list.</li>
-    <li>Tap the "Multiplayer" button on the bottom of the screen.</li>
-    <li>Tap the "Invite Friends" button on the top of the screen.</li>
-    <li>Select the friend you want to invite from the list.</li>
-    <li>Wait for your friend to accept your invitation. You will see their name appear on your players list.</li>
-    <li>Wait for them to join your world and enjoy playing with them.</li>
-    </ol></li>
-</ul>
- <h3>How to use different block types to create your realm</h3>
- <p>Crafting and Building 1.17 gives you a lot of freedom and creativity when it comes to building your realm. You can use different block types to create different effects and atmospheres for your buildings and structures. Here are some tips on how to use different block types to create your realm:</p>
- <ul>
-<li>Natural blocks: These are blocks that are found naturally in the game world, such as wood, stone, dirt, sand, etc. You can use these blocks to create realistic and organic buildings that blend in with the environment. You can also use these blocks to create natural features, such as trees, mountains, rivers, etc.</li>
-<li>Building blocks: These are blocks that are crafted from natural blocks or other materials, such as bricks, glass, concrete, etc. You can use these blocks to create modern and sophisticated buildings that stand out from the environment. You can also use these blocks to create artificial features, such as roads, bridges, towers, etc.</li>
-<li>Decoration blocks: These are blocks that are used to add some detail and beauty to your buildings, such as furniture, paintings, flowers, etc. You can use these blocks to create cozy and comfortable interiors or exteriors for your buildings. You can also use these blocks to create artistic features, such as statues, fountains, mosaics, etc.</li>
-<li>Functional blocks: These are blocks that have some functionality or purpose in the game, such as doors, chests, furnaces, etc. You can use these blocks to make your buildings more useful and convenient for you or other players. You can also use these blocks to create mechanical features, such as traps, elevators, machines, etc.</li>
-<li>Special blocks: These are blocks that have some special properties or effects in the game, such as glowstone, TNT, command blocks, etc. You can use these blocks to create magical and mysterious buildings that have some surprises or secrets for you or other players. You can also use these blocks to create fantastical features, such as portals, explosions, spells, etc.</li>
-</ul>
- <p>Here are some examples of how different block types can be used to create different realms:</p>
- <table>
-<tr>
-<th>Natural blocks</th>
-<th>Building blocks</th>
-<th>Decoration blocks</th>
-<th>Functional blocks</th>
-<th>Special blocks</th>
-</tr>
-<tr>
-<td><img src="^8^" alt="A wooden cabin in a forest"></td>
-<td><img src="^9^" alt="A glass skyscraper in a city"></td>
-<td><img src="^10^" alt="A flower garden in a park"></td>
-<td><img src="^11^" alt="A kitchen with a stove and a fridge"></td>
-<td><img src="^12^" alt="A nether portal in a cave"></td>
-</tr>
-<tr>
-<td><img src="^13^" alt="A stone castle on a hill"></td>
-<td><img src="^14^" alt="A concrete bunker in a desert"></td>
-<td><img src="^15^" alt="A painting gallery in a museum"></td>
-<td><img src="^16^" alt="A chest room with a lot of loot"></td>
-<td><img src="^17^" alt="A TNT cannon on a ship"></td>
-</tr>
-<tr>
-<td><img src="^18^" alt="A dirt hut in a swamp"></td>
-<td><img src="^19^" alt="A brick house in a village"></td>
-<td><img src="^20^" alt="A furniture store in a mall"></td>
-<td><img src="^21^" alt="A furnace room with a lot of smelting"></td>
-<td><img src="^22^" alt="A command block puzzle in a temple"></td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>Crafting and Building 1.17 is a free building game for Android devices that lets you explore, craft, and build in a sandbox world. It is similar to Minecraft, but it has some unique features and advantages that make it worth playing. You can play the game in survival or creative mode, alone or with your friends online, and use different block types to create your realm. You can also download and install various mods that add new features or change the game mechanics.</p>
- <p>If you are looking for a fun and creative game that will keep you entertained for hours, you should give Crafting and Building 1.17 a try. You will not regret it.</p>
- <h3>Summary of the main points</h3>
- <ul>
-<li>Crafting and Building 1.17 is a free building game for Android devices that lets you build anything you want in a sandbox world.</li>
-<li>Crafting and Building 1.17 is similar to Minecraft's survival mode, but it has more blocks, items, pets, skins, worlds, and mods than Minecraft.</li>
-<li>Crafting and Building 1.17 has multiplayer mode that lets you play with your friends online or join public servers.</li>
-<li>Crafting and Building 1.17 has skins editor that lets you customize your character's appearance.</li>
-<li>Crafting and Building 1.17 has tips and tricks that will help you play the game better.</li>
-</ul>
- <h3>FAQs</h3>
- <ol>
-<li>Q: Is Crafting and Building 1.17 safe to download and play?<br>A: Yes, Crafting and Building 1.17 is safe to download and play. It does not contain any viruses or malware. However, you should always download the game from the official sources (Google Play Store or Amazon Appstore) and avoid any third-party or unofficial websites that may contain harmful or fake versions of the game.</li>
-<li>Q: How can I update Crafting and Building 1.17 to the latest version?<br>A: You can update Crafting and Building 1.17 to the latest version by following these steps:     <ol>
-    <li>Open the Google Play Store or Amazon Appstore on your Android device.</li>
-    <li>Search for Crafting and Building 1.17 in the search bar.</li>
-    <li>Tap the "Update" button next to the game icon.</li>
-    <li>Wait for the update to download and install.</li>
-    <li>Launch the game and enjoy the new features and improvements.</li>
-    </ol></li>
-<li>Q: How can I backup or restore my Crafting and Building 1.17 worlds?<br>A: You can backup or restore your Crafting and Building 1.17 worlds by following these steps:     <ol>
-    <li>Open the game and tap the "Play" button on the main menu.</li>
-    <li>Select the world you want to backup or restore from the list.</li>
-    <li>Tap the "Settings" button on the bottom of the screen.</li>
-    <li>Tap the "Backup" or "Restore" button on the top of the screen.</li>
-    <li>Select a location to save or load your world data, such as your device storage, Google Drive, Dropbox, etc.</li>
-    <li>Confirm your action and wait for the process to complete.</li>
-    </ol></li>
-<li>Q: How can I report a bug or a problem in Crafting and Building 1.17?<br>A: You can report a bug or a problem in Crafting and Building 1.17 by following these steps:     <ol>
-    <li>Open the game and tap the "Settings" button on the main menu.</li>
-    <li>Tap the "Feedback" button on the bottom of the screen.</li>
-    <li>Type your message in the text box, describing the bug or problem as clearly as possible.</li>
-    <li>Tap the "Send" button on the top of the screen.</li>
-    </ol></li>
-<li>Q: How can I contact the developers of Crafting and Building 1.17?<br>A: You can contact the developers of Crafting and Building 1.17 by using one of these methods:     <ul>
-    <li>Email: craftingandbuilding@gmail.com</li>
-    <li>Facebook: https://www.facebook.com/CraftingAndBuilding</li>
-    <li>Twitter: https://twitter.com/CraftingBuild</li>
-    </ul></li>
-</ol>
- <p>I hope you enjoyed this article and learned something new about Crafting and Building 1.17. If you have any questions or comments, feel free to leave them below. Thank you for reading!</p> 401be4b1e0<br />
-<br />
-<br />
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-<br />
-<h1>BMX Bike Racing Game Download: A Guide for Beginners</h1>
-<p>If you are a fan of extreme sports and adrenaline-pumping action, you might want to try BMX bike racing games. These games let you experience the thrill and excitement of riding a BMX bike on various tracks and courses, performing tricks and stunts, and competing against other players or the clock. In this article, we will give you a brief introduction to BMX bike racing, the reasons why you should play BMX bike racing games, and how to download and play them on your device.</p>
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- <h2>What is BMX Bike Racing?</h2>
-<p>BMX bike racing is a type of off-road bicycle racing that originated in the 1970s in California. It was inspired by motocross racing, where riders use motorcycles to race on dirt tracks with jumps and obstacles. BMX stands for bicycle motocross, and it involves riders using specially designed bikes that are lightweight, durable, and agile. BMX bikes have small wheels, low seats, and high handlebars, which allow riders to maneuver easily and perform tricks.</p>
- <h3>The history and popularity of BMX racing</h3>
-<p>BMX racing started as a grassroots sport, where kids would imitate their motocross heroes by building their own tracks and bikes. Soon, local races and clubs were formed, and the sport gained popularity across the US and other countries. In 1981, the International BMX Federation was established to govern the sport, and in 1982, the first world championships were held in Dayton, Ohio. In 2008, BMX racing became an Olympic sport, with eight men and eight women competing for medals in Beijing.</p>
- <h3>The basic rules and skills of BMX racing</h3>
-<p>BMX racing is a fast-paced and exciting sport that requires speed, strength, balance, coordination, and courage. The races are usually short, lasting between 30 seconds to a minute, depending on the track length and difficulty. The tracks are typically dirt or asphalt, with various features such as hills, jumps, turns, berms, rollers, and rhythm sections. The riders start from a gate at the top of a hill, and race to the finish line while trying to avoid crashes and collisions.</p>
- <p>The basic skills of BMX racing include pedaling, braking, cornering, jumping, pumping, manualing, and bunny hopping. Pedaling is the most important skill, as it determines how fast you can go. Braking is used to slow down or stop your bike when needed. Cornering is the skill of taking turns smoothly and efficiently. Jumping is the skill of launching your bike off ramps or bumps. Pumping is the skill of using your body weight to gain speed on uneven terrain. Manualing is the skill of lifting your front wheel off the ground while balancing on your rear wheel. Bunny hopping is the skill of lifting both wheels off the ground at the same time.</p>
- <h2>Why Play BMX Bike Racing Games?</h2>
-<p>BMX bike racing games are fun and challenging games that can appeal to anyone who likes bikes, sports, or action. Here are some of the benefits of playing BMX bike racing games:</p>
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-<li>They can stimulate your creativity and imagination by letting you customize your bike and create your own tracks.</li>
-<li>They can provide you with a sense of achievement and satisfaction by completing goals and challenges.</li>
-<li>They can help you learn more about BMX culture and history by featuring real-life riders, locations, events, and tricks.</li>
-<li>They can entertain you for hours with their diverse gameplay options, such as career mode , free ride mode, multiplayer mode, and online mode.</li>
-</ul>
- <h3>The features and modes of BMX bike racing games</h3>
-<p>BMX bike racing games offer a variety of features and modes that can enhance your gaming experience. Some of the common features and modes are:</p>
- <ul>
-<li>Realistic graphics and physics that simulate the look and feel of BMX racing.</li>
-<li>Authentic sound effects and music that create an immersive atmosphere.</li>
-<li>Easy-to-use controls and tutorials that help you learn the basics and master the advanced techniques.</li>
-<li>Customizable bikes and riders that let you choose your style and preferences.</li>
-<li>Track editor and generator that let you design your own tracks or generate random ones.</li>
-<li>Career mode that lets you start from scratch and progress through different levels, leagues, and championships.</li>
-<li>Free ride mode that lets you explore the tracks and practice your skills without any pressure or limitations.</li>
-<li>Multiplayer mode that lets you play with or against your friends locally or online.</li>
-<li>Online mode that lets you compete with other players from around the world, join clans, chat, and share your achievements.</li>
-</ul>
- <h2>How to Download and Play BMX Bike Racing Games?</h2>
-<p>If you are ready to try BMX bike racing games, you will need to download them on your device first. There are many BMX bike racing games available for different platforms, such as PC, console, mobile, and web. Here are some of the best BMX bike racing games for each platform:</p>
- <h3>The best BMX bike racing games for different platforms</h3>
- <table>
-<tr>
-<th>Platform</th>
-<th>Game</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>PC</td>
-<td>Pumped BMX Pro</td>
-<td>A game that lets you ride through 60 levels of challenging courses, perform over 200 tricks, and customize your bike and rider.</td>
-</tr>
-<tr>
-<td>Console</td>
-<td>Bike Mayhem 2</td>
-<td>A game that lets you race downhill on 50 tracks, compete in tournaments, upgrade your bike and gear, and unlock new outfits and bikes.</td>
-</tr>
-<tr>
-<td>Mobile</td>
-<td>Mad Skills BMX 2</td>
-<td>A game that lets you race against other players online, earn rewards, collect bikes and parts, and join events and leaderboards.</td>
-</tr>
-<tr>
-<td>Web</td>
-<td>Bike Blast</td>
-<td>A game that lets you ride through a busy city, dodge obstacles, collect coins, power-ups, and bonuses, and perform stunts.</td>
-</tr>
-</table>
- <p>To download and play these games, you will need to follow these steps:</p>
- <ol>
-<li>Choose the platform and the game that suits your preference and device specifications.</li>
-<li>Go to the official website or app store of the game and click on the download or install button.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Launch the game and follow the instructions on the screen to set up your account and settings.</li>
-<li>Select the mode and the track that you want to play and start your BMX bike racing adventure.</li>
-</ol>
- <h3>The tips and tricks for playing BMX bike racing games</h3>
- <p>To improve your performance and enjoyment of BMX bike racing games, here are some tips and tricks that you can use:</p>
- <ul>
-<li>Practice regularly to hone your skills and learn new tricks.</li>
-<li>Watch tutorials and videos of other players to get inspired and learn from their techniques.</li>
-<li>Experiment with different bikes, parts, settings, and tracks to find what works best for you.</li>
-<li>Use the boosters, power-ups, and bonuses wisely to gain an edge over your opponents or beat your own records.</li>
-<li>Challenge yourself by trying harder levels, modes, or tracks, or by setting your own goals.</li>
-<li>Have fun and enjoy the ride!</li>
- <h2>Conclusion</h2>
- <p>BMX bike racing games are a great way to experience the thrill and excitement of BMX racing without leaving your home. They can also help you improve your skills, knowledge, creativity, and entertainment. Whether you are a beginner or a pro, there is a BMX bike racing game for you. All you need to do is download it on your device and start playing. So what are you waiting for? Grab your bike and helmet, hit the track, and have a blast!</p>
- <h2>FAQs</h2>
- <ol>
- <li><b>What is the difference between BMX racing and freestyle?</b></li>
- <p <p>BMX racing is a competitive sport that involves racing on tracks with obstacles and jumps. BMX freestyle is a creative sport that involves performing tricks and stunts on ramps, streets, parks, or flatland.</p>
- <li><b>How can I improve my BMX racing skills?</b></li>
-<p>Some of the ways to improve your BMX racing skills are: practicing regularly, learning from other riders, joining a club or a team, taking lessons or coaching, watching races and videos, reading books and magazines, and entering competitions.</p>
- <li><b>What are the best BMX bikes for racing?</b></li>
-<p>The best BMX bikes for racing are those that are lightweight, durable, and fit your size and style. Some of the factors to consider when choosing a BMX bike for racing are: frame material, wheel size, tire type, gear ratio, brake system, handlebar shape, and seat height.</p>
- <li><b>What are the safety precautions for BMX racing?</b></li>
-<p>Some of the safety precautions for BMX racing are: wearing a helmet, gloves, pads, and protective clothing, checking your bike and equipment before riding, following the rules and etiquette of the track, warming up and cooling down properly, avoiding alcohol and drugs, and seeking medical attention if injured.</p>
- <li><b>Where can I find more information about BMX racing?</b></li>
-<p>Some of the sources where you can find more information about BMX racing are: the official websites of the International Cycling Union (UCI) and the USA BMX, the online forums and communities of BMX riders, the blogs and podcasts of BMX experts and enthusiasts, and the social media accounts of BMX celebrities and influencers.</p>
- </ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_sequence_level_ft_task.py b/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_sequence_level_ft_task.py
deleted file mode 100644
index 1404571159ea95776c3953fdecb28a84031c1347..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_sequence_level_ft_task.py
+++ /dev/null
@@ -1,610 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from fengshen.models.zen1.tokenization import BertTokenizer
-from fengshen.models.zen1.modeling import ZenForSequenceClassification
-from fengshen.models.zen1.ngram_utils import ZenNgramDict
-from pytorch_lightning.callbacks import LearningRateMonitor
-import csv
-from dataclasses import dataclass
-import logging
-import math
-import numpy as np
-import os
-from tqdm import tqdm
-import json
-import torch
-import pytorch_lightning as pl
-from random import shuffle
-import argparse
-from pytorch_lightning.callbacks import ModelCheckpoint
-from torch.utils.data import Dataset, DataLoader
-
-logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt='%m/%d/%Y %H:%M:%S',
-                    level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class InputExample(object):
-    """A single training/test example for simple sequence classification."""
-
-    def __init__(self, guid, text_a, text_b=None, label=None):
-        """Constructs a InputExample.
-
-        Args:
-            guid: Unique id for the example.
-            text_a: string. The untokenized text of the first sequence. For single
-            sequence tasks, only this sequence must be specified.
-            text_b: (Optional) string. The untokenized text of the second sequence.
-            Only must be specified for sequence pair tasks.
-            label: (Optional) string. The label of the example. This should be
-            specified for train and dev examples, but not for test examples.
-        """
-        self.guid = guid
-        self.text_a = text_a
-        self.text_b = text_b
-        self.label = label
-
-
-class InputFeatures(object):
-    """A single set of features of data."""
-
-    def __init__(self, input_ids, input_mask, segment_ids, label_id, ngram_ids, ngram_positions, ngram_lengths,
-                 ngram_tuples, ngram_seg_ids, ngram_masks):
-        self.input_ids = input_ids
-        self.input_mask = input_mask
-        self.segment_ids = segment_ids
-        self.label_id = label_id
-
-        self.ngram_ids = ngram_ids
-        self.ngram_positions = ngram_positions
-        self.ngram_lengths = ngram_lengths
-        self.ngram_tuples = ngram_tuples
-        self.ngram_seg_ids = ngram_seg_ids
-        self.ngram_masks = ngram_masks
-
-
-class DataProcessor(object):
-    """Base class for data converters for sequence classification data sets."""
-
-    def get_examples(self, data_path, mode):
-        """Gets a collection of `InputExample`s for the train set."""
-        raise NotImplementedError()
-
-    @classmethod
-    def _read_tsv(cls, input_file, quotechar=None):
-        """Reads a tab separated value file."""
-        with open(input_file, "r") as f:
-            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
-            lines = []
-            for line in reader:
-                # if sys.version_info[0] == 2:
-                #     line = list(unicode(cell, 'utf-8') for cell in line)
-                lines.append(line)
-            return lines
-
-    @classmethod
-    def _read_json(cls, input_file):
-        """Reads a jsonl file."""
-        with open(input_file, "r", encoding="utf-8") as f:
-            lines = f.readlines()
-            samples = []
-            for line in tqdm(lines):
-                data = json.loads(line)
-                samples.append(data)
-            return samples
-
-
-class TnewsProcessor(DataProcessor):
-    """Processor for the tnews data set (HIT version)."""
-
-    def get_train_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(
-            self._read_json(os.path.join(data_dir, "train.json")), "train")
-
-    def get_examples(self, data_path, mode):
-        return self._create_examples(
-            self._read_json(data_path),
-            set_type=mode
-        )
-
-    def _create_examples(self, lines, set_type):
-        """Creates examples for the training and dev sets."""
-        examples = []
-        for (i, line) in enumerate(lines):
-            # if i == 0:
-            #    continue
-            guid = "%s-%s" % (set_type, i)
-            # text_a = line[0]
-            text_a = line['sentence']
-            label = line['label'] if 'label' in line.keys() else None
-            examples.append(
-                InputExample(guid=guid, text_a=text_a, label=label))
-        return examples
-
-
-class OcnliProcessor(DataProcessor):
-    """Processor for the ocnli or cmnli data set (HIT version)."""
-
-    def get_examples(self, data_path, mode):
-        return self._create_examples(
-            self._read_json(data_path),
-            set_type=mode
-        )
-
-    def _create_examples(self, lines, set_type):
-        """Creates examples for the training and dev sets."""
-        examples = []
-        for (i, line) in enumerate(lines):
-            # if i == 0:
-            #    continue
-            guid = "%s-%s" % (set_type, i)
-            # text_a = line[0]
-            text_a = line['sentence1']
-            text_b = line['sentence2']
-            label = line['label'] if 'label' in line.keys() else None
-            # 特殊处理，cmnli有label为-的
-            if label == '-':
-                label = None
-            examples.append(
-                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-        return examples
-
-
-class IflytekProcessor(DataProcessor):
-    """Processor for the iflytek data set (HIT version)."""
-
-    def get_examples(self, data_path, mode):
-        return self._create_examples(
-            self._read_json(data_path),
-            set_type=mode
-        )
-
-    def _create_examples(self, lines, set_type):
-        """Creates examples for the training and dev sets."""
-        examples = []
-        for (i, line) in enumerate(lines):
-            # if i == 0:
-            #    continue
-            guid = "%s-%s" % (set_type, i)
-            # text_a = line[0]
-            text_a = line['sentence']
-            label = line['label'] if 'label' in line.keys() else None
-            examples.append(
-                InputExample(guid=guid, text_a=text_a, label=label))
-        return examples
-
-
-def convert_examples_to_features(examples, label_map, max_seq_length, tokenizer, ngram_dict):
-    """Loads a data file into a list of `InputBatch`s."""
-
-    features = []
-    for (ex_index, example) in enumerate(examples):
-        if ex_index % 10000 == 0:
-            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
-
-        tokens_a = tokenizer.tokenize(example.text_a)
-
-        tokens_b = None
-        if example.text_b:
-            tokens_b = tokenizer.tokenize(example.text_b)
-            # Modifies `tokens_a` and `tokens_b` in place so that the total
-            # length is less than the specified length.
-            # Account for [CLS], [SEP], [SEP] with "- 3"
-            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
-        else:
-            # Account for [CLS] and [SEP] with "- 2"
-            if len(tokens_a) > max_seq_length - 2:
-                tokens_a = tokens_a[:(max_seq_length - 2)]
-
-        # The convention in BERT is:
-        # (a) For sequence pairs:
-        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
-        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
-        # (b) For single sequences:
-        #  tokens:   [CLS] the dog is hairy . [SEP]
-        #  type_ids: 0   0   0   0  0     0 0
-        #
-        # Where "type_ids" are used to indicate whether this is the first
-        # sequence or the second sequence. The embedding vectors for `type=0` and
-        # `type=1` were learned during pre-training and are added to the wordpiece
-        # embedding vector (and position vector). This is not *strictly* necessary
-        # since the [SEP] token unambiguously separates the sequences, but it makes
-        # it easier for the model to learn the concept of sequences.
-        #
-        # For classification tasks, the first vector (corresponding to [CLS]) is
-        # used as as the "sentence vector". Note that this only makes sense because
-        # the entire model is fine-tuned.
-        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
-        segment_ids = [0] * len(tokens)
-
-        if tokens_b:
-            tokens += tokens_b + ["[SEP]"]
-            segment_ids += [1] * (len(tokens_b) + 1)
-
-        input_ids = tokenizer.convert_tokens_to_ids(tokens)
-
-        # The mask has 1 for real tokens and 0 for padding tokens. Only real
-        # tokens are attended to.
-        input_mask = [1] * len(input_ids)
-
-        # Zero-pad up to the sequence length.
-        padding = [0] * (max_seq_length - len(input_ids))
-        input_ids += padding
-        input_mask += padding
-        segment_ids += padding
-
-        assert len(input_ids) == max_seq_length
-        assert len(input_mask) == max_seq_length
-        assert len(segment_ids) == max_seq_length
-
-        label_id = label_map[example.label]
-
-        # ----------- code for ngram BEGIN-----------
-        ngram_matches = []
-        #  Filter the word segment from 2 to 7 to check whether there is a word
-        for p in range(2, 8):
-            for q in range(0, len(tokens) - p + 1):
-                character_segment = tokens[q:q + p]
-                # j is the starting position of the word
-                # i is the length of the current word
-                character_segment = tuple(character_segment)
-                if character_segment in ngram_dict.ngram_to_id_dict:
-                    ngram_index = ngram_dict.ngram_to_id_dict[character_segment]
-                    ngram_matches.append([ngram_index, q, p, character_segment])
-
-        shuffle(ngram_matches)
-        # max_word_in_seq_proportion = max_word_in_seq
-        max_word_in_seq_proportion = math.ceil((len(tokens) / max_seq_length) * ngram_dict.max_ngram_in_seq)
-        if len(ngram_matches) > max_word_in_seq_proportion:
-            ngram_matches = ngram_matches[:max_word_in_seq_proportion]
-        ngram_ids = [ngram[0] for ngram in ngram_matches]
-        ngram_positions = [ngram[1] for ngram in ngram_matches]
-        ngram_lengths = [ngram[2] for ngram in ngram_matches]
-        ngram_tuples = [ngram[3] for ngram in ngram_matches]
-        ngram_seg_ids = [0 if position < (len(tokens_a) + 2) else 1 for position in ngram_positions]
-
-        ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool)
-        ngram_mask_array[:len(ngram_ids)] = 1
-
-        # record the masked positions
-        ngram_positions_matrix = np.zeros(shape=(max_seq_length, ngram_dict.max_ngram_in_seq), dtype=np.int32)
-        for i in range(len(ngram_ids)):
-            ngram_positions_matrix[ngram_positions[i]:ngram_positions[i] + ngram_lengths[i], i] = 1.0
-
-        # Zero-pad up to the max word in seq length.
-        padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids))
-        ngram_ids += padding
-        ngram_lengths += padding
-        ngram_seg_ids += padding
-
-        # ----------- code for ngram END-----------
-        label_id = label_map[example.label] if example.label is not None else 0
-        features.append(
-            InputFeatures(input_ids=input_ids,
-                          input_mask=input_mask,
-                          segment_ids=segment_ids,
-                          label_id=label_id,
-                          ngram_ids=ngram_ids,
-                          ngram_positions=ngram_positions_matrix,
-                          ngram_lengths=ngram_lengths,
-                          ngram_tuples=ngram_tuples,
-                          ngram_seg_ids=ngram_seg_ids,
-                          ngram_masks=ngram_mask_array))
-
-    return features
-
-
-def _truncate_seq_pair(tokens_a, tokens_b, max_length):
-    """Truncates a sequence pair in place to the maximum length."""
-
-    # This is a simple heuristic which will always truncate the longer sequence
-    # one token at a time. This makes more sense than truncating an equal percent
-    # of tokens from each, since if one sequence is very short then each token
-    # that's truncated likely contains more information than a longer sequence.
-    while True:
-        total_length = len(tokens_a) + len(tokens_b)
-        if total_length <= max_length:
-            break
-        if len(tokens_a) > len(tokens_b):
-            tokens_a.pop()
-        else:
-            tokens_b.pop()
-
-
-class TaskDataset(Dataset):
-    def __init__(self, data_path, processor, mode='train'):
-        super().__init__()
-        self.data = self.load_data(data_path, processor, mode)
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-    def load_data(self, data_path, processor, mode):
-        if mode == "train":
-            examples = processor.get_examples(data_path, mode)
-        elif mode == "test":
-            examples = processor.get_examples(data_path, mode)
-        elif mode == "dev":
-            examples = processor.get_examples(data_path, mode)
-        return examples
-
-
-@dataclass
-class TaskCollator:
-    args = None
-    tokenizer = None
-    ngram_dict = None
-    label2id = None
-
-    def __call__(self, samples):
-        features = convert_examples_to_features(samples, self.label2id, self.args.max_seq_length, self.tokenizer, self.ngram_dict)
-        # logger.info("  Num examples = %d", len(samples))
-        input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-        input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
-        segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
-        label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
-        ngram_ids = torch.tensor([f.ngram_ids for f in features], dtype=torch.long)
-        ngram_positions = torch.tensor([f.ngram_positions for f in features], dtype=torch.long)
-        # ngram_lengths = torch.tensor([f.ngram_lengths for f in features], dtype=torch.long)
-        # ngram_seg_ids = torch.tensor([f.ngram_seg_ids for f in features], dtype=torch.long)
-        # ngram_masks = torch.tensor([f.ngram_masks for f in features], dtype=torch.long)
-
-        return {
-            'input_ids': input_ids,
-            'input_ngram_ids': ngram_ids,
-            'ngram_position_matrix': ngram_positions,
-            'attention_mask': input_mask,
-            'token_type_ids': segment_ids,
-            'labels': label_ids,
-
-        }
-        # return default_collate(sample_list)
-
-
-class TaskDataModel(pl.LightningDataModule):
-    @staticmethod
-    def add_data_specific_args(parent_args):
-        parser = parent_args.add_argument_group('TASK NAME DataModel')
-        parser.add_argument('--data_dir', default='./data', type=str)
-        parser.add_argument('--num_workers', default=8, type=int)
-        parser.add_argument('--train_data', default='train.json', type=str)
-        parser.add_argument('--valid_data', default='dev.json', type=str)
-        parser.add_argument('--test_data', default='test.json', type=str)
-        parser.add_argument('--train_batchsize', default=16, type=int)
-        parser.add_argument('--valid_batchsize', default=32, type=int)
-        parser.add_argument('--max_seq_length', default=128, type=int)
-
-        parser.add_argument('--texta_name', default='text', type=str)
-        parser.add_argument('--textb_name', default='sentence2', type=str)
-        parser.add_argument('--label_name', default='label', type=str)
-        parser.add_argument('--id_name', default='id', type=str)
-
-        parser.add_argument('--dataset_name', default=None, type=str)
-        parser.add_argument('--vocab_file',
-                            type=str, default=None,
-                            help="Vocabulary mapping/file BERT was pretrainined on")
-        parser.add_argument("--do_lower_case",
-                            action='store_true',
-                            help="Set this flag if you are using an uncased model.")
-        parser.add_argument('--task_name', default='tnews', type=str)
-
-        return parent_args
-
-    def __init__(self, args):
-        super().__init__()
-        self.train_batchsize = args.train_batchsize
-        self.valid_batchsize = args.valid_batchsize
-        self.collator = TaskCollator()
-        self.collator.args = args
-        self.collator.tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
-        self.collator.ngram_dict = ZenNgramDict.from_pretrained(args.pretrained_model_path, tokenizer=self.collator.tokenizer)
-
-        processors = {
-            'afqmc': OcnliProcessor,
-            'tnews': TnewsProcessor,
-            'ocnli': OcnliProcessor,
-            'cmnli': OcnliProcessor,
-            'iflytek': IflytekProcessor,
-        }
-        if args.task_name not in processors:
-            raise ValueError("Task not found: %s" % (args.task_name))
-        processor = processors[args.task_name]()
-        if args.dataset_name is None:
-            self.label2id, self.id2label = self.load_schema(os.path.join(
-                args.data_dir, args.train_data), args)
-            self.train_data = TaskDataset(os.path.join(
-                args.data_dir, args.train_data), processor, mode='train')
-            self.valid_data = TaskDataset(os.path.join(
-                args.data_dir, args.valid_data), processor, mode='dev')
-            self.test_data = TaskDataset(os.path.join(
-                args.data_dir, args.test_data), processor, mode='test')
-            self.collator.label2id = self.label2id
-        else:
-            import datasets
-            ds = datasets.load_dataset(args.dataset_name)
-            self.train_data = ds['train']
-            self.valid_data = ds['validation']
-            self.test_data = ds['test']
-        self.save_hyperparameters(args)
-
-    def train_dataloader(self):
-        return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-    def val_dataloader(self):
-        return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-    def predict_dataloader(self):
-        return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-    def load_schema(self, data_path, args):
-        with open(data_path, 'r', encoding='utf8') as f:
-            lines = f.readlines()
-            label_list = []
-            for line in tqdm(lines):
-                data = json.loads(line)
-                labels = data[args.label_name] if args.label_name in data.keys(
-                ) else 0
-                if labels not in label_list:
-                    label_list.append(labels)
-
-        label2id, id2label = {}, {}
-        for i, k in enumerate(label_list):
-            label2id[k] = i
-            id2label[i] = k
-        return label2id, id2label
-
-
-class LitModel(pl.LightningModule):
-
-    @staticmethod
-    def add_model_specific_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-        parser.add_argument('--num_labels', default=2, type=int)
-
-        return parent_args
-
-    def __init__(self, args):
-        super().__init__()
-        self.model = ZenForSequenceClassification.from_pretrained(args.pretrained_model_path, num_labels=args.num_labels)
-        self.save_hyperparameters(args)
-
-    def setup(self, stage) -> None:
-        if stage == 'fit':
-            train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
-            # Calculate total steps
-            if self.trainer.max_epochs > 0:
-                world_size = self.trainer.world_size
-                tb_size = self.hparams.train_batchsize * max(1, world_size)
-                ab_size = self.trainer.accumulate_grad_batches
-                self.total_steps = (len(train_loader.dataset) *
-                                    self.trainer.max_epochs // tb_size) // ab_size
-            else:
-                self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
-
-            print('Total steps: {}' .format(self.total_steps))
-
-    def training_step(self, batch, batch_idx):
-        loss, logits = self.model(**batch)
-        acc = self.comput_metrix(logits, batch['labels'])
-        self.log('train_loss', loss)
-        self.log('train_acc', acc)
-        return loss
-
-    def comput_metrix(self, logits, labels):
-        y_pred = torch.argmax(logits, dim=-1)
-        y_pred = y_pred.view(size=(-1,))
-        y_true = labels.view(size=(-1,)).float()
-        corr = torch.eq(y_pred, y_true)
-        acc = torch.sum(corr.float())/labels.size()[0]
-        return acc
-
-    def validation_step(self, batch, batch_idx):
-        loss, logits = self.model(**batch)
-        acc = self.comput_metrix(logits, batch['labels'])
-        self.log('val_loss', loss)
-        self.log('val_acc', acc)
-
-    def predict_step(self, batch, batch_idx):
-        output = self.model(**batch)
-        return output.logits
-
-    def configure_optimizers(self):
-        from fengshen.models.model_utils import configure_optimizers
-        return configure_optimizers(self)
-
-
-class TaskModelCheckpoint:
-    @staticmethod
-    def add_argparse_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-
-        parser.add_argument('--monitor', default='train_loss', type=str)
-        parser.add_argument('--mode', default='min', type=str)
-        parser.add_argument('--dirpath', default='./log/', type=str)
-        parser.add_argument(
-            '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
-
-        parser.add_argument('--save_top_k', default=3, type=float)
-        parser.add_argument('--every_n_train_steps', default=100, type=float)
-        parser.add_argument('--save_weights_only', default=True, type=bool)
-
-        return parent_args
-
-    def __init__(self, args):
-        self.callbacks = ModelCheckpoint(monitor=args.monitor,
-                                         save_top_k=args.save_top_k,
-                                         mode=args.mode,
-                                         every_n_train_steps=args.every_n_train_steps,
-                                         save_weights_only=args.save_weights_only,
-                                         dirpath=args.dirpath,
-                                         filename=args.filename)
-
-
-def save_test(data, args, data_model):
-    with open(args.output_save_path, 'w', encoding='utf-8') as f:
-        idx = 0
-        for i in range(len(data)):
-            batch = data[i]
-            for sample in batch:
-                tmp_result = dict()
-                label_id = np.argmax(sample.numpy())
-                tmp_result['id'] = data_model.test_data.data[idx]['id']
-                tmp_result['label'] = data_model.id2label[label_id]
-                json_data = json.dumps(tmp_result, ensure_ascii=False)
-                f.write(json_data+'\n')
-                idx += 1
-    print('save the result to '+args.output_save_path)
-
-
-def main():
-    total_parser = argparse.ArgumentParser("TASK NAME")
-    total_parser.add_argument('--pretrained_model_path', default='', type=str)
-    total_parser.add_argument('--output_save_path',
-                              default='./predict.json', type=str)
-    # * Args for data preprocessing
-    total_parser = TaskDataModel.add_data_specific_args(total_parser)
-    # * Args for training
-    total_parser = pl.Trainer.add_argparse_args(total_parser)
-    total_parser = TaskModelCheckpoint.add_argparse_args(total_parser)
-
-    # * Args for base model
-    from fengshen.models.model_utils import add_module_args
-    total_parser = add_module_args(total_parser)
-    total_parser = LitModel.add_model_specific_args(total_parser)
-
-    args = total_parser.parse_args()
-
-    checkpoint_callback = TaskModelCheckpoint(args).callbacks
-    lr_monitor = LearningRateMonitor(logging_interval='step')
-    trainer = pl.Trainer.from_argparse_args(args,
-                                            callbacks=[checkpoint_callback, lr_monitor]
-                                            )
-
-    data_model = TaskDataModel(args)
-    model = LitModel(args)
-    trainer.fit(model, data_model)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/archs/rrdbnet_arch.py b/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/archs/rrdbnet_arch.py
deleted file mode 100644
index 49a2d6c204557cba53ada7550deb587541855cfb..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/archs/rrdbnet_arch.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-@ARCH_REGISTRY.register()
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out
\ No newline at end of file
diff --git a/spaces/songwy/VITS-Umamusume-voice-synthesizer/README.md b/spaces/songwy/VITS-Umamusume-voice-synthesizer/README.md
deleted file mode 100644
index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000
--- a/spaces/songwy/VITS-Umamusume-voice-synthesizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
-duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sophiamyang/panel_example/app.py b/spaces/sophiamyang/panel_example/app.py
deleted file mode 100644
index 138710fac11cbf2806c83be0d1aeaed648ec73f6..0000000000000000000000000000000000000000
--- a/spaces/sophiamyang/panel_example/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import panel as pn
-import hvplot.pandas
-
-# Load Data
-from bokeh.sampledata.autompg import autompg_clean as df
-
-# Make DataFrame Pipeline Interactive
-idf = df.interactive()
-
-# Define Panel widgets
-cylinders = pn.widgets.IntSlider(name='Cylinders', start=4, end=8, step=2)
-mfr = pn.widgets.ToggleGroup(
-    name='MFR',
-    options=['ford', 'chevrolet', 'honda', 'toyota', 'audi'], 
-    value=['ford', 'chevrolet', 'honda', 'toyota', 'audi'],
-    button_type='success')
-yaxis = pn.widgets.RadioButtonGroup(
-    name='Y axis', 
-    options=['hp', 'weight'],
-    button_type='success'
-)
-
-# Combine pipeline and widgets
-ipipeline = (
-    idf[
-        (idf.cyl == cylinders) & 
-        (idf.mfr.isin(mfr))
-    ]
-    .groupby(['origin', 'mpg'])[yaxis].mean()
-    .to_frame()
-    .reset_index()
-    .sort_values(by='mpg')  
-    .reset_index(drop=True)
-)
-
-# Pipe to hvplot
-ihvplot = ipipeline.hvplot(x='mpg', y=yaxis, by='origin', color=["#ff6f69", "#ffcc5c", "#88d8b0"], line_width=6, height=400)
-
-# Layout using Template
-template = pn.template.FastListTemplate(
-    title='Interactive DataFrame Dashboards with hvplot .interactive', 
-    sidebar=[cylinders, 'Manufacturers', mfr, 'Y axis' , yaxis],
-    main=[ihvplot.panel()],
-    accent_base_color="#88d8b0",
-    header_background="#88d8b0",
-)
-template.servable()
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py
deleted file mode 100644
index 2d78ca98708121261aa365738a65c051b5b40626..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_task import DiscriminativeRerankingNMTTask
-
-
-__all__ = [
-    "DiscriminativeRerankingNMTTask",
-]
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py
deleted file mode 100644
index 7afa40fcd195465a225c9f251734e84fe6b3c7ef..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-import numpy as np
-import re
-from pathlib import Path
-from collections import defaultdict
-
-import pandas as pd
-from torchaudio.datasets import VCTK
-from tqdm import tqdm
-
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-SPLITS = ["train", "dev", "test"]
-
-
-def normalize_text(text):
-    return re.sub(r"[^a-zA-Z.?!,'\- ]", '', text)
-
-
-def process(args):
-    out_root = Path(args.output_data_root).absolute()
-    out_root.mkdir(parents=True, exist_ok=True)
-
-    # Generate TSV manifest
-    print("Generating manifest...")
-    dataset = VCTK(out_root.as_posix(), download=False)
-    ids = list(dataset._walker)
-    np.random.seed(args.seed)
-    np.random.shuffle(ids)
-    n_train = len(ids) - args.n_dev - args.n_test
-    _split = ["train"] * n_train + ["dev"] * args.n_dev + ["test"] * args.n_test
-    id_to_split = dict(zip(ids, _split))
-    manifest_by_split = {split: defaultdict(list) for split in SPLITS}
-    progress = tqdm(enumerate(dataset), total=len(dataset))
-    for i, (waveform, _, text, speaker_id, _) in progress:
-        sample_id = dataset._walker[i]
-        _split = id_to_split[sample_id]
-        audio_dir = Path(dataset._path) / dataset._folder_audio / speaker_id
-        audio_path = audio_dir / f"{sample_id}.wav"
-        text = normalize_text(text)
-        manifest_by_split[_split]["id"].append(sample_id)
-        manifest_by_split[_split]["audio"].append(audio_path.as_posix())
-        manifest_by_split[_split]["n_frames"].append(len(waveform[0]))
-        manifest_by_split[_split]["tgt_text"].append(text)
-        manifest_by_split[_split]["speaker"].append(speaker_id)
-        manifest_by_split[_split]["src_text"].append(text)
-
-    manifest_root = Path(args.output_manifest_root).absolute()
-    manifest_root.mkdir(parents=True, exist_ok=True)
-    for _split in SPLITS:
-        save_df_to_tsv(
-            pd.DataFrame.from_dict(manifest_by_split[_split]),
-            manifest_root / f"{_split}.audio.tsv"
-        )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output-data-root", "-d", required=True, type=str)
-    parser.add_argument("--output-manifest-root", "-m", required=True, type=str)
-    parser.add_argument("--n-dev", default=50, type=int)
-    parser.add_argument("--n-test", default=100, type=int)
-    parser.add_argument("--seed", "-s", default=1234, type=int)
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/gelu.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/gelu.py
deleted file mode 100644
index a2f1ecff4a3ae3de3eb7d327b9163c46b18a15ed..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/gelu.py
+++ /dev/null
@@ -1,25 +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.
-"""
-See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with
-the corresponding GitHub repo: https://github.com/hendrycks/GELUs
-"""
-
-import math
-
-import torch
-import torch.nn as nn
-
-
-def gelu_accurate(x):
-    if not hasattr(gelu_accurate, "_a"):
-        gelu_accurate._a = math.sqrt(2 / math.pi)
-    return (
-        0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
-    )
-
-
-def gelu(x: torch.Tensor) -> torch.Tensor:
-    return torch.nn.functional.gelu(x.float()).type_as(x)
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Cimpack 13 1 Crack Erodes TOP.md b/spaces/stomexserde/gpt4-ui/Examples/Cimpack 13 1 Crack Erodes TOP.md
deleted file mode 100644
index 9b5fa27472382cf3091f7ed7ae8e88dbe24376ec..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Cimpack 13 1 Crack Erodes TOP.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>Cimpack 13.1 Crack Erodes: How to Avoid Software Piracy and Protect Your Business</h1>
- <p>If you are in the packaging industry, you might have heard of Cimpack 13.1, a powerful software that helps you design, create, and print labels for various products. Cimpack 13.1 is developed by Cimex Corp, a leading company in the field of packaging software solutions.</p>
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- <p>While using a crack might seem like a smart way to save money, it is actually a very risky and irresponsible decision that can have serious consequences for your software quality, security, business reputation, and profitability.</p>
- <p>In this article, we will explain how Cimpack 13.1 crack erodes your software quality and security, how it erodes your business reputation and profitability, and how you can avoid it by using the software legally and safely.</p>
- <h2>How Cimpack 13.1 Crack Erodes Your Software Quality and Security</h2>
- <p>One of the main reasons why you should avoid using Cimpack 13.1 crack erodes is that it can severely damage your software quality and security. Here are some of the ways how cracks work and how they can harm your software:</p>
-<p></p>
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- <h3>How cracks violate the software license agreement and the law</h3>
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- <p>A crack shows a lack of respect and appreciation for the developer's work and investment. The developer has spent a lot of time, money, and effort to create and maintain the software for your benefit. By using a crack, you are disregarding their work and investment, and taking advantage of their generosity.</p>
- <p>A crack indicates a lack of professionalism and integrity in your business practices. By using a crack, you are demonstrating that you are not willing to pay for what you use, that you are not honest about your business activities, and that you are not trustworthy in your business dealings. You are also risking your reputation and trust with your customers, partners, and suppliers who might question your reliability and quality.</p>
- <p>A crack can damage your reputation and trust with your customers, partners, and suppliers. By using a crack, you are exposing yourself to potential legal actions, fines, penalties, or even imprisonment from the developer or authorities. You are also exposing yourself to potential data breaches, cyberattacks, or malware infections from hackers or malicious programs. These can have negative impacts on your reputation and trust with your customers, partners, and suppliers who might lose confidence in your ability to deliver secure, reliable, and high-quality products or services.</p> <h2>How to Avoid Cimpack 13.1 Crack Erodes and Use the Software Legally and Safely</h2>
- <p>Now that you know how Cimpack 13.1 crack erodes can harm your software quality, security, business reputation, and profitability, you might be wondering how you can avoid it and use the software legally and safely. Here are some of the steps you can take to purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers, and to report any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes:</p>
- <h3>How to purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers</h3>
- <p>The best way to avoid Cimpack 13.1 crack erodes is to purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers. Here are some of the benefits of purchasing a genuine license of Cimpack 13.1:</p>
- <ul>
-<li>You will get the full functionality and performance of the software, without any errors, bugs, or malware.</li>
-<li>You will get the latest updates, features, and support from the developer, without any compatibility issues.</li>
-<li>You will comply with the software license agreement and the law, without any legal risks or penalties.</li>
-<li>You will respect and appreciate the developer's work and investment, without any ethical dilemmas or reputation damage.</li>
-</ul>
- <p>Here are some of the steps you can follow to purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers:</p>
- <ol>
-<li>Visit the official website of Cimex Corp at <a href="">https://www.cimexcorp.com/</a> and click on the Products tab.</li>
-<li>Select Cimpack 13.1 from the list of products and click on the Buy Now button.</li>
-<li>Choose the right license option for your needs and budget. You can choose between a single-user license, a multi-user license, or a subscription license.</li>
-<li>Complete the payment process securely using your preferred payment method, such as credit card, PayPal, or bank transfer.</li>
-<li>Receive the license key via email within a few minutes after completing the payment.</li>
-<li>Download and install Cimpack 13.1 on your computer using the link provided in the email.</li>
-<li>Activate the license key on your computer by entering it in the software activation window.</li>
-<li>Enjoy the full benefits of Cimpack 13.1 for your packaging design, creation, and printing needs.</li>
-</ol>
- <p>If you prefer to purchase a genuine license of Cimpack 13.1 from an authorized reseller, you can find a list of authorized resellers on the official website of Cimex Corp at <a href="">https://www.cimexcorp.com/resellers/</a>. You can contact them directly and follow their instructions to purchase a genuine license of Cimpack 13.1.</p>
- <h3>How to report any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes</h3>
- <p>If you encounter or witness any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes, you should report them immediately to the developer or authorities. Here are some of the steps you can take to report any Cimpack 13.1 crack erodes activities:</p>
- <ol>
-<li>Identify and avoid any websites, links, or files that offer or distribute Cimpack 13.1 crack erodes. Do not click on them, download them, or open them. They might contain viruses, malware, or other harmful programs that can infect your computer or system.</li>
-<li>Contact the developer at <a href="">support@cimexcorp.com</a> or call them at +1-800-555-1234 if you encounter or witness any Cimpack 13.1 crack erodes activities. Provide them with as much information as possible, such as the website address, link address, file name, date and time of occurrence, etc.</li>
-<li>Contact the authorities at <a href="">report@cybercrime.gov</a> or call them at +1-800-555-4321 if you encounter or witness any Cimpack 13.1 crack erodes activities that involve illegal or criminal acts, such as fraud, theft, piracy, etc. Provide them with as much information as possible, such as the website address, link address, file name, date and time of occurrence, etc.</li>
-<li>Educate yourself and others about the dangers and disadvantages of using Cimpack 13.1 crack erodes. Share this article with your colleagues, friends, or family who might be interested in or tempted by Cimpack 13.1 crack erodes. Explain to them the risks and consequences of using Cimpack 13.1 crack erodes, and the benefits of using Cimpack 13.1 legally and safely.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Cimpack 13.1 is a powerful software that helps you design, create, and print labels for various products. It is developed by Cimex Corp, a leading company in the field of packaging software solutions.</p>
- <p>Cimpack 13.1 is not a free software, however. It requires a valid license key to operate properly and legally. Some people try to avoid paying for the license by using a crack, which is a modified version of the software that bypasses the license verification process.</p>
- <p>Using a crack might seem like a smart way to save money, but it is actually a very risky and irresponsible decision that can have serious consequences for your software quality, security, business reputation, and profitability.</p>
- <p>Using a crack can damage your software quality and security by introducing errors, bugs, and malware into your software, and exposing your data and system to hackers and cyberattacks.</p>
- <p>Using a crack can harm your business reputation and profitability by violating the software license agreement and the law, and undermining your business ethics and credibility.</p>
- <p>The best way to avoid Cimpack 13.1 crack erodes is to purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers. You will get the full functionality and performance of the software, without any errors, bugs, or malware. You will get the latest updates, features, and support from the developer, without any compatibility issues. You will comply with the software license agreement and the law, without any legal risks or penalties. You will respect and appreciate the developer's work and investment, without any ethical dilemmas or reputation damage.</p>
- <p>If you encounter or witness any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes, you should report them immediately to the developer or authorities. You should also educate yourself and others about the dangers and disadvantages of using Cimpack 13.1 crack erodes, and the benefits of using Cimpack 13.1 legally and safely.</p>
- <p>We hope this article has helped you understand how Cimpack 13.1 crack erodes can affect your software quality, security, business reputation, and profitability, and how you can avoid it by using the software legally and safely.</p>
- <p>If you are interested in purchasing a genuine license of Cimpack 13.1 or learning more about the software features and benefits, please visit the official website of Cimex Corp at <a href="">https://www.cimexcorp.com/</a> or contact them at <a href="">support@cimexcorp.com</a> or +1-800-555-1234.</p>
- <p>Thank you for reading this article!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions (FAQs) about Cimpack 13.1 crack erodes:</p>
- <ol>
-<li><b>What is Cimpack 13.1?</b></li>
-<p>Cimpack 13.1 is a powerful software that helps you design, create, and print labels for various products. It is developed by Cimex Corp, a leading company in the field of packaging software solutions.</p>
-<li><b>What is a crack?</b></li>
-<p>A crack is a modified version of the original software that alters the code and bypasses the license verification process. A crack can be a file, a program, or a patch that replaces or modifies some parts of the software.</p>
-<li><b>What are the risks and consequences of using a cracked version of Cimpack 13.1?</b></li>
-<p>Using a cracked version of Cimpack 13.1 can have serious consequences for your software quality, security, business reputation, and profitability. It can damage your software quality and security by introducing errors, bugs, and malware into your software, and exposing your data and system to hackers and cyberattacks. It can harm your business reputation and profitability by violating the software license agreement and the law, and undermining your business ethics and credibility.</p>
-<li><b>How can I purchase a genuine license of Cimpack 13.1?</b></li>
-<p>You can purchase a genuine license of Cimpack 13.1 from the official website or authorized resellers of Cimex Corp. You can choose between a single-user license, a multi-user license, or a subscription license depending on your needs and budget. You can complete the payment process securely using your preferred payment method, such as credit card, PayPal, or bank transfer. You will receive the license key via email within a few minutes after completing the payment. You can then download and install Cimpack 13.1 on your computer using the link provided in the email. You can activate the license key on your computer by entering it in the software activation window. You will then enjoy the full benefits of Cimpack 13.1 for your packaging design, creation, and printing needs.</p>
-<li><b>How can I report any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes?</b></li>
-<p>If you encounter or witness any suspicious or fraudulent activities related to Cimpack 13.1 crack erodes, you should report them immediately to the developer or authorities. You can contact the developer at <a href="">support@cimexcorp.com</a> or call them at +1-800-555-1234 if you encounter or witness any Cimpack 13.1 crack erodes activities. You can contact the authorities at <a href="">report@cybercrime.gov</a> or call them at +1-800-555-4321 if you encounter or witness any Cimpack 13.1 crack erodes activities that involve illegal or criminal acts, such as fraud, theft, piracy, etc. You should also identify and avoid any websites, links, or files that offer or distribute Cimpack 13.1 crack erodes, and educate yourself and others about the dangers and disadvantages of using Cimpack 13.1 crack erodes.</p>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Html Tutorial .rar.md b/spaces/stomexserde/gpt4-ui/Examples/Html Tutorial .rar.md
deleted file mode 100644
index 1ad6d3efee2256ed28910f8223f96f1a19a72b67..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Html Tutorial .rar.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-```markdown
-<h1>How to Download and Use Html Tutorial .rar File</h1>
-<p>If you want to learn HTML, you might be interested in downloading a Html Tutorial .rar file. This is a compressed file that contains a collection of HTML files, images, and other resources that you can use to follow along with a HTML tutorial. In this article, we will show you how to download and use Html Tutorial .rar file in a few simple steps.</p>
-
-<h2>Step 1: Download Html Tutorial .rar File</h2>
-<p>The first step is to download the Html Tutorial .rar file from a reliable source. You can find many websites that offer free HTML tutorials in .rar format, such as <a href="https://www.w3schools.com/html/html_rar.asp">W3Schools</a>, <a href="https://www.tutorialspoint.com/html/html_rar.htm">TutorialsPoint</a>, or <a href="https://www.htmlgoodies.com/html_rar.zip">HTMLGoodies</a>. Make sure you choose a file that matches your level of HTML knowledge and your learning goals.</p>
-<h2>Html Tutorial .rar</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlgoal.com/2uIc6w">https://urlgoal.com/2uIc6w</a></b></p><br /><br />
-<p>Once you have found the Html Tutorial .rar file you want to download, click on the download link or button and save the file to your computer. You might need to enter your email address or complete a captcha to verify that you are not a robot. The file size may vary depending on the content and quality of the tutorial, but it should not take too long to download.</p>
-
-<h2>Step 2: Extract Html Tutorial .rar File</h2>
-<p>The next step is to extract the Html Tutorial .rar file to access the HTML files and other resources inside it. To do this, you will need a software program that can open and extract .rar files, such as <a href="https://www.win-rar.com/download.html">WinRAR</a>, <a href="https://www.7-zip.org/download.html">7-Zip</a>, or <a href="https://www.peazip.org/download-peazip.html">PeaZip</a>. These programs are free and easy to install and use.</p>
-<p>After you have installed one of these programs, locate the Html Tutorial .rar file on your computer and right-click on it. Select the option to open or extract the file with the program you have chosen. You will see a window where you can choose the destination folder where you want to extract the files. You can also change the name of the folder if you want. Click on OK or Extract to start the extraction process.</p>
-<p>When the extraction is complete, you will see a new folder with the same name as the Html Tutorial .rar file. This folder will contain all the HTML files, images, and other resources that are part of the tutorial. You can now open and view these files with any web browser or text editor.</p>
-
-<h2>Step 3: Use Html Tutorial .rar File</h2>
-<p>The final step is to use the Html Tutorial .rar file to learn HTML. You can follow the instructions and examples in the HTML files to learn the basics and advanced features of HTML. You can also modify and experiment with the code to see how it affects the output. You can use the images and other resources to enhance your web pages and make them more attractive and interactive.</p>
-<p></p>
-<p>Some Html Tutorial .rar files may also include quizzes, exercises, or projects that you can complete to test your knowledge and skills. You can check your answers or compare your results with the solutions provided in the tutorial. You can also share your work with others or upload it to a web server to showcase your HTML creations.</p>
-
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and use Html Tutorial .rar file in three easy steps. By using this method, you can access a variety of HTML tutorials in a convenient and compact format. You can learn HTML at your own pace and practice your skills with real-world examples. You can also improve your SEO ranking by using relevant keywords and creating high-quality content for your web pages.</p>
-<p>We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy learning!</p>
-```</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/document_store/faiss_store.py b/spaces/sub314xxl/MetaGPT/metagpt/document_store/faiss_store.py
deleted file mode 100644
index fbfcb3086716e3dfca5e666a8c3358ef33c191ea..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/document_store/faiss_store.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/25 10:20
-@Author  : alexanderwu
-@File    : faiss_store.py
-@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-import pickle
-from pathlib import Path
-from typing import Optional
-
-import faiss
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-
-from metagpt.const import DATA_PATH
-from metagpt.document_store.base_store import LocalStore
-from metagpt.document_store.document import Document
-from metagpt.logs import logger
-
-
-class FaissStore(LocalStore):
-    def __init__(self, raw_data: Path, cache_dir=None, meta_col='source', content_col='output'):
-        self.meta_col = meta_col
-        self.content_col = content_col
-        super().__init__(raw_data, cache_dir)
-
-    def _load(self) -> Optional["FaissStore"]:
-        index_file, store_file = self._get_index_and_store_fname()
-        if not (index_file.exists() and store_file.exists()):
-            logger.info("Missing at least one of index_file/store_file, load failed and return None")
-            return None
-        index = faiss.read_index(str(index_file))
-        with open(str(store_file), "rb") as f:
-            store = pickle.load(f)
-        store.index = index
-        return store
-
-    def _write(self, docs, metadatas, **kwargs):
-        store = FAISS.from_texts(docs,
-                                 OpenAIEmbeddings(openai_api_version="2020-11-07",
-                                                  openai_api_key=kwargs.get("OPENAI_API_KEY")),
-                                 metadatas=metadatas)
-        return store
-
-    def persist(self):
-        index_file, store_file = self._get_index_and_store_fname()
-        store = self.store
-        index = self.store.index
-        faiss.write_index(store.index, str(index_file))
-        store.index = None
-        with open(store_file, "wb") as f:
-            pickle.dump(store, f)
-        store.index = index
-
-    def search(self, query, expand_cols=False, sep='\n', *args, k=5, **kwargs):
-        rsp = self.store.similarity_search(query, k=k, **kwargs)
-        logger.debug(rsp)
-        if expand_cols:
-            return str(sep.join([f"{x.page_content}: {x.metadata}" for x in rsp]))
-        else:
-            return str(sep.join([f"{x.page_content}" for x in rsp]))
-
-    def write(self):
-        """根据用户给定的Document（JSON / XLSX等）文件，进行index与库的初始化"""
-        if not self.raw_data.exists():
-            raise FileNotFoundError
-        doc = Document(self.raw_data, self.content_col, self.meta_col)
-        docs, metadatas = doc.get_docs_and_metadatas()
-
-        self.store = self._write(docs, metadatas)
-        self.persist()
-        return self.store
-
-    def add(self, texts: list[str], *args, **kwargs) -> list[str]:
-        """FIXME: 目前add之后没有更新store"""
-        return self.store.add_texts(texts)
-
-    def delete(self, *args, **kwargs):
-        """目前langchain没有提供del接口"""
-        raise NotImplementedError
-
-
-if __name__ == '__main__':
-    faiss_store = FaissStore(DATA_PATH / 'qcs/qcs_4w.json')
-    logger.info(faiss_store.search('油皮洗面奶'))
-    faiss_store.add([f'油皮洗面奶-{i}' for i in range(3)])
-    logger.info(faiss_store.search('油皮洗面奶'))
diff --git a/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/register_ade20k_full.py b/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/register_ade20k_full.py
deleted file mode 100644
index 7121a22227583b29a6e167b560703e33371f1081..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/mask_former/data/datasets/register_ade20k_full.py
+++ /dev/null
@@ -1,964 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-
-ADE20K_SEM_SEG_FULL_CATEGORIES = [
-    {"name": "wall", "id": 2978, "trainId": 0},
-    {"name": "building, edifice", "id": 312, "trainId": 1},
-    {"name": "sky", "id": 2420, "trainId": 2},
-    {"name": "tree", "id": 2855, "trainId": 3},
-    {"name": "road, route", "id": 2131, "trainId": 4},
-    {"name": "floor, flooring", "id": 976, "trainId": 5},
-    {"name": "ceiling", "id": 447, "trainId": 6},
-    {"name": "bed", "id": 165, "trainId": 7},
-    {"name": "sidewalk, pavement", "id": 2377, "trainId": 8},
-    {"name": "earth, ground", "id": 838, "trainId": 9},
-    {"name": "cabinet", "id": 350, "trainId": 10},
-    {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11},
-    {"name": "grass", "id": 1125, "trainId": 12},
-    {"name": "windowpane, window", "id": 3055, "trainId": 13},
-    {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14},
-    {"name": "mountain, mount", "id": 1610, "trainId": 15},
-    {"name": "plant, flora, plant life", "id": 1910, "trainId": 16},
-    {"name": "table", "id": 2684, "trainId": 17},
-    {"name": "chair", "id": 471, "trainId": 18},
-    {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19},
-    {"name": "door", "id": 774, "trainId": 20},
-    {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21},
-    {"name": "sea", "id": 2264, "trainId": 22},
-    {"name": "painting, picture", "id": 1735, "trainId": 23},
-    {"name": "water", "id": 2994, "trainId": 24},
-    {"name": "mirror", "id": 1564, "trainId": 25},
-    {"name": "house", "id": 1276, "trainId": 26},
-    {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27},
-    {"name": "shelf", "id": 2329, "trainId": 28},
-    {"name": "armchair", "id": 57, "trainId": 29},
-    {"name": "fence, fencing", "id": 907, "trainId": 30},
-    {"name": "field", "id": 913, "trainId": 31},
-    {"name": "lamp", "id": 1395, "trainId": 32},
-    {"name": "rock, stone", "id": 2138, "trainId": 33},
-    {"name": "seat", "id": 2272, "trainId": 34},
-    {"name": "river", "id": 2128, "trainId": 35},
-    {"name": "desk", "id": 724, "trainId": 36},
-    {"name": "bathtub, bathing tub, bath, tub", "id": 155, "trainId": 37},
-    {"name": "railing, rail", "id": 2053, "trainId": 38},
-    {"name": "signboard, sign", "id": 2380, "trainId": 39},
-    {"name": "cushion", "id": 689, "trainId": 40},
-    {"name": "path", "id": 1788, "trainId": 41},
-    {"name": "work surface", "id": 3087, "trainId": 42},
-    {"name": "stairs, steps", "id": 2530, "trainId": 43},
-    {"name": "column, pillar", "id": 581, "trainId": 44},
-    {"name": "sink", "id": 2388, "trainId": 45},
-    {"name": "wardrobe, closet, press", "id": 2985, "trainId": 46},
-    {"name": "snow", "id": 2454, "trainId": 47},
-    {"name": "refrigerator, icebox", "id": 2096, "trainId": 48},
-    {"name": "base, pedestal, stand", "id": 137, "trainId": 49},
-    {"name": "bridge, span", "id": 294, "trainId": 50},
-    {"name": "blind, screen", "id": 212, "trainId": 51},
-    {"name": "runway", "id": 2185, "trainId": 52},
-    {"name": "cliff, drop, drop-off", "id": 524, "trainId": 53},
-    {"name": "sand", "id": 2212, "trainId": 54},
-    {"name": "fireplace, hearth, open fireplace", "id": 943, "trainId": 55},
-    {"name": "pillow", "id": 1869, "trainId": 56},
-    {"name": "screen door, screen", "id": 2251, "trainId": 57},
-    {"name": "toilet, can, commode, crapper, pot, potty, stool, throne", "id": 2793, "trainId": 58},
-    {"name": "skyscraper", "id": 2423, "trainId": 59},
-    {"name": "grandstand, covered stand", "id": 1121, "trainId": 60},
-    {"name": "box", "id": 266, "trainId": 61},
-    {"name": "pool table, billiard table, snooker table", "id": 1948, "trainId": 62},
-    {"name": "palm, palm tree", "id": 1744, "trainId": 63},
-    {"name": "double door", "id": 783, "trainId": 64},
-    {"name": "coffee table, cocktail table", "id": 571, "trainId": 65},
-    {"name": "counter", "id": 627, "trainId": 66},
-    {"name": "countertop", "id": 629, "trainId": 67},
-    {"name": "chest of drawers, chest, bureau, dresser", "id": 491, "trainId": 68},
-    {"name": "kitchen island", "id": 1374, "trainId": 69},
-    {"name": "boat", "id": 223, "trainId": 70},
-    {"name": "waterfall, falls", "id": 3016, "trainId": 71},
-    {
-        "name": "stove, kitchen stove, range, kitchen range, cooking stove",
-        "id": 2598,
-        "trainId": 72,
-    },
-    {"name": "flower", "id": 978, "trainId": 73},
-    {"name": "bookcase", "id": 239, "trainId": 74},
-    {"name": "controls", "id": 608, "trainId": 75},
-    {"name": "book", "id": 236, "trainId": 76},
-    {"name": "stairway, staircase", "id": 2531, "trainId": 77},
-    {"name": "streetlight, street lamp", "id": 2616, "trainId": 78},
-    {
-        "name": "computer, computing machine, computing device, data processor, electronic computer, information processing system",
-        "id": 591,
-        "trainId": 79,
-    },
-    {
-        "name": "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle",
-        "id": 327,
-        "trainId": 80,
-    },
-    {"name": "swivel chair", "id": 2679, "trainId": 81},
-    {"name": "light, light source", "id": 1451, "trainId": 82},
-    {"name": "bench", "id": 181, "trainId": 83},
-    {"name": "case, display case, showcase, vitrine", "id": 420, "trainId": 84},
-    {"name": "towel", "id": 2821, "trainId": 85},
-    {"name": "fountain", "id": 1023, "trainId": 86},
-    {"name": "embankment", "id": 855, "trainId": 87},
-    {
-        "name": "television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box",
-        "id": 2733,
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-    {"name": "racket", "id": 2039, "trainId": 744},
-    {"name": "tins", "id": 2774, "trainId": 745},
-    {"name": "eyeglasses", "id": 886, "trainId": 746},
-    {"name": "file", "id": 919, "trainId": 747},
-    {"name": "scarfs", "id": 2240, "trainId": 748},
-    {"name": "sugar bowl", "id": 2636, "trainId": 749},
-    {"name": "flip flop", "id": 963, "trainId": 750},
-    {"name": "headstones", "id": 1218, "trainId": 751},
-    {"name": "laptop bag", "id": 1406, "trainId": 752},
-    {"name": "leash", "id": 1420, "trainId": 753},
-    {"name": "climbing frame", "id": 526, "trainId": 754},
-    {"name": "suit hanger", "id": 2639, "trainId": 755},
-    {"name": "floor spotlight", "id": 975, "trainId": 756},
-    {"name": "plate rack", "id": 1921, "trainId": 757},
-    {"name": "sewer", "id": 2305, "trainId": 758},
-    {"name": "hard drive", "id": 1193, "trainId": 759},
-    {"name": "sprinkler", "id": 2517, "trainId": 760},
-    {"name": "tools box", "id": 2809, "trainId": 761},
-    {"name": "necklace", "id": 1647, "trainId": 762},
-    {"name": "bulbs", "id": 314, "trainId": 763},
-    {"name": "steel industry", "id": 2560, "trainId": 764},
-    {"name": "club", "id": 545, "trainId": 765},
-    {"name": "jack", "id": 1345, "trainId": 766},
-    {"name": "door bars", "id": 775, "trainId": 767},
-    {
-        "name": "control panel, instrument panel, control board, board, panel",
-        "id": 603,
-        "trainId": 768,
-    },
-    {"name": "hairbrush", "id": 1163, "trainId": 769},
-    {"name": "napkin holder", "id": 1641, "trainId": 770},
-    {"name": "office", "id": 1678, "trainId": 771},
-    {"name": "smoke detector", "id": 2450, "trainId": 772},
-    {"name": "utensils", "id": 2915, "trainId": 773},
-    {"name": "apron", "id": 42, "trainId": 774},
-    {"name": "scissors", "id": 2242, "trainId": 775},
-    {"name": "terminal", "id": 2741, "trainId": 776},
-    {"name": "grinder", "id": 1143, "trainId": 777},
-    {"name": "entry phone", "id": 862, "trainId": 778},
-    {"name": "newspaper stand", "id": 1654, "trainId": 779},
-    {"name": "pepper shaker", "id": 1826, "trainId": 780},
-    {"name": "onions", "id": 1689, "trainId": 781},
-    {
-        "name": "central processing unit, cpu, c p u , central processor, processor, mainframe",
-        "id": 3124,
-        "trainId": 782,
-    },
-    {"name": "tape", "id": 2710, "trainId": 783},
-    {"name": "bat", "id": 152, "trainId": 784},
-    {"name": "coaster", "id": 549, "trainId": 785},
-    {"name": "calculator", "id": 360, "trainId": 786},
-    {"name": "potatoes", "id": 1982, "trainId": 787},
-    {"name": "luggage rack", "id": 1478, "trainId": 788},
-    {"name": "salt", "id": 2203, "trainId": 789},
-    {"name": "street number", "id": 2612, "trainId": 790},
-    {"name": "viewpoint", "id": 2956, "trainId": 791},
-    {"name": "sword", "id": 2681, "trainId": 792},
-    {"name": "cd", "id": 437, "trainId": 793},
-    {"name": "rowing machine", "id": 2171, "trainId": 794},
-    {"name": "plug", "id": 1933, "trainId": 795},
-    {"name": "andiron, firedog, dog, dog-iron", "id": 3110, "trainId": 796},
-    {"name": "pepper", "id": 1824, "trainId": 797},
-    {"name": "tongs", "id": 2803, "trainId": 798},
-    {"name": "bonfire", "id": 234, "trainId": 799},
-    {"name": "dog dish", "id": 764, "trainId": 800},
-    {"name": "belt", "id": 177, "trainId": 801},
-    {"name": "dumbbells", "id": 817, "trainId": 802},
-    {"name": "videocassette recorder, vcr", "id": 3145, "trainId": 803},
-    {"name": "hook", "id": 1262, "trainId": 804},
-    {"name": "envelopes", "id": 864, "trainId": 805},
-    {"name": "shower faucet", "id": 2359, "trainId": 806},
-    {"name": "watch", "id": 2992, "trainId": 807},
-    {"name": "padlock", "id": 1725, "trainId": 808},
-    {"name": "swimming pool ladder", "id": 2667, "trainId": 809},
-    {"name": "spanners", "id": 2484, "trainId": 810},
-    {"name": "gravy boat", "id": 1133, "trainId": 811},
-    {"name": "notice board", "id": 1667, "trainId": 812},
-    {"name": "trash bags", "id": 2847, "trainId": 813},
-    {"name": "fire alarm", "id": 932, "trainId": 814},
-    {"name": "ladle", "id": 1392, "trainId": 815},
-    {"name": "stethoscope", "id": 2573, "trainId": 816},
-    {"name": "rocket", "id": 2140, "trainId": 817},
-    {"name": "funnel", "id": 1046, "trainId": 818},
-    {"name": "bowling pins", "id": 264, "trainId": 819},
-    {"name": "valve", "id": 2927, "trainId": 820},
-    {"name": "thermometer", "id": 2752, "trainId": 821},
-    {"name": "cups", "id": 679, "trainId": 822},
-    {"name": "spice jar", "id": 2493, "trainId": 823},
-    {"name": "night light", "id": 1658, "trainId": 824},
-    {"name": "soaps", "id": 2466, "trainId": 825},
-    {"name": "games table", "id": 1057, "trainId": 826},
-    {"name": "slotted spoon", "id": 2444, "trainId": 827},
-    {"name": "reel", "id": 2093, "trainId": 828},
-    {"name": "scourer", "id": 2248, "trainId": 829},
-    {"name": "sleeping robe", "id": 2432, "trainId": 830},
-    {"name": "desk mat", "id": 726, "trainId": 831},
-    {"name": "dumbbell", "id": 816, "trainId": 832},
-    {"name": "hammer", "id": 1171, "trainId": 833},
-    {"name": "tie", "id": 2766, "trainId": 834},
-    {"name": "typewriter", "id": 2900, "trainId": 835},
-    {"name": "shaker", "id": 2313, "trainId": 836},
-    {"name": "cheese dish", "id": 488, "trainId": 837},
-    {"name": "sea star", "id": 2265, "trainId": 838},
-    {"name": "racquet", "id": 2043, "trainId": 839},
-    {"name": "butane gas cylinder", "id": 332, "trainId": 840},
-    {"name": "paper weight", "id": 1771, "trainId": 841},
-    {"name": "shaving brush", "id": 2320, "trainId": 842},
-    {"name": "sunglasses", "id": 2646, "trainId": 843},
-    {"name": "gear shift", "id": 1089, "trainId": 844},
-    {"name": "towel rail", "id": 2826, "trainId": 845},
-    {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846},
-]
-
-
-def _get_ade20k_full_meta():
-    # Id 0 is reserved for ignore_label, we change ignore_label for 0
-    # to 255 in our pre-processing, so all ids are shifted by 1.
-    stuff_ids = [k["id"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES]
-    assert len(stuff_ids) == 847, len(stuff_ids)
-
-    # For semantic segmentation, this mapping maps from contiguous stuff id
-    # (in [0, 91], used in models) to ids in the dataset (used for processing results)
-    stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
-    stuff_classes = [k["name"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES]
-
-    ret = {
-        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
-        "stuff_classes": stuff_classes,
-    }
-    return ret
-
-
-def register_all_ade20k_full(root):
-    root = os.path.join(root, "ADE20K_2021_17_01")
-    meta = _get_ade20k_full_meta()
-    for name, dirname in [("train", "training"), ("val", "validation")]:
-        image_dir = os.path.join(root, "images_detectron2", dirname)
-        gt_dir = os.path.join(root, "annotations_detectron2", dirname)
-        name = f"ade20k_full_sem_seg_{name}"
-        DatasetCatalog.register(
-            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg")
-        )
-        MetadataCatalog.get(name).set(
-            stuff_classes=meta["stuff_classes"][:],
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=65535,  # NOTE: gt is saved in 16-bit TIFF images
-        )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_ade20k_full(_root)
diff --git a/spaces/sujitpal/clip-rsicd-demo/dashboard_text2image.py b/spaces/sujitpal/clip-rsicd-demo/dashboard_text2image.py
deleted file mode 100644
index bcb77cd23e0623986266c35f4e5ffbc19ad00c19..0000000000000000000000000000000000000000
--- a/spaces/sujitpal/clip-rsicd-demo/dashboard_text2image.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import matplotlib.pyplot as plt
-import nmslib
-import numpy as np
-import os
-import streamlit as st
-
-from PIL import Image
-from transformers import CLIPProcessor, FlaxCLIPModel
-
-import utils
-
-BASELINE_MODEL = "openai/clip-vit-base-patch32"
-MODEL_PATH = "flax-community/clip-rsicd-v2"
-IMAGE_VECTOR_FILE = "./vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"
-IMAGES_DIR = "./images"
-CAPTIONS_FILE = os.path.join(IMAGES_DIR, "test-captions.json")
-
-def app():
-    filenames, index = utils.load_index(IMAGE_VECTOR_FILE)
-    model, processor = utils.load_model(MODEL_PATH, BASELINE_MODEL)
-    image2caption = utils.load_captions(CAPTIONS_FILE)
-
-    st.title("Retrieve Images given Text")
-    st.markdown("""
-        This demo shows the image to text retrieval capabilities of this model, i.e., 
-        given a text query, we use our fine-tuned CLIP model to project the text query 
-        to the image/caption embedding space and search for nearby images (by 
-        cosine similarity) in this space.
-        
-        Our fine-tuned CLIP model was previously used to generate image vectors for 
-        our demo, and NMSLib was used for fast vector access.
-
-    """)
-    suggested_query = [
-        "ships",
-        "school house",
-        "military installation",
-        "mountains",
-        "beaches",
-        "airports",
-        "lakes"
-    ]
-    st.text("Some suggested queries to start you off with...")
-    col0, col1, col2, col3, col4, col5, col6 = st.columns(7)
-        # [1, 1.1, 1.3, 1.1, 1, 1, 1])
-    suggest_idx = -1
-    with col0:
-        if st.button(suggested_query[0]):
-            suggest_idx = 0
-    with col1:
-        if st.button(suggested_query[1]):
-            suggest_idx = 1
-    with col2:
-        if st.button(suggested_query[2]):
-            suggest_idx = 2
-    with col3:
-        if st.button(suggested_query[3]):
-            suggest_idx = 3
-    with col4:
-        if st.button(suggested_query[4]):
-            suggest_idx = 4
-    with col5:
-        if st.button(suggested_query[5]):
-            suggest_idx = 5
-    with col6:
-        if st.button(suggested_query[6]):
-            suggest_idx = 6
-    query = st.text_input("OR enter a text Query:")
-    query = suggested_query[suggest_idx] if suggest_idx > -1 else query
-
-    if st.button("Query") or suggest_idx > -1:
-        inputs = processor(text=[query], images=None, return_tensors="jax", padding=True)
-        query_vec = model.get_text_features(**inputs)
-        query_vec = np.asarray(query_vec)
-        ids, distances = index.knnQuery(query_vec, k=10)
-        result_filenames = [filenames[id] for id in ids]
-        for rank, (result_filename, score) in enumerate(zip(result_filenames, distances)):
-            caption = "{:s} (score: {:.3f})".format(result_filename, 1.0 - score)
-            col1, col2, col3 = st.columns([2, 10, 10])
-            col1.markdown("{:d}.".format(rank + 1))
-            col2.image(Image.open(os.path.join(IMAGES_DIR, result_filename)),
-                       caption=caption)
-            caption_text = []
-            for caption in image2caption[result_filename]:
-                caption_text.append("* {:s}\n".format(caption))
-            col3.markdown("".join(caption_text))                       
-            st.markdown("---")
-        suggest_idx = -1
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Maxtree Plant Models Vol. 19.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Maxtree Plant Models Vol. 19.md
deleted file mode 100644
index 11563a5e13068fcbea5b271a0a79d15dd46d0d5c..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Maxtree Plant Models Vol. 19.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Maxtree Plant Models Vol. 19</h2><br /><p><b><b>Download File</b> >> <a href="https://cinurl.com/2uEYQu">https://cinurl.com/2uEYQu</a></b></p><br /><br />
-
- 3cee63e6c2<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neverwinter Nights Diamond Cd Key Crack.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neverwinter Nights Diamond Cd Key Crack.md
deleted file mode 100644
index 48678eef291fb2b58798c5730bb761af2a2a962c..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Neverwinter Nights Diamond Cd Key Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Neverwinter Nights Diamond Cd Key Crack</h2><br /><p><b><b>Download File</b>  <a href="https://cinurl.com/2uEZcI">https://cinurl.com/2uEZcI</a></b></p><br /><br />
-<br />
- d5da3c52bf<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Atmocity HOT Free Download [License].md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Atmocity HOT Free Download [License].md
deleted file mode 100644
index 031daf95ee84eb7c5710c8565eae218655f7c689..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Atmocity HOT Free Download [License].md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>How to Download Atmocity for Free with a License Key</h1>
-<p>Atmocity is a sci-fi city building game that allows you to create impressive, gravity-defying cities. You can test your skills as a mayor in the campaign mode with set objectives, or let your creativity go wild in the free-roaming mode. The game has over 700 buildings, decorations, bridges and transportation types to build.</p>
-<p>If you want to download Atmocity for free with a license key, you can follow these steps:</p>
-<h2>Atmocity Free Download [License]</h2><br /><p><b><b>DOWNLOAD</b> ::: <a href="https://urluss.com/2uCDT2">https://urluss.com/2uCDT2</a></b></p><br /><br />
-<ol>
-<li>Go to <a href="https://soundcloud.com/ndjohtesuc/atmocity-free-download-license-portable">this link</a> on SoundCloud and listen to the track "Atmocity Free Download [License] PORTABLE" by Philip[^1^].</li>
-<li>In the description of the track, you will find a link to a Google Drive folder that contains the game files and the license key.</li>
-<li>Download the folder and extract it to your preferred location.</li>
-<li>Run the game executable and enter the license key when prompted.</li>
-<li>Enjoy playing Atmocity for free!</li>
-</ol>
-<p>Note: This method is not official or endorsed by Correcture Games AB, the developer and publisher of Atmocity. You may encounter some issues or bugs while playing the game. If you like the game and want to support the developer, you can buy it on Steam[^3^] or other platforms.</p>
-
-<p>Atmocity is a game that is currently in Early Access on Steam[^1^], which means that it is not fully finished and may change over time. The developer, Correcture Games AB, is aiming to release the full version in the second half of 2023. The game has received mixed reviews from players so far[^2^], with some praising its originality and creativity, and others criticizing its performance and complexity.</p>
-<p>If you are looking for a city building game that challenges you to think outside the box, Atmocity might be a good choice for you. You can build cities that defy gravity and physics, and experiment with different shapes and layouts. You can also manage the economy, resources, population and environment of your city, and deal with various events and disasters. The game has a campaign mode with set objectives, as well as a free-roaming mode where you can build whatever you want.</p>
-<p>However, if you are looking for a polished and easy-to-play game, Atmocity might not be for you. The game has a steep learning curve and requires a lot of trial and error. The game also has some technical issues and bugs that may affect your gameplay experience. The game is still in development and may change significantly before it is fully released. You may want to wait until the game is more stable and complete before buying it.</p>
-
-<p>Atmocity has a unique gameplay that lets you build cities in 3D space, without any ground or gravity. You can rotate and zoom in and out of your city, and place buildings on any surface. You can also connect buildings with bridges and tubes, and create transportation networks with buses, trains and monorails. The game has a realistic physics engine that simulates the weight and balance of your city, as well as the wind and weather effects.</p>
-<p>The game also has a simulation aspect that requires you to manage the needs and demands of your citizens. You have to provide them with power, water, food, education, health, entertainment and security. You also have to deal with pollution, crime, unemployment and disasters. You can choose between different ideologies that affect your city's policies and appearance. You can also trade with other cities and compete with them for resources and prestige.</p>
-<p>The game has a lot of depth and variety, as well as a high replay value. You can customize your city with different themes and styles, such as futuristic, industrial, organic or retro. You can also explore different scenarios and challenges in the campaign mode, or create your own scenarios in the sandbox mode. The game also supports modding and user-generated content, so you can add new buildings, decorations, maps and more.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sushmanth/hand_written_to_text/README.md b/spaces/sushmanth/hand_written_to_text/README.md
deleted file mode 100644
index 04a4115301bd8626a7b4b0a9569b9e7c822e667a..0000000000000000000000000000000000000000
--- a/spaces/sushmanth/hand_written_to_text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hand Written To Text
-emoji: 🔥
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/backbones/uniformer.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/backbones/uniformer.py
deleted file mode 100644
index 0c4bb88e4c928540cca9ab609988b916520f5b7a..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/backbones/uniformer.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# --------------------------------------------------------
-# UniFormer
-# Copyright (c) 2022 SenseTime X-Lab
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Kunchang Li
-# --------------------------------------------------------
-
-from collections import OrderedDict
-import math
-
-from functools import partial
-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 annotator.uniformer.mmcv_custom import load_checkpoint
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-class Mlp(nn.Module):
-    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
-
-
-class CMlp(nn.Module):
-    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.Conv2d(in_features, hidden_features, 1)
-        self.act = act_layer()
-        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
-        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
-
-
-class CBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = nn.BatchNorm2d(dim)
-        self.conv1 = nn.Conv2d(dim, dim, 1)
-        self.conv2 = nn.Conv2d(dim, dim, 1)
-        self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = nn.BatchNorm2d(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        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)
-
-    def forward(self, x):
-        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)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        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 SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        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)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, N, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.transpose(1, 2).reshape(B, N, H, W)
-        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 SABlock_Windows(nn.Module):
-    def __init__(self, dim, num_heads, window_size=14, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.window_size=window_size
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        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)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x.permute(0, 2, 3, 1)
-        B, H, W, C = x.shape
-        shortcut = x
-        x = self.norm1(x)
-
-        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
-        
-        x_windows = window_partition(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)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.permute(0, 3, 1, 2).reshape(B, C, H, W)
-        return x 
-             
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x):
-        B, _, H, W = x.shape
-        x = self.proj(x)
-        B, _, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        return x
-    
-
-@BACKBONES.register_module()   
-class UniFormer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, layers=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=80, embed_dim=[64, 128, 320, 512],
-                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
-                 pretrained_path=None, use_checkpoint=False, checkpoint_num=[0, 0, 0, 0], 
-                 windows=False, hybrid=False, window_size=14):
-        """
-        Args:
-            layer (list): number of block in each layer
-            img_size (int, tuple): input image size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            head_dim (int): dimension of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer (nn.Module): normalization layer
-            pretrained_path (str): path of pretrained model
-            use_checkpoint (bool): whether use checkpoint
-            checkpoint_num (list): index for using checkpoint in every stage
-            windows (bool): whether use window MHRA
-            hybrid (bool): whether use hybrid MHRA
-            window_size (int): size of window (>14)
-        """
-        super().__init__()
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.checkpoint_num = checkpoint_num
-        self.windows = windows
-        print(f'Use Checkpoint: {self.use_checkpoint}')
-        print(f'Checkpoint Number: {self.checkpoint_num}')
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) 
-        
-        self.patch_embed1 = PatchEmbed(
-            img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
-        self.patch_embed2 = PatchEmbed(
-            img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1])
-        self.patch_embed3 = PatchEmbed(
-            img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2])
-        self.patch_embed4 = PatchEmbed(
-            img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3])
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(layers))]  # stochastic depth decay rule
-        num_heads = [dim // head_dim for dim in embed_dim]
-        self.blocks1 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
-            for i in range(layers[0])])
-        self.norm1=norm_layer(embed_dim[0])
-        self.blocks2 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]], norm_layer=norm_layer)
-            for i in range(layers[1])])
-        self.norm2 = norm_layer(embed_dim[1])
-        if self.windows:
-            print('Use local window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock_Windows(
-                dim=embed_dim[2], num_heads=num_heads[2], 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[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        elif hybrid:
-            print('Use hybrid window for blocks in stage3')
-            block3 = []
-            for i in range(layers[2]):
-                if (i + 1) % 4 == 0:
-                    block3.append(SABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer))
-                else:
-                    block3.append(SABlock_Windows(
-                    dim=embed_dim[2], num_heads=num_heads[2], 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[i+layers[0]+layers[1]], norm_layer=norm_layer))
-            self.blocks3 = nn.ModuleList(block3)
-        else:
-            print('Use global window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        self.norm3 = norm_layer(embed_dim[2])
-        self.blocks4 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]+layers[2]], norm_layer=norm_layer)
-            for i in range(layers[3])])
-        self.norm4 = norm_layer(embed_dim[3])
-        
-        # Representation layer
-        if representation_size:
-            self.num_features = representation_size
-            self.pre_logits = nn.Sequential(OrderedDict([
-                ('fc', nn.Linear(embed_dim, representation_size)),
-                ('act', nn.Tanh())
-            ]))
-        else:
-            self.pre_logits = nn.Identity()
-        
-        self.apply(self._init_weights)
-        self.init_weights(pretrained=pretrained_path)
-        
-    def init_weights(self, pretrained):
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
-            print(f'Load pretrained model from {pretrained}')
-    def _init_weights(self, 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)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        out = []
-        x = self.patch_embed1(x)
-        x = self.pos_drop(x)
-        for i, blk in enumerate(self.blocks1):
-            if self.use_checkpoint and i < self.checkpoint_num[0]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm1(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed2(x)
-        for i, blk in enumerate(self.blocks2):
-            if self.use_checkpoint and i < self.checkpoint_num[1]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm2(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed3(x)
-        for i, blk in enumerate(self.blocks3):
-            if self.use_checkpoint and i < self.checkpoint_num[2]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm3(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed4(x)
-        for i, blk in enumerate(self.blocks4):
-            if self.use_checkpoint and i < self.checkpoint_num[3]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm4(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        return tuple(out)
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        return x
diff --git a/spaces/szukevin/VISOR-GPT/train/scripts/build_vocab.py b/spaces/szukevin/VISOR-GPT/train/scripts/build_vocab.py
deleted file mode 100644
index bbfef4a9883ed2f5fda458cfbaac96df7b812890..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/scripts/build_vocab.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""
-Build vocabulary with given tokenizer
-"""
-import sys
-import os
-import argparse
-
-tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(tencentpretrain_dir)
-
-from tencentpretrain.utils import *
-from tencentpretrain.utils.vocab import Vocab
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    parser.add_argument("--corpus_path", required=True)
-    parser.add_argument("--delimiter", choices=["char", "space"], required=True,
-                        help="Tokenizing the corpus in char-level or by the provided spaces.")
-    parser.add_argument("--output_path", required=True,
-                        help="The output path to save the vocabulary.")
-    parser.add_argument("--workers_num", type=int, default=1,
-                        help="The number of processes to build vocabulary.")
-    parser.add_argument("--min_count", type=int, default=1,
-                        help="The minimum count of words retained in the vocabulary.")
-    
-    args = parser.parse_args()
-
-    # Build tokenizer only for char and space.
-    args.vocab_path, args.spm_model_path = "./models/reserved_vocab.txt", None
-    tokenizer = str2tokenizer[args.delimiter](args)
-
-    # Build and save vocabulary using CharTokenizer or SpaceTokenizer.
-    vocab = Vocab()
-    vocab.build(args.corpus_path, tokenizer, args.workers_num, args.min_count)
-    vocab.save(args.output_path)
diff --git a/spaces/teralomaniac/clewd/README.md b/spaces/teralomaniac/clewd/README.md
deleted file mode 100644
index f962feeab32bb0e2c936c090d0d6e07765cad33e..0000000000000000000000000000000000000000
--- a/spaces/teralomaniac/clewd/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Clewd
-emoji: 🚀
-colorFrom: blue
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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deleted file mode 100644
index 90e7c2be97d1535e7250d2963ade3ff3e0fd0034..0000000000000000000000000000000000000000
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/F-Secure Antivirus SAFE Pre-Activated.md b/spaces/terfces0erbo/CollegeProjectV2/F-Secure Antivirus SAFE Pre-Activated.md
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diff --git a/spaces/tfwang/PITI-Synthesis/glide_text2im/gaussian_diffusion.py b/spaces/tfwang/PITI-Synthesis/glide_text2im/gaussian_diffusion.py
deleted file mode 100644
index 8e2b88abaa2667229fa9cfc4ca10feafea55a092..0000000000000000000000000000000000000000
--- a/spaces/tfwang/PITI-Synthesis/glide_text2im/gaussian_diffusion.py
+++ /dev/null
@@ -1,946 +0,0 @@
-"""
-This code started out as a PyTorch port of Ho et al's diffusion models:
-https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
-
-Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
-"""
-
-import enum
-import math
-from subprocess import call
-
-import numpy as np
-import torch as th
-
-from .nn import mean_flat
-from .losses import normal_kl, discretized_gaussian_log_likelihood
-
-def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
-    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
-    warmup_time = int(num_diffusion_timesteps * warmup_frac)
-    betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
-    return betas
-
-
-def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
-    """
-    This is the deprecated API for creating beta schedules.
-
-    See get_named_beta_schedule() for the new library of schedules.
-    """
-    if beta_schedule == "quad":
-        betas = (
-            np.linspace(
-                beta_start ** 0.5,
-                beta_end ** 0.5,
-                num_diffusion_timesteps,
-                dtype=np.float64,
-            )
-            ** 2
-        )
-    elif beta_schedule == "linear":
-        betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
-    elif beta_schedule == "warmup10":
-        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
-    elif beta_schedule == "warmup50":
-        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
-    elif beta_schedule == "const":
-        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
-    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
-        betas = 1.0 / np.linspace(
-            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
-        )
-    else:
-        raise NotImplementedError(beta_schedule)
-    assert betas.shape == (num_diffusion_timesteps,)
-    return betas
-
-def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
-    """
-    Get a pre-defined beta schedule for the given name.
-
-    The beta schedule library consists of beta schedules which remain similar
-    in the limit of num_diffusion_timesteps.
-    Beta schedules may be added, but should not be removed or changed once
-    they are committed to maintain backwards compatibility.
-    """
-    if schedule_name == "linear":
-        scale = 1000 / num_diffusion_timesteps
-        return get_beta_schedule(
-            "linear",
-            beta_start=scale * 0.0001,
-            beta_end=scale * 0.02,
-            num_diffusion_timesteps=num_diffusion_timesteps,
-        )
-    elif schedule_name == "squaredcos_cap_v2":
-        return betas_for_alpha_bar(
-            num_diffusion_timesteps,
-            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-    else:
-        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
-
-
-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)
-
-
-class ModelMeanType(enum.Enum):
-    """
-    Which type of output the model predicts.
-    """
-
-    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
-    START_X = enum.auto()  # the model predicts x_0
-    EPSILON = enum.auto()  # the model predicts epsilon
-
-
-class ModelVarType(enum.Enum):
-    """
-    What is used as the model's output variance.
-
-    The LEARNED_RANGE option has been added to allow the model to predict
-    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
-    """
-
-    LEARNED = enum.auto()
-    FIXED_SMALL = enum.auto()
-    FIXED_LARGE = enum.auto()
-    LEARNED_RANGE = enum.auto()
-
-
-class LossType(enum.Enum):
-    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
-    RESCALED_MSE = (
-        enum.auto()
-    )  # use raw MSE loss (with RESCALED_KL when learning variances)
-    KL = enum.auto()  # use the variational lower-bound
-    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
-
-    def is_vb(self):
-        return self == LossType.KL or self == LossType.RESCALED_KL
-
-
-class GaussianDiffusion:
-    """
-    Utilities for training and sampling diffusion models.
-
-    Ported directly from here, and then adapted over time to further experimentation.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
-
-    :param betas: a 1-D numpy array of betas for each diffusion timestep,
-                  starting at T and going to 1.
-    :param model_mean_type: a ModelMeanType determining what the model outputs.
-    :param model_var_type: a ModelVarType determining how variance is output.
-    :param loss_type: a LossType determining the loss function to use.
-    :param rescale_timesteps: if True, pass floating point timesteps into the
-                              model so that they are always scaled like in the
-                              original paper (0 to 1000).
-    """
-
-    def __init__(
-        self,
-        *,
-        betas,
-        model_mean_type,
-        model_var_type,
-        loss_type,
-        rescale_timesteps=False,
-    ):
-        self.model_mean_type = model_mean_type
-        self.model_var_type = model_var_type
-        self.loss_type = loss_type
-        self.rescale_timesteps = rescale_timesteps
-
-        # Use float64 for accuracy.
-        betas = np.array(betas, dtype=np.float64)
-        self.betas = betas
-        assert len(betas.shape) == 1, "betas must be 1-D"
-        assert (betas > 0).all() and (betas <= 1).all()
-
-        self.num_timesteps = int(betas.shape[0])
-
-        alphas = 1.0 - betas
-        self.alphas_cumprod = np.cumprod(alphas, axis=0)
-        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
-        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
-        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
-        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
-        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
-        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        self.posterior_variance = (
-            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        # log calculation clipped because the posterior variance is 0 at the
-        # beginning of the diffusion chain.
-        self.posterior_log_variance_clipped = np.log(
-            np.append(self.posterior_variance[1], self.posterior_variance[1:])
-        )
-        self.posterior_mean_coef1 = (
-            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        self.posterior_mean_coef2 = (
-            (1.0 - self.alphas_cumprod_prev)
-            * np.sqrt(alphas)
-            / (1.0 - self.alphas_cumprod)
-        )
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        )
-        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = _extract_into_tensor(
-            self.log_one_minus_alphas_cumprod, t, x_start.shape
-        )
-        return mean, variance, log_variance
-
-    def q_sample(self, x_start, t, noise=None):
-        """
-        Diffuse the data for a given number of diffusion steps.
-
-        In other words, sample from q(x_t | x_0).
-
-        :param x_start: the initial data batch.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :param noise: if specified, the split-out normal noise.
-        :return: A noisy version of x_start.
-        """
-        if noise is None:
-            noise = th.randn_like(x_start)
-        assert noise.shape == x_start.shape
-        return (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
-            * noise
-        )
-
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior:
-
-            q(x_{t-1} | x_t, x_0)
-
-        """
-        assert x_start.shape == x_t.shape
-        posterior_mean = (
-            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = _extract_into_tensor(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        assert (
-            posterior_mean.shape[0]
-            == posterior_variance.shape[0]
-            == posterior_log_variance_clipped.shape[0]
-            == x_start.shape[0]
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(
-        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
-    ):
-        """
-        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
-        the initial x, x_0.
-
-        :param model: the model, which takes a signal and a batch of timesteps
-                      as input.
-        :param x: the [N x C x ...] tensor at time t.
-        :param t: a 1-D Tensor of timesteps.
-        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample. Applies before
-            clip_denoised.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict with the following keys:
-                 - 'mean': the model mean output.
-                 - 'variance': the model variance output.
-                 - 'log_variance': the log of 'variance'.
-                 - 'pred_xstart': the prediction for x_0.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-
-        B, C = x.shape[:2]
-        assert t.shape == (B,)
-        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
-
-        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
-            assert model_output.shape == (B, C * 2, *x.shape[2:])
-            model_output, model_var_values = th.split(model_output, C, dim=1)
-            if self.model_var_type == ModelVarType.LEARNED:
-                model_log_variance = model_var_values
-                model_variance = th.exp(model_log_variance)
-            else:
-                min_log = _extract_into_tensor(
-                    self.posterior_log_variance_clipped, t, x.shape
-                )
-                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
-                # The model_var_values is [-1, 1] for [min_var, max_var].
-                frac = (model_var_values + 1) / 2
-                model_log_variance = frac * max_log + (1 - frac) * min_log
-                model_variance = th.exp(model_log_variance)
-        else:
-            model_variance, model_log_variance = {
-                # for fixedlarge, we set the initial (log-)variance like so
-                # to get a better decoder log likelihood.
-                ModelVarType.FIXED_LARGE: (
-                    np.append(self.posterior_variance[1], self.betas[1:]),
-                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
-                ),
-                ModelVarType.FIXED_SMALL: (
-                    self.posterior_variance,
-                    self.posterior_log_variance_clipped,
-                ),
-            }[self.model_var_type]
-            model_variance = _extract_into_tensor(model_variance, t, x.shape)
-            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
-
-        def process_xstart(x):
-            if denoised_fn is not None:
-                x = denoised_fn(x)
-            if clip_denoised:
-                return x.clamp(-1, 1)
-            return x
-
-        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
-            pred_xstart = process_xstart(
-                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
-            )
-            model_mean = model_output
-        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
-            if self.model_mean_type == ModelMeanType.START_X:
-                pred_xstart = process_xstart(model_output)
-            else:
-                pred_xstart = process_xstart(
-                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
-                )
-            model_mean, _, _ = self.q_posterior_mean_variance(
-                x_start=pred_xstart, x_t=x, t=t
-            )
-        else:
-            raise NotImplementedError(self.model_mean_type)
-
-        assert (
-            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
-        )
-        return {
-            "mean": model_mean,
-            "variance": model_variance,
-            "log_variance": model_log_variance,
-            "pred_xstart": pred_xstart,
-        }
-
-    def _predict_xstart_from_eps(self, x_t, t, eps):
-        assert x_t.shape == eps.shape
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
-        )
-
-    def _predict_xstart_from_xprev(self, x_t, t, xprev):
-        assert x_t.shape == xprev.shape
-        return (  # (xprev - coef2*x_t) / coef1
-            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
-            - _extract_into_tensor(
-                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
-            )
-            * x_t
-        )
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - pred_xstart
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute the mean for the previous step, given a function cond_fn that
-        computes the gradient of a conditional log probability with respect to
-        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
-        condition on y.
-        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
-        """
-        gradient = cond_fn(x, t, **model_kwargs)
-        new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
-        return new_mean
-
-    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute what the p_mean_variance output would have been, should the
-        model's score function be conditioned by cond_fn.
-        See condition_mean() for details on cond_fn.
-        Unlike condition_mean(), this instead uses the conditioning strategy
-        from Song et al (2020).
-        """
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-
-        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
-        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
-
-        out = p_mean_var.copy()
-        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
-        out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
-
-    def _scale_timesteps(self, t):
-        if self.rescale_timesteps:
-            return t.float() * (1000.0 / self.num_timesteps)
-        return t
-
-    def p_sample(
-        self, model, x, t, clip_denoised=True, denoised_fn=None, cond_fn=None, model_kwargs=None
-    ):
-        """
-        Sample x_{t-1} from the model at the given timestep.
-
-        :param model: the model to sample from.
-        :param x: the current tensor at x_{t-1}.
-        :param t: the value of t, starting at 0 for the first diffusion step.
-        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict containing the following keys:
-                 - 'sample': a random sample from the model.
-                 - 'pred_xstart': a prediction of x_0.
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        noise = th.randn_like(x)
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        if cond_fn is not None:
-            out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def p_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        call_back=None,
-        start_step=None
-    ):
-        """
-        Generate samples from the model.
-
-        :param model: the model module.
-        :param shape: the shape of the samples, (N, C, H, W).
-        :param noise: if specified, the noise from the encoder to sample.
-                      Should be of the same shape as `shape`.
-        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param device: if specified, the device to create the samples on.
-                       If not specified, use a model parameter's device.
-        :param progress: if True, show a tqdm progress bar.
-        :return: a non-differentiable batch of samples.
-        """
-        final = None
-        for sample in self.p_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            call_back=call_back,
-            start_step=start_step
-        ):
-            final = sample
-        return final["sample"]
-
-    def p_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        call_back=None,
-        start_step=None
-    ):
-        """
-        Generate samples from the model and yield intermediate samples from
-        each timestep of diffusion.
-
-        Arguments are the same as p_sample_loop().
-        Returns a generator over dicts, where each dict is the return value of
-        p_sample().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps if not start_step else start_step))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.p_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                )
-         
-                if call_back:
-                    call_back(out,t[0].item())
-                yield out
-                img = out["sample"]
-
-    def ddim_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t-1} from the model using DDIM.
-
-        Same usage as p_sample().
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
-        sigma = (
-            eta
-            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
-            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
-        )
-        # Equation 12.
-        noise = th.randn_like(x)
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
-            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
-        )
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        sample = mean_pred + nonzero_mask * sigma * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_reverse_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t+1} from the model using DDIM reverse ODE.
-        """
-        assert eta == 0.0, "Reverse ODE only for deterministic path"
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
-            - out["pred_xstart"]
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
-        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
-
-        # Equation 12. reversed
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_next)
-            + th.sqrt(1 - alpha_bar_next) * eps
-        )
-
-        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Generate samples from the model using DDIM.
-
-        Same usage as p_sample_loop().
-        """
-        final = None
-        for sample in self.ddim_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            eta=eta,
-        ):
-            final = sample
-        return final["sample"]
-
-    def ddim_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Use DDIM to sample from the model and yield intermediate samples from
-        each timestep of DDIM.
-
-        Same usage as p_sample_loop_progressive().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.ddim_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                    eta=eta,
-                )
-                yield out
-                img = out["sample"]
-
-    def _vb_terms_bpd(
-        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
-    ):
-        """
-        Get a term for the variational lower-bound.
-
-        The resulting units are bits (rather than nats, as one might expect).
-        This allows for comparison to other papers.
-
-        :return: a dict with the following keys:
-                 - 'output': a shape [N] tensor of NLLs or KLs.
-                 - 'pred_xstart': the x_0 predictions.
-        """
-        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
-            x_start=x_start, x_t=x_t, t=t
-        )
-        out = self.p_mean_variance(
-            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
-        )
-        kl = normal_kl(
-            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
-        )
-        kl = mean_flat(kl) / np.log(2.0)
-
-        decoder_nll = -discretized_gaussian_log_likelihood(
-            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
-        )
-        assert decoder_nll.shape == x_start.shape
-        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
-
-        # At the first timestep return the decoder NLL,
-        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
-        output = th.where((t == 0), decoder_nll, kl)
-        return {"output": output, "pred_xstart": out["pred_xstart"]}
-
-    def training_losses(self, model, x_start, t, vgg, adv, model_kwargs=None, noise=None):
-        """
-        Compute training losses for a single timestep.
-
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param t: a batch of timestep indices.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param noise: if specified, the specific Gaussian noise to try to remove.
-        :return: a dict with the key "loss" containing a tensor of shape [N].
-                 Some mean or variance settings may also have other keys.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-        if noise is None:
-            noise = th.randn_like(x_start)
-        x_t = self.q_sample(x_start, t, noise=noise)
-
-        terms = {}
-
-        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
-            terms["loss"] = self._vb_terms_bpd(
-                model=model,
-                x_start=x_start,
-                x_t=x_t,
-                t=t,
-                clip_denoised=False,
-                model_kwargs=model_kwargs,
-            )["output"]
-            if self.loss_type == LossType.RESCALED_KL:
-                terms["loss"] *= self.num_timesteps
-        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
-            model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
-
-            if self.model_var_type in [
-                ModelVarType.LEARNED,
-                ModelVarType.LEARNED_RANGE,
-            ]:
-                B, C = x_t.shape[:2]
-                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
-                model_output, model_var_values = th.split(model_output, C, dim=1)
-                # Learn the variance using the variational bound, but don't let
-                # it affect our mean prediction.
-                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
-                terms["vb"] = self._vb_terms_bpd(
-                    model=lambda *args, r=frozen_out: r,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t,
-                    clip_denoised=False,
-                )["output"]
-                if self.loss_type == LossType.RESCALED_MSE:
-                    # Divide by 1000 for equivalence with initial implementation.
-                    # Without a factor of 1/1000, the VB term hurts the MSE term.
-                    terms["vb"] *= self.num_timesteps / 1000.0
-
-            target = {
-                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
-                    x_start=x_start, x_t=x_t, t=t
-                )[0],
-                ModelMeanType.START_X: x_start,
-                ModelMeanType.EPSILON: noise,
-            }[self.model_mean_type]
-            assert model_output.shape == target.shape == x_start.shape
-            terms["mse"] = mean_flat((target - model_output) ** 2)
-
-            if vgg is not None:
-                terms["perceptual"] = vgg(self._predict_xstart_from_eps(x_t=x_t, t=t, eps=model_output).clamp(-1, 1) , x_start) * 0.004
-            else:
-                terms["perceptual"] = 0.
-
-            if adv is not None:
-                terms["adv"] = adv(self._predict_xstart_from_eps(x_t=x_t, t=t, eps=model_output).clamp(-1, 1) , x_start) * 0.01
-            else:
-                terms["adv"] = 0.
-
-            if "vb" in terms:
-                terms["loss"] = terms["mse"] + terms["vb"] + terms["perceptual"] + terms["adv"] 
-            else:
-                terms["loss"] = terms["mse"]
-        else:
-            raise NotImplementedError(self.loss_type)
-
-        return terms
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-
-        This term can't be optimized, as it only depends on the encoder.
-
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(
-            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
-        )
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
-        """
-        Compute the entire variational lower-bound, measured in bits-per-dim,
-        as well as other related quantities.
-
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param clip_denoised: if True, clip denoised samples.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-
-        :return: a dict containing the following keys:
-                 - total_bpd: the total variational lower-bound, per batch element.
-                 - prior_bpd: the prior term in the lower-bound.
-                 - vb: an [N x T] tensor of terms in the lower-bound.
-                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
-                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
-        """
-        device = x_start.device
-        batch_size = x_start.shape[0]
-
-        vb = []
-        xstart_mse = []
-        mse = []
-        for t in list(range(self.num_timesteps))[::-1]:
-            t_batch = th.tensor([t] * batch_size, device=device)
-            noise = th.randn_like(x_start)
-            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
-            # Calculate VLB term at the current timestep
-            with th.no_grad():
-                out = self._vb_terms_bpd(
-                    model,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t_batch,
-                    clip_denoised=clip_denoised,
-                    model_kwargs=model_kwargs,
-                )
-            vb.append(out["output"])
-            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
-            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
-            mse.append(mean_flat((eps - noise) ** 2))
-
-        vb = th.stack(vb, dim=1)
-        xstart_mse = th.stack(xstart_mse, dim=1)
-        mse = th.stack(mse, dim=1)
-
-        prior_bpd = self._prior_bpd(x_start)
-        total_bpd = vb.sum(dim=1) + prior_bpd
-        return {
-            "total_bpd": total_bpd,
-            "prior_bpd": prior_bpd,
-            "vb": vb,
-            "xstart_mse": xstart_mse,
-            "mse": mse,
-        }
-
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    """
-    Extract values from a 1-D numpy array for a batch of indices.
-
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
-                            dimension equal to the length of timesteps.
-    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
-    """
-    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
-    while len(res.shape) < len(broadcast_shape):
-        res = res[..., None]
-    return res.expand(broadcast_shape)
diff --git a/spaces/thejagstudio/procom/croma/models.py b/spaces/thejagstudio/procom/croma/models.py
deleted file mode 100644
index 71a836239075aa6e6e4ecb700e9c42c95c022d91..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/procom/croma/models.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from django.db import models
-
-# Create your models here.
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Effective Reading and Writing for COMM 170 and Beyond PDF Improve Your Communication Skills.md b/spaces/tialenAdioni/chat-gpt-api/logs/Effective Reading and Writing for COMM 170 and Beyond PDF Improve Your Communication Skills.md
deleted file mode 100644
index 0dd971758a1367ab5804628d5b728dd0fb2fbfc8..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Effective Reading and Writing for COMM 170 and Beyond PDF Improve Your Communication Skills.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-<br />
-```html
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Allan Yanna Beri Athak - Sudeera Dilshan A Song That Will Touch Your Heart.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Allan Yanna Beri Athak - Sudeera Dilshan A Song That Will Touch Your Heart.md
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-<h1>Allan Yanna Beri Durak: A Heartbreaking Sinhala Song by Sudeera Dilshan</h1>
- <p>If you are a fan of Sinhala music, you might have heard of a song called <strong>Allan Yanna Beri Durak</strong>, which translates to "Don't go far away, leaving me alone". This song is a sad and romantic ballad that expresses the pain of losing a loved one. In this article, we will explore what this song is about, why it is popular, and how you can download it legally and ethically.</p>
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- <h2>What is Allan Yanna Beri Durak?</h2>
- <p>Allan Yanna Beri Durak is a Sinhala song that was released in 2022 by Sudeera Dilshan, a young and talented singer from Sri Lanka. The song is about a man who begs his lover not to leave him alone, as he cannot bear the separation. He recalls their happy moments together and hopes that they will meet again in this life or the next. He says that his love for her is eternal and that he will always remember her in his heart.</p>
- <h3>The meaning of the song title and lyrics</h3>
- <p>The title of the song, Allan Yanna Beri Durak, means "Don't go far away, leaving me alone". This phrase is repeated throughout the chorus of the song, as the main theme. The lyrics are written by Udesh Aryan, who also wrote other popular Sinhala songs such as Dawasaka Aye Api Hamuwela and Mano Paraka. The lyrics are simple but poetic, using metaphors and imagery to convey the emotions of the singer. For example, he compares his loneliness to a dark night, his tears to raindrops, and his love to a flower that never fades.</p>
- <h3>The singer and songwriter of the song</h3>
- <p>The singer of the song, Sudeera Dilshan, is a rising star in the Sinhala music industry. He started his career as a YouTube singer, covering various songs in different genres. He has a versatile voice that can sing both high and low notes with ease. He also plays guitar and piano, and produces his own music. He has released several original songs, such as Paluwa Rajakarana, Kavikashi, Eka Diga Kathawak, and Diwa Re. He has also collaborated with other artists, such as UD Rathnayaka, Kanishka Akmeemana, and D Mass.</p>
- <h3>The music video and audio of the song</h3>
- <p>The music video of Allan Yanna Beri Durak was directed by Vidura Wijerathna and edited by Pubudu Udayanga. It features Sudeera Dilshan and Nethu Ananya as the main characters, who portray a couple who are separated by fate. The video shows their happy memories together, as well as their sorrowful goodbye. The video has over 14 million views on YouTube as of June 2023.</p>
- <p>The audio of the song was produced by Sudeera Dilshan himself, with music by D Mass. The song has a slow tempo and a soft melody that matches the mood of the lyrics. The song has over 66 thousand plays on SoundCloud as of June 2023.</p>
- <h2>Why is Allan Yanna Beri Durak popular?</h2>
- <p>Allan Yanna Beri Durak is one of the most popular Sinhala songs of 2022 and 2023. It has received positive feedback from both critics and fans, who praised the song for its emotional depth, beautiful melody, and impressive vocals. There are several reasons why this song is so popular among Sinhala music lovers.</p>
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- <h3>The emotional appeal of the song</h3>
- <p>One of the main reasons why Allan Yanna Beri Durak is popular is because it appeals to the emotions of the listeners. The song is a heartbreaking ballad that expresses the feelings of someone who has lost their lover. Many people can relate to this situation, as they might have experienced a similar loss in their lives. The song also touches on the themes of love, hope, and faith, which are universal and timeless. The song makes the listeners feel the pain and longing of the singer, as well as his love and devotion for his partner.</p>
- <h3>The relatability of the song to many listeners</h3>
- <p>Another reason why Allan Yanna Beri Durak is popular is because it is relatable to many listeners. The song is sung in Sinhala, which is the native language of most Sri Lankans. The song also reflects the culture and values of Sri Lanka, such as the importance of family, loyalty, and spirituality. The song resonates with many Sri Lankans who share these beliefs and traditions. The song also appeals to a wide range of audiences, as it can be enjoyed by people of different ages, genders, and backgrounds.</p>
- <h3>The catchy melody and vocals of the song</h3>
- <p>A third reason why Allan Yanna Beri Durak is popular is because it has a catchy melody and vocals. The song has a simple but memorable tune that sticks in the mind of the listeners. The song also has a smooth and soothing voice that delivers the lyrics with clarity and emotion. The song showcases the vocal skills and range of Sudeera Dilshan, who can sing both high and low notes with ease. The song also has a harmonious and balanced arrangement that blends well with the lyrics and the mood of the song.</p>
- <h2>How to download Allan Yanna Beri Durak mp3?</h2>
- <p>If you are interested in downloading Allan Yanna Beri Durak mp3, you might be wondering how to do it legally and ethically. There are many ways to download music online, but not all of them are fair and respectful to the artists and creators. In this section, we will discuss some of the legal and ethical ways to download Allan Yanna Beri Durak mp3, as well as some of the illegal and unethical ways to avoid downloading it.</p>
- <h3>The legal and ethical ways to download the song</h3>
- <p>The legal and ethical ways to download Allan Yanna Beri Durak mp3 are those that respect the rights and interests of the artists and creators. These ways usually involve paying a reasonable fee or obtaining permission from the owners of the song. Some of these ways are:</p>
- <h4>Streaming platforms</h4>
- <p>One of the most common and convenient ways to download Allan Yanna Beri Durak mp3 is to use streaming platforms such as Spotify, Apple Music, YouTube Music, Deezer, or Tidal. These platforms allow you to listen to millions of songs online or offline for a monthly or yearly subscription fee. You can also create playlists, discover new music, and share your favorites with your friends. By using these platforms, you are supporting the artists and creators by paying them royalties for their work.  </p>
- <h4>Official websites</h4>
- <p>Another way to download Allan Yanna Beri Durak mp3 is to visit the official websites of the artists or creators. These websites often offer direct downloads or links to other platforms where you can buy or stream their music. You can also find more information about their biography, discography, tour dates, merchandise, and social media accounts. By using these websites, you are supporting the artists and creators by giving them more exposure and recognition for their work.</p>
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- <p>A third way to download Allan Yanna Beri Durak mp3 is to use caller tunes or ringback tones. These are songs that play when someone calls you on your phone instead of the usual ringing sound. You can choose from a variety of songs from different genres and languages, including Allan Yanna Beri Durak. You can also change your caller tune anytime you want. By using caller tunes, you are supporting the artists and creators by paying them a small fee for each download or activation.</p>
- <h3>The illegal and unethical ways to avoid downloading the song</h3 <p>The illegal and unethical ways to avoid downloading Allan Yanna Beri Durak mp3 are those that violate the rights and interests of the artists and creators. These ways usually involve stealing, copying, or distributing their music without their consent or compensation. Some of these ways are:</p>
- <h4>Piracy sites</h4>
- <p>One of the most common and harmful ways to avoid downloading Allan Yanna Beri Durak mp3 is to use piracy sites such as torrent, file-sharing, or download sites. These sites offer free downloads of music, movies, games, and other digital content that are illegally obtained or uploaded by users. These sites often contain viruses, malware, or spyware that can harm your device or data. By using these sites, you are not only risking your security and privacy, but also hurting the artists and creators by depriving them of their income and recognition for their work.</p>
- <h4>YouTube converters</h4>
- <p>Another way to avoid downloading Allan Yanna Beri Durak mp3 is to use YouTube converters such as online tools, apps, or extensions that allow you to download audio or video files from YouTube. These converters often violate the terms of service and policies of YouTube, which prohibit downloading or copying any content from their platform without permission. These converters also reduce the quality and integrity of the original content, as well as the revenue and views of the creators. By using these converters, you are disrespecting the artists and creators by ignoring their rights and wishes for their work.</p>
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- <p>A third way to avoid downloading Allan Yanna Beri Durak mp3 is to use file sharing apps such as Bluetooth, Wi-Fi Direct, or AirDrop that allow you to transfer files between devices wirelessly. These apps can be useful for sharing personal or legal files, but they can also be misused for sharing pirated or unauthorized files. These apps can expose your device or data to hackers, viruses, or malware that can access your information or damage your system. By using these apps, you are undermining the artists and creators by spreading their work without their consent or compensation.</p>
- <h2>Conclusion</h2>
- <p>Allan Yanna Beri Durak is a beautiful and touching Sinhala song that has captured the hearts of many listeners. It is a song that expresses the sorrow and love of someone who has lost their partner. It is a song that is sung by a talented and passionate singer who has produced his own music. It is a song that can be downloaded legally and ethically by using streaming platforms, official websites, or caller tunes. It is a song that should not be downloaded illegally or unethically by using piracy sites, YouTube converters, or file sharing apps.</p>
- <p>If you are a fan of Allan Yanna Beri Durak, we hope that this article has helped you learn more about this song and how to download it in a respectful and responsible way. We also hope that you will continue to support the artists and creators who make such wonderful music for us to enjoy.</p>
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- <p>Here are some frequently asked questions about Allan Yanna Beri Durak:</p>
- <ol>
-<li><strong>Who is Sudeera Dilshan?</strong></li>
-<p>Sudeera Dilshan is a young and talented singer from Sri Lanka who started his career as a YouTube singer. He has released several original songs and covers in different genres and languages. He is the singer and producer of Allan Yanna Beri Durak.</p>
-<li><strong>What does Allan Yanna Beri Durak mean?</strong></li>
-<p>Allan Yanna Beri Durak means "Don't go far away, leaving me alone". It is the title and chorus of a Sinhala song that expresses the pain of losing a loved one.</p>
-<li><strong>Where can I watch the music video of Allan Yanna Beri Durak?</strong></li>
-<p>You can watch the music video of Allan Yanna Beri Durak on YouTube or on Sudeera Dilshan's official website. The video features Sudeera Dilshan and Nethu Ananya as a couple who are separated by fate.</p>
-<li><strong>Where can I listen to the audio of Allan Yanna Beri Durak?</strong></li>
-<p>You can listen to the audio of Allan Yanna Beri Durak on SoundCloud or on streaming platforms such as Spotify, Apple Music, YouTube Music, Deezer, or Tidal. You can also buy or stream the song from Sudeera Dilshan's official website.</p>
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-<p>You can download Allan Yanna Ber Durak mp3 legally and ethically by using streaming platforms, official websites, or caller tunes. These methods respect the rights and interests of the artists and creators, and pay them royalties or fees for their work. You should avoid downloading Allan Yanna Beri Durak mp3 illegally or unethically by using piracy sites, YouTube converters, or file sharing apps. These methods violate the rights and interests of the artists and creators, and deprive them of their income and recognition for their work.</p> 401be4b1e0<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Rockstar Full Movie 1080p BEST.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Rockstar Full Movie 1080p BEST.md
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/index/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/index/__init__.py
deleted file mode 100644
index 7a17b7b3b6ad49157ee41f3da304fec3d32342d3..0000000000000000000000000000000000000000
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deleted file mode 100644
index bab11b80c60f10a4f3bccb12eb5b17c48a449767..0000000000000000000000000000000000000000
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-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    # NB: This strips trailing '.0's to normalize
-    parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-    version = Version(parts[1])
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-    version = Version(version_part)
-    return (name, version)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/_pick.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/_pick.py
deleted file mode 100644
index 4f6d8b2d79406012c5f8bae9c289ed5bf4d179cc..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/_pick.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import Optional
-
-
-def pick_bool(*values: Optional[bool]) -> bool:
-    """Pick the first non-none bool or return the last value.
-
-    Args:
-        *values (bool): Any number of boolean or None values.
-
-    Returns:
-        bool: First non-none boolean.
-    """
-    assert values, "1 or more values required"
-    for value in values:
-        if value is not None:
-            return value
-    return bool(value)
diff --git a/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/__init__.py b/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/__init__.py
deleted file mode 100644
index 638918743c6d64e18514c0a0905ee7ec98abf570..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .ner_classifier import NerClassifier
-
-__all__ = ['NerClassifier']
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/seg.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/seg.py
deleted file mode 100644
index 5bc115d1cff641348e0488853f0448517d703c00..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/convertors/seg.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-import torch
-
-import mmocr.utils as utils
-from mmocr.models.builder import CONVERTORS
-from .base import BaseConvertor
-
-
-@CONVERTORS.register_module()
-class SegConvertor(BaseConvertor):
-    """Convert between text, index and tensor for segmentation based 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
-            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.
-        with_unknown (bool): If True, add `UKN` token to class.
-        lower (bool): If True, convert original string to lower case.
-    """
-
-    def __init__(self,
-                 dict_type='DICT36',
-                 dict_file=None,
-                 dict_list=None,
-                 with_unknown=True,
-                 lower=False,
-                 **kwargs):
-        super().__init__(dict_type, dict_file, dict_list)
-        assert isinstance(with_unknown, bool)
-        assert isinstance(lower, bool)
-
-        self.with_unknown = with_unknown
-        self.lower = lower
-        self.update_dict()
-
-    def update_dict(self):
-        # background
-        self.idx2char.insert(0, '<BG>')
-
-        # unknown
-        self.unknown_idx = None
-        if self.with_unknown:
-            self.idx2char.append('<UKN>')
-            self.unknown_idx = len(self.idx2char) - 1
-
-        # update char2idx
-        self.char2idx = {}
-        for idx, char in enumerate(self.idx2char):
-            self.char2idx[char] = idx
-
-    def tensor2str(self, output, img_metas=None):
-        """Convert model output tensor to string labels.
-        Args:
-            output (tensor): Model outputs with size: N * C * H * W
-            img_metas (list[dict]): Each dict contains one image info.
-        Returns:
-            texts (list[str]): Decoded text labels.
-            scores (list[list[float]]): Decoded chars scores.
-        """
-        assert utils.is_type_list(img_metas, dict)
-        assert len(img_metas) == output.size(0)
-
-        texts, scores = [], []
-        for b in range(output.size(0)):
-            seg_pred = output[b].detach()
-            valid_width = int(
-                output.size(-1) * img_metas[b]['valid_ratio'] + 1)
-            seg_res = torch.argmax(
-                seg_pred[:, :, :valid_width],
-                dim=0).cpu().numpy().astype(np.int32)
-
-            seg_thr = np.where(seg_res == 0, 0, 255).astype(np.uint8)
-            _, labels, stats, centroids = cv2.connectedComponentsWithStats(
-                seg_thr)
-
-            component_num = stats.shape[0]
-
-            all_res = []
-            for i in range(component_num):
-                temp_loc = (labels == i)
-                temp_value = seg_res[temp_loc]
-                temp_center = centroids[i]
-
-                temp_max_num = 0
-                temp_max_cls = -1
-                temp_total_num = 0
-                for c in range(len(self.idx2char)):
-                    c_num = np.sum(temp_value == c)
-                    temp_total_num += c_num
-                    if c_num > temp_max_num:
-                        temp_max_num = c_num
-                        temp_max_cls = c
-
-                if temp_max_cls == 0:
-                    continue
-                temp_max_score = 1.0 * temp_max_num / temp_total_num
-                all_res.append(
-                    [temp_max_cls, temp_center, temp_max_num, temp_max_score])
-
-            all_res = sorted(all_res, key=lambda s: s[1][0])
-            chars, char_scores = [], []
-            for res in all_res:
-                temp_area = res[2]
-                if temp_area < 20:
-                    continue
-                temp_char_index = res[0]
-                if temp_char_index >= len(self.idx2char):
-                    temp_char = ''
-                elif temp_char_index <= 0:
-                    temp_char = ''
-                elif temp_char_index == self.unknown_idx:
-                    temp_char = ''
-                else:
-                    temp_char = self.idx2char[temp_char_index]
-                chars.append(temp_char)
-                char_scores.append(res[3])
-
-            text = ''.join(chars)
-
-            texts.append(text)
-            scores.append(char_scores)
-
-        return texts, scores
diff --git a/spaces/tomofi/MMOCR/tools/deployment/mmocr_handler.py b/spaces/tomofi/MMOCR/tools/deployment/mmocr_handler.py
deleted file mode 100644
index a667f039ee2512c703a94612665baa4be1189997..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/tools/deployment/mmocr_handler.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import base64
-import os
-
-import mmcv
-import torch
-from ts.torch_handler.base_handler import BaseHandler
-
-from mmocr.apis import init_detector, model_inference
-from mmocr.datasets.pipelines import *  # NOQA
-
-
-class MMOCRHandler(BaseHandler):
-    threshold = 0.5
-
-    def initialize(self, context):
-        properties = context.system_properties
-        self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.device = torch.device(self.map_location + ':' +
-                                   str(properties.get('gpu_id')) if torch.cuda.
-                                   is_available() else self.map_location)
-        self.manifest = context.manifest
-
-        model_dir = properties.get('model_dir')
-        serialized_file = self.manifest['model']['serializedFile']
-        checkpoint = os.path.join(model_dir, serialized_file)
-        self.config_file = os.path.join(model_dir, 'config.py')
-
-        self.model = init_detector(self.config_file, checkpoint, self.device)
-        self.initialized = True
-
-    def preprocess(self, data):
-        images = []
-
-        for row in data:
-            image = row.get('data') or row.get('body')
-            if isinstance(image, str):
-                image = base64.b64decode(image)
-            image = mmcv.imfrombytes(image)
-            images.append(image)
-
-        return images
-
-    def inference(self, data, *args, **kwargs):
-
-        results = model_inference(self.model, data)
-        return results
-
-    def postprocess(self, data):
-        # Format output following the example OCRHandler format
-        return data
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/chars.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/chars.py
deleted file mode 100644
index 71772ab85dec2b42458e25593b611e5f24e465d2..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/chars.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import os
-
-import cv2
-import numpy as np
-
-
-def char2num(char):
-    if char in "0123456789":
-        num = ord(char) - ord("0") + 1
-    elif char in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ":
-        num = ord(char.lower()) - ord("a") + 11
-    else:
-        num = 0
-    return num
-
-
-def num2char(num):
-    chars = "_0123456789abcdefghijklmnopqrstuvwxyz"
-    char = chars[num]
-    # if num >=1 and num <=10:
-    # 	char = chr(ord('0') + num - 1)
-    # elif num > 10 and num <= 36:
-    # 	char = chr(ord('a') + num - 11)
-    # else:
-    # 	print('error number:%d'%(num))
-    # 	exit()
-    return char
-
-
-def getstr_grid(seg, box, threshold=192):
-    pos = 255 - (seg[0] * 255).astype(np.uint8)
-    mask_index = np.argmax(seg, axis=0)
-    mask_index = mask_index.astype(np.uint8)
-    pos = pos.astype(np.uint8)
-    string, score, rec_scores, char_polygons = seg2text(
-        pos, mask_index, seg, box, threshold=threshold
-    )
-    return string, score, rec_scores, char_polygons
-
-
-def seg2text(gray, mask, seg, box, threshold=192):
-    ## input numpy
-    img_h, img_w = gray.shape
-    box_w = box[2] - box[0]
-    box_h = box[3] - box[1]
-    ratio_h = float(box_h) / img_h
-    ratio_w = float(box_w) / img_w
-    # SE1=cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
-    # gray = cv2.erode(gray,SE1)
-    # gray = cv2.dilate(gray,SE1)
-    # gray = cv2.morphologyEx(gray,cv2.MORPH_CLOSE,SE1)
-    ret, thresh = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
-    try:
-        _, contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    except:
-        contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    chars = []
-    scores = []
-    char_polygons = []
-    for i in range(len(contours)):
-        char = {}
-        temp = np.zeros((img_h, img_w)).astype(np.uint8)
-        cv2.drawContours(temp, [contours[i]], 0, (255), -1)
-        x, y, w, h = cv2.boundingRect(contours[i])
-        c_x, c_y = x + w / 2, y + h / 2
-        perimeter = cv2.arcLength(contours[i], True)
-        epsilon = 0.01 * cv2.arcLength(contours[i], True)
-        approx = cv2.approxPolyDP(contours[i], epsilon, True)
-        pts = approx.reshape((-1, 2))
-        pts[:, 0] = pts[:, 0] * ratio_w + box[0]
-        pts[:, 1] = pts[:, 1] * ratio_h + box[1]
-        polygon = list(pts.reshape((-1,)))
-        polygon = list(map(int, polygon))
-        if len(polygon) >= 6:
-            char_polygons.append(polygon)
-        # x1 = x * ratio_w + box[0]
-        # y1 = y * ratio_h + box[1]
-        # x3 = (x + w) * ratio_w + box[0]
-        # y3 = (y + h) * ratio_h + box[1]
-        # polygon = [x1, y1, x3, y1, x3, y3, x1, y3]
-        regions = seg[1:, temp == 255].reshape((36, -1))
-        cs = np.mean(regions, axis=1)
-        sym = num2char(np.argmax(cs.reshape((-1))) + 1)
-        char["x"] = c_x
-        char["y"] = c_y
-        char["s"] = sym
-        char["cs"] = cs.reshape((-1, 1))
-        scores.append(np.max(char["cs"], axis=0)[0])
-
-        chars.append(char)
-    chars = sorted(chars, key=lambda x: x["x"])
-    string = ""
-    css = []
-    for char in chars:
-        string = string + char["s"]
-        css.append(char["cs"])
-    if len(scores) > 0:
-        score = sum(scores) / len(scores)
-    else:
-        score = 0.00
-    if not css:
-        css = [0.0]
-    return string, score, np.hstack(css), char_polygons
-
-
-# def get_tight_rect(points, start_x, start_y, image_height, image_width, scale):
-#     points = list(points)
-#     ps = sorted(points, key=lambda x: x[0])
-#
-#     if ps[1][1] > ps[0][1]:
-#         px1 = ps[0][0] * scale + start_x
-#         py1 = ps[0][1] * scale + start_y
-#         px4 = ps[1][0] * scale + start_x
-#         py4 = ps[1][1] * scale + start_y
-#     else:
-#         px1 = ps[1][0] * scale + start_x
-#         py1 = ps[1][1] * scale + start_y
-#         px4 = ps[0][0] * scale + start_x
-#         py4 = ps[0][1] * scale + start_y
-#     if ps[3][1] > ps[2][1]:
-#         px2 = ps[2][0] * scale + start_x
-#         py2 = ps[2][1] * scale + start_y
-#         px3 = ps[3][0] * scale + start_x
-#         py3 = ps[3][1] * scale + start_y
-#     else:
-#         px2 = ps[3][0] * scale + start_x
-#         py2 = ps[3][1] * scale + start_y
-#         px3 = ps[2][0] * scale + start_x
-#         py3 = ps[2][1] * scale + start_y
-#
-#     if px1 < 0:
-#         px1 = 1
-#     if px1 > image_width:
-#         px1 = image_width - 1
-#     if px2 < 0:
-#         px2 = 1
-#     if px2 > image_width:
-#         px2 = image_width - 1
-#     if px3 < 0:
-#         px3 = 1
-#     if px3 > image_width:
-#         px3 = image_width - 1
-#     if px4 < 0:
-#         px4 = 1
-#     if px4 > image_width:
-#         px4 = image_width - 1
-#
-#     if py1 < 0:
-#         py1 = 1
-#     if py1 > image_height:
-#         py1 = image_height - 1
-#     if py2 < 0:
-#         py2 = 1
-#     if py2 > image_height:
-#         py2 = image_height - 1
-#     if py3 < 0:
-#         py3 = 1
-#     if py3 > image_height:
-#         py3 = image_height - 1
-#     if py4 < 0:
-#         py4 = 1
-#     if py4 > image_height:
-#         py4 = image_height - 1
-#     return [px1, py1, px2, py2, px3, py3, px4, py4]
-
-def get_tight_rect(points, start_x, start_y, image_height, image_width, scale):
-    points = list(points)
-    ps = sorted(points, key=lambda x: x[0])
-
-    if ps[1][1] > ps[0][1]:
-        px1 = ps[0][0] * scale + start_x
-        py1 = ps[0][1] * scale + start_y
-        px4 = ps[1][0] * scale + start_x
-        py4 = ps[1][1] * scale + start_y
-    else:
-        px1 = ps[1][0] * scale + start_x
-        py1 = ps[1][1] * scale + start_y
-        px4 = ps[0][0] * scale + start_x
-        py4 = ps[0][1] * scale + start_y
-    if ps[3][1] > ps[2][1]:
-        px2 = ps[2][0] * scale + start_x
-        py2 = ps[2][1] * scale + start_y
-        px3 = ps[3][0] * scale + start_x
-        py3 = ps[3][1] * scale + start_y
-    else:
-        px2 = ps[3][0] * scale + start_x
-        py2 = ps[3][1] * scale + start_y
-        px3 = ps[2][0] * scale + start_x
-        py3 = ps[2][1] * scale + start_y
-
-    px1 = min(max(px1, 1), image_width - 1)
-    px2 = min(max(px2, 1), image_width - 1)
-    px3 = min(max(px3, 1), image_width - 1)
-    px4 = min(max(px4, 1), image_width - 1)
-    py1 = min(max(py1, 1), image_height - 1)
-    py2 = min(max(py2, 1), image_height - 1)
-    py3 = min(max(py3, 1), image_height - 1)
-    py4 = min(max(py4, 1), image_height - 1)
-    return [px1, py1, px2, py2, px3, py3, px4, py4]
diff --git a/spaces/tomofi/NDLOCR/cli/procs/layout_extraction.py b/spaces/tomofi/NDLOCR/cli/procs/layout_extraction.py
deleted file mode 100644
index 411a9e4440b70c4467050d343563ad664a80b185..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/cli/procs/layout_extraction.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) 2022, National Diet Library, Japan
-#
-# This software is released under the CC BY 4.0.
-# https://creativecommons.org/licenses/by/4.0/
-
-
-import copy
-import numpy
-
-from .base_proc import BaseInferenceProcess
-
-
-class LayoutExtractionProcess(BaseInferenceProcess):
-    """
-    レイアウト抽出推論を実行するプロセスのクラス。
-    BaseInferenceProcessを継承しています。
-    """
-    def __init__(self, cfg, pid):
-        """
-        Parameters
-        ----------
-        cfg : dict
-            本実行処理における設定情報です。
-        pid : int
-            実行される順序を表す数値。
-        """
-        super().__init__(cfg, pid, '_layer_ext')
-        from src.ndl_layout.tools.process import InferencerWithCLI
-        self._inferencer = InferencerWithCLI(self.cfg['layout_extraction'])
-        self._run_src_inference = self._inferencer.inference_wich_cli
-
-    def is_valid_input(self, input_data):
-        """
-        本クラスの推論処理における入力データのバリデーション。
-
-        Parameters
-        ----------
-        input_data : dict
-            推論処理を実行する対象の入力データ。
-
-        Returns
-        -------
-        [変数なし] : bool
-            　入力データが正しければTrue, そうでなければFalseを返します。
-        """
-        if type(input_data['img']) is not numpy.ndarray:
-            print('LayoutExtractionProcess: input img is not numpy.ndarray')
-            return False
-        return True
-
-    def _run_process(self, input_data):
-        """
-        推論処理の本体部分。
-
-        Parameters
-        ----------
-        input_data : dict
-            推論処理を実行する対象の入力データ。
-
-        Returns
-        -------
-        result : dict
-            推論処理の結果を保持する辞書型データ。
-            基本的にinput_dataと同じ構造です。
-        """
-        print('### Layout Extraction Process ###')
-        output_data = copy.deepcopy(input_data)
-        inference_output = self._run_src_inference(img=input_data['img'],
-                                                   img_path=input_data['img_file_name'],
-                                                   score_thr=self.cfg['layout_extraction']['score_thr'],
-                                                   dump=(self.cfg['dump'] or self.cfg['save_image']))
-
-        # Create result to pass xml and img data
-        result = []
-        output_data['xml'] = inference_output['xml']
-        if inference_output['dump_img'] is not None:
-            output_data['dump_img'] = inference_output['dump_img']
-        result.append(output_data)
-        return result
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/uniform_assigner.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/uniform_assigner.py
deleted file mode 100644
index 1d606dee94f53c179eaf18cf671ebd39ee6b8203..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/uniform_assigner.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from ..transforms import bbox_xyxy_to_cxcywh
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class UniformAssigner(BaseAssigner):
-    """Uniform Matching between the anchors and gt boxes, which can achieve
-    balance in positive anchors, and gt_bboxes_ignore was not considered for
-    now.
-
-    Args:
-        pos_ignore_thr (float): the threshold to ignore positive anchors
-        neg_ignore_thr (float): the threshold to ignore negative anchors
-        match_times(int): Number of positive anchors for each gt box.
-           Default 4.
-        iou_calculator (dict): iou_calculator config
-    """
-
-    def __init__(self,
-                 pos_ignore_thr,
-                 neg_ignore_thr,
-                 match_times=4,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.match_times = match_times
-        self.pos_ignore_thr = pos_ignore_thr
-        self.neg_ignore_thr = neg_ignore_thr
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-
-    def assign(self,
-               bbox_pred,
-               anchor,
-               gt_bboxes,
-               gt_bboxes_ignore=None,
-               gt_labels=None):
-        num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0)
-
-        # 1. assign -1 by default
-        assigned_gt_inds = bbox_pred.new_full((num_bboxes, ),
-                                              0,
-                                              dtype=torch.long)
-        assigned_labels = bbox_pred.new_full((num_bboxes, ),
-                                             -1,
-                                             dtype=torch.long)
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            if num_gts == 0:
-                # No ground truth, assign all to background
-                assigned_gt_inds[:] = 0
-            assign_result = AssignResult(
-                num_gts, assigned_gt_inds, None, labels=assigned_labels)
-            assign_result.set_extra_property(
-                'pos_idx', bbox_pred.new_empty(0, dtype=torch.bool))
-            assign_result.set_extra_property('pos_predicted_boxes',
-                                             bbox_pred.new_empty((0, 4)))
-            assign_result.set_extra_property('target_boxes',
-                                             bbox_pred.new_empty((0, 4)))
-            return assign_result
-
-        # 2. Compute the L1 cost between boxes
-        # Note that we use anchors and predict boxes both
-        cost_bbox = torch.cdist(
-            bbox_xyxy_to_cxcywh(bbox_pred),
-            bbox_xyxy_to_cxcywh(gt_bboxes),
-            p=1)
-        cost_bbox_anchors = torch.cdist(
-            bbox_xyxy_to_cxcywh(anchor), bbox_xyxy_to_cxcywh(gt_bboxes), p=1)
-
-        # We found that topk function has different results in cpu and
-        # cuda mode. In order to ensure consistency with the source code,
-        # we also use cpu mode.
-        # TODO: Check whether the performance of cpu and cuda are the same.
-        C = cost_bbox.cpu()
-        C1 = cost_bbox_anchors.cpu()
-
-        # self.match_times x n
-        index = torch.topk(
-            C,  # c=b,n,x c[i]=n,x
-            k=self.match_times,
-            dim=0,
-            largest=False)[1]
-
-        # self.match_times x n
-        index1 = torch.topk(C1, k=self.match_times, dim=0, largest=False)[1]
-        # (self.match_times*2) x n
-        indexes = torch.cat((index, index1),
-                            dim=1).reshape(-1).to(bbox_pred.device)
-
-        pred_overlaps = self.iou_calculator(bbox_pred, gt_bboxes)
-        anchor_overlaps = self.iou_calculator(anchor, gt_bboxes)
-        pred_max_overlaps, _ = pred_overlaps.max(dim=1)
-        anchor_max_overlaps, _ = anchor_overlaps.max(dim=0)
-
-        # 3. Compute the ignore indexes use gt_bboxes and predict boxes
-        ignore_idx = pred_max_overlaps > self.neg_ignore_thr
-        assigned_gt_inds[ignore_idx] = -1
-
-        # 4. Compute the ignore indexes of positive sample use anchors
-        # and predict boxes
-        pos_gt_index = torch.arange(
-            0, C1.size(1),
-            device=bbox_pred.device).repeat(self.match_times * 2)
-        pos_ious = anchor_overlaps[indexes, pos_gt_index]
-        pos_ignore_idx = pos_ious < self.pos_ignore_thr
-
-        pos_gt_index_with_ignore = pos_gt_index + 1
-        pos_gt_index_with_ignore[pos_ignore_idx] = -1
-        assigned_gt_inds[indexes] = pos_gt_index_with_ignore
-
-        if gt_labels is not None:
-            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
-            pos_inds = torch.nonzero(
-                assigned_gt_inds > 0, as_tuple=False).squeeze()
-            if pos_inds.numel() > 0:
-                assigned_labels[pos_inds] = gt_labels[
-                    assigned_gt_inds[pos_inds] - 1]
-        else:
-            assigned_labels = None
-
-        assign_result = AssignResult(
-            num_gts,
-            assigned_gt_inds,
-            anchor_max_overlaps,
-            labels=assigned_labels)
-        assign_result.set_extra_property('pos_idx', ~pos_ignore_idx)
-        assign_result.set_extra_property('pos_predicted_boxes',
-                                         bbox_pred[indexes])
-        assign_result.set_extra_property('target_boxes',
-                                         gt_bboxes[pos_gt_index])
-        return assign_result
diff --git a/spaces/tonyassi/face-swap/app.py b/spaces/tonyassi/face-swap/app.py
deleted file mode 100644
index a8dd32ef0b417dba5a228b0b6b14cca785f0b72d..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/face-swap/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import os
-
-exec(os.environ.get('CODE'))
\ No newline at end of file
diff --git a/spaces/tracinginsights/F1-analysis/pages/Penalty_Points.py b/spaces/tracinginsights/F1-analysis/pages/Penalty_Points.py
deleted file mode 100644
index ea37948ab578bab5b1ae81fd25d9afb4000fef4d..0000000000000000000000000000000000000000
--- a/spaces/tracinginsights/F1-analysis/pages/Penalty_Points.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from repo_directory import button
-import streamlit as st
-from repo_directory import Penalty_Points
-
-
-_, drivers, n = Penalty_Points.get_data()
-
-#Remove drivers
-DRIVERS_REMOVED = st.multiselect(
-    'Select Drivers to remove',
-    drivers)
-
-Penalty_Points.plot(DRIVERS_REMOVED)
-
-Penalty_Points.points_table()
\ No newline at end of file
diff --git a/spaces/triggah61/chingu-music/app.py b/spaces/triggah61/chingu-music/app.py
deleted file mode 100644
index 81787d6897d143fdf3985d0dfb79b987ce1f3839..0000000000000000000000000000000000000000
--- a/spaces/triggah61/chingu-music/app.py
+++ /dev/null
@@ -1,381 +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.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import typing as tp
-import warnings
-
-import torch
-import gradio as gr
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen
-
-
-MODEL = None  # Last used model
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-MAX_BATCH_SIZE = 6
-BATCHED_DURATION = 15
-INTERRUPTING = False
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomitting on the logs.
-    kwargs['stderr'] = sp.DEVNULL
-    kwargs['stdout'] = sp.DEVNULL
-    _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(3)
-pool.__enter__()
-
-
-def interrupt():
-    global INTERRUPTING
-    INTERRUPTING = True
-
-
-class FileCleaner:
-    def __init__(self, file_lifetime: float = 3600):
-        self.file_lifetime = file_lifetime
-        self.files = []
-
-    def add(self, path: tp.Union[str, Path]):
-        self._cleanup()
-        self.files.append((time.time(), Path(path)))
-
-    def _cleanup(self):
-        now = time.time()
-        for time_added, path in list(self.files):
-            if now - time_added > self.file_lifetime:
-                if path.exists():
-                    path.unlink()
-                self.files.pop(0)
-            else:
-                break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
-    # Further remove some warnings.
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        out = gr.make_waveform(*args, **kwargs)
-        print("Make a video took", time.time() - be)
-        return out
-
-
-def load_model(version='melody'):
-    global MODEL
-    print("Loading model", version)
-    if MODEL is None or MODEL.name != version:
-        MODEL = MusicGen.get_pretrained(version)
-
-
-def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
-    MODEL.set_generation_params(duration=duration, **gen_kwargs)
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
-    be = time.time()
-    processed_melodies = []
-    target_sr = 32000
-    target_ac = 1
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    if any(m is not None for m in processed_melodies):
-        outputs = MODEL.generate_with_chroma(
-            descriptions=texts,
-            melody_wavs=processed_melodies,
-            melody_sample_rate=target_sr,
-            progress=progress,
-        )
-    else:
-        outputs = MODEL.generate(texts, progress=progress)
-
-    outputs = outputs.detach().cpu().float()
-    out_files = []
-    for output in outputs:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, output, MODEL.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-            out_files.append(pool.submit(make_waveform, file.name))
-            file_cleaner.add(file.name)
-    res = [out_file.result() for out_file in out_files]
-    for file in res:
-        file_cleaner.add(file)
-    print("batch finished", len(texts), time.time() - be)
-    print("Tempfiles currently stored: ", len(file_cleaner.files))
-    return res
-
-
-def predict_batched(texts, melodies):
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    load_model('melody')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return [res]
-
-
-def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
-    global INTERRUPTING
-    INTERRUPTING = False
-    if temperature < 0:
-        raise gr.Error("Temperature must be >= 0.")
-    if topk < 0:
-        raise gr.Error("Topk must be non-negative.")
-    if topp < 0:
-        raise gr.Error("Topp must be non-negative.")
-
-    topk = int(topk)
-    load_model(model)
-
-    def _progress(generated, to_generate):
-        progress((generated, to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    outs = _do_predictions(
-        [text], [melody], duration, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
-    return outs[0]
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(source="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # ChinguMusic 🎵
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Input Text", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Submit")
-                    # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
-                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
-                with gr.Row():
-                    model = gr.Radio(["melody", "medium", "small", "large"],
-                                     label="Model", value="melody", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
-                with gr.Row():
-                    topk = gr.Number(label="Top-k", value=250, interactive=True)
-                    topp = gr.Number(label="Top-p", value=0, interactive=True)
-                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_full,
-                     inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
-                     outputs=[output])
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_full,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                    "melody"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "medium"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "medium",
-                ],
-            ],
-            inputs=[text, melody, model],
-            outputs=[output]
-        )
-        # gr.Markdown(
-        #     """
-        #     ### More details
-        #     This was built with open source technology here is the github [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        #     if you support Chingu you are supporting them.
-        #     """
-        # )
-
-        interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
-                style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
-                src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionaly provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--listen',
-        type=str,
-        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
-        help='IP to listen on for connections to Gradio',
-    )
-    parser.add_argument(
-        '--username', type=str, default='', help='Username for authentication'
-    )
-    parser.add_argument(
-        '--password', type=str, default='', help='Password for authentication'
-    )
-    parser.add_argument(
-        '--server_port',
-        type=int,
-        default=0,
-        help='Port to run the server listener on',
-    )
-    parser.add_argument(
-        '--inbrowser', action='store_true', help='Open in browser'
-    )
-    parser.add_argument(
-        '--share', action='store_true', help='Share the gradio UI'
-    )
-
-    args = parser.parse_args()
-
-    launch_kwargs = {}
-    launch_kwargs['server_name'] = args.listen
-
-    if args.username and args.password:
-        launch_kwargs['auth'] = (args.username, args.password)
-    if args.server_port:
-        launch_kwargs['server_port'] = args.server_port
-    if args.inbrowser:
-        launch_kwargs['inbrowser'] = args.inbrowser
-    if args.share:
-        launch_kwargs['share'] = args.share
-
-    # Show the interface
-    if IS_BATCHED:
-        ui_batched(launch_kwargs)
-    else:
-        ui_full(launch_kwargs)
diff --git a/spaces/trttung1610/musicgen/audiocraft/solvers/diffusion.py b/spaces/trttung1610/musicgen/audiocraft/solvers/diffusion.py
deleted file mode 100644
index 93dea2520836f458ab1b8514dca952b51d113ec2..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/solvers/diffusion.py
+++ /dev/null
@@ -1,279 +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 typing as tp
-
-import flashy
-import julius
-import omegaconf
-import torch
-import torch.nn.functional as F
-
-from . import builders
-from . import base
-from .. import models
-from ..modules.diffusion_schedule import NoiseSchedule
-from ..metrics import RelativeVolumeMel
-from ..models.builders import get_processor
-from ..utils.samples.manager import SampleManager
-from ..solvers.compression import CompressionSolver
-
-
-class PerStageMetrics:
-    """Handle prompting the metrics per stage.
-    It outputs the metrics per range of diffusion states.
-    e.g. avg loss when t in [250, 500]
-    """
-    def __init__(self, num_steps: int, num_stages: int = 4):
-        self.num_steps = num_steps
-        self.num_stages = num_stages
-
-    def __call__(self, losses: dict, step: tp.Union[int, torch.Tensor]):
-        if type(step) is int:
-            stage = int((step / self.num_steps) * self.num_stages)
-            return {f"{name}_{stage}": loss for name, loss in losses.items()}
-        elif type(step) is torch.Tensor:
-            stage_tensor = ((step / self.num_steps) * self.num_stages).long()
-            out: tp.Dict[str, float] = {}
-            for stage_idx in range(self.num_stages):
-                mask = (stage_tensor == stage_idx)
-                N = mask.sum()
-                stage_out = {}
-                if N > 0:  # pass if no elements in the stage
-                    for name, loss in losses.items():
-                        stage_loss = (mask * loss).sum() / N
-                        stage_out[f"{name}_{stage_idx}"] = stage_loss
-                out = {**out, **stage_out}
-            return out
-
-
-class DataProcess:
-    """Apply filtering or resampling.
-
-    Args:
-        initial_sr (int): Initial sample rate.
-        target_sr (int): Target sample rate.
-        use_resampling: Whether to use resampling or not.
-        use_filter (bool):
-        n_bands (int): Number of bands to consider.
-        idx_band (int):
-        device (torch.device or str):
-        cutoffs ():
-        boost (bool):
-    """
-    def __init__(self, initial_sr: int = 24000, target_sr: int = 16000, use_resampling: bool = False,
-                 use_filter: bool = False, n_bands: int = 4,
-                 idx_band: int = 0, device: torch.device = torch.device('cpu'), cutoffs=None, boost=False):
-        """Apply filtering or resampling
-        Args:
-            initial_sr (int): sample rate of the dataset
-            target_sr (int): sample rate after resampling
-            use_resampling (bool): whether or not performs resampling
-            use_filter (bool): when True filter the data to keep only one frequency band
-            n_bands (int): Number of bands used
-            cuts (none or list): The cutoff frequencies of the band filtering
-                                if None then we use mel scale bands.
-            idx_band (int): index of the frequency band. 0 are lows ... (n_bands - 1) highs
-            boost (bool): make the data scale match our music dataset.
-        """
-        assert idx_band < n_bands
-        self.idx_band = idx_band
-        if use_filter:
-            if cutoffs is not None:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, cutoffs=cutoffs).to(device)
-            else:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, n_bands=n_bands).to(device)
-        self.use_filter = use_filter
-        self.use_resampling = use_resampling
-        self.target_sr = target_sr
-        self.initial_sr = initial_sr
-        self.boost = boost
-
-    def process_data(self, x, metric=False):
-        if x is None:
-            return None
-        if self.boost:
-            x /= torch.clamp(x.std(dim=(1, 2), keepdim=True), min=1e-4)
-            x * 0.22
-        if self.use_filter and not metric:
-            x = self.filter(x)[self.idx_band]
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.initial_sr, new_sr=self.target_sr)
-        return x
-
-    def inverse_process(self, x):
-        """Upsampling only."""
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.target_sr, new_sr=self.target_sr)
-        return x
-
-
-class DiffusionSolver(base.StandardSolver):
-    """Solver for compression task.
-
-    The diffusion task allows for MultiBand diffusion model training.
-
-    Args:
-        cfg (DictConfig): Configuration.
-    """
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__(cfg)
-        self.cfg = cfg
-        self.device = cfg.device
-        self.sample_rate: int = self.cfg.sample_rate
-        self.codec_model = CompressionSolver.model_from_checkpoint(
-            cfg.compression_model_checkpoint, device=self.device)
-
-        self.codec_model.set_num_codebooks(cfg.n_q)
-        assert self.codec_model.sample_rate == self.cfg.sample_rate, (
-            f"Codec model sample rate is {self.codec_model.sample_rate} but "
-            f"Solver sample rate is {self.cfg.sample_rate}."
-            )
-        assert self.codec_model.sample_rate == self.sample_rate, \
-            f"Sample rate of solver {self.sample_rate} and codec {self.codec_model.sample_rate} " \
-            "don't match."
-
-        self.sample_processor = get_processor(cfg.processor, sample_rate=self.sample_rate)
-        self.register_stateful('sample_processor')
-        self.sample_processor.to(self.device)
-
-        self.schedule = NoiseSchedule(
-            **cfg.schedule, device=self.device, sample_processor=self.sample_processor)
-
-        self.eval_metric: tp.Optional[torch.nn.Module] = None
-
-        self.rvm = RelativeVolumeMel()
-        self.data_processor = DataProcess(initial_sr=self.sample_rate, target_sr=cfg.resampling.target_sr,
-                                          use_resampling=cfg.resampling.use, cutoffs=cfg.filter.cutoffs,
-                                          use_filter=cfg.filter.use, n_bands=cfg.filter.n_bands,
-                                          idx_band=cfg.filter.idx_band, device=self.device)
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        if self._current_stage == "evaluate":
-            return 'rvm'
-        else:
-            return 'loss'
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor) -> torch.Tensor:
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def build_model(self):
-        """Build model and optimizer as well as optional Exponential Moving Average of the model.
-        """
-        # Model and optimizer
-        self.model = models.builders.get_diffusion_model(self.cfg).to(self.device)
-        self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
-        self.register_stateful('model', 'optimizer')
-        self.register_best_state('model')
-        self.register_ema('model')
-
-    def build_dataloaders(self):
-        """Build audio dataloaders for each stage."""
-        self.dataloaders = builders.get_audio_datasets(self.cfg)
-
-    def show(self):
-        # TODO
-        raise NotImplementedError()
-
-    def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
-        """Perform one training or valid step on a given batch."""
-        x = batch.to(self.device)
-        loss_fun = F.mse_loss if self.cfg.loss.kind == 'mse' else F.l1_loss
-
-        condition = self.get_condition(x)  # [bs, 128, T/hop, n_emb]
-        sample = self.data_processor.process_data(x)
-
-        input_, target, step = self.schedule.get_training_item(sample,
-                                                               tensor_step=self.cfg.schedule.variable_step_batch)
-        out = self.model(input_, step, condition=condition).sample
-
-        base_loss = loss_fun(out, target, reduction='none').mean(dim=(1, 2))
-        reference_loss = loss_fun(input_, target, reduction='none').mean(dim=(1, 2))
-        loss = base_loss / reference_loss ** self.cfg.loss.norm_power
-
-        if self.is_training:
-            loss.mean().backward()
-            flashy.distrib.sync_model(self.model)
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-        metrics = {
-            'loss': loss.mean(), 'normed_loss': (base_loss / reference_loss).mean(),
-            }
-        metrics.update(self.per_stage({'loss': loss, 'normed_loss': base_loss / reference_loss}, step))
-        metrics.update({
-            'std_in': input_.std(), 'std_out': out.std()})
-        return metrics
-
-    def run_epoch(self):
-        # reset random seed at the beginning of the epoch
-        self.rng = torch.Generator()
-        self.rng.manual_seed(1234 + self.epoch)
-        self.per_stage = PerStageMetrics(self.schedule.num_steps, self.cfg.metrics.num_stage)
-        # run epoch
-        super().run_epoch()
-
-    def evaluate(self):
-        """Evaluate stage.
-        Runs audio reconstruction evaluation.
-        """
-        self.model.eval()
-        evaluate_stage_name = f'{self.current_stage}'
-        loader = self.dataloaders['evaluate']
-        updates = len(loader)
-        lp = self.log_progress(f'{evaluate_stage_name} estimate', loader, total=updates, updates=self.log_updates)
-
-        metrics = {}
-        n = 1
-        for idx, batch in enumerate(lp):
-            x = batch.to(self.device)
-            with torch.no_grad():
-                y_pred = self.regenerate(x)
-
-            y_pred = y_pred.cpu()
-            y = batch.cpu()  # should already be on CPU but just in case
-            rvm = self.rvm(y_pred, y)
-            lp.update(**rvm)
-            if len(metrics) == 0:
-                metrics = rvm
-            else:
-                for key in rvm.keys():
-                    metrics[key] = (metrics[key] * n + rvm[key]) / (n + 1)
-        metrics = flashy.distrib.average_metrics(metrics)
-        return metrics
-
-    @torch.no_grad()
-    def regenerate(self, wav: torch.Tensor, step_list: tp.Optional[list] = None):
-        """Regenerate the given waveform."""
-        condition = self.get_condition(wav)
-        initial = self.schedule.get_initial_noise(self.data_processor.process_data(wav))  # sampling rate changes.
-        result = self.schedule.generate_subsampled(self.model, initial=initial, condition=condition,
-                                                   step_list=step_list)
-        result = self.data_processor.inverse_process(result)
-        return result
-
-    def generate(self):
-        """Generate stage."""
-        sample_manager = SampleManager(self.xp)
-        self.model.eval()
-        generate_stage_name = f'{self.current_stage}'
-
-        loader = self.dataloaders['generate']
-        updates = len(loader)
-        lp = self.log_progress(generate_stage_name, loader, total=updates, updates=self.log_updates)
-
-        for batch in lp:
-            reference, _ = batch
-            reference = reference.to(self.device)
-            estimate = self.regenerate(reference)
-            reference = reference.cpu()
-            estimate = estimate.cpu()
-            sample_manager.add_samples(estimate, self.epoch, ground_truth_wavs=reference)
-        flashy.distrib.barrier()
diff --git a/spaces/tsailada/Chefsky/app.py b/spaces/tsailada/Chefsky/app.py
deleted file mode 100644
index 141a2a7203deba50583fa329d84e4fe0103dde44..0000000000000000000000000000000000000000
--- a/spaces/tsailada/Chefsky/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import openai
-import gradio
-
-openai.api_key = "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"
-
-messages = [{"role": "system", "content": "You are a professional Chef"}]
-
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
-
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Chefsky")
-
-demo.launch(share=False)
\ No newline at end of file
diff --git a/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/flash_attn_triton.py b/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/flash_attn_triton.py
deleted file mode 100644
index c0a42186d982283add95b63d99fc118e845bcf9d..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/flash_attn_triton.py
+++ /dev/null
@@ -1,484 +0,0 @@
-"""
-Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py
-update imports to use 'triton_pre_mlir'
-
-*Experimental* implementation of FlashAttention in Triton.
-Tested with triton==2.0.0.dev20221202.
-Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions
-other than 64:
-https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207
-We'll update this implementation with the new Triton backend once this is fixed.
-
-We use the FlashAttention implementation from Phil Tillet a starting point.
-https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
-
-Changes:
-- Implement both causal and non-causal attention.
-- Implement both self-attention and cross-attention.
-- Support arbitrary seqlens (not just multiples of 128), for both forward and backward.
-- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.
-- Support attention bias.
-- Speed up the forward pass a bit, and only store the LSE instead of m and l.
-- Make the backward for d=128 much faster by reducing register spilling.
-- Optionally parallelize the backward pass across seqlen_k, to deal with the case of
-small batch size * nheads.
-
-Caution:
-- This is an *experimental* implementation. The forward pass should be quite robust but
-I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler).
-- This implementation has only been tested on A100.
-- If you plan to use headdim other than 64 and 128, you should test for race conditions
-(due to the Triton compiler), as done in tests/test_flash_attn.py
-"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions
-for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident
-that there are none left for other head dimensions.
-
-Differences between this Triton version and the CUDA version:
-- Triton version doesn't support dropout.
-- Triton forward is generally faster than CUDA forward, while Triton backward is
-generally slower than CUDA backward. Overall Triton forward + backward is slightly slower
-than CUDA forward + backward.
-- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).
-- Triton version supports attention bias, while CUDA version doesn't.
-"""
-import math
-import torch
-import triton_pre_mlir as triton
-import triton_pre_mlir.language as tl
-
-@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
-@triton.jit
-def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
-    start_m = tl.program_id(0)
-    off_hb = tl.program_id(1)
-    off_b = off_hb // nheads
-    off_h = off_hb % nheads
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = tl.arange(0, BLOCK_N)
-    offs_d = tl.arange(0, BLOCK_HEADDIM)
-    q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :])
-    k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :])
-    v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :])
-    if BIAS_TYPE == 'vector':
-        b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
-    elif BIAS_TYPE == 'matrix':
-        b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :])
-    t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
-    lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
-    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
-    acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
-    if EVEN_M & EVEN_N:
-        if EVEN_HEADDIM:
-            q = tl.load(q_ptrs)
-        else:
-            q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
-    elif EVEN_HEADDIM:
-        q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
-    else:
-        q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
-    end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k)
-    for start_n in range(0, end_n, BLOCK_N):
-        start_n = tl.multiple_of(start_n, BLOCK_N)
-        if EVEN_N & EVEN_M:
-            if EVEN_HEADDIM:
-                k = tl.load(k_ptrs + start_n * stride_kn)
-            else:
-                k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0)
-        elif EVEN_HEADDIM:
-            k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
-        else:
-            k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
-        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
-        qk += tl.dot(q, k, trans_b=True)
-        if not EVEN_N:
-            qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf'))
-        if IS_CAUSAL:
-            qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf'))
-        if BIAS_TYPE != 'none':
-            if BIAS_TYPE == 'vector':
-                if EVEN_N:
-                    bias = tl.load(b_ptrs + start_n).to(tl.float32)
-                else:
-                    bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32)
-                bias = bias[None, :]
-            elif BIAS_TYPE == 'matrix':
-                if EVEN_M & EVEN_N:
-                    bias = tl.load(b_ptrs + start_n).to(tl.float32)
-                else:
-                    bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32)
-            qk = qk * softmax_scale + bias
-            m_ij = tl.maximum(tl.max(qk, 1), lse_i)
-            p = tl.exp(qk - m_ij[:, None])
-        else:
-            m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
-            p = tl.exp(qk * softmax_scale - m_ij[:, None])
-        l_ij = tl.sum(p, 1)
-        acc_o_scale = tl.exp(m_i - m_ij)
-        tl.store(t_ptrs, acc_o_scale)
-        acc_o_scale = tl.load(t_ptrs)
-        acc_o = acc_o * acc_o_scale[:, None]
-        if EVEN_N & EVEN_M:
-            if EVEN_HEADDIM:
-                v = tl.load(v_ptrs + start_n * stride_vn)
-            else:
-                v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0)
-        elif EVEN_HEADDIM:
-            v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
-        else:
-            v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
-        p = p.to(v.dtype)
-        acc_o += tl.dot(p, v)
-        m_i = m_ij
-        l_i_new = tl.exp(lse_i - m_ij) + l_ij
-        lse_i = m_ij + tl.log(l_i_new)
-    o_scale = tl.exp(m_i - lse_i)
-    tl.store(t_ptrs, o_scale)
-    o_scale = tl.load(t_ptrs)
-    acc_o = acc_o * o_scale[:, None]
-    start_m = tl.program_id(0)
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
-    tl.store(lse_ptrs, lse_i)
-    offs_d = tl.arange(0, BLOCK_HEADDIM)
-    out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :])
-    if EVEN_M:
-        if EVEN_HEADDIM:
-            tl.store(out_ptrs, acc_o)
-        else:
-            tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
-    elif EVEN_HEADDIM:
-        tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
-    else:
-        tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
-
-@triton.jit
-def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr):
-    start_m = tl.program_id(0)
-    off_hb = tl.program_id(1)
-    off_b = off_hb // nheads
-    off_h = off_hb % nheads
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_d = tl.arange(0, BLOCK_HEADDIM)
-    o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
-    do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
-    delta = tl.sum(o * do, axis=1)
-    tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
-
-@triton.jit
-def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr):
-    if EVEN_N & EVEN_M:
-        if EVEN_HEADDIM:
-            tl.store(dv_ptrs, dv)
-            tl.store(dk_ptrs, dk)
-        else:
-            tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
-            tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
-    elif EVEN_HEADDIM:
-        tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
-        tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
-    else:
-        tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
-        tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
-
-@triton.jit
-def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
-    begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M
-    offs_qm = begin_m + tl.arange(0, BLOCK_M)
-    offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
-    offs_m = tl.arange(0, BLOCK_M)
-    offs_d = tl.arange(0, BLOCK_HEADDIM)
-    q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
-    k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
-    v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
-    do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
-    dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
-    if BIAS_TYPE == 'vector':
-        b_ptrs = Bias + offs_n
-    elif BIAS_TYPE == 'matrix':
-        b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
-    dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
-    dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
-    if begin_m >= seqlen_q:
-        dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
-        dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
-        _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
-        return
-    if EVEN_N & EVEN_M:
-        if EVEN_HEADDIM:
-            k = tl.load(k_ptrs)
-            v = tl.load(v_ptrs)
-        else:
-            k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
-            v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
-    elif EVEN_HEADDIM:
-        k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
-        v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
-    else:
-        k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
-        v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
-    num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
-    for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
-        start_m = tl.multiple_of(start_m, BLOCK_M)
-        offs_m_curr = start_m + offs_m
-        if EVEN_M & EVEN_HEADDIM:
-            q = tl.load(q_ptrs)
-        elif EVEN_HEADDIM:
-            q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
-        else:
-            q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
-        qk = tl.dot(q, k, trans_b=True)
-        if not EVEN_N:
-            qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf'))
-        if IS_CAUSAL:
-            qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf'))
-        if BIAS_TYPE != 'none':
-            tl.debug_barrier()
-            if BIAS_TYPE == 'vector':
-                if EVEN_N:
-                    bias = tl.load(b_ptrs).to(tl.float32)
-                else:
-                    bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32)
-                bias = bias[None, :]
-            elif BIAS_TYPE == 'matrix':
-                if EVEN_M & EVEN_N:
-                    bias = tl.load(b_ptrs).to(tl.float32)
-                else:
-                    bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32)
-            qk = qk * softmax_scale + bias
-        if not EVEN_M & EVEN_HEADDIM:
-            tl.debug_barrier()
-        lse_i = tl.load(LSE + offs_m_curr)
-        if BIAS_TYPE == 'none':
-            p = tl.exp(qk * softmax_scale - lse_i[:, None])
-        else:
-            p = tl.exp(qk - lse_i[:, None])
-        if EVEN_M & EVEN_HEADDIM:
-            do = tl.load(do_ptrs)
-        else:
-            do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
-        dv += tl.dot(p.to(do.dtype), do, trans_a=True)
-        if not EVEN_M & EVEN_HEADDIM:
-            tl.debug_barrier()
-        dp = tl.dot(do, v, trans_b=True)
-        if not EVEN_HEADDIM:
-            tl.debug_barrier()
-        Di = tl.load(D + offs_m_curr)
-        ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
-        dk += tl.dot(ds, q, trans_a=True)
-        if not EVEN_M & EVEN_HEADDIM:
-            tl.debug_barrier()
-        if not ATOMIC_ADD:
-            if EVEN_M & EVEN_HEADDIM:
-                dq = tl.load(dq_ptrs, eviction_policy='evict_last')
-                dq += tl.dot(ds, k)
-                tl.store(dq_ptrs, dq, eviction_policy='evict_last')
-            elif EVEN_HEADDIM:
-                dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last')
-                dq += tl.dot(ds, k)
-                tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last')
-            else:
-                dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last')
-                dq += tl.dot(ds, k)
-                tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last')
-        else:
-            dq = tl.dot(ds, k)
-            if EVEN_M & EVEN_HEADDIM:
-                tl.atomic_add(dq_ptrs, dq)
-            elif EVEN_HEADDIM:
-                tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q)
-            else:
-                tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
-        dq_ptrs += BLOCK_M * stride_dqm
-        q_ptrs += BLOCK_M * stride_qm
-        do_ptrs += BLOCK_M * stride_dom
-        if BIAS_TYPE == 'matrix':
-            b_ptrs += BLOCK_M * stride_bm
-    dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
-    dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
-    _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
-
-def init_to_zero(name):
-    return lambda nargs: nargs[name].zero_()
-
-@triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM'])
-@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
-@triton.jit
-def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
-    off_hb = tl.program_id(1)
-    off_b = off_hb // nheads
-    off_h = off_hb % nheads
-    Q += off_b * stride_qb + off_h * stride_qh
-    K += off_b * stride_kb + off_h * stride_kh
-    V += off_b * stride_vb + off_h * stride_vh
-    DO += off_b * stride_dob + off_h * stride_doh
-    DQ += off_b * stride_dqb + off_h * stride_dqh
-    DK += off_b * stride_dkb + off_h * stride_dkh
-    DV += off_b * stride_dvb + off_h * stride_dvh
-    if BIAS_TYPE != 'none':
-        Bias += off_b * stride_bb + off_h * stride_bh
-    D += off_hb * seqlen_q_rounded
-    LSE += off_hb * seqlen_q_rounded
-    if not SEQUENCE_PARALLEL:
-        num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
-        for start_n in range(0, num_block_n):
-            _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
-    else:
-        start_n = tl.program_id(0)
-        _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
-
-def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
-    (batch, seqlen_q, nheads, d) = q.shape
-    (_, seqlen_k, _, _) = k.shape
-    assert k.shape == (batch, seqlen_k, nheads, d)
-    assert v.shape == (batch, seqlen_k, nheads, d)
-    assert d <= 128, 'FlashAttention only support head dimensions up to 128'
-    assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type'
-    assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16'
-    assert q.is_cuda and k.is_cuda and v.is_cuda
-    softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
-    has_bias = bias is not None
-    bias_type = 'none'
-    if has_bias:
-        assert bias.dtype in [q.dtype, torch.float]
-        assert bias.is_cuda
-        assert bias.dim() == 4
-        if bias.stride(-1) != 1:
-            bias = bias.contiguous()
-        if bias.shape[2:] == (1, seqlen_k):
-            bias_type = 'vector'
-        elif bias.shape[2:] == (seqlen_q, seqlen_k):
-            bias_type = 'matrix'
-        else:
-            raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
-        bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
-    bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
-    seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
-    lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
-    tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
-    o = torch.empty_like(q)
-    BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
-    BLOCK = 128
-    num_warps = 4 if d <= 64 else 8
-    grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
-    _fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1)
-    return (o, lse, softmax_scale)
-
-def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None):
-    if do.stride(-1) != 1:
-        do = do.contiguous()
-    (batch, seqlen_q, nheads, d) = q.shape
-    (_, seqlen_k, _, _) = k.shape
-    assert d <= 128
-    seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
-    assert lse.shape == (batch, nheads, seqlen_q_rounded)
-    assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
-    assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
-    softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
-    dq_accum = torch.empty_like(q, dtype=torch.float32)
-    delta = torch.empty_like(lse)
-    BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
-    grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
-    _bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM)
-    has_bias = bias is not None
-    bias_type = 'none'
-    if has_bias:
-        assert bias.dtype in [q.dtype, torch.float]
-        assert bias.is_cuda
-        assert bias.dim() == 4
-        assert bias.stride(-1) == 1
-        if bias.shape[2:] == (1, seqlen_k):
-            bias_type = 'vector'
-        elif bias.shape[2:] == (seqlen_q, seqlen_k):
-            bias_type = 'matrix'
-        else:
-            raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
-        bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
-    bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
-    grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads)
-    _bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM)
-    dq.copy_(dq_accum)
-
-class FlashAttnQKVPackedFunc(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
-        """
-            qkv: (batch, seqlen, 3, nheads, headdim)
-            bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
-                For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
-                ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
-        """
-        if qkv.stride(-1) != 1:
-            qkv = qkv.contiguous()
-        (o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale)
-        ctx.save_for_backward(qkv, o, lse, bias)
-        ctx.causal = causal
-        return o
-
-    @staticmethod
-    def backward(ctx, do):
-        (qkv, o, lse, bias) = ctx.saved_tensors
-        assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet'
-        with torch.inference_mode():
-            dqkv = torch.empty_like(qkv)
-            _flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
-        return (dqkv, None, None, None)
-flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply
-
-class FlashAttnKVPackedFunc(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
-        """
-            q: (batch, seqlen_q, nheads, headdim)
-            kv: (batch, seqlen_k, 2, nheads, headdim)
-            bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
-                For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
-                ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
-        """
-        (q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]]
-        (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale)
-        ctx.save_for_backward(q, kv, o, lse, bias)
-        ctx.causal = causal
-        return o
-
-    @staticmethod
-    def backward(ctx, do):
-        (q, kv, o, lse, bias) = ctx.saved_tensors
-        if len(ctx.needs_input_grad) >= 3:
-            assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet'
-        with torch.inference_mode():
-            dq = torch.empty_like(q)
-            dkv = torch.empty_like(kv)
-            _flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
-        return (dq, dkv, None, None, None)
-flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply
-
-class FlashAttnFunc(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
-        """
-            q: (batch_size, seqlen_q, nheads, headdim)
-            k, v: (batch_size, seqlen_k, nheads, headdim)
-            bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
-                For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
-                ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
-        """
-        (q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]]
-        (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale)
-        ctx.save_for_backward(q, k, v, o, lse, bias)
-        ctx.causal = causal
-        return o
-
-    @staticmethod
-    def backward(ctx, do):
-        (q, k, v, o, lse, bias) = ctx.saved_tensors
-        assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet'
-        with torch.inference_mode():
-            dq = torch.empty_like(q)
-            dk = torch.empty_like(k)
-            dv = torch.empty_like(v)
-            _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
-        return (dq, dk, dv, None, None, None)
-flash_attn_func = FlashAttnFunc.apply
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/(2011) Windows 7 X64 Dvd X16 96072 Iso.md b/spaces/usbethFlerru/sovits-modelsV2/example/(2011) Windows 7 X64 Dvd X16 96072 Iso.md
deleted file mode 100644
index d03f5d3a6cdb7c444aeedd6a73e1d1d7099f4141..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/(2011) Windows 7 X64 Dvd X16 96072 Iso.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-<h1>How to Download and Install (2011) Windows 7 x64 DVD x16 96072 ISO</h1>
-<p>If you are looking for a reliable and secure operating system for your PC, you might want to consider (2011) Windows 7 x64 DVD x16 96072 ISO. This is a version of Windows 7 that was released in 2011 and has been updated with the latest security patches and features. In this article, we will show you how to download and install this ISO file on your computer.</p>
-<h2>What is (2011) Windows 7 x64 DVD x16 96072 ISO?</h2>
-<p>(2011) Windows 7 x64 DVD x16 96072 ISO is an image file that contains the installation files for Windows 7 64-bit edition. It is also known as Windows 7 SP1 Ultimate x64 OEM ESD en-US March 2020. This means that it includes the Service Pack 1 update, the Ultimate edition features, the OEM activation, the ESD compression, and the English language. It also has some additional updates and drivers that were released until March 2020.</p>
-<h2>(2011) windows 7 x64 dvd x16 96072 iso</h2><br /><p><b><b>Download File</b> &#10001; <a href="https://urlcod.com/2uyWTs">https://urlcod.com/2uyWTs</a></b></p><br /><br />
-<h2>Why Choose (2011) Windows 7 x64 DVD x16 96072 ISO?</h2>
-<p>There are many reasons why you might want to choose (2011) Windows 7 x64 DVD x16 96072 ISO for your PC. Here are some of them:</p>
-<ul>
-<li>It is compatible with most hardware and software that were designed for Windows 7.</li>
-<li>It has a familiar and user-friendly interface that is easy to navigate and customize.</li>
-<li>It has a high performance and stability that can handle multiple tasks and applications.</li>
-<li>It has a strong security system that protects your data and privacy from viruses, malware, and hackers.</li>
-<li>It has a low system requirements that can run on older or low-end computers.</li>
-</ul>
-<h2>How to Download (2011) Windows 7 x64 DVD x16 96072 ISO?</h2>
-<p>To download (2011) Windows 7 x64 DVD x16 96072 ISO, you will need a torrent client such as uTorrent or BitTorrent. You can download one of these from their official websites. Then, you will need to find a torrent file that contains the ISO image. You can search for it on various torrent sites such as The Pirate Bay or Kickass Torrents. Make sure to check the comments and ratings of the torrent before downloading it to avoid fake or corrupted files. Once you have the torrent file, open it with your torrent client and start downloading the ISO image.</p>
-<h2>How to Install (2011) Windows 7 x64 DVD x16 96072 ISO?</h2>
-<p>To install (2011) Windows 7 x64 DVD x16 96072 ISO, you will need a blank DVD disc or a USB flash drive with at least 4 GB of space. You will also need a software that can burn or create bootable media such as Rufus or PowerISO. You can download one of these from their official websites. Then, follow these steps:</p>
-<ol>
-<li>Insert your DVD disc or USB flash drive into your computer.</li>
-<li>Open your software and select the ISO image as the source file.</li>
-<li>Select your DVD disc or USB flash drive as the destination device.</li>
-<li>Choose the appropriate settings for your media such as file system, partition scheme, label, etc.</li>
-<li>Click on start or burn to begin the process of creating the bootable media.</li>
-<li>Wait until the process is completed and then eject your media.</li>
-</ol>
-<p>Now you have a bootable DVD disc or USB flash drive that contains the installation files for (2011) Windows 7 x64 DVD x16 96072 ISO. To install it on your PC, follow these steps:</p>
-<ol>
-<li>Turn off your PC and insert your bootable media into it.</li>
-<li>Turn on your PC and press the appropriate key to enter the BIOS or boot menu. This key may vary depending on your PC model and manufacturer. It is usually F2, F10, F12, Esc, or Del.</li>
-<li>Select your bootable media as the first boot option and save the changes.</li>
-<li>Your PC will restart and load the Windows 7 setup screen</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/utils.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/utils.py
deleted file mode 100644
index f8636dc479d242e982fb969a313f7fd786cc3107..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/nn/modules/utils.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-Module utils
-"""
-
-import copy
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.init import uniform_
-
-__all__ = 'multi_scale_deformable_attn_pytorch', 'inverse_sigmoid'
-
-
-def _get_clones(module, n):
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
-
-
-def bias_init_with_prob(prior_prob=0.01):
-    """initialize conv/fc bias value according to a given probability value."""
-    return float(-np.log((1 - prior_prob) / prior_prob))  # return bias_init
-
-
-def linear_init_(module):
-    bound = 1 / math.sqrt(module.weight.shape[0])
-    uniform_(module.weight, -bound, bound)
-    if hasattr(module, 'bias') and module.bias is not None:
-        uniform_(module.bias, -bound, bound)
-
-
-def inverse_sigmoid(x, eps=1e-5):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def multi_scale_deformable_attn_pytorch(value: torch.Tensor, value_spatial_shapes: torch.Tensor,
-                                        sampling_locations: torch.Tensor,
-                                        attention_weights: torch.Tensor) -> torch.Tensor:
-    """
-    Multi-scale deformable attention.
-    https://github.com/IDEA-Research/detrex/blob/main/detrex/layers/multi_scale_deform_attn.py
-    """
-
-    bs, _, num_heads, embed_dims = value.shape
-    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
-    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1
-    sampling_value_list = []
-    for level, (H_, W_) in enumerate(value_spatial_shapes):
-        # bs, H_*W_, num_heads, embed_dims ->
-        # bs, H_*W_, num_heads*embed_dims ->
-        # bs, num_heads*embed_dims, H_*W_ ->
-        # bs*num_heads, embed_dims, H_, W_
-        value_l_ = (value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_))
-        # bs, num_queries, num_heads, num_points, 2 ->
-        # bs, num_heads, num_queries, num_points, 2 ->
-        # bs*num_heads, num_queries, num_points, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
-        # bs*num_heads, embed_dims, num_queries, num_points
-        sampling_value_l_ = F.grid_sample(value_l_,
-                                          sampling_grid_l_,
-                                          mode='bilinear',
-                                          padding_mode='zeros',
-                                          align_corners=False)
-        sampling_value_list.append(sampling_value_l_)
-    # (bs, num_queries, num_heads, num_levels, num_points) ->
-    # (bs, num_heads, num_queries, num_levels, num_points) ->
-    # (bs, num_heads, 1, num_queries, num_levels*num_points)
-    attention_weights = attention_weights.transpose(1, 2).reshape(bs * num_heads, 1, num_queries,
-                                                                  num_levels * num_points)
-    output = ((torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(
-        bs, num_heads * embed_dims, num_queries))
-    return output.transpose(1, 2).contiguous()
diff --git a/spaces/venz/AW-02-H5-AR-VR-IOT/style.css b/spaces/venz/AW-02-H5-AR-VR-IOT/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/venz/AW-02-H5-AR-VR-IOT/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/vialibre/edia_lmodels_en/modules/module_pllScore.py b/spaces/vialibre/edia_lmodels_en/modules/module_pllScore.py
deleted file mode 100644
index 0c077e8fb1b5ac118d42a8d92a4bd3fce1808007..0000000000000000000000000000000000000000
--- a/spaces/vialibre/edia_lmodels_en/modules/module_pllScore.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from difflib import Differ
-import torch, re
-
-
-class PllScore:
-    def __init__(
-        self, 
-        language_model  # LanguageModel class instance
-    ) -> None:
-
-        self.tokenizer = language_model.initTokenizer()
-        self.model = language_model.initModel()
-        _ = self.model.eval()
-
-        self.logSoftmax = torch.nn.LogSoftmax(dim=-1)
-
-    def sentIsCorrect(
-        self, 
-        sent: str
-    ) -> bool:
-
-        # Mod
-        is_correct = True
-
-        # Check mark existence
-        open_mark = sent.count("<")
-        close_mark = sent.count(">")
-        total_mark = open_mark + close_mark
-        if (total_mark == 0) or (open_mark != close_mark):
-            is_correct = False
-
-        # Check existence of twin marks (ie: '<<' or '>>')
-        if is_correct:
-            left_twin = sent.count("<<")
-            rigth_twin = sent.count(">>")
-            if left_twin + rigth_twin > 0:
-                is_correct = False
-
-        if is_correct:
-            # Check balanced symbols '<' and '>'
-            stack = []
-            for c in sent:
-                if c == '<':
-                    stack.append('<')
-                elif c == '>':
-                    if len(stack) == 0:
-                        is_correct = False
-                        break
-
-                    if stack.pop() != "<":
-                        is_correct = False
-                        break
-
-            if len(stack) > 0:
-                is_correct = False
-
-        if is_correct:
-            for w in re.findall("\<.*?\>", sent):
-                # Check empty interest words
-                word = w.replace("<","").replace(">","").strip() 
-                if not word:
-                    is_correct = False
-                    break
-                
-                # Check if there are any marks inside others (ie: <this is a <sentence>>)
-                word = w.strip()[1:-1]  #Delete the first and last mark
-                if '<' in word or '>' in word:
-                    is_correct = False
-                    break
-        
-        if is_correct:
-            # Check that there is at least one uninteresting word. The next examples should not be allowed 
-            # (ie: <this is a sent>, <this> <is a sent>)
-            outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
-            outside_words = [w for w in outside_words.split() if w != ""]
-            if not outside_words:
-                is_correct = False
-
-
-        return is_correct
-
-    def compute(
-        self, 
-        sent: str
-    ) -> float:
-
-        assert(self.sentIsCorrect(sent)), f"Error: The sentence '{sent}' does not have the correct format!"
-
-        outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
-        outside_words = [w for w in outside_words.split() if w != ""]
-        all_words = [w.strip() for w in sent.replace("<"," ").replace(">"," ").split() if w != ""]
-
-        tks_id_outside_words = self.tokenizer.encode(
-            " ".join(outside_words), 
-            add_special_tokens=False, 
-            truncation=True
-        )
-        tks_id_all_words = self.tokenizer.encode(
-            " ".join(all_words), 
-            add_special_tokens=False,
-            truncation=True
-        )
-
-        diff = [(tk[0], tk[2:]) for tk in Differ().compare(tks_id_outside_words, tks_id_all_words)]
-
-        cls_tk_id = self.tokenizer.cls_token_id
-        sep_tk_id = self.tokenizer.sep_token_id
-        mask_tk_id = self.tokenizer.mask_token_id
-
-        all_sent_masked = []
-        all_tks_id_masked = []
-        all_tks_position_masked = []
-
-        for i in range(0, len(diff)):
-            current_sent_masked = [cls_tk_id]
-            add_sent = True
-            for j, (mark, tk_id) in enumerate(diff):
-                if j == i:
-                    if mark == '+':
-                        add_sent = False
-                        break
-                    else:
-                        current_sent_masked.append(mask_tk_id)
-                        all_tks_id_masked.append(int(tk_id))
-                        all_tks_position_masked.append(i+1)
-                else:
-                    current_sent_masked.append(int(tk_id))
-
-            if add_sent:
-                current_sent_masked.append(sep_tk_id)
-                all_sent_masked.append(current_sent_masked)
-        
-        inputs_ids = torch.tensor(all_sent_masked)
-        attention_mask = torch.ones_like(inputs_ids)
-
-        with torch.no_grad():
-            out = self.model(inputs_ids, attention_mask)
-            logits = out.logits
-            outputs = self.logSoftmax(logits)
-
-        pll_score = 0
-        for out, tk_pos, tk_id in zip(outputs, all_tks_position_masked, all_tks_id_masked):
-            probabilities = out[tk_pos]
-            tk_prob = probabilities[tk_id]
-            pll_score += tk_prob.item()
-
-        return pll_score
\ No newline at end of file
diff --git a/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/run.sh b/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/run.sh
deleted file mode 100644
index 61af4b4950eb11334e55362e3e3c5e2796979a01..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/run.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-ps -ef | grep "train" | grep -v grep | awk '{print "kill -9 "$2}' | sh
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/info.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/info.py
deleted file mode 100644
index 29f2e5598ae2bb5866ccd15a7d3b4de33c0cd14d..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/info.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import glob
-import os
-
-import torch
-
-if torch.__version__ == 'parrots':
-    import parrots
-
-    def get_compiler_version():
-        return 'GCC ' + parrots.version.compiler
-
-    def get_compiling_cuda_version():
-        return parrots.version.cuda
-else:
-    from ..utils import ext_loader
-    ext_module = ext_loader.load_ext(
-        '_ext', ['get_compiler_version', 'get_compiling_cuda_version'])
-
-    def get_compiler_version():
-        return ext_module.get_compiler_version()
-
-    def get_compiling_cuda_version():
-        return ext_module.get_compiling_cuda_version()
-
-
-def get_onnxruntime_op_path():
-    wildcard = os.path.join(
-        os.path.abspath(os.path.dirname(os.path.dirname(__file__))),
-        '_ext_ort.*.so')
-
-    paths = glob.glob(wildcard)
-    if len(paths) > 0:
-        return paths[0]
-    else:
-        return ''
diff --git a/spaces/wangguanlin/vits_Kazari/commons.py b/spaces/wangguanlin/vits_Kazari/commons.py
deleted file mode 100644
index 2153153f527d94e2abb641ea00c80b518ff6c5bd..0000000000000000000000000000000000000000
--- a/spaces/wangguanlin/vits_Kazari/commons.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-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 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 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
diff --git a/spaces/wf-genius/Control-A-Video/model/video_diffusion/models/unet_3d_condition.py b/spaces/wf-genius/Control-A-Video/model/video_diffusion/models/unet_3d_condition.py
deleted file mode 100644
index a1c004fb9769a2f7bae72942bb8bbbbb2c3ee1f0..0000000000000000000000000000000000000000
--- a/spaces/wf-genius/Control-A-Video/model/video_diffusion/models/unet_3d_condition.py
+++ /dev/null
@@ -1,571 +0,0 @@
-# Copyright 2023 Bytedance Ltd. and/or its affiliates
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import glob
-import json
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import BaseOutput, logging
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps
-from .unet_3d_blocks import (
-    CrossAttnDownBlockPseudo3D,
-    CrossAttnUpBlockPseudo3D,
-    DownBlockPseudo3D,
-    UNetMidBlockPseudo3DCrossAttn,
-    UpBlockPseudo3D,
-    get_down_block,
-    get_up_block,
-)
-from .resnet import PseudoConv3d
-from diffusers.models.cross_attention import AttnProcessor
-from typing import Dict
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class UNetPseudo3DConditionOutput(BaseOutput):
-    sample: torch.FloatTensor
-
-
-class UNetPseudo3DConditionModel(ModelMixin, ConfigMixin):
-    """
-    这里把原来2D Unet的 2D卷积全换成新定义的PseudoConv3d。并且定义了从2D卷积继承的模型参数。
-    """
-    _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] = (
-            "CrossAttnDownBlockPseudo3D",
-            "CrossAttnDownBlockPseudo3D",
-            "CrossAttnDownBlockPseudo3D",
-            "DownBlockPseudo3D",
-        ),
-        mid_block_type: str = "UNetMidBlockPseudo3DCrossAttn",
-        up_block_types: Tuple[str] = (
-            "UpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D",
-        ),
-        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,
-        fps_embed_type: Optional[str] = None,
-        num_fps_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-        num_class_embeds=None,
-        
-    ):
-        super().__init__()
-
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = PseudoConv3d(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 fps_embed_type is None and num_fps_embeds is not None:
-            self.fps_embedding = nn.Embedding(num_fps_embeds, time_embed_dim)
-        elif fps_embed_type == "timestep":
-            self.fps_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-        elif fps_embed_type == "identity":
-            self.fps_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        else:
-            self.fps_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):
-            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,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlockPseudo3DCrossAttn":
-            self.mid_block = UNetMidBlockPseudo3DCrossAttn(
-                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,
-            )
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the images
-        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):
-            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,
-            )
-            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 = PseudoConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
-    @property
-    def attn_processors(self) -> Dict[str, AttnProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]):
-        r"""
-        Parameters:
-            `processor (`dict` of `AttnProcessor` or `AttnProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                of **all** `CrossAttention` layers.
-            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-
-    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,
-            (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D, CrossAttnUpBlockPseudo3D, UpBlockPseudo3D),
-        ):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        fps_labels: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        cross_attention_kwargs=None,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[UNetPseudo3DConditionOutput, Tuple]:
-        # 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)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            # 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.fps_embedding is not None:
-            if fps_labels is None:
-                raise ValueError("fps_labels should be provided when num_fps_embeds > 0")
-
-            if self.config.fps_embed_type == "timestep":
-                fps_labels = self.time_proj(fps_labels) # 和timesteps共用，都是sin embedding？这里的weight不更新的。
-
-            # 这里和上面timesteps does not contain any weights and will always return f32 tensors的bug一样。需要先cast过去，不然多机多卡就有问题了。
-            fps_labels = fps_labels.to(dtype=self.dtype)
-            class_emb = self.fps_embedding(fps_labels)
-
-            emb = emb + class_emb
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "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)
-
-            down_block_res_samples += res_samples
-
-        if down_block_additional_residuals is not None:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples += (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        sample = self.mid_block(
-            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
-        )
-        if mid_block_additional_residual is not None:
-            sample = sample + mid_block_additional_residual
-
-        # 5. 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,
-                )
-        # 6. 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 UNetPseudo3DConditionOutput(sample=sample)
-
-    @classmethod
-    def from_2d_model(cls, model_path, condition_on_fps=False):
-        '''
-        load a 2d model and convert it to a pseudo 3d model
-        '''
-        config_path = os.path.join(model_path, "config.json")
-        if not os.path.isfile(config_path):
-            raise RuntimeError(f"{config_path} does not exist")
-        with open(config_path, "r") as f:
-            config = json.load(f)
-
-        config.pop("_class_name")
-        config.pop("_diffusers_version")
-
-        block_replacer = {
-            "CrossAttnDownBlock2D": "CrossAttnDownBlockPseudo3D",
-            "DownBlock2D": "DownBlockPseudo3D",
-            "UpBlock2D": "UpBlockPseudo3D",
-            "CrossAttnUpBlock2D": "CrossAttnUpBlockPseudo3D",
-        }
-
-        def convert_2d_to_3d_block(block):
-            return block_replacer[block] if block in block_replacer else block
-
-        config["down_block_types"] = [
-            convert_2d_to_3d_block(block) for block in config["down_block_types"]
-        ]
-        config["up_block_types"] = [convert_2d_to_3d_block(block) for block in config["up_block_types"]]
-        
-        if condition_on_fps:
-            # config["num_fps_embeds"] = 60 # 这个在 trainable embeding时候才需要～
-            config["fps_embed_type"] = "timestep"     # 和timestep保持一致的type。
-            
-
-        model = cls(**config)   # 调用自身(init), 传入config参数全换成3d的setting
-
-        state_dict_path_condidates = glob.glob(os.path.join(model_path, "*.bin"))
-        if state_dict_path_condidates:
-            state_dict = torch.load(state_dict_path_condidates[0], map_location="cpu")
-            model.load_2d_state_dict(state_dict=state_dict)
-
-        return model
-
-    def load_2d_state_dict(self, state_dict, **kwargs):
-        '''
-        2D 部分的参数名完全不变。
-        '''
-        state_dict_3d = self.state_dict()
-
-        for k, v in state_dict.items():
-            if k not in state_dict_3d:
-                raise KeyError(f"2d state_dict key {k} does not exist in 3d model")
-            elif v.shape != state_dict_3d[k].shape:
-                raise ValueError(f"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}")
-
-        for k, v in state_dict_3d.items():
-            if "_temporal" in k:
-                continue
-            if "gamma" in k:
-                continue
-            
-            if k not in state_dict:
-                if "fps_embedding" in k:
-                    # 忽略检查fps_embedding
-                    continue
-                raise KeyError(f"3d state_dict key {k} does not exist in 2d model")
-
-        state_dict_3d.update(state_dict)
-        self.load_state_dict(state_dict_3d, **kwargs)
diff --git a/spaces/whgwd2023/bingo/src/pages/api/image.ts b/spaces/whgwd2023/bingo/src/pages/api/image.ts
deleted file mode 100644
index 26fdb31076a9c71e70d1725a630844b27f5a3221..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/pages/api/image.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, 'image')
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/wolfrage89/chaii_spaces/README.md b/spaces/wolfrage89/chaii_spaces/README.md
deleted file mode 100644
index 555e345a91c90a5faf0388177cbd2223fc82a1e1..0000000000000000000000000000000000000000
--- a/spaces/wolfrage89/chaii_spaces/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Chaii_spaces
-emoji: 🦀
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/wong26/faster-whisper-webui/src/prompts/abstractPromptStrategy.py b/spaces/wong26/faster-whisper-webui/src/prompts/abstractPromptStrategy.py
deleted file mode 100644
index 41e8cba49fdbcc294ea216fffcafee89b07ed4df..0000000000000000000000000000000000000000
--- a/spaces/wong26/faster-whisper-webui/src/prompts/abstractPromptStrategy.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import abc
-
-
-class AbstractPromptStrategy:
-    """
-    Represents a strategy for generating prompts for a given audio segment.
-
-    Note that the strategy must be picklable, as it will be serialized and sent to the workers.
-    """
-    
-    @abc.abstractmethod
-    def get_segment_prompt(self, segment_index: int, whisper_prompt: str, detected_language: str) -> str:
-        """
-        Retrieves the prompt for a given segment.
-
-        Parameters
-        ----------
-        segment_index: int
-            The index of the segment.
-        whisper_prompt: str
-            The prompt for the segment generated by Whisper. This is typically concatenated with the initial prompt.
-        detected_language: str
-            The language detected for the segment.
-        """
-        pass
-
-    @abc.abstractmethod
-    def on_segment_finished(self, segment_index: int, whisper_prompt: str, detected_language: str, result: dict):
-        """
-        Called when a segment has finished processing.
-        
-        Parameters
-        ----------
-        segment_index: int
-            The index of the segment.
-        whisper_prompt: str
-            The prompt for the segment generated by Whisper. This is typically concatenated with the initial prompt.
-        detected_language: str
-            The language detected for the segment.
-        result: dict
-            The result of the segment. It has the following format:
-                {
-                    "text": str,
-                    "segments": [
-                        {
-                            "text": str,
-                            "start": float,
-                            "end": float,
-                            "words": [words],
-                        }
-                    ],
-                    "language": str,
-                }
-        """
-        pass
-
-    def _concat_prompt(self, prompt1, prompt2):
-        """
-        Concatenates two prompts.
-
-        Parameters
-        ----------
-        prompt1: str
-            The first prompt.
-        prompt2: str
-            The second prompt.
-        """
-        if (prompt1 is None):
-            return prompt2
-        elif (prompt2 is None):
-            return prompt1
-        else:
-            return prompt1 + " " + prompt2
\ No newline at end of file
diff --git a/spaces/wuhuik/bingo/README.md b/spaces/wuhuik/bingo/README.md
deleted file mode 100644
index d65eafbc8431818f738e8e086455fa6159f101bb..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/README.md
+++ /dev/null
@@ -1,196 +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)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-> 由于目前微软封杀比较严重，推荐优先使用 [部署 Huggingface](#部署到-huggingface) 。
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge ，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/wwwwwwww2/bingo/src/components/ui/sheet.tsx b/spaces/wwwwwwww2/bingo/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/wwwwwwww2/bingo/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/xuetao/bingo3/src/lib/storage.ts b/spaces/xuetao/bingo3/src/lib/storage.ts
deleted file mode 100644
index a5b7825c4f76a28c704da512ae39e8bb45addd09..0000000000000000000000000000000000000000
--- a/spaces/xuetao/bingo3/src/lib/storage.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import { getMany, set, del, clear } from 'idb-keyval';
-
-export const Storage = {
-  async get(key: string | string[] | null): Promise<any> {
-    if (key === null) return null;
-    if (typeof key === 'string') {
-      key = [key]
-    }
-    const returnData: Record<string, any> = {}
-    const values = await getMany(key)
-    key.forEach((k, idx)=> {
-      returnData[k] = values[idx]
-    })
-    return returnData;
-  },
-  async set(object: any) {
-    for (let key of Object.keys(object)) {
-      await set(key, object[key])
-    }
-  },
-  async remove(key: string) {
-    return del(key);
-  },
-  async clear() {
-    return clear();
-  }
-}
diff --git a/spaces/xxccc/gpt-academic/theme.py b/spaces/xxccc/gpt-academic/theme.py
deleted file mode 100644
index 5ef7e9605896dbdddcaea09e7d804baf3f5696cf..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/theme.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import gradio as gr
-from toolbox import get_conf
-CODE_HIGHLIGHT, ADD_WAIFU = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU')
-# gradio可用颜色列表
-# gr.themes.utils.colors.slate (石板色)
-# gr.themes.utils.colors.gray (灰色)
-# gr.themes.utils.colors.zinc (锌色)
-# gr.themes.utils.colors.neutral (中性色)
-# gr.themes.utils.colors.stone (石头色)
-# gr.themes.utils.colors.red (红色)
-# gr.themes.utils.colors.orange (橙色)
-# gr.themes.utils.colors.amber (琥珀色)
-# gr.themes.utils.colors.yellow (黄色)
-# gr.themes.utils.colors.lime (酸橙色)
-# gr.themes.utils.colors.green (绿色)
-# gr.themes.utils.colors.emerald (祖母绿)
-# gr.themes.utils.colors.teal (青蓝色)
-# gr.themes.utils.colors.cyan (青色)
-# gr.themes.utils.colors.sky (天蓝色)
-# gr.themes.utils.colors.blue (蓝色)
-# gr.themes.utils.colors.indigo (靛蓝色)
-# gr.themes.utils.colors.violet (紫罗兰色)
-# gr.themes.utils.colors.purple (紫色)
-# gr.themes.utils.colors.fuchsia (洋红色)
-# gr.themes.utils.colors.pink (粉红色)
-# gr.themes.utils.colors.rose (玫瑰色)
-
-
-def adjust_theme():
-
-    try:
-        color_er = gr.themes.utils.colors.fuchsia
-        set_theme = gr.themes.Default(
-            primary_hue=gr.themes.utils.colors.orange,
-            neutral_hue=gr.themes.utils.colors.gray,
-            font=["sans-serif", "Microsoft YaHei", "ui-sans-serif", "system-ui",
-                  "sans-serif", gr.themes.utils.fonts.GoogleFont("Source Sans Pro")],
-            font_mono=["ui-monospace", "Consolas", "monospace", gr.themes.utils.fonts.GoogleFont("IBM Plex Mono")])
-        set_theme.set(
-            # Colors
-            input_background_fill_dark="*neutral_800",
-            # Transition
-            button_transition="none",
-            # Shadows
-            button_shadow="*shadow_drop",
-            button_shadow_hover="*shadow_drop_lg",
-            button_shadow_active="*shadow_inset",
-            input_shadow="0 0 0 *shadow_spread transparent, *shadow_inset",
-            input_shadow_focus="0 0 0 *shadow_spread *secondary_50, *shadow_inset",
-            input_shadow_focus_dark="0 0 0 *shadow_spread *neutral_700, *shadow_inset",
-            checkbox_label_shadow="*shadow_drop",
-            block_shadow="*shadow_drop",
-            form_gap_width="1px",
-            # Button borders
-            input_border_width="1px",
-            input_background_fill="white",
-            # Gradients
-            stat_background_fill="linear-gradient(to right, *primary_400, *primary_200)",
-            stat_background_fill_dark="linear-gradient(to right, *primary_400, *primary_600)",
-            error_background_fill=f"linear-gradient(to right, {color_er.c100}, *background_fill_secondary)",
-            error_background_fill_dark="*background_fill_primary",
-            checkbox_label_background_fill="linear-gradient(to top, *neutral_50, white)",
-            checkbox_label_background_fill_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
-            checkbox_label_background_fill_hover="linear-gradient(to top, *neutral_100, white)",
-            checkbox_label_background_fill_hover_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
-            button_primary_background_fill="linear-gradient(to bottom right, *primary_100, *primary_300)",
-            button_primary_background_fill_dark="linear-gradient(to bottom right, *primary_500, *primary_600)",
-            button_primary_background_fill_hover="linear-gradient(to bottom right, *primary_100, *primary_200)",
-            button_primary_background_fill_hover_dark="linear-gradient(to bottom right, *primary_500, *primary_500)",
-            button_primary_border_color_dark="*primary_500",
-            button_secondary_background_fill="linear-gradient(to bottom right, *neutral_100, *neutral_200)",
-            button_secondary_background_fill_dark="linear-gradient(to bottom right, *neutral_600, *neutral_700)",
-            button_secondary_background_fill_hover="linear-gradient(to bottom right, *neutral_100, *neutral_100)",
-            button_secondary_background_fill_hover_dark="linear-gradient(to bottom right, *neutral_600, *neutral_600)",
-            button_cancel_background_fill=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c200})",
-            button_cancel_background_fill_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c700})",
-            button_cancel_background_fill_hover=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c100})",
-            button_cancel_background_fill_hover_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c600})",
-            button_cancel_border_color=color_er.c200,
-            button_cancel_border_color_dark=color_er.c600,
-            button_cancel_text_color=color_er.c600,
-            button_cancel_text_color_dark="white",
-        )
-
-        # 添加一个萌萌的看板娘
-        if ADD_WAIFU:
-            js = """
-                <script src="file=docs/waifu_plugin/jquery.min.js"></script>
-                <script src="file=docs/waifu_plugin/jquery-ui.min.js"></script>
-                <script src="file=docs/waifu_plugin/autoload.js"></script>
-            """
-            gradio_original_template_fn = gr.routes.templates.TemplateResponse
-            def gradio_new_template_fn(*args, **kwargs):
-                res = gradio_original_template_fn(*args, **kwargs)
-                res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-                res.init_headers()
-                return res
-            gr.routes.templates.TemplateResponse = gradio_new_template_fn   # override gradio template
-    except:
-        set_theme = None
-        print('gradio版本较旧, 不能自定义字体和颜色')
-    return set_theme
-
-
-advanced_css = """
-.markdown-body table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-
-.markdown-body th, .markdown-body td {
-    border: 1.2px solid var(--border-color-primary);
-    padding: 5px;
-}
-
-.markdown-body thead {
-    background-color: rgba(175,184,193,0.2);
-}
-
-.markdown-body thead th {
-    padding: .5em .2em;
-}
-
-.markdown-body ol, .markdown-body ul {
-    padding-inline-start: 2em !important;
-}
-
-/* chat box. */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    /* padding: var(--spacing-xl) !important; */
-    /* font-size: var(--text-md) !important; */
-    /* line-height: var(--line-md) !important; */
-    /* min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */
-    /* min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */
-}
-[data-testid = "bot"] {
-    max-width: 95%;
-    /* width: auto !important; */
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 100%;
-    /* width: auto !important; */
-    border-bottom-right-radius: 0 !important;
-}
-
-/* linein code block. */
-.markdown-body code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(13, 17, 23, 0.95);
-    color: #c9d1d9;
-}
-
-.dark .markdown-body code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-
-/* code block css */
-.markdown-body pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: rgba(13, 17, 23, 0.95);
-    border-radius: 10px;
-    padding: 1em;
-    margin: 1em 2em 1em 0.5em;
-}
-
-.dark .markdown-body pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: rgba(175,184,193,0.2);
-    border-radius: 10px;
-    padding: 1em;
-    margin: 1em 2em 1em 0.5em;
-}
-
-"""
-
-if CODE_HIGHLIGHT:
-    advanced_css += """
-
-.codehilite .hll { background-color: #6e7681 }
-.codehilite .c { color: #8b949e; font-style: italic } /* Comment */
-.codehilite .err { color: #f85149 } /* Error */
-.codehilite .esc { color: #c9d1d9 } /* Escape */
-.codehilite .g { color: #c9d1d9 } /* Generic */
-.codehilite .k { color: #ff7b72 } /* Keyword */
-.codehilite .l { color: #a5d6ff } /* Literal */
-.codehilite .n { color: #c9d1d9 } /* Name */
-.codehilite .o { color: #ff7b72; font-weight: bold } /* Operator */
-.codehilite .x { color: #c9d1d9 } /* Other */
-.codehilite .p { color: #c9d1d9 } /* Punctuation */
-.codehilite .ch { color: #8b949e; font-style: italic } /* Comment.Hashbang */
-.codehilite .cm { color: #8b949e; font-style: italic } /* Comment.Multiline */
-.codehilite .cp { color: #8b949e; font-weight: bold; font-style: italic } /* Comment.Preproc */
-.codehilite .cpf { color: #8b949e; font-style: italic } /* Comment.PreprocFile */
-.codehilite .c1 { color: #8b949e; font-style: italic } /* Comment.Single */
-.codehilite .cs { color: #8b949e; font-weight: bold; font-style: italic } /* Comment.Special */
-.codehilite .gd { color: #ffa198; background-color: #490202 } /* Generic.Deleted */
-.codehilite .ge { color: #c9d1d9; font-style: italic } /* Generic.Emph */
-.codehilite .gr { color: #ffa198 } /* Generic.Error */
-.codehilite .gh { color: #79c0ff; font-weight: bold } /* Generic.Heading */
-.codehilite .gi { color: #56d364; background-color: #0f5323 } /* Generic.Inserted */
-.codehilite .go { color: #8b949e } /* Generic.Output */
-.codehilite .gp { color: #8b949e } /* Generic.Prompt */
-.codehilite .gs { color: #c9d1d9; font-weight: bold } /* Generic.Strong */
-.codehilite .gu { color: #79c0ff } /* Generic.Subheading */
-.codehilite .gt { color: #ff7b72 } /* Generic.Traceback */
-.codehilite .g-Underline { color: #c9d1d9; text-decoration: underline } /* Generic.Underline */
-.codehilite .kc { color: #79c0ff } /* Keyword.Constant */
-.codehilite .kd { color: #ff7b72 } /* Keyword.Declaration */
-.codehilite .kn { color: #ff7b72 } /* Keyword.Namespace */
-.codehilite .kp { color: #79c0ff } /* Keyword.Pseudo */
-.codehilite .kr { color: #ff7b72 } /* Keyword.Reserved */
-.codehilite .kt { color: #ff7b72 } /* Keyword.Type */
-.codehilite .ld { color: #79c0ff } /* Literal.Date */
-.codehilite .m { color: #a5d6ff } /* Literal.Number */
-.codehilite .s { color: #a5d6ff } /* Literal.String */
-.codehilite .na { color: #c9d1d9 } /* Name.Attribute */
-.codehilite .nb { color: #c9d1d9 } /* Name.Builtin */
-.codehilite .nc { color: #f0883e; font-weight: bold } /* Name.Class */
-.codehilite .no { color: #79c0ff; font-weight: bold } /* Name.Constant */
-.codehilite .nd { color: #d2a8ff; font-weight: bold } /* Name.Decorator */
-.codehilite .ni { color: #ffa657 } /* Name.Entity */
-.codehilite .ne { color: #f0883e; font-weight: bold } /* Name.Exception */
-.codehilite .nf { color: #d2a8ff; font-weight: bold } /* Name.Function */
-.codehilite .nl { color: #79c0ff; font-weight: bold } /* Name.Label */
-.codehilite .nn { color: #ff7b72 } /* Name.Namespace */
-.codehilite .nx { color: #c9d1d9 } /* Name.Other */
-.codehilite .py { color: #79c0ff } /* Name.Property */
-.codehilite .nt { color: #7ee787 } /* Name.Tag */
-.codehilite .nv { color: #79c0ff } /* Name.Variable */
-.codehilite .ow { color: #ff7b72; font-weight: bold } /* Operator.Word */
-.codehilite .pm { color: #c9d1d9 } /* Punctuation.Marker */
-.codehilite .w { color: #6e7681 } /* Text.Whitespace */
-.codehilite .mb { color: #a5d6ff } /* Literal.Number.Bin */
-.codehilite .mf { color: #a5d6ff } /* Literal.Number.Float */
-.codehilite .mh { color: #a5d6ff } /* Literal.Number.Hex */
-.codehilite .mi { color: #a5d6ff } /* Literal.Number.Integer */
-.codehilite .mo { color: #a5d6ff } /* Literal.Number.Oct */
-.codehilite .sa { color: #79c0ff } /* Literal.String.Affix */
-.codehilite .sb { color: #a5d6ff } /* Literal.String.Backtick */
-.codehilite .sc { color: #a5d6ff } /* Literal.String.Char */
-.codehilite .dl { color: #79c0ff } /* Literal.String.Delimiter */
-.codehilite .sd { color: #a5d6ff } /* Literal.String.Doc */
-.codehilite .s2 { color: #a5d6ff } /* Literal.String.Double */
-.codehilite .se { color: #79c0ff } /* Literal.String.Escape */
-.codehilite .sh { color: #79c0ff } /* Literal.String.Heredoc */
-.codehilite .si { color: #a5d6ff } /* Literal.String.Interpol */
-.codehilite .sx { color: #a5d6ff } /* Literal.String.Other */
-.codehilite .sr { color: #79c0ff } /* Literal.String.Regex */
-.codehilite .s1 { color: #a5d6ff } /* Literal.String.Single */
-.codehilite .ss { color: #a5d6ff } /* Literal.String.Symbol */
-.codehilite .bp { color: #c9d1d9 } /* Name.Builtin.Pseudo */
-.codehilite .fm { color: #d2a8ff; font-weight: bold } /* Name.Function.Magic */
-.codehilite .vc { color: #79c0ff } /* Name.Variable.Class */
-.codehilite .vg { color: #79c0ff } /* Name.Variable.Global */
-.codehilite .vi { color: #79c0ff } /* Name.Variable.Instance */
-.codehilite .vm { color: #79c0ff } /* Name.Variable.Magic */
-.codehilite .il { color: #a5d6ff } /* Literal.Number.Integer.Long */
-
-.dark .codehilite .hll { background-color: #2C3B41 }
-.dark .codehilite .c { color: #79d618; font-style: italic } /* Comment */
-.dark .codehilite .err { color: #FF5370 } /* Error */
-.dark .codehilite .esc { color: #89DDFF } /* Escape */
-.dark .codehilite .g { color: #EEFFFF } /* Generic */
-.dark .codehilite .k { color: #BB80B3 } /* Keyword */
-.dark .codehilite .l { color: #C3E88D } /* Literal */
-.dark .codehilite .n { color: #EEFFFF } /* Name */
-.dark .codehilite .o { color: #89DDFF } /* Operator */
-.dark .codehilite .p { color: #89DDFF } /* Punctuation */
-.dark .codehilite .ch { color: #79d618; font-style: italic } /* Comment.Hashbang */
-.dark .codehilite .cm { color: #79d618; font-style: italic } /* Comment.Multiline */
-.dark .codehilite .cp { color: #79d618; font-style: italic } /* Comment.Preproc */
-.dark .codehilite .cpf { color: #79d618; font-style: italic } /* Comment.PreprocFile */
-.dark .codehilite .c1 { color: #79d618; font-style: italic } /* Comment.Single */
-.dark .codehilite .cs { color: #79d618; font-style: italic } /* Comment.Special */
-.dark .codehilite .gd { color: #FF5370 } /* Generic.Deleted */
-.dark .codehilite .ge { color: #89DDFF } /* Generic.Emph */
-.dark .codehilite .gr { color: #FF5370 } /* Generic.Error */
-.dark .codehilite .gh { color: #C3E88D } /* Generic.Heading */
-.dark .codehilite .gi { color: #C3E88D } /* Generic.Inserted */
-.dark .codehilite .go { color: #79d618 } /* Generic.Output */
-.dark .codehilite .gp { color: #FFCB6B } /* Generic.Prompt */
-.dark .codehilite .gs { color: #FF5370 } /* Generic.Strong */
-.dark .codehilite .gu { color: #89DDFF } /* Generic.Subheading */
-.dark .codehilite .gt { color: #FF5370 } /* Generic.Traceback */
-.dark .codehilite .kc { color: #89DDFF } /* Keyword.Constant */
-.dark .codehilite .kd { color: #BB80B3 } /* Keyword.Declaration */
-.dark .codehilite .kn { color: #89DDFF; font-style: italic } /* Keyword.Namespace */
-.dark .codehilite .kp { color: #89DDFF } /* Keyword.Pseudo */
-.dark .codehilite .kr { color: #BB80B3 } /* Keyword.Reserved */
-.dark .codehilite .kt { color: #BB80B3 } /* Keyword.Type */
-.dark .codehilite .ld { color: #C3E88D } /* Literal.Date */
-.dark .codehilite .m { color: #F78C6C } /* Literal.Number */
-.dark .codehilite .s { color: #C3E88D } /* Literal.String */
-.dark .codehilite .na { color: #BB80B3 } /* Name.Attribute */
-.dark .codehilite .nb { color: #82AAFF } /* Name.Builtin */
-.dark .codehilite .nc { color: #FFCB6B } /* Name.Class */
-.dark .codehilite .no { color: #EEFFFF } /* Name.Constant */
-.dark .codehilite .nd { color: #82AAFF } /* Name.Decorator */
-.dark .codehilite .ni { color: #89DDFF } /* Name.Entity */
-.dark .codehilite .ne { color: #FFCB6B } /* Name.Exception */
-.dark .codehilite .nf { color: #82AAFF } /* Name.Function */
-.dark .codehilite .nl { color: #82AAFF } /* Name.Label */
-.dark .codehilite .nn { color: #FFCB6B } /* Name.Namespace */
-.dark .codehilite .nx { color: #EEFFFF } /* Name.Other */
-.dark .codehilite .py { color: #FFCB6B } /* Name.Property */
-.dark .codehilite .nt { color: #FF5370 } /* Name.Tag */
-.dark .codehilite .nv { color: #89DDFF } /* Name.Variable */
-.dark .codehilite .ow { color: #89DDFF; font-style: italic } /* Operator.Word */
-.dark .codehilite .pm { color: #89DDFF } /* Punctuation.Marker */
-.dark .codehilite .w { color: #EEFFFF } /* Text.Whitespace */
-.dark .codehilite .mb { color: #F78C6C } /* Literal.Number.Bin */
-.dark .codehilite .mf { color: #F78C6C } /* Literal.Number.Float */
-.dark .codehilite .mh { color: #F78C6C } /* Literal.Number.Hex */
-.dark .codehilite .mi { color: #F78C6C } /* Literal.Number.Integer */
-.dark .codehilite .mo { color: #F78C6C } /* Literal.Number.Oct */
-.dark .codehilite .sa { color: #BB80B3 } /* Literal.String.Affix */
-.dark .codehilite .sb { color: #C3E88D } /* Literal.String.Backtick */
-.dark .codehilite .sc { color: #C3E88D } /* Literal.String.Char */
-.dark .codehilite .dl { color: #EEFFFF } /* Literal.String.Delimiter */
-.dark .codehilite .sd { color: #79d618; font-style: italic } /* Literal.String.Doc */
-.dark .codehilite .s2 { color: #C3E88D } /* Literal.String.Double */
-.dark .codehilite .se { color: #EEFFFF } /* Literal.String.Escape */
-.dark .codehilite .sh { color: #C3E88D } /* Literal.String.Heredoc */
-.dark .codehilite .si { color: #89DDFF } /* Literal.String.Interpol */
-.dark .codehilite .sx { color: #C3E88D } /* Literal.String.Other */
-.dark .codehilite .sr { color: #89DDFF } /* Literal.String.Regex */
-.dark .codehilite .s1 { color: #C3E88D } /* Literal.String.Single */
-.dark .codehilite .ss { color: #89DDFF } /* Literal.String.Symbol */
-.dark .codehilite .bp { color: #89DDFF } /* Name.Builtin.Pseudo */
-.dark .codehilite .fm { color: #82AAFF } /* Name.Function.Magic */
-.dark .codehilite .vc { color: #89DDFF } /* Name.Variable.Class */
-.dark .codehilite .vg { color: #89DDFF } /* Name.Variable.Global */
-.dark .codehilite .vi { color: #89DDFF } /* Name.Variable.Instance */
-.dark .codehilite .vm { color: #82AAFF } /* Name.Variable.Magic */
-.dark .codehilite .il { color: #F78C6C } /* Literal.Number.Integer.Long */
-
-"""
diff --git a/spaces/yerfor/SyntaSpeech/data_gen/tts/txt_processors/en.py b/spaces/yerfor/SyntaSpeech/data_gen/tts/txt_processors/en.py
deleted file mode 100644
index be2b654cdea50ad0c8efa62054d91afb887b7efd..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/data_gen/tts/txt_processors/en.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import re
-import unicodedata
-
-from g2p_en import G2p
-from g2p_en.expand import normalize_numbers
-from nltk import pos_tag
-from nltk.tokenize import TweetTokenizer
-
-from data_gen.tts.txt_processors.base_text_processor import BaseTxtProcessor, register_txt_processors
-from utils.text.text_encoder import PUNCS, is_sil_phoneme
-
-
-class EnG2p(G2p):
-    word_tokenize = TweetTokenizer().tokenize
-
-    def __call__(self, text):
-        # preprocessing
-        words = EnG2p.word_tokenize(text)
-        tokens = pos_tag(words)  # tuples of (word, tag)
-
-        # steps
-        prons = []
-        for word, pos in tokens:
-            if re.search("[a-z]", word) is None:
-                pron = [word]
-
-            elif word in self.homograph2features:  # Check homograph
-                pron1, pron2, pos1 = self.homograph2features[word]
-                if pos.startswith(pos1):
-                    pron = pron1
-                else:
-                    pron = pron2
-            elif word in self.cmu:  # lookup CMU dict
-                pron = self.cmu[word][0]
-            else:  # predict for oov
-                pron = self.predict(word)
-
-            prons.extend(pron)
-            prons.extend([" "])
-
-        return prons[:-1]
-
-
-@register_txt_processors('en')
-class TxtProcessor(BaseTxtProcessor):
-    g2p = EnG2p()
-
-    @staticmethod
-    def preprocess_text(text):
-        text = normalize_numbers(text)
-        text = ''.join(char for char in unicodedata.normalize('NFD', text)
-                       if unicodedata.category(char) != 'Mn')  # Strip accents
-        text = text.lower()
-        text = re.sub("[\'\"()]+", "", text)
-        text = re.sub("[-]+", " ", text)
-        text = re.sub(f"[^ a-z{PUNCS}]", "", text)
-        text = re.sub(f" ?([{PUNCS}]) ?", r"\1", text)  # !! -> !
-        text = re.sub(f"([{PUNCS}])+", r"\1", text)  # !! -> !
-        text = text.replace("i.e.", "that is")
-        text = text.replace("i.e.", "that is")
-        text = text.replace("etc.", "etc")
-        text = re.sub(f"([{PUNCS}])", r" \1 ", text)
-        text = re.sub(rf"\s+", r" ", text)
-        return text
-
-    @classmethod
-    def process(cls, txt, preprocess_args):
-        txt = cls.preprocess_text(txt).strip()
-        phs = cls.g2p(txt)
-        txt_struct = [[w, []] for w in txt.split(" ")]
-        i_word = 0
-        for p in phs:
-            if p == ' ':
-                i_word += 1
-            else:
-                txt_struct[i_word][1].append(p)
-        txt_struct = cls.postprocess(txt_struct, preprocess_args)
-        return txt_struct, txt
diff --git a/spaces/ygangang/VToonify/vtoonify/model/raft/core/utils/augmentor.py b/spaces/ygangang/VToonify/vtoonify/model/raft/core/utils/augmentor.py
deleted file mode 100644
index e81c4f2b5c16c31c0ae236d744f299d430228a04..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/raft/core/utils/augmentor.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import numpy as np
-import random
-import math
-from PIL import Image
-
-import cv2
-cv2.setNumThreads(0)
-cv2.ocl.setUseOpenCL(False)
-
-import torch
-from torchvision.transforms import ColorJitter
-import torch.nn.functional as F
-
-
-class FlowAugmentor:
-    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
-        
-        # spatial augmentation params
-        self.crop_size = crop_size
-        self.min_scale = min_scale
-        self.max_scale = max_scale
-        self.spatial_aug_prob = 0.8
-        self.stretch_prob = 0.8
-        self.max_stretch = 0.2
-
-        # flip augmentation params
-        self.do_flip = do_flip
-        self.h_flip_prob = 0.5
-        self.v_flip_prob = 0.1
-
-        # photometric augmentation params
-        self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
-        self.asymmetric_color_aug_prob = 0.2
-        self.eraser_aug_prob = 0.5
-
-    def color_transform(self, img1, img2):
-        """ Photometric augmentation """
-
-        # asymmetric
-        if np.random.rand() < self.asymmetric_color_aug_prob:
-            img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
-            img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
-
-        # symmetric
-        else:
-            image_stack = np.concatenate([img1, img2], axis=0)
-            image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
-            img1, img2 = np.split(image_stack, 2, axis=0)
-
-        return img1, img2
-
-    def eraser_transform(self, img1, img2, bounds=[50, 100]):
-        """ Occlusion augmentation """
-
-        ht, wd = img1.shape[:2]
-        if np.random.rand() < self.eraser_aug_prob:
-            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
-                x0 = np.random.randint(0, wd)
-                y0 = np.random.randint(0, ht)
-                dx = np.random.randint(bounds[0], bounds[1])
-                dy = np.random.randint(bounds[0], bounds[1])
-                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
-
-        return img1, img2
-
-    def spatial_transform(self, img1, img2, flow):
-        # randomly sample scale
-        ht, wd = img1.shape[:2]
-        min_scale = np.maximum(
-            (self.crop_size[0] + 8) / float(ht), 
-            (self.crop_size[1] + 8) / float(wd))
-
-        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
-        scale_x = scale
-        scale_y = scale
-        if np.random.rand() < self.stretch_prob:
-            scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
-            scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
-        
-        scale_x = np.clip(scale_x, min_scale, None)
-        scale_y = np.clip(scale_y, min_scale, None)
-
-        if np.random.rand() < self.spatial_aug_prob:
-            # rescale the images
-            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow = flow * [scale_x, scale_y]
-
-        if self.do_flip:
-            if np.random.rand() < self.h_flip_prob: # h-flip
-                img1 = img1[:, ::-1]
-                img2 = img2[:, ::-1]
-                flow = flow[:, ::-1] * [-1.0, 1.0]
-
-            if np.random.rand() < self.v_flip_prob: # v-flip
-                img1 = img1[::-1, :]
-                img2 = img2[::-1, :]
-                flow = flow[::-1, :] * [1.0, -1.0]
-
-        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
-        x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
-        
-        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-
-        return img1, img2, flow
-
-    def __call__(self, img1, img2, flow):
-        img1, img2 = self.color_transform(img1, img2)
-        img1, img2 = self.eraser_transform(img1, img2)
-        img1, img2, flow = self.spatial_transform(img1, img2, flow)
-
-        img1 = np.ascontiguousarray(img1)
-        img2 = np.ascontiguousarray(img2)
-        flow = np.ascontiguousarray(flow)
-
-        return img1, img2, flow
-
-class SparseFlowAugmentor:
-    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
-        # spatial augmentation params
-        self.crop_size = crop_size
-        self.min_scale = min_scale
-        self.max_scale = max_scale
-        self.spatial_aug_prob = 0.8
-        self.stretch_prob = 0.8
-        self.max_stretch = 0.2
-
-        # flip augmentation params
-        self.do_flip = do_flip
-        self.h_flip_prob = 0.5
-        self.v_flip_prob = 0.1
-
-        # photometric augmentation params
-        self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
-        self.asymmetric_color_aug_prob = 0.2
-        self.eraser_aug_prob = 0.5
-        
-    def color_transform(self, img1, img2):
-        image_stack = np.concatenate([img1, img2], axis=0)
-        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
-        img1, img2 = np.split(image_stack, 2, axis=0)
-        return img1, img2
-
-    def eraser_transform(self, img1, img2):
-        ht, wd = img1.shape[:2]
-        if np.random.rand() < self.eraser_aug_prob:
-            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
-                x0 = np.random.randint(0, wd)
-                y0 = np.random.randint(0, ht)
-                dx = np.random.randint(50, 100)
-                dy = np.random.randint(50, 100)
-                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
-
-        return img1, img2
-
-    def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
-        ht, wd = flow.shape[:2]
-        coords = np.meshgrid(np.arange(wd), np.arange(ht))
-        coords = np.stack(coords, axis=-1)
-
-        coords = coords.reshape(-1, 2).astype(np.float32)
-        flow = flow.reshape(-1, 2).astype(np.float32)
-        valid = valid.reshape(-1).astype(np.float32)
-
-        coords0 = coords[valid>=1]
-        flow0 = flow[valid>=1]
-
-        ht1 = int(round(ht * fy))
-        wd1 = int(round(wd * fx))
-
-        coords1 = coords0 * [fx, fy]
-        flow1 = flow0 * [fx, fy]
-
-        xx = np.round(coords1[:,0]).astype(np.int32)
-        yy = np.round(coords1[:,1]).astype(np.int32)
-
-        v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
-        xx = xx[v]
-        yy = yy[v]
-        flow1 = flow1[v]
-
-        flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
-        valid_img = np.zeros([ht1, wd1], dtype=np.int32)
-
-        flow_img[yy, xx] = flow1
-        valid_img[yy, xx] = 1
-
-        return flow_img, valid_img
-
-    def spatial_transform(self, img1, img2, flow, valid):
-        # randomly sample scale
-
-        ht, wd = img1.shape[:2]
-        min_scale = np.maximum(
-            (self.crop_size[0] + 1) / float(ht), 
-            (self.crop_size[1] + 1) / float(wd))
-
-        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
-        scale_x = np.clip(scale, min_scale, None)
-        scale_y = np.clip(scale, min_scale, None)
-
-        if np.random.rand() < self.spatial_aug_prob:
-            # rescale the images
-            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
-
-        if self.do_flip:
-            if np.random.rand() < 0.5: # h-flip
-                img1 = img1[:, ::-1]
-                img2 = img2[:, ::-1]
-                flow = flow[:, ::-1] * [-1.0, 1.0]
-                valid = valid[:, ::-1]
-
-        margin_y = 20
-        margin_x = 50
-
-        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
-        x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
-
-        y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
-        x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
-
-        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        return img1, img2, flow, valid
-
-
-    def __call__(self, img1, img2, flow, valid):
-        img1, img2 = self.color_transform(img1, img2)
-        img1, img2 = self.eraser_transform(img1, img2)
-        img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
-
-        img1 = np.ascontiguousarray(img1)
-        img2 = np.ascontiguousarray(img2)
-        flow = np.ascontiguousarray(flow)
-        valid = np.ascontiguousarray(valid)
-
-        return img1, img2, flow, valid
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/configuration_encoder_decoder.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/configuration_encoder_decoder.py
deleted file mode 100644
index 15fed4dbd1bb53c8dedc74afaf40edadc926fe27..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/configuration_encoder_decoder.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  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 ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class EncoderDecoderConfig(PretrainedConfig):
-    r"""
-    [`EncoderDecoderConfig`] is the configuration class to store the configuration of a [`EncoderDecoderModel`]. It is
-    used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder
-    configs.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        kwargs (*optional*):
-            Dictionary of keyword arguments. Notably:
-
-                - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
-                  the encoder config.
-                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
-                  the decoder config.
-
-    Examples:
-
-    ```python
-    >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
-
-    >>> # Initializing a BERT bert-base-uncased style configuration
-    >>> config_encoder = BertConfig()
-    >>> config_decoder = BertConfig()
-
-    >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
-
-    >>> # Initializing a Bert2Bert model (with random weights) from the bert-base-uncased style configurations
-    >>> model = EncoderDecoderModel(config=config)
-
-    >>> # Accessing the model configuration
-    >>> config_encoder = model.config.encoder
-    >>> config_decoder = model.config.decoder
-    >>> # set decoder config to causal lm
-    >>> config_decoder.is_decoder = True
-    >>> config_decoder.add_cross_attention = True
-
-    >>> # Saving the model, including its configuration
-    >>> model.save_pretrained("my-model")
-
-    >>> # loading model and config from pretrained folder
-    >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained("my-model")
-    >>> model = EncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
-    ```"""
-    model_type = "encoder-decoder"
-    is_composition = True
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        assert (
-            "encoder" in kwargs and "decoder" in kwargs
-        ), "Config has to be initialized with encoder and decoder config"
-        encoder_config = kwargs.pop("encoder")
-        encoder_model_type = encoder_config.pop("model_type")
-        decoder_config = kwargs.pop("decoder")
-        decoder_model_type = decoder_config.pop("model_type")
-
-        from ..auto.configuration_auto import AutoConfig
-
-        self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
-        self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
-        self.is_encoder_decoder = True
-
-    @classmethod
-    def from_encoder_decoder_configs(
-        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
-    ) -> PretrainedConfig:
-        r"""
-        Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and
-        decoder model configuration.
-
-        Returns:
-            [`EncoderDecoderConfig`]: An instance of a configuration object
-        """
-        logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
-        decoder_config.is_decoder = True
-        decoder_config.add_cross_attention = True
-
-        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/hubert/configuration_hubert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/hubert/configuration_hubert.py
deleted file mode 100644
index 8c9f3d6929e29634ce365b5c8aa18598c766d1ab..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/hubert/configuration_hubert.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Hubert model configuration"""
-
-import functools
-import operator
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
-    # See all Hubert models at https://huggingface.co/models?filter=hubert
-}
-
-
-class HubertConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
-    Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Hubert
-    [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32):
-            Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
-            tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout(`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        activation_dropout (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for activations inside the fully connected layer.
-        attention_dropout(`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        final_dropout (`float`, *optional*, defaults to 0.1):
-            The dropout probabilitiy for the final projection layer of [`Wav2Vec2ForCTC`].
-        layerdrop (`float`, *optional*, defaults to 0.1):
-            The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
-            details.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        feat_extract_norm (`str`, *optional*, defaults to `"group"`):
-            The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
-            normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
-            convolutional layers.
-        feat_proj_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for output of the feature encoder.
-        feat_proj_layer_norm (`bool`, *optional*, defaults to `True`):
-            Whether to apply LayerNorm to the output of the feature encoder.
-        feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the 1D convolutional layers of the feature
-            extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        conv_dim (`Tuple[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
-            A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
-            feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
-        conv_stride (`Tuple[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
-            A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
-            of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
-        conv_kernel (`Tuple[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
-            A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
-            length of *conv_kernel* defines the number of convolutional layers and has to match the length of
-            *conv_dim*.
-        conv_bias (`bool`, *optional*, defaults to `False`):
-            Whether the 1D convolutional layers have a bias.
-        num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
-            Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
-            embeddings layer.
-        num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
-            Number of groups of 1D convolutional positional embeddings layer.
-        do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
-            Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
-            True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
-            False` corresponds to applying layer norm after the attention layer.
-        apply_spec_augment (`bool`, *optional*, defaults to `True`):
-            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
-            [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
-            Recognition](https://arxiv.org/abs/1904.08779).
-        mask_time_prob (`float`, *optional*, defaults to 0.05):
-            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
-            procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
-            reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
-            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
-            actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
-        mask_time_length (`int`, *optional*, defaults to 10):
-            Length of vector span along the time axis.
-        mask_time_min_masks (`int`, *optional*, defaults to 2),:
-            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
-            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
-            mask_time_min_masks''
-        mask_feature_prob (`float`, *optional*, defaults to 0.0):
-            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
-            masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
-            the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
-            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
-            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
-            True`.
-        mask_feature_length (`int`, *optional*, defaults to 10):
-            Length of vector span along the feature axis.
-        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
-            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
-            step, irrespectively of `mask_feature_prob`. Only relevant if
-            ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
-        ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
-            Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
-            instance of [`HubertForCTC`].
-        ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
-            Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
-            occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
-            of [`HubertForCTC`].
-        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
-            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
-            instance of [`HubertForSequenceClassification`].
-        classifier_proj_size (`int`, *optional*, defaults to 256):
-            Dimensionality of the projection before token mean-pooling for classification.
-
-    Example:
-
-    ```python
-    >>> from transformers import HubertModel, HubertConfig
-
-    >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
-    >>> configuration = HubertConfig()
-
-    >>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
-    >>> model = HubertModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "hubert"
-
-    def __init__(
-        self,
-        vocab_size=32,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout=0.1,
-        activation_dropout=0.1,
-        attention_dropout=0.1,
-        feat_proj_layer_norm=True,
-        feat_proj_dropout=0.0,
-        final_dropout=0.1,
-        layerdrop=0.1,
-        initializer_range=0.02,
-        layer_norm_eps=1e-5,
-        feat_extract_norm="group",
-        feat_extract_activation="gelu",
-        conv_dim=(512, 512, 512, 512, 512, 512, 512),
-        conv_stride=(5, 2, 2, 2, 2, 2, 2),
-        conv_kernel=(10, 3, 3, 3, 3, 2, 2),
-        conv_bias=False,
-        num_conv_pos_embeddings=128,
-        num_conv_pos_embedding_groups=16,
-        do_stable_layer_norm=False,
-        apply_spec_augment=True,
-        mask_time_prob=0.05,
-        mask_time_length=10,
-        mask_time_min_masks=2,
-        mask_feature_prob=0.0,
-        mask_feature_length=10,
-        mask_feature_min_masks=0,
-        ctc_loss_reduction="sum",
-        ctc_zero_infinity=False,
-        use_weighted_layer_sum=False,
-        classifier_proj_size=256,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        **kwargs,
-    ):
-        super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
-        self.hidden_size = hidden_size
-        self.feat_extract_norm = feat_extract_norm
-        self.feat_extract_activation = feat_extract_activation
-        self.conv_dim = list(conv_dim)
-        self.conv_stride = list(conv_stride)
-        self.conv_kernel = list(conv_kernel)
-        self.conv_bias = conv_bias
-        self.num_conv_pos_embeddings = num_conv_pos_embeddings
-        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
-        self.num_feat_extract_layers = len(self.conv_dim)
-        self.num_hidden_layers = num_hidden_layers
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
-        self.num_attention_heads = num_attention_heads
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.feat_proj_layer_norm = feat_proj_layer_norm
-        self.feat_proj_dropout = feat_proj_dropout
-        self.final_dropout = final_dropout
-        self.layerdrop = layerdrop
-        self.layer_norm_eps = layer_norm_eps
-        self.initializer_range = initializer_range
-        self.vocab_size = vocab_size
-        self.do_stable_layer_norm = do_stable_layer_norm
-        self.use_weighted_layer_sum = use_weighted_layer_sum
-        self.classifier_proj_size = classifier_proj_size
-
-        if (
-            (len(self.conv_stride) != self.num_feat_extract_layers)
-            or (len(self.conv_kernel) != self.num_feat_extract_layers)
-            or (len(self.conv_dim) != self.num_feat_extract_layers)
-        ):
-            raise ValueError(
-                "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
-                " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
-                f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
-                f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
-            )
-
-        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
-        self.apply_spec_augment = apply_spec_augment
-        self.mask_time_prob = mask_time_prob
-        self.mask_time_length = mask_time_length
-        self.mask_time_min_masks = mask_time_min_masks
-        self.mask_feature_prob = mask_feature_prob
-        self.mask_feature_length = mask_feature_length
-        self.mask_feature_min_masks = mask_feature_min_masks
-
-        # ctc loss
-        self.ctc_loss_reduction = ctc_loss_reduction
-        self.ctc_zero_infinity = ctc_zero_infinity
-
-    @property
-    def inputs_to_logits_ratio(self):
-        return functools.reduce(operator.mul, self.conv_stride, 1)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/levit/image_processing_levit.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/levit/image_processing_levit.py
deleted file mode 100644
index 77de1ec33366dc91ca2fd29eeac9093876e351a0..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/levit/image_processing_levit.py
+++ /dev/null
@@ -1,307 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Image processor class for LeViT."""
-
-from typing import Dict, Iterable, Optional, Union
-
-import numpy as np
-
-from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
-from ...image_transforms import (
-    get_resize_output_image_size,
-    resize,
-    to_channel_dimension_format,
-)
-from ...image_utils import (
-    IMAGENET_DEFAULT_MEAN,
-    IMAGENET_DEFAULT_STD,
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    infer_channel_dimension_format,
-    is_scaled_image,
-    make_list_of_images,
-    to_numpy_array,
-    valid_images,
-)
-from ...utils import TensorType, logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class LevitImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a LeViT image processor.
-
-    Args:
-        do_resize (`bool`, *optional*, defaults to `True`):
-            Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the
-            `do_resize` parameter in the `preprocess` method.
-        size (`Dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`):
-            Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will
-            be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest
-            edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this
-            value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width,
-            size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
-            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
-            `preprocess` method.
-        do_center_crop (`bool`, *optional*, defaults to `True`):
-            Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden
-            by the `do_center_crop` parameter in the `preprocess` method.
-        crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
-            Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess`
-            method.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
-            `do_rescale` parameter in the `preprocess` method.
-        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
-            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
-            `preprocess` method.
-        do_normalize (`bool`, *optional*, defaults to `True`):
-            Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
-            `preprocess` method.
-        image_mean (`List[int]`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`List[int]`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        do_resize: bool = True,
-        size: Dict[str, int] = None,
-        resample: PILImageResampling = PILImageResampling.BICUBIC,
-        do_center_crop: bool = True,
-        crop_size: Dict[str, int] = None,
-        do_rescale: bool = True,
-        rescale_factor: Union[int, float] = 1 / 255,
-        do_normalize: bool = True,
-        image_mean: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN,
-        image_std: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-        size = size if size is not None else {"shortest_edge": 224}
-        size = get_size_dict(size, default_to_square=False)
-        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
-        crop_size = get_size_dict(crop_size, param_name="crop_size")
-
-        self.do_resize = do_resize
-        self.size = size
-        self.resample = resample
-        self.do_center_crop = do_center_crop
-        self.crop_size = crop_size
-        self.do_rescale = do_rescale
-        self.rescale_factor = rescale_factor
-        self.do_normalize = do_normalize
-        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
-        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
-
-    def resize(
-        self,
-        image: np.ndarray,
-        size: Dict[str, int],
-        resample: PILImageResampling = PILImageResampling.BICUBIC,
-        data_format: Optional[Union[str, ChannelDimension]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Resize an image.
-
-        If size is a dict with keys "width" and "height", the image will be resized to `(size["height"],
-        size["width"])`.
-
-        If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`.
-        The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled
-        to `(size["shortest_egde"] * height / width, size["shortest_egde"])`.
-
-        Args:
-            image (`np.ndarray`):
-                Image to resize.
-            size (`Dict[str, int]`):
-                Size of the output image after resizing. If size is a dict with keys "width" and "height", the image
-                will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
-                `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
-                i.e, if height > width, then image will be rescaled to (size * height / width, size).
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
-                Resampling filter to use when resiizing the image.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        size_dict = get_size_dict(size, default_to_square=False)
-        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
-        if "shortest_edge" in size:
-            shortest_edge = int((256 / 224) * size["shortest_edge"])
-            output_size = get_resize_output_image_size(
-                image, size=shortest_edge, default_to_square=False, input_data_format=input_data_format
-            )
-            size_dict = {"height": output_size[0], "width": output_size[1]}
-        if "height" not in size_dict or "width" not in size_dict:
-            raise ValueError(
-                f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"
-            )
-        return resize(
-            image,
-            size=(size_dict["height"], size_dict["width"]),
-            resample=resample,
-            data_format=data_format,
-            input_data_format=input_data_format,
-            **kwargs,
-        )
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        do_resize: Optional[bool] = None,
-        size: Optional[Dict[str, int]] = None,
-        resample: PILImageResampling = None,
-        do_center_crop: Optional[bool] = None,
-        crop_size: Optional[Dict[str, int]] = None,
-        do_rescale: Optional[bool] = None,
-        rescale_factor: Optional[float] = None,
-        do_normalize: Optional[bool] = None,
-        image_mean: Optional[Union[float, Iterable[float]]] = None,
-        image_std: Optional[Union[float, Iterable[float]]] = None,
-        return_tensors: Optional[TensorType] = None,
-        data_format: ChannelDimension = ChannelDimension.FIRST,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> BatchFeature:
-        """
-        Preprocess an image or batch of images to be used as input to a LeViT model.
-
-        Args:
-            images (`ImageInput`):
-                Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
-                from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
-                Whether to resize the image.
-            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
-                Size of the output image after resizing. If size is a dict with keys "width" and "height", the image
-                will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
-                `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
-                i.e, if height > width, then image will be rescaled to (size * height / width, size).
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
-                Resampling filter to use when resiizing the image.
-            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
-                Whether to center crop the image.
-            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
-                Size of the output image after center cropping. Crops images to (crop_size["height"],
-                crop_size["width"]).
-            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
-                Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1.
-            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
-                Factor to rescale the image pixel values by.
-            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
-                Whether to normalize the image pixel values by `image_mean` and `image_std`.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
-                Mean to normalize the image pixel values by.
-            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
-                Standard deviation to normalize the image pixel values by.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-        """
-        do_resize = do_resize if do_resize is not None else self.do_resize
-        resample = resample if resample is not None else self.resample
-        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
-        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
-        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
-        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-
-        size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=False)
-        crop_size = crop_size if crop_size is not None else self.crop_size
-        crop_size = get_size_dict(crop_size, param_name="crop_size")
-
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        if do_resize and size is None:
-            raise ValueError("Size must be specified if do_resize is True.")
-
-        if do_center_crop and crop_size is None:
-            raise ValueError("Crop size must be specified if do_center_crop is True.")
-
-        if do_rescale and rescale_factor is None:
-            raise ValueError("Rescale factor must be specified if do_rescale is True.")
-
-        if do_normalize and (image_mean is None or image_std is None):
-            raise ValueError("Image mean and std must be specified if do_normalize is True.")
-
-        # All transformations expect numpy arrays.
-        images = [to_numpy_array(image) for image in images]
-
-        if is_scaled_image(images[0]) and do_rescale:
-            logger.warning_once(
-                "It looks like you are trying to rescale already rescaled images. If the input"
-                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
-            )
-
-        if input_data_format is None:
-            # We assume that all images have the same channel dimension format.
-            input_data_format = infer_channel_dimension_format(images[0])
-
-        if do_resize:
-            images = [self.resize(image, size, resample, input_data_format=input_data_format) for image in images]
-
-        if do_center_crop:
-            images = [self.center_crop(image, crop_size, input_data_format=input_data_format) for image in images]
-
-        if do_rescale:
-            images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
-
-        if do_normalize:
-            images = [
-                self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
-            ]
-
-        images = [
-            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
-        ]
-
-        data = {"pixel_values": images}
-        return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/luke/modeling_luke.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/luke/modeling_luke.py
deleted file mode 100644
index 6913ede09d1c7b5850a563035ad015ee60d4f09b..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/luke/modeling_luke.py
+++ /dev/null
@@ -1,2244 +0,0 @@
-# coding=utf-8
-# Copyright Studio Ousia and 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.
-"""PyTorch LUKE model."""
-
-import math
-from dataclasses import dataclass
-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, gelu
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward
-from ...utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_luke import LukeConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LukeConfig"
-_CHECKPOINT_FOR_DOC = "studio-ousia/luke-base"
-
-LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "studio-ousia/luke-base",
-    "studio-ousia/luke-large",
-    # See all LUKE models at https://huggingface.co/models?filter=luke
-]
-
-
-@dataclass
-class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
-    """
-    Base class for outputs of the LUKE model.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
-            Sequence of entity hidden-states at the output of the last layer of the model.
-        pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
-            Last layer hidden-state of the first token of the sequence (classification token) further processed by a
-            Linear layer and a Tanh activation function.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length +
-            entity_length, sequence_length + entity_length)`. Attentions weights after the attention softmax, used to
-            compute the weighted average in the self-attention heads.
-    """
-
-    entity_last_hidden_state: torch.FloatTensor = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class BaseLukeModelOutput(BaseModelOutput):
-    """
-    Base class for model's outputs, with potential hidden states and attentions.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
-            Sequence of entity hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    entity_last_hidden_state: torch.FloatTensor = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class LukeMaskedLMOutput(ModelOutput):
-    """
-    Base class for model's outputs, with potential hidden states and attentions.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            The sum of masked language modeling (MLM) loss and entity prediction loss.
-        mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Masked language modeling (MLM) loss.
-        mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Masked entity prediction (MEP) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    mlm_loss: Optional[torch.FloatTensor] = None
-    mep_loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    entity_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class EntityClassificationOutput(ModelOutput):
-    """
-    Outputs of entity classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class EntityPairClassificationOutput(ModelOutput):
-    """
-    Outputs of entity pair classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class EntitySpanClassificationOutput(ModelOutput):
-    """
-    Outputs of entity span classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`):
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class LukeSequenceClassifierOutput(ModelOutput):
-    """
-    Outputs of sentence classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class LukeTokenClassifierOutput(ModelOutput):
-    """
-    Base class for outputs of token classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class LukeQuestionAnsweringModelOutput(ModelOutput):
-    """
-    Outputs of question answering models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
-        start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-            Span-start scores (before SoftMax).
-        end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-            Span-end scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    start_logits: torch.FloatTensor = None
-    end_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class LukeMultipleChoiceModelOutput(ModelOutput):
-    """
-    Outputs of multiple choice models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
-            *num_choices* is the second dimension of the input tensors. (see *input_ids* above).
-
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
-            layer plus the initial entity embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    entity_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-class LukeEmbeddings(nn.Module):
-    """
-    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # End copy
-        self.padding_idx = config.pad_token_id
-        self.position_embeddings = nn.Embedding(
-            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
-        )
-
-    def forward(
-        self,
-        input_ids=None,
-        token_type_ids=None,
-        position_ids=None,
-        inputs_embeds=None,
-    ):
-        if position_ids is None:
-            if input_ids is not None:
-                # Create the position ids from the input token ids. Any padded tokens remain padded.
-                position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
-            else:
-                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
-
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        position_embeddings = self.position_embeddings(position_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
-        """
-        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
-
-        Args:
-            inputs_embeds: torch.Tensor
-
-        Returns: torch.Tensor
-        """
-        input_shape = inputs_embeds.size()[:-1]
-        sequence_length = input_shape[1]
-
-        position_ids = torch.arange(
-            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
-        )
-        return position_ids.unsqueeze(0).expand(input_shape)
-
-
-class LukeEntityEmbeddings(nn.Module):
-    def __init__(self, config: LukeConfig):
-        super().__init__()
-        self.config = config
-
-        self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
-        if config.entity_emb_size != config.hidden_size:
-            self.entity_embedding_dense = nn.Linear(config.entity_emb_size, config.hidden_size, bias=False)
-
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(
-        self, entity_ids: torch.LongTensor, position_ids: torch.LongTensor, token_type_ids: torch.LongTensor = None
-    ):
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(entity_ids)
-
-        entity_embeddings = self.entity_embeddings(entity_ids)
-        if self.config.entity_emb_size != self.config.hidden_size:
-            entity_embeddings = self.entity_embedding_dense(entity_embeddings)
-
-        position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
-        position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
-        position_embeddings = position_embeddings * position_embedding_mask
-        position_embeddings = torch.sum(position_embeddings, dim=-2)
-        position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(min=1e-7)
-
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = entity_embeddings + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-
-        return embeddings
-
-
-class LukeSelfAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
-                f"heads {config.num_attention_heads}."
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-        self.use_entity_aware_attention = config.use_entity_aware_attention
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        if self.use_entity_aware_attention:
-            self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
-            self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
-            self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        word_hidden_states,
-        entity_hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=False,
-    ):
-        word_size = word_hidden_states.size(1)
-
-        if entity_hidden_states is None:
-            concat_hidden_states = word_hidden_states
-        else:
-            concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
-
-        key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
-        value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
-
-        if self.use_entity_aware_attention and entity_hidden_states is not None:
-            # compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e)
-            # query layers
-            w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states))
-            w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states))
-            e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
-            e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))
-
-            # compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
-            w2w_key_layer = key_layer[:, :, :word_size, :]
-            e2w_key_layer = key_layer[:, :, :word_size, :]
-            w2e_key_layer = key_layer[:, :, word_size:, :]
-            e2e_key_layer = key_layer[:, :, word_size:, :]
-
-            # compute attention scores based on the dot product between the query and key vectors
-            w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
-            w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
-            e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
-            e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))
-
-            # combine attention scores to create the final attention score matrix
-            word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
-            entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
-            attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)
-
-        else:
-            query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
-            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in LukeModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        output_word_hidden_states = context_layer[:, :word_size, :]
-        if entity_hidden_states is None:
-            output_entity_hidden_states = None
-        else:
-            output_entity_hidden_states = context_layer[:, word_size:, :]
-
-        if output_attentions:
-            outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
-        else:
-            outputs = (output_word_hidden_states, output_entity_hidden_states)
-
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
-class LukeSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class LukeAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = LukeSelfAttention(config)
-        self.output = LukeSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        raise NotImplementedError("LUKE does not support the pruning of attention heads")
-
-    def forward(
-        self,
-        word_hidden_states,
-        entity_hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=False,
-    ):
-        word_size = word_hidden_states.size(1)
-        self_outputs = self.self(
-            word_hidden_states,
-            entity_hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions,
-        )
-        if entity_hidden_states is None:
-            concat_self_outputs = self_outputs[0]
-            concat_hidden_states = word_hidden_states
-        else:
-            concat_self_outputs = torch.cat(self_outputs[:2], dim=1)
-            concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
-
-        attention_output = self.output(concat_self_outputs, concat_hidden_states)
-
-        word_attention_output = attention_output[:, :word_size, :]
-        if entity_hidden_states is None:
-            entity_attention_output = None
-        else:
-            entity_attention_output = attention_output[:, word_size:, :]
-
-        # add attentions if we output them
-        outputs = (word_attention_output, entity_attention_output) + self_outputs[2:]
-
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate
-class LukeIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOutput
-class LukeOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class LukeLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = LukeAttention(config)
-        self.intermediate = LukeIntermediate(config)
-        self.output = LukeOutput(config)
-
-    def forward(
-        self,
-        word_hidden_states,
-        entity_hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=False,
-    ):
-        word_size = word_hidden_states.size(1)
-
-        self_attention_outputs = self.attention(
-            word_hidden_states,
-            entity_hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-        )
-        if entity_hidden_states is None:
-            concat_attention_output = self_attention_outputs[0]
-        else:
-            concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)
-
-        outputs = self_attention_outputs[2:]  # add self attentions if we output attention weights
-
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output
-        )
-        word_layer_output = layer_output[:, :word_size, :]
-        if entity_hidden_states is None:
-            entity_layer_output = None
-        else:
-            entity_layer_output = layer_output[:, word_size:, :]
-
-        outputs = (word_layer_output, entity_layer_output) + outputs
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class LukeEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        word_hidden_states,
-        entity_hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-    ):
-        all_word_hidden_states = () if output_hidden_states else None
-        all_entity_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
-                all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            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(layer_module),
-                    word_hidden_states,
-                    entity_hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    word_hidden_states,
-                    entity_hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    output_attentions,
-                )
-
-            word_hidden_states = layer_outputs[0]
-
-            if entity_hidden_states is not None:
-                entity_hidden_states = layer_outputs[1]
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
-            all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    word_hidden_states,
-                    all_word_hidden_states,
-                    all_self_attentions,
-                    entity_hidden_states,
-                    all_entity_hidden_states,
-                ]
-                if v is not None
-            )
-        return BaseLukeModelOutput(
-            last_hidden_state=word_hidden_states,
-            hidden_states=all_word_hidden_states,
-            attentions=all_self_attentions,
-            entity_last_hidden_state=entity_hidden_states,
-            entity_hidden_states=all_entity_hidden_states,
-        )
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPooler
-class LukePooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class EntityPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.entity_emb_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.entity_emb_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class EntityPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.transform = EntityPredictionHeadTransform(config)
-        self.decoder = nn.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
-        self.bias = nn.Parameter(torch.zeros(config.entity_vocab_size))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-
-        return hidden_states
-
-
-class LukePreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = LukeConfig
-    base_model_prefix = "luke"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LukeAttention", "LukeEntityEmbeddings"]
-
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            if module.embedding_dim == 1:  # embedding for bias parameters
-                module.weight.data.zero_()
-            else:
-                module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].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, LukeEncoder):
-            module.gradient_checkpointing = value
-
-
-LUKE_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 ([`LukeConfig`]): 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.
-"""
-
-LUKE_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-
-        entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
-            Indices of entity tokens in the entity vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-        entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
-            Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for entity tokens that are **not masked**,
-            - 0 for entity tokens that are **masked**.
-
-        entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
-            Segment token indices to indicate first and second portions of the entity token inputs. Indices are
-            selected in `[0, 1]`:
-
-            - 0 corresponds to a *portion A* entity token,
-            - 1 corresponds to a *portion B* entity token.
-
-        entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
-            Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, 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.
-
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **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.
-"""
-
-
-@add_start_docstrings(
-    "The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any"
-    " specific head on top.",
-    LUKE_START_DOCSTRING,
-)
-class LukeModel(LukePreTrainedModel):
-    def __init__(self, config: LukeConfig, add_pooling_layer: bool = True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = LukeEmbeddings(config)
-        self.entity_embeddings = LukeEntityEmbeddings(config)
-        self.encoder = LukeEncoder(config)
-
-        self.pooler = LukePooler(config) if add_pooling_layer else None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def get_entity_embeddings(self):
-        return self.entity_embeddings.entity_embeddings
-
-    def set_entity_embeddings(self, value):
-        self.entity_embeddings.entity_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        raise NotImplementedError("LUKE does not support the pruning of attention heads")
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=BaseLukeModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = 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, BaseLukeModelOutputWithPooling]:
-        r"""
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LukeModel
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
-        >>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
-        # Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
-
-        >>> text = "Beyoncé lives in Los Angeles."
-        >>> entity_spans = [(0, 7)]  # character-based entity span corresponding to "Beyoncé"
-
-        >>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
-        >>> outputs = model(**encoding)
-        >>> word_last_hidden_state = outputs.last_hidden_state
-        >>> entity_last_hidden_state = outputs.entity_last_hidden_state
-        # Input Wikipedia entities to obtain enriched contextualized representations of word tokens
-
-        >>> text = "Beyoncé lives in Los Angeles."
-        >>> entities = [
-        ...     "Beyoncé",
-        ...     "Los Angeles",
-        ... ]  # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
-        >>> entity_spans = [
-        ...     (0, 7),
-        ...     (17, 28),
-        ... ]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
-
-        >>> encoding = tokenizer(
-        ...     text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
-        ... )
-        >>> outputs = model(**encoding)
-        >>> word_last_hidden_state = outputs.last_hidden_state
-        >>> entity_last_hidden_state = outputs.entity_last_hidden_state
-        ```"""
-        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
-
-        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:
-            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
-            input_shape = input_ids.size()
-        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")
-
-        batch_size, seq_length = input_shape
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length), device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-        if entity_ids is not None:
-            entity_seq_length = entity_ids.size(1)
-            if entity_attention_mask is None:
-                entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
-            if entity_token_type_ids is None:
-                entity_token_type_ids = torch.zeros((batch_size, entity_seq_length), dtype=torch.long, device=device)
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        # First, compute word embeddings
-        word_embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-        )
-
-        # Second, compute extended attention mask
-        extended_attention_mask = self.get_extended_attention_mask(attention_mask, entity_attention_mask)
-
-        # Third, compute entity embeddings and concatenate with word embeddings
-        if entity_ids is None:
-            entity_embedding_output = None
-        else:
-            entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)
-
-        # Fourth, send embeddings through the model
-        encoder_outputs = self.encoder(
-            word_embedding_output,
-            entity_embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        # Fifth, get the output. LukeModel outputs the same as BertModel, namely sequence_output of shape (batch_size, seq_len, hidden_size)
-        sequence_output = encoder_outputs[0]
-
-        # Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseLukeModelOutputWithPooling(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            entity_last_hidden_state=encoder_outputs.entity_last_hidden_state,
-            entity_hidden_states=encoder_outputs.entity_hidden_states,
-        )
-
-    def get_extended_attention_mask(
-        self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]
-    ):
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            word_attention_mask (`torch.LongTensor`):
-                Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
-            entity_attention_mask (`torch.LongTensor`, *optional*):
-                Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
-
-        Returns:
-            `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
-        """
-        attention_mask = word_attention_mask
-        if entity_attention_mask is not None:
-            attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)
-
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape})")
-
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
-        return extended_attention_mask
-
-
-def create_position_ids_from_input_ids(input_ids, padding_idx):
-    """
-    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
-    are ignored. This is modified from fairseq's `utils.make_positions`.
-
-    Args:
-        x: torch.Tensor x:
-
-    Returns: torch.Tensor
-    """
-    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
-    mask = input_ids.ne(padding_idx).int()
-    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
-    return incremental_indices.long() + padding_idx
-
-
-# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead
-class LukeLMHead(nn.Module):
-    """Roberta Head for masked language modeling."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-        self.decoder.bias = self.bias
-
-    def forward(self, features, **kwargs):
-        x = self.dense(features)
-        x = gelu(x)
-        x = self.layer_norm(x)
-
-        # project back to size of vocabulary with bias
-        x = self.decoder(x)
-
-        return x
-
-    def _tie_weights(self):
-        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
-        # For accelerate compatibility and to not break backward compatibility
-        if self.decoder.bias.device.type == "meta":
-            self.decoder.bias = self.bias
-        else:
-            self.bias = self.decoder.bias
-
-
-@add_start_docstrings(
-    """
-    The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
-    masked entity prediction.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForMaskedLM(LukePreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.luke = LukeModel(config)
-
-        self.lm_head = LukeLMHead(config)
-        self.entity_predictions = EntityPredictionHead(config)
-
-        self.loss_fn = nn.CrossEntropyLoss()
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def tie_weights(self):
-        super().tie_weights()
-        self._tie_or_clone_weights(self.entity_predictions.decoder, self.luke.entity_embeddings.entity_embeddings)
-
-    def get_output_embeddings(self):
-        return self.lm_head.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=LukeMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.LongTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        entity_labels: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = 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, LukeMaskedLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (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]`
-        entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (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
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        loss = None
-
-        mlm_loss = None
-        logits = self.lm_head(outputs.last_hidden_state)
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
-            if loss is None:
-                loss = mlm_loss
-
-        mep_loss = None
-        entity_logits = None
-        if outputs.entity_last_hidden_state is not None:
-            entity_logits = self.entity_predictions(outputs.entity_last_hidden_state)
-            if entity_labels is not None:
-                mep_loss = self.loss_fn(entity_logits.view(-1, self.config.entity_vocab_size), entity_labels.view(-1))
-                if loss is None:
-                    loss = mep_loss
-                else:
-                    loss = loss + mep_loss
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    loss,
-                    mlm_loss,
-                    mep_loss,
-                    logits,
-                    entity_logits,
-                    outputs.hidden_states,
-                    outputs.entity_hidden_states,
-                    outputs.attentions,
-                ]
-                if v is not None
-            )
-
-        return LukeMaskedLMOutput(
-            loss=loss,
-            mlm_loss=mlm_loss,
-            mep_loss=mep_loss,
-            logits=logits,
-            entity_logits=entity_logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
-    token) for entity classification tasks, such as Open Entity.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForEntityClassification(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.luke = LukeModel(config)
-
-        self.num_labels = config.num_labels
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=EntityClassificationOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, EntityClassificationOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
-            Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
-            used for the single-label classification. In this case, labels should contain the indices that should be in
-            `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
-            loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
-            and 1 indicate false and true, respectively.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LukeForEntityClassification
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
-        >>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
-
-        >>> text = "Beyoncé lives in Los Angeles."
-        >>> entity_spans = [(0, 7)]  # character-based entity span corresponding to "Beyoncé"
-        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
-        >>> outputs = model(**inputs)
-        >>> logits = outputs.logits
-        >>> predicted_class_idx = logits.argmax(-1).item()
-        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
-        Predicted class: person
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        feature_vector = outputs.entity_last_hidden_state[:, 0, :]
-        feature_vector = self.dropout(feature_vector)
-        logits = self.classifier(feature_vector)
-
-        loss = None
-        if labels is not None:
-            # When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
-            # cross entropy is used otherwise.
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            if labels.ndim == 1:
-                loss = nn.functional.cross_entropy(logits, labels)
-            else:
-                loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
-                if v is not None
-            )
-
-        return EntityClassificationOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
-    tokens) for entity pair classification tasks, such as TACRED.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForEntityPairClassification(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.luke = LukeModel(config)
-
-        self.num_labels = config.num_labels
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size * 2, config.num_labels, False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=EntityPairClassificationOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, EntityPairClassificationOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
-            Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
-            used for the single-label classification. In this case, labels should contain the indices that should be in
-            `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
-            loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
-            and 1 indicate false and true, respectively.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LukeForEntityPairClassification
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
-        >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
-
-        >>> text = "Beyoncé lives in Los Angeles."
-        >>> entity_spans = [
-        ...     (0, 7),
-        ...     (17, 28),
-        ... ]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
-        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
-        >>> outputs = model(**inputs)
-        >>> logits = outputs.logits
-        >>> predicted_class_idx = logits.argmax(-1).item()
-        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
-        Predicted class: per:cities_of_residence
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        feature_vector = torch.cat(
-            [outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1
-        )
-        feature_vector = self.dropout(feature_vector)
-        logits = self.classifier(feature_vector)
-
-        loss = None
-        if labels is not None:
-            # When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
-            # cross entropy is used otherwise.
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            if labels.ndim == 1:
-                loss = nn.functional.cross_entropy(logits, labels)
-            else:
-                loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
-                if v is not None
-            )
-
-        return EntityPairClassificationOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks
-    such as named entity recognition.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForEntitySpanClassification(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.luke = LukeModel(config)
-
-        self.num_labels = config.num_labels
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=EntitySpanClassificationOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.LongTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        entity_start_positions: Optional[torch.LongTensor] = None,
-        entity_end_positions: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, EntitySpanClassificationOutput]:
-        r"""
-        entity_start_positions (`torch.LongTensor`):
-            The start positions of entities in the word token sequence.
-
-        entity_end_positions (`torch.LongTensor`):
-            The end positions of entities in the word token sequence.
-
-        labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
-            Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
-            entropy loss is used for the single-label classification. In this case, labels should contain the indices
-            that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
-            num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
-            labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LukeForEntitySpanClassification
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
-        >>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
-
-        >>> text = "Beyoncé lives in Los Angeles"
-        # List all possible entity spans in the text
-
-        >>> word_start_positions = [0, 8, 14, 17, 21]  # character-based start positions of word tokens
-        >>> word_end_positions = [7, 13, 16, 20, 28]  # character-based end positions of word tokens
-        >>> entity_spans = []
-        >>> for i, start_pos in enumerate(word_start_positions):
-        ...     for end_pos in word_end_positions[i:]:
-        ...         entity_spans.append((start_pos, end_pos))
-
-        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
-        >>> outputs = model(**inputs)
-        >>> logits = outputs.logits
-        >>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
-        >>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
-        ...     if predicted_class_idx != 0:
-        ...         print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
-        Beyoncé PER
-        Los Angeles LOC
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-        hidden_size = outputs.last_hidden_state.size(-1)
-
-        entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
-        if entity_start_positions.device != outputs.last_hidden_state.device:
-            entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
-        start_states = torch.gather(outputs.last_hidden_state, -2, entity_start_positions)
-
-        entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
-        if entity_end_positions.device != outputs.last_hidden_state.device:
-            entity_end_positions = entity_end_positions.to(outputs.last_hidden_state.device)
-        end_states = torch.gather(outputs.last_hidden_state, -2, entity_end_positions)
-
-        feature_vector = torch.cat([start_states, end_states, outputs.entity_last_hidden_state], dim=2)
-
-        feature_vector = self.dropout(feature_vector)
-        logits = self.classifier(feature_vector)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            # When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
-            # cross entropy is used otherwise.
-            if labels.ndim == 2:
-                loss = nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
-            else:
-                loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
-                if v is not None
-            )
-
-        return EntitySpanClassificationOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the
-    pooled output) e.g. for GLUE tasks.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForSequenceClassification(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.luke = LukeModel(config)
-        self.dropout = nn.Dropout(
-            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
-        )
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=LukeSequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, LukeSequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        pooled_output = outputs.pooler_output
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(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:
-            return tuple(
-                v
-                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
-                if v is not None
-            )
-
-        return LukeSequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To
-    solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this
-    class.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForTokenClassification(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.luke = LukeModel(config, add_pooling_layer=False)
-        self.dropout = nn.Dropout(
-            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
-        )
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=LukeTokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, LukeTokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
-            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
-            `input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        sequence_output = outputs.last_hidden_state
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
-                if v is not None
-            )
-
-        return LukeTokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForQuestionAnswering(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-
-        self.luke = LukeModel(config, add_pooling_layer=False)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=LukeQuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.FloatTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, LukeQuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        sequence_output = outputs.last_hidden_state
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
-        end_logits = end_logits.squeeze(-1)
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions.clamp_(0, ignored_index)
-            end_positions.clamp_(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    total_loss,
-                    start_logits,
-                    end_logits,
-                    outputs.hidden_states,
-                    outputs.entity_hidden_states,
-                    outputs.attentions,
-                ]
-                if v is not None
-            )
-
-        return LukeQuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The LUKE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    LUKE_START_DOCSTRING,
-)
-class LukeForMultipleChoice(LukePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.luke = LukeModel(config)
-        self.dropout = nn.Dropout(
-            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
-        )
-        self.classifier = nn.Linear(config.hidden_size, 1)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=LukeMultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        entity_ids: Optional[torch.LongTensor] = None,
-        entity_attention_mask: Optional[torch.FloatTensor] = None,
-        entity_token_type_ids: Optional[torch.LongTensor] = None,
-        entity_position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, LukeMultipleChoiceModelOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
-            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
-            `input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
-        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
-        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
-        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
-        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        inputs_embeds = (
-            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
-            if inputs_embeds is not None
-            else None
-        )
-
-        entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
-        entity_attention_mask = (
-            entity_attention_mask.view(-1, entity_attention_mask.size(-1))
-            if entity_attention_mask is not None
-            else None
-        )
-        entity_token_type_ids = (
-            entity_token_type_ids.view(-1, entity_token_type_ids.size(-1))
-            if entity_token_type_ids is not None
-            else None
-        )
-        entity_position_ids = (
-            entity_position_ids.view(-1, entity_position_ids.size(-2), entity_position_ids.size(-1))
-            if entity_position_ids is not None
-            else None
-        )
-
-        outputs = self.luke(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            entity_ids=entity_ids,
-            entity_attention_mask=entity_attention_mask,
-            entity_token_type_ids=entity_token_type_ids,
-            entity_position_ids=entity_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-        )
-
-        pooled_output = outputs.pooler_output
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = logits.view(-1, num_choices)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(reshaped_logits.device)
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(reshaped_logits, labels)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    loss,
-                    reshaped_logits,
-                    outputs.hidden_states,
-                    outputs.entity_hidden_states,
-                    outputs.attentions,
-                ]
-                if v is not None
-            )
-
-        return LukeMultipleChoiceModelOutput(
-            loss=loss,
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            entity_hidden_states=outputs.entity_hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mluke/tokenization_mluke.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mluke/tokenization_mluke.py
deleted file mode 100644
index 028de5d4f79c8c7ae2f9329bca909d2a601719a5..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mluke/tokenization_mluke.py
+++ /dev/null
@@ -1,1631 +0,0 @@
-# coding=utf-8
-# Copyright 2021 Studio Ousia and 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
-""" Tokenization classes for mLUKE."""
-
-
-import itertools
-import json
-import os
-from collections.abc import Mapping
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import sentencepiece as spm
-
-from ...tokenization_utils import PreTrainedTokenizer
-from ...tokenization_utils_base import (
-    ENCODE_KWARGS_DOCSTRING,
-    AddedToken,
-    BatchEncoding,
-    EncodedInput,
-    PaddingStrategy,
-    TensorType,
-    TextInput,
-    TextInputPair,
-    TruncationStrategy,
-    to_py_obj,
-)
-from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging
-
-
-logger = logging.get_logger(__name__)
-
-EntitySpan = Tuple[int, int]
-EntitySpanInput = List[EntitySpan]
-Entity = str
-EntityInput = List[Entity]
-
-SPIECE_UNDERLINE = "▁"
-
-VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/vocab.json",
-    },
-    "merges_file": {
-        "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/merges.txt",
-    },
-    "entity_vocab_file": {
-        "studio-ousia/mluke-base": "https://huggingface.co/studio-ousia/mluke-base/resolve/main/entity_vocab.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "studio-ousia/mluke-base": 512,
-}
-
-ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
-            return_token_type_ids (`bool`, *optional*):
-                Whether to return token type IDs. If left to the default, will return the token type IDs according to
-                the specific tokenizer's default, defined by the `return_outputs` attribute.
-
-                [What are token type IDs?](../glossary#token-type-ids)
-            return_attention_mask (`bool`, *optional*):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the `return_outputs` attribute.
-
-                [What are attention masks?](../glossary#attention-mask)
-            return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
-                of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
-                of returning overflowing tokens.
-            return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
-                Whether or not to return special tokens mask information.
-            return_offsets_mapping (`bool`, *optional*, defaults to `False`):
-                Whether or not to return `(char_start, char_end)` for each token.
-
-                This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
-                Python's tokenizer, this method will raise `NotImplementedError`.
-            return_length  (`bool`, *optional*, defaults to `False`):
-                Whether or not to return the lengths of the encoded inputs.
-            verbose (`bool`, *optional*, defaults to `True`):
-                Whether or not to print more information and warnings.
-            **kwargs: passed to the `self.tokenize()` method
-
-        Return:
-            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model.
-
-              [What are input IDs?](../glossary#input-ids)
-
-            - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
-              if *"token_type_ids"* is in `self.model_input_names`).
-
-              [What are token type IDs?](../glossary#token-type-ids)
-
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
-
-              [What are attention masks?](../glossary#attention-mask)
-
-            - **entity_ids** -- List of entity ids to be fed to a model.
-
-              [What are input IDs?](../glossary#input-ids)
-
-            - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model.
-
-            - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when
-              `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`).
-
-              [What are token type IDs?](../glossary#token-type-ids)
-
-            - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model
-              (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`).
-
-              [What are attention masks?](../glossary#attention-mask)
-
-            - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when
-              `task="entity_span_classification"`).
-            - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when
-              `task="entity_span_classification"`).
-            - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
-              `return_overflowing_tokens=True`).
-            - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
-              `return_overflowing_tokens=True`).
-            - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
-              regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
-            - **length** -- The length of the inputs (when `return_length=True`)
-
-"""
-
-
-class MLukeTokenizer(PreTrainedTokenizer):
-    """
-    Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on
-    [SentencePiece](https://github.com/google/sentencepiece).
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        entity_vocab_file (`str`):
-            Path to the entity vocabulary file.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the beginning of
-            sequence. The token used is the `cls_token`.
-
-            </Tip>
-
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
-            The token used is the `sep_token`.
-
-            </Tip>
-
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (`str`, *optional*, defaults to `"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (`str`, *optional*, defaults to `"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        task (`str`, *optional*):
-            Task for which you want to prepare sequences. One of `"entity_classification"`,
-            `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
-            sequence is automatically created based on the given entity span(s).
-        max_entity_length (`int`, *optional*, defaults to 32):
-            The maximum length of `entity_ids`.
-        max_mention_length (`int`, *optional*, defaults to 30):
-            The maximum number of tokens inside an entity span.
-        entity_token_1 (`str`, *optional*, defaults to `<ent>`):
-            The special token used to represent an entity span in a word token sequence. This token is only used when
-            `task` is set to `"entity_classification"` or `"entity_pair_classification"`.
-        entity_token_2 (`str`, *optional*, defaults to `<ent2>`):
-            The special token used to represent an entity span in a word token sequence. This token is only used when
-            `task` is set to `"entity_pair_classification"`.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
-        sp_model_kwargs (`dict`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-    Attributes:
-        sp_model (`SentencePieceProcessor`):
-            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        entity_vocab_file,
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        task=None,
-        max_entity_length=32,
-        max_mention_length=30,
-        entity_token_1="<ent>",
-        entity_token_2="<ent2>",
-        entity_unk_token="[UNK]",
-        entity_pad_token="[PAD]",
-        entity_mask_token="[MASK]",
-        entity_mask2_token="[MASK2]",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> None:
-        # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
-
-        # we add 2 special tokens for downstream tasks
-        # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778
-        entity_token_1 = (
-            AddedToken(entity_token_1, lstrip=False, rstrip=False)
-            if isinstance(entity_token_1, str)
-            else entity_token_1
-        )
-        entity_token_2 = (
-            AddedToken(entity_token_2, lstrip=False, rstrip=False)
-            if isinstance(entity_token_2, str)
-            else entity_token_2
-        )
-        additional_special_tokens = kwargs.pop("additional_special_tokens", [])
-        additional_special_tokens += [entity_token_1, entity_token_2]
-
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(str(vocab_file))
-        self.vocab_file = vocab_file
-
-        # Original fairseq vocab and spm vocab must be "aligned":
-        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
-        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
-        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
-        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'
-
-        # Mimic fairseq token-to-id alignment for the first 4 token
-        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
-
-        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
-        self.fairseq_offset = 1
-
-        self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset
-        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
-
-        with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
-            self.entity_vocab = json.load(entity_vocab_handle)
-        for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
-            if entity_special_token not in self.entity_vocab:
-                raise ValueError(
-                    f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. "
-                    f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}."
-                )
-        self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
-        self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
-        self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
-        self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
-
-        self.task = task
-        if task is None or task == "entity_span_classification":
-            self.max_entity_length = max_entity_length
-        elif task == "entity_classification":
-            self.max_entity_length = 1
-        elif task == "entity_pair_classification":
-            self.max_entity_length = 2
-        else:
-            raise ValueError(
-                f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification',"
-                " 'entity_span_classification'] only."
-            )
-
-        self.max_mention_length = max_mention_length
-
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            sp_model_kwargs=self.sp_model_kwargs,
-            task=task,
-            max_entity_length=max_entity_length,
-            max_mention_length=max_mention_length,
-            entity_token_1=entity_token_1,
-            entity_token_2=entity_token_2,
-            entity_unk_token=entity_unk_token,
-            entity_pad_token=entity_pad_token,
-            entity_mask_token=entity_mask_token,
-            entity_mask2_token=entity_mask2_token,
-            additional_special_tokens=additional_special_tokens,
-            **kwargs,
-        )
-
-    @property
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.vocab_size
-    def vocab_size(self):
-        return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_vocab
-    def get_vocab(self):
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._tokenize
-    def _tokenize(self, text: str) -> List[str]:
-        # TODO check if the t5/llama PR also applies here
-        return self.sp_model.encode(text, out_type=str)
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._convert_token_to_id
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        if token in self.fairseq_tokens_to_ids:
-            return self.fairseq_tokens_to_ids[token]
-        spm_id = self.sp_model.PieceToId(token)
-
-        # Need to return unknown token if the SP model returned 0
-        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        if index in self.fairseq_ids_to_tokens:
-            return self.fairseq_ids_to_tokens[index]
-        return self.sp_model.IdToPiece(index - self.fairseq_offset)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
-        return out_string
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-
-        # for backward compatibility
-        if not hasattr(self, "sp_model_kwargs"):
-            self.sp_model_kwargs = {}
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.__call__
-    def __call__(
-        self,
-        text: Union[TextInput, List[TextInput]],
-        text_pair: Optional[Union[TextInput, List[TextInput]]] = None,
-        entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None,
-        entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None,
-        entities: Optional[Union[EntityInput, List[EntityInput]]] = None,
-        entities_pair: Optional[Union[EntityInput, List[EntityInput]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        stride: int = 0,
-        is_split_into_words: Optional[bool] = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
-        sequences, depending on the task you want to prepare them for.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
-                tokenizer does not support tokenization based on pretokenized strings.
-            text_pair (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
-                tokenizer does not support tokenization based on pretokenized strings.
-            entity_spans (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*):
-                The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
-                with two integers denoting character-based start and end positions of entities. If you specify
-                `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor,
-                the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
-                sequence must be equal to the length of each sequence of `entities`.
-            entity_spans_pair (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*):
-                The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
-                with two integers denoting character-based start and end positions of entities. If you specify the
-                `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the
-                length of each sequence must be equal to the length of each sequence of `entities_pair`.
-            entities (`List[str]`, `List[List[str]]`, *optional*):
-                The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
-                representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
-                Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
-                each sequence must be equal to the length of each sequence of `entity_spans`. If you specify
-                `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences
-                is automatically constructed by filling it with the [MASK] entity.
-            entities_pair (`List[str]`, `List[List[str]]`, *optional*):
-                The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
-                representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
-                Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
-                each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
-                `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
-                sequences is automatically constructed by filling it with the [MASK] entity.
-            max_entity_length (`int`, *optional*):
-                The maximum length of `entity_ids`.
-        """
-        # Input type checking for clearer error
-        is_valid_single_text = isinstance(text, str)
-        is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str)))
-        if not (is_valid_single_text or is_valid_batch_text):
-            raise ValueError("text input must be of type `str` (single example) or `List[str]` (batch).")
-
-        is_valid_single_text_pair = isinstance(text_pair, str)
-        is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
-            len(text_pair) == 0 or isinstance(text_pair[0], str)
-        )
-        if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
-            raise ValueError("text_pair input must be of type `str` (single example) or `List[str]` (batch).")
-
-        is_batched = bool(isinstance(text, (list, tuple)))
-
-        if is_batched:
-            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
-            if entities is None:
-                batch_entities_or_entities_pairs = None
-            else:
-                batch_entities_or_entities_pairs = (
-                    list(zip(entities, entities_pair)) if entities_pair is not None else entities
-                )
-
-            if entity_spans is None:
-                batch_entity_spans_or_entity_spans_pairs = None
-            else:
-                batch_entity_spans_or_entity_spans_pairs = (
-                    list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
-                )
-
-            return self.batch_encode_plus(
-                batch_text_or_text_pairs=batch_text_or_text_pairs,
-                batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
-                batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                stride=stride,
-                is_split_into_words=is_split_into_words,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                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_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.encode_plus(
-                text=text,
-                text_pair=text_pair,
-                entity_spans=entity_spans,
-                entity_spans_pair=entity_spans_pair,
-                entities=entities,
-                entities_pair=entities_pair,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                stride=stride,
-                is_split_into_words=is_split_into_words,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                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_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._encode_plus
-    def _encode_plus(
-        self,
-        text: Union[TextInput],
-        text_pair: Optional[Union[TextInput]] = None,
-        entity_spans: Optional[EntitySpanInput] = None,
-        entity_spans_pair: Optional[EntitySpanInput] = None,
-        entities: Optional[EntityInput] = None,
-        entities_pair: Optional[EntityInput] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        stride: int = 0,
-        is_split_into_words: Optional[bool] = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast. "
-                "More information on available tokenizers at "
-                "https://github.com/huggingface/transformers/pull/2674"
-            )
-
-        if is_split_into_words:
-            raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
-
-        (
-            first_ids,
-            second_ids,
-            first_entity_ids,
-            second_entity_ids,
-            first_entity_token_spans,
-            second_entity_token_spans,
-        ) = self._create_input_sequence(
-            text=text,
-            text_pair=text_pair,
-            entities=entities,
-            entities_pair=entities_pair,
-            entity_spans=entity_spans,
-            entity_spans_pair=entity_spans_pair,
-            **kwargs,
-        )
-
-        # prepare_for_model will create the attention_mask and token_type_ids
-        return self.prepare_for_model(
-            first_ids,
-            pair_ids=second_ids,
-            entity_ids=first_entity_ids,
-            pair_entity_ids=second_entity_ids,
-            entity_token_spans=first_entity_token_spans,
-            pair_entity_token_spans=second_entity_token_spans,
-            add_special_tokens=add_special_tokens,
-            padding=padding_strategy.value,
-            truncation=truncation_strategy.value,
-            max_length=max_length,
-            max_entity_length=max_entity_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            prepend_batch_axis=True,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            verbose=verbose,
-        )
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_encode_plus
-    def _batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]],
-        batch_entity_spans_or_entity_spans_pairs: Optional[
-            Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]]
-        ] = None,
-        batch_entities_or_entities_pairs: Optional[
-            Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]]
-        ] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        stride: int = 0,
-        is_split_into_words: Optional[bool] = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast."
-            )
-
-        if is_split_into_words:
-            raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
-
-        # input_ids is a list of tuples (one for each example in the batch)
-        input_ids = []
-        entity_ids = []
-        entity_token_spans = []
-        for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
-            if not isinstance(text_or_text_pair, (list, tuple)):
-                text, text_pair = text_or_text_pair, None
-            else:
-                text, text_pair = text_or_text_pair
-
-            entities, entities_pair = None, None
-            if batch_entities_or_entities_pairs is not None:
-                entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
-                if entities_or_entities_pairs:
-                    if isinstance(entities_or_entities_pairs[0], str):
-                        entities, entities_pair = entities_or_entities_pairs, None
-                    else:
-                        entities, entities_pair = entities_or_entities_pairs
-
-            entity_spans, entity_spans_pair = None, None
-            if batch_entity_spans_or_entity_spans_pairs is not None:
-                entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
-                if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
-                    entity_spans_or_entity_spans_pairs[0], list
-                ):
-                    entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
-                else:
-                    entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
-
-            (
-                first_ids,
-                second_ids,
-                first_entity_ids,
-                second_entity_ids,
-                first_entity_token_spans,
-                second_entity_token_spans,
-            ) = self._create_input_sequence(
-                text=text,
-                text_pair=text_pair,
-                entities=entities,
-                entities_pair=entities_pair,
-                entity_spans=entity_spans,
-                entity_spans_pair=entity_spans_pair,
-                **kwargs,
-            )
-            input_ids.append((first_ids, second_ids))
-            entity_ids.append((first_entity_ids, second_entity_ids))
-            entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
-
-        batch_outputs = self._batch_prepare_for_model(
-            input_ids,
-            batch_entity_ids_pairs=entity_ids,
-            batch_entity_token_spans_pairs=entity_token_spans,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            max_entity_length=max_entity_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            return_tensors=return_tensors,
-            verbose=verbose,
-        )
-
-        return BatchEncoding(batch_outputs)
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._check_entity_input_format
-    def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
-        if not isinstance(entity_spans, list):
-            raise ValueError("entity_spans should be given as a list")
-        elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
-            raise ValueError(
-                "entity_spans should be given as a list of tuples containing the start and end character indices"
-            )
-
-        if entities is not None:
-            if not isinstance(entities, list):
-                raise ValueError("If you specify entities, they should be given as a list")
-
-            if len(entities) > 0 and not isinstance(entities[0], str):
-                raise ValueError("If you specify entities, they should be given as a list of entity names")
-
-            if len(entities) != len(entity_spans):
-                raise ValueError("If you specify entities, entities and entity_spans must be the same length")
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._create_input_sequence
-    def _create_input_sequence(
-        self,
-        text: Union[TextInput],
-        text_pair: Optional[Union[TextInput]] = None,
-        entities: Optional[EntityInput] = None,
-        entities_pair: Optional[EntityInput] = None,
-        entity_spans: Optional[EntitySpanInput] = None,
-        entity_spans_pair: Optional[EntitySpanInput] = None,
-        **kwargs,
-    ) -> Tuple[list, list, list, list, list, list]:
-        def get_input_ids(text):
-            tokens = self.tokenize(text, **kwargs)
-            return self.convert_tokens_to_ids(tokens)
-
-        def get_input_ids_and_entity_token_spans(text, entity_spans):
-            if entity_spans is None:
-                return get_input_ids(text), None
-
-            cur = 0
-            input_ids = []
-            entity_token_spans = [None] * len(entity_spans)
-
-            split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
-            char_pos2token_pos = {}
-
-            for split_char_position in split_char_positions:
-                orig_split_char_position = split_char_position
-                if (
-                    split_char_position > 0 and text[split_char_position - 1] == " "
-                ):  # whitespace should be prepended to the following token
-                    split_char_position -= 1
-                if cur != split_char_position:
-                    input_ids += get_input_ids(text[cur:split_char_position])
-                    cur = split_char_position
-                char_pos2token_pos[orig_split_char_position] = len(input_ids)
-
-            input_ids += get_input_ids(text[cur:])
-
-            entity_token_spans = [
-                (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans
-            ]
-
-            return input_ids, entity_token_spans
-
-        first_ids, second_ids = None, None
-        first_entity_ids, second_entity_ids = None, None
-        first_entity_token_spans, second_entity_token_spans = None, None
-
-        if self.task is None:
-            if entity_spans is None:
-                first_ids = get_input_ids(text)
-            else:
-                self._check_entity_input_format(entities, entity_spans)
-
-                first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
-                if entities is None:
-                    first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
-                else:
-                    first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities]
-
-            if text_pair is not None:
-                if entity_spans_pair is None:
-                    second_ids = get_input_ids(text_pair)
-                else:
-                    self._check_entity_input_format(entities_pair, entity_spans_pair)
-
-                    second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
-                        text_pair, entity_spans_pair
-                    )
-                    if entities_pair is None:
-                        second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
-                    else:
-                        second_entity_ids = [
-                            self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair
-                        ]
-
-        elif self.task == "entity_classification":
-            if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)):
-                raise ValueError(
-                    "Entity spans should be a list containing a single tuple "
-                    "containing the start and end character indices of an entity"
-                )
-            first_entity_ids = [self.entity_mask_token_id]
-            first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
-
-            # add special tokens to input ids
-            entity_token_start, entity_token_end = first_entity_token_spans[0]
-            first_ids = (
-                first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:]
-            )
-            first_ids = (
-                first_ids[:entity_token_start]
-                + [self.additional_special_tokens_ids[0]]
-                + first_ids[entity_token_start:]
-            )
-            first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
-
-        elif self.task == "entity_pair_classification":
-            if not (
-                isinstance(entity_spans, list)
-                and len(entity_spans) == 2
-                and isinstance(entity_spans[0], tuple)
-                and isinstance(entity_spans[1], tuple)
-            ):
-                raise ValueError(
-                    "Entity spans should be provided as a list of two tuples, "
-                    "each tuple containing the start and end character indices of an entity"
-                )
-
-            head_span, tail_span = entity_spans
-            first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
-            first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
-
-            head_token_span, tail_token_span = first_entity_token_spans
-            token_span_with_special_token_ids = [
-                (head_token_span, self.additional_special_tokens_ids[0]),
-                (tail_token_span, self.additional_special_tokens_ids[1]),
-            ]
-            if head_token_span[0] < tail_token_span[0]:
-                first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
-                first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
-                token_span_with_special_token_ids = reversed(token_span_with_special_token_ids)
-            else:
-                first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
-                first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
-
-            for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids:
-                first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
-                first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:]
-
-        elif self.task == "entity_span_classification":
-            if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)):
-                raise ValueError(
-                    "Entity spans should be provided as a list of tuples, "
-                    "each tuple containing the start and end character indices of an entity"
-                )
-
-            first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
-            first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
-
-        else:
-            raise ValueError(f"Task {self.task} not supported")
-
-        return (
-            first_ids,
-            second_ids,
-            first_entity_ids,
-            second_entity_ids,
-            first_entity_token_spans,
-            second_entity_token_spans,
-        )
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_prepare_for_model
-    def _batch_prepare_for_model(
-        self,
-        batch_ids_pairs: List[Tuple[List[int], None]],
-        batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]],
-        batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]],
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        """
-        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
-        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
-        manages a moving window (with user defined stride) for overflowing tokens
-
-
-        Args:
-            batch_ids_pairs: list of tokenized input ids or input ids pairs
-            batch_entity_ids_pairs: list of entity ids or entity ids pairs
-            batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
-            max_entity_length: The maximum length of the entity sequence.
-        """
-
-        batch_outputs = {}
-        for input_ids, entity_ids, entity_token_span_pairs in zip(
-            batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
-        ):
-            first_ids, second_ids = input_ids
-            first_entity_ids, second_entity_ids = entity_ids
-            first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
-            outputs = self.prepare_for_model(
-                first_ids,
-                second_ids,
-                entity_ids=first_entity_ids,
-                pair_entity_ids=second_entity_ids,
-                entity_token_spans=first_entity_token_spans,
-                pair_entity_token_spans=second_entity_token_spans,
-                add_special_tokens=add_special_tokens,
-                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
-                truncation=truncation_strategy.value,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                stride=stride,
-                pad_to_multiple_of=None,  # we pad in batch afterward
-                return_attention_mask=False,  # we pad in batch afterward
-                return_token_type_ids=return_token_type_ids,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_length=return_length,
-                return_tensors=None,  # We convert the whole batch to tensors at the end
-                prepend_batch_axis=False,
-                verbose=verbose,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        batch_outputs = self.pad(
-            batch_outputs,
-            padding=padding_strategy.value,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-        )
-
-        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-        return batch_outputs
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.prepare_for_model
-    def prepare_for_model(
-        self,
-        ids: List[int],
-        pair_ids: Optional[List[int]] = None,
-        entity_ids: Optional[List[int]] = None,
-        pair_entity_ids: Optional[List[int]] = None,
-        entity_token_spans: Optional[List[Tuple[int, int]]] = None,
-        pair_entity_token_spans: Optional[List[Tuple[int, int]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        prepend_batch_axis: bool = False,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
-        entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
-        while taking into account the special tokens and manages a moving window (with user defined stride) for
-        overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
-        or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
-        error.
-
-        Args:
-            ids (`List[int]`):
-                Tokenized input ids of the first sequence.
-            pair_ids (`List[int]`, *optional*):
-                Tokenized input ids of the second sequence.
-            entity_ids (`List[int]`, *optional*):
-                Entity ids of the first sequence.
-            pair_entity_ids (`List[int]`, *optional*):
-                Entity ids of the second sequence.
-            entity_token_spans (`List[Tuple[int, int]]`, *optional*):
-                Entity spans of the first sequence.
-            pair_entity_token_spans (`List[Tuple[int, int]]`, *optional*):
-                Entity spans of the second sequence.
-            max_entity_length (`int`, *optional*):
-                The maximum length of the entity sequence.
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        # Compute lengths
-        pair = bool(pair_ids is not None)
-        len_ids = len(ids)
-        len_pair_ids = len(pair_ids) if pair else 0
-
-        if return_token_type_ids and not add_special_tokens:
-            raise ValueError(
-                "Asking to return token_type_ids while setting add_special_tokens to False "
-                "results in an undefined behavior. Please set add_special_tokens to True or "
-                "set return_token_type_ids to None."
-            )
-        if (
-            return_overflowing_tokens
-            and truncation_strategy == TruncationStrategy.LONGEST_FIRST
-            and pair_ids is not None
-        ):
-            raise ValueError(
-                "Not possible to return overflowing tokens for pair of sequences with the "
-                "`longest_first`. Please select another truncation strategy than `longest_first`, "
-                "for instance `only_second` or `only_first`."
-            )
-
-        # Load from model defaults
-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        encoded_inputs = {}
-
-        # Compute the total size of the returned word encodings
-        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
-
-        # Truncation: Handle max sequence length and max_entity_length
-        overflowing_tokens = []
-        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
-            # truncate words up to max_length
-            ids, pair_ids, overflowing_tokens = self.truncate_sequences(
-                ids,
-                pair_ids=pair_ids,
-                num_tokens_to_remove=total_len - max_length,
-                truncation_strategy=truncation_strategy,
-                stride=stride,
-            )
-
-        if return_overflowing_tokens:
-            encoded_inputs["overflowing_tokens"] = overflowing_tokens
-            encoded_inputs["num_truncated_tokens"] = total_len - max_length
-
-        # Add special tokens
-        if add_special_tokens:
-            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
-            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
-            entity_token_offset = 1  # 1 * <s> token
-            pair_entity_token_offset = len(ids) + 3  # 1 * <s> token & 2 * <sep> tokens
-        else:
-            sequence = ids + pair_ids if pair else ids
-            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
-            entity_token_offset = 0
-            pair_entity_token_offset = len(ids)
-
-        # Build output dictionary
-        encoded_inputs["input_ids"] = sequence
-        if return_token_type_ids:
-            encoded_inputs["token_type_ids"] = token_type_ids
-        if return_special_tokens_mask:
-            if add_special_tokens:
-                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
-            else:
-                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
-
-        # Set max entity length
-        if not max_entity_length:
-            max_entity_length = self.max_entity_length
-
-        if entity_ids is not None:
-            total_entity_len = 0
-            num_invalid_entities = 0
-            valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)]
-            valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
-
-            total_entity_len += len(valid_entity_ids)
-            num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
-
-            valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
-            if pair_entity_ids is not None:
-                valid_pair_entity_ids = [
-                    ent_id
-                    for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
-                    if span[1] <= len(pair_ids)
-                ]
-                valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)]
-                total_entity_len += len(valid_pair_entity_ids)
-                num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
-
-            if num_invalid_entities != 0:
-                logger.warning(
-                    f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the"
-                    " truncation of input tokens"
-                )
-
-            if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
-                # truncate entities up to max_entity_length
-                valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(
-                    valid_entity_ids,
-                    pair_ids=valid_pair_entity_ids,
-                    num_tokens_to_remove=total_entity_len - max_entity_length,
-                    truncation_strategy=truncation_strategy,
-                    stride=stride,
-                )
-                valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)]
-                if valid_pair_entity_token_spans is not None:
-                    valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)]
-
-            if return_overflowing_tokens:
-                encoded_inputs["overflowing_entities"] = overflowing_entities
-                encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length
-
-            final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids
-            encoded_inputs["entity_ids"] = list(final_entity_ids)
-            entity_position_ids = []
-            entity_start_positions = []
-            entity_end_positions = []
-            for token_spans, offset in (
-                (valid_entity_token_spans, entity_token_offset),
-                (valid_pair_entity_token_spans, pair_entity_token_offset),
-            ):
-                if token_spans is not None:
-                    for start, end in token_spans:
-                        start += offset
-                        end += offset
-                        position_ids = list(range(start, end))[: self.max_mention_length]
-                        position_ids += [-1] * (self.max_mention_length - end + start)
-                        entity_position_ids.append(position_ids)
-                        entity_start_positions.append(start)
-                        entity_end_positions.append(end - 1)
-
-            encoded_inputs["entity_position_ids"] = entity_position_ids
-            if self.task == "entity_span_classification":
-                encoded_inputs["entity_start_positions"] = entity_start_positions
-                encoded_inputs["entity_end_positions"] = entity_end_positions
-
-            if return_token_type_ids:
-                encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"])
-
-        # Check lengths
-        self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
-
-        # Padding
-        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
-            encoded_inputs = self.pad(
-                encoded_inputs,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                padding=padding_strategy.value,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-
-        if return_length:
-            encoded_inputs["length"] = len(encoded_inputs["input_ids"])
-
-        batch_outputs = BatchEncoding(
-            encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
-        )
-
-        return batch_outputs
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.pad
-    def pad(
-        self,
-        encoded_inputs: Union[
-            BatchEncoding,
-            List[BatchEncoding],
-            Dict[str, EncodedInput],
-            Dict[str, List[EncodedInput]],
-            List[Dict[str, EncodedInput]],
-        ],
-        padding: Union[bool, str, PaddingStrategy] = True,
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        """
-        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
-        in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
-        `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
-        are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless
-        you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
-        specific device of your tensors however.
-
-        Args:
-            encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`):
-                Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of
-                tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str,
-                List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
-                collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or
-                TensorFlow tensors), see the note above for the return type.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
-                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
-                 index) among:
-
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            max_length (`int`, *optional*):
-                Maximum length of the returned list and optionally padding length (see above).
-            max_entity_length (`int`, *optional*):
-                The maximum length of the entity sequence.
-            pad_to_multiple_of (`int`, *optional*):
-                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
-                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
-            return_attention_mask (`bool`, *optional*):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention
-                masks?](../glossary#attention-mask)
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors instead of list of python integers. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return Numpy `np.ndarray` objects.
-            verbose (`bool`, *optional*, defaults to `True`):
-                Whether or not to print more information and warnings.
-        """
-        # If we have a list of dicts, let's convert it in a dict of lists
-        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
-        if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
-            encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}
-
-        # The model's main input name, usually `input_ids`, has be passed for padding
-        if self.model_input_names[0] not in encoded_inputs:
-            raise ValueError(
-                "You should supply an encoding or a list of encodings to this method "
-                f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
-            )
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-
-        if not required_input:
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = []
-            return encoded_inputs
-
-        # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
-        # and rebuild them afterwards if no return_tensors is specified
-        # Note that we lose the specific device the tensor may be on for PyTorch
-
-        first_element = required_input[0]
-        if isinstance(first_element, (list, tuple)):
-            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
-            index = 0
-            while len(required_input[index]) == 0:
-                index += 1
-            if index < len(required_input):
-                first_element = required_input[index][0]
-        # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
-        if not isinstance(first_element, (int, list, tuple)):
-            if is_tf_tensor(first_element):
-                return_tensors = "tf" if return_tensors is None else return_tensors
-            elif is_torch_tensor(first_element):
-                return_tensors = "pt" if return_tensors is None else return_tensors
-            elif isinstance(first_element, np.ndarray):
-                return_tensors = "np" if return_tensors is None else return_tensors
-            else:
-                raise ValueError(
-                    f"type of {first_element} unknown: {type(first_element)}. "
-                    "Should be one of a python, numpy, pytorch or tensorflow object."
-                )
-
-            for key, value in encoded_inputs.items():
-                encoded_inputs[key] = to_py_obj(value)
-
-        # Convert padding_strategy in PaddingStrategy
-        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
-            padding=padding, max_length=max_length, verbose=verbose
-        )
-
-        if max_entity_length is None:
-            max_entity_length = self.max_entity_length
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-        if required_input and not isinstance(required_input[0], (list, tuple)):
-            encoded_inputs = self._pad(
-                encoded_inputs,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                padding_strategy=padding_strategy,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-            return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
-
-        batch_size = len(required_input)
-        if any(len(v) != batch_size for v in encoded_inputs.values()):
-            raise ValueError("Some items in the output dictionary have a different batch size than others.")
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = max(len(inputs) for inputs in required_input)
-            max_entity_length = (
-                max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0
-            )
-            padding_strategy = PaddingStrategy.MAX_LENGTH
-
-        batch_outputs = {}
-        for i in range(batch_size):
-            inputs = {k: v[i] for k, v in encoded_inputs.items()}
-            outputs = self._pad(
-                inputs,
-                max_length=max_length,
-                max_entity_length=max_entity_length,
-                padding_strategy=padding_strategy,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        return BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-    # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._pad
-    def _pad(
-        self,
-        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-        max_length: Optional[int] = None,
-        max_entity_length: Optional[int] = None,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
-
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            max_entity_length: The maximum length of the entity sequence.
-            padding_strategy: PaddingStrategy to use for padding.
-
-
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-
-
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        entities_provided = bool("entity_ids" in encoded_inputs)
-
-        # Load from model defaults
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(encoded_inputs["input_ids"])
-            if entities_provided:
-                max_entity_length = len(encoded_inputs["entity_ids"])
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        if (
-            entities_provided
-            and max_entity_length is not None
-            and pad_to_multiple_of is not None
-            and (max_entity_length % pad_to_multiple_of != 0)
-        ):
-            max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (
-            len(encoded_inputs["input_ids"]) != max_length
-            or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length)
-        )
-
-        # Initialize attention mask if not present.
-        if return_attention_mask and "attention_mask" not in encoded_inputs:
-            encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
-        if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs:
-            encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
-
-        if needs_to_be_padded:
-            difference = max_length - len(encoded_inputs["input_ids"])
-            if entities_provided:
-                entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
-            if self.padding_side == "right":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
-                    if entities_provided:
-                        encoded_inputs["entity_attention_mask"] = (
-                            encoded_inputs["entity_attention_mask"] + [0] * entity_difference
-                        )
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference
-                    if entities_provided:
-                        encoded_inputs["entity_token_type_ids"] = (
-                            encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
-                        )
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
-                encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
-                if entities_provided:
-                    encoded_inputs["entity_ids"] = (
-                        encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference
-                    )
-                    encoded_inputs["entity_position_ids"] = (
-                        encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference
-                    )
-                    if self.task == "entity_span_classification":
-                        encoded_inputs["entity_start_positions"] = (
-                            encoded_inputs["entity_start_positions"] + [0] * entity_difference
-                        )
-                        encoded_inputs["entity_end_positions"] = (
-                            encoded_inputs["entity_end_positions"] + [0] * entity_difference
-                        )
-
-            elif self.padding_side == "left":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
-                    if entities_provided:
-                        encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[
-                            "entity_attention_mask"
-                        ]
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"]
-                    if entities_provided:
-                        encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[
-                            "entity_token_type_ids"
-                        ]
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
-                if entities_provided:
-                    encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[
-                        "entity_ids"
-                    ]
-                    encoded_inputs["entity_position_ids"] = [
-                        [-1] * self.max_mention_length
-                    ] * entity_difference + encoded_inputs["entity_position_ids"]
-                    if self.task == "entity_span_classification":
-                        encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[
-                            "entity_start_positions"
-                        ]
-                        encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[
-                            "entity_end_positions"
-                        ]
-            else:
-                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
-
-        return encoded_inputs
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        entity_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"]
-        )
-
-        with open(entity_vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        return out_vocab_file, entity_vocab_file
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.build_inputs_with_special_tokens
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. An XLM-RoBERTa sequence has the following format:
-
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s></s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_special_tokens_mask
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.create_token_type_ids_from_sequences
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
-        not make use of token type ids, therefore a list of zeros is returned.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of zeros.
-
-        """
-
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py
deleted file mode 100644
index f25e166ef917cbb45a9531099508e24825eb533a..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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 Reformer checkpoint."""
-
-
-import argparse
-import pickle
-
-import numpy as np
-import torch
-from torch import nn
-
-from transformers import ReformerConfig, ReformerModelWithLMHead
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def set_param(torch_layer, weight, bias=None):
-    # set parameter of one layer
-    assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
-    torch_layer.weight = nn.Parameter(weight)
-    if bias is not None:
-        assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
-        torch_layer.bias = nn.Parameter(bias)
-
-
-def set_layer_weights_in_torch_lsh(weights, torch_layer, hidden_size):
-    # set torch weights for 1-to-1 comparison
-    np_query_key = np.asarray(weights[0])
-    np_value = np.asarray(weights[1])
-    np_dense = np.asarray(weights[2])
-
-    set_param(
-        torch_layer.self_attention.query_key,
-        torch.tensor(np_query_key).transpose(1, 2).contiguous().view(-1, hidden_size),
-    )
-    set_param(
-        torch_layer.self_attention.value,
-        torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
-    )
-    set_param(
-        torch_layer.output.dense,
-        torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
-    )
-
-
-def set_layer_weights_in_torch_local(weights, torch_layer, hidden_size):
-    # set torch weights for 1-to-1 comparison
-    np_query = np.asarray(weights[0])
-    np_key = np.asarray(weights[1])
-    np_value = np.asarray(weights[2])
-    np_dense = np.asarray(weights[3])
-
-    set_param(
-        torch_layer.self_attention.query,
-        torch.tensor(np_query).transpose(1, 2).contiguous().view(-1, hidden_size),
-    )
-    set_param(
-        torch_layer.self_attention.key,
-        torch.tensor(np_key).transpose(1, 2).contiguous().view(-1, hidden_size),
-    )
-    set_param(
-        torch_layer.self_attention.value,
-        torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
-    )
-    set_param(
-        torch_layer.output.dense,
-        torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
-    )
-
-
-def set_block_weights_in_torch(weights, torch_block, hidden_size):
-    # layernorm 1
-    layer_norm_1 = weights[0][0][0]
-    layer_norm_1_weight = np.asarray(layer_norm_1[0])
-    layer_norm_1_bias = np.asarray(layer_norm_1[1])
-    set_param(
-        torch_block.attention.layer_norm,
-        torch.tensor(layer_norm_1_weight),
-        torch.tensor(layer_norm_1_bias),
-    )
-
-    # lsh weights + output
-    attn_weights = weights[0][1]
-    if len(attn_weights) < 4:
-        set_layer_weights_in_torch_lsh(attn_weights, torch_block.attention, hidden_size)
-    else:
-        set_layer_weights_in_torch_local(attn_weights, torch_block.attention, hidden_size)
-
-    # intermediate weighs
-    intermediate_weights = weights[2][0][1][2]
-
-    # Chunked Feed Forward
-    if len(intermediate_weights) == 4:
-        intermediate_weights = intermediate_weights[2]
-
-    # layernorm 2
-    layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
-    layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
-    set_param(
-        torch_block.feed_forward.layer_norm,
-        torch.tensor(layer_norm_2_weight),
-        torch.tensor(layer_norm_2_bias),
-    )
-
-    # intermediate dense
-    inter_dense_weight = np.asarray(intermediate_weights[1][0])
-    inter_dense_bias = np.asarray(intermediate_weights[1][1])
-    set_param(
-        torch_block.feed_forward.dense.dense,
-        torch.tensor(inter_dense_weight).transpose(0, 1).contiguous(),
-        torch.tensor(inter_dense_bias),
-    )
-
-    # intermediate out
-    out_dense_weight = np.asarray(intermediate_weights[4][0])
-    out_dense_bias = np.asarray(intermediate_weights[4][1])
-    set_param(
-        torch_block.feed_forward.output.dense,
-        torch.tensor(out_dense_weight).transpose(0, 1).contiguous(),
-        torch.tensor(out_dense_bias),
-    )
-
-
-def set_model_weights_in_torch(weights, torch_model, hidden_size):
-    # reformer model
-    torch_model_reformer = torch_model.reformer
-
-    # word embeds
-    word_embeddings = np.asarray(weights[1])
-    set_param(
-        torch_model_reformer.embeddings.word_embeddings,
-        torch.tensor(word_embeddings),
-    )
-
-    if isinstance(weights[3], tuple):
-        position_embeddings = torch_model_reformer.embeddings.position_embeddings
-        for emb_idx in range(len(position_embeddings.weights)):
-            emb_weights = np.asarray(weights[3][emb_idx][0])
-            assert (
-                position_embeddings.weights[emb_idx].shape == emb_weights.shape
-            ), f"{position_embeddings[emb_idx]} emb does not match"
-            position_embeddings.weights[emb_idx] = nn.Parameter(torch.tensor(emb_weights))
-
-    trax_layer_weights = weights[5]
-    assert len(torch_model_reformer.encoder.layers) * 4 == len(
-        trax_layer_weights
-    ), "HF and trax model do not have the same number of layers"
-    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers):
-        block_weights = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
-        set_block_weights_in_torch(block_weights, layer, hidden_size)
-
-    # output layer norm
-    layer_norm_out_weight = np.asarray(weights[7][0])
-    layer_norm_out_bias = np.asarray(weights[7][1])
-    set_param(
-        torch_model_reformer.encoder.layer_norm,
-        torch.tensor(layer_norm_out_weight),
-        torch.tensor(layer_norm_out_bias),
-    )
-
-    # output embeddings
-    output_embed_weights = np.asarray(weights[9][0])
-    output_embed_bias = np.asarray(weights[9][1])
-    set_param(
-        torch_model.lm_head.decoder,
-        torch.tensor(output_embed_weights).transpose(0, 1).contiguous(),
-        torch.tensor(output_embed_bias),
-    )
-
-
-def convert_trax_checkpoint_to_pytorch(trax_model_pkl_path, config_file, pytorch_dump_path):
-    # Initialise PyTorch model
-    config = ReformerConfig.from_json_file(config_file)
-    print(f"Building PyTorch model from configuration: {config}")
-    model = ReformerModelWithLMHead(config)
-
-    with open(trax_model_pkl_path, "rb") as f:
-        model_weights = pickle.load(f)["weights"]
-
-    set_model_weights_in_torch(model_weights, model, config.hidden_size)
-
-    # Save pytorch-model
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    torch.save(model.state_dict(), pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained Reformer model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/mel_processing.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/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/yl12053/so-vits-4.1-Grass-Wonder/vencoder/wavlm/WavLM.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/vencoder/wavlm/WavLM.py
deleted file mode 100644
index 777befb7865c298fad94bd48003bd071feef7064..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/vencoder/wavlm/WavLM.py
+++ /dev/null
@@ -1,743 +0,0 @@
-# --------------------------------------------------------
-# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
-# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Based on fairseq code bases
-# https://github.com/pytorch/fairseq
-# --------------------------------------------------------
-
-import math
-import logging
-from typing import List, Optional, Tuple
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn import LayerNorm
-from vencoder.wavlm.modules import (
-    Fp32GroupNorm,
-    Fp32LayerNorm,
-    GradMultiply,
-    MultiheadAttention,
-    SamePad,
-    init_bert_params,
-    get_activation_fn,
-    TransposeLast,
-    GLU_Linear,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def compute_mask_indices(
-    shape: Tuple[int, int],
-    padding_mask: Optional[torch.Tensor],
-    mask_prob: float,
-    mask_length: int,
-    mask_type: str = "static",
-    mask_other: float = 0.0,
-    min_masks: int = 0,
-    no_overlap: bool = False,
-    min_space: int = 0,
-) -> np.ndarray:
-    """
-    Computes random mask spans for a given shape
-
-    Args:
-        shape: the the shape for which to compute masks.
-            should be of size 2 where first element is batch size and 2nd is timesteps
-        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
-        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
-            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
-            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
-        mask_type: how to compute mask lengths
-            static = fixed size
-            uniform = sample from uniform distribution [mask_other, mask_length*2]
-            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
-            poisson = sample from possion distribution with lambda = mask length
-        min_masks: minimum number of masked spans
-        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
-        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
-    """
-
-    bsz, all_sz = shape
-    mask = np.full((bsz, all_sz), False)
-
-    all_num_mask = int(
-        # add a random number for probabilistic rounding
-        mask_prob * all_sz / float(mask_length)
-        + np.random.rand()
-    )
-
-    all_num_mask = max(min_masks, all_num_mask)
-
-    mask_idcs = []
-    for i in range(bsz):
-        if padding_mask is not None:
-            sz = all_sz - padding_mask[i].long().sum().item()
-            num_mask = int(
-                # add a random number for probabilistic rounding
-                mask_prob * sz / float(mask_length)
-                + np.random.rand()
-            )
-            num_mask = max(min_masks, num_mask)
-        else:
-            sz = all_sz
-            num_mask = all_num_mask
-
-        if mask_type == "static":
-            lengths = np.full(num_mask, mask_length)
-        elif mask_type == "uniform":
-            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
-        elif mask_type == "normal":
-            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
-            lengths = [max(1, int(round(x))) for x in lengths]
-        elif mask_type == "poisson":
-            lengths = np.random.poisson(mask_length, size=num_mask)
-            lengths = [int(round(x)) for x in lengths]
-        else:
-            raise Exception("unknown mask selection " + mask_type)
-
-        if sum(lengths) == 0:
-            lengths[0] = min(mask_length, sz - 1)
-
-        if no_overlap:
-            mask_idc = []
-
-            def arrange(s, e, length, keep_length):
-                span_start = np.random.randint(s, e - length)
-                mask_idc.extend(span_start + i for i in range(length))
-
-                new_parts = []
-                if span_start - s - min_space >= keep_length:
-                    new_parts.append((s, span_start - min_space + 1))
-                if e - span_start - keep_length - min_space > keep_length:
-                    new_parts.append((span_start + length + min_space, e))
-                return new_parts
-
-            parts = [(0, sz)]
-            min_length = min(lengths)
-            for length in sorted(lengths, reverse=True):
-                lens = np.fromiter(
-                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
-                    np.int,
-                )
-                l_sum = np.sum(lens)
-                if l_sum == 0:
-                    break
-                probs = lens / np.sum(lens)
-                c = np.random.choice(len(parts), p=probs)
-                s, e = parts.pop(c)
-                parts.extend(arrange(s, e, length, min_length))
-            mask_idc = np.asarray(mask_idc)
-        else:
-            min_len = min(lengths)
-            if sz - min_len <= num_mask:
-                min_len = sz - num_mask - 1
-
-            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
-
-            mask_idc = np.asarray(
-                [
-                    mask_idc[j] + offset
-                    for j in range(len(mask_idc))
-                    for offset in range(lengths[j])
-                ]
-            )
-
-        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
-
-    min_len = min([len(m) for m in mask_idcs])
-    for i, mask_idc in enumerate(mask_idcs):
-        if len(mask_idc) > min_len:
-            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
-        mask[i, mask_idc] = True
-
-    return mask
-
-
-class WavLMConfig:
-    def __init__(self, cfg=None):
-        self.extractor_mode: str = "default"     # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
-        self.encoder_layers: int = 12     # num encoder layers in the transformer
-
-        self.encoder_embed_dim: int = 768     # encoder embedding dimension
-        self.encoder_ffn_embed_dim: int = 3072     # encoder embedding dimension for FFN
-        self.encoder_attention_heads: int = 12     # num encoder attention heads
-        self.activation_fn: str = "gelu"     # activation function to use
-
-        self.layer_norm_first: bool = False     # apply layernorm first in the transformer
-        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"     # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
-        self.conv_bias: bool = False     # include bias in conv encoder
-        self.feature_grad_mult: float = 1.0     # multiply feature extractor var grads by this
-
-        self.normalize: bool = False  # normalize input to have 0 mean and unit variance during training
-
-        # dropouts
-        self.dropout: float = 0.1     # dropout probability for the transformer
-        self.attention_dropout: float = 0.1     # dropout probability for attention weights
-        self.activation_dropout: float = 0.0     # dropout probability after activation in FFN
-        self.encoder_layerdrop: float = 0.0     # probability of dropping a tarnsformer layer
-        self.dropout_input: float = 0.0     # dropout to apply to the input (after feat extr)
-        self.dropout_features: float = 0.0     # dropout to apply to the features (after feat extr)
-
-        # masking
-        self.mask_length: int = 10     # mask length
-        self.mask_prob: float = 0.65     # probability of replacing a token with mask
-        self.mask_selection: str = "static"     # how to choose mask length
-        self.mask_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
-        self.no_mask_overlap: bool = False     # whether to allow masks to overlap
-        self.mask_min_space: int = 1     # min space between spans (if no overlap is enabled)
-
-        # channel masking
-        self.mask_channel_length: int = 10     # length of the mask for features (channels)
-        self.mask_channel_prob: float = 0.0     # probability of replacing a feature with 0
-        self.mask_channel_selection: str = "static"     # how to choose mask length for channel masking
-        self.mask_channel_other: float = 0     # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
-        self.no_mask_channel_overlap: bool = False     # whether to allow channel masks to overlap
-        self.mask_channel_min_space: int = 1     # min space between spans (if no overlap is enabled)
-
-        # positional embeddings
-        self.conv_pos: int = 128     # number of filters for convolutional positional embeddings
-        self.conv_pos_groups: int = 16     # number of groups for convolutional positional embedding
-
-        # relative position embedding
-        self.relative_position_embedding: bool = False     # apply relative position embedding
-        self.num_buckets: int = 320     # number of buckets for relative position embedding
-        self.max_distance: int = 1280     # maximum distance for relative position embedding
-        self.gru_rel_pos: bool = False     # apply gated relative position embedding
-
-        if cfg is not None:
-            self.update(cfg)
-
-    def update(self, cfg: dict):
-        self.__dict__.update(cfg)
-
-
-class WavLM(nn.Module):
-    def __init__(
-        self,
-        cfg: WavLMConfig,
-    ) -> None:
-        super().__init__()
-        logger.info(f"WavLM Config: {cfg.__dict__}")
-
-        self.cfg = cfg
-        feature_enc_layers = eval(cfg.conv_feature_layers)
-        self.embed = feature_enc_layers[-1][0]
-
-        self.feature_extractor = ConvFeatureExtractionModel(
-            conv_layers=feature_enc_layers,
-            dropout=0.0,
-            mode=cfg.extractor_mode,
-            conv_bias=cfg.conv_bias,
-        )
-
-        self.post_extract_proj = (
-            nn.Linear(self.embed, cfg.encoder_embed_dim)
-            if self.embed != cfg.encoder_embed_dim
-            else None
-        )
-
-        self.mask_prob = cfg.mask_prob
-        self.mask_selection = cfg.mask_selection
-        self.mask_other = cfg.mask_other
-        self.mask_length = cfg.mask_length
-        self.no_mask_overlap = cfg.no_mask_overlap
-        self.mask_min_space = cfg.mask_min_space
-
-        self.mask_channel_prob = cfg.mask_channel_prob
-        self.mask_channel_selection = cfg.mask_channel_selection
-        self.mask_channel_other = cfg.mask_channel_other
-        self.mask_channel_length = cfg.mask_channel_length
-        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
-        self.mask_channel_min_space = cfg.mask_channel_min_space
-
-        self.dropout_input = nn.Dropout(cfg.dropout_input)
-        self.dropout_features = nn.Dropout(cfg.dropout_features)
-
-        self.feature_grad_mult = cfg.feature_grad_mult
-
-        self.mask_emb = nn.Parameter(
-            torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
-        )
-
-        self.encoder = TransformerEncoder(cfg)
-        self.layer_norm = LayerNorm(self.embed)
-
-    def apply_mask(self, x, padding_mask):
-        B, T, C = x.shape
-        if self.mask_prob > 0:
-            mask_indices = compute_mask_indices(
-                (B, T),
-                padding_mask,
-                self.mask_prob,
-                self.mask_length,
-                self.mask_selection,
-                self.mask_other,
-                min_masks=2,
-                no_overlap=self.no_mask_overlap,
-                min_space=self.mask_min_space,
-            )
-            mask_indices = torch.from_numpy(mask_indices).to(x.device)
-            x[mask_indices] = self.mask_emb
-        else:
-            mask_indices = None
-
-        if self.mask_channel_prob > 0:
-            mask_channel_indices = compute_mask_indices(
-                (B, C),
-                None,
-                self.mask_channel_prob,
-                self.mask_channel_length,
-                self.mask_channel_selection,
-                self.mask_channel_other,
-                no_overlap=self.no_mask_channel_overlap,
-                min_space=self.mask_channel_min_space,
-            )
-            mask_channel_indices = (
-                torch.from_numpy(mask_channel_indices)
-                .to(x.device)
-                .unsqueeze(1)
-                .expand(-1, T, -1)
-            )
-            x[mask_channel_indices] = 0
-
-        return x, mask_indices
-
-    def forward_padding_mask(
-            self, features: torch.Tensor, padding_mask: torch.Tensor,
-    ) -> torch.Tensor:
-        extra = padding_mask.size(1) % features.size(1)
-        if extra > 0:
-            padding_mask = padding_mask[:, :-extra]
-        padding_mask = padding_mask.view(
-            padding_mask.size(0), features.size(1), -1
-        )
-        padding_mask = padding_mask.all(-1)
-        return padding_mask
-
-    def extract_features(
-        self,
-        source: torch.Tensor,
-        padding_mask: Optional[torch.Tensor] = None,
-        mask: bool = False,
-        ret_conv: bool = False,
-        output_layer: Optional[int] = None,
-        ret_layer_results: bool = False,
-    ):
-
-        if self.feature_grad_mult > 0:
-            features = self.feature_extractor(source)
-            if self.feature_grad_mult != 1.0:
-                features = GradMultiply.apply(features, self.feature_grad_mult)
-        else:
-            with torch.no_grad():
-                features = self.feature_extractor(source)
-
-        features = features.transpose(1, 2)
-        features = self.layer_norm(features)
-
-        if padding_mask is not None:
-            padding_mask = self.forward_padding_mask(features, padding_mask)
-
-        if self.post_extract_proj is not None:
-            features = self.post_extract_proj(features)
-
-        features = self.dropout_input(features)
-
-        if mask:
-            x, mask_indices = self.apply_mask(
-                features, padding_mask
-            )
-        else:
-            x = features
-
-        # feature: (B, T, D), float
-        # target: (B, T), long
-        # x: (B, T, D), float
-        # padding_mask: (B, T), bool
-        # mask_indices: (B, T), bool
-        x, layer_results = self.encoder(
-            x,
-            padding_mask=padding_mask,
-            layer=None if output_layer is None else output_layer - 1
-        )
-
-        res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results}
-
-        feature = res["features"] if ret_conv else res["x"]
-        if ret_layer_results:
-            feature = (feature, res["layer_results"])
-        return feature, res["padding_mask"]
-
-
-class ConvFeatureExtractionModel(nn.Module):
-    def __init__(
-            self,
-            conv_layers: List[Tuple[int, int, int]],
-            dropout: float = 0.0,
-            mode: str = "default",
-            conv_bias: bool = False,
-            conv_type: str = "default"
-    ):
-        super().__init__()
-
-        assert mode in {"default", "layer_norm"}
-
-        def block(
-                n_in,
-                n_out,
-                k,
-                stride,
-                is_layer_norm=False,
-                is_group_norm=False,
-                conv_bias=False,
-        ):
-            def make_conv():
-                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
-                nn.init.kaiming_normal_(conv.weight)
-                return conv
-
-            assert (
-                           is_layer_norm and is_group_norm
-                   ) == False, "layer norm and group norm are exclusive"
-
-            if is_layer_norm:
-                return nn.Sequential(
-                    make_conv(),
-                    nn.Dropout(p=dropout),
-                    nn.Sequential(
-                        TransposeLast(),
-                        Fp32LayerNorm(dim, elementwise_affine=True),
-                        TransposeLast(),
-                    ),
-                    nn.GELU(),
-                )
-            elif is_group_norm:
-                return nn.Sequential(
-                    make_conv(),
-                    nn.Dropout(p=dropout),
-                    Fp32GroupNorm(dim, dim, affine=True),
-                    nn.GELU(),
-                )
-            else:
-                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
-
-        self.conv_type = conv_type
-        if self.conv_type == "default":
-            in_d = 1
-            self.conv_layers = nn.ModuleList()
-            for i, cl in enumerate(conv_layers):
-                assert len(cl) == 3, "invalid conv definition: " + str(cl)
-                (dim, k, stride) = cl
-
-                self.conv_layers.append(
-                    block(
-                        in_d,
-                        dim,
-                        k,
-                        stride,
-                        is_layer_norm=mode == "layer_norm",
-                        is_group_norm=mode == "default" and i == 0,
-                        conv_bias=conv_bias,
-                    )
-                )
-                in_d = dim
-        elif self.conv_type == "conv2d":
-            in_d = 1
-            self.conv_layers = nn.ModuleList()
-            for i, cl in enumerate(conv_layers):
-                assert len(cl) == 3
-                (dim, k, stride) = cl
-
-                self.conv_layers.append(
-                    torch.nn.Conv2d(in_d, dim, k, stride)
-                )
-                self.conv_layers.append(torch.nn.ReLU())
-                in_d = dim
-        elif self.conv_type == "custom":
-            in_d = 1
-            idim = 80
-            self.conv_layers = nn.ModuleList()
-            for i, cl in enumerate(conv_layers):
-                assert len(cl) == 3
-                (dim, k, stride) = cl
-                self.conv_layers.append(
-                    torch.nn.Conv2d(in_d, dim, k, stride, padding=1)
-                )
-                self.conv_layers.append(
-                    torch.nn.LayerNorm([dim, idim])
-                )
-                self.conv_layers.append(torch.nn.ReLU())
-                in_d = dim
-                if (i + 1) % 2 == 0:
-                    self.conv_layers.append(
-                        torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
-                    )
-                    idim = int(math.ceil(idim / 2))
-        else:
-            pass
-
-    def forward(self, x, mask=None):
-
-        # BxT -> BxCxT
-        x = x.unsqueeze(1)
-        if self.conv_type == "custom":
-            for conv in self.conv_layers:
-                if isinstance(conv, nn.LayerNorm):
-                    x = x.transpose(1, 2)
-                    x = conv(x).transpose(1, 2)
-                else:
-                    x = conv(x)
-            x = x.transpose(2, 3).contiguous()
-            x = x.view(x.size(0), -1, x.size(-1))
-        else:
-            for conv in self.conv_layers:
-                x = conv(x)
-            if self.conv_type == "conv2d":
-                b, c, t, f = x.size()
-                x = x.transpose(2, 3).contiguous().view(b, c * f, t)
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-
-        self.dropout = args.dropout
-        self.embedding_dim = args.encoder_embed_dim
-
-        self.pos_conv = nn.Conv1d(
-            self.embedding_dim,
-            self.embedding_dim,
-            kernel_size=args.conv_pos,
-            padding=args.conv_pos // 2,
-            groups=args.conv_pos_groups,
-        )
-        dropout = 0
-        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
-        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
-        nn.init.constant_(self.pos_conv.bias, 0)
-
-        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
-        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
-
-        if hasattr(args, "relative_position_embedding"):
-            self.relative_position_embedding = args.relative_position_embedding
-            self.num_buckets = args.num_buckets
-            self.max_distance = args.max_distance
-        else:
-            self.relative_position_embedding = False
-            self.num_buckets = 0
-            self.max_distance = 0
-
-        self.layers = nn.ModuleList(
-            [
-                TransformerSentenceEncoderLayer(
-                    embedding_dim=self.embedding_dim,
-                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
-                    num_attention_heads=args.encoder_attention_heads,
-                    dropout=self.dropout,
-                    attention_dropout=args.attention_dropout,
-                    activation_dropout=args.activation_dropout,
-                    activation_fn=args.activation_fn,
-                    layer_norm_first=args.layer_norm_first,
-                    has_relative_attention_bias=(self.relative_position_embedding and i == 0),
-                    num_buckets=self.num_buckets,
-                    max_distance=self.max_distance,
-                    gru_rel_pos=args.gru_rel_pos,
-                )
-                for i in range(args.encoder_layers)
-            ]
-        )
-
-        self.layer_norm_first = args.layer_norm_first
-        self.layer_norm = LayerNorm(self.embedding_dim)
-        self.layerdrop = args.encoder_layerdrop
-
-        self.apply(init_bert_params)
-
-    def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
-        x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)
-
-        if self.layer_norm_first and layer is None:
-            x = self.layer_norm(x)
-
-        return x, layer_results
-
-    def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None):
-
-        if padding_mask is not None:
-            x[padding_mask] = 0
-
-        x_conv = self.pos_conv(x.transpose(1, 2))
-        x_conv = x_conv.transpose(1, 2)
-        x = x + x_conv
-
-        if not self.layer_norm_first:
-            x = self.layer_norm(x)
-
-        x = F.dropout(x, p=self.dropout, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        layer_results = []
-        z = None
-        if tgt_layer is not None:
-            layer_results.append((x, z))
-        r = None
-        pos_bias = None
-        for i, layer in enumerate(self.layers):
-            dropout_probability = np.random.random()
-            if not self.training or (dropout_probability > self.layerdrop):
-                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False,
-                                       self_attn_mask=streaming_mask, pos_bias=pos_bias)
-            if tgt_layer is not None:
-                layer_results.append((x, z))
-            if i == tgt_layer:
-                r = x
-                break
-
-        if r is not None:
-            x = r
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        return x, layer_results
-
-
-class TransformerSentenceEncoderLayer(nn.Module):
-    """
-    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
-    models.
-    """
-
-    def __init__(
-            self,
-            embedding_dim: float = 768,
-            ffn_embedding_dim: float = 3072,
-            num_attention_heads: float = 8,
-            dropout: float = 0.1,
-            attention_dropout: float = 0.1,
-            activation_dropout: float = 0.1,
-            activation_fn: str = "relu",
-            layer_norm_first: bool = False,
-            has_relative_attention_bias: bool = False,
-            num_buckets: int = 0,
-            max_distance: int = 0,
-            rescale_init: bool = False,
-            gru_rel_pos: bool = False,
-    ) -> None:
-
-        super().__init__()
-        # Initialize parameters
-        self.embedding_dim = embedding_dim
-        self.dropout = dropout
-        self.activation_dropout = activation_dropout
-
-        # Initialize blocks
-        self.activation_name = activation_fn
-        self.activation_fn = get_activation_fn(activation_fn)
-        self.self_attn = MultiheadAttention(
-            self.embedding_dim,
-            num_attention_heads,
-            dropout=attention_dropout,
-            self_attention=True,
-            has_relative_attention_bias=has_relative_attention_bias,
-            num_buckets=num_buckets,
-            max_distance=max_distance,
-            rescale_init=rescale_init,
-            gru_rel_pos=gru_rel_pos,
-        )
-
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(self.activation_dropout)
-        self.dropout3 = nn.Dropout(dropout)
-
-        self.layer_norm_first = layer_norm_first
-
-        # layer norm associated with the self attention layer
-        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
-
-        if self.activation_name == "glu":
-            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
-        else:
-            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
-        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
-
-        # layer norm associated with the position wise feed-forward NN
-        self.final_layer_norm = LayerNorm(self.embedding_dim)
-
-    def forward(
-            self,
-            x: torch.Tensor,
-            self_attn_mask: torch.Tensor = None,
-            self_attn_padding_mask: torch.Tensor = None,
-            need_weights: bool = False,
-            pos_bias=None
-    ):
-        """
-        LayerNorm is applied either before or after the self-attention/ffn
-        modules similar to the original Transformer imlementation.
-        """
-        residual = x
-
-        if self.layer_norm_first:
-            x = self.self_attn_layer_norm(x)
-            x, attn, pos_bias = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=self_attn_padding_mask,
-                need_weights=False,
-                attn_mask=self_attn_mask,
-                position_bias=pos_bias
-            )
-            x = self.dropout1(x)
-            x = residual + x
-
-            residual = x
-            x = self.final_layer_norm(x)
-            if self.activation_name == "glu":
-                x = self.fc1(x)
-            else:
-                x = self.activation_fn(self.fc1(x))
-            x = self.dropout2(x)
-            x = self.fc2(x)
-            x = self.dropout3(x)
-            x = residual + x
-        else:
-            x, attn, pos_bias = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=self_attn_padding_mask,
-                need_weights=need_weights,
-                attn_mask=self_attn_mask,
-                position_bias=pos_bias
-            )
-
-            x = self.dropout1(x)
-            x = residual + x
-
-            x = self.self_attn_layer_norm(x)
-
-            residual = x
-            if self.activation_name == "glu":
-                x = self.fc1(x)
-            else:
-                x = self.activation_fn(self.fc1(x))
-            x = self.dropout2(x)
-            x = self.fc2(x)
-            x = self.dropout3(x)
-            x = residual + x
-            x = self.final_layer_norm(x)
-
-        return x, attn, pos_bias
-
diff --git a/spaces/youngtsai/Mandarin-TTS/text/symbols.py b/spaces/youngtsai/Mandarin-TTS/text/symbols.py
deleted file mode 100644
index 80fd41ea8ee57725ce0f76aa5347a3a1fdd0047d..0000000000000000000000000000000000000000
--- a/spaces/youngtsai/Mandarin-TTS/text/symbols.py
+++ /dev/null
@@ -1,71 +0,0 @@
-_pause = ["sil", "eos", "sp", "#0", "#1", "#2", "#3"]
-
-_initials = [
-    "^",
-    "b",
-    "c",
-    "ch",
-    "d",
-    "f",
-    "g",
-    "h",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "x",
-    "z",
-    "zh",
-]
-
-_tones = ["1", "2", "3", "4", "5"]
-
-_finals = [
-    "a",
-    "ai",
-    "an",
-    "ang",
-    "ao",
-    "e",
-    "ei",
-    "en",
-    "eng",
-    "er",
-    "i",
-    "ia",
-    "ian",
-    "iang",
-    "iao",
-    "ie",
-    "ii",
-    "iii",
-    "in",
-    "ing",
-    "iong",
-    "iou",
-    "o",
-    "ong",
-    "ou",
-    "u",
-    "ua",
-    "uai",
-    "uan",
-    "uang",
-    "uei",
-    "uen",
-    "ueng",
-    "uo",
-    "v",
-    "van",
-    "ve",
-    "vn",
-]
-
-symbols = _pause + _initials + [i + j for i in _finals for j in _tones]
\ No newline at end of file
diff --git a/spaces/ysharma/ernie_vilg_english/app.py b/spaces/ysharma/ernie_vilg_english/app.py
deleted file mode 100644
index 3b186b018f72371cd5ca6dcf3bcf745c212a4933..0000000000000000000000000000000000000000
--- a/spaces/ysharma/ernie_vilg_english/app.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import gradio as gr
-import os 
-import requests
-import time 
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import paddlehub as hub
-# Importing the essential libraries for monitoring
-import psutil
-
-HF_TOKEN = os.environ["HF_TOKEN"]
-model = hub.Module(name='ernie_vilg')
-
-def get_ernie_vilg(text_prompts, style): 
-  style = style.split('-')[0]
-  results = model.generate_image(text_prompts=text_prompts, style=style, visualization=False)
-  #for CPU monitoring
-  # Testing the psutil library for both CPU and RAM performance details
-  print(f"ERNIE CPU percent is: {psutil.cpu_percent()}")
-  print(f"ERNIE virtual memory is : {psutil.virtual_memory().percent}")
-
-  return results[0]
-  
-sd_inf = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion", use_auth_token=HF_TOKEN)
- 
-nllb_model_name = 'facebook/nllb-200-distilled-600M'
-nllb_model = AutoModelForSeq2SeqLM.from_pretrained(nllb_model_name)
-nllb_tokenizer = AutoTokenizer.from_pretrained(nllb_model_name)
-
-def get_chinese_translation(text):  #in_language_first, in_language_second, 
-  print("********Inside get_chinese_translation ********")
-  src = 'eng_Latn' 
-  tgt= 'zho_Hans'
-  print(f"text is :{text}, source language is : {src}, target language is : {tgt} ") 
-
-  translator = pipeline('translation', model=nllb_model, tokenizer=nllb_tokenizer, src_lang=src, tgt_lang=tgt)
-  output = translator(text, max_length=400)
-  print(f"initial output is:{output}")
-  output = output[0]['translation_text']
-  print(f"output is:{output}")
-  
-  # for CPU monitoring
-  # Testing the psutil library for both CPU and RAM performance details
-  print(f"CPU percent is: {psutil.cpu_percent()}")
-  print(f"virtual memory is : {psutil.virtual_memory().percent}")
-
-  return output 
-  
-#Block inference not working for stable diffusion   
-def get_sd(translated_txt, samples, steps, scale, seed):
-  print("******** Inside get_SD ********")
-  print(f"translated_txt is : {translated_txt}")
-  sd_img_gallery = sd_inf(translated_txt, samples, steps, scale, seed, fn_index=1)[0] 
-  
-  return sd_img_gallery
-
-demo = gr.Blocks()
-
-with demo:
-  gr.Markdown("<h1><center>ERNIE in English !</center></h1>")
-  gr.Markdown("<h3><center>ERNIE-ViLG is a state-of-the-art text-to-image model that generates images from simplified Chinese text.</center></h3>")
-  gr.Markdown("<h3><center>This app helps you in checking-out ERNIE in English. Note that due to limitations on available Ram, only one image is being generated at the moment<br><br>Please access the original model here - [ERNIE-ViLG](https://huggingface.co/spaces/PaddlePaddle/ERNIE-ViLG)</center></h3>")
-  with gr.Row():
-    with gr.Column():
-      in_text_prompt = gr.Textbox(label="Enter English text here")
-      out_text_chinese = gr.Textbox(label="Text in Simplified Chinese")
-      
-    b1 = gr.Button("English to Simplified Chinese")
-    
-    #s1 = gr.Slider(label='samples', value=4, visible=False)
-    #s2 = gr.Slider(label='steps', value=45, visible=False)
-    #s3 = gr.Slider(label='scale', value=7.5, visible=False)
-    #s4 = gr.Slider(label='seed', value=1024, visible=False)
-    
-  with gr.Row():
-    with gr.Column():
-      in_styles = gr.Dropdown(['水彩-WaterColor', '油画-OilPainting', '粉笔画-Painting', '卡通-Cartoon', '蜡笔画-Pencils', '儿童画-ChildrensPaintings', '探索无限-ExploringTheInfinite'])
-      b2 = gr.Button("Generate Images from Ernie")
-      
-    out_ernie = gr.Image(type="pil", label="Ernie output for the given prompt")
-    #out_gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery") #.style(grid=[2, 3], height="auto")
-    #in_language_first = gr.Textbox(visible=False, value= 'eng_Latn') #'English'
-    #in_language_second = gr.Textbox(visible=False, value= 'zho_Hans') #'Chinese (Simplified)'
-    
-    
-    #out_sd = gr.Image(type="pil", label="SD output for the given prompt")
-    #b3 = gr.Button("Generate Images from SD")
-    
-    b1.click(get_chinese_translation, in_text_prompt, out_text_chinese )  #[in_language_first, in_language_second, 
-    b2.click(get_ernie_vilg, [out_text_chinese, in_styles], out_ernie)  
-    
-    #b3.click(get_sd, [in_text_prompt,s1,s2,s3,s4], out_sd) #out_gallery )  
-    
-demo.launch(enable_queue=True, debug=True) 
\ No newline at end of file
diff --git a/spaces/yuzu34/rvc-hololive/README.md b/spaces/yuzu34/rvc-hololive/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/yuzu34/rvc-hololive/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/zekewilliams/ControlNet/app_scribble_interactive.py b/spaces/zekewilliams/ControlNet/app_scribble_interactive.py
deleted file mode 100644
index 94fce120e69463bdee121804ea2c48f4a0475dff..0000000000000000000000000000000000000000
--- a/spaces/zekewilliams/ControlNet/app_scribble_interactive.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_scribble2image_interactive.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-import numpy as np
-
-
-def create_canvas(w, h):
-    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown(
-                '## Control Stable Diffusion with Interactive Scribbles')
-        with gr.Row():
-            with gr.Column():
-                canvas_width = gr.Slider(label='Canvas Width',
-                                         minimum=256,
-                                         maximum=512,
-                                         value=512,
-                                         step=1)
-                canvas_height = gr.Slider(label='Canvas Height',
-                                          minimum=256,
-                                          maximum=512,
-                                          value=512,
-                                          step=1)
-                create_button = gr.Button(label='Start',
-                                          value='Open drawing canvas!')
-                input_image = gr.Image(source='upload',
-                                       type='numpy',
-                                       tool='sketch')
-                gr.Markdown(
-                    value=
-                    'Do not forget to change your brush width to make it thinner. (Gradio do not allow developers to set brush width so you need to do it manually.) '
-                    'Just click on the small pencil icon in the upper right corner of the above block.'
-                )
-                create_button.click(fn=create_canvas,
-                                    inputs=[canvas_width, canvas_height],
-                                    outputs=input_image,
-                                    queue=False)
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process, inputs=inputs, outputs=result)
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_scribble_interactive)
-    demo.queue().launch()
diff --git a/spaces/zhang-wei-jian/docker/node_modules/debug/src/node.js b/spaces/zhang-wei-jian/docker/node_modules/debug/src/node.js
deleted file mode 100644
index 79bc085cb0230c69aaeb76bbc1374f43ab6414f4..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/debug/src/node.js
+++ /dev/null
@@ -1,263 +0,0 @@
-/**
- * Module dependencies.
- */
-
-const tty = require('tty');
-const util = require('util');
-
-/**
- * This is the Node.js implementation of `debug()`.
- */
-
-exports.init = init;
-exports.log = log;
-exports.formatArgs = formatArgs;
-exports.save = save;
-exports.load = load;
-exports.useColors = useColors;
-exports.destroy = util.deprecate(
-	() => {},
-	'Instance method `debug.destroy()` is deprecated and no longer does anything. It will be removed in the next major version of `debug`.'
-);
-
-/**
- * Colors.
- */
-
-exports.colors = [6, 2, 3, 4, 5, 1];
-
-try {
-	// Optional dependency (as in, doesn't need to be installed, NOT like optionalDependencies in package.json)
-	// eslint-disable-next-line import/no-extraneous-dependencies
-	const supportsColor = require('supports-color');
-
-	if (supportsColor && (supportsColor.stderr || supportsColor).level >= 2) {
-		exports.colors = [
-			20,
-			21,
-			26,
-			27,
-			32,
-			33,
-			38,
-			39,
-			40,
-			41,
-			42,
-			43,
-			44,
-			45,
-			56,
-			57,
-			62,
-			63,
-			68,
-			69,
-			74,
-			75,
-			76,
-			77,
-			78,
-			79,
-			80,
-			81,
-			92,
-			93,
-			98,
-			99,
-			112,
-			113,
-			128,
-			129,
-			134,
-			135,
-			148,
-			149,
-			160,
-			161,
-			162,
-			163,
-			164,
-			165,
-			166,
-			167,
-			168,
-			169,
-			170,
-			171,
-			172,
-			173,
-			178,
-			179,
-			184,
-			185,
-			196,
-			197,
-			198,
-			199,
-			200,
-			201,
-			202,
-			203,
-			204,
-			205,
-			206,
-			207,
-			208,
-			209,
-			214,
-			215,
-			220,
-			221
-		];
-	}
-} catch (error) {
-	// Swallow - we only care if `supports-color` is available; it doesn't have to be.
-}
-
-/**
- * Build up the default `inspectOpts` object from the environment variables.
- *
- *   $ DEBUG_COLORS=no DEBUG_DEPTH=10 DEBUG_SHOW_HIDDEN=enabled node script.js
- */
-
-exports.inspectOpts = Object.keys(process.env).filter(key => {
-	return /^debug_/i.test(key);
-}).reduce((obj, key) => {
-	// Camel-case
-	const prop = key
-		.substring(6)
-		.toLowerCase()
-		.replace(/_([a-z])/g, (_, k) => {
-			return k.toUpperCase();
-		});
-
-	// Coerce string value into JS value
-	let val = process.env[key];
-	if (/^(yes|on|true|enabled)$/i.test(val)) {
-		val = true;
-	} else if (/^(no|off|false|disabled)$/i.test(val)) {
-		val = false;
-	} else if (val === 'null') {
-		val = null;
-	} else {
-		val = Number(val);
-	}
-
-	obj[prop] = val;
-	return obj;
-}, {});
-
-/**
- * Is stdout a TTY? Colored output is enabled when `true`.
- */
-
-function useColors() {
-	return 'colors' in exports.inspectOpts ?
-		Boolean(exports.inspectOpts.colors) :
-		tty.isatty(process.stderr.fd);
-}
-
-/**
- * Adds ANSI color escape codes if enabled.
- *
- * @api public
- */
-
-function formatArgs(args) {
-	const {namespace: name, useColors} = this;
-
-	if (useColors) {
-		const c = this.color;
-		const colorCode = '\u001B[3' + (c < 8 ? c : '8;5;' + c);
-		const prefix = `  ${colorCode};1m${name} \u001B[0m`;
-
-		args[0] = prefix + args[0].split('\n').join('\n' + prefix);
-		args.push(colorCode + 'm+' + module.exports.humanize(this.diff) + '\u001B[0m');
-	} else {
-		args[0] = getDate() + name + ' ' + args[0];
-	}
-}
-
-function getDate() {
-	if (exports.inspectOpts.hideDate) {
-		return '';
-	}
-	return new Date().toISOString() + ' ';
-}
-
-/**
- * Invokes `util.format()` with the specified arguments and writes to stderr.
- */
-
-function log(...args) {
-	return process.stderr.write(util.format(...args) + '\n');
-}
-
-/**
- * Save `namespaces`.
- *
- * @param {String} namespaces
- * @api private
- */
-function save(namespaces) {
-	if (namespaces) {
-		process.env.DEBUG = namespaces;
-	} else {
-		// If you set a process.env field to null or undefined, it gets cast to the
-		// string 'null' or 'undefined'. Just delete instead.
-		delete process.env.DEBUG;
-	}
-}
-
-/**
- * Load `namespaces`.
- *
- * @return {String} returns the previously persisted debug modes
- * @api private
- */
-
-function load() {
-	return process.env.DEBUG;
-}
-
-/**
- * Init logic for `debug` instances.
- *
- * Create a new `inspectOpts` object in case `useColors` is set
- * differently for a particular `debug` instance.
- */
-
-function init(debug) {
-	debug.inspectOpts = {};
-
-	const keys = Object.keys(exports.inspectOpts);
-	for (let i = 0; i < keys.length; i++) {
-		debug.inspectOpts[keys[i]] = exports.inspectOpts[keys[i]];
-	}
-}
-
-module.exports = require('./common')(exports);
-
-const {formatters} = module.exports;
-
-/**
- * Map %o to `util.inspect()`, all on a single line.
- */
-
-formatters.o = function (v) {
-	this.inspectOpts.colors = this.useColors;
-	return util.inspect(v, this.inspectOpts)
-		.split('\n')
-		.map(str => str.trim())
-		.join(' ');
-};
-
-/**
- * Map %O to `util.inspect()`, allowing multiple lines if needed.
- */
-
-formatters.O = function (v) {
-	this.inspectOpts.colors = this.useColors;
-	return util.inspect(v, this.inspectOpts);
-};
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-loose.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-loose.js
deleted file mode 100644
index 4881fbe00250c53f1508d86192be31bae9781f3e..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-loose.js
+++ /dev/null
@@ -1,3 +0,0 @@
-const compare = require('./compare')
-const compareLoose = (a, b) => compare(a, b, true)
-module.exports = compareLoose
diff --git a/spaces/zhang-wei-jian/docker/node_modules/to-regex-range/index.js b/spaces/zhang-wei-jian/docker/node_modules/to-regex-range/index.js
deleted file mode 100644
index 77fbaced17fc86d8cd3f73912b5dd56f3cfb3aaf..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/to-regex-range/index.js
+++ /dev/null
@@ -1,288 +0,0 @@
-/*!
- * to-regex-range <https://github.com/micromatch/to-regex-range>
- *
- * Copyright (c) 2015-present, Jon Schlinkert.
- * Released under the MIT License.
- */
-
-'use strict';
-
-const isNumber = require('is-number');
-
-const toRegexRange = (min, max, options) => {
-  if (isNumber(min) === false) {
-    throw new TypeError('toRegexRange: expected the first argument to be a number');
-  }
-
-  if (max === void 0 || min === max) {
-    return String(min);
-  }
-
-  if (isNumber(max) === false) {
-    throw new TypeError('toRegexRange: expected the second argument to be a number.');
-  }
-
-  let opts = { relaxZeros: true, ...options };
-  if (typeof opts.strictZeros === 'boolean') {
-    opts.relaxZeros = opts.strictZeros === false;
-  }
-
-  let relax = String(opts.relaxZeros);
-  let shorthand = String(opts.shorthand);
-  let capture = String(opts.capture);
-  let wrap = String(opts.wrap);
-  let cacheKey = min + ':' + max + '=' + relax + shorthand + capture + wrap;
-
-  if (toRegexRange.cache.hasOwnProperty(cacheKey)) {
-    return toRegexRange.cache[cacheKey].result;
-  }
-
-  let a = Math.min(min, max);
-  let b = Math.max(min, max);
-
-  if (Math.abs(a - b) === 1) {
-    let result = min + '|' + max;
-    if (opts.capture) {
-      return `(${result})`;
-    }
-    if (opts.wrap === false) {
-      return result;
-    }
-    return `(?:${result})`;
-  }
-
-  let isPadded = hasPadding(min) || hasPadding(max);
-  let state = { min, max, a, b };
-  let positives = [];
-  let negatives = [];
-
-  if (isPadded) {
-    state.isPadded = isPadded;
-    state.maxLen = String(state.max).length;
-  }
-
-  if (a < 0) {
-    let newMin = b < 0 ? Math.abs(b) : 1;
-    negatives = splitToPatterns(newMin, Math.abs(a), state, opts);
-    a = state.a = 0;
-  }
-
-  if (b >= 0) {
-    positives = splitToPatterns(a, b, state, opts);
-  }
-
-  state.negatives = negatives;
-  state.positives = positives;
-  state.result = collatePatterns(negatives, positives, opts);
-
-  if (opts.capture === true) {
-    state.result = `(${state.result})`;
-  } else if (opts.wrap !== false && (positives.length + negatives.length) > 1) {
-    state.result = `(?:${state.result})`;
-  }
-
-  toRegexRange.cache[cacheKey] = state;
-  return state.result;
-};
-
-function collatePatterns(neg, pos, options) {
-  let onlyNegative = filterPatterns(neg, pos, '-', false, options) || [];
-  let onlyPositive = filterPatterns(pos, neg, '', false, options) || [];
-  let intersected = filterPatterns(neg, pos, '-?', true, options) || [];
-  let subpatterns = onlyNegative.concat(intersected).concat(onlyPositive);
-  return subpatterns.join('|');
-}
-
-function splitToRanges(min, max) {
-  let nines = 1;
-  let zeros = 1;
-
-  let stop = countNines(min, nines);
-  let stops = new Set([max]);
-
-  while (min <= stop && stop <= max) {
-    stops.add(stop);
-    nines += 1;
-    stop = countNines(min, nines);
-  }
-
-  stop = countZeros(max + 1, zeros) - 1;
-
-  while (min < stop && stop <= max) {
-    stops.add(stop);
-    zeros += 1;
-    stop = countZeros(max + 1, zeros) - 1;
-  }
-
-  stops = [...stops];
-  stops.sort(compare);
-  return stops;
-}
-
-/**
- * Convert a range to a regex pattern
- * @param {Number} `start`
- * @param {Number} `stop`
- * @return {String}
- */
-
-function rangeToPattern(start, stop, options) {
-  if (start === stop) {
-    return { pattern: start, count: [], digits: 0 };
-  }
-
-  let zipped = zip(start, stop);
-  let digits = zipped.length;
-  let pattern = '';
-  let count = 0;
-
-  for (let i = 0; i < digits; i++) {
-    let [startDigit, stopDigit] = zipped[i];
-
-    if (startDigit === stopDigit) {
-      pattern += startDigit;
-
-    } else if (startDigit !== '0' || stopDigit !== '9') {
-      pattern += toCharacterClass(startDigit, stopDigit, options);
-
-    } else {
-      count++;
-    }
-  }
-
-  if (count) {
-    pattern += options.shorthand === true ? '\\d' : '[0-9]';
-  }
-
-  return { pattern, count: [count], digits };
-}
-
-function splitToPatterns(min, max, tok, options) {
-  let ranges = splitToRanges(min, max);
-  let tokens = [];
-  let start = min;
-  let prev;
-
-  for (let i = 0; i < ranges.length; i++) {
-    let max = ranges[i];
-    let obj = rangeToPattern(String(start), String(max), options);
-    let zeros = '';
-
-    if (!tok.isPadded && prev && prev.pattern === obj.pattern) {
-      if (prev.count.length > 1) {
-        prev.count.pop();
-      }
-
-      prev.count.push(obj.count[0]);
-      prev.string = prev.pattern + toQuantifier(prev.count);
-      start = max + 1;
-      continue;
-    }
-
-    if (tok.isPadded) {
-      zeros = padZeros(max, tok, options);
-    }
-
-    obj.string = zeros + obj.pattern + toQuantifier(obj.count);
-    tokens.push(obj);
-    start = max + 1;
-    prev = obj;
-  }
-
-  return tokens;
-}
-
-function filterPatterns(arr, comparison, prefix, intersection, options) {
-  let result = [];
-
-  for (let ele of arr) {
-    let { string } = ele;
-
-    // only push if _both_ are negative...
-    if (!intersection && !contains(comparison, 'string', string)) {
-      result.push(prefix + string);
-    }
-
-    // or _both_ are positive
-    if (intersection && contains(comparison, 'string', string)) {
-      result.push(prefix + string);
-    }
-  }
-  return result;
-}
-
-/**
- * Zip strings
- */
-
-function zip(a, b) {
-  let arr = [];
-  for (let i = 0; i < a.length; i++) arr.push([a[i], b[i]]);
-  return arr;
-}
-
-function compare(a, b) {
-  return a > b ? 1 : b > a ? -1 : 0;
-}
-
-function contains(arr, key, val) {
-  return arr.some(ele => ele[key] === val);
-}
-
-function countNines(min, len) {
-  return Number(String(min).slice(0, -len) + '9'.repeat(len));
-}
-
-function countZeros(integer, zeros) {
-  return integer - (integer % Math.pow(10, zeros));
-}
-
-function toQuantifier(digits) {
-  let [start = 0, stop = ''] = digits;
-  if (stop || start > 1) {
-    return `{${start + (stop ? ',' + stop : '')}}`;
-  }
-  return '';
-}
-
-function toCharacterClass(a, b, options) {
-  return `[${a}${(b - a === 1) ? '' : '-'}${b}]`;
-}
-
-function hasPadding(str) {
-  return /^-?(0+)\d/.test(str);
-}
-
-function padZeros(value, tok, options) {
-  if (!tok.isPadded) {
-    return value;
-  }
-
-  let diff = Math.abs(tok.maxLen - String(value).length);
-  let relax = options.relaxZeros !== false;
-
-  switch (diff) {
-    case 0:
-      return '';
-    case 1:
-      return relax ? '0?' : '0';
-    case 2:
-      return relax ? '0{0,2}' : '00';
-    default: {
-      return relax ? `0{0,${diff}}` : `0{${diff}}`;
-    }
-  }
-}
-
-/**
- * Cache
- */
-
-toRegexRange.cache = {};
-toRegexRange.clearCache = () => (toRegexRange.cache = {});
-
-/**
- * Expose `toRegexRange`
- */
-
-module.exports = toRegexRange;
diff --git a/spaces/zhanpj/ChatGPT/modules/shared.py b/spaces/zhanpj/ChatGPT/modules/shared.py
deleted file mode 100644
index 4046900a39b2fc7bdd8005844a92dc7d4eb669b6..0000000000000000000000000000000000000000
--- a/spaces/zhanpj/ChatGPT/modules/shared.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from modules.presets import API_URL
-
-class State:
-    interrupted = False
-    api_url = API_URL
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def recover(self):
-        self.interrupted = False
-
-    def set_api_url(self, api_url):
-        self.api_url = api_url
-
-    def reset_api_url(self):
-        self.api_url = API_URL
-        return self.api_url
-
-    def reset_all(self):
-        self.interrupted = False
-        self.api_url = API_URL
-
-state = State()
diff --git a/spaces/zideliu/styledrop/timm/models/dla.py b/spaces/zideliu/styledrop/timm/models/dla.py
deleted file mode 100644
index a41ec3260b9aacf01e2f9fb953b31649c374fd55..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/models/dla.py
+++ /dev/null
@@ -1,438 +0,0 @@
-""" Deep Layer Aggregation and DLA w/ Res2Net
-DLA original adapted from Official Pytorch impl at:
-DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
-
-Res2Net additions from: https://github.com/gasvn/Res2Net/
-Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
-"""
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import create_classifier
-from .registry import register_model
-
-__all__ = ['DLA']
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 0.875, 'interpolation': 'bilinear',
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'base_layer.0', 'classifier': 'fc',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'dla34': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth'),
-    'dla46_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth'),
-    'dla46x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth'),
-    'dla60x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth'),
-    'dla60': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth'),
-    'dla60x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth'),
-    'dla102': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth'),
-    'dla102x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth'),
-    'dla102x2': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth'),
-    'dla169': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth'),
-    'dla60_res2net': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'),
-    'dla60_res2next': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth'),
-}
-
-
-class DlaBasic(nn.Module):
-    """DLA Basic"""
-
-    def __init__(self, inplanes, planes, stride=1, dilation=1, **_):
-        super(DlaBasic, self).__init__()
-        self.conv1 = nn.Conv2d(
-            inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.stride = stride
-
-    def forward(self, x, residual=None):
-        if residual is None:
-            residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class DlaBottleneck(nn.Module):
-    """DLA/DLA-X Bottleneck"""
-    expansion = 2
-
-    def __init__(self, inplanes, outplanes, stride=1, dilation=1, cardinality=1, base_width=64):
-        super(DlaBottleneck, self).__init__()
-        self.stride = stride
-        mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
-        mid_planes = mid_planes // self.expansion
-
-        self.conv1 = nn.Conv2d(inplanes, mid_planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_planes)
-        self.conv2 = nn.Conv2d(
-            mid_planes, mid_planes, kernel_size=3, stride=stride, padding=dilation,
-            bias=False, dilation=dilation, groups=cardinality)
-        self.bn2 = nn.BatchNorm2d(mid_planes)
-        self.conv3 = nn.Conv2d(mid_planes, outplanes, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(outplanes)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x, residual=None):
-        if residual is None:
-            residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class DlaBottle2neck(nn.Module):
-    """ Res2Net/Res2NeXT DLA Bottleneck
-    Adapted from https://github.com/gasvn/Res2Net/blob/master/dla.py
-    """
-    expansion = 2
-
-    def __init__(self, inplanes, outplanes, stride=1, dilation=1, scale=4, cardinality=8, base_width=4):
-        super(DlaBottle2neck, self).__init__()
-        self.is_first = stride > 1
-        self.scale = scale
-        mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
-        mid_planes = mid_planes // self.expansion
-        self.width = mid_planes
-
-        self.conv1 = nn.Conv2d(inplanes, mid_planes * scale, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_planes * scale)
-
-        num_scale_convs = max(1, scale - 1)
-        convs = []
-        bns = []
-        for _ in range(num_scale_convs):
-            convs.append(nn.Conv2d(
-                mid_planes, mid_planes, kernel_size=3, stride=stride,
-                padding=dilation, dilation=dilation, groups=cardinality, bias=False))
-            bns.append(nn.BatchNorm2d(mid_planes))
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList(bns)
-        if self.is_first:
-            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
-
-        self.conv3 = nn.Conv2d(mid_planes * scale, outplanes, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(outplanes)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x, residual=None):
-        if residual is None:
-            residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        spx = torch.split(out, self.width, 1)
-        spo = []
-        for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
-            sp = spx[i] if i == 0 or self.is_first else sp + spx[i]
-            sp = conv(sp)
-            sp = bn(sp)
-            sp = self.relu(sp)
-            spo.append(sp)
-        if self.scale > 1:
-            spo.append(self.pool(spx[-1]) if self.is_first else spx[-1])
-        out = torch.cat(spo, 1)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class DlaRoot(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, residual):
-        super(DlaRoot, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2)
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU(inplace=True)
-        self.residual = residual
-
-    def forward(self, *x):
-        children = x
-        x = self.conv(torch.cat(x, 1))
-        x = self.bn(x)
-        if self.residual:
-            x += children[0]
-        x = self.relu(x)
-
-        return x
-
-
-class DlaTree(nn.Module):
-    def __init__(self, levels, block, in_channels, out_channels, stride=1,
-                 dilation=1, cardinality=1, base_width=64,
-                 level_root=False, root_dim=0, root_kernel_size=1, root_residual=False):
-        super(DlaTree, self).__init__()
-        if root_dim == 0:
-            root_dim = 2 * out_channels
-        if level_root:
-            root_dim += in_channels
-        self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity()
-        self.project = nn.Identity()
-        cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
-        if levels == 1:
-            self.tree1 = block(in_channels, out_channels, stride, **cargs)
-            self.tree2 = block(out_channels, out_channels, 1, **cargs)
-            if in_channels != out_channels:
-                # NOTE the official impl/weights have  project layers in levels > 1 case that are never
-                # used, I've moved the project layer here to avoid wasted params but old checkpoints will
-                # need strict=False while loading.
-                self.project = nn.Sequential(
-                    nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
-                    nn.BatchNorm2d(out_channels))
-        else:
-            cargs.update(dict(root_kernel_size=root_kernel_size, root_residual=root_residual))
-            self.tree1 = DlaTree(
-                levels - 1, block, in_channels, out_channels, stride, root_dim=0, **cargs)
-            self.tree2 = DlaTree(
-                levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, **cargs)
-        if levels == 1:
-            self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_residual)
-        self.level_root = level_root
-        self.root_dim = root_dim
-        self.levels = levels
-
-    def forward(self, x, residual=None, children=None):
-        children = [] if children is None else children
-        bottom = self.downsample(x)
-        residual = self.project(bottom)
-        if self.level_root:
-            children.append(bottom)
-        x1 = self.tree1(x, residual)
-        if self.levels == 1:
-            x2 = self.tree2(x1)
-            x = self.root(x2, x1, *children)
-        else:
-            children.append(x1)
-            x = self.tree2(x1, children=children)
-        return x
-
-
-class DLA(nn.Module):
-    def __init__(self, levels, channels, output_stride=32, num_classes=1000, in_chans=3,
-                 cardinality=1, base_width=64, block=DlaBottle2neck, residual_root=False,
-                 drop_rate=0.0, global_pool='avg'):
-        super(DLA, self).__init__()
-        self.channels = channels
-        self.num_classes = num_classes
-        self.cardinality = cardinality
-        self.base_width = base_width
-        self.drop_rate = drop_rate
-        assert output_stride == 32  # FIXME support dilation
-
-        self.base_layer = nn.Sequential(
-            nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
-            nn.BatchNorm2d(channels[0]),
-            nn.ReLU(inplace=True))
-        self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
-        self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2)
-        cargs = dict(cardinality=cardinality, base_width=base_width, root_residual=residual_root)
-        self.level2 = DlaTree(levels[2], block, channels[1], channels[2], 2, level_root=False, **cargs)
-        self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs)
-        self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs)
-        self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs)
-        self.feature_info = [
-            dict(num_chs=channels[0], reduction=1, module='level0'),  # rare to have a meaningful stride 1 level
-            dict(num_chs=channels[1], reduction=2, module='level1'),
-            dict(num_chs=channels[2], reduction=4, module='level2'),
-            dict(num_chs=channels[3], reduction=8, module='level3'),
-            dict(num_chs=channels[4], reduction=16, module='level4'),
-            dict(num_chs=channels[5], reduction=32, module='level5'),
-        ]
-
-        self.num_features = channels[-1]
-        self.global_pool, self.fc = create_classifier(
-            self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
-        modules = []
-        for i in range(convs):
-            modules.extend([
-                nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1,
-                          padding=dilation, bias=False, dilation=dilation),
-                nn.BatchNorm2d(planes),
-                nn.ReLU(inplace=True)])
-            inplanes = planes
-        return nn.Sequential(*modules)
-
-    def get_classifier(self):
-        return self.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.num_classes = num_classes
-        self.global_pool, self.fc = create_classifier(
-            self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
-
-    def forward_features(self, x):
-        x = self.base_layer(x)
-        x = self.level0(x)
-        x = self.level1(x)
-        x = self.level2(x)
-        x = self.level3(x)
-        x = self.level4(x)
-        x = self.level5(x)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.global_pool(x)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.fc(x)
-        if not self.global_pool.is_identity():
-            x = x.flatten(1)  # conv classifier, flatten if pooling isn't pass-through (disabled)
-        return x
-
-
-def _create_dla(variant, pretrained=False, **kwargs):
-    return build_model_with_cfg(
-        DLA, variant, pretrained, default_cfg=default_cfgs[variant],
-        pretrained_strict=False, feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)), **kwargs)
-
-
-@register_model
-def dla60_res2net(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
-        block=DlaBottle2neck, cardinality=1, base_width=28, **kwargs)
-    return _create_dla('dla60_res2net', pretrained, **model_kwargs)
-
-
-@register_model
-def dla60_res2next(pretrained=False,**kwargs):
-    model_kwargs = dict(
-        levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
-        block=DlaBottle2neck, cardinality=8, base_width=4, **kwargs)
-    return _create_dla('dla60_res2next', pretrained, **model_kwargs)
-
-
-@register_model
-def dla34(pretrained=False, **kwargs):  # DLA-34
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512],
-        block=DlaBasic, **kwargs)
-    return _create_dla('dla34', pretrained, **model_kwargs)
-
-
-@register_model
-def dla46_c(pretrained=False, **kwargs):  # DLA-46-C
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
-        block=DlaBottleneck, **kwargs)
-    return _create_dla('dla46_c', pretrained, **model_kwargs)
-
-
-@register_model
-def dla46x_c(pretrained=False, **kwargs):  # DLA-X-46-C
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
-        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
-    return _create_dla('dla46x_c', pretrained, **model_kwargs)
-
-
-@register_model
-def dla60x_c(pretrained=False, **kwargs):  # DLA-X-60-C
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 64, 64, 128, 256],
-        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
-    return _create_dla('dla60x_c', pretrained, **model_kwargs)
-
-
-@register_model
-def dla60(pretrained=False, **kwargs):  # DLA-60
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, **kwargs)
-    return _create_dla('dla60', pretrained, **model_kwargs)
-
-
-@register_model
-def dla60x(pretrained=False, **kwargs):  # DLA-X-60
-    model_kwargs = dict(
-        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
-    return _create_dla('dla60x', pretrained, **model_kwargs)
-
-
-@register_model
-def dla102(pretrained=False, **kwargs):  # DLA-102
-    model_kwargs = dict(
-        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, residual_root=True, **kwargs)
-    return _create_dla('dla102', pretrained, **model_kwargs)
-
-
-@register_model
-def dla102x(pretrained=False, **kwargs):  # DLA-X-102
-    model_kwargs = dict(
-        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, cardinality=32, base_width=4, residual_root=True, **kwargs)
-    return _create_dla('dla102x', pretrained, **model_kwargs)
-
-
-@register_model
-def dla102x2(pretrained=False, **kwargs):  # DLA-X-102 64
-    model_kwargs = dict(
-        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, cardinality=64, base_width=4, residual_root=True, **kwargs)
-    return _create_dla('dla102x2', pretrained, **model_kwargs)
-
-
-@register_model
-def dla169(pretrained=False, **kwargs):  # DLA-169
-    model_kwargs = dict(
-        levels=[1, 1, 2, 3, 5, 1], channels=[16, 32, 128, 256, 512, 1024],
-        block=DlaBottleneck, residual_root=True, **kwargs)
-    return _create_dla('dla169', pretrained, **model_kwargs)